Whole Genome Sequencing Assembly and Annotation of Halobacillus Trueperi S61 Isolated From the Qarhan Salt Lake

Background: Halophilic microbial as prospective resources of biotechnology due to the advantages of flexible survivability. Qarhan Salt Lake is the second largest Salt Lake in the world which contains rich-unique extremophiles and deserved in-depth exploration. Results: Present study first time isolated novel strain Halobacillus trueperi S61 from Qarhan Salt Lake and performed whole-genome sequencing through combined third-generation PacBio and second-generation Illumina technology. The whole genome of Halobacillus trueperi S61 identified 57549 total reads and consists a complete circular chromosome of 4047887 bp with 43.86% GC content without gaps. Total number of 139 non-coding RNA (included 86 tRNA, 30 rRNA and 23 sRNA), 16 gene islands with 260275 bp and two prophages (with 82682 length) were predicted. In addition, the whole genome of Halobacillus trueperi S61 summarized basic annotation for 3982 protein-coding genes, 3980, 3667, 2998 and 2303 unigenes were annotated with Nr, Swissport, KOG and KEGG database. Combined with advanced analysis, 561 carbohydrate enzymes and 4416 pathogen host interactions related genes were identified. The protein function of Halobacillus trueperi S61 was mainly focus on biological processes, and the protein function was mainly distributed in gene transcription and amino acids, and carbohydrates metabolism. Conclusions: The complete whole genome sequence assembly and annotation of novel strain Halobacillus trueperi S61 isolated from Qarhan Salt Lake mainly focus on protein biological processes and antibiotic resistance, provides a potential resource for biotechnology.

Predicting Students’ Performance In Basic Algorithms Programming In an E-Learning Environment Using Decision Tree Approach

Predicting the performance of students plays an important role in every institution to protect their students from failures and leverage their quality in higher education. Algorithm and Programming is a fundamental course for the students who start their studies in Informatics. Hence, the scope of this research is to identify the critical attributes which influence student performance in the E-learning Environment on Moodle LMS (Learning Management System) Platform and its accuracy. Data mining helps the process of preprocessing data in a dataset from raw data to quality data for advanced analysis. Dataset set is consisting of student academic performance such as grades of Quizzes, Mid exams, Final exams, and Final projects. Moreover, the dataset from LMS is considered as well in the process of modeling, in terms of constructing the decision tree, such as punctuality submission of Quizzes, Assignments, and Final Projects. Regarding the Basic Algorithm and Programming course, which is separated into two subjects in the first and second semester, thus the research will predict the student performance in the Basic Algorithm and programming course in the second semester based on the Introduction to programming course in the first semester. Decision Tree techniques are applied by using information gain in ID3 algorithm to get the important feature which is the PP index has the highest information gain with value 0.44, also the accuracy between ID3 and J48 algorithm that shows ID3 has the highest accuracy of modeling which is 84.80% compared to J48 82.34%.

Identification of an early-stage Parkinson’s disease neuromarker using event-related potentials, brain network analytics and machine-learning

Objective The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson’s disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms. Background Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD. Methods Nineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation. Results The ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task). Conclusions This pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.

A replication study separates polymorphisms behind migraine with and without depression

