Notes

A report for the biosorption kinetics involving Cu (The second) and also Zn (II) ions from aqueous period (sulphate method) employing squander sawdust generated from Acacia nilotica wood carpentry.
The ability to design efficient enzymes from scratch would have a profound effect on chemistry, biotechnology and medicine. Rapid progress in protein engineering over the past decade makes us optimistic that this ambition is within reach. The development of artificial enzymes containing metal cofactors and noncanonical organocatalytic groups shows how protein structure can be optimized to harness the reactivity of nonproteinogenic elements. In parallel, computational methods have been used to design protein catalysts for diverse reactions on the basis of fundamental principles of transition state stabilization. Although the activities of designed catalysts have been quite low, extensive laboratory evolution has been used to generate efficient enzymes. Structural analysis of these systems has revealed the high degree of precision that will be needed to design catalysts with greater activity. To this end, emerging protein design methods, including deep learning, hold particular promise for improving model accuracy. Here we take stock of key developments in the field and highlight new opportunities for innovation that should allow us to transition beyond the current state of the art and enable the robust design of biocatalysts to address societal needs.
We compared the efficacy, safety, and immunogenicity of biosimilar insulin aspart premix SAR341402 Mix 70/30 (70% intermediate SAR341402 protamine and 30% rapid SAR341402 solution) (SAR
-Mix) with its originator NovoMix 30 insulin aspart mix (NN-Mix) in adults with type 1 or type 2 diabetes switching from different premix insulin analogs.

This phase 3, randomized, open-label, multinational, 26-week trial (GEMELLI M) enrolled 402 participants with type 1 or type 2 diabetes. At randomization, participants switched from their prestudy premix insulin NovoMix 30 (n = 341) or Humalog Mix 25/Liprolog Mix 25 (n = 61) to equivalent (11) doses of either SAR
-Mix or NN-Mix at least twice daily (11 randomization). In this subgroup analysis, efficacy measures [change in hemoglobin A1c (HbA1c), daily insulin dose], and safety outcomes [hypoglycemia incidence, adverse events (including hypersensitivity and injection site reactions), anti-insulin aspart antibodies] of SAR
-Mix were compared with those of NN-Mix separately according to the participants' prestudy premix insulin.

At week 26, change from baseline in HbA1c (primary efficacy endpoint) was similar between SAR
-Mix and NN-Mix in those participants pretreated with NovoMix 30 [least squares (LS) mean difference 0.05%, 95% confidence interval (CI)  -0.195% to 0.289%] or Humalog Mix 25/Liprolog Mix 25 (LS mean difference 0.28%, 95% CI -0.279% to 0.830%) (P value for treatment-by-subgroup interaction = 0.46). In both subgroups, safety outcomes, including immunogenicity, and changes in daily insulin doses were similar between treatments over 26weeks.

Efficacy, safety, and immunogenicity profiles of SAR
-Mix are similar to NN-Mix over 26weeks in adults with diabetes irrespective of prior type of premix insulin.

EudraCT number 2017-000092-84.
EudraCT number 2017-000092-84.
Perceived discomfort could indicate an early sign of pain, for example, as a result of a biomechanical load on the musculoskeletal system. Assessing discomfort can, therefore, help to identify workers at increased risk of musculoskeletal disorders for targeted intervention development. We aimed (1) to identify the optimal cut-off value of neck and low back discomfort among office workers and (2) to evaluate its predictive validity with future neck and low back pain, respectively.

At baseline healthy participants (n = 100) completed questionnaires, including the Borg CR-10 discomfort scale (on a 0-10 scale), and were followed for six months, during which musculoskeletal pain was assessed monthly. Logistic regression analyses were performed to assess the associations of baseline discomfort with the onset of future neck or low back pain. Sensitivity, specificity, and the area under the receiver operating characteristics curve were estimated to identify the optimal discomfort cut-off value predicting future pain.

Borg CR-10 scores ≥ 3.5 for perceived neck and low back discomfort had acceptable sensitivity and specificity to predict future neck and low back pain, respectively. SAR405 datasheet Perceived discomfort at baseline as a dichotomous measure (using the ≥ 3.5 cut-off) was a statistically significant predictor of future neck pain (OR = 10.33) and low back pain (OR = 11.81).

We identified the optimal cut-off value of the Borg CR-10 discomfort scale to identify office workers at increased risk of developing neck and low back pain. These findings might benefit ergonomists, primary health care providers, and occupational health researchers in developing targeted interventions.
We identified the optimal cut-off value of the Borg CR-10 discomfort scale to identify office workers at increased risk of developing neck and low back pain. These findings might benefit ergonomists, primary health care providers, and occupational health researchers in developing targeted interventions.
Automatic retinal fundus image quality analysis is one of the most essential preliminary stages in automatic computer-aided retinal disease diagnosis system, which allows good-quality fundus images for accurate disease prediction through localization and segmentation of retinal regions. This paper presents new feature extraction methods using full-reference and no-reference image quality metrics for image quality classification.

