1073 A Wide World 1947 Newcastle set on registered illustrated cover airmail to New Zealand, Port Fairy cds of 8SP47.
1111 ✉ A 1932 use of 5d brown KGV Small Multi P13½x12½, cancelled with 'PERTH/13JAN/1932/1245AM/W.A.' (A1) machine, on "Per Aerial Mail" endorsed cover to Yalbago Station, via Carnarvon.
1116 ✉ B 1936 use of 5d KGV, cancelled with 'AIR MAIL HOBART/3-P20AP36/TAS.' (A1) on long pre-addressed air cover to Sydney, folded.
1123 ✉ A 1947 use of 5d & 1/4d on air cover to England, cancelled with light 'NUNAW[ADING]/1JY47/VIC-AUST' (A2-) with blue registration label.
1761 ✉ A- B1 Cowell: - 'COWELL/19FE38/S.A' on 1d & 2d KGVI x2 on air cover to Adelaide.
3642 ✉ A- 1948 (Dec 10) use of 1p Air & 2p Fruit on registered air cover to Melbourne, endorsed 'VIA B.S.A.A.', Miami & San Francisco backstamps, small faults.
3939 ✉ A- 1948 (Nov 4) use of 2d & 6d on air cover from Raki Raki to Melbourne, faults.


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by Mike Errico | Apr 9, 2017 | audio, Press, So Good, Video |
Long story short:
Initially, I had planned on recording a five-song, tight, straightforward EP that I would be proud to give to you. However, as soon as we started recording, I knew something special was going on, and that an EP was not going to cut it. That's not bragging, it's just that when you do something long enough, you kinda get a feel for when it's the real thing, and in this case it was obvious. It also meant that an EP was not going to cut it. So I crashed through the DEAD END sign and kept on driving. I now have a full-length record that is edgy, Americana-leaning, story-based, and deeply connected to all my work that came before it.
Minor Fits is being fueled by a Pledge campaign, and I'm asking you to help me see it through to completion. In addition to the finished product, I'm psyched to offer a bunch of things I've been itching to give you, including many other studio and live recordings, podcasts, interviews with the people who worked on the record, a quarterly subscription to the all-new and still infamous Tallboy magazine, and more. You'll be a part of the first-ever non-holiday expansion of the Holiday Omens, which is explained below. And hey, if you don't see something that you want, just ask me. Let's make this work.
For decades, you have been there for me and I have tried to express my gratitude by making the best possible work I can. I hope you'll join me now, and continue to play along.
So that's it. Let's do this.

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text:
When the teachers' union supports school choice
American Experiment Policy Fellow Isaac Orr Applies for Governor's Advisory Council on Climate Change
Minnesotans are fleeing to avoid estate tax, so repeal it
Minnesota Teacher's Stunt to Kneel for National Anthem Near Trump Falls Flat
Center of the American Experiment Testifies on the Minnesota Senate's Clean Energy First Bill in Rochester
Minnesota Economy
Employee Freedom
Make the Twin Cities Great Again!
Written by John Phelan
in Economics, Minnesota Economy
The very real challenges facing Minneapolis will not go away because Mayor Frey gets into a Twitter spat with President Trump. ...
Waverly Couple Says Forty Acre Solar Installation Isn't a Good Neighbor
Written by Isaac Orr
in Climate, Energy, Environment, Minnesota Economy
Why aren't any of the state's dozens of environmental groups upset this 40 acre solar facility isn't complying with MPCA stormwater standards? ...
With manufacturing slowing, Minnesota's uncharacteristically low taxes on manufacturers are more important than ever
in Economics, Employment, Minnesota Economy, Taxes
Manufacturing in Minnesota is a bit of a success story and much of that is down to lower than average taxes. They helped the state weather the 'China Shock' comparatively well and they can help insulate our state's manufacturers from any economic downturn. ...
Long Island Liberals Wake Up to How Green Extremism Hurts Constituents
Renewable energy groups like Fresh Energy are encouraging the City of Minneapolis to follow the precedent set by the City of Berkeley in California by banning natural gas in new construction within the City to limit carbon dioxide emissions. This would be an incredibly bad idea, as liberal State Senators in New York, which has banned fracking for natural gas and opposed natural gas pipelines, are now finding out. It appears reality is crashing down on liberal elected officials in the Empire state, where the natural gas shortages they caused are threatening public safety and the state's economic growth. According to...
Minnesota's economic news, w/e 10/4/19
in Minnesota Economy
A round-up of the last week's economic news stories in Minnesota....
It Costs $532,000 to Decommission A Single Wind Turbine
It looks like Minnesota will have a very expensive mess to clean up when the wind turbines currently operating in the state reach the end of their 20 year useful lifetimes....
Numbers on job openings and wages in Minnesota deal a severe blow to the 'Fight for 15'
in Economics, Minimum wages, Minnesota Economy
New numbers from the Department of Employment and Economic Development's Job Vacancy Survey deal a severe blow to the case for a state minimum wage of $15 per hour. ...
Another One Bites the Dust: Federal Judge Rejects Challenges to PolyMet Land Swap
in Environment, Mining, Minnesota Economy
Excellent news, as yet another lawsuit designed to delay the PolyMet mining project has been struck down in federal court. According to CBS: A federal judge has rejected challenges by several environmental groups to a land swap needed for the planned PolyMet copper-nickel mine in northeastern Minnesota. U.S. District Judge Joan Ericksen dismissed the four challenges late Monday. She ruled that the groups lacked the necessary legal standing to block the land exchange between PolyMet and the federal government. She said that's because PolyMet had not yet secured all the necessary permits to begin work, so the groups couldn't establish that they'd been harmed. PolyMet...
What does data on job openings say about Minnesota's labor market?
in Economics, Employment, Minnesota Economy
1.3 million Minnesotans of working age are not in the labor force. 94% of them are cool with that, mostly because they are retired. Finding ways to get them into the labor force could pay economic dividends for the state....
Looming Nickel Shortage Threatens to Slam the Brakes on Governor Walz's Electric Vehicle Mandates
in Climate, Energy, Environment, Mining, Minnesota Economy
Governor Walz recently announced that he wants hundreds of thousands of electric vehicles on Minnesota roads by 2030, but a world-wide shortage of nickel threatens to slam the brakes on EV's....
© 2016 American Experiment. All Rights Reserved. Designed & Developed by Kegan Quimby.


اولین گام در روابط زناشویی نحوه ی صحبت کردن است ، طرز صحیح صحبت کردن با همسرتان تاثیر بسزایی را در روابظ شما میگذارد البته لازم به ذکر است نه هر صحبتی .
بعضی از خانمها یا آقایان عادت دارند تمام اتفاقاتی را که در طول روز برای شان اتفاق افتاده است را برای همسر حود تعریف کنند . با این وجود گاهی اوقات صحبت کردن بسیار خظرناک است و توصیه نمیشود که مدام صحبت نکنید ، سکوت در بسیاری از موارد بهتر از صحبت کردن میباشد .
1- افرادی که همه مطالب زندگی خود چه در حال ، چه در گذشته را برای همسران خود بازگو میکنند به بلوغ فکری نرسیده اند ، ذهنشان مانند دوران نوجوانی شان عمل میکند که عادت داشتند همه چیز را برای خانواده خود تعریف کنند .
2- افرادی که در تعاریف خود ، صحبتهای شان اغراق میکنند و حتی گاهی وقتها به دروغ نیز متوسل میشوند ، افرادی هستند که اعتماد به نفس کمی به خود دارند ، با این کار قصد دارند تا اعتماد به نفس خود را افزایش دهند . امروزه پیوند بین زوجهای با ازدواجهایی که در سابق صورت میگرفت کاملا متفاوت میباشد . در زمان امروز اکثر زوجین هر دو با هم به کار بیرون از خانه اشتغال دارند ، زمان کمی را برای صحبت کردن با یکدیگر دارند ، هنگامی که فرصتی برای صحبت کردن پیش میآید در اغلب موارد راجع به کار بیرون از خانه ی خانم یا آقا میگذرد ، به ندرت پیش میآید که زوجین در مورد رابطهی خودشان ، زندگی دو نفره ای یا چهار نفرهاشان به صحبت بنشینند .