The largest migraine genome-wide association study identified 38 candidate loci. In this study we assessed whether these results replicate on a gene level in our European cohort and whether effects are altered by lifetime depression. We tested SNPs of the loci and their vicinity with or without interaction with depression in regression models. Advanced analysis methods such as Bayesian relevance analysis and a neural network based classifier were used to confirm findings. Main effects were found for rs2455107 of PRDM16 (OR = 1.304, p = 0.007) and five intergenic polymorphisms in 1p31.1 region: two of them showed risk effect (OR = 1.277, p = 0.003 for both rs11209657 and rs6686879), while the other three variants were protective factors (OR = 0.4956, p = 0.006 for both rs12090642 and rs72948266; OR = 0.4756, p = 0.005 for rs77864828). Additionally, 26 polymorphisms within ADGRL2, 2 in REST, 1 in HPSE2 and 33 mostly intergenic SNPs from 1p31.1 showed interaction effects. Among clumped results representing these significant regions, only rs11163394 of ADGRL2 showed a protective effect (OR = 0.607, p = 0.002), all other variants were risk factors (rs1043215 of REST with the strongest effect: OR = 6.596, p = 0.003). Bayesian relevance analysis confirmed the relevance of intergenic rs6660757 and rs12128399 (p31.1), rs1043215 (REST), rs1889974 (HPSE2) and rs11163394 (ADGRL2) from depression interaction results, and the moderate relevance of rs77864828 and rs2455107 of PRDM16 from main effect analysis. Both main and interaction effect SNPs could enhance predictive power with the neural network based classifier. In summary, we replicated p31.1, PRDM16, REST, HPSE2 and ADGRL2 genes with classic genetic and advanced analysis methods. While the p31.1 region and PRDM16 are worthy of further investigations in migraine in general, REST, HPSE2 and ADGRL2 may be prime candidates behind migraine pathophysiology in patients with comorbid depression.

Case studies on seismic assessment of historical buildings using advanced analysis

Seismic risk mitigation in existing buildings requires an engineering assessment of the current condition and expected seismic performance and an identification of possible deficiencies that should be addressed. For heritage and historical buildings in particular, there is significant benefit in using the most detailed analysis methods available to avoid the conservatism inherent in simpler methods and thereby minimise unnecessary interventions and more precisely pinpoint where strengthening is required. On recent heritage projects, Arup has used the analysis software LS-DYNA and a new material model, calibrated against experimental tests on unreinforced masonry components and buildings to carry out (or supplement) seismic assessments. The analysis method (non-linear response history analysis) is not new, but its application on detailed finite-element models of complex historic structures has previously been computationally prohibitive and requires significant analyst experience to deliver reliable results. This paper summarises three of these recent Arup projects: Woltersum Church (Netherlands), Procuratie Vecchie (Venice) and a building cluster in the historical centre of Appingedam (Netherlands). The case studies show that these analyses allow complex features of seismic performance to be considered, such as damage or modifications to the building over time, pounding (separate buildings colliding into one another due to seismic movements) and load sharing between adjacent structures.

The Placental Innate Immune System Is Altered in Early-Onset Preeclampsia, but Not in Late-Onset Preeclampsia

Preeclampsia is a severe placenta-related pregnancy disorder that is generally divided into two subtypes named early-onset preeclampsia (onset <34 weeks of gestation), and late-onset preeclampsia (onset ≥34 weeks of gestation), with distinct pathophysiological origins. Both forms of preeclampsia have been associated with maternal systemic inflammation. However, alterations in the placental immune system have been less well characterized. Here, we studied immunological alterations in early- and late-onset preeclampsia placentas using a targeted expression profile approach. RNA was extracted from snap-frozen placenta samples (healthy n=13, early-onset preeclampsia n=13, and late-onset preeclampsia n=6). The expression of 730 immune-related genes from the Pan Cancer Immune Profiling Panel was measured, and the data were analyzed in the advanced analysis module of nSolver software (NanoString Technology). The results showed that early-onset preeclampsia placentas displayed reduced expression of complement, and toll-like receptor (TLR) associated genes, specifically TLR1 and TLR4. Mast cells and M2 macrophages were also decreased in early-onset preeclampsia compared to healthy placentas. The findings were confirmed by an immunohistochemistry approach using 20 healthy, 19 early-onset preeclampsia, and 10 late-onset preeclampsia placentas. We conclude that the placental innate immune system is altered in early-onset preeclampsia compared to uncomplicated pregnancies. The absence of these alterations in late-onset preeclampsia placentas indicates dissimilar immunological profiles. The study revealed distinct pathophysiological processes in early-onset and late-onset preeclampsia placentas and imply that a tailored treatment to each subtype is desirable.

