Miracle-dz/UniFAR-FTUs huggingface dataset:
Difficulty in Cessation of Undesired Habits: Goal-Based Reduced Successor Representation and Reward Prediction Errors

Difficulty in cessation of drinking, smoking, or gambling, even with strong intention, has been widely recognized. Reasons for this, and whether there are reasons common to substance and non-substance reward, remain elusive. We present a computational model of common potential mechanisms underlying the difficulty in resisting habitual behavior to obtain reward. Consider that a person has long been regularly taking a series of actions leading to a purchase of alcohol, cigarette, or betting ticket without any hesitation. Referring to the recently suggested representation of states by their successors in human reinforcement learning as well as the dimension reduction in state representations in the brain, we assumed that the person has acquired a rigid representation of states along the series of habitual actions by the discounted future occupancy of the final successor state, namely, the rewarded goal state, under the established non-resistant policy. Then, we show that if the person takes a different policy to resist temptation of habitual behavior, negative reward prediction error (RPE) is generated when s/he makes "No-Go" decisions whereas no RPE occurs upon "Go" decisions, and a large positive RPE is generated upon eventually reaching the goal, given that the state representation acquired under the non-resistant policy is so rigid that it does not easily change. In the cases where the states are instead represented in the punctate manner or by the discounted future occupancies of all the states (i.e., by the genuine successor representation), negative and positive RPEs are generated upon "No-Go" and "Go" decisions, respectively, whereas no or little RPE occurs at the goal. We suggest that these RPEs, especially the large positive RPE generated upon goal reaching in the case with the goal-based reduced successor representation, might underlie the difficulty in cessation of undesired habitual or addictive behavior to obtain substance and non-substance reward.
Dissociation of tonotopy and pitch in human auditory cortex

Frequency-to-place mapping, or tonotopy, is a fundamental organizing principle from the earliest stages of auditory processing in the cochlea to subcortical and cortical regions. Although cortical maps are referred to as tonotopic, previous studies employed sounds that covary in spectral content and higher-level perceptual features such as pitch, making it unclear whether these maps are inherited from cochlear organization and are indeed tonotopic, or instead reflect transformations based on higher-level features. We used high-resolution fMRI to measure BOLD responses in 10 participants as they listened to pure tones that varied in frequency or complex tones that independently varied in either spectral content or fundamental frequency (pitch). We show that auditory cortical gradients are in fact a mixture of maps organized both by spectral content and pitch. Consistent with hierarchical organization, primary regions were tuned predominantly to spectral content, whereas higher-level pitch tuning was observed bilaterally in surrounding non-primary regions.
Network Modeling and Inference of Peroxisome Proliferator-Activated Receptor Pathway in High fat diet-linked Obesity

Systems biology aims to understand how holistic systems theory can be used to explain the observable living system characteristics, and mathematical modeling tools have been successful in understanding the intricate relationships underlying cellular functions. Lately, researchers have been interested in understanding molecular mechanisms underlying obesity, which is a major health concern worldwide and has been linked to several diseases. Various mechanisms such as peroxisome proliferator-activated receptors (PPARs) are known to modulate obesity-induced inflammation and its consequences. In this study, we have modeled the PPAR pathway using a Bayesian model and inferred the sub-pathways that are potentially responsible for the activation of the output processes that are associated with high fat diet (HFD)-induced obesity. We examined a previously published dataset from a study that compared gene expression profiles of 40 mice maintained on HFD against 40 mice fed with chow diet (CD). Our simulations have highlighted that GPCR and FATCD36 sub-pathways were aberrantly active in HFD mice and are therefore favorable targets for anti-obesity strategies. We further cross-validated our observations with experimental results from the literature. We believe that mathematical models such as those presented in the present study can help in inferring other pathways and deducing significant biological relationships.
A soluble ACE2 microbody protein fused to a single immunoglobulin Fc domain is a potent inhibitor of SARS-CoV-2 infection in cell culture

Soluble forms of ACE2 have recently been shown to inhibit SARS-CoV-2 infection. We report on an improved soluble ACE2, termed a “microbody” in which the ACE2 ectodomain is fused to Fc domain 3 of the immunoglobulin heavy chain. The protein is smaller than previously described ACE2-Ig Fc fusion proteins and contains an H345A mutation in the ACE2 catalytic active site that inactivates the enzyme without reducing its affinity for the SARS-CoV-2 spike. The disulfide-bonded ACE2 microbody protein inhibited entry of lentiviral SARS-CoV-2 spike protein pseudotyped virus and live SARS-CoV-2 with a potency 10-fold higher than unmodified soluble ACE2 and was active after initial virus binding to the cell. The ACE2 microbody inhibited the entry of ACE2-specific β coronaviruses and viruses with the high infectivity variant D614G spike. The ACE2 microbody may be a valuable therapeutic for COVID-19 that is active against SARS-CoV-2 variants and future coronaviruses that may arise.
Multiple measures of depression to enhance validity of major depressive disorder in the UK Biobank

