Miracle-dz/UniFAR-FTUs huggingface dataset:
PV Cell Parameter Extraction Using Data Prediction–Based Meta-Heuristic Algorithm via Extreme Learning Machine

To reliably evaluate the practical performance and to undertake optimal control of PV systems, a precise PV cell parameter extraction–based accurate modeling of PV cells is extremely crucial. However, its inherent high nonlinear and multimodal characteristics usually hinder conventional optimization methods to obtain a fast and satisfactory performance. Besides, insufficient current–voltage (I–V) data provided by manufacturers cannot guarantee high accuracy and flexibility of PV cell parameter extraction under various operation scenarios. Hence, this article proposes a novel parameter extraction strategy by data prediction–based meta-heuristic algorithm (DPMhA). An extreme learning machine (ELM) is adopted to predict output I–V data from measured data, which can provide a more reliable fitness function to meta-heuristic algorithms (MhAs). Consequently, MhAs can undertake a more stable search for optimal solution through extended I–V data; thus, PV cell parameters can be obtained with high accuracy and convergence rate. Its effectiveness is validated via three typical PV cell models, that is, single diode model (SDM), double diode model (DDM), and three diode model (TDM). Last, comprehensive case studies illustrate that the DPMhA can considerably enhance the accuracy and effectiveness compared with those without data prediction.
First Sequencing of Caprine Mdr1 (Abcb1) mRNA Due to Suspected Neurological Adverse Drug Reaction in a Thuringian Goat Following Extra-Label Use of Doramectin

The multidrug resistance gene MDR1 encodes for an efflux transporter called P-glycoprotein (P-gp). In the canine Mdr1 gene, a nonsense mutation was identified in certain dog breeds causing increased drug sensitivity to various P-gp substrates such as antiparasitic macrocyclic lactones. Symptoms of neurologic toxicity include ataxia, depression, salivation, tremor, apparent blindness, and mydriasis. In the current report, a Thuringian goat developed similar neurological signs after treatment with doramectin, a compound from the macrocyclic lactone class. Therefore, Mdr1 might be defective in this individual goat. For diagnostic purposes, sequencing of the complete mRNA transcript coding for caprine Mdr1 was performed to investigate a potential missense mutation. The Mdr1 transcripts of two related goats without drug sensitivity were also sequenced to allow differential diagnosis and were compared to the suspected drug-sensitive goat. The only position where the Mdr1 sequence from the suspected drug-sensitive goat differed was in the 3′-untranslated region, being a heterozygous single nucleotide polymorphism c.3875C>A. It can be suspected that this variant affects the expression level, stability, or translation efficiency of the Mdr1 mRNA transcript and therefore might be associated with the suspected drug sensitivity. To clarify this, further studies are needed, particularly investigating the Mdr1 mRNA and protein expression levels from brain material of affected goats. In conclusion, Mdr1 variants may exist not only in dogs, but also in individual goats. The current report provides the first Mdr1 mRNA transcript sequence of a goat and therefore represents the basis for more detailed Mdr1 sequence and expression analyses.
The ‘Shellome’ of the Crocus Clam Tridacna crocea Emphasizes Essential Components of Mollusk Shell Biomineralization

