Miracle-dz/UniFAR-FTUs huggingface dataset:
quEHRy: a question answering system to query electronic health records

OBJECTIVE We propose a system, quEHRy, to retrieve precise, interpretable answers to natural language questions from structured data in electronic health records (EHRs). MATERIALS AND METHODS We develop/synthesize the main components of quEHRy: concept normalization (MetaMap), time frame classification (new), semantic parsing (existing), visualization with question understanding (new), and query module for FHIR mapping/processing (new). We evaluate quEHRy on 2 clinical question answering (QA) datasets. We evaluate each component separately as well as holistically to gain deeper insights. We also conduct a thorough error analysis for a crucial subcomponent, medical concept normalization. RESULTS Using gold concepts, the precision of quEHRy is 98.33% and 90.91% for the 2 datasets, while the overall accuracy was 97.41% and 87.75%. Precision was 94.03% and 87.79% even after employing an automated medical concept extraction system (MetaMap). Most incorrectly predicted medical concepts were broader in nature than gold-annotated concepts (representative of the ones present in EHRs), eg, Diabetes versus Diabetes Mellitus, Non-Insulin-Dependent. DISCUSSION The primary performance barrier to deployment of the system is due to errors in medical concept extraction (a component not studied in this article), which affects the downstream generation of correct logical structures. This indicates the need to build QA-specific clinical concept normalizers that understand EHR context to extract the "relevant" medical concepts from questions. CONCLUSION We present an end-to-end QA system that allows information access from EHRs using natural language and returns an exact, verifiable answer. Our proposed system is high-precision and interpretable, checking off the requirements for clinical use.
CONSORT Harms 2022 statement, explanation, and elaboration: updated guideline for the reporting of harms in randomised trials

Randomised controlled trials remain the reference standard for healthcare research on effects of interventions, and the need to report both benefits and harms is essential. The Consolidated Standards of Reporting Trials (the main CONSORT) statement includes one item on reporting harms (ie, all important harms or unintended effects in each group). In 2004, the CONSORT group developed the CONSORT Harms extension; however, it has not been consistently applied and needs to be updated. Here, we describe CONSORT Harms 2022, which replaces the CONSORT Harms 2004 checklist, and shows how CONSORT Harms 2022 items could be incorporated into the main CONSORT checklist. Thirteen items from the main CONSORT were modified to improve harms reporting. Three new items were added. In this article, we describe CONSORT Harms 2022 and how it was integrated into the main CONSORT checklist, and elaborate on each item relevant to complete reporting of harms in randomised controlled trials. Until future work from the CONSORT group produces an updated checklist, authors, journal reviewers, and editors of randomised controlled trials should use the integrated checklist presented in this paper.
Trumpet plots: visualizing the relationship between allele frequency and effect size in genetic association studies

Recent advances in genome-wide association study (GWAS) and sequencing studies have shown that the genetic architecture of complex diseases and traits involves a combination of rare and common genetic variants, distributed throughout the genome. One way to better understand this architecture is to visualize genetic associations across a wide range of allele frequencies. However, there is currently no standardized or consistent graphical representation for effectively illustrating these results. Here we propose a standardized approach for visualizing the effect size of risk variants across the allele frequency spectrum. The proposed plots have a distinctive trumpet shape, with the majority of variants having low frequency and small effects, while a small number of variants have higher frequency and larger effects. These plots, which we call 'trumpet plots', can help to provide new and valuable insights into the genetic basis of traits and diseases, and can help prioritize efforts to discover new risk variants. To demonstrate the utility of trumpet plots in illustrating the relationship between the number of variants, their frequency, and the magnitude of their effects in shaping the genetic architecture of complex diseases and traits, we generated trumpet plots for more than one hundred traits in the UK Biobank. To facilitate their broader use, we have developed an R package 'TrumpetPlots' and R Shiny application, available at https://juditgg.shinyapps. io/shinytrumpets/, that allows users to explore these results and submit their own data.
Multi-User Room-Scale Respiration Tracking Using COTS Acoustic Devices

