Miracle-dz/UniFAR-FTUs huggingface dataset:
Menopausal Symptoms and Associated Social and Environmental Factors in Midlife Chinese Women

Objective To investigate the dependency of menopausal symptoms on age and/or menopausal status and association with social and environmental factors. Methods The cross-sectional study was conducted on 4595 women (40–83 years) coming from 31 provinces during two years to our “Menopause Clinic”, the first official center in China. Menopausal symptoms were assessed: negative mood, cognitive symptoms, sleep disorder, vasomotor symptoms (VMS), urogenital symptoms, autonomic nervous disorder, limb pain/paresthesia. Social and environmental factors were collected; simple and unconditional logistic regression with adjustments by all analyzed factors were used to assess associations. Results Urogenital symptoms were the most common and VMS the least common complaints. All symptoms, except cognitive and urogenital symptoms, worsened age-dependently up to 60 years but improved beyond this age. Most symptoms also were associated with menopause, except negative mood and autonomic nervous disorders. Soya-rich diet decreased all symptoms, but only if consumed daily. Exercise was beneficial for some symptoms. Hormone replacement therapy (HRT) was most effective but only with regular use. Increased alcohol consumption aggravated VMS. Higher education was associated with less symptoms; no relationship was found for smoking, gravidity, parity, and menarche. Conclusion All symptoms, except cognitive and urogenital symptoms, worsened age-dependently up to 60 years but improved beyond this age; most were also associated with menopause. For the first time in a large study population, it was observed that soy-rich diet is protective but only with daily consumption. Exercising can protect against some of the symptoms. HRT decreased all symptoms, but regular use is necessary. Women with higher education reported less symptoms, but after adjustments no other relationships were observed (ChiCTR2000035047).
A data driven change-point epidemic model for assessing the impact of large gathering and subsequent movement control order on COVID-19 spread in Malaysia

The second wave of COVID-19 in Malaysia is largely attributed to a mass gathering held in Sri Petaling between February 27, 2020 and March 1, 2020, which contributed to an exponential rise of COVID-19 cases in the country. Starting March 18, 2020, the Malaysian government introduced four consecutive phases of a Movement Control Order (MCO) to stem the spread of COVID-19. The MCO was implemented through various non-pharmaceutical interventions (NPIs). The reported number of cases reached its peak by the first week of April and then started to reduce, hence proving the effectiveness of the MCO. To gain a quantitative understanding of the effect of MCO on the dynamics of COVID-19, this paper develops a class of mathematical models to capture the disease spread before and after MCO implementation in Malaysia. A heterogeneous variant of the Susceptible-Exposed-Infected-Recovered (SEIR) model is developed with additional compartments for asymptomatic transmission. Further, a change-point is incorporated to model the before and after disease dynamics, and is inferred based on data. Related statistical analyses for inference are developed in a Bayesian framework and are able to provide quantitative assessments of (1) the impact of the Sri Petaling gathering, and (2) the extent of decreasing transmission during the MCO period. The analysis here also quantitatively demonstrates how quickly transmission rates fall under effective NPI implemention within a short time period.
Progress and Prospects in Weather and Climate Modelling.

This popular article provides a short summary of the progress and prospects in Weather and Climate Modelling for the benefit of high school and undergraduate college students and early career researchers. Although this is not a comprehensive scientific article, the basic information provided here is intended to introduce students and researchers to the topic of Weather and Climate Modelling - which comes under the broad discipline of Atmospheric / Oceanic / Climate / Earth Sciences. This article briefly summarizes the historical developments, progress, scientific challenges in weather and climate modelling and career opportunities.
Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery

Illegal wildlife poaching is driving the loss of biodiversity. To combat poaching, rangers patrol expansive protected areas for illegal poaching activity. However, rangers often cannot comprehensively search such large parks. Thus, the Protection Assistant for Wildlife Security (PAWS) was introduced as a machine learning approach to help identify the areas with highest poaching risk. As PAWS is deployed to parks around the world, we recognized that many parks have limited resources for data collection and therefore have scarce feature sets. To ensure under-resourced parks have access to meaningful poaching predictions, we introduce the use of publicly available remote sensing data to extract features for parks. By employing this data from Google Earth Engine, we also incorporate previously unavailable dynamic data to enrich predictions with seasonal trends. We automate the entire data-to-deployment pipeline and find that, with only using publicly available data, we recuperate prediction performance comparable to predictions made using features manually computed by park specialists. We conclude that the inclusion of satellite imagery creates a robust system through which parks of any resource level can benefit from poaching risks for years to come.
Preparing for the Maximum Emergency with a Simulation: A Table-Top Test to Evaluate Bed Surge Capacity and Staff Compliance with Training

Introduction The sudden increase in the number of critically ill patients following a disaster can be overwhelming. Study Objective The main objective of this study was to assess the real number of available and readily freeable beds (“bed surge capacity”) and the availability of emergency operating rooms (OR) in a maximum emergency using a theoretical simulation. Patients and Methods The proportion of dismissible patients in four areas (Medical Area, Surgical Area, Sub-intensive Care Units, Intensive Care Units) and three emergency OR was assessed at 2 and 24 hours after a simulated maximum emergency. Four scenarios were modeled. Hospitalization and surgical capacities were assessed on weekdays and holidays. The creation of new beds was presumed by the possibility of moving patients to a lower level of care than that provided at the time of detection, of dislocation of patients to a discharge room, with care transferred to lower-intensity hospitals, rehabilitation, or discharge facilities. The Phase 1 table-top simulations were conducted during the weekday morning hours. In particular, the 24-hour table-top simulations of a hypothetical event lasted about 150 minutes compared to those conducted at 2 hours, which were found to be longer (about 195 minutes). Phase 2 was conducted on two public holidays and a quick response time was observed within the first 40 minutes of the start of the test (about 45% of departments). Results The availability of simulated beds was greater than that indicated in the maximum emergency plans (which was based solely on the census of beds). Patients admitted to Intensive Care and The Sub-Intensive Area may be more difficult to move than those in low-intensity care. The availability of emergency OR was not problematic. Age influenced the possibility of remitting/transferring patients. Conclusion Simulation in advance of a maximum emergency is helpful in designing an efficient response plan.
Learning Control Barrier Functions with High Relative Degree for Safety-Critical Control

Control barrier functions have shown great success in addressing control problems with safety guarantees. These methods usually find the next safe control input by solving an online quadratic programming problem. However, model uncertainty is a big challenge in synthesizing controllers. This may lead to the generation of unsafe control actions, resulting in severe consequences. In this paper, we develop a learning framework to deal with system uncertainty. Our method mainly focuses on learning the dynamics of the control barrier function, especially for high relative degree with respect to a system. We show that for each order, the time derivative of the control barrier function can be separated into the time derivative of the nominal control barrier function and a remainder. This implies that we can use a neural network to learn the remainder so that we can approximate the dynamics of the real control barrier function. We show by simulation that our method can generate safe trajectories under parametric uncertainty using a differential drive robot model.
Near-wall forces on a neutrally buoyant spherical particle in an axisymmetric stagnation-point flow

Abstract Hydrodynamic forces acting on a neutrally buoyant spherical particle immersed in a wall-bounded axisymmetric stagnation-point flow (Hiemenz–Homann flow) are predicted, based on a suitable form of the reciprocal theorem. An approximate algebraic form of the undisturbed velocity field is set up, mimicking the gradual transition of the actual carrying flow throughout the boundary layer, from a pure linear straining flow in the bulk to a parabolic flow at the wall. The particle Reynolds number is assumed to be small and predictions based on the creeping-flow assumption are first derived. Then, inertial corrections are computed, assuming that the particle stands close enough to the wall for the latter to be in the inner region of the disturbance. Predictions for the time-dependent slip velocity between the particle and ambient fluid are obtained in the form of a differential equation, first assuming that the particle moves along the flow symmetry axis, then extending the analysis to particles released at an arbitrary radial position. In the former case, these predictions are compared with results provided by numerical simulations. When the strain-based Reynolds number (built on the particle radius and strain rate in the bulk) exceeds $0.1$, finite-inertia effects due to particle–wall interactions and to the relative acceleration between the particle and fluid are found to substantially modify the way the slip velocity varies with the distance to the wall.
Contextual Interference Reduction by Selective Fine-Tuning of Neural Networks

