Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of 3,4‐methylenedioxymethamphetamine and methamphetamine on motor vehicle driving performance: A systematic review of experimental and observational studies

Methamphetamine (METH) and 3,4‐methylenedioxymethamphetamine (MDMA) are common drugs of abuse and driving under their influence may occur in 1 million people yearly in the United States. This systematic review fills the currently unmet need in understanding the effects of METH and MDMA on motor vehicle driving performance (MVP) and provides insight into the forensic community. A PubMed search on September 24, 2020, for experimental and observational studies, which evaluated the impact of METH and MDMA on MVP was performed. After a review of 208 abstracts, 103 were considered potentially interesting and full texts were obtained. After the exclusion of non‐English articles, review articles, single case reports, and articles which did not evaluate METH or MDMA on MVP, a total of nine experimental studies, 10 traditional observational studies, and 35 case series were included. The clinical rigor of experimental studies was evaluated using the Jadad scale. Experimental studies often demonstrated no significant MVP safety signals for METH or MDMA use, which was contrary to the overwhelming MVP safety risks found in observational studies. Common driving behaviors while using METH or MDMA include: errors in judgment, traveling at high speeds, failure to stop, merging inappropriately, lane weaving, and crashes. Limitations of experimental studies that led to dissimilar MVP outcomes from observational studies include: the common use of driving simulators, as opposed to actual driving examinations, and doses of METH or MDMA administered may not be representative of blood concentrations seen in observational studies. This systematic review has no funding source and was not registered.
Investigating nanomotion-based technology (Resistell AST) for rapid antibiotic susceptibility testing among adult patients admitted to a tertiary-care hospital with Gram-negative bacteraemia: protocol for a prospective, observational, cross-sectional, single-arm study

Introduction Effective treatment of bloodstream infections (BSIs) is relying on rapid identification of the causing pathogen and its antibiotic susceptibility. Still, most commercially available antibiotic susceptibility testing (AST) methods are based on monitoring bacterial growth, thus impacting the time to results. The Resistell AST is based on a new technology measuring the nanomotion caused by physiologically active bacterial cells and detecting the changes in nanomotion caused by the exposure to a drug. Methods and analysis This is a single-centre, prospective, cross-sectional, single-arm diagnostic accuracy study to determine the agreement of the Resistell AST on Gram-negative bacteria isolated from blood cultures among patients admitted to a tertiary-care hospital with the reference method. Up to 300 patients will be recruited. Starting with a pilot phase, enrolling 10%–20% of the subjects and limited to Escherichia coli BSI tested for ceftriaxone susceptibility, the main phase will follow, extending the study to Klebsiella pneumoniae and ciprofloxacin. Ethics and dissemination This study has received ethical approval from the Swiss Ethics Committees (swissethics, project 2020-01622). All the case report forms and clinical samples will be assigned a study code by the local investigators and stored anonymously at the reference centre (Lausanne University Hospital). The results will be broadly distributed through conference presentations and peer-reviewed publications. Trial registration number ClinicalTrials.gov Registry (NCT05002413).
Gut microbiome in tumorigenesis and therapy of colorectal cancer

Colorectal cancer (CRC) is the malignant tumor with the highest incidence in the digestive system, and the gut microbiome plays a crucial role in CRC tumorigenesis and therapy. The gastrointestinal tract is the organ harboring most of the microbiota in humans. Changes in the gut microbiome in CRC patients suggest possible host–microbe interactions, thereby hinting the potential tumorigenesis, which provides new perspective for preventing, diagnosing, or treating CRC. In this review, we discuss the effects of gut microbiome dysbiosis on CRC, and reveal the mechanisms by which gut microbiome dysbiosis leads to CRC. Gut microbiome modulation with the aim to reverse the established gut microbial dysbiosis is a novel strategy for the prevention and treatment of CRC. In addition, this review summarizes that probiotic antagonize CRC tumorigenesis by protecting intestinal barrier function, inhibiting cancer cell proliferation, resisting oxidative stress, and enhancing host immunity. Finally, we highlight clinical applications of the gut microbiome, such as gut microbiome analysis‐based biomarker screening and prediction, and microbe modulation‐based CRC prevention, treatment enhancement, and treatment side effect reduction. This review provides the reference for the clinical application of gut microbiome in the prevention and treatment of CRC.
On the problem of local adaptive estimation in tomography

