Miracle-dz/UniFAR-FTUs huggingface dataset:
The possibility of using shogaol for treatment of ulcerative colitis

Objective(s): This study aimed to investigate the effect of Shogaol on dextran sodium sulfate (DSS)- induced ulcerative colitis (UC) in mice compared to an immune-suppressant chemotherapeutic medicine, known as 6-thioguanine (6-TG). Materials and Methods: Thirty-six adult BALB/c mice were divided into six groups: group 1 (positive control): no DSS exposure and no treatment; group 2 (negative control): DSS exposure without treatment; group 3 (vehicle control): DSS exposure and olive oil treatment; group 4: DSS exposure and 0.3 mg/kg 6-TG treatment; group 5: DSS exposure and 20 mg/kg Shogaol treatment; and group 6: DSS exposure and 40 mg/kg Shogaol treatment. At day 16, the mice were euthanized and UC was evaluated according to colon length, histologically index score and expression scores of the epidermal growth factor receptor (EGFR). Results: The disease activity index (DAI) and histological index scores of mice treated with 40 mg/kg body weight (BW) Shogaol were approximately lower than the corresponding scores of mice treated with 6-TG. In addition, the rate of healing in the former mice was approximately 3 folds higher than that of the latter ones as indicated by the lack of EGFR expression in colonic glands and macrophages. Conclusion: These findings showed that the therapeutic effect of 40 mg/kg BW Shogaol could be better than 6-TG in the treatment of UC, and it may draw the attention regarding the priority of using this cheap plant-derived substance for treatment of the inflammatory bowel diseases because treatment with 6-TG is usually associated with adverse side effects.
Enjoy the Benefit of Network Coding: Combat Pollution Attacks in 5G Multihop Networks

In the upcoming 5G era, many new types of networks will greatly expand the connectivity of the world such as vehicular ad hoc networks (VANETs), Internet of Things (IoT), and device-to-device communications (D2D). Network coding is a promising technology that can significantly improve the throughput and robustness of these emerging 5G multihop networks. However, network coding is generally very fragile to malicious attacks such as message content corruption and node compromise attacks. To take advantage of network coding in performance gain while refraining malicious network attacks is an interesting and challenging research issue. In this paper, we propose a new error-detection and error-correction (EDEC) scheme that can jointly detect and remove the malicious attacks based on the underlying error-control scheme for general multihop networks that can model the 5G multihop networks. The proposed scheme can increase the throughput for network with pollution attacks compared to existing error-detection based schemes. Then we propose a low-density parity check (LDPC) decoding based EDEC (LEDEC) scheme. Our theoretical analysis demonstrates that the LEDEC scheme can further increase the throughput for heavily polluted network environments. We also provide extensive performance evaluation and simulation results to validate the proposed schemes. This research ensures the expected performance gain for the application of network coding in the 5G network under malicious pollution attacks.
Shared Capacity Routing Problem - An Omni-channel Retail Study

More and more retailers allow customers to order goods online and then pick them up in a store. In this setting, these orders are typically served from a dedicated warehouse. This often means that the stores are visited by different vehicles to replenish the store inventory and to supply the pick-up points. Motivated by a collaboration with an omni-channel grocery retailer in the Netherlands, we study how to best share capacity between the routes associated with these different sales channels. As operational constraints prevent jointly planning the routes, we consider the replenishment routes as fixed when planning the routes to serve the pick-up orders. An order can be transferred to the replenishment route, if capacity allows. We consider the problem of deciding which customer orders to transfer and which to deliver directly such that the total costs are minimized. We present an exact and a heuristic approach to solve this problem. Computational experiments on both real-world and artificial instances show that substantial savings can be achieved by sharing vehicle capacity across different channels.
A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft

In aircraft industry, market needs evolve quickly in a high competitiveness context. This requires adapting a given aircraft model in minimum time considering for example an increase of range or the number of passengers (cf A330 NEO family). The computation of loads and stress to resize the airframe is on the critical path of this aircraft variant definition: this is a consuming and costly process, one of the reason being the high dimen-sionality and the large amount of data. This is why Airbus has invested since a couple of years in Big Data approaches (statistic methods up to machine learning) to improve the speed, the data value extraction and the responsiveness of this process. This paper presents recent advances in this work made in cooperation between Airbus, ENAC and Institut de Mathe-matiques de Toulouse in the framework of a proof of value sprint project. It compares the influence of three dimensional reduction techniques (PCA, polynomial fitting, combined) on the extrapolation capabilities of Regression Trees based algorithms for loads prediction. It shows that AdaBoost with Random Forest offers promising results in average in terms of accuracy and computational time to estimate loads on which a PCA is applied only on the outputs.
Leader-following fixed-time output feedback consensus for second-order multi-agent systems with input saturation

