Characterization of quaternary chalcogenide As-Ge-Te-Si thin films

Investigated in this paper is the effect of replacement of Te by Si on the optical gap and some other physical operation parameters of quaternary chalcogenide As₃₀Ge₁₀Te₆₀₋xSix (where x = 0, 5, 10, 12 and 20 at.%) thin films. Thin films with the thickness 100-200 nm of As₃₀Ge₁₀Te₆₀₋xSix were prepared using thermal evaporation of bulk samples. Increasing Si content was found to affect the average heat of atomization, average coordination number, number of constraints and cohesive energy of the As₃₀Ge₁₀Te₆₀₋xSix alloys. Optical absorption is due to allowed non-direct transition, and the energy gap increases with increasing Si content. The chemical bond approach has been applied successfully to interpret the increase in the optical gap with increasing silicon content

Top management team networking as an imperative predictor of the firm performance: A case of permodalan nasional berhad invested companies

Networking is an important medium in reaching different resources and facilitating the information exchange.Therefore, the integration of various resources within Top Management Team (TMT) through networking is an essential aspect to boost the firm performance. While earlier literature has emphasised the empirical concerns on the influence of networking on firm performance, some have also upstretched concern relating to the impact of TMT towards the firm outcome. Despite its apparent importance, relatively little research has addressed networking as a critical factor for firm performance.Therefore, this study intended to fill this gap by studying the effect of TMT networking on firm performance of Permodalan Nasional Berhad (PNB) invested companies in Malaysia. Data was collected through survey questionnaire from 45 TMT leaders of 45 PNB invested companies and it was analysed using SmartPLS3 through the assessment of measurement model for testing the reliability and validities of the measurement and the assessment of structural model for hypothesis testing. Results indicated that TMT networking, measured as the knowledge embedded within the TMT, within and across the organization, as well as with individuals and organizations outside the firm, has a strong positive effect on firm performance. The results therefore emphasized the importance of TMT backgrounds specifically networking as an essential factor for firm performance. Findings of the study has added into the Upper Echelon Theory. The study is important for practitioners, PNB invested companies and also for PNB since it offers further suggestions and guidelines for the importance of TMT networking for improving the firm performance

Early decompressive hemicraniectomy in thrombolyzed acute ischemic stroke patients from the international ENCHANTED trial

Abstract Decompressive hemicraniectomy (DHC) can improve outcomes for patients with severe forms of acute ischemic stroke (AIS), but the evidence is mainly derived from non-thrombolyzed patients. We aimed to determine the characteristics and outcomes of early DHC in thrombolyzed AIS participants of the international Enhanced Control of Hypertension and Thrombolysis Stroke Study (ENCHANTED). Post-hoc analyses of ENCHANTED, an international, partial-factorial, open, blinded outcome-assessed, controlled trial in 4557 thrombolysis-eligible AIS patients randomized to low- versus standard-dose intravenous alteplase (Arm A, n = 2350), intensive versus guideline-recommended blood pressure control (Arm B, n = 1280), or both (Arms A + B, n = 947). Logistic regression models were used to identify baseline variables associated with DHC, with inverse probability of treatment weights employed to eliminate baseline imbalances between those with and without DHC. Logistic regression was also used to determine associations of DHC and clinical outcomes of death/disability, major disability, and death (defined by scores 2–6, 3–5, and 6, respectively, on the modified Rankin scale) at 90 days post-randomization. There were 95 (2.1%) thrombolyzed AIS patients who underwent DHC, who were significantly younger, of non-Asian ethnicity, and more likely to have had prior lipid-lowering treatment and severe neurological impairment from large vessel occlusion than other patients. DHC patients were more likely to receive other management interventions and have poor functional outcomes than non-DHC patients, with no relation to different doses of intravenous alteplase. Compared to other thrombolyzed AIS patients, those who received DHC had a poor prognosis from more severe disease despite intensive in-hospital management

Examining the Classification Accuracy of TSVMs with Feature Selection in Comparison with the GLAD Algorithm

Gene expression data sets are used to classify and predict patient diagnostic categories. As we know, it is extremely difficult and expensive to obtain gene expression labelled examples. Moreover, conventional supervised approaches cannot function properly when labelled data (training examples) are insufficient using Support Vector Machines (SVM) algorithms. Therefore, in this paper, we suggest Transductive Support Vector Machines (TSVMs) as semi-supervised learning algorithms, learning with both labelled samples data and unlabelled samples to perform the classification of microarray data. To prune the superfluous genes and samples we used a feature selection method called Recursive Feature Elimination (RFE), which is supposed to enhance the output of classification and avoid the local optimization problem. We examined the classification prediction accuracy of the TSVM-RFE algorithm in comparison with the Genetic Learning Across Datasets (GLAD) algorithm, as both are semi-supervised learning methods. Comparing these two methods, we found that the TSVM-RFE surpassed both a SVM using RFE and GLAD

Locality-Convolution Kernel and Its Application to Dependency Parse Ranking

Abstract. We propose a Locality-Convolution (LC) kernel in applica-tion to dependency parse ranking. The LC kernel measures parse similar-ities locally, within a small window constructed around each matching feature. Inside the window it makes use of a position sensitive func-tion to take into account the order of the feature appearance. The sim-ilarity between two windows is calculated by computing the product of their common attributes and the kernel value is the sum of the window similarities. We applied the introduced kernel together with Regular-ized Least-Squares (RLS) algorithm to a dataset containing dependency parses obtained from a manually annotated biomedical corpus of 1100 sentences. Our experiments show that RLS with LC kernel performs bet-ter than the baseline method. The results outline the importance of local correlations and the order of feature appearance within the parse. Final validation demonstrates statistically significant increase in parse ranking performance.

