Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecological and Molecular Interactions between Insects and Fungi

Insects and fungi represent two of the most widespread groupings of organisms in nature, occurring in every kind of ecological context and impacting agriculture and other human activities in various ways. Moreover, they can be observed to reciprocally interact, establishing a wide range of symbiotic relationships, from mutualism to antagonism. The outcome of these relationships can in turn affect the extent at which species of both organisms can exert their noxious effects, as well as the management practices which are to be adopted to counter them. In conjunction with the launch of a Special Issue of Microorganisms with the same title, this article offers a general overview of the manifold aspects related to such interactions from the perspective of implementing our capacity to regulate them in a direction more favorable for the environment, crop production and human health.
Assessing the drug resistance profiles of oral probiotic lozenges

ABSTRACT Background Probiotic lozenges have been developed to harvest the benefits of probiotics for oral health, but their long-term consumption may encourage the transfer of resistance genes from probiotics to commensals, and eventually to disease-causing bacteria. Aim To screen commercial probiotic lozenges for resistance to antibiotics, characterize the resistance determinants, and examine their transferability in vitro. Results Probiotics of all lozenges were resistant to glycopeptide, sulfonamide, and penicillin antibiotics, while some were resistant to aminoglycosides and cephalosporins. High minimum inhibitory concentrations (MICs) were detected for streptomycin (>128 µg/mL) and chloramphenicol (> 512 µg/mL) for all probiotics but only one was resistant to piperacillin (MIC = 32 µg/mL). PCR analysis detected erythromycin (erm(T), ermB or mefA) and fluoroquinolone (parC or gyr(A)) resistance genes in some lozenges although there were no resistant phenotypes. The dfrD, cat-TC, vatE, aadE, vanX, and aph(3”)-III or ant(2”)-I genes conferring resistance to trimethoprim, chloramphenicol, quinupristin/dalfopristin, vancomycin, and streptomycin, respectively, were detected in resistant probiotics. The rifampicin resistance gene rpoB was also present. We found no conjugal transfer of streptomycin resistance genes in our co-incubation experiments. Conclusion Our study represents the first antibiotic resistance profiling of probiotics from oral lozenges, thus highlighting the health risk especially in the prevailing threat of drug resistance globally.
The new horizon of biomarker in melanoma patients

Supplemental Digital Content is available in the text Abstract Autophagy-related long non-coding RNAs (arlncRNAs) play a crucial role in the pathogenesis and development of the tumor. However, there is a lack of systematic analysis of arlncRNAs in melanoma patients. Melanoma data for analysis were obtained from The Cancer Genome Atlas (TCGA) database. By establishing a co-expression network of autophagy-related mRNAs-lncRNAs, we identified arlncRNAs in melanoma patients. We evaluated the prognostic value of arlncRNAs by univariate and multivariate Cox analysis and constructed an arlncRNAs risk model. Patients were divided into high- and low-risk groups based on the arlncRNAs risk score. This model was evaluated by Kaplan–Meier (K–M) analysis, univariate-multivariate Cox regression analysis, and receiver operating characteristic (ROC) curve analysis. Characteristics of autophagy genes and co-expressive tendency were analyzed by principal component analysis and Gene Set Enrichment Analysis (GSEA) functional annotation. Nine arlncRNAs (USP30-AS1, LINC00665, PCED1B-AS1, LINC00324, LINC01871, ZEB1-AS1, LINC01527, AC018553.1, and HLA-DQB1-AS1) were identified to be related to the prognosis of melanoma patients. Otherwise, the 9 arlncRNAs constituted an arlncRNAs prognostic risk model. K–M analysis and ROC curve analysis showed that the arlncRNAs risk model has good discrimination. Univariate and multivariate Cox regression analysis showed that arlncRNAs risk model was an independent prognostic factor in melanoma patients. Principal component analysis and GSEA functional annotation showed different autophagy and carcinogenic status in the high- and low-risk groups. This novel arlncRNAs risk model plays an essential role in predicting of the prognosis of melanoma patients. The model reveals new prognosis-related biomarkers for autophagy, promotes precision medicine, and provides a lurking target for melanoma's autophagy-related treatment.
Changes in Metabolic Regulation and the Microbiota Composition after Supplementation with Different Fatty Acids in db/db Mice