ABSTRACT This paper investigates the leader-following fixed-time output feedback consensus problem for second-order multi-agent systems with input saturation. By combing fixed-time control technique and bi-limit homogeneous systems theory, a class of bounded fixed-time consensus protocols are developed for leader-following multi-agent systems. The protocol design is divided into two parts. First, when all the state information of the followers are measurable, a state feedback consensus protocol is designed to achieve fixed-time consensus. Then, when the velocity information is unmeasurable, an observer-based fixed-time consensus protocol is proposed. With the help of Lyapunov stability theorem and the property of a homogeneous function, it is theoretically shown that the states of all followers can track that of the leader in fixed-time in the presence of input saturation. Finally, numerical simulation is carried out to illustrate the effectiveness of theoretical results.
Analysis of the Factors Influencing Enterprise and Government Participation in the Medicines Patent Pool Based on System Dynamics Model

Background: The participation of enterprises and governments in the Medicines Patent Pool (MPP) improves the disease management levels by enhancing the accessibility of medical resources. Non-participation of the stakeholders restricts the development of the MPP. Hence, systematic analysis of the key factors influencing MPP participation is necessary. Methods: A system dynamics model of the market before and after enterprises and governments join the MPP was constructed by considering the economic benefits of both stakeholders. The effects of generic drug prices, royalty rates, pooling subsidies, and enterprise scale on the relevant benefit difference were analyzed. Data from the China Medical and Economic Network for the period 2003–2016, as well as the 2017 annual report of Celgene Corporation, were used as test data. Results: The proper pooling subsidy coefficient ranges between 0.05 and 0.08 when the generic drug price ratio and royalty rate are lower than 36% and 34%, respectively. These factors could enhance the willingness of both stakeholders to join the MPP. Initial R&D investments and the relative drug patent intensity of enterprises respectively exert positive and negative impacts on their willingness to join the MPP. Conclusion: To encourage stakeholders to join the MPP, generic drug prices should be lowered, license fees and subsidies should be adjusted appropriately, and the R&D scale and strength of original drug enterprises should be taken into account. The research results provide a reference for formulating the rules of MPP and other policies aiming to facilitate the sharing and innovation of medical resources.
On Counting Oracles for Path Problems

We initiate the study of counting oracles for various path problems in graphs. Distance oracles have gained a lot of attention in recent years, with studies of the underlying space and time tradeoffs. For a given graph G, a distance oracle is a data structure which can be used to answer distance queries for pairs of vertices s, t ∈ V (G). In this work, we extend the set up to answering counting queries: for a pair of vertices s, t, the oracle needs to provide the number of (shortest or all) paths from s to t. We present O(n1.5) preprocessing time, O(n1.5) space, and O( √ n) query time algorithms for oracles counting shortest paths in planar graphs and for counting all paths in planar directed acyclic graphs. We extend our results to other graphs which admit small balanced separators and present applications where our oracle improves the currently best known running times. 2012 ACM Subject Classification Theory of computation → Graph algorithms analysis
Gastrodin Rescues Autistic-Like Phenotypes in Valproic Acid-Induced Animal Model

Autism spectrum disorder (ASD) is an immensely challenging developmental disorder characterized by impaired social interaction, restricted/repetitive behavior, and anxiety. GABAergic dysfunction has been postulated to underlie these autistic symptoms. Gastrodin is widely used clinically in the treatment of neurological disorders and showed to modulate GABAergic signaling in the animal brain. The present study aimed to determine whether treatment with gastrodin can rescue valproic acid (VPA) induced autistic-like phenotypes, and to determine its possible mechanism of action. Our results showed that administration of gastrodin effectively alleviated the autistic-associated behavioral abnormalities as reflected by an increase in social interaction and decrement in repetitive/stereotyped behavior and anxiety in mice as compared to those in untreated animals. Remarkably, the amelioration in autistic-like phenotypes was accompanied by the restoration of inhibitory synaptic transmission, α5 GABAA receptor, and type 1 GABA transporter (GAT1) expression in the basolateral amygdala (BLA) of VPA-treated mice. These findings indicate that gastrodin may alleviate the autistic symptoms caused by VPA through regulating GABAergic synaptic transmission, suggesting that gastrodin may be a potential therapeutic target in autism.
β-VAEs can retain label information even at high compression

In this paper, we investigate the degree to which the encoding of a $\beta$-VAE captures label information across multiple architectures on Binary Static MNIST and Omniglot. Even though they are trained in a completely unsupervised manner, we demonstrate that a $\beta$-VAE can retain a large amount of label information, even when asked to learn a highly compressed representation.
Variational Coarse-Graining for Molecular Dynamics

