Transforming Motor Imagery Analysis: A Novel EEG Classification Framework Using AtSiftNet Method.

This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., 350×18). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., 99.946%), Mutual Information (i.e., 98.902%), Independent Component Analysis (i.e., 99.62%), and Principal Component Analysis (i.e., 98.884%) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is 99.89%. The experiments' findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain-Computer Interfaces (BCI).

Comparison of the Latarjet Procedure With the Modified Putti-Platt Surgery for Recurrent Anterior Shoulder Dislocation With Respect to Functional Outcome.

Introduction Shoulder dislocation is the most common injury encountered in orthopedic outpatient department. The choice of procedure depends on the expertise of surgeons. The objective of this study was to compare the Latarjet procedure with the modified Putti-Platt surgery for recurrent anterior shoulder dislocation in terms of functional outcomes. Materials and methods A quasi-experimental study evaluated 60 patients with recurrent anterior shoulder instability. Patients were assigned to either Latarjet or modified Putti-Platt surgery. Functional outcomes were assessed at six months using the Constant-Murley shoulder score. Results This study encompassed 60 patients (mean age: 23.93±5.88 years) undergoing shoulder procedures. Functional outcomes exhibited a majority of 55% excellent, 35% good, 6.7% fair, and 3.3% poor outcomes. No significant differences in functional outcomes were found between the procedures. Conclusion Both procedures may be viable options for recurrent shoulder instability. The choice may depend on patient factors and surgeon preference. Further research is needed to refine techniques and identify ideal candidates.

G-ACP: a machine learning approach to the prediction of therapeutic peptides for gastric cancer.

Conventional Gastrointestinal (GI) cancer treatments are quite expensive and have major hazards. Nowadays, a different strategy places more emphasis on creating tiny biologically active peptides that do not cause severe poisoning. Anticancer peptides (ACPs) are found through experimental screening, which is time-dependent and frequently fraught with difficulties. Gastric ACPs are emerging as a promising GI cancer treatment in the current day. It is crucial to identify novel gastric ACPs to have an improved knowledge of their functioning processes and treatment of gastric cancer. As a result of the post-genomic era's massive production of peptide sequences, rapid and effective ACPs using a computational method are essential. Several adaptive statistical techniques for distinguishing ACPs and non-ACPs have recently been developed. A variety of adapted statistically significant methods have been developed to differentiate between ACPs and non-ACPs. Despite significant progress, there is no specific model for the prediction of gastric ACPs because the specific model will predict a particular type of peptide more accurately and quickly. To overcome this, an initiative is taken for the creation of a reliable framework for the accurate identification of gastric ACPs. The current technique in particular contains four possible features along with one hybrid feature encoding mechanisms which are the target-class motif previously indicated by Amino Acid Composition, Dipeptide Composition, Tripeptide Composition (TPC), Pseudo Amino Acid Composition (PAAC), and their Hybrid. Machine Learning algorithms make high-performance and accurate prediction tools. Moreover, highly variable and ideal deep feature selection is done using an ANOVA-based F score for feature pruning. Experiments on a range of algorithms are carried out to identify the optimal operating strategy due to the diverse nature of learning. Following analysis of the empirical results, Naïve Bayes with TPC and Hybrid feature space outperforms other methods with 0.99 accuracy score on the testing dataset. To find the model generalization an external validation is carried out. In external datasets, the Extra Trees with PAAC features outperforms with the accuracy of 0.94. The comparison study shows that our suggested model will predict gastric ACPs more accurately and will be useful in drug development and gastric cancer. The predictive model can be freely accessed at https://github.com/humeraazad10/G-ACP.git.Communicated by Ramaswamy H. Sarma.

Disability pension and sociodemographic & work-related risk factors among 2.3 million migrants and natives in Finland (2011-2019): a prospective population study.

