Miracle-dz/UniFAR-FTUs huggingface dataset:
SynaptoPAC, an Optogenetic Tool for Induction of Presynaptic Plasticity

Optogenetic manipulations have transformed neuroscience in recent years. While sophisticated tools now exist for controlling the firing patterns of neurons, it remains challenging to optogenetically define the plasticity state of individual synapses. A variety of synapses in the mammalian brain express presynaptic long-term potentiation (LTP) upon elevation of presynaptic cyclic adenosine monophosphate (cAMP), but the molecular expression mechanisms as well as the impact of presynaptic LTP on network activity and behavior are not fully understood. In order to establish optogenetic control of presynaptic cAMP levels and thereby presynaptic potentiation, we developed synaptoPAC, a presynaptically targeted version of the photoactivated adenylyl cyclase bPAC. In cultures of hippocampal granule cells, activation of synaptoPAC with blue light increases action potential-evoked transmission, an effect not seen in hippocampal cultures of non-granule cells. In acute brain slices, synaptoPAC activation immediately triggers a strong presynaptic potentiation at mossy fiber terminals in CA3, but not at Schaffer collateral synapse in CA1. Following light-triggered potentiation, mossy fiber transmission decreases within 20 minutes, but remains enhanced still after 30 min. Optogenetic potentiation alters the short-term plasticity dynamics of release, reminiscent of presynaptic LTP. SynaptoPAC is the first optogenetic tool that allows acute light-controlled potentiation of transmitter release at specific synapses of the brain, and will enable to investigate the role of presynaptic potentiation in network function and the animal’s behavior in an unprecedented manner. Significance Statement SynaptoPAC is a novel optogenetic tool that allows increasing synaptic transmission by light-controlled induction of presynaptic plasticity.
Acquired Immunodeficiency from Maternal Chemotherapy and Severe Primary Pneumocystis Infection in an Infant

Pneumocystis jirovecii is recognized as an opportunistic pathogen in immunosuppressed patients. We report a case of severe Pneumocystis pneumonia (PCP) in an infant with acquired combined immunodeficiency secondary to maternal chemotherapy exposure during the second and third trimesters of pregnancy. The infant required cardiorespiratory support with veno-venous extracorporeal membrane oxygenation (VV-ECMO) for severe respiratory failure. This case highlights the potential for severe acquired immunodeficiency in this patient cohort and further postnatal surveillance is highly recommended.
How many ontogenetic points are needed to accurately describe the ontogeny of a cephalopod conch? A case study of the modern nautilid Nautilus pompilius

Recent advancements in tomographic techniques allow for detailed morphological analysis of various organisms, which has proved difficult in the past. However, the time and cost required for the post-processing of highly resolved tomographic data are considerable. Cephalopods are an ideal group to study ontogeny using tomography as the entire life history is preserved within a conch. Although an increasing number of studies apply tomography to cephalopod conchs, the number of conch measurements needed to adequately characterize ontogeny remains unknown. Therefore, the effect of different ontogenetic sampling densities on the accuracy of the resultant growth trajectories needs to be investigated. Here, we reconstruct ontogenetic trajectories of a single conch of Nautilus pompilius using different numbers of ontogenetic points to assess the resulting accuracies. To this end, conch parameters were measured every 10°, 30°, 45°, 90°, and 180°. Results reveal that the overall patterns of reconstructed growth trajectories are nearly identical. Relatively large errors appear to occur where growth changes occur, such as the points of hatching and the onset of morphogenetic countdown before the attainment of maturity. In addition, a previously undocumented growth change before hatching was detected when measurements were taken every 10°, 30°, and 45°, though this growth change was obscured when fewer measurements were used (90° and 180°). The lower number of measurements also masks the subtle fluctuating patterns of conch parameters in middle ontogeny. We conclude that the measurements of a conch every 30° and 45° permit a reasonably precise description of conch ontogeny in nautilids. Since ammonoids were likely more responsive to external stimuli than to nautilids, a much denser sampling may be required for ammonoids.
Temporal changes in the most effective pollinator of a bromeliad pollinated by bees and hummingbirds