Molecular dynamics simulations provide theoretical insight into the microscopic behavior of materials in condensed phase and, as a predictive tool, enable computational design of new compounds. However, because of the large temporal and spatial scales involved in thermodynamic and kinetic phenomena in materials, atomistic simulations are often computationally unfeasible. Coarse-graining methods allow simulating larger systems, by reducing the dimensionality of the simulation, and propagating longer timesteps, by averaging out fast motions. Coarse-graining involves two coupled learning problems; defining the mapping from an all-atom to a reduced representation, and parametrizing a Hamiltonian over coarse-grained coordinates. Multiple statistical mechanics approaches have addressed the latter, but the former is generally a hand-tuned process based on chemical intuition. Here we present Autograin, an optimization framework based on auto-encoders to learn both tasks simultaneously. Autograin automatically learns the optimal mapping between all-atom and reduced representation, using the reconstruction loss to facilitate the learning of coarse-grained variables. In addition, a force-matching method is applied to variationally determine the coarse-grained potential energy function. This procedure is tested on a number of model systems including single-molecule and bulk-phase periodic simulations.
Multi-Scale Spectrum Sensing in Dense Multi-Cell Cognitive Networks

Multi-scale spectrum sensing is proposed to overcome the cost of full network state information on the spectrum occupancy of primary users (PUs) in dense multi-cell cognitive networks. Secondary users (SUs) estimate the local spectrum occupancies and aggregate them hierarchically to estimate spectrum occupancy at multiple spatial scales. Thus, SUs obtain fine-grained estimates of spectrum occupancies of nearby cells, more relevant to scheduling tasks, and coarse-grained estimates of those of distant cells. An agglomerative clustering algorithm is proposed to design a cost-effective aggregation tree, matched to the structure of interference, robust to local estimation errors, and delays. Given these multi-scale estimates, the SU traffic is adapted in a decentralized fashion in each cell, to optimize the trade-off among SU cell throughput, interference caused to PUs, and mutual SU interference. Numerical evaluations demonstrate a small degradation in SU cell throughput (up to 15% for a 0 dB interference-to-noise ratio experienced at PUs) compared to a scheme with full network state information, using only one-third of the cost incurred in the exchange of spectrum estimates. The proposed interference-matched design is shown to significantly outperform a random tree design, by providing more relevant information for network control, and a state-of-the-art consensus-based algorithm, which does not leverage the spatio-temporal structure of interference across the network.
Predicting pregnancy using large-scale data from a women's health tracking mobile application

Predicting pregnancy has been a fundamental problem in women's health for more than 50 years. Previous datasets have been collected via carefully curated medical studies, but the recent growth of women's health tracking mobile apps offers potential for reaching a much broader population. However, the feasibility of predicting pregnancy from mobile health tracking data is unclear. Here we develop four models - a logistic regression model, and 3 LSTM models - to predict a woman's probability of becoming pregnant using data from a women's health tracking app, Clue by BioWink GmbH. Evaluating our models on a dataset of 79 million logs from 65,276 women with ground truth pregnancy test data, we show that our predicted pregnancy probabilities meaningfully stratify women: women in the top 10% of predicted probabilities have a 89% chance of becoming pregnant over 6 menstrual cycles, as compared to a 27% chance for women in the bottom 10%. We develop a technique for extracting interpretable time trends from our deep learning models, and show these trends are consistent with previous fertility research. Our findings illustrate the potential that women's health tracking data offers for predicting pregnancy on a broader population; we conclude by discussing the steps needed to fulfill this potential.
Reasoning with 'Some'

There has been substantial debate about the question of whether ‘some’ is normally interpreted as ‘some but not all’ when it is embedded under a quantifying expression. Experiments using a sentence-picture veriﬁcation paradigm have been equivocal: while Geurts & Pouscoulous (2009) report that embedded upper-bounded construals of ‘some’ are almost non-existent, Potts et al. (2016) observed substantial rates of upper-bounded construals in at least some embedding environments. In this paper, we investigate how it is possible that these two superﬁcially similar studies provide such diverging sets of results. We show that subtle features of the displays inﬂuence the frequency of embedded upper-bounded construals. We discuss the consequences of these ﬁndings for theories of upper-bounded construals and for experimental research in pragmatics in general.
Evaluation and Recommendations for Routine Genotyping Using Skim Whole Genome Re-sequencing in Canola

