Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined Transcriptomics and Metabolomics Analysis Reveals the Molecular Mechanism of Salt Tolerance of Huayouza 62, an Elite Cultivar in Rapeseed (Brassica napus L.)

Soil salinity is one of the most significant abiotic stresses affecting crop yield around the world. To explore the molecular mechanism of salt tolerance in rapeseed (Brassica napus L.), the transcriptome analysis and metabolomics analysis were used to dissect the differentially expressed genes and metabolites in two rapeseed varieties with significant differences in salt tolerance; one is an elite rapeseed cultivar, Huayouza 62. A total of 103 key differentially expressed metabolites (DEMs) and 53 key differentials expressed genes (DEGs) that might be related to salt stress were identified through metabolomics and transcriptomics analysis. GO and KEGG analysis revealed that the DEGs were mainly involved in ion transport, reactive oxygen scavenging, osmotic regulation substance synthesis, and macromolecular protein synthesis. The DEMs were involved in TCA cycle, proline metabolism, inositol metabolism, carbohydrate metabolic processes, and oxidation-reduction processes. In addition, overexpression of BnLTP3, which was one of the key DEGs, could increase tolerance to salt stress in Arabidopsis plants. This study reveals that the regulation mechanism of salt tolerance in rapeseed at the transcriptome and metabolism level and provides abundant data for further in-depth identification of essential salt tolerance genes.
Hierarchical Regression Modeling of Some Factors Affecting Weight of Child at Birth

According to World Health Organization (WHO), normal weight of baby at term delivery is (2.5 – 4.2) kilograms. Every child’s birth weight below 2.5 kilograms, regardless of gestational age, is regarded as Low birth weight (LBW). WHO estimates that globally, over 20 million LBW babies are born annually and nearly 95.6% of them in developing countries. Half of all perinatal and 1/3rd of all infant deaths occur in babies with LBW. It is therefore, essential to study some of the factors that causes LBW. Hierarchical Multiple Regression analysis was used to study the effects of mother’s weight, age and height above and beyond mother’s education level in predicting the weight of the child at birth. The results showed that mother’s education level explains about 6.1% of the unexplained variations in the weight of the child at birth in block 1 and mother’s age, weight and height explained about 3.9% above and beyond mother’s education level. This implies that all the variables studied affects the baby’s weight at birth but, the mother’s educational level affects the baby’s weight much more. It was concluded that mother’s education level plays a vital role in predicting the weight of the child at birth because it has a causal effect on the use of prenatal care and improves marriage prospects.
Vonoprazan in the management of gastric/peptic ulcers: a systematic review of safety data

Introduction Although potassium-competitive acid blockers (P-CABs) prompted safety concerns when first developed, they ultimately proved to have a favourable safety profile. Aim To assess the safety of vonoprazan in the management of gastroesophageal reflux disease (GERD), peptic ulcers, or gastroduodenal mucosal lesions induced by chronic use of aspirin or non-steroidal anti-inflammatory drugs (NSAIDs). Material and methods From March to June 2021, a literature search was conducted using Medline via PubMed, Cochrane library, Lilacs, SciELO, and Centre for Reviews and Dissemination (CRD) electronic databases. After applying the eligibility criteria, 10 studies were included in this review. Of these 10 articles, vonoprazan was used as initial therapy in 6 and as maintenance therapy in 4. Adverse event rates were similar for vonoprazan and proton-pump inhibitors (PPIs). Conclusions Our findings suggest that vonoprazan is a safe option for the management of erosive oesophagitis, gastric/peptic ulcers, or peptic ulcers induced by chronic use of aspirin or NSAIDs.
Linear actuator–based knee exoskeleton for stand–sit–stand motions: a bond graph approach

