Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of non-coding small-RNAs in P. aeruginosa Keratitis Strains with Different Antibiotic Susceptibility

Pseudomonas aeruginosa, is a gram-negative bacterium causes opportunistic or nosocomial infections in immunocompromised individuals. In recent years, a steady increase in human corneal infections of P. aeruginosa has been reported with increased multi-drug resistance (MDR) or extensively drug resistance (XDR). Several non-coding sRNAs, has been identified to regulate various physiological processes in P. aeruginosa, including biofilm formation, quorum sensing. However, the regulatory mechanism of sRNAs in MDR/XDR pathways of P. aeruginosa keratitis strains is not yet studied. In this study, we identified bacterial sRNAs in publicly available P. aeruginosa keratitis genomes and investigated their regulatory role in MDR/XDR pathways using bioinformatic analysis. Totally, 46 P. aeruginosa keratitis strains from different geographical regions were included. Of 46, Eight (30%) out of Twentyseven and Nine (52%) out of Nineteen P. aeruginosa strains from India and Australia were identified as not-MDR. Whereas, 10 (38%) Indian and 9 (47%) Australian strains were identified as MDR. Eight Indian strains were identified as XDR. Out of 46 strains, 23 (50%) carried ExoU, 21(45%) carried ExoS and two (5%) strains carried both ExoU and ExoS, exotoxins for their virulence. The sRNA, SPA0021 was identified in 18 MDR/XDR and 6 not-MDR strains along with UCBPP-PA14. Interestingly, majority of the imipenem resistant P. aeruginosa keratitis strains from the present study was found to be carried SPA0023 sRNA (18 out of 30 strains). The outer membrane porin protein OprD, identified as binding target of SPA0023. Negative regulation or inactivation of OprD, reported in increased imipenem resistance in P. aeruginosa. Mutation analysis revealed that SPA0023 carrying P. aeruginosa keratitis strains contains a lesser number of amino acid changes in OprD protein than other strains. These findings indicate, imipenem resistance in SPA0023 carried strains might arose from the negative regulation or inhibition of OprD by SPA0023. However, functional studies are warranted with large number of P. aeruginosa keratitis strains to confirm the negative regulation of OprD by SPA0023 and imipenem resistance. was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted July 3, 2021. ; https://doi.org/10.1101/2021.07. 02.450871 doi: bioRxiv preprint
Mechanism of the Conformational Change of the Protein Methyltransferase SMYD3: A Molecular Dynamics Simulation Study

SMYD3 is a SET-domain-containing methyltransferase that catalyzes the transfer of methyl groups onto lysine residues of substrate proteins. Methylation of MAP3K2 by SMYD3 has been implicated in Ras-driven tumorigenesis, which makes SMYD3 a potential target for cancer therapy. Of all SMYD family proteins, SMYD3 adopt a closed conformation in a crystal structure. Several studies have suggested that the conformational changes between the open and closed forms may regulate the catalytic activity of SMYD3. In this work, we carried out extensive molecular dynamics simulations on a series of complexes with a total of 21 μs sampling to investigate the conformational changes of SMYD3 and unveil the molecular mechanisms. Based on the C-terminal domain movements, the simulated models could be depicted in three different conformational states: the closed, intermediate and open states. Only in the case that both the methyl donor binding pocket and the target lysine-binding channel had bound species did the simulations show SMYD3 maintaining its conformation in the closed state, indicative of a synergetic effect of the cofactors and target lysine on regulating the conformational change of SMYD3. In addition, we performed analyses in terms of structure and energy to shed light on how the two regions might regulate the C-terminal domain movement. This mechanistic study provided insights into the relationship between the conformational change and the methyltransferase activity of SMYD3. The more complete understanding of the conformational dynamics developed here together with further work may lay a foundation for the rational drug design of SMYD3 inhibitors.
Synthesis, Characterization, DNA/HSA Interactions, and Anticancer Activity of Two Novel Copper(II) Complexes with 4-Chloro-3-Nitrobenzoic Acid Ligand