Molluscan shells are among the most fascinating research objects because of their diverse morphologies and textures. The formation of these delicate biomineralized structures is a matrix-mediated process. A question that arises is what are the essential components required to build these exoskeletons. In order to understand the molecular mechanisms of molluscan shell formation, it is crucial to identify organic macromolecules in different shells from diverse taxa. In the case of bivalves, however, taxon sampling in previous shell proteomics studies are focused predominantly on representatives of the class Pteriomorphia such as pearl oysters, edible oysters and mussels. In this study, we have characterized the shell organic matrix from the crocus clam, Tridacna crocea, (Heterodonta) using various biochemical techniques, including SDS-PAGE, FT-IR, monosaccharide analysis, and enzyme-linked lectin assay (ELLA). Furthermore, we have identified a number of shell matrix proteins (SMPs) using a comprehensive proteomics approach combined to RNA-seq. The biochemical studies confirmed the presence of proteins, polysaccharides, and sulfates in the T. crocea shell organic matrix. Proteomics analysis revealed that the majority of the T. crocea SMPs are novel and dissimilar to known SMPs identified from the other bivalve species. Meanwhile, the SMP repertoire of the crocus clam also includes proteins with conserved functional domains such as chitin-binding domain, VWA domain, and protease inhibitor domain. We also identified BMSP (Blue Mussel Shell Protein, originally reported from Mytilus), which is widely distributed among molluscan shell matrix proteins. Tridacna SMPs also include low-complexity regions (LCRs) that are absent in the other molluscan genomes, indicating that these genes may have evolved in specific lineage. These results highlight the diversity of the organic molecules – in particular proteins – that are essential for molluscan shell formation.
Isolation, Antimicrobial Resistance Phenotypes, and Virulence Genes of Bordetella bronchiseptica From Pigs in China, 2018–2020

Bordetella bronchiseptica is a leading cause of respiratory diseases in pigs. However, epidemiological data of B. bronchiseptica in pigs particularly in China, the largest pig rearing country in the world is still limited. We isolated 181 B. bronchiseptica strains from 4259 lung samples of dead pigs with respiratory diseases in 14 provinces in China from 2018 to 2020. The average isolation rate of this 3-year period was 4.25% (181/4259). Antimicrobial susceptibility testing performed by disc diffusion method revealed that most of the B. bronchiseptica isolates in this study were resistant to ampicillin (83.98%), while a proportion of isolates were resistant to cefotaxime (30.39%%), chloramphenicol (12.71%), gentamicin (11.60%), florfenicol (11.60%), tetracycline (8.84%), amoxicillin (8.29%), tobramycin (6.63%), ceftriaxone (4.97%), and cefepime (0.55%). There were no isolates with resistant phenotypes to imipenem, meropenem, polymyxin B, ciprofloxacin, enrofloxacin, and amikacin. In addition, ~13.18% of the isolates showed phenotypes of multidrug resistance. Detection of antimicrobial resistance genes (ARGs) by PCR showed that 16.57% of the B. bronchiseptica isolates in this study was positive to aac(3)-IV, while 3.87%, 2.21%, 1.10%, 0.55%, 0.55%, and 0.55% of the isolates were positive to aac6'-Ib, rmtA, blaTEM, blaSHV, oqxB, and tetA, respectively. Detection of virulence factors encoding genes (VFGs) by conventional PCR showed that over 90% of the pig B. bronchiseptica isolates in this study were positive to the five VFGs examined (fhaB, 97.24%; prn, 91.16%; cyaA, 98.34%; dnt, 98.34%; betA, 92.82%). These results demonstrate B. bronchiseptica as an important pathogen associated with pig respiratory disorders in China. The present work contributes to the current understanding of the prevalence, antimicrobial resistance and virulence genes of B. bronchiseptica in pigs.
Prokaryotic, Fungal, and Unicellular Eukaryotic Core Communities Across Three Sympatric Marine Sponges From the Southwestern Atlantic Coast Are Dominated Largely by Deterministic Assemblage Processes