People with knee disorders often find it difficult to perform common mobility tasks, such as stand–sit–stand motions. High knee torque is required to complete such transitions, as the chances of toppling increase during these motions. Most of the existing conventional approaches, such as wheelchairs and crutches, have failed to provide complete independence to the users. Conversely, contemporary systems like lower body exoskeletons which are bulky, complex, and expensive do not specifically target the knee joint instead of assisting other joints. Hence, there is a need to aid the knee joint using a robotic knee exoskeleton capable of accurately providing the desired knee torque. In the present work, to assist the user in performing the stand–sit–stand motions, an electromyography sensor-based four-bar knee exoskeleton actuated by a linear actuator is proposed. The modeling of the complete exoskeleton is developed using bond graph technique, as the components exist in different energy domains and it is possible to frame a dynamic bond graph model using only kinematic equations. The prototype is fabricated, and experiments are carried out on an artificial limb to prove the efficacy of the design of the current knee exoskeleton. The assistive torque developed by the actuator at the knee joint of the exoskeleton is found to be suitable to assist the wearer. As a result, little effort is required by the wearer for performing the stand–sit–stand motions. The rotation of the thigh link of the developed exoskeleton was found to be suitable for performing the stand–sit–stand activity.
Combinatorial Path Planning for a System of Multiple Unmanned Vehicles

Combinatorial Path Planning for a System of Multiple Unmanned Vehicles. (December 2010) Sai Yadlapalli, B.Tech., Indian Institute of Technology, Madras; M.S., Texas A&M University Co–Chairs of Advisory Committee: Dr. Swaroop Darbha Dr. K. R. Rajagopal In this dissertation, the problem of planning the motion ofmUnmanned Vehicles (UVs) (or simply vehicles) through n points in a plane is considered. A motion plan for a vehicle is given by the sequence of points and the corresponding angles at which each point must be visited by the vehicle. We require that each vehicle return to the same initial location(depot) at the same heading after visiting the points. The objective of the motion planning problem is to choose at most q(≤ m) UVs and find their motion plans so that all the points are visited and the total cost of the tours of the chosen vehicles is a minimum amongst all the possible choices of vehicles and their tours. This problem is a generalization of the wellknown Traveling Salesman Problem (TSP) in many ways: (1) each UV takes the role of salesman (2) motion constraints of the UVs play an important role in determining the cost of travel between any two locations; in fact, the cost of the travel between any two locations depends on direction of travel along with the heading at the origin and destination, and (3) there is an additional combinatorial complexity stemming from the need to partition the points to be visited by each UV and the set of UVs that must be employed by the mission. In this dissertation, a sub-optimal, two-step approach to motion planning is presented to solve this problem:(1) the combinatorial problem of choosing the vehicles and their associated tours is based on Euclidean distances between points and (2) once the sequence of points to be visited is specified, the heading at each point is determined based on a Dynamic Programming scheme. The solution to the first step is based on a generalization of
Computationally Efficient Assembly of Pseudomonas aeruginosa Gene Expression Compendia

Pseudomonas aeruginosa is a causative agent of a wide range of infections, including chronic infections associated with cystic fibrosis. These P. aeruginosa infections are difficult to treat and often have negative outcomes. ABSTRACT Thousands of Pseudomonas aeruginosa RNA sequencing (RNA-seq) gene expression profiles are publicly available via the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). In this work, the transcriptional profiles from hundreds of studies performed by over 75 research groups were reanalyzed in aggregate to create a powerful tool for hypothesis generation and testing. Raw sequence data were uniformly processed using the Salmon pseudoaligner, and this read mapping method was validated by comparison to a direct alignment method. We developed filtering criteria to exclude samples with aberrant levels of housekeeping gene expression or an unexpected number of genes with no reported values and normalized the filtered compendia using the ratio-of-medians method. The filtering and normalization steps greatly improved gene expression correlations for genes within the same operon or regulon across the 2,333 samples. Since the RNA-seq data were generated using diverse strains, we report the effects of mapping samples to noncognate reference genomes by separately analyzing all samples mapped to cDNA reference genomes for strains PAO1 and PA14, two divergent strains that were used to generate most of the samples. Finally, we developed an algorithm to incorporate new data as they are deposited into the SRA. Our processing and quality control methods provide a scalable framework for taking advantage of the troves of biological information hibernating in the depths of microbial gene expression data and yield useful tools for P. aeruginosa RNA-seq data to be leveraged for diverse research goals. IMPORTANCE Pseudomonas aeruginosa is a causative agent of a wide range of infections, including chronic infections associated with cystic fibrosis. These P. aeruginosa infections are difficult to treat and often have negative outcomes. To aid in the study of this problematic pathogen, we mapped, filtered for quality, and normalized thousands of P. aeruginosa RNA-seq gene expression profiles that were publicly available via the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). The resulting compendia facilitate analyses across experiments, strains, and conditions. Ultimately, the workflow that we present could be applied to analyses of other microbial species.
Novel Insights into the Emerging Role of Neat1 and Its Effects Downstream in the Regulation of Inflammation

