Beneficiation of Commiphora africana Plant: Extraction and Application of Green Softener on Cotton Fabric

This paper aims at the extraction and application of eco- and user-friendly natural gum obtained from Commiphora Africana tree. The result obtained is also compared with fabric treated with a commercial softener of the same concentration. The gum was extracted by puncturing the stem of the plant and the extracted gum was applied directly to 100% cotton fabric through a padding process with different concentrations of extracted gum (i.e., 20 gram per litre (g/l), 25 g/l, and 30 g/l). Another similar fabric sample was treated with a silicon softener of the same concentration. The fabric samples treated with both natural gum and silicon softener were tested for their stiffness, crease recovery, and drapability. The results show that the change in fabric softness depends on the concentration of softener used in both cases. As the concentration of the softener increased, there was a decrease in bending length and drape coefficient for both fabric samples. The drape coefficient of fabric sample treated with natural gum has a comparable result with fabric treated with silicon/commercial softener. Maximum increases in recovery angle were seen in fabric treated with natural gum at a concentration of 30 g/l.

Prediction of Blood-Brain Barrier Penetration (BBBP) Based on Molecular Descriptors of the Free-Form and In-Blood-Form Datasets

The blood-brain barrier (BBB) controls the entry of chemicals from the blood to the brain. Since brain drugs need to penetrate the BBB, rapid and reliable prediction of BBB penetration (BBBP) is helpful for drug development. In this study, free-form and in-blood-form datasets were prepared by modifying the original BBBP dataset, and the effects of the data modification were investigated. For each dataset, molecular descriptors were generated and used for BBBP prediction by machine learning (ML). For ML, the dataset was split into training, validation, and test data by the scaffold split algorithm MoleculeNet used. This creates an unbalanced split and makes the prediction difficult; however, we decided to use that algorithm to evaluate the predictive performance for unknown compounds dissimilar to existing ones. The highest prediction score was obtained by the random forest model using 212 descriptors from the free-form dataset, and this score was higher than the existing best score using the same split algorithm without using any external database. Furthermore, using a deep neural network, a comparable result was obtained with only 11 descriptors from the free-form dataset, and the resulting descriptors suggested the importance of recognizing the glucose-like characteristics in BBBP prediction.

Linear Independence of T-Spline Blending Functions of Degree One for Isogeometric Analysis

Linear independence of the blending functions is a necessary requirement for T-spline in isogeometric analysis. The main work in this paper focuses on the analysis about T-splines of degree one, we demonstrate that all the blending functions of such T-spline of degree one are linearly independent. The advantage owned by one degree T-spline is that it can avoid the problem of judging whether the model is analysis-suitable or not, especially for occasions that need a quick response from the analysis results. This may provide a new way of using T-spline for a CAD and CAE integrating scenario, since one degree T-spline still guarantees the topology flexibility and is compatible with the spline-based modeling system. In addition, we compare the numerical approximations of isogeometric analysis and finite element analysis, and the experiment indicates that isogeometric analysis using T-spline of degree one can reach a comparable result with classical method.

Focus on radioiodine-131 biokinetics: the influence of methylprednisolone on intratherapeutic effective half-life of 131I during radioiodine therapy of Graves’ disease

Aim Radioiodine therapy (RIT) may trigger the development of Graves’ ophthalmopathy (GO) or exacerbate pre-existing subclinical GO. Therefore, glucocorticoid administration is recommended for patients with pre-existing GO. Aim of this study was to analyze the influence of glucocorticoid therapy with methylprednisolone on intratherapeutic effective half-life (EHL) of radioiodine-131 in patients with Graves’ disease (GD) as recent studies showed an effect for prednisolone. Methods In a retrospective study, 264 patients with GD who underwent RIT without any additional antithyroid medication were evaluated. Intrathyroidal EHL was determined pre- and intratherapeutically. Patients with co-existing GO (n = 43) received methylprednisolone according to a fixed scheme starting 1 day prior to RIT, patients without GO (n = 221) did not receive any protective glucocorticoid medication. The ratios of EHL during RIT and during radioiodine uptake test (RIUT) were compared. Results Patients receiving methylprednisolone showed a slight decrease of the mean EHL from 5.63 d (RIUT) to 5.39 d (RIT) (p > 0.05). A comparable result was obtained in patients without glucocorticoids (5.71 d (RIUT) to 5.47 d (RIT); p > 0.05). The ratios of the EHL between RIT and RIUT failed to show a significant difference between the two groups. EHL is therefore not significantly influenced by an additional protective treatment with methylprednisolone. Conclusions In the present study a decreased intrathyroidal EHL under glucocorticoid medication with methylprednisolone could not be detected. Therefore, co-medication with methylprednisolone in patients with GO may be preferred to avoid an intratherapeutic decrease of EHL by accompanying protective glucocorticoides.

Optimal Control for a COVID-19 Model Accounting for Symptomatic and Asymptomatic

Building on an SEIR-type model of COVID-19 where the infecteds are further divided into symptomatic and asymptomatic, a system incorporating the various possible interventions is formulated. Interventions, also referred to as controls, include transmission reduction (e.g., lockdown, social distancing, barrier gestures); testing/isolation on the exposed, symptomatic and asymptomatic compartments; and medical controls such as enhancing patients’ medical care and increasing bed capacity. By considering the government’s capacity, the best strategies for implementing the controls were obtained using optimal control theory. Results show that, if all the controls are to be used, the more able the government is, the more it should implement transmission reduction, testing, and enhancing patients’ medical care without increasing hospital beds. However, if the government finds it very difficult to implement the controls for economic reasons, the best approach is to increase the hospital beds. Moreover, among the testing/isolation controls, testing/isolation in the exposed compartment is the least needed when there is significant transmission reduction control. Surprisingly, when there is no transmission reduction control, testing/isolation in the exposed should be optimal. Testing/isolation in the exposed could seemingly replace the transmission reduction control to yield a comparable result to that when the transmission reduction control is being implemented.

