Miracle-dz/UniFAR-FTUs huggingface dataset:
Topography: A Biophysical Approach to Direct the Fate of Mesenchymal Stem Cells in Tissue Engineering Applications

Tissue engineering is a promising strategy to treat tissue and organ loss or damage caused by injury or disease. During the past two decades, mesenchymal stem cells (MSCs) have attracted a tremendous amount of interest in tissue engineering due to their multipotency and self-renewal ability. MSCs are also the most multipotent stem cells in the human adult body. However, the application of MSCs in tissue engineering is relatively limited because it is difficult to guide their differentiation toward a specific cell lineage by using traditional biochemical factors. Besides biochemical factors, the differentiation of MSCs also influenced by biophysical cues. To this end, much effort has been devoted to directing the cell lineage decisions of MSCs through adjusting the biophysical properties of biomaterials. The surface topography of the biomaterial-based scaffold can modulate the proliferation and differentiation of MSCs. Presently, the development of micro- and nano-fabrication techniques has made it possible to control the surface topography of the scaffold precisely. In this review, we highlight and discuss how the main topographical features (i.e., roughness, patterns, and porosity) are an efficient approach to control the fate of MSCs and the application of topography in tissue engineering.
Discrepancies in Assessing Diastolic Function in Pre-Clinical Heart Failure Using Different Algorithms—A Primary Care Study

Background: Current guidelines on diastolic function (DF) by the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) have been disputed and two alternative algorithms have been proposed by Johansen et al. and Oh et al. We sought (a) to assess the concordance of ASE/EACVI guidelines on DF using these proposed alternative approaches and (b) to evaluate the prevalence of indeterminate diastolic dysfunction (DD) by each method, exploring means for reducing their number. Methods: We retrospectively analyzed the echocardiographic reports of 1158 outpatients including subjects at risk of heart failure without (n = 644) or with (n = 241) structural heart disease, and 273 healthy individuals. Concordance was calculated using the k coefficient and overall proportion of DD reclassification rate. The effectiveness of pulmonary vein flow (PVF), Valsalva maneuver, and left atrial volume index/late diastolic a’-ratio (LAVi/a’) over indeterminate grading was assessed. Results: The DD reclassification rate was 30.1% (k = 0.35) for ASE/EACVI and OH, 36.5% (k = 0.27) for ASE/EACVI and JOHANSEN and 31.1% (k = 0.37) for OH and JOHANSEN (p < 0.0001 for all comparisons). DF could not be graded only by ASE/EACVI and OH in 9% and 11% patients, respectively. The majority of patients could be reclassified using PVF or Valsalva maneuver or LAVi/a’, with the latter being the single most effective parameter. Conclusion: Inconsistencies between updated guidelines and independent approaches to assess and grade DF impede their interchangeable clinical use. The inconclusive diagnoses can be reconciled by conventional echocardiography in most patients, and LAVi/a’ emerges as a simple and effective approach to this aim.
Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning

Image-based plant disease detection is among the essential activities in precision agriculture for observing incidence and measuring the severity of variability in crops. 70% to 80% of the variabilities are attributed to diseases caused by pathogens, and 60% to 70% appear on the leaves in comparison to the stem and fruits. This work provides a comparative analysis through the model implementation of the two renowned machine learning models, the support vector machine (SVM) and deep learning (DL), for plant disease detection using leaf image data. Until recently, most of these image processing techniques had been, and some still are, exploiting what some considered as "shallow" machine learning architectures. The DL network is fast becoming the benchmark for research in the field of image recognition and pattern analysis. Regardless, there is a lack of studies concerning its application in plant leaves disease detection. Thus, both models have been implemented in this research on a large plant leaf disease image dataset using standard settings and in consideration of the three crucial factors of architecture, computational power, and amount of training data to compare the duos. Results obtained indicated scenarios by which each model best performs in this context, and within a particular domain of factors suggests improvements and which model would be more preferred. It is also envisaged that this research would provide meaningful insight into the critical current and future role of machine learning in food security
Understanding nonverbal communication cues of human personality traits in human-robot interaction

With the increasing presence of robots in our daily life, there is a strong need and demand for the strategies to acquire a high quality interaction between robots and users by enabling robots to understand usersʼ mood, intention, and other aspects. During human-human interaction, personality traits have an important influence on human behavior, decision, mood, and many others. Therefore, we propose an efficient computational framework to endow the robot with the capability of understanding the userʼ s personality traits based on the userʼ s nonverbal communication cues represented by three visual features including the head motion, gaze, and body motion energy, and three vocal features including voice pitch, voice energy, and mel-frequency cepstral coefficient ( MFCC ) . We used the Pepper robot in this study as a communication robot to interact with each participant by asking questions, and meanwhile, the robot extracts the nonverbal features from each participantʼ s habitual behavior using its on-board sensors. On the other hand, each participantʼ s personality traits are evaluated with a questionnaire. We then train the ridge regression and linear support vector machine ( SVM ) classifiers using the nonverbal features and personality trait labels from a questionnaire and evaluate the performance of the classifiers. We have verified the validity of the proposed models that showed promising binary classification performance on recognizing each of the Big Five personality traits of the participants based on individual differences in nonverbal communication cues.
Spray drying conditions for orange juice incorporated with lactic acid bacteria

