Micro‐morphological,environmental and phytochemical investigation of herbal medicines by using LM and SEM: A quality control tool

The use of quality control tool for authentication of Jadwar (Delphinium denudatum Wall. ex Hook.f. & Thomson), a folk herbal drug used for the treatment of different ailments, was studied. People face problems of adulteration for this drug at global, regional, national and local levels. Two different plant species are commercially marketed in the Indo‐Pak Subcontinent under the same trade name of Jadwar. One is D. denudatum Wall. ex Hook.f. & Thomson and the other is Aconitum heterophyllum Wall. ex Royle. To focus on this problem, a marketable available drug sample of Jadwar was authenticated by using basic microscopy tools (LM) and advanced chemo‐taxonomic markers. Authentication, quality and standardization of this drug was achieved using morphology, organoleptography, UV and IR analyses, scanning electron microscopy of pollen and anatomical investigations. The techniques used for authentication marked the clear difference between the studied plants. Microscopic studies, chemotaxonomic investigation and other techniques used in this project provided the basis for the authentication of this species.     

Do IT freelancers increase their entrepreneurial behavior and performance by using IT self-efficacy and social capital? Evidence from Bangladesh

This study applies entrepreneurship theory and social capital perspective to IT freelancing to facilitate a better understanding of it. Our model hypothesizes that both IT self-efficacy and social capital obtained through social media interactions contribute to IT freelancers’ entrepreneurial behavior and then this entrepreneurial behavior influences their performance. We collected primary data from one of the leading online freelancing marketplaces, oDesk.com (currently Upwork.com). Our empirical findings confirm the core assertion of our research model. The implications of these findings for research and practice are discussed. Directions for future research are also provided.     

Preparation of Au nanoparticles by Q switched laser ablation and their application in 4-nitrophenol reduction

Clean Technologies and Environmental Policy - This work reports the fabrication of Au nanoparticles (NPs) by laser ablation of a gold metal plate immersed in water in the absence of stabilizing...     

Hikami-Larkin-Nagaoka (HLN) Treatment of the Magneto-Conductivity of Bi<Subscript>2</Subscript>Te<Subscript>3</Subscript> Topological Insulator

We report the magneto-conductivity analysis at different temperatures under a magnetic field of up to 5 T of a well- characterized Bi₂Te₃ crystal. Details of crystal growth and various physical properties including high linear magneto-resistance are already reported by some of us. To elaborate upon the transport properties of Bi₂Te₃ crystal, the magneto-conductivity is fitted to the known Hikami-Larkin-Nagaoka (HLN) equation and it is found that the conduction mechanism is dominated by both surface-driven weak anti-localization (WAL) and the bulk weak localization (WL) states. The value of HLN equation coefficient (α) signifying the type of localization (WL, WAL or both WL and WAL) falls within the range of ??0.5 to ??1.5. In our case, the low-field (±?0.25 T) fitting of studied crystal exhibited a value of α close to ??0.86 for studied temperatures of up to 50 K, indicating both WAL and WL contributions. The phase coherence length (l_φ) is found to decrease from 98.266 to 40.314 nm with increasing temperature. Summarily, the short letter reports the fact that bulk Bi₂Te₃ follows the HLN equation and quantitative analysis of the same facilitates to know the quality of studied crystal in terms of WAL to WL contributions and thus the surface to bulk conduction ratio.     

Improving Routine Immunization Coverage Through Optimally Designed Predictive Models

Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.     

Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile,the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet + SVM hybrid technique. The hybrid model AlexNet + SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively.     

Pikeperch,Sander lucioperca Invasion in the Neretva River Watershed (Bosnia and Herzegovina,Croatia) After Alteration of River Flow

The invasion history and the recent distribution, abundance and prey composition of pikeperch, Sander lucioperca in Neretva River watershed (Bosnia and Herzegovina and Croatia) are presented. The species was illegally/unintentionally introduced in Ramski Reservoir in the upper river part in 1986, while establishment of its abundant populations was reported during the 1990s in two connected artificial reservoirs. The research on the present abundance and food composition was executed during the 2012–2014 period. The results showed that pikeperch occupied most of the water bodies of the Neretva River, with an exception of natural river parts with gravel sediment and fast flow: all five artificial reservoirs (Ramski, Jablani?ki, Grabovica, Salakovac and Mostarski reservoirs) and lower wetland zone. However, the success and impact of colonization differ among water bodies, indicating pikeperch preference for warm and shallow reservoirs. Pikeperch prey is composed of different fish species abundant in specific water bodies. In artificial reservoirs, it preys mostly on introduced fish species, with a potential for controlling pumpkinseed abundance and biomass. Sampling carried out in Ramski Reservoir between 2013 and 1014 showed exclusive feeding on the introduced Leucaspius delineatus, and the establishment of a new food web, where only introduced species play significant roles. Copyright © 2015 John Wiley & Sons, Ltd.     

Citrulline malate supplementation does not improve German Volume Training performance or reduce muscle soreness in moderately trained males and females

Background

Use of supplements to aid performance is common practice amongst recreationally active individuals, including those without a sufficient evidence base. This investigation sought to assess whether acute supplementation with 8 g of citrulline malate (CM) (1.11: 1 ratio) would improve anaerobic performance.

Methods

A randomised double blind placebo control trial was employed, using a counterbalanced design. We recruited recreationally active men and women to take part in an isokinetic chair protocol, based on German Volume Training (GVT) whereby participants attempted to perform 10 sets of 10 repetitions against a force representing 70% of their peak concentric force.

Results

The number of repetitions achieved over the course of the GVT was 94.0?±?7.9 and 90.9?±?13.9 for placebo and CM respectively. There was no significant difference between the placebo and CM treatment for number of repetitions (P?=?0.33), isometric (P?=?0.60), concentric (P?=?0.38), or eccentric (P?=?0.65) peak force following the GVT. Total muscle soreness was significantly higher in the CM compared to the placebo treatment following the GVT protocol over 72 h (P?=?0.01); although this was not accompanied by a greater workload/number of repetitions in the CM group.

Conclusions

We conclude that an acute dose of CM does not significantly affect anaerobic performance using an isokinetic chair in recreational active participants. Practical implications include precaution in recommending CM supplementation. Coaches and athletes should be aware of the disparity between the chemical analyses of the products reviewed in the present investigation versus the manufacturers’ claims.

     

Evaluation of ZnO-Vitamin B1 Nanoparticles on Bioactivity and Physiochemical Properties of the Polycaprolactone-Based Nanocomposites

In the present study, we investigated the use of thiamine chloride hydrochloride (vitamin B₁)-modified ZnO nanoparticles (ZnO-VB₁ NPs) to reinforce polycaprolactone matrix. The stable and bioactive PCL/ZnO-VB₁ nanocomposites were fabricated with the combination of ultrasonication and solution casting methods. Transmission electron microscope results indicated that the ZnO-VB₁ NPs were uniformly dispersed in the matrix. The nanocomposites showed high hydroxyapatite formation (high bioactivity) in the simulated body fluid. The nanocomposites with 2?wt% of the modified nanoparticles were found to have highest mechanical strength. The nanocomposites with more nanofiller concentrations exhibited high wettability.