BACKGROUND: Increasing employment and immigration have been proposed as possible solutions to tackle the problem of the labour force shortage in aging societies. Ensuring sufficient health and work ability among migrants is a key factor in increasing and maintaining their employment. Many studies have found higher disability pension (DP) rates among migrants compared to natives but such studies lack in determining the risk of DP by occupational class and industrial sector. This study explores the risk of DP and the contribution of sociodemographic and work-related factors between migrants and natives in Finland. METHODS: Full-population panel data obtained from the administrative registers of Statistics Finland were used to study 2.3 million individuals aged 25-60 years in 2010. We calculated hazard ratios (HR) and their 95% confidence intervals (CI) to estimate the risk of having a DP in 2011-2019 using Cox proportional hazard models adjusting for different sociodemographic and work-related factors. RESULTS: Compared to natives, migrants had a lower risk of a DP (HR 0.58, 95% CI 0.53-0.63). We found great variation between countries of origin, where compared to natives, migrants from refugee-exporting countries (HR 1.37, 95% CI 1.22-1.53) and other non-European countries (HR 1.30; CI 1.18-1.43) had a higher risk of DP, but migrants from other countries did not differ or had a slightly lower risk of DP than natives. The associations between sociodemographic factors and the risk of DP were very similar between natives and migrants. CONCLUSION: Migrants had a lower risk of a DP than natives except for migrants from outside Europe. The associations between different sociodemographic and work-related factors and the risk of DP were similar between natives and migrants and did not completely explain the differences in the risk of DP.

The Effect of Soil-Structure Interaction on the Seismic Response of Structures Using Machine Learning, Finite Element Modeling and ASCE 7-16 Methods.

Seismic design of structures taking into account the soil-structure interaction (SSI) methods is considered to be more efficient, cost effective, and safer then fixed-base designs, in most cases. Finite element methods that use direct equations to solve SSI problems are very popular, but the prices of the software are very high, and the analysis time is very long. Even though some low-cost and efficient software are available, the structures are mostly analyzed for the superstructure only, without using the geotechnical properties of the ground and its interaction effects. The reason is that a limited number of researchers have the knowledge of both geotechnical and structural engineering to model accurately the coupled soil-structure system. However, a cost-effective, less time-consuming and easy-to-implement technique is to analyze the structure along with ground properties using machine learning methods. The database techniques using machine learning are robust and provide reliable results. Thus, in this study, machine learning techniques, such as artificial neural networks and support vector machines are used to investigate the effect of soil-structure interactions on the seismic response of structures for different earthquake scenarios. Four frame structures are investigated by varying the soil and seismic properties. In addition, varying sample sizes and different optimization algorithms are used to obtain the best machine learning framework. The input parameters contain both soil and seismic properties, while the outputs consist of three engineering demand parameters. The network is trained using three and five-story buildings and tested on a three-story building with mass irregularity and a four-story building. Furthermore, the proposed method is compared with the dynamic responses obtained using fixed-base and ASCE 7-16 SSI methods. The proposed machine learning method showed better results compared with fixed-base and ASCE 7-16 methods with the nonlinear time history analysis results as a reference.

Assessment of risk factors associated with outbreak of hepatitis A in Shakrial, Rawalpindi, Pakistan.

INTRODUCTION: Two patients with jaundice reported to the National Institute of Health (NIH), Islamabad from Shakrial, Rawalpindi in April 2017. An outbreak investigation team was formulated to assess the disease magnitude, risk factors and control measures. METHODOLOGY: A case-control study was conducted in 360 houses in May 2017. The case definition was: onset of acute jaundice with any symptom including fever, right upper-quadrant pain, loss of appetite, dark urine, nausea and vomiting among Shakrial residents from March 10 - May 19, 2017. Four age and gender matched controls were selected for each case. Blood samples were sent to the NIH for laboratory confirmation. Frequencies, attack rates (AR), odd ratios, and logistic regression were computed at 95% confidence interval and p < 0.05. RESULTS: A total of 25 cases (23 new) were identified with mean age 8 years and male to female ratio 1.5:1. Overall AR was 1.39% and the most severely affected age-group was 5-10 years (AR of 3.92%). Multivariate analysis revealed that raw vegetable consumption, lack of awareness and poor handwashing practices had significant association with disease spread. All blood samples were positive for hepatitis A, and no resident was previously vaccinated. Lack of awareness of disease spread among the community was the most probable reason for the outbreak. There were no new cases during follow up until May 30, 2017. CONCLUSIONS: Healthcare departments should implement public policies towards the management of hepatitis A in Pakistan. Health awareness sessions and vaccination for children ≤ 16 years age is recommended.