Continuous domestic respiration monitoring provides vital information for diagnosing assorted diseases. In this article, we introduce RespTracker, the first continuous, multiple-person respiration tracking system in domestic settings using acoustic-based COTS devices. RespTracker uses a multi-stage algorithm to separate and recombine respiration signals from multiple paths so that it can track the respiration rate of multiple moving subjects. And it leverages features from multiple dimensions to separate different users in the same area. Our experimental results show that our two-stage algorithm can distinguish the respiration of at least four subjects and cover a distance of three meters.
Using acoustic recording units to investigate the effects of logging of indigenous trees in the Amathole forests, South Africa on Cape Parrot Poicephalus robustus breeding and the presence of three primary cavity-excavating bird species

Summary The Amathole forest complex is the breeding stronghold of the endemic and vulnerable Cape Parrot Poicephalus robustus, and is also one of only two forest complexes in South Africa formally harvested for timber. The aim of this study was to determine if formal harvesting of indigenous trees, primarily the two yellowwood species Afrocarpus falcatus and Podocarpus latifolius, in 9 of 16 Amathole forests has had any effect on the presence of Cape Parrots and three primary nest-excavating species, as well as on parrot breeding. The study used logging data from the past 25 years (1997–2021) as well as data collected by acoustic recording units over two breeding seasons from 2019 to 2021. Cape Parrots were present in 15 of 16 forests, but breeding calls were identified in only seven forests: five in logged and two in unlogged forests. Fourteen of the forests harboured all three primary excavators: Knysna Woodpecker Campethera notata, Olive Woodpecker Dendropicos griseocephalus, and Red-fronted Tinkerbird Pogoniulus pusillus. The last two species were absent from the adjacent Mount Thomas and Kologha forests, respectively, in which parrots were present, but no breeding calls were recorded. Logging of yellowwoods was not found to affect parrot breeding. However, due to the overlap between preferred parrot breeding sites and preferred trees for harvesting, we recommend that harvesting in the five harvested forest blocks where parrot breeding occurs be limited to tree falls, with no standing dead, dying, or damaged trees harvested, to ensure that potential nesting trees are not harvested.
Unbounded perturbations of the generator domain

Let $X,U$ and $Z$ be Banach spaces such that $Z\subset X$ (with continuous and dense embedding), $L:Z\to X$ be a closed linear operator and consider closed linear operators $G,M:Z\to U$. Putting conditions on $G$ and $M$ we show that the operator $\mathcal{A}=L$ with domain $D(\mathcal{A})=\big\{z\in Z:Gz=Mz\big\}$ generates a $C_0$-semigroup on $X$. Moreover, we give a variation of constants formula for the solution of the following inhomogeneous problem \begin{align*} \begin{cases} \dot{z}(t)=L z(t)+f(t),& t\ge 0,\cr G z(t)=Mz(t)+g(t),& t\ge 0,\cr z(0)=z^0. \end{cases} \end{align*} Several examples will be given, in particular a heat equation with distributed unbounded delay at the boundary condition.
The role of attachment styles in attention-deficit hyperactivity disorder: A meta-analytic review from the perspective of a transactional development model

ABSTRACT Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by pervasive alterations in self-regulation. Attachment patterns could be involved in maintaining the clinical features of ADHD during development. This meta-analytic review summarizes the findings of 26 cross-sectional and longitudinal studies that assessed correlations between attachment styles and ADHD features among children and adolescents. Results show that insecure attachment highlighted significant correlations with ADHD inattention and hyperactivity symptoms. Dismissive/avoidant attachment was associated with inattention features. Ambivalent/preoccupied attachment correlated with hyperactivity symptoms. Disorganized attachment was associated with inattentive symptoms. Insecure attachment was prospectively associated with later ADHD symptoms. Cross-sectional correlations between insecure attachment and ADHD symptoms were comparable to longitudinal ones. This might suggest a transactional developmental model of ADHD considering mutual reinforcement effects of insecure attachment patterns and core clinical features of the disorder. Clinical samples with ADHD should be assessed in order to support this theoretical framework.
Allosteric coupling asymmetry mediates paradoxical activation of BRAF by type II inhibitors