Marine sponges are known to harbor a diverse and complex microbiota; however, a vast majority of surveys have been investigating the prokaryotic communities in the north hemisphere and Australia. In addition, the mechanisms of microbial community assembly are poorly understood in this pivotal player of the ecosystem. Thus, this survey addressed the holobiome of the sponge species in the São Paulo region (Brazil) for the first time and investigated the contribution of neutral and niche processes of prokaryotic, fungal, and unicellular eukaryotic assemblage in three sympatric species Aplysina caissara, Aplysina fulva, and Tedania ignis along with environmental samples. The compositions of the holobiome associated with the sponges and detected in environmental samples were strikingly different. Remarkably, between 47 and 88% of the assigned operational taxonomic units (OTUs) were specifically associated with sponge species. Moreover, around 77, 69, and 53% of the unclassified OTUs from prokaryotic, fungal, and unicellular eukaryotic communities, respectively, showed less than 97% similarity with well-known databases, suggesting that sponges from the southwestern Atlantic coast are an important source of microbial novelty. These values are even higher, around 80 and 61% of the unclassified OTUs, when excluding low abundance samples from fungal and unicellular eukaryotic datasets, respectively. Host species were the major driver shaping the sponge-associated microbial community. Deterministic processes were primarily responsible for the assembly of microbial communities in all sponge species, while neutral processes of prokaryotic and fungal community assembly were also detected in the sympatric A. caissara and T. ignis replicates, respectively. Most of the species-rich sponge-associated lineages from this region are also found in the Northern seas and many of them might play essential roles in the symbioses, such as biosynthesis of secondary metabolites that exhibit antimicrobial and antiviral activities, as well as provide protection against host predation. Overall, in this study the microbiota was assembled by interactions with the host sponge in a deterministic-based manner; closely related sponge species shared a strong phylogenetic signal in their associated prokaryotic and fungal community traits and Brazilian sponges were a reservoir of novel microbial species.
Reduced PHLPP Expression Leads to EGFR-TKI Resistance in Lung Cancer by Activating PI3K-AKT and MAPK-ERK Dual Signaling

Background Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are effective in advanced EGFR-mutation non-small cell lung cancer (NSCLC) but the magnitude of tumor regression varies, and drug resistance is unavoidable. The pleckstrin homology domain leucine-rich repeat protein phosphatase (PHLPP) levels are reduced or lost and acts as a tumor suppressor in many cancers. Here, we hypothesized that PHLPP is a key regulator of EGFR-TKI sensitivity and a potential treatment target for overcoming resistance to EGFR-TKI in lung cancer. Methods Cell proliferation and growth inhibition were measured by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and colony formation assay. PHLPP- knockdown stable cell lines were generated by lentivirus-mediated delivery of PHLPP shRNAs. The expression of PHLPP mRNA and protein levels was detected by real-time quantitative polymerase chain reaction (qPCR) and Western blotting. Immunohistochemical (IHC) staining was performed to detect the PHLPP expression in clinical patient tissue samples. A transcriptomic assay of genome-wide RNA expressions of PHLPP in NSCLC cell lines according to gefitinib sensitivity was obtained from Gene Expression Omnibus (GEO) database. Murine xenograft model was established to verify the function of PHLPP in gefitinib resistance in vivo. Results PHLPP highly expressed in gefitinib-sensitive NSCLC cell lines than gefitinib-resistant NSCLC cell lines. In gefitinib-acquired resistance cell line HCC827-GR, PHLPP expression even dramatically reduced. Knockdown of PHLPP in NSCLC cells decreased cell death induced by the EGFR-TKI, while overexpression PHLPP in gefitinib-resistance NSCLC cells can enhance or restore EGFR-TKIs sensitivity. Mechanism study indicated that PHLPP downregulation attenuates the effect of EGFR-TKI on the both AKT and ERK pathway, thereby decreasing the cell death sensitivity to EGFR inhibitors. In xenograft mice, knockdown of PHLPP decreased tumor response to gefitinib and advanced tumor cells re-growth after gefitinib treatment. In clinical, PHLPP expression were reduced in the post-relapse tumor compared to that of pre-treatment, and lower pre-treatment PHLPP levels were significantly correlated with shorter progression-free survival (PFS) in patients with EGFR-mutant lung adenocarcinoma whom treated with EGFR-TKI. Conclusions Our data strongly demonstrated that loss of PHLPP function was a key factor of EGFR-TKI resistance in NSCLC. Downregulated PHLPP expression activated PI3K-AKT and MAPK-ERK pathway which strengthened cell survival to EGFR-TKI. Therefore, PHLPP expression level was not only a potential biomarker to predict EGFR-TKIs sensitivity but also as a therapeutic target in EGFR-TKIs therapy, enhancing PHLPP expression may be a valuable strategy for delaying or overcoming EGFR-TKIs drug resistance.
Disease Outbreak in a Keystone Grazer Population Brings Hope to the Recovery of Macroalgal Forests in a Barren Dominated Island