1- از دیدگاه بعضی از روانشناسان افرادی که گذشته تلخ و سخت داشته اند باید توجه کنند که ، هنگامی تجربه های تلخ گذشته باید چراغ راه آینده باشد و بس. سعی کنید با گذشته های تلخ تان خداحافظی کنید و با عنوان کردن تجربه ها ذهن همسر تان را مغشوش نکنید . توجه کنید که شما دیگر در دوران مجردی به سر نمیبرید ، شما تشکیل خانواده داده اید و از همان روز اول که وارد زندگی زناشویی میشوید باید سعی کنید همهی گذشته ی خود را فراموش کنید ، و الان زمان آن است که زندگی دو نفره ی که در آینده خواهید داشت فکر کنید ، صحبتهایتان نیز باید حول زندگی مشترکتان باشد نه گذشته تلختان .
2- اگر از اشتباهات گذشته عبرت گرفته اید و تحول خاصی در رفتارتان ایجاد کرده اید لزومی به بازگو کردن اشتباهات گذشته تان نیست چون ممکن است بر دیدگاه طرف مقابل به شما اثر منفی بگذارد و یا برخی قابلیت های فعلی تان نادیده گرفته شود. مگر در مواردی که بازگو کردن تجربه شما میتواند مانع تکرار اشتباه برای طرف مقابلتان شود.
3- صحبتهایی را از گذشته ، حال برای همسرتان باز گو و تعریف کنید اما سعی کنید وارد جزییات نشوید ، مسائل را برای او کاملا باز نکنید و نشکافید .
مشکلاتی که در دوران قبل از ازدواج برایتان بوجود آمده و بازگو کردن آن تنها باعث مشغول کردن ذهن همسرتان میشود. نا گفته نماند که مسائلی است که گفتن آنها به همسرتان ضروری است مثلا تجربه نامزدی ناموفقی اگر در گذشته داشته اید به همسر تان بگویید اما آیا واقعا لازم است همسرتان از جزئیات آن مطلع باشد؟ مشکلاتی که با پدرو مادر، خواهر یا برادر تان داشته اید و عنوان کردن آنها به دیدگاه همسرتان نسبت به خانواده تان خدشه وارد میکند ، کمبودهایی که در زندگی با آنها مواجه بوده اید ، رنجها و مشقتهایی را که متحمل شده اید ، و مسائلی مانند این موضوعات بهتر است بازگو نکنید.
مطلب بسیار ارزشمندی بود، اما باید مراقب بود بعضی اوقات ما در خصوص اینکه چه مطالبی را باید به همسرمان بگوئیم و چه مسائلی را نگوئیم دچار سردرگمی می شویم. این حالت خوش بینانه قضیه است حالت دیگر اینست که ما با همین بهانه خلوتی را برای خود بنا کنیم و با اتکاء به این مطلب که نباید همه چیز را با شریک زندگی مان در میان بگذاریم، اندوخته این خلوت را زیاد کرده و در نهایت خود این خلوت با محتویاتش بلای جان ما خواهد شد.
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< p > I remember the iron is how tempered "Paul & middot; kecha Jinguan saying" the most valuable life, life belongs to everyone only once. One's life should be spent like this. Looking back on the past, I should not wasted the mood for love and remorse,yeezy 350 pas cher, not because of mediocrity and shame, dying to say: all my life, all my strength are dedicated to the world's most magnificent career -- for human liberation struggle "I want to if we can put our body are dedicated to when people, as * * * * life and struggle that we have is to realize the self value.
< p > in socialist society, in the relationship between the individual and the society, the human value includes two aspects: the society of personal respect and meet; the individuals of social responsibility and dedication. The former is the condition and premise to realize the value of life, and the latter is the means and purpose to realize the value of life, that is, the purpose of life and the meaning of life. These two aspects are unified. But the essence of the value of life is the individual's responsibility and contribution to the society. This is because of the development of human society. As citizens, in the relationship between individuals and society,louboutin soldes, they are mutually dependent and inseparable, people can provide the level of contribution to determine the level of people's rights to enjoy. The supreme value of life is to contribute to the liberation of the proletariat and all mankind. Yes, as Einstein said, "a person's value, should see what he contributed to,golden goose pas cher, and should not see what he achieved" the value of life lies in the dedication.
is to offer. As a famous singer,golden goose sneakers, Cong Fei commercial performances frequently, this should be rich in life. But he is ten years to tilt all accumulated donations donations of more than 300 million yuan,gioielli pandora outlet, 183 poverty-stricken children out of school funding, he has been living (debt 17 million yuan) life of the poor,scarpe hogan outlet, even at the expense of borrowing to life. By Cong Fei funded the Guizhou Province Zhijin County middle school students Wang Weishan wrote in a letter to Cong Fei's letter said: "I never know great can be so specific and can be moved by such a complete, life can live so broad and unselfish. The meaning of life lies not in the length of time, but in the quality of life,adidas nmd runing pas cher, in the value of life ". This is a summary of Cong Fei died on the meaning of life to explore and he died for fighting the portrayal of so in today's China, is not the lack of wealth, but lack of caring; more will enjoy the rich, less is Cong Fei like the devotees.


This is a very interesting build and shows what happens when you combine an efficient and high revving motor with a bit of boost. Obviously the GT3 is naturally aspirated in factory form but this one makes use of that high redline and efficiency with a bunch of boost.
A bunch of boost to the tune of over 1500 horsepower at the wheels.
What do we know about this car? Well, it is tuned on a Syvecs ECU and the engine is a built 4.0 liter variant. John Bray built the motor and Wayne Potts tuned the ECU.
There does not seem to be any other manual Porsche 997 or 911 for that matter in its class.
So cool. Way more power than I'd ever want, but literally my dream build. The sound and rate that it tears through gears is an eargasm.
The GT3 is a dream with a little bit of boost but the existence of the Turbo means most just go that route I assume.
Hard to beat the sound and revs here though.
Craziest part is he did it all basically one handed.


text:
{ "item_title" : "The Girl Who Loved Tom Gordon", "item_author" : [" Stephen King "], "item_description" : "The acclaimed #1 New York Times bestseller from Stephen King--uniquely frightening suspense about a young girl lost in the woods as night falls, with only the voice of her beloved Red Sox relief pitcher to sustain her and help her survive--maybe. During a six-mile hike on the Maine-New Hampshire branch of the Appalachian Trail, nine-year-old Trisha McFarland quickly tires of the constant bickering between her older brother and her recently divorced mother. But when she wanders off by herself, she becomes lost in a wilderness maze full of peril and terror. As night falls, Trisha has only her ingenuity as a defense against the elements, and only her courage and faith to withstand her mounting fears. For solace she tunes her headphones to broadcasts of Boston Red Sox baseball games and follows the gritty performances of her hero, relief pitcher Tom Gordon. And when the reception begins to fade, Trisha imagines that Tom Gordon is with her--the protector from an enemy who may or may not be imagined...one who is watching her, waiting for her in the dense, dark woods...", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/1/50/115/751/1501157515_b.jpg", "price_data" : { "retail_price" : "9.99", "online_price" : "9.99", "our_price" : "9.99", "club_price" : "9.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "9.99" } }
The Girl Who Loved Tom Gordon
by Stephen King
The acclaimed #1 New York Times bestseller from Stephen King--uniquely frightening suspense about a young girl lost in the woods as night falls, with only the voice of her beloved Red Sox relief pitcher to sustain her and help her survive--maybe.
