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Automating the amino acid identification in elliptical dichroism spectrometer with Machine Learning
Amino acid identification is crucial across various scientific disciplines, including biochemistry, pharmaceutical research, and medical diagnostics. However, traditional methods such as mass spectrometry require extensive sample preparation and are time-consuming, complex and costly. Therefore, this study presents a pioneering Machine Learning (ML) approach for automatic amino acid identification by utilizing the unique absorption profiles from an Elliptical Dichroism (ED) spectrometer. Advanced data preprocessing techniques and ML algorithms to learn patterns from the absorption profiles that distinguish different amino acids were investigated to prove the feasibility of this approach. The results show that ML can potentially revolutionize the amino acid analysis and detection paradigm.
Study on the linkage mechanism between key strata fracture in overburden and surface subsidence during caving mining in extra-thick coal seams
The widely used Ucp1-Cre transgene elicits complex developmental and metabolic phenotypes
Adolescent reports of subjective socioeconomic status: An adequate alternative to parent-reported objective and subjective socioeconomic status?
Socioeconomic status (SES) is associated with well-being outcomes across studies; however, there is wide variation in its measurement, particularly in adolescence. One key difference in measures of SES concerns whether participants relay objective information—for example, years of education, household income—or subjective perceptions of socioeconomic status, either with or without reference to others or society. Although parents are often considered the best source of SES information—especially objective SES—within families, interviewing parents within the context of adolescent research is costly, time-consuming, and not always feasible. Given the importance of SES for outcomes in adolescence and cumulative effects over the lifespan, we used data from adolescents (N = 702) and parents (Ns = 664–730) to examine whether adolescent reports of SES serve as reasonable proxies for parent reports of both objective and subjective SES, as well as administrative data assessing family SES and neighborhood SES. Consistent with our hypotheses, adolescents’ reports of subjective SES were moderately correlated with parent reports and administrative data tapping family SES. Moreover, adolescents’ reports of subjective SES predicted adolescent-reported measures of well-being, including mental health, physical health, school performance, problem behavior, and alcohol use to the same degree as or better than parent reports of both subjective and objective SES and administrative data. These findings suggest that adolescent reports of subjective SES—using two different, easily understood measures—can stand in as reasonable alternatives to parent-reported SES and administrative data.
Spin transport properties in a topological insulator sandwiched between two-dimensional magnetic layers
Abstract Non-trivial band topology along with magnetism leads to different novel quantum phases. When time-reversal symmetry is broken in three-dimensional topological insulators (TIs) through, e.g., the proximity effect, different phases such as the quantum Hall phase or the quantum anomalous Hall(QAH) phase emerge, displaying interesting transport properties for spintronic applications. The QAH phase displays sidewall chiral edge states, which leads to the QAH effect. We have considered a heterostructure consisting of a TI, namely Bi $$_2$$ Se $$_3$$ , sandwiched between two two-dimensional ferromagnetic monolayers of CrI $$_3$$ , to study how its topological and transport properties change due to the proximity effect. Combining DFT and tight-binding calculations, along with non-equilibrium Green’s function formalism, we show that a well-defined exchange gap appears in the band structure in which spin-polarised edge states flow. In a finite slab, the nature of the surface states depends on both the cross-section and thickness of the system. Therefore, we also study the width and finite-size effects on the transmission and topological properties of this magnetised TI nanoribbon.
Microbial biogeography along a 2578 km transect on the East Antarctic Plateau
Interpretable and integrative deep learning for discovering brain-behaviour associations
Author Correction: A sedimentary ancient DNA perspective on human and carnivore persistence through the Late Pleistocene in El Mirón Cave, Spain
Stability indicating RP-HPLC technique for simultaneous estimation of nirmatrelvir and ritonavir in their new copackaged dosage form for COVID-19 treatment
Abstract RP-HPLC technique was developed and optimized for simultaneous identification and estimation of nirmatrelvir (NIR) and ritonavir (RIT) in their new copackaged tablet. Stability of nirmatrelvir (NIR) was studied after exposure to different five stress conditions; alkali, acid, heat, photo and oxidation degradation. The chromatographic separation was achieved using VDSpher PUR 100 ODS (4.6-mm x 15-mm), 3.5 μm column and mixture of 0.03 M potassium di-hydrogen phosphate buffer pH 4 and acetonitrile (45:55, v/v) as mobile phase. The column temperature was set at 40 °C, flow rate at 1mL/min and UV detection at 215 nm. The NIR and RIT retention times were 3.94 ± 0.08 min and 9.08 ± 0.1 min, respectively. Linear relationship was established in range of (1.5–105 µg/mL) for NIR and (1–70 µg/mL) for RIT with good reproducibility. The found mean percentage recoveries of nirmatrelvir (NIR) and ritonavir (RIT) were 100.03% and 99.85%, respectively. The developed method shows very good sensitivity as the LOQ and LOD were found to be 3.001 & 0.990 µg/mL, respectively for NIR and 2.765 & 0.912 µg/mL, respectively for RIT. The developed approach was validated concerning to ICH guidelines and applied successfully for the simultaneous estimation of NIR and RIT in their new copackaged dosage from. The results of assay using the proposed approach were compared statistically to the results found by applying the published one with good agreement.