The type II class of RAF inhibitors currently in clinical trials paradoxically activate BRAF at subsaturating concentrations. Activation is mediated by induction of BRAF dimers, but why activation rather than inhibition occurs remains unclear. Using biophysical methods tracking BRAF dimerization and conformation we built an allosteric model of inhibitor-induced dimerization that resolves the allosteric contributions of inhibitor binding to the two active sites of the dimer, revealing key differences between type I and type II RAF inhibitors. For type II inhibitors the allosteric coupling between inhibitor binding and BRAF dimerization is distributed asymmetrically across the two dimer binding sites, with binding to the first site dominating the allostery. This asymmetry results in efficient and selective induction of dimers with one inhibited and one catalytically active subunit. Our allosteric models quantitatively account for paradoxical activation data measured for 11 RAF inhibitors. Unlike type II inhibitors, type I inhibitors lack allosteric asymmetry and do not activate BRAF homodimers. Finally, NMR data reveal that BRAF homodimers are dynamically asymmetric with only one of the subunits locked in the active αC-in state. This provides a structural mechanism for how binding of only a single αC-in inhibitor molecule can induce potent BRAF dimerization and activation.
Chronic exposure of mosquito larvae to pesticide residues endangers a new generation of agrochemicals repurposed for malaria prevention

Agrochemicals have been successfully repurposed to control mosquitoes worldwide, but pesticides used in agriculture challenge their effectiveness by contaminating surface waters and helping larval populations develop resistance. Thus, knowledge of the lethal and sublethal effects of residual pesticide exposure on mosquitoes is critical for selecting effective insecticides. Here we implemented a new experimental approach to predict the efficacy of agricultural pesticides newly repurposed for malaria vector control. We mimicked insecticide resistance selection as it occurs in contaminated aquatic habitats by rearing field-collected mosquito larvae in water containing a dose of insecticide capable of killing individuals from a susceptible strain within 24 h. We then simultaneously monitored short-term lethal toxicity within 24 h and sublethal effects for 7 days. We found that due to chronic exposure to agricultural pesticides, some mosquito populations are currently pre-adapt to resist neonicotinoids if those were used in vector control. Larvae collected from rural and agricultural areas where neonicotinoid formulations are intensively used for insect pest management were able to survive, grow, pupate and emerge in water containing a lethal dose of acetamiprid, imidacloprid or clothianidin. These results emphasize the importance of addressing exposure of larval populations to formulations applied in agriculture prior to using agrochemicals against malaria vectors.
A novel Energy Landscape method incorporating the Latent Dirichlet Allocation topic model and the pairwise Maximum Entropy model, revealing the significant contribution of Bactericides to the development of Inflammatory Bowel Disease

The dysbiosis of microbiota has been reported to be associated with numerous human pathophysiological processes, including Inflammatory Bowel Disease (IBD). With advancements in highthroughput sequencing, various methods have been developed to study the alteration of microbiota in the development and progression of diseases. However, a suitable approach to assess the global stability of the microbiota in disease states through time-series microbiome data is yet to be established. In this study, we introduce a novel Energy Landscape construction method, which incorporates the Latent Dirichlet Allocation (LDA) model and the pairwise Maximum Entropy (MaxEnt) model, and demonstrate its utility by applying it to an IBD time-series dataset. Through this method, we obtained the “energy” profile for the potential patterns of microbiota to occur under disease, indicating their stability and prevalence. The results suggest the potential contribution of several microbial genera, including Bacteroides, Alistipes, and Faecalibacterium, as well as their interactions, to the development of IBD. Our proposed method provides a novel and insightful tool for understanding the alteration and stability of the microbiota under disease states and offers a more holistic view of its complex dynamics at play in microbiota-mediated disease.
Design principles of Cdr2 node patterns in fission yeast cells