Basic image features, reference and no-reference features are extracted from the fundus image and applied through different classification techniques to determine the image quality for further diagnosis. In this paper, human-made categorization including good and non-good-quality fundus image classification is constructed by considering major features of retinal fundus images are illumination, clarity, image intensity, contrast and region visibility. The proposed system presented fundus image quality classification by automatic extraction of features from fundus images through imaage analysis during retinal disease diagnosis.
Immunotherapy has dramatically improved the prognosis of patients with metastatic melanoma (MM). Yet, there is a lack of biomarkers to predict whether a patient will benefit from immunotherapy. Our aim was to create radiomics models on pretreatment computed tomography (CT) to predict overall survival (OS) and treatment response in patients with MM treated with anti-PD-1 immunotherapy.

We performed a monocentric retrospective analysis of 503 metastatic lesions in 71 patients with 46 radiomics features extracted following lesion segmentation. Predictive accuracies for OS < 1year versus > 1year and treatment response versus no response was compared for five feature selection methods (sequential forward selection, recursive, Boruta, relief, random forest) and four classifiers (support vector machine (SVM), random forest, K-nearest neighbor, logistic regression (LR)) used with or without SMOTE data augmentation. A fivefold cross-validation was performed at the patient level, with a tumour-based classificct OS and treatment response in MM treated with anti-PD-1.
This is the first study to investigate CT texture parameter selection and classification methods for predicting MM prognosis with treatment by immunotherapy. Combining pretreatment CT radiomics features from a single tumor with data selection and classifiers may accurately predict OS and treatment response in MM treated with anti-PD-1.China's pilot free trade zone (PFTZ) is an important national strategy to achieve high-quality development, so it is necessary to discuss the effect of PFTZ implementation on green total factor productivity (GTFP). Based on the data from 279 cities in China from 2004 to 2018, this study takes the establishment of PFTZ as a quasi-natural experiment and uses the difference-in-differences (DID) method to systematically evaluate the effect of PFTZ on urban GTFP. The empirical results of this paper are shown as follows Firstly, the construction of PFTZ has a significant effect on urban GTFP, and this effect has increased gradually over time. Secondly, the construction of PFTZ mainly promotes the urban GTFP by increasing the level of science and technology innovation, reducing government intervention and improving the level of human capital. Thirdly, the effect of PFTZ construction on GTFP is more pronounced in regions with earlier waves of PFTZ and in western regions where environmental concerns are higher. In addition, there is a significant positive linkage between the construction of the PFTZ and the Belt and Road Initiative to improve the urban GTFP. The findings of this paper enrich the relevant literature on PFTAs and sustainable development and provide a theoretical basis for further promotion of PFTZ construction.To explore the emission characteristics of vehicle's pollutants is of great significance to prevent and control the diffusion of pollutants. Limited by geographic location and economic condition, the model- and guideline-based studies on vehicle's emission factor have become more concerned measures than the actual measurement. By analyzing the actual operating conditions of motor vehicles, this study obtains the emission factors of typical pollutants from different motor vehicles by adopting international vehicle emission (IVE) model and guideline method, respectively. Furthermore, the resulting emission factors by the above methods were compared and analyzed with on-road method. The results show that (1) the emission factors of vehicle pollutants change regularly with velocity, emission standard, and accumulated mileage. Taking CO as an example, its emission factor shows a downward trend with the increase of velocity and emission standard and an upward trend with the increase of accumulated mileage; (2) compared with the actual measurement, the vehicle emission factor obtained by the guideline method has a large error, while the IVE model is close to the actual.Climate change affects the change of vegetation, and the analysis of vegetation change and its drivers in different globe climate zones is important for ecological conservation, energy balances, and climate change in different global climate zones. Based on the vegetation leaf area index (LAI) and climate factor datasets, this paper uses an integrated empirical model decomposition, sensitivity rate, contribution rate, and geographic detector analysis method to study the vegetation drivers and their changes in 14 different climate zones around the globe from 1981 to 2018. The results showed that (1) Vegetation changes were sensitive to precipitation and evapotranspiration in arid climate zones and to temperature and soil temperature in cold climate zones. In the tundra climate zone, the sensitivity of vegetation change to temperature was higher than that to precipitation and evapotranspiration. (2) Soil moisture has the highest contribution to vegetation change, and the areas with absolute contribution rates or, with the largest interaction effect of specific humidity and soil moisture on LAI changes. This research provides a scientific basis for vegetation change monitoring, driving mechanisms, and ecological protection in different climate regions around the globe.
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