Miracle-dz/UniFAR-FTUs huggingface dataset:
Innate Immunity and Influenza A Virus Pathogenesis: Lessons for COVID-19

There is abundant evidence that the innate immune response to influenza A virus (IAV) is highly complex and plays a key role in protection against IAV induced infection and illness. Unfortunately it also clear that aspects of innate immunity can lead to severe morbidity or mortality from IAV, including inflammatory lung injury, bacterial superinfection, and exacerbation of reactive airways disease. We review broadly the virus and host factors that result in adverse outcomes from IAV and show evidence that inflammatory responses can become damaging even apart from changes in viral replication per se, with special focus on the positive and adverse effects of neutrophils and monocytes. We then evaluate in detail the role of soluble innate inhibitors including surfactant protein D and antimicrobial peptides that have a potential dual capacity for down-regulating viral replication and also inhibiting excessive inflammatory responses and how these innate host factors could possibly be harnessed to treat IAV infection. Where appropriate we draw comparisons and contrasts the SARS-CoV viruses and IAV in an effort to point out where the extensive knowledge existing regarding severe IAV infection could help guide research into severe COVID 19 illness or vice versa.
Comparative Analysis of Column Family NoSQL Cloud Data Stores for Managing Big Data

With the advancement in technology, bulk of data is generated. To store this data traditional database management systems were used but they were incapable to handle unstructured data. For managing this data NoSQL cloud data stores came into existence. This paper provides comparative analysis of column family NoSQL cloud data stores named HBase, Cassandra and SimpleDB. Comparative analysis is performed on the basis of different important parameters named Query language, partitioning, replication, consistency and concurrency.
Exploring Personal Memories and Video Content as Context for Facial Behavior in Predictions of Video-Induced Emotions

Empirical evidence suggests that the emotional meaning of facial behavior in isolation is often ambiguous in real-world conditions. While humans complement interpretations of others' faces with additional reasoning about context, automated approaches rarely display such context-sensitivity. Empirical findings indicate that the personal memories triggered by videos are crucial for predicting viewers' emotional response to such videos ? - in some cases, even more so than the video's audiovisual content. In this article, we explore the benefits of personal memories as context for facial behavior analysis. We conduct a series of multimodal machine learning experiments combining the automatic analysis of video-viewers' faces with that of two types of context information for affective predictions: \beginenumerate* [label=(\arabic*)] \item self-reported free-text descriptions of triggered memories and \item a video's audiovisual content \endenumerate*. Our results demonstrate that both sources of context provide models with information about variation in viewers' affective responses that complement facial analysis and each other.
Infrared inhibition impacts on locally initiated and propagating action potentials and the downstream synaptic transmission

Abstract. Significance: Systematic studies of the physiological outputs induced by infrared (IR)-mediated inhibition of motor nerves can provide guidance for therapeutic applications and offer critical insights into IR light modulation of complex neural networks. Aim: We explore the IR-mediated inhibition of action potentials (APs) that either propagate along single axons or are initiated locally and their downstream synaptic transmission responses. Approach: APs were evoked locally by two-electrode current clamp or at a distance for propagating APs. The neuromuscular transmission was recorded with intracellular electrodes in muscle cells or macro-patch pipettes on terminal bouton clusters. Results: IR light pulses completely and reversibly terminate the locally initiated APs firing at low frequencies, which leads to blocking of the synaptic transmission. However, IR light pulses only suppress but do not block the amplitude and duration of propagating APs nor locally initiated APs firing at high frequencies. Such suppressed APs do not influence the postsynaptic responses at a distance. While the suppression of AP amplitude and duration is similar for propagating and locally evoked APs, only the former exhibits a 7% to 21% increase in the maximum time derivative of the AP rising phase. Conclusions: The suppressed APs of motor axons can resume their waveforms after passing the localized IR light illumination site, leaving the muscular and synaptic responses unchanged. IR-mediated modulation on propagating and locally evoked APs should be considered as two separate models for axonal and somatic modulations.
Effects of NT5C2 germline variants on 6-mecaptopurine metabolism in children with acute lymphoblastic leukemia