A Machine Learning Architecture Replacing Heavy Instrumented Laboratory Tests: In Application to the Pullout Capacity of Geosynthetic Reinforced Soils.

For economical and sustainable benefits, conventional retaining walls are being replaced by geosynthetic reinforced soil (GRS). However, for safety and quality assurance purposes, prior tests of pullout capacities of these materials need to be performed. Conventionally, these tests are conducted in a laboratory with heavy instruments. These tests are time-consuming, require hard labor, are prone to error, and are expensive as a special pullout machine is required to perform the tests and acquire the data by using a lot of sensors and data loggers. This paper proposes a data-driven machine learning architecture (MLA) to predict the pullout capacity of GRS in a diverse environment. The results from MLA are compared with actual laboratory pullout capacity tests. Various input variables are considered for training and testing the neural network. These input parameters include the soil physical conditions based on water content and external loading applied. The soil used is a locally available weathered granite soil. The input data included normal stress, soil saturation, displacement, and soil unit weight whereas the output data contains information about the pullout strength. The data used was obtained from an actual pullout capacity test performed in the laboratory. The laboratory test is performed according to American Society for Testing and Materials (ASTM) standard D 6706-01 with little modification. This research shows that by using machine learning, the same pullout resistance of a geosynthetic reinforced soil can be achieved as in laboratory testing, thus saving a lot of time, effort, and money. Feedforward backpropagation neural networks with a different number of neurons, algorithms, and hidden layers have been examined. The comparison of the Bayesian regularization learning algorithm with two hidden layers and 12 neurons each showed the minimum mean square error (MSE) of 3.02 × 10-5 for both training and testing. The maximum coefficient of regression (R) for the testing set is 0.999 and the training set is 0.999 for the prediction interval of 99%.

Alleviation of cadmium stress in rice by inoculation of Bacillus cereus.

Heavy metal resistant bacteria are of great importance because they play a crucial role in bioremediation. In the present study, 11 bacterial strains isolated from industrial waste were screened under different concentrations of cadmium (Cd) (100 µM and 200 µM). Among 11 strains, the Cd tolerant Bacillus cereus (S6D1-105) strain was selected for in vitro and in vivo studies. B. cereus was able to solubilize potassium, and phosphate as well as produce protease and siderophores during plate essays. Moreover, we observed the response of hydroponically grown rice plants, inoculated with B. cereus which was able to promote plant growth, by increasing plant biomass, chlorophyll contents, relative water content, different antioxidant enzymatic activity such as catalase, superoxide dismutase, ascorbate peroxidase, polyphenol oxidase and phenylalanine ammonia-lyase and reducing malondialdehyde content in both roots and leaves of rice plants under Cd stress. Our results showed that the B. cereus can be used as a biofertilizer which might be beneficial for rice cultivation in Cd contaminated soils.

Exploring the genetic potential of Pakistani soybean cultivars through RNA-seq based transcriptome analysis.

BACKGROUND: Soybean is largely grown and considered among the top oilseed crops. Three Pakistani cultivars, NARC-II (N), Swat-84 (S), and Rawal-I (R) were employed for RNA-Seq based transcriptome analysis to explore their genetic potential and performance in our local environment. METHODS AND RESULTS: We grew the plants in glass house at same conditions and sampled leaves for RNA-Seq analysis in triplicate for each variety. We retrieved 2225 differentially expressed genes (DEGs) between S vs R, 2591 DEGs between S vs N, and 1221 DEGs between R vs N cultvars. These genes consist of transcription factors representing Basic Helix-loop Helix, myeloblastosis, ethylene response factors, and WRKY amino acid motif (WRKY) type major families that were up-regulated. KEGG pathway analysis revealed that MAPK, plant hormone signal transduction, and Phenylpropanoid biosynthesis pathways were the most dominant pathways involved in plant defense and growth. Comparative analysis showed that Swat-84 (S) cultivar had better gene expression among these varieties having higher number of DEGs, where mostly genes related to important phenotypic traits were up regulated. CONCLUSIONS: This is a pilot study to investigate and functionally characterise the DEG involved in the stress response in the cultivars studied.