Introduction The effects of fatty acids on health vary and depend on the type, amount, and route of consumption. EPA and DHA have a defined role in health, unlike coconut oil. Objective The aim was to investigate the changes in metabolic regulation and the composition of the culture-dependent microbiota after supplementation with different fatty acids in db/db mice. Material and Methods. We were using 32 8-week-old db/db mice, supplemented for eight weeks with EPA/DHA derived from microalgae as well as coconut oil. The lipid, hormonal profiles, and composition of the culture-dependent microbiota and the phylogenetic analysis based on the 16S rRNA gene sequencing were determined for identification of the intestinal microbiota. Results Enriched diet with EPA/DHA reduced TNF-α, C-peptide, insulin resistance, resistin, and the plasma atherogenic index, but increased TC, LDL-c, VLDL-c, and TG without changes in HDL-c. Coconut oil raised the HDL-c, GIP, and TNF-α, with TG, insulin resistance, adiponectin, and C-peptide reduced. Conclusion The most abundant microbial populations were Firmicutes and the least Proteobacteria. EPA/DHA derived from microalgae contributes to improving the systemic inflammatory status, but depressed the diversity of the small intestine microbiota. Coconut oil only decreased the C-peptide, raising TNF-α, with an unfavorable hormonal and lipid profile.
Affinity, Specificity, and Cooperativity of DNA Binding by Bacterial Gene Regulatory Proteins

Nearly all of biology depends on interactions between molecules: proteins with small molecules, proteins with other proteins, nucleic acids with small molecules, and nucleic acids with proteins that regulate gene expression, our concern in this Special Issue. All those kinds of interactions, and others, constitute the vast majority of biology at the molecular level. An understanding of those interactions requires that we quantify them to learn how they interact: How strongly? With which partners? How—and how well—are different partners distinguished? This review addresses the evolution of our current understanding of the molecular origins of affinity and specificity in regulatory protein–DNA interactions, and suggests that both these properties can be modulated by cooperativity.
The Music Studio Challenge: effective practice in developing enterprise and employability skills while supporting transition

Creative industries attract many people who “start and run diverse kinds of enterprise, from self employed artists to owners of globally significant business organisations” (Bjorkegren, 1996, cited in Rae, 2004, p. 492). The graduate website, prospects, suggests the creative sector is experiencing a “golden age” (2009); however, for graduates seeking to obtain entry it remains a difficult marketplace. A significant part of the workforce is self-employed and works from home or a small office/studio; part-time and seasonal work is commonplace and hours are frequently unsociable. Competition for graduate level jobs is fierce and many artists find they must support themselves through alternative work. The UK music industry alone is worth £5 billion a year, generating 126,000 fulltime jobs (UK Trade & Investment, 2007 as cited by prospects, 2009); yet the reality of these jobs is that they are for the most part secured by individuals who have successfully developed a network of contacts over time. Creative arts students and graduates struggle to access relevant work experience which provides opportunities to develop employability skills such as creative enterprise and networking. Related work experience and industry contacts are valuable and indeed essential for successful career progression within this sector.
Modeling Human Cardiac Arrhythmias: Insights from Zebrafish

Cardiac arrhythmia, or irregular heart rhythm, is associated with morbidity and mortality and is described as one of the most important future public health challenges. Therefore, developing new models of cardiac arrhythmia is critical for understanding disease mechanisms, determining genetic underpinnings, and developing new therapeutic strategies. In the last few decades, the zebrafish has emerged as an attractive model to reproduce in vivo human cardiac pathologies, including arrhythmias. Here, we highlight the contribution of zebrafish to the field and discuss the available cardiac arrhythmia models. Further, we outline techniques to assess potential heart rhythm defects in larval and adult zebrafish. As genetic tools in zebrafish continue to bloom, this model will be crucial for functional genomics studies and to develop personalized anti-arrhythmic therapies.
Influence of salinity on the horizontal distribution of soldier crab Mictyris guinotae in a subtropical river in Japan

