Accurate and efficient method for the treatment of exchange in a plane-wave basis.

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Generalized Hyers-Ulam Stability of the Second-Order Linear Differential Equations

We prove the generalized Hyers-Ulam stability of the 2nd-order linear differential equation of the form +()+()=(), with condition that there exists a nonzero 1∶→ in 2() such that 1+()1+()1=0 and is an open interval. As a consequence of our main theorem, we prove the generalized Hyers-Ulam stability of several important well-known differential equations

Finite-size errors in continuum quantum Monte Carlo calculations

We analyze the problem of eliminating finite-size errors from quantum Monte Carlo (QMC) energy data. We demonstrate that both (i) adding a recently proposed [S. Chiesa et al., Phys. Rev. Lett. 97, 076404 (2006)] finite-size correction to the Ewald energy and (ii) using the model periodic Coulomb (MPC) interaction [L. M. Fraser et al., Phys. Rev. B 53, 1814 (1996); P. R. C. Kent et al., Phys. Rev. B 59, 1917 (1999); A. J. Williamson et al., Phys. Rev. B 55, 4851 (1997)] are good solutions to the problem of removing finite-size effects from the interaction energy in cubic systems, provided the exchange-correlation (XC) hole has converged with respect to system size. However, we find that the MPC interaction distorts the XC hole in finite systems, implying that the Ewald interaction should be used to generate the configuration distribution. The finite-size correction of Chiesa et al. is shown to be incomplete in systems of low symmetry. Beyond-leading-order corrections to the kinetic energy are found to be necessary at intermediate and high densities, and we investigate the effect of adding such corrections to QMC data for the homogeneous electron gas. We analyze finite-size errors in two-dimensional systems and show that the leading-order behavior differs from that which has hitherto been supposed. We compare the efficiency of different twist-averaging methods for reducing single-particle finite-size errors and we examine the performance of various finite-size extrapolation formulas. Finally, we investigate the system-size scaling of biases in diffusion QMC

Neuropsychological function is related to irritable bowel syndrome in women with premenstrual syndrome and dysmenorrhea

Background There is increasing evidence demonstrating the co-occurrence of primary dysmenorrhea (PD), premenstrual syndrome (PMS), and irritable bowel syndrome (IBS) in women. This study aimed to investigate whether women who have symptoms of IBS in addition to PD and PMS also report more severe or frequent menstruation-associated symptoms and psychological complications compared to women with PD and PMS alone. Methods The study group included 182 female University students aged 18–25 years. IBS was diagnosed using the Rome III criteria. The severity of PMS and PD was determined using a 10-point visual analog scale and PSST (Premenstrual Syndrome Screening Tool), respectively. Neuropsychological functions including cognitive function, depression score, anxiety score, stress, insomnia, daytime sleepiness, quality of life and personality were assessed using standard questionnaires. Results Of the 182 young females, 31 (17.0%) had IBS. Average days of bleeding during the menstrual cycle and mean pain severity on the PSST scale were significantly greater in the group with IBS compared to the non-IBS group (p < 0.01). The non-IBS individuals scored more favorably than the women with IBS with respect to severity of depression, insomnia, daytime sleepiness (p < 0.05). The PSST scores were significantly correlated with scores for depression (r = 0.29; p < 0.001), anxiety (r = 0.28; p < 0.001), stress (r = 0.32; p < 0.001), insomnia (r = 0.34; p < 0.001) and daytime sleepiness (r = 0.31; p < 0.001); while, they were negatively correlated with cognitive abilities (r = − 0.20; p = 0.006) and quality of life (r = − 0.42; p < 0.001). Linear regression analysis showed that the PSST scores were possibly significant factors in determining the scores for depression, anxiety, stress, quality of life, insomnia and daytime sleepiness (p < 0.05). Conclusion IBS is related to psychological comorbidities, in particular depression, sleep problems and menstrual-associated disorders. IBS may exacerbate the features of PMS which should be taken into account in the management of PMS

Production of full length and splicing form of chymosin using pETexpression system in E-coli and investigation its enzyme activity and preplasmic secretion

Introduction: Chymosin (Rennin EC 3.4.23.4) is an aspartyl proteinas (the major proteolyticenzyme in the fourth stomach of the unweaned calf) that is formed by proteolytic activation fromzymogene prochymosin. The aim of his study was to produce the full length and splicing form ofchymosin using pETexpression system in E-coli and to assay the activity of expressed enzyme andpreplasmic secretion.Materials and Methods: The sense primer F-prochy(+) (5´-ggggccatgGCTGAGATCACCAGGA)including NCOI restriction site). The anti sense R-prochy(-) (5´-gggcggccgcGATGGCTTTGGCCAGC -3´) hybridizing to the C-terminal end of calf preprocymosincDNA and contains an additional NotI restriction site at its 5´-end . The cells were disrupted bysonication and proteins were purified by using Ni-NTA beads from Qiagen under native conditional.The preprochymosin and AS6 preprochymosin were activated at pH 4.7. The enzyme solutions werediluted 20-fold with 50 mM phosphate buffer .Results: Sequencing data analysis showed that the exon six has been spliced out and, therefore thegene product is 114 bp shorter in length, both chymosin forms were expressed together in E.coli.Under the same expression conditions, at least AS6 preprochymosin was produced 7-fold highexpression in comparison to a full-length recombinant chymosin. Following acid activation andneutralization, the purified fractions were tested in a qualitative milk clotting assay. The clottingactivity of preprochymosin and exon6-less preprochymosin were comparable.Conclusion: High expression of this alternatively expressed transcript in bacteria, and properfolding of the AS6 chymosin protein molecule in the absence of exon six are the two most importantaspects distinguished in this research

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

Objective: To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. Methods: Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients� history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. Results: For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95CI: 0.95�0.96), accuracy = 0.88 ± 0.046 (95 CI: 0.88�0.89), sensitivity = 0.88 ± 0.066 (95 CI = 0.87�0.9) and specificity = 0.89 ± 0.07 (95 CI = 0.87�0.9)). Conclusion: Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients. © 2021 The Author(s

PRAME induces genomic instability in uveal melanoma

PRAME is a CUL2 ubiquitin ligase subunit that is normally expressed in the testis but becomes aberrantly overexpressed in many cancer types in association with aneuploidy and metastasis. Here, we show that PRAME is expressed predominantly in spermatogonia around the time of meiotic crossing-over in coordination with genes mediating DNA double strand break repair. Expression of PRAME in somatic cells upregulates pathways involved in meiosis, chromosome segregation and DNA repair, and it leads to increased DNA double strand breaks, telomere dysfunction and aneuploidy in neoplastic and non-neoplastic cells. This effect is mediated at least in part by ubiquitination of SMC1A and altered cohesin function. PRAME expression renders cells susceptible to inhibition of PARP1/2, suggesting increased dependence on alternative base excision repair pathways. These findings reveal a distinct oncogenic function of PRAME that can be targeted therapeutically in cancer