During a six-mile hike on the Maine-New Hampshire branch of the Appalachian Trail, nine-year-old Trisha McFarland quickly tires of the constant bickering between her older brother and her recently divorced mother. But when she wanders off by herself, she becomes lost in a wilderness maze full of peril and terror. As night falls, Trisha has only her ingenuity as a defense against the elements, and only her courage and faith to withstand her mounting fears. For solace she tunes her headphones to broadcasts of Boston Red Sox baseball games and follows the gritty performances of her hero, relief pitcher Tom Gordon. And when the reception begins to fade, Trisha imagines that Tom Gordon is with her--the protector from an enemy who may or may not be imagined...one who is watching her, waiting for her in the dense, dark woods...
Publisher: Pocket Books
Publish Date: April 2017


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Robert Califf to resign as US FDA commissioner
Robert Califf is to step down as US Food and Drug Administration (FDA) commissioner upon the inauguration of Donald Trump as US President on 20 January 2017. Democrat Califf, who was appointed to the post in September 2016 by Barack Obama, is required to resign along with other presidential appointees.
Califf, a cardiologist and researcher, had received endorsement from The American College of Cardiology, the New England Journal of Medicine and the American Heart Association, as well as others.
Califf's tenure was not without controversy, with his ties to the pharmaceutical industry questioned by US senator and former presidential candidate Bernie Sanders. Senator Edward Markey expressed concern that Califf "did not give [him] confidence" that the FDA would act appropriately to quell the growing US opioid epidemic under his sterwardship.
Current deputy commissioner Stephen Ostroff is likely to serve as commissioner on a deputy basis, as he did between Califf's appointment and former commissioner Margaret Hamburg's departure.
According to Politico, venture capitalist Scott Gottlieb—a past FDA deputy commissioner—is likely to take over the position on a long-term basis. Other names suggested by the Wall Street Journal include Jim O'Neill, managing director of Mithril Capital Management and Balaji Srinivasan, founder of Counsyl, a genetic-counselling firm.
comissioner
Robert Califf
US FDA
Swiss parliament votes to accept US FDA-approved medical devices
RapidAI receives FDA 510(k) clearance for PE Triage & Notification
FDA issues two final guidances for including patient perspectives in medical device studies


The lateral amygdala (LA) plays a critical role in the formation of fear-conditioned associative memories. Previous studies have used c-fos regulated expression to identify a spatially restricted population of neurons within the LA that is specifically activated by fear learning. These neurons are likely to be a part of a memory engram, but, to date, functional evidence for this has been lacking. We show that neurons within a spatially restricted region of the LA had an increase in both the frequency and amplitude of spontaneous postsynaptic currents (sPSC) when compared to neurons recorded from home cage control mice. We then more specifically addressed if this increased synaptic activity was limited to learning-activated neurons. Using a fos-tau-LacZ (FTL) transgenic mouse line, we developed a fluorescence-based method of identifying and recording from neurons activated by fear learning (FTL+) in acute brain slices. An increase in frequency and amplitude of sPSCs was observed in FTL+ neurons when compared to nonactivated FTL− neurons in fear-conditioned mice. No learning-induced changes were observed in the action potential (AP) input-output relationships. These findings support the idea that a discrete LA neuron population forms part of a memory engram through changes in synaptic connectivity.
The lateral amygdala (LA) is critical for the formation of associative fear memories, such as those formed by fear conditioning. A subset of neurons in LA are activated by fear conditioning suggesting that they may be part of a memory engram. However, there is no functional evidence to support this view. Electrophysiological characterization of LA neurons shows that the frequency and amplitude of synaptic events increase following fear conditioning. A method based on β-galactosidase as a reporter was developed to locate activated neurons in acute slice. An increase in frequency and amplitude of synaptic events was observed in activated compared to nonactivated neurons supporting the idea that a discrete LA neuron population forms part of a memory engram.
Memory formation has long been hypothesized to occur via enhanced synaptic connectivity between populations of neurons in the brain (Hebb, 1949). While there is much indirect evidence supporting this hypothesis (Thompson, 2005; Josselyn et al., 2015; Sweatt, 2016), direct evidence is lacking because of the difficulty in identifying neurons that are specifically involved in the formation of a particular memory. One model of learning and memory which has facilitated the identification of neurons involved in memory is classical fear conditioning. Fear conditioning is a well-validated model of associative learning, with the lateral amygdala (LA) strongly implicated in the formation of the associative memories (Maren, 2001; Pape and Stork, 2003; Fanselow and Poulos, 2005; Kim and Jung, 2006; LeDoux, 2007; Johansen et al., 2011). Extracellular single-unit recordings of LA neurons during auditory fear conditioning show that a subpopulation of LA neurons alters their rate of tone-evoked firing (Repa et al., 2001). In addition, selective ablation of a subset of CREB-overexpressing neurons in the LA results in the erasure of a conditioned fear memory (Han et al., 2009; Rogerson et al., 2016). These studies suggest that plasticity in only a subset of LA neurons forms part of the memory trace in fear learning.
Immediate early gene expression provides one method of identifying neurons that have been functionally activated during the learning process (Dragunow, 1996). The immediate early gene c-fos has been extensively used as a marker of neuronal activation (Knapska et al., 2007) and previous studies have employed c-fos regulated expression in the transgenic fos-tau-LacZ (FTL) mouse line to identify functionally activated circuitry in the brain (Murphy et al., 2007; Wilson and Murphy, 2009). Using FTL mice, a number of discrete, anatomically defined populations of neurons in different parts of the brain were identified as being specifically activated by fear learning (Wilson and Murphy 2009; Trogrlic et al., 2011; Butler et al., 2015). In particular, a population of learning-activated FTL+ neurons was found within a spatially restricted region of the ventrolateral nucleus of the LA (LAvl). Other studies are also consistent with neurons specifically in LAvl playing a key role in fear memory (Schafe et al., 2000; Bergstrom et al., 2013; Besnard et al., 2014). Thus, where it is established that activation of the ERK1/2 kinase in LA is required for fear memory formation, ERK1/2 activation following fear conditioning occurs predominantly in neurons in LAvl (Schafe et al., 2000), in a similar pattern to that seen with FTL+ neurons.
Given the importance of the LA in the formation of fear memory, the learning-activated FTL+ neurons are prime candidates for direct involvement in fear memory. However, it has not been established if these neurons undergo specific changes in their synaptic or intrinsic properties following activation in fear learning. Such studies may provide further evidence both for involvement of these neurons in fear memory and for the nature of the "memory engram." In this study, we first trained wild-type mice to simultaneously record the synaptic and firing properties of LAvl neurons after fear learning. Recordings from neurons in brain slices revealed significant increases in the amplitude and frequency of spontaneous synaptic events in a subpopulation of LAvl neurons from fear-conditioned mice compared with neurons from home cage mice. Subsequently, we used the activity-dependent expression of the FTL transgene to target recordings to the learning activated neurons in brain slices from trained FTL mice. Similar increases in amplitude and number of spontaneous synaptic events recorded from FTL+ neurons were observed, relative to their nonactivated neighbors. We suggest that these synaptic changes, which occur within a specific population of LAvl neurons following learning, are central to the formation of the fear memory engram.
All animal procedures were performed in accordance with the University of Melbourne Animal Care Committee's regulations. Male FTL− and FTL+ mice, on a C57BL6/J background, aged four to seven weeks, were housed in standard 15 × 30 × 12 cm cages on a 12/12 h light/dark cycle and had food and water supplied ad libitum. Two days before the commencement of the experiment, mice were singly housed in cages and relocated to a dedicated behavioral laboratory. In this facility, mice were housed under quiet (60 dB), low-light (15–20 Lux) conditions and a 12/12 h light/dark cycle.