Feature disentanglement of the foreground target objects and the background surrounding context has not been yet fully accomplished. The lack of network interpretability prevents advancing for feature disentanglement and better generalization robustness. We study the role of the context on interfering with a disentangled foreground target object representation in this work. We hypothesize that the representation of the surrounding context is heavily tied with the foreground object due to the dense hierarchical parametrization of convolutional networks with under-constrained learning algorithms. Working on a framework that benefits from the bottom-up and top-down processing paradigms, we investigate a systematic approach to shift learned representations in feedforward networks from the emphasis on the irrelevant context to the foreground objects. The top-down processing provides importance maps as the means of the network internal self-interpretation that will guide the learning algorithm to focus on the relevant foreground regions towards achieving a more robust representations. We define an experimental evaluation setup with the role of context emphasized using the MNIST dataset. The experimental results reveal not only that the label prediction accuracy is improved but also a higher degree of robustness to the background perturbation using various noise generation methods is obtained.
Notes on the Hankel transform of linear combinations of consecutive pairs of Catalan numbers

We provide a context around a conjectured closed form for the Hankel transform of linear combinations of consecutive pairs of Catalan numbers. This generalizes the formula for the Hankel transforms of the shifted Catalan numbers and the known results for linear combinations of pairs of Catalan numbers. Many interesting number triangles emerge, some of which we analyze using the language of Riordan arrays.
Gallic acid from Terminalia chebula inhibited the growth of esophageal carcinoma cells by suppressing the Hippo signal pathway

Objective(s): To explore the molecular mechanism of gallic acid (GA) from Terminalia chebula in suppressing the growth of esophageal carcinoma (EC). Materials and Methods: Human EC cells (EC9706 and KYSE450) were treated with different concentrations of GA (10, 20, and 40 μg/ml), which were subjected to 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide (MTT) assay, plate clone formation assay, Annexin V-fluorescein isothiocyanate (FITC)/propidium iodide (PI) staining, and Western blotting. EC mice were divided into Model, 0.3% GA, and 1% GA groups to observe the tumor volume and the expressions of YAP, TAZ, Ki-67, and Caspase-3 in tumor tissues. Results: GA decreased cell viability and colony formation of EC9706 and KYSE450 cells, which was more obvious as the concentration increased. In addition, GA promoted cell apoptosis in a concentration-dependent manner with the up-regulation of pro-apoptotic proteins (Bax, cleaved caspase-3, and cleaved caspase-9) and nuclear YAP/TAZ, as well as the down-regulation of anti-apoptotic protein Bcl-2 and the levels of p-YAP and p-TAZ. Moreover, GA decreased the growth of xenograft tumor in vivo, with the reduction in the tumor volume and the reduction of YAP and TAZ expressions in the tumor tissues. In addition, Ki-67 expression in GA groups was lower than those in the Model group, with the increase in caspase-3 expression in the tumor tissues. Changes aforementioned were obviously shown in the 0.3% GA group. Conclusion: GA blocked the activity of the Hippo pathway to suppress cell proliferation of EC and facilitate cell apoptosis, which is expected to be a novel strategy for treatment of EC.
HIGHER REGULARITY FOR PARABOLIC EQUATIONS BASED ON MAXIMAL Lp-Lq SPACES