6-mercaptopurine (6-MP) is widely used in the treatment of acute lymphoblastic leukemia (ALL), and its cytotoxicity is primarily mediated by thioguanine nucleotide metabolites (TGN). Recent genome-wide association study has identified germline polymorphisms (e.g., rs72846714) in the NT5C2 gene associated with 6-MP metabolism in patients with ALL. However, the full spectrum of genetic variation in NT5C2 is unclear and its impact on 6-MP drug activation has not been comprehensively examined. To this end, we performed targeted sequencing of NT5C2 in 588 children with ALL and identified 121 single nucleotide polymorphisms (SNPs) nominally associated with erythrocyte TGN during 6-MP treatment (P < 0.05). Of these, 61 variants were validated in a replication cohort of 372 children with ALL. After considering linkage disequilibrium and multivariate analysis, we confirmed two clusters of variants, represented by rs72846714 and rs58700372, that independently affected 6-MP metabolism. Functional studies showed that rs58700372 directly altered the activity of an intronic enhancer, with the variant allele linked to higher transcription activity and reduced 6-MP metabolism (lower TGN). By contrast, rs72846714 was not located in a regulatory element and instead its association signal was explained by linkage disequilibrium with a proximal functional variant rs12256506 that activated NT5C2 transcription in-cis. Our results indicated that NT5C2 germline variation significantly contributes to inter-patient variability in thiopurine drug disposition.
An Integrated Biology & Mathematics Activity to Investigate Photosynthesis & Linear Relationships

Abstract We share a STEM activity designed to integrate biology and mathematics through technology. The proposed activity was completed in two class hours (80 minutes), during which middle school students worked with LabQuest and Vernier probeware to collect data and Geogebra to graph and study data. The activity was designed to foster understanding of photosynthesis and linear relationships. With this activity we were able to effectively integrate both mathematics and biology concepts through technology and propose multiple learning modes for students through real-world context.
TMEA: A Thermodynamically Motivated Framework for Functional Characterization of Biological Responses to System Acclimation

The objective of gene set enrichment analysis (GSEA) in modern biological studies is to identify functional profiles in huge sets of biomolecules generated by high-throughput measurements of genes, transcripts, metabolites, and proteins. GSEA is based on a two-stage process using classical statistical analysis to score the input data and subsequent testing for overrepresentation of the enrichment score within a given functional coherent set. However, enrichment scores computed by different methods are merely statistically motivated and often elusive to direct biological interpretation. Here, we propose a novel approach, called Thermodynamically Motivated Enrichment Analysis (TMEA), to account for the energy investment in biological relevant processes. Therefore, TMEA is based on surprisal analysis, which offers a thermodynamic-free energy-based representation of the biological steady state and of the biological change. The contribution of each biomolecule underlying the changes in free energy is used in a Monte Carlo resampling procedure resulting in a functional characterization directly coupled to the thermodynamic characterization of biological responses to system perturbations. To illustrate the utility of our method on real experimental data, we benchmark our approach on plant acclimation to high light and compare the performance of TMEA with the most frequently used method for GSEA.
Advances in Understanding and Managing Catastrophic Ecosystem Shifts in Mediterranean Ecosystems