Pattern forming networks have diverse roles in cell biology. Rod-shaped fission yeast cells use pattern formation to control the localization of mitotic signaling proteins and the cytokinetic ring. During interphase, the kinase Cdr2 forms membrane-bound multiprotein complexes termed nodes, which are positioned in the cell middle due in part to the node inhibitor Pom1 enriched at cell tips. Node positioning is important for timely cell cycle progression and positioning of the cytokinetic ring. Here, we combined experimental and modeling approaches to investigate pattern formation by the Pom1-Cdr2 system. We found that Cdr2 nodes accumulate near the nucleus, and Cdr2 undergoes nucleocytoplasmic shuttling when cortical anchoring is reduced. We generated particle-based simulations based on tip inhibition, nuclear positioning, and cortical anchoring. We tested model predictions by investigating Pom1-Cdr2 localization patterns after perturbing each positioning mechanism, including in both anucleate and multinucleated cells. Experiments show that tip inhibition and cortical anchoring alone are sufficient for the assembly and positioning of nodes in the absence of the nucleus, but that the nucleus and Pom1 facilitate the formation of unexpected node patterns in multinucleated cells. These findings have implications for spatial control of cytokinesis by nodes and for spatial patterning in other biological systems.
Electrophysiological correlates of attention in the locus coeruleus - anterior cingulate cortex circuit during the rodent continuous performance test

Sustained attention, the ability to focus on an activity or stimulus over time, is significantly impaired in many psychiatric disorders, and there remains a major unmet need in treating impaired attention. Continuous performance tests (CPTs) were developed to measure sustained attention in humans, non-human primates, rats, and mice, and similar neural circuits are engaged across species during CPT performance, supporting their use in translational studies to identify novel therapeutics. Here, we identified electrophysiological correlates of attentional performance in a touchscreen-based rodent CPT (rCPT) in the locus coeruleus (LC) and anterior cingulate cortex (ACC), two inter-connected regions that are implicated in attentional processes. We used viral labeling and molecular techniques to demonstrate that neural activity is recruited in LC-ACC projections during the rCPT, and that this recruitment increases with cognitive demand. We implanted male mice with depth electrodes within the LC and ACC for local field potential (LFP) recordings during rCPT training, and identified an increase in ACC delta and theta power, and an increase in LC delta power during correct responses in the rCPT. We also found that the LC leads the ACC in theta frequencies during correct responses while the ACC leads the LC in gamma frequencies during incorrect responses. These findings may represent translational biomarkers that can be used to screen novel therapeutics for drug discovery in attention.
An ex vivo human precision-cut lung slice platform provides insight into SARS-CoV-2 pathogenesis and antiviral drug efficacy

COVID-19 has claimed millions of lives since the emergence of SARS-CoV-2, and lung disease appears the primary cause of the death in COVID-19 patients. However, the underlying mechanisms of COVID-19 pathogenesis remain elusive, and there is no existing model where the human disease can be faithfully recapitulated and conditions for the infection process can be experimentally controlled. Herein we report the establishment of an ex vivo human precision-cut lung slice (hPCLS) platform for studying SARS-CoV-2 pathogenicity and innate immune responses, and for evaluating the efficacy of antiviral drugs against SARS-CoV-2. We show that while SARS-CoV-2 continued to replicate during the course of infection of hPCLS, infectious virus production peaked within 2 days, and rapidly declined thereafter. Although most proinflammatory cytokines examined were induced by SARS-CoV-2 infection, the degree of induction and types of cytokines varied significantly among hPCLS from individual donors, reflecting the heterogeneity of human populations. In particular, two cytokines (IP-10 and IL-8) were highly and consistently induced, suggesting a role in the pathogenesis of COVID-19. Histopathological examination revealed focal cytopathic effects late in the infection. Transcriptomic and proteomic analyses identified molecular signatures and cellular pathways that are largely consistent with the progression of COVID-19 in patients. Furthermore, we show that homoharringtonine, a natural plant alkaloid derived from Cephalotoxus fortunei, not only inhibited virus replication but also production of pro-inflammatory cytokines, and ameliorated the histopathological changes of the lungs caused by SARS-CoV-2 infection, demonstrating the usefulness of the hPCLS platform for evaluating antiviral drugs. SIGNIFICANCE Here we established an ex vivo human precision-cut lung slice platform for assessing SARS-CoV-2 infection, viral replication kinetics, innate immune response, disease progression, and antiviral drugs. Using this platform, we identified early induction of specific cytokines, especially IP-10 and IL-8, as potential predictors for severe COVID-19, and uncovered a hitherto unrecognized phenomenon that while infectious virus disappears at late times of infection, viral RNA persists and lung histopathology commences. This finding may have important clinical implications for both acute and post-acute sequelae of COVID-19. This platform recapitulates some of the characteristics of lung disease observed in severe COVID-19 patients and is therefore a useful platform for understanding mechanisms of SARS-CoV-2 pathogenesis and for evaluating the efficacy of antiviral drugs.
Identification of Multiple Outliers in Logistic Regression