ABSTRACT The horizontal distribution of Mictyris guinotae was studied in relation to gradients of salinity and sediment characteristics in sandy flats between 0 and 8.2 km from the mouth of a subtropical river. Lower salinity tolerance was also tested at seven salinity levels (0, 2, 4, 8, 12, 24 and 32) by a survival experiment for 14 days. Environmental clines were found in salinity but not in sediment characteristics. The upper limit of the occurrence of crab was 5.07 km upstream where seawater could reach during the spring flood tide. The densities of age-0 crabs tended to be higher at lower stations (0–0.65 km upstream), suggesting more recruits settle near the river mouth. Carapace length was significantly larger in the upper section (2.50–5.07 km upstream) in which salinity fluctuated or remained low (<10) for longer periods. It is unlikely that low salinity has a negative effect on post-larval growth. In contrast, body size was smaller in the lower section, possibly due to the effect of density. Some individuals died at the end of the experiment at salinity ≤12. The upper limit of appearance was explained by the salinity of riverine water and low salinity tolerance of the crab.
Motor Imagery EEG Decoding Based on New Spatial-Frequency Feature and Hybrid Feature Selection Method

Feature extraction and selection are important parts of motor imagery electroencephalogram (EEG) decoding and have always been the focus and difficulty of brain-computer interface (BCI) system research. In order to improve the accuracy of EEG decoding and reduce model training time, new feature extraction and selection methods are proposed in this paper. First, a new spatial-frequency feature extraction method is proposed. The original EEG signal is preprocessed, and then the common spatial pattern (CSP) is used for spatial filtering and dimensionality reduction. Finally, the filter bank method is used to decompose the spatially filtered signals into multiple frequency subbands, and the logarithmic band power feature of each frequency subband is extracted. Second, to select the subject-specific spatial-frequency features, a hybrid feature selection method based on the Fisher score and support vector machine (SVM) is proposed. The Fisher score of each feature is calculated, then a series of threshold parameters are set to generate different feature subsets, and finally, SVM and cross-validation are used to select the optimal feature subset. The effectiveness of the proposed method is validated using two sets of publicly available BCI competition data and a set of self-collected data. The total average accuracy of the three data sets achieved by the proposed method is 82.39%, which is 2.99% higher than the CSP method. The experimental results show that the proposed method has a better classification effect than the existing methods, and at the same time, feature extraction and feature selection time also have greater advantages.
Alkaline Stable Anion Exchange Membranes Based on Cross-Linked Poly(arylene ether sulfone) Bearing Dual Quaternary Piperidines for Enhanced Anion Conductivity at Low Water Uptake

Alkaline stable anion exchange membranes based on the cross-linked poly(arylene ether sulfone) grafted with dual quaternary piperidine (XPAES-DP) units were synthesized. The chemical structure of the synthesized PAES-DP was validated using 1H-NMR and FT-IR spectroscopy. The physicochemical, thermal, and mechanical properties of XPAES-DP membranes were compared with those of two linear PAES based membranes grafted with single piperidine (PAES-P) unit and conventional trimethyl amine (PAES-TM). XPAES-DP membrane showed the ionic conductivity of 0.021 S cm−1 at 40 °C which was much higher than that of PAES-P and PAES-TM because of the possession of more quaternary ammonium groups in the cross-linked structure. This cross-linked structure of the XPAES-DP membrane resulted in a higher tensile strength of 18.11 MPa than that of PAES-P, 17.09 MPa. In addition, as the XPAES-DP membrane shows consistency in the ionic conductivity even after 96 h in 3 M KOH solution with a minor change, its chemical stability was assured for the application of anion exchange membrane fuel cell. The single-cell assembled with XPAES-DP membrane displayed a power density of 109 mWcm−2 at 80 °C under 100% relative humidity.
Discussion on Health Service System of Mobile Medical Institutions Based on Internet of Things and Cloud Computing

