Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping Transcriptome Data to Protein–Protein Interaction Networks of Inflammatory Bowel Diseases Reveals Disease-Specific Subnetworks

Inflammatory bowel disease (IBD) is the common name for chronic disorders associated with the inflammation of the gastrointestinal tract. IBD is triggered by environmental factors in genetically susceptible individuals and has a significant number of incidences worldwide. Crohn’s disease (CD) and ulcerative colitis (UC) are the two distinct types of IBD. While involvement in ulcerative colitis is limited to the colon, Crohn’s disease may involve the whole gastrointestinal tract. Although these two disorders differ in macroscopic inflammation patterns, they share various molecular pathogenesis, yet the diagnosis can remain unclear, and it is important to reveal their molecular signatures in the network level. Improved molecular understanding may reveal disease type-specific and even individual-specific targets. To this aim, we determine the subnetworks specific to UC and CD by mapping transcriptome data to protein–protein interaction (PPI) networks using two different approaches [KeyPathwayMiner (KPM) and stringApp] and perform the functional enrichment analysis of the resulting disease type-specific subnetworks. TP63 was identified as the hub gene in the UC-specific subnet and p63 tumor protein, being in the same family as p53 and p73, has been studied in literature for the risk associated with colorectal cancer and IBD. APP was identified as the hub gene in the CD-specific subnet, and it has an important role in the pathogenesis of Alzheimer’s disease (AD). This relation suggests that some similar genetic factors may be effective in both AD and CD. Last, in order to understand the biological meaning of these disease-specific subnets, they were functionally enriched. It is important to note that chemokines—special types of cytokines—and antibacterial response are important in UC-specific subnets, whereas cytokines and antimicrobial responses as well as cancer-related pathways are important in CD-specific subnets. Overall, these findings reveal the differences between IBD subtypes at the molecular level and can facilitate diagnosis for UC and CD as well as provide potential molecular targets that are specific to disease subtypes.
The Interaction of Facial Expression and Donor-Recipient Eye Contact in Donation Intentions: Based on the Intensity of Emotion

Both happy and sad facial expressions of recipients are frequently used in charity advertisements. However, the relative effectiveness of these two types of facial expressions has been found paradoxical in the past. In this study, we examine when happy facial expression can more effectively increase donation intentions of consumers and when vice versa. Specially, we propose that eye contact between a donor and a potential recipient may moderate the relative effectiveness of happy and sad facial expressions, and further explain the interaction effect from the perspective of emotional intensity. Results from two experiments suggest that, when donor-recipient eye contact is present, consumers tend to have stronger emotional intensity, and, in turn, show higher donation intentions when the recipient is with a happy rather than sad facial expression. In contrast, when the eye contact is absent, consumers may show stronger emotional intensity and donation intentions toward the charity advertisement with a recipient showing sad rather than happy expression.
Arginine:Glycine Amidinotransferase Is Essential for Creatine Supply in Mice During Chronic Hypoxia

Objective: Chronic hypoxia induces pulmonary and cardiovascular pathologies, including pulmonary hypertension (PH). L-arginine:glycine amidinotransferase (AGAT) is essential for homoarginine (hArg) and guanidinoacetate synthesis, the latter being converted to creatine by guanidinoacetate methyltransferase. Low hArg concentrations are associated with cardiovascular morbidity and predict mortality in patients with PH. We therefore aimed to investigate the survival and cardiac outcome of AGAT knockout (Agat−/−) mice under hypoxia and a possible rescue of the phenotype. Methods: Agat−/− mice and wild-type (WT) littermates were subjected to normoxia or normobaric hypoxia (10% oxygen) for 4 weeks. A subgroup of Agat−/− mice was supplemented with 1% creatine from weaning. Survival, hematocrit, blood lactate and glucose, heart weight-to-tibia length (HW/TL) ratio, hArg plasma concentration, and Agat and Gamt expression in lung, liver, and kidneys were evaluated. Results: After 6 h of hypoxia, blood lactate was lower in Agat−/−-mice as compared to normoxia (p < 0.001). Agat−/− mice died within 2 days of hypoxia, whereas Agat−/− mice supplemented with creatine and WT mice survived until the end of the study. In WT mice, hematocrit (74 ± 4 vs. 55 ± 2%, mean ± SD, p < 0.001) and HW/TL (9.9 ± 1.3 vs. 7.3 ± 0.7 mg/mm, p < 0.01) were higher in hypoxia, while hArg plasma concentration (0.25 ± 0.06 vs. 0.38 ± 0.12 μmol/L, p < 0.01) was lower. Agat and Gamt expressions were differentially downregulated by hypoxia in lung, liver, and kidneys. Conclusion: Agat and Gamt are downregulated in hypoxia. Agat−/− mice are nonviable in hypoxia. Creatine rescues the lethal phenotype, but it does not reduce right ventricular hypertrophy of Agat−/− mice in hypoxia.
Early Origins of Psoriatic Arthritis: Clinical, Genetic and Molecular Biomarkers of Progression From Psoriasis to Psoriatic Arthritis

