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The study was performed to evaluate the neuroprotective effects of Benfotiamine (BFT) in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced Parkinson's disease (PD) in rats. The rats were given daily doses of BFT (100mg/kg, 200mg/kg) through oral administration for 42 days. The rats were given a single bilateral dosage of MPTP (0.1 mg/nostril) intranasally once before the drug treatment to induce PD. On day 42, the animals were subjected to various behavioral paradigms. Post-treatment with BFT for 42 days significantly improved the motor and nonmotor fluctuations of MPTP. The results demonstrated that treatment with BFT ameliorated MPTP-induced disorders in behavior, body balance, and dopamine levels in the mid-brain. Among the post-treated groups, a high dose of BFT was the most effective treatment. Mean values are indicated in ±SEM, n = 5***(p < 0.001) when compared with the vehicle control, n = 5 ### (p < 0.001) when compared with the disease control; (p < 0.001) when compared with the BFT per se; (p < 0.001) when compared with the low dose of BFT; (p < 0.001) when compared with the high dose of BFT. Our finding suggests that BFT contributed to superior antioxidant, and anti-inflammatory and could be a novel therapeutic method for PD management. In conclusion, BFT could be a potential drug candidate for curbing and preventing PD.

The inclusion of physiographic and atmospheric influences is critical for spatial modeling of orographic precipitation in complex terrains. However, attempts to incorporate cloud cover frequency (CCF) data when interpolating precipitation are limited. CCF considers the rain shadow effect during interpolation to avoid an overly strong relationship between elevation and precipitation in areas at equivalent altitudes across rain shadows. Conventional multivariate regression or geostatistical methods assume the precipitation–explanatory variable relationship to be steady, even though this relation is often non-stationarity in complex terrains. This study proposed a novel spatial mapping approach for precipitation that combines regression-kriging (RK) to leverage its advantages over conventional multivariate regression and the spatial autocorrelation structure of residuals via kriging. The proposed hybrid model, RK (GT + CCF), utilized CCF and other physiographic factors to enhance the accuracy of precipitation interpolation. The implementation of this approach was examined in a mountainous region of southern Syria using in situ monthly precipitation data from 57 rain gauges. The RK model’s performance was compared with conventional multivariate regression models (CMRMs) that used geographical and topographical (GT) factors and CCF as predictors. The results indicated that the RK model outperformed the CMRMs with a root mean square error of <8 mm, a mean absolute percentage error range of 5–15%, and an R2 range of 0.75–0.96. The findings of this study showed that the incorporation of MODIS–CCF with physiographic variables as covariates significantly improved the interpolation accuracy by 5–20%, with the largest improvement in modeling precipitation in March.

The influence of inserting WO3 in a barium-phosphate glass system (BaWP) with the chemical composition of (50-x)BaO+ (x + y)WO3 + (50-y)P2O5, where x = y and x + y = 10, 20, 30, 40, 50, and 60 mol% was evaluated. Makishima-Mackenzie's theory was applied to describe the tested BaWP glasses’ mechanical properties. The mechanical moduli (Young's, shear, bulk, and longitudinal) were predicted based on packing factor, coordination number, and the ionic radius of elements constituting the current glasses. Moreover, The shielding ability of the investigated glass samples was assessed using the Monte Carlo. The simulated results showed the best mass attenuation coefficient recorded for the BaWP7 glass sample. It is ranged between 0.3191 and 0.0517 cm2/g for gamma photons ranging from 0.284 to 1.406 MeV, respectively.

This work shows how it is possible to reconstruct SuperKEKB's beam parameters using a Neural Network with beamstrahlung signal from the Large Angle Beamstrahlung Monitor (LABM) as input. We describe the device, the model, and discuss the results., Comment: 22 pages, 12 Figures, submitted to Nuclear Instr. Methods A

BackgroundPatients with cancer may be at high risk of adverse outcomes from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We analyzed a cohort of patients with cancer and coronavirus 2019 (COVID-19) reported to the COVID-19 and Cancer Consortium (CCC19) to identify prognostic clinical factors, including laboratory measurements and anticancer therapies.Patients and methodsPatients with active or historical cancer and a laboratory-confirmed SARS-CoV-2 diagnosis recorded between 17 March and 18 November 2020 were included. The primary outcome was COVID-19 severity measured on an ordinal scale (uncomplicated, hospitalized, admitted to intensive care unit, mechanically ventilated, died within 30 days). Multivariable regression models included demographics, cancer status, anticancer therapy and timing, COVID-19-directed therapies, and laboratory measurements (among hospitalized patients).ResultsA total of 4966 patients were included (median age 66 years, 51% female, 50% non-Hispanic white); 2872 (58%) were hospitalized and 695 (14%) died; 61% had cancer that was present, diagnosed, or treated within the year prior to COVID-19 diagnosis. Older age, male sex, obesity, cardiovascular and pulmonary comorbidities, renal disease, diabetes mellitus, non-Hispanic black race, Hispanic ethnicity, worse Eastern Cooperative Oncology Group performance status, recent cytotoxic chemotherapy, and hematologic malignancy were associated with higher COVID-19 severity. Among hospitalized patients, low or high absolute lymphocyte count; high absolute neutrophil count; low platelet count; abnormal creatinine; troponin; lactate dehydrogenase; and C-reactive protein were associated with higher COVID-19 severity. Patients diagnosed early in the COVID-19 pandemic (January-April 2020) had worse outcomes than those diagnosed later. Specific anticancer therapies (e.g. R-CHOP, platinum combined with etoposide, and DNA methyltransferase inhibitors) were associated with high 30

The arboreal ecosystem is vitally important to global and local biogeochemical processes, the maintenance of biodiversity in natural systems, and human health in urban environments. The ability to collect samples, observations, and data to conduct meaningful scientific research is similarly vital. The primary methods and modes of access remain limited and difficult. In an online survey, canopy researchers (n = 219) reported a range of challenges in obtaining adequate samples, including ∼10% who found it impossible to procure what they needed. Currently, these samples are collected using a combination of four primary methods: (1) sampling from the ground; (2) tree climbing; (3) constructing fixed infrastructure; and (4) using mobile aerial platforms, primarily rotorcraft drones. An important distinction between instantaneous and continuous sampling was identified, allowing more targeted engineering and development strategies. The combination of methods for sampling the arboreal ecosystem provides a range of possibilities and opportunities, particularly in the context of the rapid development of robotics and other engineering advances. In this study, we aim to identify the strategies that would provide the benefits to a broad range of scientists, arborists, and professional climbers and facilitate basic discovery and applied management. Priorities for advancing these efforts are (1) to expand participation, both geographically and professionally; (2) to define 2–3 common needs across the community; (3) to form and motivate focal teams of biologists, tree professionals, and engineers in the development of solutions to these needs; and (4) to establish multidisciplinary communication platforms to share information about innovations and opportunities for studying arboreal ecosystems.