Macroalgal forests play a key role in shallow temperate rocky reefs worldwide, supporting communities with high productivity and providing several ecosystem services. Sea urchin grazing has been increasingly influencing spatial and temporal variation in algae distributions and it has become the main cause for the loss of these habitats in many coastal areas, causing a phase shift from macroalgae habitats to barren grounds. The low productive barrens often establish as alternative stable states and only a major reduction in sea urchin density can trigger the recovery of macroalgal forests. The present study aims to assess if the 2018 disease outbreak, responsible for a strong reduction in the sea urchin Diadema africanum densities in Madeira Island, was able to trigger a reverse shift from barren grounds into macroalgae-dominated state. By assessing the diversity and abundance of benthic sessile organisms, macroinvertebrates and fishes before, during and after that particular mass mortality event, we evaluate changes in benthic assemblages and relate them to variations in grazer and herbivore densities. Our results revealed a clear shift from barren state to a macroalgae habitat, with barrens characterized by bare substrate, sessile invertebrate and Crustose Coralline Algae (CCA) disappearing after the mortality event. Overall variations in benthic assemblages was best explained by four taxa (among grazers and herbivores species). However, it was the 2018 demise of D. africanum and its density reduction that most contributed to the reverse shift from a long stable barren state to a richer benthic assemblage with higher abundance of macroalgae. Despite this recent increase in macroalgae dominated habitats, their stability and persistence in Madeira Island is fragile, since it was triggered by an unpredictable disease outbreak and depends on how D. africanum populations will recover. With no control mechanisms, local urchin populations can easily reach the tipping point needed to promote a new shift into barren states. New conservation measures and active restoration are likely required to maintain and promote the local stability of macroalgal forests.
Implications of Climate Change: How Does Increased Water Temperature Influence Biofilm and Water Quality of Chlorinated Drinking Water Distribution Systems?

Temperature variation can promote physico-chemical and microbial changes in the water transported through distribution systems and influence the dynamics of biofilms attached to pipes, thus contributing to the release of pathogens into the bulk drinking water. An experimental real-scale chlorinated DWDS was used to study the effect of increasing temperature from 16 to 24°C on specific pathogens, bacterial-fungal communities (biofilm and water samples) and determine the risk of material accumulation and mobilisation from the pipes into the bulk water. Biofilm was developed for 30 days at both temperatures in the pipe walls, and after this growth phase, a flushing was performed applying 4 gradual steps by increasing the shear stress. The fungal-bacterial community characterised by Illumina MiSeq sequencing, and specific pathogens were studied using qPCR: Mycobacterium spp. , Mycobacterium avium complex, Acanthamoeba spp. , Pseudomonas aeruginosa, Legionella pneumophilia, and Stenotrophomonas maltophilia. Sequencing data showed that temperature variation significantly modified the structure of biofilm microbial communities from the early stages of biofilm development. Regarding bacteria, Pseudomonas increased its relative abundance in biofilms developed at 24°C, while fungal communities showed loss of diversity and richness, and the increase in dominance of Fusarium genus. After the mobilisation phase, Pseudomonas continued being the most abundant genus at 24°C, followed by Sphingobium and Sphingomonas. For biofilm fungal communities after the mobilisation phase, Helotiales incertae sedis and Fusarium were the most abundant taxa. Results from qPCR showed a higher relative abundance of Mycobacterium spp. on day 30 and M. avium complex throughout the growth phase within the biofilms at higher temperatures. The temperature impacts were not only microbial, with physical mobilisation showing higher discolouration response and metals release due to the increased temperature. While material accumulation was accelerated by temperature, it was not preferentially to either stronger or weaker biofilm layers, as turbidity results during the flushing steps showed. This research yields new understanding on microbial challenges that chlorinated DWDS will undergo as global temperature rises, this information is needed in order to protect drinking water quality and safety while travelling through distribution systems.
A Study of Dramatic Action and Emotion Using a Systematic Scan of Stick Figure Configurations