Fear conditioning was conducted using a computerized system (Clever Sys Inc.). The conditioning chamber (32 × 26 × 21 cm) was contained within a sound-attenuating compartment. The floor of the chamber consisted of stainless steel rods connected to a shocker-scrambler unit capable of delivering electric shocks of defined duration and intensity. The chamber was cleaned with 70% ethanol between training and testing sessions. For fear conditioning, mice were placed in the chamber for 3 min, after which they received a 1.0-mA, 2-s footshock. The mice then remained in the chamber for a further 30 s before being returned to their home cages. For the histochemistry experiment, in addition to the previous training conditions, an immediate shock group was exposed to a 1.0-mA, 2-s footshock immediately on entry into the training chamber. Mice in this group then remained in the training chamber for 3 min and 30 s. The immediately shocked mice did not exhibit any context-associated fear learning when tested. Furthermore, non-learning control mice are not reported to exhibit appreciable FTL expression in the LAvl (Trogrlic et al., 2011).
Mice remained in their home cages for 3 h after training, to induce FTL expression in activated neurons. Mice were then tested for context-associated fear by placing the mice back in the same training chamber for 3 min. Freezing and moving behavior was recorded, using automated software (Clever Sys Inc.). Mice were anaesthetized immediately after testing. Home cage mice were anaesthetized immediately on removal from their home cages. In total, 13 FTL− (eight home cage, six context fear-conditioned) and seven FTL+ (all context fear-conditioned) were used.
FTL+ neurons can be identified in fixed tissue using β-galactosidase histochemistry. To show learning-induced FTL+ neurons in the LAvl region, context fear-conditioned and immediate shock mice were sacrificed immediately following testing (3 h after training). Brains were removed from the mice and 300-μm coronal slices were cut on a vibratome (VT1200; Leica). Slices were immersion fixed in 4% paraformaldehyde for 10 min and transferred to separate wells containing 400-µl X-gal solution (10 mM potassium ferricyanide, 10 mM potassium ferrocyanide, 40 mM MgCl2, and 2 mg/ml 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside; Roche, Life Sciences) in 0.1 M PBS for 24 h at room temperature, with agitation. The slices were then rinsed with PBS and mounted onto Superfrost+ glass slides (Thermo Fisher Scientific). Sections were left to dry and coverslipped.
Mice were anaesthetized with 2% isofluorane, before decapitation. The brains were quickly removed and placed into ice cold cutting solution (125 mM choline chloride, 2.5 mM KCl, 0.4 mM CaCl2, 6 mM MgCl2, 1.25 mM NaH2PO4, 26 mM NaHCO3, and 20 mM D-glucose) saturated with carbogen gas (95% O2–5% CO2), and 300-μm coronal slices were cut on a Vibratome (VT1200; Leica). Slices containing the LA (approximately two slices per mouse) were incubated at room temperature for a minimum of 1 h in artificial CSF (aCSF; 125 mM NaCl, 2.5 mM KCl, 2 mM CaCl2, 2 mM MgCl2, 1.25 mM NaH2PO4, 26 mM NaHCO3, and 10 mM D-glucose) saturated with carbogen. For FTL experiments, 25 µM C12-fluorescein-di-(β-D-galactopyranoside) (C12FDG, Imagene Green, Thermo Fisher Scientific) was added to the aCSF solution.
For patch clamping experiments, slices were transferred to a submerged chamber and perfused (8 ml/min) with aCSF at room temperature. The LAvl was located, and neurons were visualized with IR-DIC microscopy. Glutamatergic projection neurons were targeted for recording, distinguished from interneurons by large pyramidal-like somata (Sosulina et al., 2006). Electrodes were pulled using a Sutter P-2000 puller (Sutter Instruments) from borosilicate micropipettes (World Precision Instruments) with an initial resistance of around 3–6 MΩ. Electrodes were filled with: 125 mM K-gluconate, 5 mM KCl, 2 mM MgCl2, 10 mM HEPES, 4 mM ATP-Mg, 0.3 mM GTP-Na2, 10 mM tris-phosphocreatine, and 10 mM EGTA (pH 7.2, 290 OsM). Biocytin (2 mg/ml) was included to allow morphologic examination and verification of FTL+ neurons post hoc. Whole-cell patch clamp recordings were made using a MultiClamp 700A amplifier and pClamp acquisition software (Molecular Devices) from neurons visually identified using infrared DIC imaging (BX51, Olympus). For FTL experiments, neurons were identified as either FTL+ or FTL– by the presence or absence of C12FDG fluorescence using a GFP filter set (excitation 457–487 nm and emission 502–538 nm). The number of neurons recorded for the experiment involving the application of C12FDG was 13 from FTL mice (six FTL −, seven FTL+; all context fear-conditioned). In a subset of these experiments we were able to record at –50 mV (four FTL− neurons, six FTL+ neurons). For the nontargeted experiment, 61 neurons were recorded from wild-type mice (28 from home cage mice, 33 from context fear-conditioned mice). No more than two neurons were recorded from each brain slice.
Firing properties were recorded in current clamp mode. Bridge balance and capacitance compensation was applied to all recordings. Voltage recordings were filtered at 30 kHz and sampled at 100 kHz. Resting membrane potential was measured and neurons recorded as being more than –55 mV were excluded. A holding current was injected into neurons, if required, setting their holding potential to approximately –65 mV. A current injection/action potential (AP) frequency relationship was established by injecting 30 current steps of 500-ms duration (10-pA incremental steps from –30 to 260 pA). All electrophysiological data were analyzed using AxoGraph X software. To calculate membrane time constant, an exponential function was fit to the voltage trace following a –20-pA current injection for each cell. An automated detection algorithm was used to detect AP with visual confirmation. AP threshold voltage was defined as the voltage at which gradient reached 10 mV/ms−1. For the average AP wave form analysis, APs were aligned to the threshold. AP properties measured were peak height, width at 50% of height, rise time (10–90% height), and decay time (100–50%). Peak AP amplitude was measured from threshold to peak. Rheobase was defined as the current injection that first fired an AP.
Spontaneous postsynaptic currents (sPSCs) were measured in voltage-clamp mode in the absence of any neurotransmitter blockers. Currents were recorded at –70 and –50 mV and sampled at 10 kHz with filtering set at 3 kHz. sPSCs were identified using event detection in Axograph X (Axograph Scientific Software). The detection threshold of sPSCs was set at four times the SD of the noise. Each automatically identified event was manually confirmed. Amplitude and interevent interval (IEI) were calculated and cumulative probability curves constructed using 60-s gap-free recording with at least 25 events per neuron. No series resistance compensation was applied. NBQX (20 µM) and APV (50 µM) were added to ACSF to determine the reversal potential of GABAA receptor-mediated Cl− current (measured from seven wild-type neurons). Synaptic events were recorded at various voltages and their amplitudes measured using the automated method described above. We have not accounted for liquid junction potential in any recordings.
All statistical tests were performed with GraphPad software (Prism). Freezing and moving data were analyzed using two-tailed Student's t tests. sPSC IEIs and amplitudes were analyzed using Student's t tests with Welch's correction, while cumulative probability curves were compared using the Kolmogorov–Smirnov test. IO curves were analyzed using two-way ANOVAs, examining current injection and FTL status as sources of variation. AP parameters were compared using unpaired Student's t tests with Welch's correction. A peak fitting algorithm was applied to the data using an unconstrained nonlinear optimization algorithm to decompose the percentage of recorded neurons versus sPSC frequency and amplitude data into Gaussian peaks (only the number of peaks was specified). The peak fits used the MATLAB script of "findpeak.m" (https://terpconnect.umd.edu/~toh/spectrum/InteractivePeakFitter.htm). Bar graphs presented in the figures represent mean ± SEM.