Exergy, Exergoeconomic, Exergoenvironmental, Emergy-based Assessment and Advanced Exergy-based Analysis of an Integrated Solar Combined Cycle Power Plant

The high amount of solar energy as clean and sustainable energy has increased awareness in solar energy concentration, especially in integrated concepts. One of the best and promising hybrid configurations for converting solar energy into power is an integrated solar combined cycle system (ISCCS). In this study, conventional and advanced analysis tools for the ISCCS located in Yazd (Iran) have been investigated. In this paper, thermodynamic simulation, exergy, exergoeconomic, and exergoenvironmental analysis based on Life Cycle Assessment (LCA) have been performed. In addition, an emergy-based concept, including emergoeconomic and emergoenvironmental assessment, has been performed. In-depth analysis of exergy, exergoeconomic, and exergoenvironmental modelling, advanced exergy analysis based on endogenous/exogenous and avoidable/unavoidable parts have been done. In this regard, MATLAB code has been developed for thermodynamic simulation, exergy, exergoeconomic, exergoenvironment, emergoeconomic and emergoenvironment analysis. Furthermore, THERMOFLEX (commercial software) applied for thermodynamic simulation and verification. The Sankey diagram based on each analysis tool has been constructed. Furthermore, the priority of improvement based on each analysis has been identified. The thermal efficiency and net power generation of ISCCS are 48.25% and 419600 kW, respectively. It was obsereved that in most equipment, less than 10% of exergy destruction and cost and environmental impact rates were avoidable/endogenous.

Heart age estimated using explainable advanced electrocardiography

BackgroundElectrocardiographic (ECG) Heart Age conveying cardiovascular risk has been estimated by both Bayesian and artificial intelligence approaches. We hypothesized that explainable measures from the 10-second 12-lead ECG could successfully predict Bayesian ECG Heart Age.MethodsAdvanced analysis was performed on ECGs from healthy subjects and patients with cardiovascular risk or proven heart disease. Regression models were used to predict a Bayesian 5-minute ECG Heart Age from the standard resting 10-second 12-lead ECG. The difference between 10-second ECG Heart Age and chronological age was compared.ResultsIn total, 2,771 subjects were included (n=1682 healthy volunteers, n=305 with cardiovascular risk factors, n=784 with cardiovascular disease). Overall, 10-second Heart Age showed strong agreement with the 5-minute Heart Age (R2=0.94, p<0.001, mean±SD bias 0.0±5.1 years). The difference between 10-second ECG Heart Age and chronological age was 0.0±5.7 years in healthy individuals, 7.4±7.3 years in subjects with cardiovascular risk factors (p<0.001), and 14.3±9.2 years for patients with cardiovascular disease (p<0.001).ConclusionsECG Heart Age can be accurately estimated from a 10-second 12-lead ECG in a transparent and explainable fashion based on known ECG measures, without artificial intelligence techniques. The difference between ECG Heart Age and chronological age increases markedly with cardiovascular risk and disease.

Comprehensive Imaging Characterization of Colorectal Liver Metastases

Colorectal liver metastases (CRLM) have heterogenous histopathological and immunohistochemical phenotypes, which are associated with variable responses to treatment and outcomes. However, this information is usually only available after resection, and therefore of limited value in treatment planning. Improved techniques for in vivo disease assessment, which can characterise the variable tumour biology, would support further personalization of management strategies. Advanced imaging of CRLM including multiparametric MRI and functional imaging techniques have the potential to provide clinically-actionable phenotypic characterisation. This includes assessment of the tumour-liver interface, internal tumour components and treatment response. Advanced analysis techniques, including radiomics and machine learning now have a growing role in assessment of imaging, providing high-dimensional imaging feature extraction which can be linked to clinical relevant tumour phenotypes, such as a the Consensus Molecular Subtypes (CMS). In this review, we outline how imaging techniques could reproducibly characterize the histopathological features of CRLM, with several matched imaging and histology examples to illustrate these features, and discuss the oncological relevance of these features. Finally, we discuss the future challenges and opportunities of CRLM imaging, with a focus on the potential value of advanced analytics including radiomics and artificial intelligence, to help inform future research in this rapidly moving field.