One of the most challenging issues in Mediterranean ecosystems to date has been to understand the emergence of discontinuous changes or catastrophic shifts. In the era of the 2030 Sustainable Development Goals, which encompass ideas around Land Degradation Neutrality, advancing this understanding has become even more critical and urgent. The aim of this paper is to synthesize insights into the drivers, processes and management of catastrophic shifts to highlight ways forward for the management of Mediterranean ecosystems. We use a multidisciplinary approach that extends beyond the typical single site, single scale, single approach studies in the current literature. We link applied and theoretical ecology at multiple scales with analyses and modeling of human–environment–climate relations and stakeholder engagement in six field sites in Mediterranean ecosystems to address three key questions: i) How do major degradation drivers affect ecosystem functioning and services in Mediterranean ecosystems? ii) What processes happen in the soil and vegetation during a catastrophic shift? iii) How can management of vulnerable ecosystems be optimized using these findings? Drawing together the findings from the use of different approaches allows us to address the whole pipeline of changes from drivers through to action. We highlight ways to assess ecosystem vulnerability that can help to prevent ecosystem shifts to undesirable states; identify cost-effective management measures that align with the vision and plans of land users; and evaluate the timing of these measures to enable optimization of their application before thresholds are reached. Such a multidisciplinary approach enables improved identification of early warning signals for discontinuous changes informing more timely and cost-effective management, allowing anticipation of, adaptation to, or even prevention of, undesirable catastrophic ecosystem shifts.
Metagenômica de um consórcio microbiano termofílico na detecção de genes de enzimas celuloliticas e expressão heteróloga em Kluyveromyces marxianus CCT 7735 (UFV-3)

xi INTRODUÇÃO GERAL . 1 CAPÍTULO 1 . 9 Applying functional metagenomics to search for novel lignocellulosic enzymes in a microbial consortium derived from a thermophilic composting phase of sugarcane bagasse and cow manure.9 ABSTRACT . 10.10 INTRODUCTION.10 MATERIALS AND METHODS.12 Total DNA Extraction and Metagenomic Fosmid Library Construction . 12 Functional Screening and Enzymatic assays . 13 Sequence and Phylogenetic Analysis of Fosmid DNA . 14 Nucleotide Sequence Accession Numbers . 15 RESULTS . 17 Metagenomic Library Screening for Clones Expressing Lignocellulolytic Activity and General Characteristics . 17 Fosmid sequence, phylogenetic and functional analyses . 17 Contig analysis of clones associated with carbohydrate hydrolysis . 19 DISCUSSION . 27 ACKNOWLEDGMENTS . 33 REFERENCES . 34 CAPÍTULO 2 . 40 Construção de um sistema de expressão para Kluyveromyces marxianus CCT 7735 (UFV-3): Obtenção de linhagem hospedeira ura3 ̄ e de vetor integrativo para clonagem e transformação . 40 RESUMO . 41 INTRODUÇÃO . 41
A Level-wise Taxonomic Perspective on Automated Machine Learning to Date and Beyond: Challenges and Opportunities

Automated machine learning (AutoML) is essentially automating the process of applying machine learning to real-world problems. The primary goals of AutoML tools are to provide methods and processes to make Machine Learning available for non-Machine Learning experts (domain experts), to improve efficiency of Machine Learning and to accelerate research on Machine Learning. Although automation and efficiency are some of AutoML's main selling points, the process still requires a surprising level of human involvement. A number of vital steps of the machine learning pipeline, including understanding the attributes of domain-specific data, defining prediction problems, creating a suitable training data set etc. still tend to be done manually by a data scientist on an ad-hoc basis. Often, this process requires a lot of back-and-forth between the data scientist and domain experts, making the whole process more difficult and inefficient. Altogether, AutoML systems are still far from a "real automatic system". In this review article, we present a level-wise taxonomic perspective on AutoML systems to-date and beyond, i.e., we introduce a new classification system with seven levels to distinguish AutoML systems based on their level of autonomy. We first start with a discussion on how an end-to-end Machine learning pipeline actually looks like and which sub-tasks of Machine learning Pipeline has indeed been automated so far. Next, we highlight the sub-tasks which are still done manually by a data-scientist in most cases and how that limits a domain expert's access to Machine learning. Then, we introduce the novel level-based taxonomy of AutoML systems and define each level according to their scope of automation support. Finally, we provide a road-map of future research endeavor in the area of AutoML and discuss some important challenges in achieving this ambitious goal.
Improving Spare Part Search for Maintenance Services using Topic Modelling