Fluorescence immunohistochemistry was used to verify the FTL status of recorded and biocytin-filled neurons from FTL mice. After patch-clamp experiments, slices were immersion fixed with 4% paraformaldehyde for 1 h. Slices were incubated with blocking solution (3% bovine serum albumin, 0.3% Triton X-100, and 50 µM glycine, diluted in PBS) for 1 h on an orbital shaker, followed by primary antibody solution [chicken anti-β-galactosidase (Abcam, RRID: AB_307210), diluted 1:1000 in 0.1% Triton X-100/PBS] overnight. Slices were then exposed to secondary antibody solution (goat anti-chicken Alexa Fluor 488 1:500, Streptavidin 594 1:500, diluted in 0.1% Triton X-100/PBS) for 6 h. Slices were mounted in PBS onto Superfrost+ glass slides (Thermo Fisher Scientific), and coverslipped using fluorescent mounting medium (Dako). Slides were visualized using a confocal microscope (LSM5, Carl Zeiss) equipped with a 63× oil-immersion objective (NA 1.4). FTL expression was detected after illumination via an Argon laser at 488 nm (emission bandpass filter 515–530 nm). Biocytin fluorescence was excited via a DPSS laser at 561 nm and the emission collected using a bandpass filter (575–615 nm). Dual channel z-stack images were taken (1024 pixels × 1024 pixels × 55 planes, voxel size 0.12 × 0.12 × 0.43 µm) and then projected using maximum intensity (ImageJ, NIH).
To see whether fear learning had any effects on the electrophysiological characteristics of neurons in LA, a group of wild-type mice were fear-conditioned for comparison with a home cage control group. Consistent with having learnt contextual fear, the fear-conditioned mice had significantly increased freezing episodes and reduced movement (Fig. 1A,B; pre-shock freezing M = 0.66, SD = 0.82; test freezing M = 29.5, SD = 12; t(5) = 6, p = 0.0018; pre-shock moving M = 32, SD = 13; test moving M = 5.4, SD = 2.7; t(5) = 6.3, p = 0.0015). A small subset of neurons within the LAvl has been previously identified to be specifically activated by fear conditioning (Wilson and Murphy, 2009; Trogrlic et al., 2011; Butler et al., 2015). Whole-cell recordings were made from neurons in this LAvl subnucleus region from fear-conditioned and home cage control mice as marked in Figure 1C. There were no differences in the recorded passive neuron properties as reported in Table 1.
Recorded neurons in the LAvl region from fear-conditioned and home cage mice. fear-conditioned mice were shown to have acquired a context-fear memory by a significant increase in freezing behavior (p = 0.0018; A) and a significant decrease in moving behavior (p = 0.0015, n = 6; B). C, Cartoon illustrating the position of whole-cell recorded neurons within the LAvl region of the amygdala from fear-conditioned mice and home cage control mice. Blue markers = home cage; yellow markers = fear-conditioned trained mice; ** indicates statistical significant change.
We measured sPSCs at a –70-mV holding potential to evaluate excitatory/inhibitory synaptic network activity onto these neurons (Fig. 2A,B). The cumulative frequency plot of the IEI for sPSCs showed a significant left-shift of the curve for neurons recorded from fear-conditioned mice compared to home cage mice (Fig. 2C; Kolmogorov–Smirnov D = 0.222, p < 0.0001, averaged data: HC M = 0.38 s, SD = 0.5 s; FC M = 0.23 s, SD = 0.38 s; t(6239) = 17.1, p < 0.0001). Furthermore, the cumulative frequency plot of the sPSC amplitude showed a significant right shift for neurons recorded from fear-conditioned mice compared to home caged mice (Fig. 2D; Kolmogorov–Smirnov D = 0.2159, p < 0.0001, averaged data: HC M = 10.4 pA, SD = 5.9 pA; FC M = 15.3 pA, SD = 11.7 pA; t(12210) = 30.8, p < 0.0001). These findings are consistent with a change in synaptic properties of neurons within the LAvl after fear conditioning. However, based on previous reports identifying activated neurons histochemically, only a subset of neurons within the LAvl are expected to have altered properties (Wilson and Murphy, 2009; Trogrlic et al., 2011; Butler et al., 2015). To explore this further, we applied a Gaussian peak-fitting algorithm to the average sPSC amplitude of each cell recorded from either fear-conditioned or home-caged mice (Fig. 2E,F). The algorithm identified a single Gaussian distribution peaking at 9.2 pA for home-caged mice but identified two Gaussian peaks (at 9.9 and 16 pA) in conditioned mice. The Gaussian peak-fitting algorithm was also applied to sPSC frequency data. The algorithm identified three peaks (at 2.5, 5, and 10 Hz), which were at similar frequencies in each condition (data not shown). However, the proportions of cells that fell within each peak differed between conditions. The Gaussian fit to the highest frequency accounted for 25% of cells in the fear learning mice but only 4% in home-caged mice. These empirical observations are consistent with the idea that only a subpopulation of neurons in LAvl exhibit altered properties following fear learning (Repa et al., 2001; Sehgal et al., 2014), and with previous findings that fear learning activates a specific subpopulation of neurons in LAvl (Wilson and Murphy, 2009; Trogrlic et al., 2011; Butler et al., 2015). We next used an approach that allowed the electrophysiological recording of neurons specifically activated by fear conditioning.
Learning induces increased synaptic activity in a subpopulation of LAvl neurons. Raw traces of sPSCs from (A) home cage mice (HC) and (B) fear-conditioned mice (FC). C, The cumulative frequency graph of the IEI of sPSCs shows a significant left shift in fear-conditioned mice compared to home cage controls (Kolmogorov–Smirnov test p < 0.0001). D, The cumulative frequency graph of the amplitude of sPSCs shows a significant right shift in fear-conditioned mice, compared to home cage controls (Kolmogorov–Smirnov test p < 0.0001, n = 28 HC and n = 33 FC). E, A Gaussian fit to the frequency histogram of the sPSC amplitude of neurons from home cage controls shows a single peak. F, A second peak is evident in fear-conditioned mice.
To test the electrophysiological properties of activated neurons, we developed a method to label and identify them in live brain slices based on the FTL mouse. The FTL mouse consists of a tau-LacZ transgene driven by the c-fos promoter, which results in expression of β-galactosidase throughout neurons in which c-fos has been expressed. First, we determined if the expected pattern of learning activated neurons in LAvl could be identified in the thick brain slices used for electrophysiology. For this, FTL mice were context fear-conditioned and then tested for context-associated fear 3 h later. Mice trained in fear conditioning spent a significantly higher percentage of time freezing during this testing session than during the pre-shock period in training (Fig. 3A; pre-shock M = 28, SD = 14; Test M = 0.14, SD = 0.38; t(12) = 5.2, p = 0.0002). The trained mice also spent a significantly lower percentage of time moving during testing when compared to the pre-shock training session (Fig. 3B; pre-shock M = 6.9, SD = 4; Test M = 25, SD = 4; t(12) = 8.2, p < 0.0001;). Conditioned mice had therefore acquired a context-associated fear memory. Thick brain slices prepared for electrophysiological analysis were taken from these mice and stained for β-galactosidase. These slices showed a clear pattern of FTL activation in a small subset of neurons along the border of the LAvl, with only a few FTL+ neurons visualized in equivalent sections from immediate shock mice (Fig. 3C,D). The pattern of FTL+ neurons we observed in conditioned mice is consistent with the pattern of FTL activity described in previous studies (Wilson and Murphy, 2009; Trogrlic et al., 2011; Butler et al., 2015).
Restricted expression of FTL+ neurons in thick brain slices following fear conditioning. Context fear-conditioned mice acquired a fear memory illustrated by increased (A) freezing relative to pre-shock levels (p = 0.0002) and (B) decreased movement (p < 0.0001, n = 7). C, D, Micrograph of the amygdala region of an FTL mouse stained with x-gal histochemistry following immediate shock training (non-learning control; C) and following delayed shock training (fear-conditioned; D), showing an increase in FTL+ neurons specifically in the LAvl. Scale bars in C, D: 150 µm. *** indicates statistically significant change.