Abstract Nuclear paraspeckle assembly transcript 1 (Neat1) located at chromosome 11 is a long non-coding RNA that is widely expressed in mammalian cell types, and which is overexpressed in several inflammation-related disorders. Inflammation implies a plethora of mutual interactions between both soluble factors and cells due to various stimuli including tissue injury. Although there is no doubt that inflammation is critically involved in multiple biological and pathological processes alike, the precise mechanisms being involved are still open for debate. In this context, the role of Neat1 as a regulator of inflammation, microglial activation, and lipid accumulation under various inflammatory conditions remains elusive. Herein, we review the regulation of Neat1 and how it modulates the expression of its target genes. Thereafter, we will review the impact of Neat1 on inflammation by activating or inhibiting various signaling pathways, such as microRNAs, AKT, TLR4, TRAF6, and NF-κB.
Residential Electricity Load Forecasting Based on Fuzzy Cluster Analysis and LSSVM with Optimization by the Fireworks Algorithm

As the construction of the energy internet progresses, the proportion of residential electricity consumption in end-use energy consumption is increasing, the peak load on the grid is growing year on year, and seasonal and regional peak power supply tensions, mainly for residential electricity consumption, have become common problems across the country. Accurate residential load forecasting can provide strong data support for the operation of electricity demand response and the incentive setting of the response. For the accuracy and stability of residential electricity load forecasting, a forecasting model is presented in this paper based on fuzzy cluster analysis (FC), least-squares support vector machine (LSSVM), and a fireworks algorithm (FWA). First of all, to reduce the redundancy of input data, it is necessary to reduce the dimension of data features. Then, FWA is used to optimize the arguments γ and σ2 of LSSVM, where γ is the penalty factor and σ2 denotes the kernel width. Finally, a load forecasting method of FC–FWA–LSSVM is developed. Relevant data from Beijing, China, are selected for training tests to demonstrate the effectiveness of the proposed model. The results show that the FC–FWA–LSSVM hybrid model proposed in this paper has high accuracy in residential power load forecasting, and the model has good stability and versatility.
Computationally Efficient Approximations for Distributionally Robust Optimization Under Moment and Wasserstein Ambiguity

