Miracle-dz/UniFAR-FTUs huggingface dataset:
Species-specific DNA Markers for Improving the Genetic Management of Tilapia

The tilapias are a group of African and Middle Eastern cichlid fish that are widely cultured in developed and developing countries. With many different species and sub-species, and extensive use of interspecies hybrids, identification of tilapia species is of importance in aquaculture and in wild populations where introductions occur. This research set out to distinguish between tilapia species and sub-species by retrieving species-specific nuclear DNA markers (SNPs) using two approaches: (i) sequencing of the coding regions of the ADA gene; and (ii) next-generation sequencing, both standard RADseq and double-digest RADseq (ddRADseq). The mitochondrial DNA (mtDNA) marker cytochrome c oxidase subunit I (COI) was used to verify tilapia species status. ADA gene sequence analysis was partially successful, generating SNP markers that distinguished some species pairs. Most species could also be discriminated using the COI sequence. Reference based analysis (RBA: using only markers found in the O. niloticus genome sequence) of standard RADseq data identified 1,613 SNPs in 1,002 shared RAD loci among seven species. De novo based analysis (DBA: based on the entire data set) identified 1,358 SNPs in 825 loci and RBA detected 938 SNPs in 571 shared RAD loci from ddRADseq among 10 species. Phylogenetic trees based on shared SNP markers indicated similar patterns to most prior phylogenies based on other characteristics. The standard RADseq detected 677 species-specific SNP markers from the entire data set (seven species), while the ddRADseq retrieved 38 (among ten species). Furthermore, 37 such SNP markers were identified from ddRADseq data from a subset of four economically important species which are often involved in hybridization in aquaculture, and larger numbers of SNP markers distinguished between species pairs in this group. In summary, these SNPs are a valuable resource in further investigating hybridization and introgression in a range of captive and wild stocks of tilapias.
Effects of Abscisic Acid and its Esters on Stomatal Aperture and the Transpiration Ratio

A single surface application of abscisic acid or its methyl and phenyl esters suppressed stomatal opening on leaves of Xanthium strumarium. The effect was restricted to the treated parts of the leaf blades, there being no detectable translocation to untreated parts. There were no increases in CO2 compensation to which stomatal closure could be attributed. Abscisic acid and its esters acted successfully as antitranspirants when applied once to leaf surfaces of young barley plants. Over a 9-day period there was a reduction of about 50% in the amount of water transpired without any detectable reduction in the rate of dry weight increase. The treatments reduced transpiration relatively more than dry matter accumulation, and hence there was an increase in the water use efficiency. The effect of the treatments became progressively less over 9 days, but even at the end of the experiment (day 9) both the esters reduced transpiration by 20–25%. The esters were slightly more effective than abscisic acid itself. On the basis of the data presented here, field trials of the antitranspirant properties of these compounds are recommended.
Application of Electronic Nose for Diagnosing Azotemia from Urinalysis Using Principal Component Analysis

Azotemia is a disease that is easy to be treated, but when left undiagnosed and untreated could lead to more fatal diseases like those of Kidney failure or Renal Diseases. Azotemia is a common disease; however, the lack of information from urinalysis showed to be one of the problems, detecting Azotemia thru urinalysis could prevent more hazardous diseases to develop. This study aims to gather more information about Azotemia through urinalysis using Electronic Nose and help diagnose Azotemia faster using urinalysis. Based from the result, The Electronic Nose system proved that there is a difference on the odor of a Healthy Urine sample and an Azotemic Urine sample. The Electronic Nose is made of common MQ Gas sensors and Raspberry Pi for its hardware, while for its software the system used Principal Component Analysis. Using the E-Nose chamber the system gathers data from the pre-classified urine sample provided by the hospital to test and differentiate the Azotemic urine and Healthy urine. The result shows that there are four sensors that are very responsive to the system namely: Combustible Gas, Carbon Monoxide, Hydrogen Gas and Air Quality. Based from the result the system has an accuracy of 90% and an error rate of 10%. In conclusion, the researches successfully developed a system that can differentiate Healthy Urine to an Azotemic Urine sample using Electronic Nose System with Principal Component Analysis.
Mutant glucocorticoid receptor binding elements on the interleukin-6 promoter regulate dexamethasone effects

