tbuckley/cabot-search huggingface dataset:
Neither hype nor gloom do DNNs justice
Authors: Felix A. Wichmann, Simon Kornblith, Robert Geirhos
Journal: Behavioral and Brain Sciences (2023)

Abstract Neither the hype exemplified in some exaggerated claims about deep neural networks (DNNs), nor the gloom expressed by Bowers et al. do DNNs as models in vision science justice: DNNs rapidly evolve, and today's limitations are often tomorrow's successes. In addition, providing explanations as well as prediction and image-computability are model desiderata; one should not be favoured at the expense of the other.
Efficacy and Safety of the Heat Shock Protein 90 Inhibitor RGRN-305 in Hidradenitis Suppurativa
Authors: Hakim Ben Abdallah, Anne Bregnhøj, Thomas Emmanuel, Gautam Ghatnekar, Claus Johansen, Lars Iversen
Journal: JAMA Dermatology (2023)

Importance Hidradenitis suppurativa is a painful immune-mediated disorder with limited treatment options; hence, a need exists for new treatments. Objective To evaluate the feasibility of heat shock protein 90 inhibition by RGRN-305 as a novel mechanism of action in treating moderate to severe hidradenitis suppurativa. Design, Setting, and Participants This was a parallel-design, double-blind, proof-of-concept, placebo-controlled randomized clinical trial conducted between September 22, 2021, and August 29, 2022, at the Department of Dermatology, Aarhus University Hospital in Denmark. The study included a 1- to 30-day screening period, a 16-week treatment period, and a 4-week follow-up period. Eligibility criteria included age 18 years or older and moderate to severe hidradenitis suppurativa with 6 or more inflammatory nodules or abscesses in at least 2 distinct anatomic regions. Of 19 patients screened, 15 patients were enrolled in the study. Intention-to-treat analysis was performed. Interventions Patients were randomly assigned (2:1) to receive oral RGRN-305, 250-mg tablet, or matching placebo once daily for 16 weeks. Main Outcomes and Measures The primary efficacy end point was the percentage of patients achieving Hidradenitis Suppurativa Clinical Response 50 (HiSCR-50) at week 16. Secondary efficacy end points included HiSCR-75 or HiSCR-90, Hidradenitis Suppurativa Physician’s Global Assessment, Dermatology Life Quality Index scores, and a pain numeric rating scale. Safety was assessed by adverse events, physical examinations, clinical laboratory measurements, and electrocardiograms. Results A total of 15 patients were enrolled, completed the study, and were included in all analyses (10 [67%] female; median age, 29 [IQR, 23-41] years). The primary end point HiSCR-50 at week 16 was achieved by a higher percentage in the RGRN-305 group (60% [6 of 10]) than in the placebo group (20% [1 of 5]). Improvements were also observed across all secondary end points at week 16, including higher rates of the harder-to-reach HiSCR levels; 50% (5 of 10) achieved HiSCR-75 and 30% (3 of 10) achieved HiSCR-90, whereas none of the placebo-treated patients achieved HiSCR-75 or HiSCR-90. RGRN-305 was well tolerated, with no deaths or serious adverse events, and treatment-emergent adverse events were similarly frequent between the RGRN-305 and placebo groups. Conclusions and Relevance The findings of this trial suggest that heat shock protein 90 inhibition by RGRN-305 offers a novel mechanism of action in treating hidradenitis suppurativa, warranting further evaluation in larger trials. Trial Registration ClinicalTrials.gov Identifier: NCT05286567
Psilocybin in Bipolar II Study Provides Preliminary Data on Safety
Authors: David B. Yaden, Natalie Gukasyan, Sandeep M. Nayak
Journal: JAMA Psychiatry (2023)

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Psychiatry HomeNew OnlineCurrent IssueFor Authors Podcast Journals JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2024 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Psychiatry journal
Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses
Authors: Tal Golan, JohnMark Taylor, Heiko Schütt, Benjamin Peters, Rowan P. Sommers, Katja Seeliger, Adrien Doerig, Paul Linton, Talia Konkle, Marcel van Gerven, Konrad Kording, Blake Richards, Tim C. Kietzmann, Grace W. Lindsay, Nikolaus Kriegeskorte
Journal: Behavioral and Brain Sciences (2023)