Background The UK Biobank contains data with varying degrees of reliability and completeness for assessing depression. A third of participants completed a Mental Health Questionnaire (MHQ) containing the gold-standard Composite International Diagnostic Interview (CIDI) criteria for assessing mental health disorders. Aims To investigate whether multiple observations of depression from sources other than the MHQ can enhance the validity of major depressive disorder (MDD). Method In participants who did not complete the MHQ, we calculated the number of other depression measures endorsed, for example from hospital episode statistics and interview data. We compared cases defined this way with CIDI-defined cases for several estimates: the variance explained by polygenic risk scores (PRS), area under the curve attributable to PRS, single nucleotide polymorphisms (SNPs)-based heritability and genetic correlations with summary statistics from the Psychiatric Genomics Consortium MDD genome-wide association study. Results The strength of the genetic contribution increased with the number of measures endorsed. For example, SNP-based heritability increased from 7% in participants who endorsed only one measure of depression, to 21% in those who endorsed four or five measures of depression. The strength of the genetic contribution to cases defined by at least two measures approximated that for CIDI-defined cases. Most genetic correlations between UK Biobank and the Psychiatric Genomics Consortium MDD study exceeded 0.7, but there was variability between pairwise comparisons. Conclusions Multiple measures of depression can serve as a reliable approximation for case status where the CIDI measure is not available, indicating sample size can be optimised using the entire suite of UK Biobank data.
Single-cell immune landscape of human recurrent spontaneous abortion

Successful pregnancy in placental mammals substantially depends on the establishment of maternal immune tolerance to the semi-allogenic fetus. Disorders in this process are tightly associated with adverse pregnancy outcomes including recurrent spontaneous abortion (RSA). However, an in-depth understanding of the disorders from the aspect of systematic and decidual immune environment in RSA remains largely lacking. In this study, we utilized single-cell RNA-sequencing to comparably analyze the cellular and molecular signatures of decidual and peripheral leukocytes in normal and RSA pregnancies at the early stage of gestation. Integrative analysis identified 22 distinct cell clusters in total, and a dramatic difference in leukocyte subsets and molecular properties in RSA cases was revealed. Specifically, the cytotoxic properties of CD8T effector, NK, and MAIT cells in peripheral blood indicated apparently enhanced immune inflammatory status, and the subpopulation proportions and ligand-receptor interactions of the decidual leukocyte subsets demonstrated preferential immune activation in RSA patients. The molecular features, spatial distribution and the developmental trajectories of five decidual NK (dNK) subsets were illustrated. The proportion of a dNK subset responsible for fetal protection was reduced, while the ratio of another dNK subset with cytotoxic and immune-active signature was significantly increased. Notably, a unique pro-inflammatory CD56+CD16+ dNK subpopulation was substantially accumulated in RSA decidua. These findings reveal a comprehensive cellular and molecular atlas of decidual and peripheral leukocytes in human early pregnancy, which provides an in-depth insight into the immune pathogenesis for early pregnancy loss.
An associative learning account of sensorimotor adaptation

1 Two influential paradigms, sensorimotor adaptation and eyeblink conditioning, have deepened 2 our understanding of the theoretical and neural foundations of motor learning, and in particular, 3 the role of the cerebellum. Although there has been some cross-pollination between these two 4 lines of research, they typically operate within distinct theoretical frameworks, with the incremental 5 updating of an internal forward model explaining adaptation, and associative learning processes 6 explaining eyeblink conditioning. Here we ask if a unified framework might be parsimonious, 7 directly linking sensorimotor adaptation to associative learning. Using a task that isolates implicit 8 sensorimotor adaptation, we paired movement-related feedback with neutral auditory or visual 9 cues that served as conditioning stimuli (CSs) to test two key signatures of associative learning- 10 differential conditioning and compound conditioning. We observed clear Pavlovian effects in both 11 cases: Implicit trial-by-trial changes in movement kinematics were reliably modulated by the CSs 12 in the predicted directions. Moreover, after compound conditioning, we observed a robust 13 negative correlation between the responses of individuals to the two elemental CSs of the 14 compound, consistent with the additivity principle posited by the Rescorla-Wagner model of 15 classical conditioning. Computational modelling demonstrates that these results cannot be 16 captured by the conventional algorithm used to explain the operation of a forward model. We 17 believe that associative learning effects in implicit sensorimotor adaptation provide a proof-of- 18 concept for linking multiple motor learning paradigms within a similar theoretical framework. 19
Predicting MEG resting-state functional connectivity using microstructural information

Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from ninety healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity and a myelin measure (derived from multi-component relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer timeseries at four frequency bands: delta (1 − 4 Hz), theta (3 − 8 Hz), alpha (8 − 13 Hz) and beta (13 − 30 Hz). Non-negative matrix factorization was performed to identify the components of the functional connectivity. Shortest-path-length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructurally-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. The shortest-path-length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.
Targeting ischemia-induced KCC2 hypofunction rescues refractory neonatal seizures and mitigates epileptogenesis in a mouse model

Neonatal seizures pose a clinical challenge for their early detection, acute management, and mitigation of long-term comorbidities. A major cause of neonatal seizures is hypoxic-ischemic encephalopathy that results in seizures that are frequently refractory to the first-line anti-seizure medication phenobarbital (PB). One proposed mechanism for PB-inefficacy during neonatal seizures is the reduced expression and function of the neuron-specific K+/Cl− cotransporter 2 (KCC2), the main neuronal Cl− extruder that maintains chloride homeostasis and influences the efficacy of GABAergic inhibition. To determine if PB-refractoriness after ischemic neonatal seizures is dependent upon KCC2 hypofunction and can be rescued by KCC2 functional enhancement, we investigated the recently developed KCC2 functional enhancer CLP290 in a CD-1 mouse model of refractory ischemic neonatal seizures quantified with vEEG. We report that acute CLP290 intervention can rescue PB-resistance, KCC2 expression, and the development of epileptogenesis after ischemic neonatal seizures. KCC2 phosphorylation sites have a strong influence over KCC2 activity and seizure susceptibility in adult experimental epilepsy models. Therefore, we investigated seizure susceptibility in two different knock-in mice in which either phosphorylation of S940 or T906/T1007 was prevented. We report that KCC2 phosphorylation regulates both neonatal seizure susceptibility and CLP290-mediated KCC2 functional enhancement. Our results validate KCC2 as a clinically relevant target for refractory neonatal seizures and provide insights for future KCC2 drug development.
OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing

SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing. It can simulate a population of 1 million people in seconds per day allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 is its Python interface, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.
Quantifying the Effects of Social Distancing on the Spread of COVID-19

This paper studies the interplay between the social distancing and the spread of COVID-19 disease, a widely spread pandemic that has affected nearly most of the world population. Starting in China, now the virus has reached the United States of America with devastating consequences. Other countries severely affected by the pandemic are Brazil, Russia, United Kingdom, Spain, India, Italy, and France. Even though it is not possible to eliminate the spread of the virus from the world or any other country, it might be possible to reduce its effect by decreasing the amount of infected people. Implementing such policies need a good understanding of the systems dynamics, generally not possible with mathematical linear equations or Monte Carlo methods because human society is a complex adaptive system with complex and continuous feedback loops. As a result, we use agent-based methods to conduct our study. Moreover, recent agent-based modeling studies for the COVID-19 pandemic show significant promise assisting decision-makers in managing the crisis through applying some policies such as social distancing, disease testing, contact tracing, home isolation, providing good emergency and hospitalization strategies, and preventing traveling would lead to reduce the infection rates. Based on imperial college modeling studies that prove increasing levels of interventions could slow down the spread of disease and infection cases as much as possible, and taking into account that social distancing policy is considered to be the most important factor that was recommended to follow. Our proposed model is designed to test if increasing the social distancing policies strictness can slow down the spread of disease significantly or not, and find out what is the required safe level of social distancing. So, the model was run six times with six different percentages of social distancing with keeping the other parameters levels fixed for all experiments. The results of our study show that social distancing affects the spread of COVID-19 significantly, where the spread of disease and infection rates decrease once social distancing procedures are implemented at higher levels. Also, the behavior space tool was used to run ten experiments with different levels of social distancing which enough more supported the previous results. We conclude that applying and increasing social distancing policy levels led to significantly reduced infection rates which result in decreasing deaths. Both types of experiments prove that 80% to 100% of following social distancing policies reduces the infection rates dramatically.
Epidemiology of COVID-19 vs. influenza: Differential failure of COVID-19 mitigation among Hispanics, Cook County Health, Illinois