A generalist pollination system may be characterized through the interaction of a plant species with two or more functional groups of pollinators. The spatiotemporal variation of the most effective pollinator is the factor most frequently advocated to explain the emergence and maintenance of generalist pollination systems. There are few studies merging variation in floral visitor assemblages and the efficacy of pollination by different functional groups. Thus, there are gaps in our knowledge about the variation in time of pollinator efficacy and frequency of generalist species. In this study, we evaluated the pollination efficacy of the floral visitors of Edmundoa lindenii (Bromeliaceae) and their frequency of visits across four reproductive events. We analyzed the frequency of the three groups of floral visitors (large bees, small bees, and hummingbirds) through focal observations in the reproductive events of 2015, 2016, 2017, and 2018. We evaluated the pollination efficacy (fecundity after one visit) through selective exposure treatments and the breeding system by manual pollinations. We tested if the reproductive success after natural pollination varied between the reproductive events and also calculated the pollen limitation index. E. lindenii is a self-incompatible and parthenocarpic species, requiring the action of pollinators for sexual reproduction. Hummingbirds had higher efficacy than large bees and small bees acted only as pollen larcenists. The relative frequency of the groups of floral visitors varied between the reproductive events. Pollen limitation has occurred only in the reproductive event of 2017, when visits by hummingbirds were scarce and reproductive success after natural pollination was the lowest. We conclude that hummingbirds and large bees were the main and the secondary pollinators of E. lindenii, respectively, and that temporal variations in the pollinator assemblages had effects on its reproductive success. Despite their lower pollination efficacy, large bees ensured seed set when hummingbirds failed. Thus, we provide evidence that variable pollination environments may favor generalization, even under differential effectiveness of pollinator groups if secondary pollinators provide reproductive assurance.
Grant Report on Anxiety-CBT: Dimensional Brain Behavior Predictors of CBT Outcomes in Pediatric Anxiety

In the following grant report, we describe initial and planned work supported by our National Institute of Mental Health R01-funded, Research Domain Criteria (RDoc) informed project, “Dimensional Brain Behavior Predictors of CBT Outcomes in Pediatric Anxiety”. This project examines response to cognitive behavioral therapy (CBT) in a large sample of anxiety-affected and low-anxious youth ages 7 to 18 years using multiple levels of analysis, including brain imaging, behavioral performance, and clinical measures. The primary goal of the project is to understand how brain-behavioral markers of anxiety-relevant constructs, namely acute threat, cognitive control, and their interaction, associate with CBT response in youth with clinically significant anxiety. A secondary goal is to determine whether child age influences how these markers predict, and/or change, across varying degrees of CBT response. Now in its fourth year, data from this project has informed the examination of (1) baseline (i.e., pre-CBT) anxiety severity as a function of brain-behavioral measures of cognitive control, and (2) clinical characteristics of youth and parents that associate with anxiety severity and/or predict response to CBT. Analysis of brain-behavioral markers before and after CBT will assess mechanisms of CBT effect, and will be conducted once the data collection in the full sample has been completed. This knowledge will help guide the treatment of clinically anxious youth by informing for whom and how does CBT work.
Development and Pretesting of a New Functional-Based Health Literacy Measurement Tool for Chronic Obstructive Pulmonary Disease (COPD) and Asthma Management

Background Health literacy (HL) is a person’s ability to practically apply a wide range of cognitive and non-cognitive skills in health-related decisions. HL includes five domains: navigate/access, understand, communicate, evaluate, and use of health information and services. Currently, no tool accurately captures and measures HL in adult patients with asthma and COPD, while utilizing all 5-HL domains. Objective Develop a comprehensive functional-based measurement tool for adult asthma and/or COPD patients, while assessing HL on routine actions required to manage their chronic respiratory condition(s). Methods We developed our HL tool based on a conceptualization of the link between HL and asthma and COPD management, during needs assessment stage including; a systematic review, which was followed by patient-oriented focus groups, and key-informant and respirologist interviews. Preliminary face and content validation were obtained by patients’ and health professionals’ input prior to the pretesting stage. The needs assessment information enabled us to develop passages in scenario-format and corresponding items to assess HL core domains, in addition to numeracy skills, across nine self-management topics: peak flow meters, prednisone use, pulmonary rehabilitation, action plans, flu shots, inhaler technique, lifestyle (nutrition and exercise), trigger control, and map navigation. The tool was pretested with asthma and COPD patients to assess its relevance, clarity, and difficulty. Results Our systematic review identified the deficiencies of existing HL tools that assessed the HL skills of asthma and COPD patients. The patient-oriented focus groups (n=93) enabled us to identify self-management topics and develop items for our proposed HL tool, which were enriched by input from 45 key informants (eg, policy makers, clinicians, etc.) and 17 respiratory physicians. Preliminary pretesting with a new cohort of participants (36 asthma and COPD patients and 39 key informants) aided in the refinement and finalized our tool. The modified tool included passages and corresponding items related to asthma and COPD management was pretested with 75 asthma/COPD patients who completed the questionnaire and provided their feedback on the clarity, relevance, and difficulty of the tool. The main barrier to self-management pertained to “communication” skills. The flu shot was the most relevant topic (91.2%), while map navigation was the least relevant (63.9%). Action plans were the most difficult topic, where only 55% knew when to utilize their action plans. Numeracy items challenged COPD patients the most. Conclusion We summarized findings from the development and preliminary testing stages of a new asthma/COPD HL tool. This tool will now be validated with a new cohort of patients. Practice Implications Knowledge gained in this study has been applied to the final version of the tool, which is currently being validated.
Relationship of Two-Hour Plasma Glucose and Abdominal Visceral Fat with Bone Mineral Density and Bone Mineral Content in Women with Different Glucose Metabolism Status