Photosynthesis and Salt Exclusion Are Key Physiological Processes Contributing to Salt Tolerance of Canola (Brassica napus L.): Evidence from Physiology and Transcriptome Analysis.

Plant salt tolerance is controlled by various physiological processes such as water and ion homeostasis, photosynthesis, and cellular redox balance, which are in turn controlled by gene expression. In the present study, plants of six canola cultivars (DGL, Dunkled, Faisal Canola, Cyclone, Legend, and Oscar) were evaluated for salt tolerance by subjecting them to 0 or 200 mM NaCl stress. Based on growth, cultivars DGL, Dunkled, and Faisal Canola were ranked as salt tolerant, while cultivars Cyclone, Legend, and Oscar were ranked as salt-sensitive ones. Differential salt tolerance in these canola cultivars was found to be associated with a relatively lower accumulation of Na+ and greater accumulation of K+ in the leaves, lower oxidative damage (MDA), and better antioxidative defense system (Superoxide dismutase, SOD; peroxidase, POD, and catalase, CAT). Cultivar Oscar was the poorest to discriminate Na+ and K+ uptake and accumulation in leaves and had poor antioxidant potential to scavenge ROS. Salt stress did not affect the structural stability of photosystem-II (PSII) till three weeks, thereafter it caused a significant decrease. Salt stress increased the performance index (PIABS) by increasing the density of active reaction centers in Oscar. Salt stress decreased the antenna size thereby lowering the absorption and trapping energy flux, and maintaining the electron transport with an increase in heat dissipation. This may represent a potential mechanism to cope with salt stress. Transcriptome analysis of salt-sensitive cultivar Oscar further revealed that salt stress down-regulated DEGs related to hormonal signal transduction pathways, photosynthesis, and transcription factors, while DEGs related to the biosynthesis of amino acid and ion transport were up-regulated. In conclusion, salt tolerance in canola cultivars was associated with ion exclusion and maintenance of photosynthesis. Salt stress sensitivity in cultivar Oscar was mainly associated with poor control of ion homeostasis which caused oxidative stress and reduced photosynthetic efficiency.

Community-based social and demographic assessment of knowledge, attitudes, practices and medical conditions related to vitamin D deficiency in Gilgit Baltistan, Pakistan.

Vitamin D is an important nutrient for bone health, and vitamin D deficiency increases the risk of various diseases. Gilgit Baltistan, the northern-most area of Pakistan, has a high prevalence of vitamin D deficiency, despite many nutritional and food safety programmes. The present study aimed to find how knowledge, attitudes and practices associated with vitamin D related to the prevalence of vitamin D deficiency among people residing in different areas of Gilgit Baltistan. The cross-sectional study was descriptive and used data from a survey carried out between February 2019 and December 2020 on individuals of both sexes aged 10 years or over in Gilgit Baltistan. Of the 575 survey participants, 306 (53.2%) had experienced signs and symptoms of vitamin D deficiency, i.e. tiredness, fatigue and bone weakness. Approximately 64.8% had some general knowledge of vitamin D and its relation to health. Participants aged 19-25 years had the highest scores on knowledge of vitamin D. Only 22.7% of interviewees had ever taken any supplements and only 25.6% often exposed themselves to sunlight. Females' mean knowledge score (28.7; SD 7.02) was higher than that of males (24; SD 9.01). A lack of consistency was observed between attitude towards daylight exposure and knowledge of vitamin D. There was a large correlation between knowledge and attitude (p = 0.001), while a non-significant association was demonstrated between knowledge and practices (p = 0.1). Better knowledge, attitude and practices by people living in cities or more-developed regions indicates that education can be an effective way to provide awareness regarding micronutrient deficiencies. More emphasis is needed on enhancing knowledge, awareness and practices associated with vitamin D deficiency in rural areas of Pakistan. It is strongly recommended that an awareness campaign on micronutrients is launched in both rural and urban areas of Pakistan, concentrating on poor socioeconomic settings.