Distributionally robust optimization (DRO) is a modeling framework in decision making under uncertainty in which the probability distribution of a random parameter is unknown although its partial information (e.g., statistical properties) is available. In this framework, the unknown probability distribution is assumed to lie in an ambiguity set consisting of all distributions that are compatible with the available partial information. Although DRO bridges the gap between stochastic programming and robust optimization, one of its limitations is that its models for large-scale problems can be significantly difficult to solve, especially when the uncertainty is of high dimension. In this paper, we propose computationally efficient inner and outer approximations for DRO problems under a piecewise linear objective function and with a moment-based ambiguity set and a combined ambiguity set including Wasserstein distance and moment information. In these approximations, we split a random vector into smaller pieces, leading to smaller matrix constraints. In addition, we use principal component analysis to shrink uncertainty space dimensionality. We quantify the quality of the developed approximations by deriving theoretical bounds on their optimality gap. We display the practical applicability of the proposed approximations in a production–transportation problem and a multiproduct newsvendor problem. The results demonstrate that these approximations dramatically reduce the computational time while maintaining high solution quality. The approximations also help construct an interval that is tight for most cases and includes the (unknown) optimal value for a large-scale DRO problem, which usually cannot be solved to optimality (or even feasibility in most cases). Summary of Contribution: This paper studies an important type of optimization problem, that is, distributionally robust optimization problems, by developing computationally efficient inner and outer approximations via operations research tools. Specifically, we consider several variants of such problems that are practically important and that admit tractable yet large-scale reformulation. We accordingly utilize random vector partition and principal component analysis to derive efficient approximations with smaller sizes, which, more importantly, provide a theoretical performance guarantee with respect to low optimality gaps. We verify the significant efficiency (i.e., reducing computational time while maintaining high solution quality) of our proposed approximations in solving both production–transportation and multiproduct newsvendor problems via extensive computing experiments.
Toward Predicting Student’s Academic Performance Using Artificial Neural Networks (ANNs)

Student performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. Moreover, the very limited tangible findings referred to the use of techniques in the actual context and target objective of improving student outcomes, performance, and achievement. An important recommendation of this work is to overcome the identified gap related to the only theoretical and limited application of the ANN in a real-life situation to help achieve the educational goals.
A Reliable Low Standby Power 10T SRAM Cell With Expanded Static Noise Margins

This paper explores a low standby power 10T (LP10T) SRAM cell with high read stability and write-ability (RSNM/WSNM/WM). The proposed LP10T SRAM cell uses a strong cross-coupled structure consisting standard inverter with a stacked transistor and Schmitt-trigger inverter with a double-length pull-up transistor. This along with the read path separated from true internal storage nodes eliminates the read-disturbance. Furthermore, it performs its write operation in pseudo differential form through write bitline and control signal with a write-assist technique. To estimate the proposed LP10T SRAM cell’s performance, it is compared with some state-of-the-art SRAM cells using HSPICE in 16-nm CMOS predictive technology model at 0.7 V supply voltage under harsh manufacturing process, voltage, and temperature variations. The proposed SRAM cell offers 4.65X/1.57X/1.46X improvement in RSNM/WSNM/WM and 4.40X/1.69X narrower spread in RSNM/WM compared to the conventional 6T SRAM cell. Furthermore, it shows 1.26X/1.08X/1.01X higher RSNM/WSNM/WM and 1.71X/1.25X tighter/wider spread in RSNM/WM compared to the best studied SRAM cells. The proposed SRAM cell indicates 74.48%/1.41% higher/lower read/write delay compared to the 6T SRAM cell. Moreover, it exhibits the third-(second-) best read (write) dynamic power, consuming 29.69% (26.87%) lower than the 6T SRAM cell. The leakage power is minimized by the proposed design, which is 37.35% and 12.08% lower than that of the 6T and best studied cells, respectively. Nonetheless, the proposed LP10T SRAM cell occupies 1.313X higher area compared to the 6T SRAM cell.
Olla, obús, olvido: análisis comparativo de léxico en los cuentos de Arturo Barea