Greater than 90% of patients with psoriatic arthritis (PsA) first develop their arthritis on a background of known psoriasis (Pso). Thus, having skin/nail Pso certainly is an important risk factor for PsA but as PsA develops in <30% of those affected with Pso, the presence of Pso alone is insufficient as a means of identifying which patients with Pso will develop PsA. It is hoped that with further molecular assessment of Pso patients who do not have any evidence of inflammatory musculoskeletal disease compared to those with early PsA features, that the “at risk” profile of Pso patients destined to develop PsA can be refined such that disease prevention studies can be designed and a new era of treatment for PsA can emerge. In this article, the early stages in the development of PsA are outlined and what is currently known about clinical features, genetic factors and soluble or tissue biomarkers associated with the development of PsA in patients with Pso is reviewed in detail. Finally, proposals are outlined regarding the approaches required in order to address this important research area.
Unveiling the Relationship Between Sea Surface Hydrographic Patterns and Tuna Larval Distribution in the Central Mediterranean Sea

Thunnus thynnus (Atlantic bluefin tuna, ABT) and other tuna species reproduce in the Mediterranean Sea during the summer period. Despite the Central Mediterranean Sea, the Strait of Sicily in particular, being a key spawning site for many tuna species, little is known on the effects of oceanographic variability on their larval distribution in this area. The abundance and presence-absence of larval specimens for three tuna species (ABT, bullet tuna and albacore) were modeled in order to examine their relationships with environmental factors, by analysing historical in situ information collected during seven annual surveys (2010–2016). The results revealed that most tuna larvae for the three species were found in the easternmost part of the study area, south of Capo Passero. This area is characterized by a stable saline front and warmer nutrient-poor water, and it has different environmental conditions, compared with the surrounding areas. The models used to investigate the presence-absence and abundance of the three species showed that ABT was the most abundant, followed by bullet tuna and albacore. The presence and abundance data collected are comparable with those of other spawning areas in the Mediterranean. Regarding biological and physical parameters, the results suggest that temperature, salinity, and day of the year are the key factors for understanding the ecological mechanisms and geographical distribution of these species in this area. Temperature affects the presence of ABT larvae and salinity, which, with a physical barrier effect, is a key factor for the presence-absence of bullet and albacore and for albacore abundance.
COVID-19 Prediction Applying Supervised Machine Learning Algorithms with Comparative Analysis Using WEKA

Early diagnosis is crucial to prevent the development of a disease that may cause danger to human lives. COVID-19, which is a contagious disease that has mutated into several variants, has become a global pandemic that demands to be diagnosed as soon as possible. With the use of technology, available information concerning COVID-19 increases each day, and extracting useful information from massive data can be done through data mining. In this study, authors utilized several supervised machine learning algorithms in building a model to analyze and predict the presence of COVID-19 using the COVID-19 Symptoms and Presence dataset from Kaggle. J48 Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes algorithms were applied through WEKA machine learning software. Each model’s performance was evaluated using 10-fold cross validation and compared according to major accuracy measures, correctly or incorrectly classified instances, kappa, mean absolute error, and time taken to build the model. The results show that Support Vector Machine using Pearson VII universal kernel outweighs other algorithms by attaining 98.81% accuracy and a mean absolute error of 0.012.
Optimal Coronavirus Optimization Algorithm Based PID Controller for High Performance Brushless DC Motor