We then developed a novel methodology using the fluorogenic β-galactosidase substrate C12FDG as a marker of FTL expressing neurons in acute brain slices (Fig. 4A). C12FDG is a β-galactosidase substrate that is cell permeable (Zhang et al., 1991; Plovins et al., 1994), and in the presence of β-galactosidase, the galactopyranose moieties are cleaved to yield a fluorescent product (Rotman et al., 1963). FTL+ neurons in the LAvl of context- fear-conditioned mice were identified based on their fluorescent signal. Post hoc analysis of biocytin-filled neurons confirmed their FTL identity: the biocytin signal overlapped with that of β-galactosidase for FTL+ neurons while, if the neuron was FTL−, only the biocytin signal was seen (Fig. 4B,C). These findings demonstrated that it was possible to identify FTL+ neurons in acute brain slices using C12FDG, which could then be targeted for whole-cell patch clamp recordings. We observed no differences in the recorded passive properties of FTL+ and FTL− neurons as reported in Table 1.
FTL+ neurons in the LAvl show increases in sPSC frequency and amplitude. A, C12FDG is converted to the fluorescent product C12-fluorescein by β-galactosidase, enabling the identification of FTL+ neurons in acute brain slices. FTL+ (B) and FTL− (C) neurons identified post hoc by double labeling for β-galactosidase and biotin from intracellular recording. Representative voltage-clamp traces from (D) FTL− and (E) FTL+ neurons in the LAvl of context fear-conditioned mice. F, Cumulative frequency histogram of the IEIs of recorded sPSCs from FTL+ and FTL− neurons (Kolmogorov–Smirnov test p < 0.0001). G, Cumulative frequency histogram of the amplitudes of recorded sPSCs from FTL+ and FTL− neurons (Kolmogorov–Smirnov test p < 0.0001, n = 6 FTL− and n = 7 FTL+). Scale bars in B, C: 25 µm.
To examine synaptic changes that may have occurred following context fear conditioning, we recorded sPSCs from the FTL+ and FTL− neurons in the LAvl of the fear-conditioned mice at a holding potential of –70 mV (Fig. 4D,E). The cumulative frequency plot of the IEI for sPSCs showed a significant left-shift of the curve for FTL+ neurons compared to FTL− neurons (Fig. 4F; Kolmogorov–Smirnov D = 0.29, p < 0.0001, averaged data: FTL− M = 0.52 s, SD =0.57; FTL+ M = 0.25 s, SD = 0.25; t(562) = 10.7, p < 0.0001). Furthermore, the cumulative frequency plot of the sPSC amplitude showed a significant right shift of the curve for FTL+ neurons compared to FTL− neurons (Fig. 4G; Kolmogorov–Smirnov D = 0.27, p < 0.0001, FTL− M = 14 pA, SD = 8; FTL+ M = 19 pA, SD = 11; t(1228) = 11.0, p < 0.0001). Together, these observations illustrate an increase in both the frequency and amplitude of synaptic activity onto FTL+ neurons when compared with neighboring FTL− neurons.
In a subset of these neurons we were able to record sPSC at a holding potential of –50 mV, which is near the reversal potential of Cl− (Fig. 5A). The pattern of reduced sPSCs IEI (Fig. 5B; Kolmogorov–Smirnov D = 0.53, p < 0.0001; averaged data: FTL− M = 1.8 s, SD = 2; FTL+ M = 0.43 s, SD = 0.50; t(133) = 7.6, p < 0.0001) and increased amplitude (Fig. 5C; Kolmogorov–Smirnov D = 0.273, p < 0.0001; averaged data: FTL− M = 14 pA, SD = 8; FTL+ M = 16 pA, SD = 7; t(164) = 3.0, p = 0.0027) was still observed under these conditions, suggesting that the observed increase in synaptic activity was, at least in part, through excitatory transmission. Together, these findings indicate that the neurons in LAvl activated to express FTL by fear conditioning have undergone significant learning-induced synaptic plasticity.
Increases in the spontaneous excitatory synaptic current event amplitude and frequency. A, Average spontaneous inhibitory PSC (sIPSC) amplitude at varying holding potentials in the presence of NBQX and APV from neurons in the mouse LA, showing a reversal potential of Cl− of approximately –50 mV (n = 7). B, The cumulative frequency graph of the IEI of sPSCs from FTL+ and FTL− neurons at –50 mV (Kolmogorov–Smirnov test p < 0.0001). D, The cumulative frequency graph of the amplitude of sPSCs at –50 mV FTL+ and FTL− neurons (Kolmogorov–Smirnov test p < 0.0001, n = 4 FTL− and n = 6 FTL+).
Analysis of the firing properties of LAvl neurons in wild type experimental mice reveals a similar input/output relationship (Fig. 6A–C) and rheobase (home caged (HC) M = 60 pA, SD = 40 pA; fear conditioned (FC) M = 57 pA, SD = 18 pA; t(29) = 0.34, ns) between conditioned and home cage control mice. Fear conditioning did induce significant changes in the shape of LAvl APs, as measured by the peak height, peak width, rise parameter, and decay parameter (Table 2).
LAvl neurons show no differences in firing properties following fear conditioning. A, B, Representative traces from current-clamped (A) home cage (HC) and (B) fear-conditioned (FC) neurons in the LAvl. C, Mean AP count versus current injection for neurons from context fear-conditioned mice and from untrained home cage mice (n = 28 HC and n = 33 FC). Representative traces from current-clamped (D) FTL− and (E) FTL+ neurons in the LAvl of context fear-conditioned mice. F, Plot of the mean AP count versus current injection for FTL+ and FTL− neurons from fear-conditioned mice (n = 6 FTL− and n = 7 FTL+).
Firing properties of FTL− and FTL+ neurons recorded from fear-conditioned mice were similar with no differences in the input/output relationship (Fig. 6D–F) or rheobase (FTL+ M = 87 pA, SD = 51 pA; FTL− M = 104 pA, SD = 44 pA; t(10) = 0.59, ns). There was also no difference in the shape of APs produced by FTL+ and FTL− neurons, as measured by the peak height, peak width, rise parameter, and decay parameter (Table 2). It should be noted, given the low number of recorded neurons, that small changes in AP properties may not have been detected.
Previous studies identified a spatially discrete population of neurons within LAvl which was specifically activated by fear conditioning (Wilson and Murphy, 2009; Trogrlic et al., 2011; Butler et al., 2015). These neurons are, therefore, specifically implicated in fear memory formation. To look for memory related electrophysiological changes within LAvl, we first recorded from neurons in wild-type mice following fear conditioning. These recordings revealed a subpopulation of neurons in LAvl that exhibited increases in sPSC frequency and amplitude compared with neurons from home-caged mice. to target the discrete population of neurons within LAvl which was specifically activated by fear conditioning, we then developed a method of identifying and recording from FTL+ neurons in FTL brain slices labeled for β-galactosidase. Neurons that exhibited learning-specific β-galactosidase activation displayed increased sPSC frequency and amplitude compared to neighboring non-labeled neurons. Recordings of a subset of these labeled neurons at the reversal potential of Cl− suggest that, at least in part, the increases in amplitude and frequency of synaptic events are mediated through excitatory transmission. These findings provide evidence that increases in synaptic strength in a small, spatially discrete population of neurons in LA form part of the fear memory engram.