Summary This work aimed to develop an orange juice powder by spray drying with lactic acid bacteria (Lactobacillus plantarum 299v and Pediococcus acidilactici HA-6111-2), testing their survival both during drying and storage (room temperature and 4 °C). Initially, the best conditions for spray drying were chosen to allow the best survival of each LAB: (i) inlet air temperature of 120 °C and (ii) 0.5:2 ratio of the orange juice soluble solids and drying agent added (prebiotics: 10 DE maltodextrin or gum Arabic). Survival of LAB was not affected by drying process, and it was higher when cultures were stored at 4 °C. A slightly higher protection was conferred by 10 DE maltodextrin, in the case of L. plantarum and at 4 °C. Pediococcus acidilactici was more resistant during storage at 4 °C, with logarithmic reductions lower than 1 log-unit. It was demonstrated that it is possible to produce a functional nondairy product, orange juice powder supplemented with prebiotic compounds, containing viable LAB for at least 7 months, when stored at 4 °C.
Protein palmitoylation and its pathophysiological relevance

Protein palmitoylation, in which C16 fatty acid chains are attached to cysteine residues via a reversible thioester linkage, is one of the most common lipid modifications and plays important roles in regulating protein stability, subcellular localization, membrane trafficking, interactions with effector proteins, enzymatic activity, and a variety of other cellular processes. Moreover, the unique reversibility of palmitoylation allows proteins to be rapidly shuttled between biological membranes and cytoplasmic substrates in a process usually controlled by a member of the DHHC family of protein palmitoyl transferases (PATs). Notably, mutations in PATs are closely related to a variety of human diseases, such as cancer, neurological disorders, and immune deficiency conditions. In addition to PATs, intracellular palmitoylation dynamics are also regulated by the interplay between distinct posttranslational modifications, including ubiquitination and phosphorylation. Understanding the specific mechanisms of palmitoylation may reveal novel potential therapeutic targets for many human diseases.
Anesthetic considerations in medical cannabis patients

Purpose of review Growing numbers of patients, consuming cannabinoids admitted to surgery, create a challenge to anesthesia providers. This review provides a summary of recent literature related to cannabis and anesthesia, with specific recommendations to the anesthetic management of medical cannabis consumers. Recent findings At present, cannabis has found its way to public consensus in many countries and is penetrating slower to different medical fields. We relate and discuss recent findings investigating effects of cannabis consumption on the various aspects including perioperative measures, post-operative pain, PONV, cardiovascular stability, and anesthesia monitoring. Summary Recent surveys estimate that 10–20% of adult populations have consumed cannabis in the past year. Medical cannabis consumers are a newer group of cannabis users. Anesthesia providers have to update their knowledge on cannabis and possible anesthetic interaction. It is unreasonable to make recommendations that apply to the whole heterogeneous group of cannabis users, but is easier with the more homogenous group of Medical cannabis users, characterized by frequent use and relatively high cannabis doses, combined with good knowledge of administered composition and protocol, as well as adverse and withdrawal effects. Anesthesia providers have to know the effects and modify anesthetic plan accordingly. We provide perioperative anesthetic recommendations related to medical cannabis consumers. Collecting information of the effects of medical cannabis use in perioperative setting will further create a highly useful database for anesthetics in the close future.
Comparison of 2 Sagittal Craniosynostosis Repair Techniques: Spring-Assisted Surgery Versus Endoscope-Assisted Craniectomy With Helmet Molding Therapy