The principle of tomography is to reconstruct a multidimensional function from observations of its integrals over hyperplanes. We consider here a model of stochastic tomography where we observe the Radon transform Rf of the function f with a stochastic error. Then we construct a “data-driven” estimator which does not depend on any a priori smoothness assumptions on the function f . Considering pointwise mean-squared error, we prove that it has (up to a log) the same asymptotic properties as an oracle. We give an example of Sobolev classes of functions where our estimator converges to f(x) with the optimal rate of convergence up to a log factor.
Early Efficacy of Antipsychotic Medications at Week 2 Predicts Subsequent Responses at Week 6 in a Large-scale Randomized Controlled Trial

Methods: A total of 3010 patients with schizophrenia enrolled in a randomized controlled trial (RCT) and received a 6-week treatment with one antipsychotic drug randomly chosen from five atypical antipsychotics (risperidone 2-6 mg/d, olanzapine 5-20 mg/d, quetiapine 400-750 mg/d, aripiprazole 10-30 mg/d, and ziprasidone 80-160 mg/d) and two typical antipsychotics (perphenazine 20-60 mg/d and haloperidol 6-20 mg/d). Early efficacy was defined as the reduction rate using the Positive and Negative Syndrome Scale (PANSS) total score at week 2. With cut-offs at 50% reduction, logistic regression, receiver operating characteristic (ROC) and random forests were adopted. Results: The reduction rate of PANSS total score and improvement of psychotic symptoms at week 2 enabled subsequent responses to 7 antipsychotics to be predicted, in which improvements in delusions, lack of judgment and insight, unusual thought content, and suspiciousness/ persecution were endowed with the greatest weight. Conclusion: It is robust enough to clinically predict treatment responses to antipsychotics at week 6 using the reduction rate of PANSS total score and symptom relief at week 2. Psychiatric clinicians had better determine whether to switch the treatment plan by the first 2 weeks. Clinical Trial Registration Number: This RCT was registered at the Chinese Clinical Trials Registry Identifier: ChiCTR-TRC-10000934).
Maximum knee extension velocity without external load is a stronger determinant of gait function than quadriceps strength in the early postoperative period following total knee arthroplasty

Objective Quadriceps weakness is considered the primary determinant of gait function after total knee arthroplasty (TKA). However, many patients have shown a gap in improvement trends between gait function and quadriceps strength in clinical situations. Factors other than quadriceps strength in the recovery of gait function after TKA may be essential factors. Because muscle power is a more influential determinant of gait function than muscle strength, the maximum knee extension velocity without external load may be a critical parameter of gait function in patients with TKA. This study aimed to identify the importance of knee extension velocity in determining the gait function early after TKA by comparing the quadriceps strength. Methods This prospective observational study was conducted in four acute care hospitals. Patients scheduled for unilateral TKA were recruited (n = 186; age, 75.9 ± 6.6 years; 43 males and 143 females). Knee extension velocity was defined as the angular velocity of knee extension without external load as quickly as possible in a seated position. Bilateral knee function (knee extension velocity and quadriceps strength), lateral knee function (pain and range of motion), and gait function (gait speed and Timed Up and Go test (TUG)) were evaluated before and at 2 and 3 weeks after TKA. Results Both bilateral knee extension velocities and bilateral quadriceps strengths were significantly correlated with gait function. The knee extension velocity on the operation side was the strongest predictor of gait function at all time points in multiple regression analysis. Conclusion These findings identified knee extension velocity on the operation side to be a more influential determinant of gait function than impairments in quadriceps strength. Therefore, training that focuses on knee extension velocity may be recommended as part of the rehabilitation program in the early postoperative period following TKA. Trial registration UMIN Clinical Trials Registry (UMIN-CTR) UMIN000020036.
Healthcare workers’ attitudes on mandates, incentives, and strategies to improve COVID-19 vaccine uptake: A mixed methods study