Background Several studies have reported the relationship of diabetes mellitus (DM) and obesity with bone mineral density (BMD), but the conclusions remain unclear. This study aimed to provide more information for the relationship of plasma glucose and abdominal visceral fat (AVF) with BMD and bone mineral content (BMC) in women with different glucose metabolism status. Methods Patients were screened by oral glucose tolerance test (OGTT) and were divided into three groups: normal glucose tolerance (NGT, n=132), pre-diabetes mellitus (pre-DM, n=28) and newly diagnosed type 2 DM (T2DM, n=27) groups. Plasma glucose concentrations, anthropometric measurements, body composition, and BMD were measured. Analysis of variance (ANOVA), pearson correlation, and multiple linear regression models were used to evaluate the relationship between BMD, plasma glucose, AVF, and other variables. Results The percentage of subjects with osteoporosis or low BMD was 29.9%, and 66.7% subjects in T2DM group were significantly higher than that in the pre-DM (28.6%) and NGT (22.7%) groups (p=0.005 and p<0.001, respectively). Both BMD at femoral neck (FN) and lumbar spine (LS) of T2DM group were lower than those in NGT group (p=0.009 and p=0.003, respectively), and BMC of T2DM group was lower than those of NGT and pre-DM groups (p<0.001). The results of statistical analysis revealed that both two-hour plasma glucose (2-h PG) and age showed negative correlation with BMC, FN BMD, and LS BMD. AVF showed positive correlation with BMC and LS BMD. Furthermore, the lean mass (LM) showed independent positive effects on BMC. Conclusion Our findings suggest that 1) Age is a strong negative predictor of bone mass. 2) A direct negative effect of increasing 2-h PG might be more prominent at bone mass in women. 3) A moderate increase in AVF is beneficial to bone mass, while excessive increase might be harmful. 4) LM is a positive predictor of BMC.
Mice IL-23-Deficient Hypersensitivity Responses in Compromised Humoral and Delayed-Type

Austin L. Gurney and Frederic J. de SauvageNico Ghilardi, Noelyn Kljavin, Qi Chen, Sophie Lucas,http://www.jimmunol.org/content/172/5/2827J Immunol€2004; 172:2827-2833; ;Referenceshttp://www.jimmunol.org/content/172/5/2827.full#ref-list-1This article cites 40 articles, 20 of which you can access for free at: Subscriptionshttp://jimmunol.org/subscriptionsInformation about subscribing to The Journal of Immunology is online at: Permissionshttp://www.aai. org/ji/copyright.htmlSubmit copyright permission requests at: Email Alertshttp://jimmunol.org/cgi/alerts/etocReceive free email-alerts when new articles cite this article. Sign up at:
Finding Multiple Solutions of ODEs with Neural Networks

Applications of neural networks to numerical problems have gained increasing interest. Among different tasks, ﬁnding solutions of ordinary differential equations (ODEs) is one of the most intriguing problems, as there may be advantages over well established and robust classical approaches. In this paper, we propose an algorithm to ﬁnd all possible solutions of an ordinary differential equation that has multiple solutions, using artiﬁcial neural networks. The key idea is the introduction of a new loss term that we call the interaction loss . The interaction loss prevents different solutions families from co-inciding. We carried out experiments with two nonlinear differential equations, all admitting more than one solution, to show the effectiveness of our algorithm, and we performed a sensitivity analysis to investigate the impact of different hyper-parameters.
A Weighted Sparse-Input Neural Network Technique Applied to Identify Important Features for Vortex-Induced Vibration

Flow-induced vibration depends on a large number of parameters or features. On the one hand, the number of candidate physical features may be too big to construct an interpretable and transferrable model. On the other hand, failure to account for key dependence among features may oversimplify the model. Feature selection is found to be able to reduce the dimension of the physical problem by identifying the most important features for a certain prediction task. In this paper, a weighted sparse-input neural network (WSPINN) is proposed, where the prior physical knowledge is leveraged to constrain the neural network optimization. The effectiveness of this approach is evaluated when applied to the vortex-induced vibration of a long flexible cylinder with Reynolds number from 104 to 105. The important physical features affecting the flexible cylinders’ crossflow vibration amplitude are identified.
Generalized Physics-Informed Learning through Language-Wide Differentiable Programming