Background Glucocorticoids (GCs) have been extensively used as essential modulators in clinical infectious and inflammatory diseases. The GC receptor (GR) is a transcription factor belonging to the nuclear receptor family that regulates anti-inflammatory processes and releases pro-inflammatory cytokines, such as interleukin (IL)-6. Results Five putative GR binding sites and other transcriptional factor binding sites were identified on theIL-6 promoter, and dexamethasone (DEX) was noted to reduce the lipopolysaccharide (LPS)-induced IL-6 production. Among mutant transcriptional factor binding sites, nuclear factor-kappa B (NF-κB), activator protein (AP)-1, and specificity protein (Sp)1–2 sites reduced basal and LPS-induced IL-6 promoter activities through various responses. The second GR binding site (GR2) was noted to play a crucial role in both basal and inducible promoter activities in LPS-induced inflammation. Conclusions We concluded that selective GR2 modulator might exert agonistic and antagonistic effects and could activate crucial signaling pathways during the LPS-stimulated inflammatory process.
Improving classification performance of hybrid EEG-fNIRS BCI system by channel optimization

Hybrid brain-computer interface system (hBCIs) is an emerging technique that addresses the limitations of uni-modal EEG and fNIRS-based BCIs. Improvements have been shown through hBCIs in recent studies; however, there still exists a void to fill. Some recent studies have shown improvements in classification accuracies and the number of control commands, however, no study has so far addressed the issue of optimization of channels for EEG-fNIRS hBCIs. In this study, a novel technique for the determination of optimal features and optimal channels is discussed to get maximum classification performance for a hybrid EEG-fNIRS system. Motor-imagery (MI) signals are obtained from the motor cortex, and butter-worth bandpass filtering is applied to remove physiological noises. Five basic statical features (mean, peak, variance, slope, and kurtosis) are obtained from oxygenated hemoglobin (HbO) signal for fNIRS. Optimal temporal features were selected by applying the Principle Component Analysis (PCA) algorithm. 10-fold cross-validation is used for the Medium Gaussian Support Vector Machine (MGSVM) classifier and maximum average accuracy of 79.86 ± 5.57% is improved to 85.58 ± 5.42% for 18 subjects for fNIRS, and the computational cost is reduced significantly.
DeepFRET: Rapid and automated single molecule FRET data classification using deep learning

Single molecule Förster Resonance energy transfer (smFRET) is a mature and adaptable method for studying the structure of biomolecules and integrating their dynamics into structural biology. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and fully automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histogram of biomolecule behavior, is a user-adjustable quality threshold. Integrating all standard features of smFRET analysis, DeepFRET will consequently output common kinetic information metrics for biomolecules. We validated the utility of DeepFRET by performing quantitative analysis on simulated, ground truth, data and real smFRET data. The accuracy of classification by DeepFRET outperformed human operators and current commonly used hard threshold and reached >95% precision accuracy only requiring a fraction of the time (<1% as compared to human operators) on ground truth data. Its flawless and rapid operation on real data demonstrates its wide applicability. This level of classification was achieved without any preprocessing or parameter setting by human operators, demonstrating DeepFRET’s capacity to objectively quantify biomolecular dynamics. The provided a standalone executable based on open source code capitalises on the widespread adaptation of machine learning and may contribute to the effort of benchmarking smFRET for structural biology insights.
Understanding Mycobacterium avium subspecies hominissuis microaggregate mediated pathogenesis

Mycobacterium avium subspecies hominissuis (MAH) is an opportunistic pathogen and causes nontuberculous infections in immune compromised individuals, an emerging problem that has been recognized worldwide. Understanding the pathogenesis of this organism is important as better treatment and prevention options are needed. Microaggregates form when two or more bacterial cells join at a surface. MAH forms micgroaggregates to promote its entry in to epithelial cells and cause infection. The mechanisms involved in the interaction between the microaggregate and the host are becoming clearer as the molecules involved in this process are being uncovered. Microaggregate Invasion Protein-1 (MIP-1) is now described as having a major role in the invasion of epithelial cells by MAH.
Spatial augmented reality: a tool for operator guidance and training evaluated in five industrial case studies

Spatial Augmented Reality (sAR) as an assistive technology is a promising tool for experienced and novice industrial workers. Five industrial case studies in Dutch manufacturing companies are described to study the effects of sAR assistance on task completion time, learning speed, product quality, work load, technology acceptance and employability in manual assembly guidance and training. Although case study outcomes were rather positive and user acceptance was high, 2 out of 5 use case companies decided not to invest in this technology after the initial pilot project. The main barriers for implementation were concerns about the relatively high system costs, the initial instruction programming time and the required expertise to do so. Future system developments should improve the system's usability from a business process engineering perspective and thereby support zero programming of sAR systems and adaptive work instructions.
A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation

Background: Stroke is a disease with a high associated disability burden. Robotic-assisted gait training offers an opportunity for the practice intensity levels associated with good functional walking outcomes in this population. Neural interfacing technology, electroencephalography (EEG), or electromyography (EMG) can offer new strategies for robotic gait re-education after a stroke by promoting more active engagement in movement intent and/or neurophysiological feedback. Objectives: This study identifies the current state-of-the-art and the limitations in direct neural interfacing with robotic gait devices in stroke rehabilitation. Methods: A pre-registered systematic review was conducted using standardized search operators that included the presence of stroke and robotic gait training and neural biosignals (EMG and/or EEG) and was not limited by study type. Results: From a total of 8,899 papers identified, 13 articles were considered for the final selection. Only five of the 13 studies received a strong or moderate quality rating as a clinical study. Three studies recorded EEG activity during robotic gait, two of which used EEG for BCI purposes. While demonstrating utility for decoding kinematic and EMG-related gait data, no EEG study has been identified to close the loop between robot and human. Twelve of the studies recorded EMG activity during or after robotic walking, primarily as an outcome measure. One study used multisource information fusion from EMG, joint angle, and force to modify robotic commands in real time, with higher error rates observed during active movement. A novel study identified used EMG data during robotic gait to derive the optimal, individualized robot-driven step trajectory. Conclusions: Wide heterogeneity in the reporting and the purpose of neurobiosignal use during robotic gait training after a stroke exists. Neural interfacing with robotic gait after a stroke demonstrates promise as a future field of study. However, as a nascent area, direct neural interfacing with robotic gait after a stroke would benefit from a more standardized protocol for biosignal collection and processing and for robotic deployment. Appropriate reporting for clinical studies of this nature is also required with respect to the study type and the participants' characteristics.
Blood Vessel Extraction and Optic Disk Localization for Diabetic Retinopathy

This paper presents methods of vascular extraction and optic disk localization in the retinal images. Our approach begins with preprocessing to improve the quality of blood vessels. In the next step, a matrix filter was applied to express blood vessels. Finally, the blood vessel structure was used to estimate the location of the optic disk. The proposed method was tested on all different forty retinal images from the DRIVE database, which public retinal image dataset. The results of vessel extraction were compared with the ground truth image. The error of vascular extraction showed that the average sensitivity and accuracy were 79.81% and 94.98%, respectively. The optic disk localization achieved 97.5%.
CRISPR-finder: A high throughput and cost effective method for identifying successfully edited A. thaliana individuals

Background Genome editing with the CRISPR/Cas9 system allows the user to mutate a targeted region of the genome using an endonuclease (Cas9) and an artificial single-guide RNA (sgRNA). Both because of variable efficiency with which such mutations arise and because the repair process produces a spectrum of mutations, one needs to ascertain the genome sequence at the targeted locus for many individuals that have been subjected to CRISPR/Cas9 mutagenesis. This process can be laborious, expensive and inefficient with conventional methods such as the T7E1 assay or Sanger sequencing. An alternative comprises methods for amplicon sequencing, but most available protocols do not include a facile way for high throughput generation of the samples for sequencing. Results In this study we provide a full pipeline based on amplicon sequencing, CRISPR-finder. We provide a complete protocol for the generation of amplicons up until the identification of the exact mutations in the targeted region. CRISPR-finder can be used to process thousands of individuals in a single sequencing run. For example, we were able to analyze in one sequencing reaction over 900 Arabidopsis thaliana individuals whose genomes had been targeted with the CRISPR/Cas9 system. Conclusions In order to validate the potential of CRISPR-finder, we targeted the ISOCHORISMATE SYNTHASE 1 gene in A. thaliana using the CRISPR/Cas9 system. We successfully identified a mutant line in which the production of salicylic acid was impaired compared to the wild type, as expected. These features establish CRISPR-finder as a high-throughput, cost-effective and -efficient genotyping method of individuals whose genomes have been targeted using the CRISPR/Cas9 system.
Factor Structure and Psychometric Properties of the Body Perception Questionnaire–Short Form (BPQ-SF) Among Chinese College Students

Objective Body perception, including body awareness and reactivity, is featured in a range of mental health conditions. However, research on Chinese questionnaires assessing body perception has been surprisingly absent. The present study aimed to investigate the factor structure and psychometric properties of the Body Perception Questionnaire–Short Form (BPQ-SF) among Chinese. Methods the current sample included 688 Chinese college students. Self-report scales were used to measure body perception, somatization, and depressive somatic and psychological symptoms. Results Confirmatory factor analysis supported a three-factor model of the BPQ-SF, involving body awareness, supradiaphragmatic reactivity, and subdiaphragmatic reactivity. Good internal consistency and test–retest reliability were observed. Convergent validity was established by significant correlations with scores of somatization and somatic symptoms of depression. Divergent validity was evidenced by non-significant association with ratings on psychological symptoms of depression. The very short form of the body awareness subscale of BPQ can be an alternative to the body awareness subscale when scale length is the priority. Conclusion The BPQ-SF possessed three latent factors and demonstrated good psychometric properties that can measure body perception among Chinese in a reliable and valid way.
Investigating the accuracy of imputed genotypes in Nellore cattle using the ARS-UCD1.2 assembly of the bovine genome