Whole genome sequencing offers genome wide, unbiased markers, and inexpensive library preparation. With the cost of sequencing decreasing rapidly, many plant genomes of modest size are amenable to skim whole genome resequencing (skim WGR). The use of skim WGR in diverse sample sets without the use of imputation was evaluated in silico in 149 canola samples representative of global diversity. Fastq files with an average of 10x coverage of the reference genome were used to generate skim samples representing 0.25x, 0.5x, 1x, 2x, 3x, 4x, and 5x sequencing coverage. Applying a pre-defined list of SNPs versus de novo SNP discovery was evaluated. As skim WGR is expected to result in some degree of insufficient allele sampling, all skim coverage levels were filtered at a range of minimum read depths from a relaxed minimum read depth of 2 to a stringent read depth of 5, resulting in 28 list-based SNP sets. As a broad recommendation, genotyping pre-defined SNPs between 1x and 2x coverage with relatively stringent depth filtering is appropriate for a diverse sample set of canola due to a balance between marker number, sufficient accuracy, and sequencing cost, but depends on the intended application. This was experimentally examined in two sample sets with different genetic backgrounds: 1x coverage of 1,590 individuals from 84 Australian spring type four-parent crosses aimed at maximizing diversity as well as one commercial F1 hybrid, and 2x coverage of 379 doubled haploids (DHs) derived from a subset of the four-parent crosses. To determine optimal coverage in a simpler genetic background, the DH sample sequence coverage was further down sampled in silico. The flexible and cost-effective nature of the protocol makes it highly applicable across a range of species and purposes.
Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing

ABSTRACT Artificial neural networks (ANNs) have been extensively used for the spatially explicit modeling of complex geographic phenomena. However, because of the complexity of the computational process, there has been an inadequate investigation on the parameter configuration of neural networks. Most studies in the literature from GIScience rely on a trial-and-error approach to select the parameter setting for ANN-driven spatial models. Hyperparameter optimization provides support for selecting the optimal architectures of ANNs. Thus, in this study, we develop an automated hyperparameter selection approach to identify optimal neural networks for spatial modeling. Further, the use of hyperparameter optimization is challenging because hyperparameter space is often large and the associated computational demand is heavy. Therefore, we utilize high-performance computing to accelerate the model selection process. Furthermore, we involve spatial statistics approaches to improve the efficiency of hyperparameter optimization. The spatial model used in our case study is a land price evaluation model in Mecklenburg County, North Carolina, USA. Our results demonstrate that the automated selection approach improves the model-level performance compared with linear regression, and the high-performance computing and spatial statistics approaches are of great help for accelerating and enhancing the selection of optimal neural networks for spatial modeling.
Human Retinal Pigment Epithelium: In Vivo Cell Morphometry, Multispectral Autofluorescence, and Relationship to Cone Mosaic

Purpose To characterize in vivo morphometry and multispectral autofluorescence of the retinal pigment epithelial (RPE) cell mosaic and its relationship to cone cell topography across the macula. Methods RPE cell morphometrics were computed in regularly spaced regions of interest (ROIs) from contiguous short-wavelength autofluorescence (SWAF) and photoreceptor reflectance images collected across the macula in one eye of 10 normal participants (23–65 years) by using adaptive optics scanning light ophthalmoscopy (AOSLO). Infrared autofluorescence (IRAF) images of the RPE were collected with AOSLO in seven normal participants (22–65 years), with participant overlap, and compared to SWAF quantitatively and qualitatively. Results RPE cell statistics could be analyzed in 84% of SWAF ROIs. RPE cell density consistently decreased with eccentricity from the fovea (participant mean ± SD: 6026 ± 1590 cells/mm2 at fovea; 4552 ± 1370 cells/mm2 and 3757 ± 1290 cells/mm2 at 3.5 mm temporally and nasally, respectively). Mean cone-to-RPE cell ratio decreased rapidly from 16.6 at the foveal center to <5 by 1 mm. IRAF revealed cells in six of seven participants, in agreement with SWAF RPE cell size and location. Differences in cell fluorescent structure, contrast, and visibility beneath vasculature were observed between modalities. Conclusions Improvements in AOSLO autofluorescence imaging permit efficient visualization of RPE cells with safe light exposures, allowing individual characterization of RPE cell morphometry that is variable between participants. The normative dataset and analysis of RPE cell IRAF and SWAF herein are essential for understanding microscopic characteristics of cell fluorescence and may assist in interpreting disease progression in RPE cells.
Refactoring Multi-Level Models

Multi-level modelling promotes flexibility in modelling by enabling the use of several meta-levels instead of just two, as is the case in mainstream two-level modelling approaches. While this approach leads to simpler models for some scenarios, it introduces an additional degree of freedom as designers can decide the meta-level where an element should reside, having to ascertain the suitability of such decisions. In this respect, model refactorings have been successfully applied in the context of two-level modelling to rearrange the elements of a model while preserving its meaning. Following this idea, we propose a catalogue of 17 novel refactorings specific to multi-level models. Their objective is to help designers in rearranging elements across and within meta-levels and exploring the consequences. In this article, we detail each refactoring in the catalogue, show a classification across different dimensions, and describe the support we provide in our MetaDepth tool. We present two experiments to assess two aspects of our refactorings. The first one validates the predicted semantic side effects of the refactorings on the basis of more than 210.000 refactoring applications. The second one measures the impact of refactorings on three quality attributes of multi-level models.