Comprehending the meaning of body postures is essential for social organisms such as humans. For example, it is important to understand at a glance whether two people seen at a distance are in a friendly or conflictual interaction. However, it is still unclear what fraction of the possible body configurations carry meaning, and what is the best way to characterize such meaning. Here, we address this by using stick figures as a low-dimensional, yet evocative, representation of body postures. We systematically scanned a set of 1,470 upper-body postures of stick figures in a dyad with a second stick figure with a neutral pose. We asked participants to rate the stick figure in terms of 20 emotion adjectives like sad or triumphant and in terms of eight active verbs that connote intent like to threaten and to comfort. The stick figure configuration space was dense with meaning: people strongly agreed on more than half of the configurations. The meaning was generally smooth in the sense that small changes in posture had a small effect on the meaning, but certain small changes had a large effect. Configurations carried meaning in both emotions and intent, but the intent verbs covered more configurations. The effectiveness of the intent verbs in describing body postures aligns with a theory, originating from the theater, called dramatic action theory. This suggests that, in addition to the well-studied role of emotional states in describing body language, much can be gained by using also dramatic action verbs which signal the effort to change the state of others. We provide a dictionary of stick figure configurations and their perceived meaning. This systematic scan of body configurations might be useful to teaching people and machines to decipher body postures in human interactions.
Prediction and Detection of Diabetes using Machine Learning

Data mining and machine learning have become a vital part of different disease detection and prevention. One of them is diabetes. The purpose of this paper is to evaluate data mining methods and their performances that can be used for analyzing the collected data about the diabetes. We identified the most appropriate data mining methods to analyze the data by comparing them theoretically and practically. Some attributes of this dataset are: Age, Body Mass Index, Insulin, Glucose, etc. Methods are applied on these data to determine their effectiveness in analyzing and preventing diabetes. Evaluations on the data showed that the method with a higher performance is “Decision Tree”. This was achieved by some performance measures, such as the number of instances correctly classified, accuracy, precision, recall and F-measure, that has brought better results compared to other methods. We come to the conclusion that the data mining methods and machine learning contribute to the predictions on the possibility of occurrence of the diabetes.
A Framework for Mobile-Assisted Formative Assessment to Promote Students' Self-Determination

Motivation is an important issue to consider when designing learning activities, including mobile learning and assessment. While previous research provides evidence for the motivational impact of mobile learning, not many pedagogical frameworks exist for the design of mobile-assisted learning and assessment. The current study is grounded in the Self-Determination Theory of motivation and proposes a pedagogical framework for mobile-assisted formative assessment, aiming at enhancing student motivation. For a preliminary evaluation of the framework, fifty-one students from a public European high school participated in a series of formative assessment activities. The tasks that were implemented according to the proposed mobile-based formative assessment framework had a significant positive impact on student perceived levels of autonomy, competence, and relatedness, enhancing students’ intrinsic motivation levels. Study findings highlighted the capacity of the proposed framework to guide the design of mobile-based formative assessment activities that enhance and promote student motivation. The study makes a theoretical contribution by proposing a framework that aligns mobile learning and assessment with elements of the Self-Determination Theory of motivation and also has a practical contribution by implementing mobile learning and assessment practices that have the potential to promote student motivation.
Evaluating Meta-Reinforcement Learning through a HVAC Control Benchmark (Student Abstract)

Meta-Reinforcement Learning (RL) algorithms promise to leverage prior task experience to quickly learn new unseen tasks. Unfortunately, evaluating meta-RL algorithms is complicated by a lack of suitable benchmarks. In this paper we propose adapting a challenging real-world heating, ventilation and air-conditioning (HVAC) control benchmark for meta-RL. Unlike existing benchmark problems, HVAC control has a broader task distribution, and sources of exogenous stochasticity from price and weather predictions which can be shared across task definitions. This can enable greater differentiation between the performance of current meta-RL approaches, and open the way for future research into algorithms that can adapt to entirely new tasks not sampled from the current task distribution.