To support the decision-making process in various industrial applications, many companies use knowledge management and Information Retrieval (IR). In an industrial setting, knowledge is extracted from data that is often stored in a semi-structured or unstructured format. As a result, Natural Language Processing (NLP) methods have been applied to a number of IR steps. Inthiswork, weexplorehowNLPandparticularlytopicmodellingcanbeusedtoimprovetherelevanceofsparepartretrieval in the context of maintenance services. A proposed methodology extracts topics from short maintenance service reports that also include part replacement data. An intuition behind the proposed methodology is that every topic should represent a specific root cause. Experimental were conducted for an ad-hoc retrieval system of service case descriptions and spare parts. The results have shown that our modification improves a baseline system thus boosting the performance of maintenance service solution recommendation.
Characterizing the Complexity of Weighted Networks via Graph Embedding and Point Pattern Analysis

We propose a new metric to characterize the complexity of weighted complex networks. Weighted complex networks represent a highly organized interactive process, for example, co-varying returns between stocks (financial networks) and coordination between brain regions (brain connectivity networks). Although network entropy methods have been developed for binary networks, the measurement of non-randomness and complexity for large weighted networks remains challenging. We develop a new analytical framework to measure the complexity of a weighted network via graph embedding and point pattern analysis techniques in order to address this unmet need. We first perform graph embedding to project all nodes of the weighted adjacency matrix to a low dimensional vector space. Next, we analyze the point distribution pattern in the projected space, and measure its deviation from the complete spatial randomness. We evaluate our method via extensive simulation studies and find that our method can sensitively detect the difference of complexity and is robust to noise. Last, we apply the approach to a functional magnetic resonance imaging study and compare the complexity metrics of functional brain connectivity networks from 124 patients with schizophrenia and 103 healthy controls. The results show that the brain circuitry is more organized in healthy controls than schizophrenic patients for male subjects while the difference is minimal in female subjects. These findings are well aligned with the established sex difference in schizophrenia.
Imported Schistosomiasis: A New Public Health Challenge for China

Significantly increased imported schistosomiasis cases have been reported in China as the economy grows. The aim of this study is to review and summarize the current status, clinical features, and transmission risk of imported infections of Schistosoma mansoni and Schistosoma haematobium in China. A retrospective study was performed to review all information regarding the imported cases of schistosomiasis collected from published literature and the database of the National Notifiable Disease Report System from 1979 to 2019. The characterization of epidemiological and clinical features was analyzed. A total of 355 cases of imported schistosomiasis have been reported in 15 provinces (autonomous regions, municipalities) in China since 1979, including 78 cases of infection with S. mansoni (21.97%), 262 cases with S. haematobium (73.80%), and 15 cases with unidentified Schistosoma species. Eosinophilia was the most common sign of the infection with S. mansoni (91.03%). The parasitological findings were confirmed in 89.74% (70/78) of cases infected with S. mansoni and 32.06% (84/262) of cases infected with S. haematobium. There was no imported case of infection of Schistosoma japonicum, Schistosoma intercalatum, or Schistosoma mekongi reported in China during this period. Praziquantel is the best therapeutic drug for curing imported schistosomiasis. In addition, Biomphalaria straminea, the intermediate host of S. mansoni, has already been found in Guangdong province in south China. There is a rising risk that the existence of the intermediate host B. straminea and the imported cases of S. mansoni infection could cause the spread of the infections and make these endemic. Thus, better understanding of the clinical features and the transmission pattern of these Schistosoma infections would assist Chinese physicians in the diagnosis and treatment of these imported schistosomiasis cases.
Efeito da cabergolina no adenoma hipofisário clinicamente não funcionante e correlação dos polimorfismos do gene do receptor do glicocorticoide e da proteína AIP nos corticotropinomas

