Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Large-Scale OMIC Data Using Self Organizing Maps

ion and decision making and to apply such ‘natural’ principles in ‘artificial’ computer algorithms. The method of ‘self-organizing maps’ (SOM, also called ‘Kohonen-maps’ according to its developer Teuvo Kohonen [1]) applies concepts of neuronal data perception to the processing of vast amounts of information. It occurs as a promising attempt to analyze molecular-biological high throughput data. The SOM method accomplishes essential tasks such as clustering, dimension reduction, multidimensional scaling and visualization. Particularly it transforms original data from the high-dimensional ‘input’ space into a low-dimensional ‘map’ space using machine learning based on artificial neuronal networks. The mapped data can be visualized in terms of two-dimensional mosaic pictures ‘portraying’ the individual visual identity of each sample. Such ‘molecular portraits’ highlight relevant intrinsic substructures in the data which can be interpreted by researchers on an intuitive basis usually without special knowledge about the particular algorithm used [2]. This makes the method very attractive for biologists and medical doctors. However SOMs are still relatively infrequently used in high-throughput bioanalytics possibly because involved bioinformaticians and statisticians are typically trained in ‘classical’ approaches for feature selection, class discovery and classification based on rigorous significance testing of single features. On the other hand, researchers with background in machine learning are often affiliated at engineering and technology departments not or only peripherally involved into life science problems. Therefore machine learning and particularly SOM are still somewhat unorthodox methods in molecular biology and health science. In consequence application of the concept of SOM learning, data transformation and visualization still require explanation and adaptation. Moreover, the SOM algorithm accomplishes ‘only’ basal sorting and visualization tasks. It needs to be supplemented with add-ons for significance testing and marker extraction, visualization of biological properties inherent in the data and finally for information mining of the biological context to become an attractive application tool in life sciences.
An Introduction to Audio Content Analysis

No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading an introduction to audio content analysis is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.
Current trends of hemagglutinin and M2 protein inhibition for the treatment of influenza using antivirals

Influenza is an annually returning illness with a great physical and economic burden. Currently, the most commonly used medication for influenza is vaccination. However, every year, a new vaccine has to be created to combat a newly formed influenza strain as a result of antigenic drift and shift. Moreover, creating new vaccines to combat new influenza strains costs money and time. Another way of treating influenza is via the use of antivirals. Production of these drugs is faster and they are not strain-specific. Currently used antivirals focus on targeting neuraminidase and cap-dependent nuclease proteins of the exit and replication phase of influenza virus, respectively. This review investigates ongoing and future research in antivirals targeting hemagglutinin and matrix protein 2 of the entry phase of influenza. The most promising upcoming antivirals are hemagglutinin inhibiting drugs, such as Arbidol, Nitazoxanide, and Urumin. More research in matrix protein 2 inhibition is still necessary, with a low number of viable candidates and low barrier of drug-resistance being the main problems.
ESPM—An Algorithm to Mine Frequent Subtrees

With the development of Internet, frequent pattern mining generalizes to more complex patterns like tree mining and graph mining Such applications arise in complex domains like Bioinformatics, web mining, etc In this paper a novel algorithm, named ESPM (expanded subtree pattern miner), is presented to discover frequent subtrees from ordered labeled trees Unlike previous works, the work of distinguishing isomorphism is left in the later part of the algorithm, which minimizes the cost of the whole process The performance of the algorithm is evaluated with experiments on synthetic and real datasets The experimental result shows that the algorithm can do the job well and is better than previous algorithms Finally the potential improvement of ESPM is mentioned
Understanding online communities by using semantic web technologies

The work presented in this chapter has been funded in part by Science Foundation Ireland under Grant No. SFI/08/CE/I1380 (Lion 2). We would also like to acknowledge the winners of the SIOC data competition whose entries were referenced in this chapter, as well as Eyal Oren and Benjamin Heitmann for their work on the SIOC Explorer.
Clinical utility of brain age prediction in stroke patients: A longitudinal study