Transcriptome Analysis of Host Inflammatory Responses to the Ectoparasitic Mite Sarcoptes scabiei var. hominis.

Scabies, a human skin infestation caused by the ectoparasitic mite Sarcoptes scabiei var. hominis, affects more than 200 million people globally. The prevailing knowledge of the disease process and host immune response mechanisms is limited. A better understanding of the host-parasite relationship is essential for the identification of novel vaccine and drug targets. Here we aimed to interrogate the transcriptomic profiles of mite-infested human skin biopsies with clinical manifestations of ordinary scabies subjects ("OS"; n = 05) and subjects naive to scabies ("control"; n = 03) using RNASeq data analysis. A combined clustering, network, and pathway mapping approach enabled us to identify key signaling events in the host immune and pro-inflammatory responses to S. scabiei infestation. The clustering patterns showed various differentially expressed genes including inflammatory responses and innate immunity genes (DEFB4A, IL-19, CXCL8, CSF3, SERPINB4, S100A7A, HRNR) and notably upregulation of the JAK-STAT pathway in scabies-infested samples. Mite-infested human skin biopsies (GSE178563) were compared with an ex-vivo porcine infested model (E-MTAB-6433) and human skin equivalents (GSE48459). Marked enrichment of immune response pathways (JAK-STAT signaling, IL-4 and IL-13 pathway, and Toll receptor cascade), chemokine ligands and receptors (CCL17, CCL18, CCL3L1, CCL3L3, CCR7), and cytokines (IL-13 and IL-20) were observed. Additionally, genes known for their role in psoriasis and atopic dermatitis were upregulated, e.g., IL-19. The detailed transcriptomic profile has provided an insight into molecular functions, biological processes, and immunological responses and increased our understanding about transcriptomic regulation of scabies in human.

Community Based Assessment of Behavior and Awareness of Risk Factors of Cystic Echinococcosis in Major Cities of Pakistan: A One Health Perspective.

Background: The parasitic disease, cystic echinococcosis (CE), is a serious health problem in Pakistan. Risk of disease transmission is increased by economic and political instability, poor living conditions, and limited awareness of hygienic practices. The current study aimed to investigate the community perception and awareness regarding the risk factors of CE in Pakistan, from a One Health perspective. Methods: We conducted a community-based survey involving 454 participants in the major cities of Pakistan. Quantitative data based on knowledge, attitude, and practices (KAP), the One Health concept, risk factors, and community perception of CE among the general population of the major cities of Pakistan were collected. The questions included those related to knowledge, attitude, practices, One Health concept, risk factors, and community perception. The Chi-squared test was applied to determine the associations regarding KAPs across socio-demographic parameters. Results: KAPs had no significant associations with sociodemographic aspects such as age, sex, religion, ethnicity, education, marital status, occupation, or financial status of the participants. The findings indicated a lack of awareness about CE among the participants. Respondents were unaware of the risk factors and the One Health concept of CE. However, the community attitude and perception were positive toward the control of CE. Conclusion: Illiteracy, deficient sanitation systems and lack of awareness are the contributing factors to CE in Pakistan. It is necessary to make the community aware regarding CE and its importance. Increasing this awareness represents an important step toward the eradication and control of CE.

Sodium nitroprusside application improves morphological and physiological attributes of soybean (Glycine max L.) under salinity stress.