Factors affecting internet use among university students in Sarawak, Malaysia: an empirical study

Background:The internet has become an indispensable tool for communication, academic research, information and entertainment. However, heavy users of the internet lead to less confidence in social skills and the tendency to be isolated. The study aimed to assess the pattern of internet use and factors affecting problematic internet use among university students.Methods:This cross-sectional study conducted among the students of a university in Sarawak, Malaysia. A multistage cluster sampling technique was adapted to select the participants. Data were collected from 463 students by self-administered questionnaire. Hierarchical binary logistic regression analysis was done to determine the potential factors for problematic internet use.Results:The mean age of the students was 22 years, with a standard deviation of 1.6 years.Two-fifths (61.8%) of the students had no problematic internet use. However, 35.4% had moderate and 2.8% had severe problematic internet use. Hierarchical binary logistic regression analysis found that age of the students, year of study, duration of daily internet use and use of social networking like Skype appeared to be potential predictors of problematic internet use (p<0.05).Conclusions:This study was conducted in only one university, thus did not depict the overall scenarios of the country. The implications of the findings are still worth noting in the process of designing internet addiction studies among university students. Overall, this study has unearthed some useful insights which can serve as a guide to more elaborate studies

Src activation by β-adrenoreceptors is a key switch for tumor metastasis

Norepinephrine (NE) can modulate multiple cellular functions important for cancer progression; however, how this single extracellular signal regulates such a broad array of cellular processes is unknown. Here, we identify Src as a key regulator of phosphoproteomic signaling networks activated in response to beta-adrenergic signaling in cancer cells. These results also identify a new mechanism of Src phosphorylation that mediates beta-adrenergic/PKA regulation of downstream networks, thereby enhancing tumor cell migration, invasion and growth. In human ovarian cancer samples, high tumoral NE levels were correlated with high pSrcY419 levels. Moreover, among cancer patients, the use of beta blockers was significantly associated with reduced cancer-related mortality. Collectively, these data provide a pivotal molecular target for disrupting neural signaling in the tumor microenvironment

MetWAMer: eukaryotic translation initiation site prediction

<p>Abstract</p> <p>Background</p> <p>Translation initiation site (TIS) identification is an important aspect of the gene annotation process, requisite for the accurate delineation of protein sequences from transcript data. We have developed the MetWAMer package for TIS prediction in eukaryotic open reading frames of non-viral origin. MetWAMer can be used as a stand-alone, third-party tool for post-processing gene structure annotations generated by external computational programs and/or pipelines, or directly integrated into gene structure prediction software implementations.</p> <p>Results</p> <p>MetWAMer currently implements five distinct methods for TIS prediction, the most accurate of which is a routine that combines weighted, signal-based translation initiation site scores and the contrast in coding potential of sequences flanking TISs using a perceptron. Also, our program implements clustering capabilities through use of the <it>k</it>-medoids algorithm, thereby enabling cluster-specific TIS parameter utilization. In practice, our static weight array matrix-based indexing method for parameter set lookup can be used with good results in data sets exhibiting moderate levels of 5'-complete coverage.</p> <p>Conclusion</p> <p>We demonstrate that improvements in statistically-based models for TIS prediction can be achieved by taking the class of each potential start-methionine into account pending certain testing conditions, and that our perceptron-based model is suitable for the TIS identification task. MetWAMer represents a well-documented, extensible, and freely available software system that can be readily re-trained for differing target applications and/or extended with existing and novel TIS prediction methods, to support further research efforts in this area.</p

Kidney, Cardiovascular, and Safety Outcomes of Canagliflozin according to Baseline Albuminuria:A CREDENCE Secondary Analysis

BACKGROUND AND OBJECTIVES: The kidney protective effects of renin-angiotensin system inhibitors are greater in people with higher levels of albuminuria at treatment initiation. Whether this applies to sodium-glucose cotransporter 2 (SGLT2) inhibitors is uncertain, particularly in patients with a very high urine albumin-to-creatinine ratio (UACR; ≥3000 mg/g). We examined the association between baseline UACR and the effects of the SGLT2 inhibitor, canagliflozin, on efficacy and safety outcomes in the Canagliflozin and Renal Endpoints in Diabetes with Established Nephropathy Clinical Evaluation (CREDENCE) randomized controlled trial. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The study enrolled 4401 participants with type 2 diabetes, an eGFR of 30 to 300 to 5000 mg/g. Using Cox proportional hazards regression, we examined the relative and absolute effects of canagliflozin on kidney, cardiovascular, and safety outcomes according to a baseline UACR of ≤1000 mg/g (n=2348), >1000 to 1000 to <3000 mg/g, and 37% (HR, 0.63; 95% CI, 0.47 to 0.84) in the highest subgroup (Pheterogeneity=0.55). Absolute risk reductions for kidney outcomes were greater in participants with higher baseline albuminuria; the number of primary composite events prevented across ascending UACR categories were 17 (95% CI, 3 to 38), 45 (95% CI, 9 to 81), and 119 (95% CI, 35 to 202) per 1000 treated participants over 2.6 years (Pheterogeneity=0.02). Rates of kidney-related adverse events were lower with canagliflozin, with a greater relative reduction in higher UACR categories. CONCLUSIONS: Canagliflozin safely reduces kidney and cardiovascular events in people with type 2 diabetes and severely increased albuminuria. In this population, the relative kidney benefits were consistent over a range of albuminuria levels, with greatest absolute kidney benefit in those with an UACR ≥3000 mg/g. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: ClinicalTrials.gov: CREDENCE, NCT02065791. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_02_22_CJN15260920_final.mp3