Cognitive deficits are important predictors for outcome, independence and quality of life after stroke, but often remain unnoticed and unattended because other impairments are more evident. Computerized cognitive training (CCT) is among the candidate interventions that may alleviate cognitive difficulties, but the evidence supporting its feasibility and effectiveness is scarce, partly due to the lack of tools for outcome prediction and monitoring. Magnetic resonance imaging (MRI) provides candidate markers for disease monitoring and outcome prediction. By integrating information not only about lesion extent and localization, but also regarding the integrity of the unaffected parts of the brain, advanced MRI provides relevant information for developing better prediction models in order to tailor cognitive intervention for patients, especially in a chronic phase.nnUsing brain age prediction based on MRI based brain morphometry and machine learning, we tested the hypotheses that stroke patients with a younger-appearing brain relative to their chronological age perform better on cognitive tests and benefit more from cognitive training compared to patients with an older-appearing brain. In this randomized double-blind study, 54 patients who suffered mild stroke (>6 months since hospital admission, NIHSS<7 at hospital discharge) underwent 3-weeks CCT and MRI before and after the intervention. In addition, patients were randomized to one of two groups receiving either active or sham transcranial direct current stimulation (tDCS). We tested for main effects of brain age gap (estimated age - chronological age) on cognitive performance, and associations between brain age gap and task improvement. Finally, we tested if longitudinal changes in brain age gap during the intervention were sensitive to treatment response. Briefly, our results suggest that longitudinal brain age prediction based on automated brain morphometry is feasible and reliable in stroke patients. However, no significant association between brain age and both performance and response to cognitive training were found.
Intercropping Agricultural Practices by Improving Maize Early Growth Process: A Bio-Statistical Approach

Fertile soil pressure represents a crucial concern vis-a-vis of agricultural crop yield improvement in several southern countries. Environmental concerns and soil low fertility as well as a rapid demographic development and as well intensive industrial exploitation with regard ground resource, drastically contribute in reducing agricultural land availability. We believe that multiple culture and/or intercropping experimentation designs, integration in southern countries agricultural practices, could partially overcame fertile soil pressure issues. The main types of intercropping include mixed intercropping, row intercropping and strip intercropping. Here we assessed maize/cowpea intercropping patterns as well as maize monoculture systems. For this purpose, experimental dispositive is as following; 5 parcels including sole maize plants, 5 parcels with alternation of maize and cowpea plots on the same row (strip intercropping) and 5 parcels including maize and cowpea alternation rows (row intercropping), by a computational statistical approach with the purpose to promote mixed crops practices in contrasting fertile soil availability concerns, optimizing maize plants growth process. Growth data (plant height and plant leave number) apropos 52 maize plants for each above described experimental sites were collected during 9 weeks and processed by own R script, including descriptive and analytical statistical surveys. Findings clearly shown a positive impact of intercropping practices in accelerating maize early growth process. Maize and cowpea rows intercropping exhibited a good performance in term of accelerating maize growth process as opposite to maize monoculture (p<0.03) and maize/cowpea strip intercropping parcels (p<0.001). This study highlighted the usefulness mixed cultures intercropping system based on maize/cowpea rows alternation planting pattern in improving maize early development and as well promoted experimental design as a valuable solution in agronomical research for contrasting agricultural concerns vis-a-vis of limited productive and as well low fertile ground resources.
CETA: A Consensus Enhanced Training Approach for Denoising in Distantly Supervised Relation Extraction

Distantly supervised relation extraction aims to extract relational facts from texts but suffers from noisy instances. Existing methods usually select reliable sentences that rely on potential noisy labels, resulting in wrongly selecting many noisy training instances or underutilizing a large amount of valuable training data. This paper proposes a sentence-level DSRE method beyond typical instance selection approaches by preventing samples from falling into the wrong classification space on the feature space. Specifically, a theorem for denoising and the corresponding implementation, named Consensus Enhanced Training Approach (CETA), are proposed in this paper. By training the model with CETA, samples of different classes are separated, and samples of the same class are closely clustered in the feature space. Thus the model can easily establish the robust classification boundary to prevent noisy labels from biasing wrongly labeled samples into the wrong classification space. This process is achieved by enhancing the classification consensus between two discrepant classifiers and does not depend on any potential noisy labels, thus avoiding the above two limitations. Extensive experiments on widely-used benchmarks have demonstrated that CETA significantly outperforms the previous methods and achieves new state-of-the-art results.
Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses

Background Single-cell RNA sequencing (scRNAseq) is a powerful tool for studying complex biological systems, such as tumour heterogeneity and tissue microenvironments. However, the sources of technical and biological variation in primary solid tumour tissues and patient-derived mouse xenografts for scRNAseq, are not well understood. Here, we used low temperature (6°C) protease and collagenase (37°C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNAseq dataset comprising 128,481 cells from patient cancer tissues, patient-derived breast cancer xenografts and cancer cell lines. Results We observe substantial variation in standard quality control (QC) metrics of cell viability across conditions and tissues. From FACS sorted populations gated for cell viability, we identify a sub-population of dead cells that would pass standard data filtering practices, and quantify the extent to which their transcriptomes differ from live cells. We identify a further subpopulation of transcriptomically “dying” cells that exhibit up-regulation of MHC class I transcripts, in contrast with live and fully dead cells. From the contrast between tissue protease dissociation at 37°C or 6°C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, including FOS and JUN, induced by collagenase (37°C), which are minimized by dissociation with a cold active protease (6°C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues. We observe that the yield of cancer and non-cancer cell types varies between tissues and dissociation methods. Conclusions The method and conditions of tumour dissociation influence cell yield and transcriptome state and are both tissue and cell type dependent. Interpretation of stress pathway expression differences in cancer single cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with identification of such effects in dissociated scRNA-seq experiments.
Evaluating Word Embeddings in Extremely Under-Resourced Languages: A Case Study in Bribri