In this paper we prove higher regularity for 2m-th order parabolic equations with general boundary conditions. This is a kind of maximal L_p-L_q regularity with differentiability, i.e. the main theorem is isomorphism between the solution space and the data space using Besov and Triebel--Lizorkin spaces. The key is compatibility conditions for the initial data. We are able to get a unique smooth solution if the data satisfying compatibility conditions are smooth.
An Approximation Algorithm for Covering Linear Programs and its Application to Bin-Packing

We give an $\alpha(1+\epsilon)$-approximation algorithm for solving covering LPs, assuming the presence of a $(1/\alpha)$-approximation algorithm for a certain optimization problem. Our algorithm is based on a simple modification of the Plotkin-Shmoys-Tardos algorithm (MOR 1995). We then apply our algorithm to $\alpha(1+\epsilon)$-approximately solve the configuration LP for a large class of bin-packing problems, assuming the presence of a $(1/\alpha)$-approximate algorithm for the corresponding knapsack problem (KS). Previous results give us a PTAS for the configuration LP using a PTAS for KS. Those results don't extend to the case where KS is poorly approximated. Our algorithm, however, works even for polynomially-large $\alpha$.
PI3K inhibition sensitizes EGFR wild-type NSCLC cell lines to erlotinib chemotherapy

Tyrosine kinase inhibitors (TKIs) bring significant benefits for patients with cancers harboring epidermal growth factor receptor (EGFR) mutations. However, after treatment for a certain period, most patients ultimately acquire resistance. Numerous studies indicated that PI3K has an important role in tumor cell growth and drug sensitivity. Furthermore, inhibition of PI3K may lead to sensitization of non-small cell lung cancer (NSCLC) cells to EGFR-TKIs. The aim of the present study was to explore whether LY294002, an inhibitor of PI3K, is able to improve the sensitivity of NSCLC cell lines with wild-type EGFR to the EGFR-TKI erlotinib. An MTT assay was used to examine the effect of combined treatment with LY294002 and erlotinib on cell survival of two EGFR wild-type NSCLC cell lines, NCI-H661 and NCI-H460. Furthermore, flow cytometry was used to assess apoptosis in NCI-H661 and NCI-H460 cells after treatment with erlotinib and LY294002. In addition, the expression of downstream proteins was detected by western blot analysis. The results indicated that the number of viable NCI-H661 and NCI-H460 cells was dose-dependently reduced by erlotinib or LY294002. Compared to treatment with erlotinib alone, the cell apoptosis was enhanced if combined treatment of erlotinib and LY294002 was performed in NCI-H661 cells. Furthermore, combination treatment of erlotinib and LY294002 resulted in a significant reduction of phosphorylated p70S6K levels in NCI-H661 [PI3K catalytic subunit alpha (PI3KCA) wild-type] cells. However, this phenomenon was not observed in the NCI-H460 cell line (PIK3CA mutant-type). In conclusion, the present study indicated that inhibition of PI3K may have the potential to improve the sensitivity of NSCLC cells to an EGFR-TKI. However, the therapeutic effect may depend on the mutation status of PIK3CA.
Computing Properties of Thermodynamic Binding Networks: An Integer Programming Approach

The thermodynamic binding networks (TBN) model was recently developed as a tool for studying engineered molecular systems. The TBN model allows one to reason about their behavior through a simplified abstraction that ignores details about molecular composition, focusing on two key determinants of a system's energetics common to any chemical substrate: how many molecular bonds are formed, and how many separate complexes exist in the system. We formulate as an integer program the NP-hard problem of computing stable configurations of a TBN (a.k.a., minimum energy: those that maximize the number of bonds and complexes). We provide open-source software that solves these formulations, and give empirical evidence that this approach enables dramatically faster computation of TBN stable configurations than previous approaches based on SAT solvers. Our setup can also reason about TBNs in which some molecules have unbounded counts. These improvements in turn allow us to efficiently automate verification of desired properties of practical TBNs. Finally, we show that the TBN's Graver basis (a kind of certificate of optimality in integer programming) has a natural interpretation as the "fundamental components" out of which locally minimal energy configurations are composed. This characterization helps verify correctness of not only stable configurations, but entire "kinetic pathways" in a TBN.
Reinforcement-Learning-Based Disturbance Rejection Control for Uncertain Nonlinear Systems