Salinity is among the major abiotic stresses negatively affecting the growth and productivity of crop plants. Sodium nitroprusside (SNP) -an external nitric oxide (NO) donor- has been found effective to impart salinity tolerance to plants. Soybean (Glycine max L.) is widely cultivated around the world; however, salinity stress hampers its growth and productivity. Therefore, the current study evaluated the role of SNP in improving morphological, physiological and biochemical attributes of soybean under salinity stress. Data relating to biomass, chlorophyll and malondialdehyde (MDA) contents, activities of various antioxidant enzymes, ion content and ultrastructural analysis were collected. The SNP application ameliorated the negative effects of salinity stress to significant extent by regulating antioxidant mechanism. Root and shoot length, fresh and dry weight, chlorophyll contents, activities of various antioxidant enzymes, i.e., catalase (CAT), superoxide dismutase (SOD), peroxidase (POD) and ascorbate peroxidase (APX) were improved with SNP application under salinity stress compared to control treatment. Similarly, plants treated with SNP observed less damage to cell organelles of roots and leaves under salinity stress. The results revealed pivotal functions of SNP in salinity tolerance of soybean, including cell wall repair, sequestration of sodium ion in the vacuole and maintenance of normal chloroplasts with no swelling of thylakoids. Minor distortions of cell membrane and large number of starch grains indicates an increase in the photosynthetic activity. Therefore, SNP can be used as a regulator to improve the salinity tolerance of soybean in salt affected soils.

Protein quantification and enzyme activity estimation of Pakistani wheat landraces.

Wheat is a major food grain in Pakistan having a prominent role in agriculture as well as the economic status of the country. In the current study, seeds of 99 wheat landraces were characterized for the quantification of seed storage proteins (Albumins, Globulin, Gliadins, and Glutenin), enzyme activities of antioxidant enzymes i.e. Ascorbate peroxidase (APX), Catalase (CAT), Superoxide dismutase (SOD), Peroxidase (POD), one hydrolytic enzyme Protease (PROT) and non-enzymatic antioxidant enzyme Ascorbic acid (AsA). The landraces were categorized into low, medium, and high based on protein concentration and enzymes activities/content. The majority of the landraces were placed in the medium category. However, for the AsA parameter majority of the landraces were placed in the low category. The highest concentration of total extracted protein (184.88±0.7 mg/g. wt.), globulins (21.35±0.43 mg/g. wt.) and glutenin (20±0.04 mg/g. wt.) as well as the high activity of SOD (303±16.80 Units/g. wt.), and Ascorbic acid (533±36.1 Units/g. wt.) was identified in the wheat landrace "11757" collected from district Panjgur (Balochistan). The wheat landrace "11760", collected from district Kech (Balochistan), contained the highest albumins concentration (65.42±0.02 mg/g. wt.) and highest activity for CAT (589.5±61.20 Units/g. wt.). The highest activity of POD (32341± 91.3) and PROT was observed in seeds of the wheat landrace "11618" collected from the Gilgit Baltistan region of Pakistan. The principal component analysis showed that the great variations existed for the tested parameters among the wheat landraces. The landraces with a high concentration of seed storage proteins and antioxidant enzyme activities can be used for breeding purposes to improve the nutrimental quality of wheat cultivars.

Controlling the dissolution of iron through the development of nanostructured Fe-Mg for biomedical applications.