Word embeddings are critical for numerous NLP tasks but their evaluation in actual under-resourced settings needs further examination. This paper presents a case study in Bribri, a Chibchan language from Costa Rica. Four experiments were adapted from English: Word similarities, WordSim353 correlations, odd-one-out tasks and analogies. Here we discuss their adaptation to an under-resourced Indigenous language and we use them to measure semantic and morphological learning. We trained 96 word2vec models with different hyperparameter combinations. The best models for this under-resourced scenario were Skip-grams with an intermediate size (100 dimensions) and large window sizes (10). These had an average correlation of r=0.28 with WordSim353, a 76% accuracy in semantic odd-one-out and 70% accuracy in structural/morphological odd-one-out. The performance was lower for the analogies: The best models could find the appropriate semantic target amongst the first 25 results approximately 60% of the times, but could only find the morphological/structural target 11% of the times. Future research needs to further explore the patterns of morphological/structural learning, to examine the behavior of deep learning embeddings, and to establish a human baseline. This project seeks to improve Bribri NLP and ultimately help in its maintenance and revitalization.
FeatureBART: Feature Based Sequence-to-Sequence Pre-Training for Low-Resource NMT

In this paper we present FeatureBART, a linguistically motivated sequence-to-sequence monolingual pre-training strategy in which syntactic features such as lemma, part-of-speech and dependency labels are incorporated into the span prediction based pre-training framework (BART). These automatically extracted features are incorporated via approaches such as concatenation and relevance mechanisms, among which the latter is known to be better than the former. When used for low-resource NMT as a downstream task, we show that these feature based models give large improvements in bilingual settings and modest ones in multilingual settings over their counterparts that do not use features.
Speeding up Transformer Decoding via an Attention Refinement Network

Despite the revolutionary advances made by Transformer in Neural Machine Translation (NMT), inference efficiency remains an obstacle due to the heavy use of attention operations in auto-regressive decoding. We thereby propose a lightweight attention structure called Attention Refinement Network (ARN) for speeding up Transformer. Specifically, we design a weighted residual network, which reconstructs the attention by reusing the features across layers. To further improve the Transformer efficiency, we merge the self-attention and cross-attention components for parallel computing. Extensive experiments on ten WMT machine translation tasks show that the proposed model yields an average of 1.35x faster (with almost no decrease in BLEU) over the state-of-the-art inference implementation. Results on widely used WMT14 En-De machine translation tasks demonstrate that our model achieves a higher speed-up, giving highly competitive performance compared to AAN and SAN models with fewer parameter numbers.
THE INCIDENCE AND RISK FACTORS OF CONTRAST INDUCED NEPHROPATHY IN PATIENTS UNDERGOING CORONARY ANGIOPLASTY.

Background: Contrast induced nephropathy remains important cause of hospital-acquired acute kidney injury and affects between 2% of the general population to 50% of high-risk subgroups following coronary intervention. Objectives: To determine the incidence and to study the various risk factors associated with CIN. Patients and Methods: In our study, total of 212 patients (154 males and 58 females) who received non ionic iso osmolar contrast media during percutaneous transluminal coronary intervention (PCI) were included. We diagnosed CIN by a relative increase of >25% or an absolute increase of >0.5 mg/dl in serum creatinine levels two days after procedure. Results: The incidence of CIN in our study was 15.1%. In univariate analysis: age >75 years, hypertention diabetes mellitus (DM), heart failure, hypotension, CKD [creatinine >1.5mg/dl] and increased contrast volume were associated with increased risk of CIN whereas, gender, anemia and use of IABP failed to reach statistical significance. As the number of risk factor and/or volume of contrast increases, the incidence of CIN increases exponentially. Conclusion: Since there is no established treatment for CIN, all possible efforts should be taken to prevent it from occurring by identifying patients at risk. CIN is related with many risk factors, so whenever multiple risk factors are present in an individual patient, lowest dose of contrast should be used and adequate hydration should be given. Key Words: Contrast-induced nephropathy, Non ionic iso-osmolar contrast media, percutaneous coronary intervention.
LKB1 negatively regulates AKT1 signaling via DBC1 and TRB3