The purpose of this study was to identify new metal-based anticancer drugs; to this end, we synthesized two new copper(II) complexes, namely [Cu(ncba)4(phen)] (1) and [Cu(ncba)4(bpy)] (2), comprised 4-chloro-3-nitrobenzoic acid as the main ligand. The single-crystal XRD approach was employed to determine the copper(II) complex structures. Binding between these complexes and calf thymus DNA (CT-DNA) and human serum albumin (HSA) was explored by electronic absorption, fluorescence spectroscopy, and viscometry. Both complexes intercalatively bound CT-DNA and statically and spontaneously quenched DNA/HSA fluorescence. A CCK-8 assay revealed that complex 1 and complex 2 had substantial antiproliferative influences against human cancer cell lines. Moreover, complex 1 had greater antitumor efficacy than the positive control cisplatin. Flow cytometry assessment of the cell cycle demonstrated that these complexes arrested the HepG2 cell cycle and caused the accumulation of G0/G1-phase cells. The mechanism of cell death was elucidated by flow cytometry-based apoptosis assays. Western blotting revealed that both copper(II) complexes induced apoptosis by regulating the expression of the Bcl-2(Bcl-2, B cell lymphoma 2) protein family.
Elektrikli Araç Batarya Aktif Dengeleme Sistemleri için Çift Yönlü Flyback Dönüştürücü Tasarımı

The battery balancing systems are essential for balanced charging and discharging the battery cells. Lifecyle, efficiency and safety of battery packs can be increased by balancing systems. Battery balancing systems are divided into two group as passive and active. In passive balancing systems the amount of charge causing imbalance between cells is discharged by a resistor while in active balancing sytems imbalance between the cells is eliminated by transferring energy from high state of charge cell to low one. Increasing number of cells is led to complexity of balancing systems. Converter based balancing systems present a feasible solution to battery packs with a large number of cells. In this study, a bidirectional flyback converter in order to use in battery active balancing systems has been developed. Simulation and test results revealed that the developed bidirectional flyback converter is suitable for battery active balancing systems. The highest efficiencies were obtained as 67.84% at 32.5% duty cycle at charge test and 66.02% at 50% duty cycle at discharge test.
Measuring Shared Electrons in Extended Molecular Systems: Covalent Bonds from Plane-Wave Representation of Wave Function

In the study of materials and macromolecules by first-principle methods, the bond order is a useful tool to represent molecules, bulk materials and interfaces in terms of simple chemical concepts. Despite the availability of several methods to compute the bond order, most applications have been limited to small systems because a high spatial resolution of the wave function and an all-electron representation of the electron density are typically required. Both limitations are critical for large-scale atomistic calculations, even within approximate density-functional theory (DFT) approaches. In this work, we describe our methodology to quickly compute delocalization indices for all atomic pairs, while keeping the same representation of the wave function used in most compute-intensive DFT calculations on high-performance computing equipment. We describe our implementation into a post-processing tool, designed to work with Quantum ESPRESSO, a popular open-source DFT package. In this way, we recover a description in terms of covalent bonds from a representation of wave function containing no explicit information about atomic types and positions.
Comparative Study on Synchronized Permutation Tests in Balanced I J Designs in Case of Non-Normally Distributed Error Terms

ANOVA methodology is one of the important models in the experimental design theory. Synchronized permutation provides exact solutions in factorial designs within a nonparametric framework. The synchronized permutation tests have quadratic form test statistics. In this paper, we study the synchronized permutation tests with absolute value form in the test statistics for main factors and interaction for I J  balanced factorial designs in case of non-normal distributed error terms. The behavior of synchronized permutation tests with the absolute value form is investigated and the quadratic form in the test statistics in a comparative simulation study with the parametric ANOVA is reported in 3 2  design when the error terms are normally and non-normally distributed.
Inclusion of Children with Disabilities in Disaster Management