This article investigates the reinforcement-learning (RL)-based disturbance rejection control for uncertain nonlinear systems having nonsimple nominal models. An extended state observer (ESO) is first designed to estimate the system state and the total uncertainty, which represents the perturbation to the nominal system dynamics. Based on the output of the observer, the control compensates for the total uncertainty in real time, and simultaneously, online approximates the optimal policy for the compensated system using a simulation of experience-based RL technique. Rigorous theoretical analysis is given to show the practical convergence of the system state to the origin and the developed policy to the ideal optimal policy. It is worth mentioning that the widely used restrictive persistence of excitation (PE) condition is not required in the established framework. Simulation results are presented to illustrate the effectiveness of the proposed method.
Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles

Seamlessly operating an autonomous vehicles in a crowded pedestrian environment is a very challenging task. This is because human movement and interactions are very hard to predict in such environments. Recent work has demonstrated that reinforcement learning-based methods have the ability to learn to drive in crowds. However, these methods can have very poor performance due to inaccurate predictions of the pedestrians’ future state as human motion prediction has a large variance. To overcome this problem, we propose a new method, SARL-SGAN-KCE, that combines a deep socially aware attentive value network with a human multimodal trajectory prediction model to help identify the optimal driving policy. We also introduce a novel technique to extend the discrete action space with minimal additional computational requirements. The kinematic constraints of the vehicle are also considered to ensure smooth and safe trajectories. We evaluate our method against the state of art methods for crowd navigation and provide an ablation study to show that our method is safer and closer to human behaviour.
Anti-inflammatory activity of Radix Angelicae biseratae in the treatment of osteoarthritis determined by systematic pharmacology and in vitro experiments

Radix Angelicae biseratae is a widely used Chinese traditional herbal medicine for osteoarthritis (OA). Its therapeutic efficacy has been confirmed in clinical practice. However, its mechanisms of action in treating OA have remained elusive. The purpose of the present study was to identify active components with good oral bioavailability and drug-like properties from Radix Angelicae biseratae through systematic pharmacology and in vitro experiments to determine targets of Radix Angelicae biseratae in the treatment of OA. The functional components of Radix Angelicae biseratae were screened from the Traditional Chinese Medicine Systems Pharmacology database based on oral bioavailability and drug-like properties. Subsequently, the databases STITCH, Open Targets Platform and DrugBank were searched and microarray analysis was performed to screen the active components of Radix Angelicae biseratae to treat OA and predict its potential target proteins. The interaction network and protein interaction network were then generated and examined, molecular docking between active components and targets was performed and the enrichment of potential target proteins was analyzed. Finally, reverse transcription-quantitative (RT-q)PCR and western blot analyses were used to verify the therapeutic effect of Radix Angelicae biseratae extract on the expression of OA-associated target proteins. The results provided eight active components in Radix Angelicae biseratae, which were firmly linked to 20 targets of OA. In combination with molecular docking and the analysis of the interaction network between components and targets, it was suggested that sitosterol was a major active component of Radix Angelicae biseratae in the treatment of OA. Protein interaction network analysis suggested that prostaglandin-endoperoxide synthase 2 (PTGS2), nitric oxide synthase 3 and cytochrome P450 2B6 may be critical targets for Radix Angelicae biseratae in the treatment of OA. In addition, RT-qPCR and western blot analyses suggested that Radix Angelicae biseratae extract inhibited the mRNA and protein expression of PTGS2 in degenerative articular cartilage cells in vitro, whilst other targets remain to be verified. Functional enrichment analysis indicated that Radix Angelicae biseratae confers pharmacological efficacy towards OA through exerting anti-inflammatory effects and immune regulation.