Abstract An ideal vision model accounts for behavior and neurophysiology in both naturalistic conditions and designed lab experiments. Unlike psychological theories, artificial neural networks (ANNs) actually perform visual tasks and generate testable predictions for arbitrary inputs. These advantages enable ANNs to engage the entire spectrum of the evidence. Failures of particular models drive progress in a vibrant ANN research program of human vision.
Clarifying status of DNNs as models of human vision
Authors: Jeffrey S. Bowers, Gaurav Malhotra, Marin Dujmović, Milton L. Montero, Christian Tsvetkov, Valerio Biscione, Guillermo Puebla, Federico Adolfi, John E. Hummel, Rachel F. Heaton, Benjamin D. Evans, Jeffrey Mitchell, Ryan Blything
Journal: Behavioral and Brain Sciences (2023)

On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN-human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in the literature. In our view, these latter issues are contributing to many unjustified claims regarding DNN-human correspondences in vision and other domains of cognition. We explore all these issues in this response.
Addressing State-Based Differences in Suicide Fatalities Among Children and Adolescents—Finding Another Way
Authors: Cordelia Mannix, Rebekah Mannix
Journal: JAMA Psychiatry (2023)

This Viewpoint uses state-based data obtained from the National Center for Health Statistics’ Web-Based Injury Statistics Query and Reporting System to discuss the importance of legislatively addressing firearm-related suicide fatalities among children and adolescents in the US.
The Consideration of Cyclosporine in the Treatment of Stevens-Johnson Syndrome—Reply
Authors: Yuki Miyamoto, Hiroyuki Ohbe, Hideo Yasunaga
Journal: JAMA Dermatology (2023)

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Dermatology HomeNew OnlineCurrent IssueFor Authors Podcast Journals JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2024 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Dermatology journal
Even deeper problems with neural network models of language
Authors: Thomas G. Bever, Noam Chomsky, Sandiway Fong, Massimo Piattelli-Palmarini
Journal: Behavioral and Brain Sciences (2023)

Abstract We recognize today's deep neural network (DNN) models of language behaviors as engineering achievements. However, what we know intuitively and scientifically about language shows that what DNNs are and how they are trained on bare texts, makes them poor models of mind and brain for language organization, as it interacts with infant biology, maturation, experience, unique principles, and natural law.
The Consideration of Cyclosporine in the Treatment of Stevens-Johnson Syndrome
Authors: Hamza Malick, Kyle Lauck, Dario Kivelevitch
Journal: JAMA Dermatology (2023)

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Dermatology HomeNew OnlineCurrent IssueFor Authors Podcast Journals JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Dermatology journal
Models of vision need some action
Authors: Constantin Rothkopf, Frank Bremmer, Katja Fiehler, Katharina Dobs, Jochen Triesch
Journal: Behavioral and Brain Sciences (2023)

Abstract Bowers et al. focus their criticisms on research that compares behavioral and brain data from the ventral stream with a class of deep neural networks for object recognition. While they are right to identify issues with current benchmarking research programs, they overlook a much more fundamental limitation of this literature: Disregarding the importance of action and interaction for perception.
Why psychologists should embrace rather than abandon DNNs
Authors: Galit Yovel, Naphtali Abudarham
Journal: Behavioral and Brain Sciences (2023)

Deep neural networks (DNNs) are powerful computational models, which generate complex, high-level representations that were missing in previous models of human cognition. By studying these high-level representations, psychologists can now gain new insights into the nature and origin of human high-level vision, which was not possible with traditional handcrafted models. Abandoning DNNs would be a huge oversight for psychological sciences.
Implications of capacity-limited, generative models for human vision
Authors: Joseph Scott German, Robert A. Jacobs
Journal: Behavioral and Brain Sciences (2023)

Although discriminative deep neural networks are currently dominant in cognitive modeling, we suggest that capacity-limited, generative models are a promising avenue for future work. Generative models tend to learn both local and global features of stimuli and, when properly constrained, can learn componential representations and response biases found in people's behaviors.
Economic Burden of Health Conditions Associated With Adverse Childhood Experiences Among US Adults
Authors: Cora Peterson, Maria V. Aslam, Phyllis H. Niolon, Sarah Bacon, Mark A. Bellis, James A. Mercy, Curtis Florence
Journal: JAMA Network Open (2023)