Background Imputation accuracy among other things depends on the size of the reference panel, the marker’s minor allele frequency (MAF), and the correct placement of variants on the reference genome assembly. Using high-density genotypes of 3938 Nellore cattle from Brazil, we investigated the accuracy of imputation from 50K to 777K SNP density, using map positions determined according to the bovine genome assemblies UMD3.1 and ARS-UCD1.2. We assessed the effect of reference and target panel sizes on the pre-phasing-based imputation quality using ten-fold cross-validation. Further, we compared the reliability of the model-based imputation quality score (Rsq) from Minimac3 to empirical imputation accuracy. Results The overall accuracy of imputation measured as the squared correlation between true and imputed allele dosages (R2dose) was virtually identical using either the UMD3.1 or ARS-UCD1.2 genome assembly. When the size of the reference panel increased from 250 to 2000, R2dose increased from 0.845 to 0.917, and the number of polymorphic markers in the imputed data set increased from 586,701 to 618,660. Advantages in both accuracy and marker density were also observed when larger target panels were imputed, likely resulting from more accurate haplotype inference. Imputation accuracy and the marker density in the imputed data increased from 0.903 to 0.913 and from 593,239 to 595,570 when haplotypes were inferred in 500 and 2900 target animals, respectively. The model-based imputation quality scores from Minimac3 (Rsq) were highly correlated to but systematically higher than empirically estimated accuracies. The correlation between these metrics increased with the size of the reference panel and MAF of imputed variants. Conclusions Accurate imputation of BovineHD BeadChip markers is possible in Nellore cattle using the new bovine reference genome assembly ARS-UCD1.2. The use of large reference and target panels improves the accuracy of the imputed genotypes and provides genotypes for more markers segregating at low frequency for downstream genomic analyses. The model-based imputation quality score from Minimac3 (Rsq) can be used to detect poorly imputed variants but its reliability depends on the size of the reference panel used and MAF of the imputed variants.
The distributed Gauss-Newton methods for solving the inverse of approximated Hessian with application to target localization

Gauss-Newton method is a Hessian free second-order Newton's optimization method, which can be used to solve the common nonlinear least squares (NLLS) problems. In this paper, we consider the collaborative target localization problem in a wireless sensor network with a random topology structure. By using a specialized decomposition technique, the centralized Gauss-Newton method is modified as a distributed solution by exchanging locally information with the neighboring anchor nodes. In the recent diffusion Gauss-Newton algorithm, the main difficulty is the computation of inverse because the approximated Hessian is not always full column rank. To address this problem, we propose that two methods can be used to approximate the inverse matrix, i.e., Levenberg-Marquardt parameter and Moore-Penrose inverse methods. The former in essence is a damped iteration method, while the latter depends largely on the adjustment of step size. In addition to proposing the methods above for diffusion Gauss-Newton algorithm, we also compare their performance to solve the collaborative localization in wireless sensor networks, which can be modeled as a NLLS problem. Simulation results show both of them are applicable to diffusion GN, and LM method has some slight advantages in these respects such as resistance to environment noise while guaranteeing convergence, and dynamical increasing/decreasing to step size.
Neuromodulation of Gamma-Range Auditory Steady-State Responses: A Scoping Review of Brain Stimulation Studies

Neural oscillations represent a fundamental mechanism that enables coordinated action during normal brain functioning. Auditory steady-state responses (ASSRs) are used to test the ability to generate gamma-range activity. Different non-invasive brain stimulation (NIBS) techniques have the potential to modulate neural activation patterns that are aberrant in a variety of neuropsychiatric disorders. Here, we summarize the current state of knowledge on how different methods of NIBS (transcranial altering current stimulation—tACS, transcranial direct current stimulation—tDCS, transcranial random noise stimulation—tRNS, paired associative stimulation—PAS, repetitive transcranial magnetic stimulation—rTMS) affect the gamma-range ASSRs in both healthy and clinical populations. We show that the current research has been far from systematic and methodologically heterogeneous. Nevertheless, some brain stimulation techniques, especially tACS and rTMS show strong potential for further exploration. We outline the main findings and provide directions for further research into neuromodulation of ASSRs as a promising biomarker of different psychopathological conditions such as schizophrenia, bipolar disorder, attention deficit hyperactivity disorder (ADHD), autism.