The approach of hypothalamic-pituitary diseases represents a challenge in endocrinology. In general, pathologies involving the pituitary have high morbidity, mortality rates above what is observed in the general population, and complex diagnostic and therapeutic. A better understanding of the mechanisms involved in the pathogenesis of these conditions is certainly a pathway for the development of therapeutic alternatives in this context. In the present study, we investigated the efficacy of a drug treatment modality for nonfunctioning pituitary adenoma (NFPA), and analyzed genetic aspects in correlation with tumorigenesis and phenotype in patients with corticotropinomas Cushing's disease (CD). In the investigation of the NFPA, we evaluated the efficacy of cabergoline (2mg / week) in the treatment of these tumors in a short-term follow-up (6 months). Nineteen patients (10 men and 9 women) underwent treatment at the Hospital Universitário de Brasília (HUB). Eleven of these had undergone transsphenoidal surgery (TSS) previously and 8 of them had undergone no procedure. 57.89% of the patients had tumor progression in the last year before the onset of cabergoline. After the use of this medication for 6 months, only 21% presented increased tumor volume. There was a reduction of the initial tumor volume in 78.5% of patients (significant reduction, above 25%, in 31.5% and reduction of at least 10% in 47% of patients). The Cushing's disease study included 53 patients matched in HUB and 2 genes were investigated (the glucocorticoid receptor gene GR and the aryl hydrocarbon receptor binding protein gene AIP). There is evidence that polymorphisms in the GR gene lead to vulnerability to psychiatric disorders in the general population. As Cushing's disease is extensively associated with various psychiatric disorders, and its signs and symptoms are a consequence of the chronic hypersecretion of glucocorticoids, a better understanding of the genetic mechanisms involved in the phenotypic expression of this disease may contribute, in the future, to a better conduction of these patients. The polymorphisms rs6190 (R23K) and rs41423247 (BclI) were investigated. The R23K polymorphism of the GR gene was not found among the 53 patients, however the BclI polymorphism was found at high frequency in this sample. The group with psychiatric disorder presented BclI in homozygosis significantly higher than the control group (45% and 8%, respectively).
RNA regulates Glycolysis and Embryonic Stem Cell Differentiation via Enolase 1

Cells must coordinate their metabolism and fate trajectories (1, 2), but the underlying mechanisms are only beginning to be discovered. To understand why the glycolytic enzyme enolase 1 (ENO1) binds RNA (3–6), we studied this phenomenon in vitro, in human cells, and during mouse embryonic stem cell differentiation. We find specific cellular RNA ligands that inhibit ENO1’s enzymatic activity in vitro. Increasing the concentration of these ligands in cultured cells inhibits glycolysis. We demonstrate that pluripotent stem cells expressing an ENO1 mutant that is hyper-inhibited by RNA are severely impaired in their glycolytic capacity and in endodermal differentiation, whereas cells with an RNA binding-deficient ENO1 mutant display disproportionately high endodermal marker expression. Our findings uncover ENO1 riboregulation as a novel form of metabolic control. They also describe an unprecedented mechanism involved in the regulation of stem cell differentiation. One Sentence Summary RNA directly regulates enzyme activity to control metabolism and stem cell fate
Transcranial Magnetic Stimulation as an Interventional Tool for Addiction

Repetitive transcranial magnetic stimulation (rTMS) is implemented to treat many clinical diagnoses. The most common clinical patients receiving rTMS are those suffering from treatment resistant depression (TRD). As a treatment for TRD, rTMS is thought to modulate circuits dysregulated by the disease (Fox et al., 2012). Considering many clinical populations have overlapping dysregulated circuits (Goodkind et al., 2015; McTeague et al., 2017), rTMS holds tremendous potential to treat myriad diseases. One such disease that is manifest by dysregulated circuits is substance use disorder (SUD) (Volkow et al., 2016). The dysregulation spawns from the mesocorticolimbic dopamine (MCL-DA) system and linked to initiation and maintenance of addictive behaviors (Goldstein and Volkow, 2002). Drug use increases DA release in MCL-DA system (Jay, 2003; Kelley, 2004; Nestler, 2005), which is thought to be an important element in learning, goal-directed behavior, and reward processing (Everitt and Robbins, 2005; Kalivas and O’Brien, 2008). The MCL-DA system was modulated with repeated drug exposures to increase dysregulations in SUDs. Cortical rTMSmodulates dopamine release in theMCL-DA (Strafella et al., 2001; Strafella, 2003) suggesting rTMS has therapeutic potential for clinical disorders related to DA, such as SUDs.