Importance Adverse childhood experiences (ACEs) are preventable, potentially traumatic events in childhood, such as experiencing abuse or neglect, witnessing violence, or living in a household with substance use disorder, mental health problems, or instability from parental separation or incarceration. Adults who had ACEs have more harmful risk behaviors and worse health outcomes; the economic burden associated with these issues is uncertain. Objective To estimate the economic burden of ACE-associated health conditions among US adults. Design, Setting, and Participants In this economic evaluation, regression models of cross-sectional survey data from the 2019-2020 Behavioral Risk Factor Surveillance System (BRFSS) and previous studies were used to estimate ACE population-attributable fractions (PAFs) (ie, the fraction of total cases associated with a specific exposure) for selected health outcomes (anxiety, arthritis, asthma, cancer, chronic obstructive pulmonary disease, depression, diabetes, heart disease, kidney disease, stroke, and violence) and risk factors (heavy drinking, illicit drug use, overweight and obesity, and smoking) among the 2019 US adult population. Adverse childhood experience PAFs were used to calculate the proportion of total condition-specific medical spending and lost healthy life-years related to ACEs using Global Burden of Disease Study data. Data analysis was performed from September 10, 2021, to November 29, 2022. Exposure Adverse childhood experiences (age &amp;amp;lt;18 years). Main Outcomes and Measures Monetary valuation of ACE-associated morbidity and mortality using standard US value of statistical life methods and presented in terms of annual and lifetime per affected person and total population estimates at the national and state levels. Results A total of 820 673 adults, representing 255 million individuals, participated in the BRFSS in 2019 and 2020. An estimated 160 million of the total 255 million US adult population (63%) had 1 or more ACE, associated with an annual economic burden of $14.1 trillion ($183 billion in direct medical spending and $13.9 trillion in lost healthy life-years). This was $88 000 per affected adult annually and $2.4 million over their lifetimes. The lifetime economic burden per affected adult was lowest in North Dakota ($1.3 million) and highest in Arkansas ($4.3 million). Twenty-two percent of adults had 4 or more ACEs and comprised 58% of the total economic burden—the estimated per person lifetime economic burden for those adults was $4.0 million. Conclusions and Relevance In this cross-sectional analysis of the US adult population, the economic burden of ACE-related health conditions was substantial. The findings suggest that measuring the economic burden of ACEs can support decision-making about investing in strategies to improve population health.
Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision
Authors: James J. DiCarlo, Daniel L. K. Yamins, Michael E. Ferguson, Evelina Fedorenko, Matthias Bethge, Tyler Bonnen, Martin Schrimpf
Journal: Behavioral and Brain Sciences (2023)

Abstract In the target article, Bowers et al. dispute deep artificial neural network (ANN) models as the currently leading models of human vision without producing alternatives. They eschew the use of public benchmarking platforms to compare vision models with the brain and behavior, and they advocate for a fragmented, phenomenon-specific modeling approach. These are unconstructive to scientific progress. We outline how the Brain-Score community is moving forward to add new model-to-human comparisons to its community-transparent suite of benchmarks.
Single-Dose Synthetic Psilocybin With Psychotherapy for Treatment-Resistant Bipolar Type II Major Depressive Episodes
Authors: Scott T. Aaronson, Andrew van der Vaart, Tammy Miller, Jeffrey LaPratt, Kimberly Swartz, Audrey Shoultz, Margo Lauterbach, Harold A. Sackeim, Trisha Suppes
Journal: JAMA Psychiatry (2023)

Bipolar II disorder (BDII) is a debilitating condition frequently associated with difficult-to-treat depressive episodes. Psilocybin has evidence for rapid-acting antidepressant effects but has not been investigated in bipolar depression.
Nucleic acid‐based small molecules as targeted transcription therapeutics for immunoregulation
Authors: Dan Bai, Reihane Ziadlou, Thangavel Vaijayanthi, Subramani Karthikeyan, Shanmugavel Chinnathambi, Anutthaman Parthasarathy, Li Cai, Marie‐Charlotte Brüggen, Hiroshi Sugiyama, Ganesh N. Pandian
Journal: Allergy (2023)

Abstract Transcription therapy is an emerging approach that centers on identifying the factors associated with the malfunctioning gene transcription machinery that causes diseases and controlling them with designer agents. Until now, the primary research focus in therapeutic gene modulation has been on small‐molecule drugs that target epigenetic enzymes and critical signaling pathways. However, nucleic acid‐based small molecules have gained popularity in recent years for their amenability to be pre‐designed and realize operative control over the dynamic transcription machinery that governs how the immune system responds to diseases. Pyrrole–imidazole polyamides (PIPs) are well‐established DNA‐based small‐molecule gene regulators that overcome the limitations of their conventional counterparts owing to their sequence‐targeted specificity, versatile regulatory efficiency, and biocompatibility. Here, we emphasize the rational design of PIPs, their functional mechanisms, and their potential as targeted transcription therapeutics for disease treatment by regulating the immune response. Furthermore, we also discuss the challenges and foresight of this approach in personalized immunotherapy in precision medicine.
Early‐Responsive Immunoregulation Therapy Improved Microenvironment for Bone Regeneration Via Engineered Extracellular Vesicles
Authors: Tingwei Lu, Yuanqi Liu, Xiangru Huang, Siyuan Sun, Hongyuan Xu, Anting Jin, Xinyu Wang, Xin Gao, Jingyi Liu, Yanfei Zhu, Qinggang Dai, Chao Wang, Kaili Lin, Lingyong Jiang
Journal: Advanced Healthcare Materials (2023)