Because modern human beings pay more and more attention to physical health, and there are many problems in the traditional medical service system, human beings have a higher and higher voice for the new medical model. At present, there are many researches on the application of modern science and technology to put forward solutions to medical development, but they generally pay attention to some details and ignore the construction of the whole medical service system. In order to solve the problems of low efficiency of the traditional medical model, difficult communication between doctors and patients, unreasonable allocation of medical resources, and so on, this article proposes establishing a perfect medical and health service system. First, the correlation functions are used, such as cosine correlation, to calculate the correlation of various medical products, and then the correlation measurement methods of cloud computing and the Internet of Things are used to realize the network connection of smart medical equipment, efficiently store, calculate and analyze health data, and realize online outpatient services, health file management, data analysis, and other functions. Then, the energy consumption formula of the wireless transceiver was used to reduce the resource loss in the operation of the system. Then, we use the questionnaire to understand the current situation of mobile medical and put forward improvement suggestions. This article also scores the performance of the system. The experimental results show that the performance rating of traditional medical institutions is B, while the model rating of mobile medical institutions is a, and the efficiency is optimized by 4.42%.
A Space Phasor Based Current Hysteresis Controller Using Adjacent Inverter Voltage Vectors with Smooth Transition to Six Step Operation for a Three Phase Voltage Source Inverter

Abstract In this paper, a space phasor based current hysteresis controller for a three-phase voltage source inverter is proposed. The current errors are determined along three axes, which are orthogonal to the A, B, C phases, and the current error space phasor is held within a hexagonal boundary. The proposed controller does not require any computation of machine voltage vector and uses only those inverter voltage vectors, which are adjacent to the machine voltage vector for the entire range of operation. The region detection logic employed in the proposed controller ensures that, the vector (among the three adjacent vectors), which has the largest deviation in the opposite direction, is selected, for all the regions of the hexagonal boundary. A simple self-adapting logic is used to effect sector changes and smooth transition to six-step mode of operation is achieved. The proposed controller is implemented for a 5hp induction motor drive.
Review on model reference adaptive system for sensorless vector control of induction motor drives

The most frequently used electrical machine in various modern high-performance drive applications is the induction motor (IM), especially its squirrel cage type rotor counterpart. The high-precision, efficient control of sensorless IM drives throughout its operating range demands an exact knowledge of a few of the IM parameters. The stator resistance, rotor resistances are such important IM parameters whose variations may lead to improper estimation of speed over the whole operating range in sensorless drives. Numerous methods for the estimation of speed of high-performance sensorless IM drives taking into account the effect of parameter variations have been developed. This paper aims at providing a review of the major model reference adaptive system (MRAS) based techniques applied for the estimation of speed of such sensorless drives. This paper is illustrated throughout with relevant mathematical equations, experimental and simulation results, associated to different MRAS-based sensorless vector control of the IM drive.
Lanzhang Granules Ameliorate Nonalcoholic Fatty Liver Disease by Regulating the PPARα Signaling Pathway