The fluorogenic β-galactosidase substrate C12FDG has been previously used primarily in flow cytometry applications, including for the analysis of mammalian cell types (Gu et al., 1993; Miao et al., 1993; Beattie et al., 1994). However, the use of C12FDG to identify cells for electrophysiological analysis has not been previously reported. A number of other fluorogenic β-galactosidase substrates exist (Haugland and Johnson, 1993) however C12FDG was chosen as it is nontoxic, well retained by cells, and able to passively enter mammalian cells in an aqueous environment (Zhang et al., 1991; Plovins et al., 1994). Our study highlights the use of C12FDG as a robust method of identifying labeled neurons expressing any construct that uses β-galactosidase expression as a marker.
Two recent studies investigated the synaptic properties of Arc + neurons in LA after fear conditioning (Nonaka et al., 2014; Gouty-Colomer et al., 2016), based on the premise that Arc expression is indicative of a neuron's involvement in fear learning and memory. Nonaka and colleagues report an increase in the frequency of synaptic currents on Arc + neurons compared to neighboring Arc− neurons in conditioned mice (Nonaka et al., 2014). Furthermore, Gouty-Colomer and colleagues also demonstrate synaptic plasticity with increased evoked excitatory currents in Arc + neurons postlearning (Gouty-Colomer et al., 2016). These increases in synaptic event frequency and amplitude were comparable to those observed in the current study. However, it is worth noting that Arc expression in these studies may not have been indicative of a neuron's involvement in memory since there were substantial numbers of Arc + neurons in the non-learning controls, and fear learning stimulated only a small (Gouty-Colomer et al., 2016) or insignificant increase (Nonaka et al., 2014) in Arc expression. Thus, the synaptic changes observed in Arc + neurons may have been associated with either learning or non-learning stimuli, such as footshock. In contrast, the LAvl neurons from which we recorded express FTL only following fear learning with an average 4-fold increase in FTL + neurons in trained mice compared to unpaired or shock controls (Wilson and Murphy 2009; Trogrlic et al., 2011; Butler et al., 2015). We can thus directly argue that the synaptic changes we observed occurred in neurons that were specifically involved in fear learning.
Our findings of synaptic plasticity can be incorporated into a model of fear learning and memory within the LA as follows. Both context and auditory fear learning induce c-fos in a small subset of LAvl neurons (Butler et al., 2015). Given that the LA is crucial for the formation of the association between the conditioned and unconditioned stimuli (Fanselow and LeDoux, 1999; LeDoux, 2000; Blair et al., 2001; Maren, 2001, 2005), neurons involved in fear memory in the LA are possibly a site of convergence of these stimuli. Tracing studies from regions of the thalamus which deliver conditioned stimuli and unconditioned stimuli to the LA show extensive projections to LAvl (LeDoux, 2000). These findings are thus consistent with conditioned stimuli and unconditioned stimuli inputs converging on LAvl neurons, with associative firing of these two pathways resulting in the induction of synaptic plasticity during fear learning. In this model, these LAvl neurons project to downstream regions of the brain which control fear pathways and which are intrinsically activated during signaling from aversive unconditioned stimuli (Fanselow and LeDoux, 1999; LeDoux, 2000; Blair et al., 2001; Maren, 2001, 2005). We propose that the key synaptic changes on these neurons occur at the conditioned stimuli inputs which were activated during fear conditioning and would result in an altered response of these neurons to the conditioned stimuli. This altered response, such as increased frequency and/or amplitude of sPSC, would increase the probability of AP firing and consequently activate the downstream fear pathways in the absence of a unconditional stimulus. After fear learning, the animal would thus display a fear response when exposed to the conditioned stimuli alone. While key aspects of this model need to be examined and tested, it provides a reasonable explanation for how the changes we observed within a small population of neurons in LAvl may underlie part of fear learning and memory.
A number of recent studies have proposed an alternative model of fear memory, whereby neurons in the BLA are incorporated into the memory trace based on their excitability directly before the time of training (Han et al., 2007; Zhou et al., 2009; Gouty-Colomer et al., 2016; Rogerson et al., 2016). Viral-mediated overexpression of CREB in LA neurons has been shown to result in increased neuronal excitability, and transfected neurons were more likely to form part of the fear memory trace (Zhou et al., 2009). Furthermore, optogenetic inactivation of these CREB-overexpressing neurons in the BLA results in attenuation of an acquired fear memory (Rogerson et al., 2016). We find no evidence of changes in the AP input-output relationship of FTL+ and FTL− neurons. This finding differs from that of Gouty-Colomer and colleagues, who demonstrated that Arc + neurons had increased AP input-output gain (Gouty-Colomer et al., 2016). The basis of this discrepancy is unclear although it could reflect the relatively small number of neurons recorded. Additionally, EGTA in our internal solution may mask conditioning-associated changes in neuronal firing due to its effects on calcium-activated currents. We have also labeled neurons using a different marker (c-fos, cf. Arc), and only neurons within the LAvl were targeted, which is a more anatomically restricted region than the wider BLA. Interestingly, we do see subtle changes in AP morphology at a population level in the LAvl of fear-conditioned mice. This was not observed in our targeted approach with no difference in AP morphology observed between FTL+ and FTL− neurons. The physiologic relevance of this is unclear and warrants future investigation.
In summary, we show that synaptic changes occur specifically on neurons activated by context fear conditioning in the LAvl, with no differences in intrinsic neuronal excitability being detected. These findings suggest a model of fear learning whereby learning-induced synapse modification occurs on neurons within an anatomically restricted region of the LA.
Acknowledgements: We thank the Biological Optical Microscopy Platform (BOMP) for their assistance.
Joint corresponding authorship: C.A.R. and M.M.
This work was supported by the National Health and Medical Research Council, Australia, Program Grant 10915693 (to S.P. and C.A.R.). C.A.R. was supported by a Dowd Fellowship. The Florey Institute of Neuroscience and Mental Health is supported by Victorian State Government infrastructure funds.
Hebb DO (1949) The organisation of behaviour. New York: Wiley.
Decisions are customarily a result of the Reviewing Editor and the peer reviewers coming together and discussing their recommendations until a consensus is reached. When revisions are invited, a fact-based synthesis statement explaining their decision and outlining what is needed to prepare a revision will be listed below. The following reviewer(s) agreed to reveal their identity: Newton Canteras, John Power.
The manuscript examines synaptic strength in lateral amygdala neurons that are "activated" during fear conditioning. The lateral amygdala is a critical brain region for the formation of associative fear memories, including Pavlovian fear conditioning. Previous research has identified several neurophysiological changes in the lateral amygdala following fear conditioning, including an enhancement of the strength of synaptic inputs, increased intrinsic neuronal excitability, and increased expression of immediate early genes such as c-fos. In the current manuscript the authors use a transgenic mouse to fluorescently label neurons expressing the activity dependent transcription factor c-fos, allowing targeted electrophysiological recordings from these neurons. The authors report that following fear conditioning the amplitude and frequency of spontaneous synaptic events is greater in c-fos expressing lateral amygdala neurons than in non-fos expressing neurons. They suggest that these neurons are a critical component of the fear conditioning engram. Overall, the reviewers and I find this to be an interesting study that contributes to our knowledge of memory formation. However, there are concerns about the small number of neurons from FTL mice in the study (12 neurons, 5 FTL- and 7 FTL+) and the conclusions made from the data particularly from negative findings. More detail about some aspects of the methods and result is also needed to enable the data to be interpreted. Major and minor comments are listed below.
1. The methodology underlying sPSC cumulative probability curve construction lacks detail. How many events were used per neuron?
2. Exact p-values and the number of neurons per group should be reported for each comparison. It is unclear how many neurons were used to generate the data in figure 5, one of the most critical figures in the manuscript.
3. Authors should add a column or scatter plots to complement the cumulative histograms.
4. There is concern that soma size is not sufficient to distinguish between projection and interneurons in the lateral amygdala. The authors have collected the information on the action potential and should also use AP width to distinguish between cell types.