Introduction: This study compares anthropometric outcomes of 2 sagittal synostosis repair techniques: spring-assisted surgery and endoscope-assisted craniectomy with molding helmet therapy. Methods: Patients undergoing spring-assisted surgery (n = 27) or endoscope-assisted craniectomy with helmet therapy (n = 40) at separate institutions were retrospectively reviewed. Pre- and 1-year postoperative computed tomography (CT) or laser scans were analyzed for traditional cranial index (CI), adjusted cranial index (aCI), and cranial vault volume (CVV). Nine patient-matched scans were analyzed for measurement consistency. Results: The spring-assisted group was older at both time points (P < .050) and spring-assisted group CVV was larger preoperatively and postoperatively (P < .01). However, the change in CVV did not differ between the groups (P = .210). There was no difference in preoperative CI (helmet vs spring: 70.1 vs 71.2, P = .368) between the groups. Postoperatively, helmet group CI (77.0 vs 74.3, P = .008) was greater. The helmet group also demonstrated a greater increase in CI (6.9 vs 3.1, P < .001). The proportion of patients achieving CI of 75 or greater was not significantly different between the groups (helmet vs spring: CI, 65% vs 52%, P = .370). There was no detectable bias in CI between matched CT and laser scans. Differences were identified between scan types in aCI and CVV measurements; subsequent analyses used corrected CVV and aCI measures for laser scan measures. Conclusions: Both techniques had equivalent proportions of patients achieving normal CI, comparable effects on cranial volume, and similar operative characteristics. The study suggests that there may be greater improvement in CI in the helmet group. However, further research should be performed.
Evaluation of the Pharmacophoric Role of the O–O Bond in Synthetic Antileishmanial Compounds: Comparison between 1,2-Dioxanes and Tetrahydropyrans

Leishmaniases are neglected diseases that can be treated with a limited drug arsenal; the development of new molecules is therefore a priority. Recent evidence indicates that endoperoxides, including artemisinin and its derivatives, possess antileishmanial activity. Here, 1,2-dioxanes were synthesized with their corresponding tetrahydropyrans lacking the peroxide bridge, to ascertain if this group is a key pharmacophoric requirement for the antileishmanial bioactivity. Newly synthesized compounds were examined in vitro, and their mechanism of action was preliminarily investigated. Three endoperoxides and their corresponding tetrahydropyrans effectively inhibited the growth of Leishmania donovani promastigotes and amastigotes, and iron did not play a significant role in their activation. Further, reactive oxygen species were produced in both endoperoxide- and tetrahydropyran-treated promastigotes. In conclusion, the peroxide group proved not to be crucial for the antileishmanial bioactivity of endoperoxides, under the tested conditions. Our findings reveal the potential of both 1,2-dioxanes and tetrahydropyrans as lead compounds for novel therapies against Leishmania.
A Development of Fuzzy Logic Expert-Based Recommender System for Improving Students’Employability

Most of the higher education institutions in the world have already been evaluating their strategies of enhancing the employability of their students and introducing different ways to improve and strengthen it. Identifying student’s employability and recommend the areas for improvement before graduation will increase the chance to be employed if the students developed their employability skills. That is why in this paper the rule-based algorithm that is commonly used in developing a recommender system were replaced by Fuzzy logic based because rule-based cannot eliminate the ambiguity issues in decision-making, whether they are hirable or not before graduation. Various factors may affect the employability of undergraduate students. In this paper, the employability prediction and recommender system for students were built using fuzzy logic to resolve the issue. The most significant attributes that affect the undergraduate students’ employability were determined using feature selection filtering techniques and used as inputs. The result shows that the developed fuzzy model performs a high predictive accuracy based on the computed mean absolute error (MAE) and root-mean-square error (RMSE) scores which decrease from the training to the validation and test sets.
A genome-wide association study of severe asthma exacerbations in Latino children and adolescents

Severe asthma exacerbations are a major cause of school absences and healthcare costs in children, particularly those in high-risk racial/ethnic groups. To identify susceptibility genes for severe asthma exacerbations in Latino children and adolescents, we conducted a meta-analysis of genome-wide association studies (GWAS) in 4010 Latino youth with asthma in four independent cohorts, including 1693 Puerto Ricans, 1019 Costa Ricans, 640 Mexicans, 256 Brazilians and 402 members of other Latino subgroups. We then conducted methylation quantitative trait locus, expression quantitative trait locus and expression quantitative trait methylation analyses to assess whether the top single nucleotide polymorphism (SNP) in the meta-analysis is linked to DNA methylation and gene expression in nasal (airway) epithelium in separate cohorts of Puerto Rican and Dutch children and adolescents. In the meta-analysis of GWAS, an SNP in FLJ22447 (rs2253681) was significantly associated with 1.55 increased odds of severe asthma exacerbation (95% CI 1.34–1.79, p=6.3×10−9). This SNP was significantly associated with DNA methylation of a CpG site (cg25024579) at the FLJ22447 locus, which was in turn associated with increased expression of KCNJ2-AS1 in nasal airway epithelium from Puerto Rican children and adolescents (β=0.10, p=2.18×10−7). SNP rs2253681 was significantly associated with both DNA methylation of a cis-CpG in FLJ22447 and severe asthma exacerbations in Latino youth. This may be partly explained by changes in airway epithelial expression of a gene recently implicated in atopic asthma in Puerto Rican children and adolescents (KCNJ2-AS1). A novel SNP in FLJ22447 is significantly associated with both severe asthma exacerbations in Latino youth and DNA methylation of a cis-CpG in nasal epithelium. This CpG is linked to nasal epithelial expression of a gene implicated in atopic asthma. https://bit.ly/33D9Joc
Zoonotic infection with swine A/H1avN1 influenza virus in a child, Germany, June 2020