A strategic and data production frameworks for the development of business statistics

This paper highlights the key characteristics and implications of the strategic and data production frameworks designed and progressively implemented by the United Nations Committee of Experts on Business and Trade Statistics (UNCEBTS) to enhance the relevance, accuracy and coverage of business statistics, according to an internationally comparable, result-oriented and sustainable approach. The strategic framework aims to expand the traditional scope of official business statistics by including all relevant environmental and social related issues. NSOs may achieve relevant improvements by focusing their efforts upon specific global goals consistent with their national ones, and sourcing from knowledge sharing with other countries and international coordination. It also highlights the relevance of an enterprise-centered approach for a better understanding of emerging phenomena by official statisticians, and for priority setting in improving the quality of business statistics. The data production framework is dominated by the crucial role of the Statistical Business Register (SBR) as the backbone of any current and future improvements in the relevance and accuracy of business statistics. Its implications, both in terms of sustainability of production lines, data integration and production of new indicators that exploit the variability dimension of business statistics are further investigated in the paper.

Top-Down RST Parsing Utilizing Granularity Levels in Documents

Some downstream NLP tasks exploit discourse dependency trees converted from RST trees. To obtain better discourse dependency trees, we need to improve the accuracy of RST trees at the upper parts of the structures. Thus, we propose a novel neural top-down RST parsing method. Then, we exploit three levels of granularity in a document, paragraphs, sentences and Elementary Discourse Units (EDUs), to parse a document accurately and efficiently. The parsing is done in a top-down manner for each granularity level, by recursively splitting a larger text span into two smaller ones while predicting nuclearity and relation labels for the divided spans. The results on the RST-DT corpus show that our method achieved the state-of-the-art results, 87.0 unlabeled span score, 74.6 nuclearity labeled span score, and the comparable result with the state-of-the-art, 60.0 relation labeled span score. Furthermore, discourse dependency trees converted from our RST trees also achieved the state-of-the-art results, 64.9 unlabeled attachment score and 48.5 labeled attachment score.

One Central Bank to Rule Them All*

While global stock markets enjoy high returns on days surrounding Federal Open Market Committee (FOMC) meetings, there is no comparable result for other central banks either internationally or, more surprisingly, domestically. Neither announcement surprises nor currency moves drive these findings, which hold even for stocks with a domestic focus. The difference in announcement premia is not explained by economy size, exposure to multinationals, or policy activism. We conclude that the Fed exerts a unique impact on global equities. Consistent with this hypothesis, uncertainty drops across global markets following FOMC announcements but not those of other central banks. Furthermore, the Fed is generally the leader among central banks in setting monetary policy.

Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering

Most of the recent progresses on visual question answering are based on recurrent neural networks (RNNs) with attention. Despite the success, these models are often timeconsuming and having difficulties in modeling long range dependencies due to the sequential nature of RNNs. We propose a new architecture, Positional Self-Attention with Coattention (PSAC), which does not require RNNs for video question answering. Specifically, inspired by the success of self-attention in machine translation task, we propose a Positional Self-Attention to calculate the response at each position by attending to all positions within the same sequence, and then add representations of absolute positions. Therefore, PSAC can exploit the global dependencies of question and temporal information in the video, and make the process of question and video encoding executed in parallel. Furthermore, in addition to attending to the video features relevant to the given questions (i.e., video attention), we utilize the co-attention mechanism by simultaneously modeling “what words to listen to” (question attention). To the best of our knowledge, this is the first work of replacing RNNs with selfattention for the task of visual question answering. Experimental results of four tasks on the benchmark dataset show that our model significantly outperforms the state-of-the-art on three tasks and attains comparable result on the Count task. Our model requires less computation time and achieves better performance compared with the RNNs-based methods. Additional ablation study demonstrates the effect of each component of our proposed model.

Comparison of metabolic effects of glimepride and sitagliptin with metformin in patients suffering from type 2 diabetes mellitus in a tertiary care hospital

Background: Diabetes mellitus (DM) is one of the major causes of mortality & morbidity, and patient’s with better control of glycaemic parameters have lesser chronic complications associated with it. Though monotherapy with metformin is first choice for T2DM but is effective in less than 50% of patient and they should be managed with two drug therapy. Both Glimepiride and Sitagliptin are effective with metformin but there has been no study done in this region hence, we planned to study comparison of effects of glimepiride and sitagliptin with metformin in patient of T2DM.Methods: This prospective, open-label, randomized study was done in all patient diagnosed with T2DM, not adequately managed by metformin alone. The patient was divided into two group G (Glimepiride with Metformin) and Group S (Sitagliptin with Metformin) and had a follow up at 3 and 6 months. The biochemical parameters were assessed at 12 weeks and 24 weeks.Results: The result of this study show that both glimepiride and sitagliptin with metformin significantly (p<0.05) lowered both the fasting blood sugar as well as postprandial blood glucose at 3 and 6 months. Glimepiride was more effective in lowering (p<0.05) the plasma glucose at 3 months but both the drugs had comparable result at 6 months. This study also showed that glycosylated haemoglobin was lowered in both groups at three and six months as compared to Day 0 (p<0.05), with glimepiride having better control of glycosylated haemoglobin at 3 months with both groups having comparable result at 6 months.Conclusions: To conclude, this study compared effects of sitagliptin and glimepiride on glycaemic parameters in patients of T2DM and found that both drugs had comparable results.