This paper presents an efficient coronavirus optimization algorithm (CVOA) to find the optimal values of the PID controller to track a preselected reference speed of a brushless DC (BLDC) motor under several types of disturbances. This work simulates how the coronavirus (COVID-19) spreads and infects healthy people. The initial values of PID controller parameters consider the zero patient, who infects new patients (other values of PID controller parameters). The model aims to simulate as accurately as possible the coronavirus activity. The CVOA has two major advantages compared to other similar strategies. First, the CVOA parameters are already adjusted according to disease statistics to prevent designers from initializing them with arbitrary values. Second, the approach has the ability to finish after several iterations where the infected population initially grows at an exponential rate. The proposed CVOA was investigated with well-known optimization techniques such as the genetic algorithm (GA) and Harmony Search (HS) optimization. A multi-objective function was used to allow the designer to select the desired rise time, the desired settling time, the desired overshoot, and the desired steady-state error. Several tests were performed to investigate the obtained proper values of PID controller parameters. In the first test, the BLDC motor was exposed to sudden load at a steady speed. In the second test, the continuous sinusoidal load was applied to the rotor of the BLDC motor. In the third test, different operating points of reference speed were selected to the rotor of the BLDC motor. The results proved that the CVOA-based PID controller has the best performance among the techniques. In the first test, the CVOA-based PID controller has a minimum rise time (0.0042 s), minimum settling time (0.0079 s), and acceptable overshoot (0.0511%). In the second test, the CVOA-based PID controller has the minimum deviation about the reference speed (±4 RPM). In the third test, the CVOA-based PID controller can accurately track the reference speed among other techniques.
An Automatic Nuclei Image Segmentation Based on Multi-Scale Split-Attention U-Net

Nuclei segmentation is an important step in the task of medical image analysis. Nowadays, deep learning techniques based on Convolutional Neural Networks (CNNs) have become prevalent methods in nuclei segmentation. In this paper, we propose a network called Multi-scale Split-Attention U-Net (MSAU-Net) for further improving the performance of cell segmentation. MSAU-Net is based on U-Net architecture and the original blocks used to down-sampling and up-sampling paths are replaced with Multi-scale Split-Attention blocks for capturing independent semantic information of nuclei images. A public microscopy image dataset from 2018 Data Science Bowl grand challenge is selected to train and evaluate MSAU-Net. By running trained models on the test set, our model reaches average Inter-section over Union (IoU) of 0.851, which is better than other prominent models, especially 4.8 percent higher than the original U-Net. For other evaluation metrics including accuracy, precision, recall and F1-score, MSAU-Net shows better performance in the most of indicators. The outstanding result reveals that our proposed model presents a promising nuclei segmentation method for the microscopy image analysis.
A SAXS-based approach to rationally evaluate radical scavengers – toward eliminating radiation damage in solution and crystallographic studies

A system engineered to produce a large-scale structural change from X-ray-induced disulfide bond cleavage allows residue-specific changes to be interpreted with SAXS. This system is used to investigate how radical scavengers reduce bond breakage during data collection at 10°C with the results providing insight into mitigation strategies for crystallographic and solution experiments.
Research Hotspots and Frontiers of Management Innovation at Home and Abroad: Citespace Visual Analysis Based on Literature from 2010 to 2020

Management innovation has always been a research hotspot in the field of management. Existing companies cannot have sustainable development without innovation in management. In this regard, this article adopts a bibliometric method to review the authoritative journals of management from 2010 to 2020 in the CNKI and WOS database which are analyzed quantitatively. By using citespace software to form a scientific knowledge map, this article analyzes the domestic and foreign research hotspots about management innovation, such as corporate dynamic capabilities, corporate performance, corporate technological innovation, organizational routines, organizational changes, innovation drive, etc., to provide a clear view for the research on management innovation Ideas.
SOLO taxonomy supporting practical chemistry instruction