Disability inclusion of children in disaster management means to identify and then eliminate the challenges faced by children with disabilities during disaster occurrence. The present research aimed to explore how the challenges of children with disabilities can be resolved in disaster management. Qualitative content analysis was used to compare individual-stakeholder-based disaster management with all-stakeholder disaster management considering three stakeholders: developed nations, developing nations, and international organizations. A key finding is that these stakeholders must shift from the individual-stakeholder-based approach to the all-stakeholders approach while enhancing disaster medicine, education, monitoring, and implementation stages. A comprehensive framework of disability inclusion is proposed to reflect effective disaster management for these children.
Forecasting Hydrogen Energy Production in China base on Multi Grey Model

As hydrogen energy becomes an important fuel energy, the demand is also increasing. With the rapid development of hydrogen energy industry in China, the application of hydrogen energy is more and more extensive. In order to predict the hydrogen energy production in China, three grey models, GM(1, 1) model, DGM model and Grey Verhulst model, were established according to the data of hydrogen energy production in China from 2009 to 2019. Through the comparison and analysis of the predicted data, it is concluded that the Verhulst model is more accurate and more suitable for the prediction and analysis of China’s hydrogen energy production. At the same time, the prediction results also show that the hydrogen energy production prediction model established by using grey theory is feasible in practical work. According to the prediction model, China’s hydrogen energy from 2020 to 2021 will be 22.81 million tons and 22.96 million tons respectively.
Küresel optimizasyon problemlerinin çözümü için zamanla değişen rastgele atalet ağırlıklı jaya algoritması Time-varying random inertia weighted jaya algorithm for the solution of global optimization

The Jaya algorithm is a population-based optimization algorithm that has been used recently to solve global optimization problems. In this study, the time-varying random inertia-weighted Jaya (ZR-Jaya) algorithm has been developed for the solution of global optimization problems. In the proposed algorithm, it is aimed to solve optimization problems in earlier iterations, reduce the convergence time and obtain a better solution according to Jaya. ZR-Jaya has been applied to the five composite spherical optimization problems, consisting of ten well-known bencmark functions in the literature for experimental studies and the combination of these functions. The results of the ZR-Jaya algorithm were compared with the Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Jaya algorithms and three different weighting strategies applied to Jaya's update procedure. In order to compare the success of the developed ZR-Jaya algorithm with other weight strategies of Jaya, ABC, PSO, Jaya, random inertia weight Jaya (RIW-Jaya), linear decreasing inertia weight Jaya (LDIW-Jaya) and Chaotic inertia weight Jaya (CIW-Jaya). The results are given in tables and shown with graphics. According to the experimental study results, ZR-Jaya's PSO, ABC, Jaya and other Jaya inertia weight strategies have achieved a 75% success rate in unimodal functions and 61.11% in multimodal functions. The proposed ZR-Jaya algorithm, it has been determined by experimental studies that the time-varying random inertia weight factor is highly effective and feasible.
A Cyber-Physical System and Graph-Based Approach for Transportation Management in Smart Cities

In the last decade, technological advancements in the cyber-physical system have set the basis for real-time and context-aware services to ease human lives. The citizens, especially travelers, want to experience a safe, healthy, and timely journey to their destination. Smart and on-ground real-time traffic analysis helps authorities further improve decision-making to ensure safe and convenient traveling. In this paper, we proposed a transport-control model that exploits cyber-physical systems (CPS) and sensor-technology to continuously monitor and mine the big city data for smart decision-making. The system makes use of travel-time, traffic intensity, vehicle’s speed, and current road conditions to construct a weighted city graph representing the road network. Traditional graph algorithms with efficient implementation technologies are employed to respond to commuters’ and authorities’ needs in order to achieve a smart and optimum transportation system. To efficiently process the incoming big data streams, the proposed architecture uses the Apache GraphX tool with several parallel processing nodes, along with Spark and Hadoop that ultimately provide better performance against various state-of-the-art solutions. The system is thoroughly evaluated in terms of system throughput and processing time, revealing that the proposed system is efficient, robust, and scalable.
Anthropometric measurements as predictors of hypertension in Busia, Vihiga, Trans Nzoia and Siaya counties of Western Kenya