DBC1 plays a critical role in various cellular functions notably cell proliferation, transcription, histone modification and adipogenesis. Current reports about the role of DBC1 in tumorigenesis are paradoxical and designate DBC1 both as a tumor suppressor or an oncogene. Here, using small T antigen of polyoma virus (PyST) as a tool, we have delineated a signaling mechanism that connects LKB1 to AKT1 via DBC1. We report that PyST associates with DBC1 and leads to its down-regulation. Our results also show that PyST expression promotes LKB1 activation which in turn leads to in the downregulation of DBC1 protein. Absence of DBC1 results in transcriptional upregulation and consequently enhanced protein levels of TRB3. TRB3 sequesters AKT1, and consequently the phosphorylation and activity of AKT1 is compromised. This ultimately results in inactivation of pro-survival pathways triggered via AKT1 signaling. Our studies thus provide an insight into a signaling pathway that connects LKB1, DBC1, TRB3 and AKT1.
Effects of dietary probiotic
 Lactobacillus plantarum
 and whey protein concentrate on the productive parameters, immunity response and susceptibility of Nile tilapia,
 Oreochromis niloticus
 (L.), to
 Aeromonas sobria
 infection

Immunostimulatory feed supplements have an increasingly interest in aquaculture management. Generally, an individual supplement was used in fish diets but it is expected that the use of multi‐supplements may show synergistic enhancements in fish performance, health, and immunity. Therefore, the present investigation was carried out to evaluate the use of dietary probiotic Lactobacillus plantarum and whey protein concentrate (WPC) in practical diets for Nile tilapia, Oreochromis niloticus. Hence, probiotic L. plantarum, WPC and their mixture were incorporated into a basal fish diet (300 g/kg crude protein) as follows: T1 = a basal control diet, T2 = a basal diet containing L. plantarum, T3 = a basal diet containing 1.0 g WCP/kg diet and T4, T5 or T6 = basal diets containing probiotic L. plantarum + 1.0, 2.0 or 3.0 g WCP/kg diet, respectively. Fish (15.2 ± 0.6 g) were fed on one of the tested diets up to apparent satiation twice a day for 60 days. After that, fish were intraperitoneally injected with pathogenic bacteria Aeromonas sobria and fish mortality was observed for 10 days postchallenge. Fish growth and feed intake were significantly improved by dietary probiotic L. plantarum (T2) and/or WPC (T3) over the control group (T1), and highest fish performance was observed in T5–T6 fish groups. Similarly, highest values of haematocrit, glucose, total proteins, albumin, and globulin were significantly observed in T5–T6 fish groups. Likewise, fish fed dietary probiotic L. plantarum (T2), WPC (T3), and their mixture (T4–T6) showed antioxidants and immune‐stimulating activities better than the control group. Fish fed the control diet were more susceptible to A. sobria infection showing highest fish mortality (75.0%). Meanwhile, dietary probiotic L. plantarum (T2), WPC (T3), and their mixture (T4–T6) enhanced significantly the fish resistance to A. sobria infection resulting in maximum values of relative percent of fish survival (73.3%–80.0%) in T5–T6 groups. The present investigation recommended the use of probiotic L. plantarum with 2.0 g WPC/kg diet to improve the growth, antioxidant, immunity responses and tolerance of Nile tilapia to A. sobria infection.
Morphological variation, ultrastructure, pigment composition and phylogeny of the star-shaped dinoflagellate Asterodinium gracile (Kareniaceae, Dinophyceae)

ABSTRACT Morphological variation, ultrastructure, pigment composition and phylogeny of an oceanic, star-shaped, unarmored dinoflagellate Asterodinium gracile were examined using a culture established from Nagasaki, Japan, in 2017. Our examinations were based on LM, SEM, TEM, high-performance liquid chromatography (HPLC) and molecular phylogeny inferred from nuclear-encoded internal transcribed spacer (ITS) and 28S rDNA and chloroplast-encoded 16S rDNA sequences. Typical cells were stellate, possessing an apical, two lateral, and two antapical extensions. Cells ranged in size from 62.5 to 105.6 µm long and 21.2 to 39.5 µm in cingular width. The length and orientation of cellular extensions were variable; that is, two lateral extensions were usually symmetrically bent or retracted. Ellipsoidal cells without extensions were also observed. The apical structure complex was straight or slightly sigmoid. A spherical nucleus was located at the left side in the hyposome. The numerous discoid, yellowish-brown chloroplasts were variable in size and distributed also in the cell extensions. Among Kareniaceae, the lack of a pyrenoid in Asterodinium is unique. Nuclear-encoded ITS and 28S phylogenies suggested that A. gracile is closely related to Karenia papilionacea in the Kareniaceae, but its affinity to Brachidinium capitatum, another species with extensions, was unclear. A phylogeny of 16S rDNA showed that chloroplasts of A. gracile are related to other kareniaceans derived from a haptophyte. These chloroplasts were of the fucoxanthin type, composed of pigments similar to those in other Kareniaceae; however, the detection of 19′-hexanoyloxyfucoxanthin as a major carotenoid, the low amount of fucoxanthin, and lack of 19′-butanoyloxyfucoxanthin in A. gracile were unusual for the family.