This article is based on a doctoral dissertation (Tomperi, 2015) which examines the design and development process of a STEM professional training course implementing SOLO taxonomy. SOLO taxonomy provides a common language for teachers to construct knowledge collaboratively. In the created research-based training model the modification of laboratory assignments in chemistry books into inquiry tasks using SOLO taxonomy preceded the enactment phase in the classroom. According to research, it is well known that a conceptual change will not take place until teachers practice new teaching strategies and experience how the created new material supports student learning.
6.867 Project: Neural Network with Structure Growth During Training

This project studies the effect of structure growth for neural networks during training. The general idea is to start with a simple network and gradually crank up the network size. With the small and simple network, the big features of the data can be captured roughly, and adding some more neurons/layers after training the small network allows better capturing the details of the data. Several heuristic network structure update methods are tested, and the experimental results with several standard benchmark datasets are discussed. It is shown that compared with directly training a large (in depth and width) neural net, the growing network will allow better accuracy and convergence.
Pre-trained CNNs Models for Content based Image Retrieval

Content based image retrieval (CBIR) systems is a common recent method for image retrieval and is based mainly on two pillars extracted features and similarity measures. Low level image presentations, based on colour, texture and shape properties are the most common feature extraction methods used by traditional CBIR systems. Since these traditional handcrafted features require good prior domain knowledge, inaccurate features used for this type of CBIR systems may widen the semantic gap and could lead to very poor performance retrieval results. Hence, features extraction methods, which are independent of domain knowledge and have automatic learning capabilities from input image are highly useful. Recently, pre-trained deep convolution neural networks (CNN) with transfer learning facilities have ability to generate and extract accurate and expressive features from image data. Unlike other types of deep CNN models which require huge amount of data and massive processing time for training purposes, the pretrained CNN models have already trained for thousands of classes of large-scale data, including huge images and their information could be easily used and transferred. ResNet18 and SqueezeNet are successful and effective examples of pretrained CNN models used recently in many machine learning applications, such as classification, clustering and object recognition. In this study, we have developed CBIR systems based on features extracted using ResNet18 and SqueezeNet pretrained CNN models. Here, we have utilized these pre-trained CNN models to extract two groups of features that are stored separately and then later are used for online image searching and retrieval. Experimental results on two popular image datasets Core-1K and GHIM-10K show that ResNet18 features based on the CBIR method have overall accuracy of 95.5% and 93.9% for the two datasets, respectively, which greatly outperformed the traditional handcraft features based on the CBIR method. Keywords—Pre-trained deep neural networks; transfer learning; content based image retrieval
Using Ontology for Natural Sciences and Technologies for Vacancies Analysis

In the paper, the ontology-based approach for online vacancy posts is considered. The Ontology on Natural Sciences and Technologies is used as a basic resource for automatic processing. Vacancies are categorized using two large systems of categories. Besides, for a vacancy post, prerequisites can be inferred, which are implicitly contained in the post and required to be learned by a job seeker
Biomass burning plume chemistry: OH radical initiated oxidation of 3-penten-2-one and its main oxidation product 2-hydroxypropanal

Abstract. In order to enlarge our understanding of biomass burning plume chemistry, the OH radical initiated oxidation of 3-penten-2-one (3P2), identified in biomass burning emissions, and 2-hydroxypropanal (2HPr) were investigated at 298 ± 3 K and 990 ± 15 mbar in two atmospheric simulation chambers using long-path FTIR spectroscopy. The rate coefficient of 3P2 + OH was determined to be (6.2 ± 1.0) × 10−11 cm3 molecule−1 s−1 and the molar first-generation yields for acetaldehyde, methyl glyoxal, 2HPr and the sum of PAN and CO2, used to determine the CH3C(O) radical yield, were 0.39 ± 0.07, 0.32 ± 0.08, 0.68 ± 0.27, and 0.56 ± 0.14, respectively, under conditions where the 3P2 derived peroxy radicals react solely with NO. The 2HPr + OH reaction was investigated using 3P2 + OH as a source of the α-hydroxyaldehyde adjusting the experimental conditions to shift the reaction system towards secondary oxidation processes. The rate coefficient was estimated to be (2.2 ± 0.6) × 10−11 cm3 molecule−1 s−1. Employing an Euler-Cauchy model to describe the temporal behaviour of the experiments, the further oxidation of 2HPr was shown to form methyl glyoxal, acetaldehyde and CO2 with estimated yields of 0.21 ± 0.05, 0.79 ± 0.05, and 0.79 ± 0.05, respectively.
Antibiotic resistance of Staphylococcus aureus isolated from raw cow and goat milk produced in the Tiaret and Souk Ahras areas of Algeria