Enhanced sampling of protein conformational changes via true reaction coordinates from energy relaxation
Physical and emotional abuse with internet addiction and anxiety as a mediator and physical activity as a moderator
Improving polyketide biosynthesis by rescuing the translation of truncated mRNAs into functional polyketide synthase subunits
Exploring the anticancer activities of Sulfur and magnesium oxide through integration of deep learning and fuzzy rough set analyses based on the features of Vidarabine alkaloid
Abstract Drug discovery and development is a challenging and time-consuming process. Laboratory experiments conducted on Vidarabine showed IC50 6.97 µg∕mL, 25.78 µg∕mL, and ˃ 100 µg∕mL against non-small Lung cancer (A-549), Human Melanoma (A-375), and Human epidermoid Skin carcinoma (skin/epidermis) (A-431) respectively. To address these challenges, this paper presents an Artificial Intelligence (AI) model that combines the capabilities of Deep Learning (DL) to identify potential new drug candidates, Fuzzy Rough Set (FRS) theory to determine the most important chemical compound features, Explainable Artificial Intelligence (XAI) to explain the features’ importance in the last layer, and medicinal chemistry to rediscover anticancer drugs based on natural products like Vidarabine. The proposed model aims to identify potential new drug candidates. By analyzing the results from laboratory experiments on Vidarabine, the model identifies Sulfur and magnesium oxide (MgO) as new potential anticancer agents. The proposed model selected Sulfur and MgO based on Interpreting their promising features, and further laboratory experiments were conducted to validate the model’s predictions. The results demonstrated that, while Vidarabine was inactive against the A-431 cell line (IC50 ˃ 100 µg∕mL), Sulfur and MgO exhibited significant anticancer activity (IC50 4.55 and 17.29 µg/ml respectively). Sulfur displayed strong activity against A-549 and A-375 cell lines (IC50 3.06 and 1.86 µg/ml respectively) better than Vidarabine (IC50 6.97 and 25.78 µg/ml respectively). However, MgO showed weaker activity against these two cell lines. This paper emphasizes the importance of uncovering hidden chemical features that may not be discernible without the assistance of AI. This highlights the ability of AI to discover novel compounds with therapeutic potential, which can significantly impact the field of drug discovery. The promising anticancer activity exhibited by Sulfur and MgO warrants further preclinical studies.
NKAPL facilitates transcription pause-release and bridges elongation to initiation during meiosis exit
A hybrid Harris Hawks Optimization with Support Vector Regression for air quality forecasting
Abstract This paper proposes a hybridized model for air quality forecasting that combines the Support Vector Regression (SVR) method with Harris Hawks Optimization (HHO) called (HHO-SVR). The proposed HHO-SVR model utilizes five datasets from the environmental protection agency’s Downscaler Model (DS) to predict Particulate Matter ( $$PM_{2.5}$$ ) levels. In order to assess the efficacy of the suggested HHO-SVR forecasting model, we employ metrics such as Mean Absolute Percentage Error (MAPE), Average, Standard Deviation (SD), Best Fit, Worst Fit, and CPU time. Additionally, we contrast our methodology with recently created models that have been published in the literature, such as the Grey Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Henry Gas Solubility Optimization (HGSO), Barnacles Mating Optimizer (BMO), Whale Optimization Algorithm (WOA), and Manta Ray Foraging Optimization (MRFO). In particular, the proposed HHO-SVR model outperforms other approaches, establishing it as the optimal model based on its superior results.