A zoonotic A/sw/H1avN1 1C.2. 2 influenza virus infection was detected in a German child that presented with influenza-like illness, including high fever. There was a history of close contact with pigs 3 days before symptom onset. The child recovered within 3 days. No other transmissions were observed. Serological investigations of the virus isolate revealed cross-reactions with ferret antisera against influenza A(H1N1)pdm09 virus, indicating a closer antigenic relationship with A(H1N1)pdm09 than with the former seasonal H1N1 viruses.
Alpine lakes and ponds – a promising source of high genetic diversity in metapopulations of aquatic insects

Abstract Alpine lakes and ponds are unique and vulnerable aquatic habitats inhabited by specific species assemblages. In contrast to alpine streams, the genetic diversity and population structure of the organisms found in alpine standing waters are almost completely unknown. Here we present a population genetic study of a macroinvertebrate species from alpine lakes, the diving beetle Agabus bipustulatus (Linnaeus, 1767). We used partial cytochrome b (Cyt b) mtDNA gene of 560 specimens from 43 lakes and ponds distributed across 20 mountain valleys of the Tatra Mountains (Western Carpathians) and their foothills. The aims of this study were to provide detailed genetic metapopulation analysis of A. bipustulatus within a geographically small mountain system, to explore the role of ridges in isolating lake populations, and to test for the applicability of Cyt b in insect population genetic studies. We detected high genetic variability (42 haplotypes), comparable to many large spatial scale studies. There was a star-shaped genetic structure with few dominant haplotypes, which confirmed recent origin, rapid expansion of metapopulation, and multiple colonization of the Tatra Mountain region by A. bipustulatus. The populations within the mountain system differed from each other, suggesting that the Tatra Mountain lakes and ponds were colonized through multiple colonization events, and also that colonization of the region and evolution of metapopulations are to a certain extent influenced by ridge barriers.
Exploring the Environmental Manifestation of Types of Music on Reinforcing Mindfulness and Concurrent Calorie Intake

Background The role of music on energy intake is conflicting, and recent research has suggested a positive association between classical music listening and mindfulness. Objective The purpose of the present study was to investigate the effect of music, specifically classical music on state mindfulness and calorie intake of energy-dense foods. Method One hundred participants were randomly assigned to either a classical, popular or no music condition, and were served a variety of sweet (i.e., chocolate and cookies) and savoury (i.e., crisps) energy-dense foods. Results: The results found no significant differences in state mindfulness, overall calorie intake, or intake of sweet foods across the three conditions. However, participants in the classical music condition did consume significantly less savoury food than those in the no music condition. Conclusion Playing classical music may be beneficial in reducing intake of savoury foods, but not through the association to changes in state mindfulness. Future research should explore extended sessions of music listening on state mindfulness and other experiential evaluations of mindfulness to conclude on the direct and indirect effects of music on sweet and savoury foods.
Secure Federated Averaging Algorithm with Differential Privacy

Federated learning (FL), as a recent advance of distributed machine learning, is capable of learning a model over the network without directly accessing the client's raw data. Nevertheless, the clients' sensitive information can still be exposed to adversaries via differential attacks on messages exchanged between the parameter server and clients. In this paper, we consider the widely used federating averaging (FedAvg) algorithm and propose to enhance the data privacy by the differential privacy (DP) technique, which obfuscates the exchanged messages by properly adding Gaussian noise. We analytically show that the proposed secure FedAvg algorithm maintains an O(l/T) convergence rate, where $T$ is the total number of stochastic gradient descent (SGD) updates for local model parameters. Moreover, we demonstrate how various algorithm parameters can impact on the algorithm communication efficiency. Experiment results are presented to justify the obtained analytical results on the performance of the proposed algorithm in terms of testing accuracy.
RX Equalization for a High-Speed Channel based on Bayesian Active Learning using Dropout

Determining optimal equalization settings in highspeed bus design simulations is becoming more important due to increased complexity and data rates of current server systems, but it is also time and resource consuming. In this paper, a probabilistic machine learning technique, Bayesian Active Learning using Dropout (BAL-DO), is utilized to perform RX equalization and optimization to address this issue. Largest HEYE opening and corresponding equalization settings are obtained with high prediction accuracy without performing extensive time-domain analysis, thereby significantly reducing the cost of engineering time and computational resources.
The efficacy of the biological control agents Comostolopsis germana (Bitou Tip Moth) and Mesoclanis polana (Bitou Tip Fly) in controlling the invasive weed Chrysanthemoides monilifera spp. rotundata (Bitou Bush)

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