ABSTRACT Healthcare workers are a trusted health information source and are uniquely positioned to reduce the burden of the COVID-19 pandemic. The purpose of this sequential exploratory mixed methods study was to understand attitudes of healthcare workers working in Massachusetts during the COVID-19 pandemic regarding strategies to improve COVID-19 vaccine utilization, including vaccine mandates and incentives. Fifty-two individuals completed one-on-one interviews between April 22nd and September 7th, 2021. The survey was developed based on findings from the interviews; 209 individuals completed the online survey between February 17th and March 23rd, 2022. Both the interview and survey asked about attitudes toward COVID-19 vaccine and booster mandates, incentives, and strategies to improve vaccination rates. Most participants were female (79%-interview, 81%-survey), Caucasian (56%, 73%), and worked as physicians (37%, 34%) or nurses (10%, 18%). Overall, nuanced attitudes regarding vaccine and booster mandates were expressed; many supported mandates to protect their patients’ health, others emphasized personal autonomy, while some were against mandates if job termination was the consequence of declining vaccines. Similarly, views regarding vaccine incentives differed; some considered incentives helpful, yet many viewed them as coercive. Strategies believed to be most effective to encourage vaccination included improving accessibility to vaccination sites, addressing misinformation, discussing vaccine safety, tailored community outreach via trusted messengers, and one-on-one conversations between patients and healthcare workers. Healthcare workers’ experiences with strategies to improve utilization of COVID-19 vaccines and boosters have implications for public health policies. Generally, efforts to improve access and education were viewed more favorably than incentives and mandates.
Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields

Contents: Introduction: Differential Equations and Dynamical Systems. - An Introduction to Chaos: Four Examples. - Local Bifurcations. - Averaging and Perturbation from a Geometric Viewpoint. - Hyperbolic Sets, Sympolic Dynamics, and Strange Attractors. - Global Bifurcations. - Local Codimension Two Bifurcations of Flows. - Appendix: Suggestions for Further Reading. Postscript Added at Second Printing. Glossary. References. Index.
Manipulating risk of infection and appeal to public benefit increase compliance with infection control measures in a hypothetical pandemic scenario

To limit an infectious outbreak, the public must be informed about the infection risk and be motivated to comply with infection control measures. Perceiving a situation as threatening and seeing benefits to complying may be necessary to motivate for compliance. The current study used a preregistered survey experiment with a 2-by-2 between-subject design to investigate if emphasizing high infection risk and appealing to societal benefits impacted intention to comply with infection control measures. The results from a representative Norwegian sample (N = 2533) show that describing a high (as opposed to low) personal risk scenario had a small main effect on compliance. Further, appealing to public (as opposed to self-interested) benefits also had a small main effect. There was no interaction between risk scenario and motivational emphasis. The results suggest that to maximize compliance, information about disease outbreak should emphasize the individual risk of contracting the disease, and could also underline the public value of limiting infection spread. These findings can inform health authorities about the motives underlying compliance with infection control measures during an infectious disease outbreak.
Evolution of complexity in non-viral oligonucleotide delivery systems: from gymnotic delivery through bioconjugates to biomimetic nanoparticles