BACKGROUND: During the early phases of the COVID-19 pandemic, predominantly African-American or Hispanic communities were disproportionately impacted. We sought to better understand the epidemiology of COVID-19 among hospitalized Hispanic patients by comparing individual and census-tract level characteristics of patients diagnosed with COVID-19 to those diagnosed with influenza, another viral infection with respiratory transmission. We evaluated the temporal changes in epidemiology across race-ethnicity related to a shelter-in-place mandate. METHODS: We evaluated patients hospitalized at Cook County Health, the safety-net health system for the Chicago metropolitan area. Among self-identified hospitalized Hispanic patients, we compared those with influenza (2019-2020 influenza season) to COVID-19 infection during March 16, 2020 through May 11, 2020. We used multivariable analysis to identify differences in individual and census-tract level characteristics between the two groups. RESULTS: Relative to non-Hispanic blacks and whites, COVID-19 rapidly increased among Hispanics during promotion of social-distancing policies. Whereas non-Hispanic blacks were more likely to be hospitalized for influenza, Hispanic patients predominated among COVID-19 infections (40% relative increase compared to influenza). In the comparative analysis of influenza and COVID-19, Hispanic patients with COVID-19 were more likely to reside in census tracts with higher proportions of residents with the following characteristics: Hispanic; no high school diploma; non-US citizen; limited English speaking ability; employed in manufacturing or construction; and, overcrowding. By multivariable analysis, Hispanic patients hospitalized with COVID-19 compared to those with influenza were more likely to be male (adjusted OR=1.8; 95% CI 1.1 to 2.9) or obese (aOR=2.5; 95% CI 1.5 to 4.2), and to reside in a census tract with [≥]40% of residents without a high-school diploma (aOR=2.5; 95% CI 1.3 to 4.8). CONCLUSIONS: The rapid and disproportionate increase in COVID-19 hospitalizations among Hispanics after the shelter-in-place mandate indicates that public health strategies were inadequate in protecting this population. In particular, those residing in neighborhoods with lower levels of educational attainment.
Synthesis of unsymmetrical sulfamides and polysulfamides via SuFEx click chemistry†

As hydrogen-bond donors and acceptors, N,N′-disubstituted sulfamides have been used in a range of applications from medicinal chemistry to anion-binding catalysis. However, compared to ureas or thioureas, the utilization of this unique moiety remains marginal, in part because of a lack of general synthetic methods to access unsymmetrical sulfamides. Specifically, polysulfamides are a virtually unknown type of polymer despite their potential utility in non-covalent dynamic networks, an intense area of research in materials science. We report herein a practical and efficient process to prepare unsymmetrical sulfamides via Sulfur(vi)-Fluoride Exchange (SuFEx) click chemistry. This process was then applied to synthesize polysulfamides. Thermal analysis showed that this family of polymers possess high thermal stability and tunable glass transition temperatures. Finally, hydrolysis studies indicated that aromatic polysulfamides could be recycled back to their constituting monomers at the end of their life cycle.
Lightning strikes and resultant fires from archival (1912- 1917) and current (1960-1997) information in Pennsylvania

RUFFNER, C. M. AND M. D. ABRAMS (School of Forest Resources, Pennsylvania State University, University Park, PA 16802). Lightning strikes and resultant fires from archival (1912-1917) and current (1960-1997) information in Pennsylvania. J. Torrey Bot. Soc. 125:249-252. 1998. -Archival information was searched for evidence of lightning strikes and subsequent ignitions in Pennsylvania forests. Between 1912-1917, twentyeight different tree species were struck by lightning 2553 times igniting 77 individual trees. These data probably underestimate the number of ignited fires due to rugged terrain, inefficient fire reporting, and minimal fire control resources of this early era. Lightning strikes occurred most often between June-August, peaking in July with 877 strikes. These data provide direct evidence that lightning strikes and tree ignitions occur relatively frequently in Pennsylvania forests. Recent data provide substantial evidence for large areas burned by lightning ignitions during drought years in the past thirty-seven years.
Detection of PRDV from Wild Adult Kuruma Prawn

1996年7月から1998年4月にかけて, 5つの海域で漁獲された天然クルマエビ成体から PCR 法により PRDV (penaeid rod-shaped DNA virus: クルマエビ類の急性ウイルス血症原因ウイルス)の検出を定期的に行った。 検査部位は雌雄とも血リンパ, 胃上皮および生殖腺とした。 その結果, 雌エビにおいては卵巣, 雄エビにおいては胃上皮で, それぞれ最も高い検出率を示した。 また, PRDV の検出率は夏期(7月から8月)に高く, 冬期(12月から1月)に低かった。 さらに, 雄親エビよりも雌の方が高い検出率を示した。