Scientiﬁc computing is increasingly incorporating the advancements in machine learning to allow for data-driven physics-informed modeling approaches. However, re-targeting existing scientiﬁc computing workloads to machine learning frameworks is both costly and limiting, as scientiﬁc simulations tend to use the full feature set of a general purpose programming language. In this manuscript we develop an infrastructure for incorporating deep learning into existing scientiﬁc computing code through Differentiable Programming ( ∂P ). We describe a ∂P system that is able to take gradients of full Julia programs, making Automatic Differentiation a ﬁrst class language feature and compatibility with deep learning pervasive. Our system utilizes the one-language nature of Julia package development to augment the existing package ecosystem with deep learning, supporting almost all language constructs (control ﬂow, recursion, mutation, etc.) while generating high-performance code without requiring any user intervention or refactoring to stage computations. We showcase several examples of physics-informed learning which directly utilizes this extension to existing simulation code: neural surrogate models, machine learning on simulated quantum hardware, and data-driven stochastic dynamical model discovery with neural stochastic differential equations.
Economic Aspects of Sanitation in Developing Countries

Background: Improved sanitation has been shown to have great impacts on people's health and economy. However, the progress of achieving the Millennium Development Goals (MDGs) on halving the proportion of people without access to clean water and basic sanitation by 2015 has thus far been delayed. One of the reasons for the slow progress is that policy makers, as well as the general public, have not fully understood the importance of the improved sanitation solutions. This paper, by gathering relevant research findings, aims to report and discuss currently available evidence on the economic aspects of sanitation, including the economic impacts of unimproved sanitation and the costs and economic benefits of some common improved sanitation options in developing countries. Methods: Data used in this paper were obtained from different information sources: international and national journal articles and reports, web-based statistics, and fact sheets. We used both online search and hand search methods to gather the information. Results: Scientific evidence has demonstrated that the economic cost associated with poor sanitation is substantial. At the global level, failure to meet the MDG water and sanitation target would have ramifications in the area of US$38 billion, and sanitation accounts for 92% of this amount. In developing countries, the spending required to provide new coverage to meet the MDG sanitation target (not including program costs) is US$142 billion (US$ year 2005). This translates to a per capita spending of US$28 for sanitation. Annually, this translates to roughly US$14 million. The evidence complied in this paper demonstrates that investing in sanitation is socially and economically worthwhile. For every US$1 invested, achieving the sanitation MDG target and universal sanitation access in the non-OECD countries would result in a global return of US$9.1 and US$11.2, respectively. Conclusion: Given the current state of knowledge, sanitation is undeniably a profitable investment. It is clear that achieving the MDG sanitation target not only saves lives but also provides a foundation for economic growth.
Continuous Representation of Molecules using Graph Variational Autoencoder

In order to continuously represent molecules, we propose a generative model in the form of a VAE which is operating on the 2D-graph structure of molecules. A side predictor is employed to prune the latent space and help the decoder in generating meaningful adjacency tensor of molecules. Other than the potential applicability in drug design and property prediction, we show the superior performance of this technique in comparison to other similar methods based on the SMILES representation of the molecules with RNN based encoder and decoder.
Internationally mobile scientists as knowledge transmitters: A lexical‐based approach to detect knowledge transfer

This paper explores the knowledge transfer of internationally mobile scientists. It builds upon previous work on the development of methods for detecting the knowledge transfer of German scientists. Using abstract terms of publications covered in Scopus, this paper proposes a lexical‐based approach to identify knowledge transmitters. These scientists are characterized by acquiring knowledge from their co‐workers during their international stay and transferring it upon return to German co‐workers. Knowledge is operationalized as the co‐occurrence of rarely used abstract terms. Knowledge transfer is expressed as the diffusion of these term combinations in co‐authorship networks. The method developed was validated by contacting the bibliometrically identified knowledge transmitters and asking them what they believe they learned during their stay abroad. A control group of internationally mobile scientists without traceable knowledge transfer was similarly asked to report on their knowledge acquisition. The findings suggest that bibliometric data are capable of detecting knowledge transmitters among German scientists who were internationally mobile. The juxtaposition of the responses on their perceived knowledge acquisition and the bibliometrically identified lexical terms shows that the method proposed is well suited to studying the knowledge transfer of internationally mobile scientists. The strength of the method is its simplicity and high precision.
Deep Learning for Climate Models of the Atlantic Ocean

A deep neural network is trained to predict sea surface temperature variations at two important regions of the Atlantic ocean, using 800 years of simulated climate dynamics based on the ﬁrst-principles physics models. This model is then tested against 60 years of historical data. Our statistical model learns to approximate the physical laws governing the simulation, providing signiﬁcant improvement over simple statistical forecasts and comparable to most state-of-the-art dynamical/conventional forecast models for a fraction of the computational cost.