Arturo Barea es un autor español exiliado en 1939, principalmente conocido por su trilogía narrativa La forja de un rebelde. Sus cuentos tuvieron que esperar hasta el año 2001 para ser editados en un solo volumen, llamado Cuentos completos, que agrupa los cuentos en tres apartados: «Cuentos misceláneos», «Cuentos de valor y miedo», y «El centro de la pista». Este artículo es una revisión de los cuentos de Arturo Barea desde un punto de vista de analítica léxica que requiere reagrupar los textos en una taxonomía de cuatro categorías: cuentos de guerra, cuentos de ciudad, cuentos de exilio y cuentos futuristas. En primer lugar, se presenta el análisis de distintividad como herramienta informatizada que nos capacita para aplicar a los textos un análisis comparativo basado en las palabras que los construyen. Este análisis se enfoca en el recuento de las frecuencias de aparición de las palabras con el fin de caracterizar el texto a partir de datos objetivos, y de este modo hacer aflorar la motivación última del autor en el proceso creativo que conduce a la selección léxica consciente o inconsciente. En segundo lugar, se presentan los resultados de este análisis junto con un breve comentario, con el fin de comprobar la validez de la taxonomía elegida y la pertinencia del análisis realizado.
Role of Efflux Pump-Mediated Antibiotic Resistance in Quorum Sensing-Regulated Biofilm Formation by Salmonella Typhimurium

This study was designed to assess the influence of efflux pump activity on the biofilm formation in Salmonella Typhimurium. Salmonella enterica subsp. enterica serovar Typhimurium ATCC 19585 (STWT) and clinically isolated S. Typhimurium CCARM 8009 (STCI) were treated with ceftriaxone (CEF), chloramphenicol (CHL), ciprofloxacin (CIP), erythromycin (ERY), norfloxacin (NOR), and tetracycline (TET) in autoinducer-containing media in the absence and presence of phenylalanine-arginine β-naphthylamide (PAβN) to compare efflux pump activity with biofilm-forming ability. The susceptibilities of STWT and STCI were increased in the presence of PAβN. ERY+PAβN showed the highest decrease in the minimum inhibitory concentration (MIC) of ERY from 256 to 2 μg/mL against STWT and STCI. The antimicrobial activity of NOR against planktonic cells was significantly increased in the presence of PAβN, showing the lowest numbers of STWT (3.2 log CFU/cm2), and the TET+PAβN effectively inhibited the growth of STCI (5.2 log CFU/cm2). The lowest biofilm-forming abilities were observed at NOR+PAβN against STWT (biofilm-forming index, BFI < 0.41) and CEF+PAβN against STCI (BFI = 0.32). The bacteria swimming motility and relative fitness varied depending on the antibiotic and PAβN treatments. The motility diameters of STWT were significantly decreased by NOR+PAβN (6 mm) and TET+PAβN (15 mm), while the lowest motility of STCI was observed at CIP+PAβN (8 mm). The significant decrease in the relative fitness levels of STWT and STCI was observed at CIP+PAβN and NOR+PAβN. The PAβN as an efflux pump inhibitor (EPI) can improve the antimicrobial and anti-biofilm efficacy of antibiotics against S. Typhimurium. This study provides useful information for understanding the role of efflux pump activity in quorum sensing-regulated biofilm formation and also emphasizes the necessity of the discovery of novel EPIs for controlling biofilm formation by antibiotic-resistant pathogens.
Auto-Exposure Algorithm for Enhanced Mobile Robot Localization in Challenging Light Conditions

The success of robot localization based on visual odometry (VO) largely depends on the quality of the acquired images. In challenging light conditions, specialized auto-exposure (AE) algorithms that purposely select camera exposure time and gain to maximize the image information can therefore greatly improve localization performance. In this work, an AE algorithm is introduced which, unlike existing algorithms, fully leverages the camera’s photometric response function to accurately predict the optimal exposure of future frames. It also features feedback that compensates for prediction inaccuracies due to image saturation and explicitly balances motion blur and image noise effects. For validation, stereo cameras mounted on a custom-built motion table allow different AE algorithms to be benchmarked on the same repeated reference trajectory using the stereo implementation of ORB-SLAM3. Experimental evidence shows that (1) the gradient information metric appropriately serves as a proxy of indirect/feature-based VO performance; (2) the proposed prediction model based on simulated exposure changes is more accurate than using γ transformations; and (3) the overall accuracy of the estimated trajectory achieved using the proposed algorithm equals or surpasses classic exposure control approaches. The source code of the algorithm and all datasets used in this work are shared openly with the robotics community.