Background and Aim: Staphylococcus aureus is a leading cause of infection in both humans and animals. Most livestock strains have shown antibiotic resistance to the many molecules used in veterinary therapeutics. This study aimed to assess the resistance patterns of these bacteria, we carried out our study in the Tiaret and Souk Ahras areas of Algeria. Materials and Methods: We collected 116 samples of bovine and goat milk to detect S. aureus. We used a selective media to isolate the strains, followed by biochemically identifying the isolates. We determined the susceptibility of the strains to antibiotic molecules using the disk diffusion method and confirmed the methicillin-resistant S. aureus (MRSA) with oxacillin minimum inhibitory concentration (MIC). Results: Our results showed that 26.72% of the samples were contaminated with S. aureus, and we recovered 31 isolates from the positive samples. We ascribed a high resistance profile to penicillin G (96.77%), fusidic acid (67.74%), and tobramycin (45.16%) and isolated 4MRSA strains. Conclusion: The presence of S. aureus, including MRSA strains in raw milk, can present a public health hazard, because these strains can cause widespread food poisoning. This finding will be useful to the veterinarians to choose an adequate treatment and to sensitize livestock breeders and milk producers to ensure the health of consumers.
Prosody and Information in Naturally-Occurring Discourse

We assume (with Liberman [22]) that for any utterance u, it makes sense to regard its phonetic and phonological properties-jointly, phon(u)-as a combination of a lexical support lex(u) and a prosodic structure pros(u). Motivation for this division of linguistic labor can be found in the distinct felt equivalences corresponding to the rows and columns below (using common orthographic conventions):
WASTE HEAT RECOVERY FOR THE LHC COOOLING TOWERS: CONTROL SYSTEM VALIDATION USING DIGITAL TWINS

In order to improve its energy utilization, CERN will deploy a Waste Heat Recovery (WHR) system at one of the surface sites of the Large Hadron Collider (LHC) which will provide heating power to a local municipality. To study the effects that the heat recovery plant will have on the cooling system, a ‘digital twin’ of the cooling plant was created in the simulation tool EcosimPro. The primary question of interest was whether the existing control system of the cooling plant would be capable of handling transients arising from a sudden shutdown of the heat recovery plant. The simulation was connected via the communication protocol OPC Unified Architecture (OPC-UA) to a Programmable Logic Controller (PLC) implementing the cooling plant control system. This ‘virtual commissioning’ setup was used to study a number of scenarios representing different cooling loads, ambient temperature conditions, and heat recovery plant operating points. Upon completion of the investigation it was found that the current cooling plant control system will be sufficient to deal with the transients arising from a sudden stop of heat recovery plant operation. In addition, it was shown that an improvement in the controls could also enhance the energy savings of the cooling towers.
UNITED STATES DISTRICT COURT DISTRICT OF MASSACHUSETTS

Leora Ben-Ami (pro hac vice) Mark S. Popofsky (pro hac vice) Patricia A. Carson (pro hac vice) Thomas F. Fleming (pro hac vice) Howard S. Suh (pro hac vice) Christopher T. Jagoe (pro hac vice) Peter Fratangelo (BBO# 639775) Krista M. Rycroft (pro hac vice) Kaye Scholer LLP 425 Park Avenue New York, New York 10022 Tel. (212) 836-8000 Lee Carl Bromberg (BBO# 058480) Timothy M. Murphy (BBO# 551926) Julia Huston (BBO# 562160) Keith E. Toms (BBO# 663369) Nicole A. Rizzo (BBO# 663853) Bromberg & Sunstein LLP 125 Summer Street Boston, MA 02110 Tel. (617) 443-9292