5. The authors should report input resistance, resting membrane potential, and membrane time constant values.
6. Given the limited sample size the authors should use caution when discussing the lack of firing property differences between FTL+ and FTL- neurons. Additionally, the 10 mM EGTA in the internal solution may mask conditioning-associated changes in neuronal firing due to EGTA's effect on calcium-activated currents.THis should be acknowledged.
1. Line 57. "Other studies are also.." - The referenced studies should be cited.
2. Suggest a table of passive membrane properties and firing properties.
The manuscript examines synaptic strength in lateral amygdala neurons that are "activated" during fear conditioning. The lateral amygdala is a critical brain region for the formation of associative fear memories, including Pavlovian fear conditioning. Previous research has identified several neurophysiological changes in the lateral amygdala following fear conditioning, including an enhancement of the strength of synaptic inputs, increased intrinsic neuronal excitability, and increased expression of immediate early genes such as c-fos. In the current manuscript the authors use a transgenic mouse to fluorescently label neurons expressing the activity dependent transcription factor c-fos, allowing targeted electrophysiological recordings from these neurons. The authors report that following fear conditioning the amplitude and frequency of spontaneous synaptic events is greater in c-fos expressing lateral amygdala neurons than in non-fos expressing neurons. They suggest that these neurons are a critical component of the fear conditioning engram. Overall, the reviewers and I find this to be an interesting study that contributes to our knowledge of memory formation. However, there are concerns about the small number of neurons from FTL mice in the study (12 neurons, 5 FTL- and 7 FTL+) and the conclusions made from the data particularly from negative findings. More detail about some aspects of the methods and result is also needed to enable the data to be interpreted. Major and minor comments are listed below. 1. The methodology underlying sPSC cumulative probability curve construction lacks detail. How many events were used per neuron?
Cumulative probability curves were constructed using all events recorded in a standardised 60s period from each neuron, with at least 25 events per neuron represented. These details have been added to the Materials and Methods section at line 183-184.
Exact p-values have been added for each significant comparison, except where the p-value is < 0.0001 (the statistics software package used does not provide exact values below this level). Some incorrect D values for KS analyses have also been corrected. The number of neurons used to generate Figure 5 has been added to the methods and figure legends (line 154, 186 and 569).
We felt that adding these would have resulted in the figure being too cluttered and less clear and have instead added mean and SD values in the text of the Results (p11, p14). However, we are happy to add this data to the figures if the reviewer thinks it is necessary.
All neurons included in analysis had comparable peak half-widths (M = 2.18 ms, SD = 0.30 ms). No neurons included in the analysis had a peak half-width of less than 1.6 ms. These data suggest no interneurons were included in the analysis (which typically have much narrower AP peaks). The observed membrane time constants of recorded neurons (40-50 ms, Table 1) also suggest that the analysis included primarily projection neurons, given that interneurons typically show membrane time constants of ~10 ms (Kaneko et al., 2008). Furthermore, previous studies have shown that all FTL+ neurons in the LAvl are glutamatergic (Wilson and Murphy, 2009).
Input resistance, resting membrane potential and membrane time constants have been added as a table (Table 1).
6. Given the limited sample size the authors should use caution when discussing the lack of firing property differences between FTL+ and FTL- neurons. Additionally, the 10 mM EGTA in the internal solution may mask conditioning-associated changes in neuronal firing due to EGTA's effect on calcium-activated currents. This should be acknowledged.
We agree with this and have added a sentence about the low numbers in both the Results (334-35) and Discussion sections. We have also acknowledged, in the Discussion, that EGTA may be a confound (ln 417-20). 7. Reported firing properties need units These have been added. Minor 1. Line 57. "Other studies are also.." - The referenced studies should be cited. References cited. 2. Suggest a table of passive membrane properties and firing properties.
We have added a table of both the passive and active properties (Table 1 and Table 2).


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Great Lakes Ghosts
More like inland seas than lakes, the Great Lakes of North America are among the most treacherous bodies of water in the world.
Great Lake
Updated 2/9/2020 – Terrible seasonal storms batter ships that choose to sail the lakes at the wrong time of year.
These great gales, called the Witch of November, have claimed many lives and ships over the years, the most famous of which was the Edmund Fitzgerald in 1975.
Strange things happen on the lakes that are unique to them, and sailors have long told stories of ghost ships on the great lakes in the same maritime tradition of the salt water ocean.
The oldest of these ghost ships is the Griffon, launched in 1678 by the early French explorer and trader La Salle.
The ship vanished on the return leg of its maiden voyage loaded with furs for trade.
No trace of the ship was ever found until recently, when a possible candidate for the wreck was found by divers in the form of a very old hand-carved mast sticking out of the muck.
In any case, sailors have seen a ghostly apparition of the Griffon under full sail on several occasions crossing lake Michigan.
At least 10 more modern ghost ships are occasionally sighted plying the lakes, and even the giant 729 foot Edmund Fitzgerald was sighted a decade after its infamous sinking.
But the stories do not end with ships.
A diver working the Lake Superior wreck Emperor in the late 1980's is said to have seen the ghost of a a crewman lying on a bunk looking at him.
Easily the strangest and most macabre story is the tale of Grandpa.
The great lakes are very cold in the depths, so cold that the frigid water will preserve almost anything through natural refrigeration.
This includes human remains, and the story goes that there is a preserved body in the engine room of the wreck of the SS Kamloops, which went down in 1927.
Locals and divers call him Grandpa, and he is known to float quietly behind divers, following them as they swim around the compartment.
Perhaps this is just due to currents created by the divers, or maybe its something else, but the effect has scared the daylights out of more than a few divers.
All the better, as they probably shouldn't be disturbing a wreck that clearly is also the final resting place of preserved human remains.
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The great lakes may be the most fruitful ground in the world for paranormal stories of ghost ships and their crew.
Sightings are almost common, and show no signs of slowing.
What's most interesting is that, unlike the oceans, the majority of the ghost ships seen on the lakes are modern, with many having been lost within living memory.
What makes these inland seas so haunted? Maybe its the cold of the water, or the inordinate amount of tragedies that the lakes have seen.
We'll never know, but if you ever visit one of the lakes, watch carefully as the strange ship you see in the distance may just be a ghost ship.
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Twitter is banning multiple alt-right accounts
Following the release of new features to tackle abuse on its network yesterday, Twitter has begun suspending the accounts of users associated with the alt-right political movement, reports USA Today.
That includes the one belonging to Richard Spencer, who heads up the National Policy Institute (NPI) think tank. The organization concerns itself with the identity of "people of European descent in the United States, and around the world," and publishes related writings on its sister site, Radix Journal.
Accounts for both bodies have also been suspended, along with those of well-know alt-righters Paul Town, Pax Dickinson, Ricky Vaughn and John Rivers.
In a YouTube clip titled 'The Knight of Long Knives' (possibly a nod to the 1934 purge of Nazi leaders meant to help Hitler consolidate power) uploaded yesterday, Spencer said that he wasn't being more of a troll or engaging in any untoward behavior prior to his ban. He noted:
I am alive physically but digitally speaking there has been execution squads across the alt-right.
Spencer added:
There is a great purge going on and they are purging people based on their views.
Twitter previously terminated the account belonging to Milo Yiannopoulos, the outspoken tech editor at Breitbart, after he harassed Ghostbusters actor Leslie Jones on the site.
Spencer clearly believes that there's a coordinated effort being made to stamp out the alt-right movement on Twitter. He also said that he'd consider switching to Gab, a social network that focuses on allowing people the right to free speech with minimal censorship.
Twitter told USA Today that it doesn't comment on the actions taken with regard to individual accounts, for privacy and security reasons – so we may not hear from the company about this. However, it'd be naive to think that this is the last we'll hear on the subject. Spencer said in the video that he believed "this is a clear sign that we have power." How things will play out from here is anybody's guess.
Published November 16, 2016 - 8:47 am UTC
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