Abstract Overactivated inflammatory reactions hinder the bone regeneration process. Timely transformation of microenvironment from pro‐inflammatory to anti‐inflammatory after acute immune response is favorable for osteogenesis. Macrophages play an important role in the immune response to inflammation. Therefore, this study adopts TIM3 high expression extracellular vesicles (EVs) with immunosuppressive function to reshape the early immune microenvironment of bone injury, mainly by targeting macrophages. These EVs can be phagocytosed by macrophages, thereby increasing the infiltration of TIM3‐positive macrophages (TIM3 + macrophages) and M2 subtypes. The TIM3 + macrophage group has some characteristics of M2 macrophages and secretes cytokines, such as IL‐10 and TGF‐β1 to regulate inflammation. TIM3, which is highly expressed in the engineered EVs, mediates the release of anti‐inflammatory cytokines by inhibiting the p38/MAPK pathway and promotes osseointegration by activating the Bmp2 promoter to enhance macrophage BMP2 secretion. After evenly loading the engineered EVs into the hydrogel, the continuous and slow release of EVs TIM3OE recruits more anti‐inflammatory macrophages during the early stages of bone defect repair, regulating the immune microenvironment and eliminating the adverse effects of excessive inflammation. In summary, this study provides a new strategy for the treatment of refractory wounds through early inflammation control.
Where do the hypotheses come from? Data-driven learning in science and the brain
Authors: Barton L. Anderson, Katherine R. Storrs, Roland W. Fleming
Journal: Behavioral and Brain Sciences (2023)

Abstract Everyone agrees that testing hypotheses is important, but Bowers et al. provide scant details about where hypotheses about perception and brain function should come from. We suggest that the answer lies in considering how information about the outside world could be acquired – that is, learned – over the course of evolution and development. Deep neural networks (DNNs) provide one tool to address this question.
Neural networks, AI, and the goals of modeling
Authors: Walter Veit, Heather Browning
Journal: Behavioral and Brain Sciences (2023)

Deep neural networks (DNNs) have found many useful applications in recent years. Of particular interest have been those instances where their successes imitate human cognition and many consider artificial intelligences to offer a lens for understanding human intelligence. Here, we criticize the underlying conflation between the predictive and explanatory power of DNNs by examining the goals of modeling.
Optimizing Anticoagulation for Venovenous Extracorporeal Membrane Oxygenation: Finding the Right Balance
Authors: Christiaan L. Meuwese, Jerrold H. Levy
Journal: American Journal of Respiratory and Critical Care Medicine (2023)

"Optimizing Anticoagulation for VV ECMO: Finding the Right Balance." American Journal of Respiratory and Critical Care Medicine, 0(ja), pp.
Antigen-specific T cell activation through targeted delivery of in-situ generated antigen and calcium ionophore to enhance antitumor immunotherapy
Authors: Xue Chen, Hongling Ouyang, Yunxiao Zhang, Conglin Chen, Simin Nan, Ximing Pu, Tao Gong, Zhi-Rong Zhang, Renhe Liu, Yao Fu
Journal: Journal of Controlled Release (2023)
For deep networks, the whole equals the sum of the parts
Authors: Philip J. Kellman, Nicholas Baker, Patrick Garrigan, Austin Phillips, Hongjing Lu
Journal: Behavioral and Brain Sciences (2023)

Abstract Deep convolutional networks exceed humans in sensitivity to local image properties, but unlike biological vision systems, do not discover and encode abstract relations that capture important properties of objects and events in the world. Coupling network architectures with additional machinery for encoding abstract relations will make deep networks better models of human abilities and more versatile and capable artificial devices.
Fixing the problems of deep neural networks will require better training data and learning algorithms
Authors: Drew Linsley, Thomas Serre
Journal: Behavioral and Brain Sciences (2023)

Bowers et al. argue that deep neural networks (DNNs) are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those of humans. We show that this problem is worsening as DNNs are becoming larger-scale and increasingly more accurate, and prescribe methods for building DNNs that can reliably model biological vision.