ABSTRACT From the early days of research on RNA biology and biochemistry, there was an interest to utilize this knowledge and RNA itself for therapeutic applications. Today, we have a series of oligonucleotide therapeutics on the market and many more in clinical trials. These drugs - exploit different chemistries of oligonucleotides, such as modified DNAs and RNAs, peptide nucleic acids (PNAs) or phosphorodiamidate morpholino oligomers (PMOs), and different mechanisms of action, such as RNA interference (RNAi), targeted RNA degradation, splicing modulation, gene expression and modification. Despite major successes e.g. mRNA vaccines developed against SARS-CoV-2 to control COVID-19 pandemic, development of therapies for other diseases is still limited by inefficient delivery of oligonucleotides to specific tissues and organs and often prohibitive costs for the final drug. This is even more critical when targeting multifactorial disorders and patient-specific biological variations. In this review, we will present the evolution of complexity of oligonucleotide delivery methods with focus on increasing complexity of formulations from gymnotic delivery to bioconjugates and to lipid nanoparticles in respect to developments that will enable application of therapeutic oligonucleotides as drugs in personalized therapies.
Hierarchical Graph Structures for Congestion and ETA Prediction

Traffic4cast is an annual competition to predict spatio temporal traffic based on real world data. We propose an approach using Graph Neural Networks that directly works on the road graph topology which was extracted from OpenStreetMap data. Our architecture can incorporate a hierarchical graph representation to improve the information flow between key intersections of the graph and the shortest paths connecting them. Furthermore, we investigate how the road graph can be compacted to ease the flow of information and make use of a multi-task approach to predict congestion classes and ETA simultaneously. Our code and models are released here: https://github.com/floriangroetschla/NeurIPS2022-traffic4cast
A Reinforcement Learning Badminton Environment for Simulating Player Tactics (Student Abstract)

Recent techniques for analyzing sports precisely has stimulated various approaches to improve player performance and fan engagement. However, existing approaches are only able to evaluate offline performance since testing in real-time matches requires exhaustive costs and cannot be replicated. To test in a safe and reproducible simulator, we focus on turn-based sports and introduce a badminton environment by simulating rallies with different angles of view and designing the states, actions, and training procedures. This benefits not only coaches and players by simulating past matches for tactic investigation, but also researchers from rapidly evaluating their novel algorithms. Our code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/Strategic%20Environment.
Revisiting the Spectral Energy Correlations of GRBs with Fermi Data. I. Model-wise Properties

Gamma-ray bursts (GRBs) exhibit a diversity of spectra. Several spectral models (e.g., Band, cutoff power law (CPL), and blackbody) and their hybrid versions (e.g., Band+blackbody) have been widely used to fit the observed GRB spectra. Here, we attempt to collect all the bursts detected by Fermi/GBM with known redshifts from 2008 July to 2022 May, having been motivated to (i) provide a parameter catalog independent of the official Fermi/GBM team and (ii) achieve a “clean” model-based GRB spectral energy correlation analysis. A nearly complete GRB sample is created, containing 153 such bursts (136 long GRBs and 17 short GRBs). Using the sample and by performing detailed spectral analysis and model comparisons, we investigate two GRB spectral energy correlations: the correlation of the cosmological rest-frame peak energy (E p,z ) of the ν F ν prompt emission spectrum with (i) the isotropic-bolometric-equivalent emission energy E γ,iso (the Amati relation) and (ii) the isotropic-bolometric-equivalent peak luminosity L p,iso (the Yonetoku relation). From a linear regression analysis, a tight correlation between E p,z and E γ,iso (and L γ,iso) is found for both Band-like and CPL-like bursts (except for CPL-like long burst E p,z –E γ,iso correlation). More interestingly, CPL-like bursts do not fall on the Band-like burst Amati and Yonetoku correlations, suggesting distinct radiation processes, and pointing to the fact that these spectral energy correlations are tightly reliant on the model-wise properties.
BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion

We present the award-winning submission to the WikiKG90Mv2 track of OGB-LSC@NeurIPS 2022. The task is link-prediction on the large-scale knowledge graph WikiKG90Mv2, consisting of 90M+ nodes and 600M+ edges. Our solution uses a diverse ensemble of $85$ Knowledge Graph Embedding models combining five different scoring functions (TransE, TransH, RotatE, DistMult, ComplEx) and two different loss functions (log-sigmoid, sampled softmax cross-entropy). Each individual model is trained in parallel on a Graphcore Bow Pod$_{16}$ using BESS (Balanced Entity Sampling and Sharing), a new distribution framework for KGE training and inference based on balanced collective communications between workers. Our final model achieves a validation MRR of 0.2922 and a test-challenge MRR of 0.2562, winning the first place in the competition. The code is publicly available at: https://github.com/graphcore/distributed-kge-poplar/tree/2022-ogb-submission.
Contract-Based Specification Refinement and Repair for Mission Planning