Emerging data suggest a rise in the incidence rate of hypertension in many countries within Sub-Saharan Africa. This has been attributed to socioeconomic factors that have influenced diet and reduced physical activity further deranging anthropometric measurements. We assessed the predictive power of three anthropometric indicators namely: waist circumference (WC), waist to height ratio (WHtR) and body mass index (BMI) in detecting hypertension. This cross-sectional community survey was conducted in four counties within Western Kenya between October 2018 to April 2019 among 3594 adults. The participants sociodemographic data were collected using an interviewer-administered questionnaire and anthropometric measurements taken. We used the R-software for descriptive and inferential statistical analysis. Pearson chi-square test was used to assess the association between anthropometric measurements and hypertension while logistic regressions estimated the likelihood of hypertension. Youden method was used to identify optimal anthropometric cut-offs for sensitivity, specificity and area under the receiver operating characteristics (ROC) curve computation. The crude prevalence of hypertension was 23.3%, however it rose with advancement in age. Furthermore, obese individuals had a three-fold (AOR=2.64; 95% CI: 2.09, 3.35) increased likelihood of hypertension compared to those with a normal BMI. The optimal WC cut-off was 82.5cm for men and 87cm for women, an optimal WHtR of 0.47 for men and 0.55 for women; while the optimal BMI cut-off was 23.7 kg/m2 and 22.6 kg/m2 for men and women respectively. The sensitivity of WC, WHtR and BMI for men was 0.60, 0.65 and 0.39 respectively and 0.71, 0.65 and 0.78 respectively for women. BMI is the best predictor for hypertension among women but a poor predictor for men; WC had a high hypertension predictive power for both gender while WHtR is the best hypertension predictor for men.
Industry 4.0 and Triple Bottom Line Sustainability - a Crunch Point for Malaysian SMEs

Current industrialization has generated many challenges, including ecological hazards, climate change, and overuse of non-renewable natural resources creating an increasing demand for achieving the goal of Triple Bottom Line (TBL). In this regard, Industry 4.0 can contribute to the the production process that can help in achieving sustainable development. Using Dynamic Capability view (DCV), this paper explores the role of Industry 4.0 as firm-level Dynamic Capabilities (DC) to achieving the goals of the triple botton line. Employing simple random sampling, the study adopted a quantitative approach based on 199 useable respondent’s feedback collected through a survey of 900 employees from the Malaysian SMEs. The results exhibit that top management and IT infrastructure significantly impact Industry 4.0 implementation and sustainability. In contrast, the analysis also demonstrates that supply chain integration is insignificant to Industry 4.0 implementation in SMEs. The study findings also show that the relationship between the firm-level DC and TBL sustainability can be mediated by the “effective implementation” of Industry 4.0. The study highlights the practical consequences of the role and use of firm capabilities in Industry 4.0 implementation and findings to notify managers and policy-makers to optimize value creation to achieve the sustainable development goals.
Modelling silo clogging with non-local granular rheology

Abstract Granular flow in a silo demonstrates multiple non-local rheological phenomena due to the finite size of grains. We solve the non-local granular fluidity continuum model in quasi-two-dimensional silo geometries and evaluate its ability to predict these non-local effects, including flow spreading and, importantly, clogging (arrest) when the opening is small enough. The model is augmented to include a free-separation criterion and is implemented numerically with an extension of the trans-phase granular flow solver described in Dunatunga & Kamrin (J. Fluid Mech., vol. 779, 2015, pp. 483–513), to produce full-field solutions. The implementation is validated against analytical results of the model in the inclined chute geometry, such as the solution for the critical thickness for flow arrest, and the velocity profile as a function of layer height. We then implement the model in the silo geometry and vary the apparent grain size. The model predicts a clogging criterion when the opening competes with the scale of the mean grain size, which agrees with previous experimental studies. For larger openings, the flow within the silo obtains a diffusive characteristic whose spread depends on the model's non-local amplitude and the mean grain size. The numerical tests are controlled for grid effects and a comparison study of coarse vs refined numerical simulations shows agreement in the pressure field, the shape of the arch in a clogged silo configuration and the velocity field in a flowing configuration.
Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting

Pedestrian attribute recognition aims to assign multiple attributes to one pedestrian image captured by a video surveillance camera. Although numerous methods are proposed and make tremendous progress, we argue that it is time to step back and analyze the status quo of the area. We review and rethink the recent progress from three perspectives. First, given that there is no explicit and complete definition of pedestrian attribute recognition, we formally define and distinguish pedestrian attribute recognition from other similar tasks. Second, based on the proposed definition, we expose the limitations of the existing datasets, which violate the academic norm and are inconsistent with the essential requirement of practical industry application. Thus, we propose two datasets, PETA\textsubscript{$ZS$} and RAP\textsubscript{$ZS$}, constructed following the zero-shot settings on pedestrian identity. In addition, we also introduce several realistic criteria for future pedestrian attribute dataset construction. Finally, we reimplement existing state-of-the-art methods and introduce a strong baseline method to give reliable evaluations and fair comparisons. Experiments are conducted on four existing datasets and two proposed datasets to measure progress on pedestrian attribute recognition.
MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications

Recent breakthroughs in ML have produced new classes of models that allow ML inference to run directly on milliwatt-powered IoT devices. On one hand, existing ML-to-FPGA compilers are designed for deep neural-networks on large FPGAs. On the other hand, general-purpose HLS tools fail to exploit properties specific to ML inference, thereby resulting in suboptimal performance. We propose MAFIA, a tool to compile ML inference on small form-factor FPGAs for IoT applications. MAFIA provides native support for linear algebra operations and can express a variety of ML algorithms, including state-of-the-art models. We show that MAFIA-generated programs outperform best-performing variant of a commercial HLS compiler by 2.5 × on average.
Geometric averages of partitioned datasets

We introduce a method for jointly registering ensembles of partitioned datasets in a way which is both geometrically coherent and partition-aware. Once such a registration has been defined, one can group partition blocks across datasets in order to extract summary statistics, generalizing the commonly used order statistics for scalar-valued data. By modeling a partitioned dataset as an unordered $k$-tuple of points in a Wasserstein space, we are able to draw from techniques in optimal transport. More generally, our method is developed using the formalism of local Fr\'{e}chet means in symmetric products of metric spaces. We establish basic theory in this general setting, including Alexandrov curvature bounds and a verifiable characterization of local means. Our method is demonstrated on ensembles of political redistricting plans to extract and visualize basic properties of the space of plans for a particular state, using North Carolina as our main example.
Encoding Domain Information with Sparse Priors for Inferring Explainable Latent Variables

Latent variable models are powerful statistical tools that can uncover relevant variation between patients or cells, by inferring unobserved hidden states from observable high-dimensional data. A major shortcoming of current methods, however, is their inability to learn sparse and interpretable hidden states. Additionally, in settings where partial knowledge on the latent structure of the data is readily available, a statistically sound integration of prior information into current methods is challenging. To address these issues, we propose spex-LVM, a factorial latent variable model with s parse p riors to encourage the inference of ex plainable factors driven by domain-relevant information. spex-LVM utilizes existing knowledge of curated biomedical pathways to automatically assign annotated attributes to latent factors, yielding interpretable results tailored to the corresponding domain of interest. Evalu-ations on simulated and real single-cell RNA-seq datasets demonstrate that our model robustly identiﬁes relevant structure in an inher-ently explainable manner, distinguishes technical noise from sources of biomedical variation, and provides dataset-speciﬁc adaptations of existing pathway annotations. Implementation is available at https://github.com/MLO-lab/spexlvm.