The use of logistic regression modeling has seen a great deal of attention in the literature in recent years. This includes all aspects of the logistic regression model including the identification of outliers. A variety of methods for the identification of outliers, such as the standardized Pearson residuals, are now available in the literature. These methods, however, are successful only if the data contain a single outlier. In the presence of multiple outliers in the data, which is often the case in practice, these methods fail to detect the outliers. This is due to the well-known problems of masking (false negative) and swamping (false positive) effects. In this article, we propose a new method for the identification of multiple outliers in logistic regression. We develop a generalized version of standardized Pearson residuals based on group deletion and then propose a technique for identifying multiple outliers. The performance of the proposed method is then investigated through several examples.
Anoikis-related genes combined with single cell sequencing: Insights into model specification of lung adenocarcinoma and applicability for prognosis and therapy

Background: Anoikis has therapeutic potential against different malignancies including lung adenocarcinoma. This study used anoikis and bioinformatics to construct a prognostic model for lung adenocarcinoma and explore new therapeutic strategies. Methods: Several bioinformatic algorithms (co-expression analysis, univariate Cox analysis, multivariate Cox analysis, and cross-validation) were used to screen anoikis-related genes (ARGs) to construct a risk model. Lung adenocarcinoma patients were divided into training and testing groups at a ratio of 1:1. The prognostic model was validated by risk score comparison between high- and low-risk groups using receiver operating characteristic curve (ROC), nomograms, independent prognostic analysis and principal component analysis. In addition, two anoikis-related genes patterns were classified utilizing consensus clustering method and were compared with each other in survival time, immune microenvironment, and regulation in pathway. Single cell sequencing was applied to analyze anoikis-related genes constructed the model. Results: This study demonstrated the feasibility of the model based on seven anoikis-related genes, as well as identifying axitinib, nibtinib and sorafenib as potential therapeutic strategies for LUAD. Risk score based on this model had could be used as an independent prognostic factor for lung adenocarcinoma (HR > 1; p < 0.001) and had the highest accuracy to predict survival compared with the clinical characteristics. Single cell sequencing analysis discovered Keratin 14 (KRT14, one of the seven anoikis-related genes) was mainly expressed in malignant cells in various cancers. Conclusion: We identified seven anoikis-related genes and constructed an accurate risk model based on bioinformatics analysis that can be used for prognostic prediction and for the design of therapeutic strategies in clinical practice.
Recent trends in robot learning and evolution for swarm robotics

Swarm robotics is a promising approach to control large groups of robots. However, designing the individual behavior of the robots so that a desired collective behavior emerges is still a major challenge. In recent years, many advances in the automatic design of control software for robot swarms have been made, thus making automatic design a promising tool to address this challenge. In this article, I highlight and discuss recent advances and trends in offline robot evolution, embodied evolution, and offline robot learning for swarm robotics. For each approach, I describe recent design methods of interest, and commonly encountered challenges. In addition to the review, I provide a perspective on recent trends and discuss how they might influence future research to help address the remaining challenges of designing robot swarms.