We address the problem of modeling, refining, and repairing formal specifications for robotic missions using assume-guarantee contracts. We show how to model mission specifications at various levels of abstraction and implement them using a library of pre-implemented specifications. Suppose the specification cannot be met using components from the library. In that case, we compute a proxy for the best approximation to the specification that can be generated using elements from the library. Afterward, we propose a systematic way to either 1) search for and refine the ‘missing part’ of the specification that the library cannot meet or 2) repair the current specification such that the existing library can refine it. Our methodology for searching and repairing mission requirements leverages the quotient, separation, composition, and merging operations between contracts.
Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images

In this work, we develop convolutional neural generative coding (Conv-NGC), a generalization of predictive coding to the case of convolution/deconvolution-based computation. Specifically, we concretely implement a flexible neurobiologically-motivated algorithm that progressively refines latent state feature maps in order to dynamically form a more accurate internal representation/reconstruction model of natural images. The performance of the resulting sensory processing system is evaluated on complex datasets such as Color-MNIST, CIFAR-10, and Street House View Numbers (SVHN). We study the effectiveness of our brain-inspired model on the tasks of reconstruction and image denoising and find that it is competitive with convolutional auto-encoding systems trained by backpropagation of errors and outperforms them with respect to out-of-distribution reconstruction (including the full 90k CINIC-10 test set).
A Lightweight Modular Continuum Manipulator with IMU-based Force Estimation

Most aerial manipulators use serial rigid-link designs, which results in large forces when initiating contacts during manipulation and could cause flight stability difficulty. This limitation could potentially be improved by the compliance of continuum manipulators. To achieve this goal, we present the novel design of a compact, lightweight, and modular cable-driven continuum manipulator for aerial drones. We then derive a complete modeling framework for its kinematics, statics, and stiffness (compliance). The modeling framework can guide the control and design problems to integrate the manipulator to aerial drones. In addition, thanks to the derived stiffness (compliance) matrix, and using a low-cost IMU sensor to capture deformation angles, we present a simple method to estimate manipulation force at the tip of the manipulator. We report preliminary experimental validations of the hardware prototype, providing insights on its manipulation feasibility. We also report preliminary results of the IMU-based force estimation method.
SLUGBOT, an Aplysia-inspired Robotic Grasper for Studying Control

Living systems can use a single periphery to perform a variety of tasks and adapt to a dynamic environment. This multifunctionality is achieved through the use of neural circuitry that adaptively controls the reconfigurable musculature. Current robotic systems struggle to flexibly adapt to unstructured environments. Through mimicry of the neuromechanical coupling seen in living organisms, robotic systems could potentially achieve greater autonomy. The tractable neuromechanics of the sea slug $\textit{Aplysia californica's}$ feeding apparatus, or buccal mass, make it an ideal candidate for applying neuromechanical principles to the control of a soft robot. In this work, a robotic grasper was designed to mimic specific morphology of the $\textit{Aplysia}$ feeding apparatus. These include the use of soft actuators akin to biological muscle, a deformable grasping surface, and a similar muscular architecture. A previously developed Boolean neural controller was then adapted for the control of this soft robotic system. The robot was capable of qualitatively replicating swallowing behavior by cyclically ingesting a plastic tube. The robot's normalized translational and rotational kinematics of the odontophore followed profiles observed $\textit{in vivo}$ despite morphological differences. This brings $\textit{Aplysia}$-inspired control $\textit{in roboto}$ one step closer to multifunctional neural control schema $\textit{in vivo}$ and $\textit{in silico}$. Future additions may improve SLUGBOT's viability as a neuromechanical research platform.