Background There is still a lack of effective therapeutic drugs for nonalcoholic fatty liver disease (NAFLD) to date. In this study, we applied mouse model experiments to clarify the effect of Chinese herbal medicine “Lanzhang Granules (LZG)” on NAFLD and further explore the potential mechanism to provide an alternative method for NAFLD treatment. Methods Male C57BL/6J mice were fed with a high-fat diet (HFD) for twenty-two weeks to induce the NAFLD model. LZG intervention was then performed by gavage daily for another eight weeks. At the end of the treatment, serum and liver tissues were collected. Serum biochemical indexes, insulin levels, and liver histopathology were measured to assess the effect of LZG on NAFLD. The liver tissues were then analyzed by RNA sequence for differentially expressed genes and signaling pathways. Results were further analyzed by Protein-Protein Interaction (PPI) networks between the LZG and model groups. The selected different genes and signaling pathways were further verified by RT-PCR and Western blot analysis. Moreover, alpha mouse liver 12 (AML12) cells with lipid accumulation induced by fatty acid were treated with LZG, Fenofibrate (PPARα agonist), or Gw6471 (PPARα antagonist) to confirm the potential pharmacological mechanism. Results LZG was found to downregulate liver weight, body weight, liver index, and serum levels of ALT, AST, and serum lipid in HFD-induced NAFLD mice. HE and Oil Red O staining showed the improvement of hepatic steatosis and inflammatory infiltration in the mice with LZG treatment. The homeostasis model assessment-insulin resistance (HOMA-IR) index indicated that LZG improved the insulin resistance of NAFLD mice. The RNA sequencing and PPI analysis confirmed the role of LZG in lipid metabolism regulation and identified the peroxisome proliferator-activated receptor alpha (PPARα) signaling pathway as one of the major underlying mechanisms. Western blot and RT-PCR results verified the regulatory effect of LZG on the PPARα pathway, including the upregulation of PPARα, acyl-coenzyme A oxidase 1 (ACOX1), and enoyl-CoA hydratase and 3-hydroxyacyl-CoA dehydrogenase (EHHADH) and the downregulation of TNFα. In vitro experiments showed the effect of LZG in improving lipid accumulation and cell viability in AML12 cells induced by fatty acids, which were alleviated by Gw6471 coincubation. Gw6471could also reverse the transcription of PPAR target genes ACOX1 and EHHADH, which were upregulated by LZG treatment. Conclusion LZG can improve NAFLD in mice or cell models. A major underlying mechanism may be the regulation of the PPARα signaling pathway to improve lipid metabolism and inhibit the inflammatory response. This study will help to promote the clinical application of LZG for the treatment of NAFLD.
Predicting Asset Maintenance Failure Using
Supervised Machine Learning Techniques

 Abstract — Maintenance activities can be broadly divided into three major categories and are corrective, preventive, and predictive maintenance. Our research focused on condition monitoring which is a form of predictive maintenance for brake pad failure for heavy-duty vehicles asset. The failure of a machine can stop production and cause a huge number of losses of money and people, moreover, it may take several months to order a new one. At the same time, excessive maintenance actions may slow production. Existing works of literature on predicting maintenance were studied in this research. Different machine learning techniques have been used for predicting maintenance, and to the best of our knowledge, Neural Network was only used for the prediction of the brake pad failure. Neural Network makes accurate prediction if the dataset is very large and also consume a lot of computational power. However, due to the fact that the problem is a classification problem, it is a necessity to carry out performance check of the best supervised model for the dataset downloaded from GitHub. Gaussian Naïve bayes, Decision tree and K-Nearest Neighbour are used to check for accuracy of our dataset. The dataset was divided into training and testing data where the training data has larger rows than the testing data. We then compared the performances of the selected supervised algorithm. Python is the preferred language used in this research. For our result, we showed that Decision tree performed well more than Gaussian Naïve bayes and K-Nearest Neighbour with an accuracy of 95%.
Potsdam Eye-Movement Corpus for Scene Memorization and Search With Color and Spatial-Frequency Filtering

The availability of large eye-movement corpora has become increasingly important over the past years. In scene viewing, scan-path analyses of time-ordered fixations, for example, allow for investigating individual differences in spatial correlations between fixation locations, or for predicting individual viewing behavior in the context of computational models. However, time-dependent analyses require many fixations per scene, and only few large eye-movement corpora are publicly available. This manuscript presents a new corpus with eye-movement data from two hundred participants. Viewers memorized or searched either color or grayscale scenes while high or low spatial frequencies were filtered in central or peripheral vision. Our database provides the scenes from the experiment with corresponding object annotations, preprocessed eye-movement data, and heatmaps and fixation clusters based on empirical fixation locations. Besides time-dependent analyses, the corpus data allow for investigating questions that have received little attention in scene-viewing research so far: (i) eye-movement behavior under different task instructions, (ii) the importance of color and spatial frequencies when performing these tasks, and (iii) the individual roles and interaction of central and peripheral vision during scene viewing. Furthermore, the corpus allows for validation of computational models of attention and eye-movement control, and finally, analyses on an object- or cluster-based level.