In the field of biodegradable metallic materials, rapid and non-uniform biodegradation, caused by uncontrolled corrosion rates, is a potential shortcoming. Among the prominent biodegradable materials, magnesium is an attractive choice, however, it is prone to rapid dissolution. In contrast, iron possesses a slow dissolution rate. To approach the middle ground, instead of making magnesium more corrosion-resistant, the less-explored approach of making iron less corrosion-resistant is employed here. In this study, iron, and magnesium, having contrasting corrosion rates, are combined via magnetron co-sputtering. The idea of combinatorial synthesis is employed to fabricate two model nanostructured Fe-Mg samples, i.e. CSFM-1 (Fe85Mg15), and CSFM-2 (Fe65Mg35), exhibiting a controlled and uniform degradation in phosphate-buffer saline solution. The structural characterization of the two samples demonstrates a substitutional solid solution of bcc-Fe-Mg in CSFM-1 and an amorphous short-range-ordered structure in the CSFM-2 sample. Electrochemical investigation shows increased corrosion rates for the two Fe-Mg samples in comparison to pure Fe, validated by relatively active corrosion potentials, higher corrosion current densities, faster anodic dissolution, and lower charge transfer resistances, governed by chemical composition and non-equilibrium nanostructures. Finally, nano-indentation testing of the two samples reveals relatively higher hardness and lower elastic moduli, a suitable combination for bio-implants. STATEMENT OF SIGNIFICANCE: The use of Mg as a biodegradable in-vivo  implant material is problematic because of its high dissolution rate and potential for hydrogen gas generation. This is the first time that the idea of combinatorial synthesis is employed to fabricate two model nanostructured Fe-Mg systems, i.e. CSFM-1 (Fe85Mg15), and CSFM-2 (Fe65Mg35), exhibiting a controlled and uniform degradation. The structural characterization of the two systems demonstrates a substitutional solid solution of bcc-Fe-Mg in CSFM-1 and an amorphous short-range-ordered structure in the CSFM-2 system. Electrochemical investigation shows increased biodegradation rates for the two Fe-Mg systems in comparison to pure Fe, validated by relatively active corrosion potentials, higher corrosion current densities, faster anodic dissolution, and lower charge transfer resistances, governed by chemical composition and non-equilibrium nanostructures.

Microstructural examination and corrosion behavior of selective laser melted and conventionally manufactured Ti6Al4V for dental applications.

The selective laser melting of Ti6Al4V would induce definite changes in the microstructure that may affect its corrosion properties. Microstructural examination showed the formation of relatively thin beta (ß) lamella in selective laser melted (SLM) Ti6Al4V compared to wrought Ti6Al4V. X-ray diffraction analysis (XRD) analysis confirmed the presence of alpha and beta phases in both SLM and wrought Ti6Al4V. However, the higher concentration of the ß phase in SLM Ti6Al4V compared to wrought Ti6Al4V was evident in the microstructure. As candidate dental implant materials, the corrosion behavior of both SLM and wrought Ti6Al4V was assessed in artificial saliva (AS) and deionized water (DI) containing various species i.e. fluoride (F), calcium chloride (CaCl2) and lactic acid (LA). Electrochemical impedance spectroscopy and potentiodynamic polarization analysis was carried out to estimate the corrosion behavior of SLM and wrought Ti6Al4V at room temperature. SLM Ti6Al4V offered better corrosion resistance than wrought Ti6Al4V in all solutions at pH > 6. However, wrought Ti6Al4V comparatively presented high corrosion resistance in AS + LA, DI + CaCl2 and DI + LA solutions (pH < 6). The lower dissolution rate of SLM Ti6Al4V (at pH > 6) was attributed to larger ß content in the microstructure compared to wrought Ti6Al4V.

Transcriptome-Enabled Network Inference Revealed the GmCOL1 Feed-Forward Loop and Its Roles in Photoperiodic Flowering of Soybean.

Photoperiodic flowering, a plant response to seasonal photoperiod changes in the control of reproductive transition, is an important agronomic trait that has been a central target of crop domestication and modern breeding programs. However, our understanding about the molecular mechanisms of photoperiodic flowering regulation in crop species is lagging behind. To better understand the regulatory gene networks controlling photoperiodic flowering of soybeans, we elucidated global gene expression patterns under different photoperiod regimes using the near isogenic lines (NILs) of maturity loci (E loci). Transcriptome signatures identified the unique roles of the E loci in photoperiodic flowering and a set of genes controlled by these loci. To elucidate the regulatory gene networks underlying photoperiodic flowering regulation, we developed the network inference algorithmic package CausNet that integrates sparse linear regression and Granger causality heuristics, with Gaussian approximation of bootstrapping to provide reliability scores for predicted regulatory interactions. Using the transcriptome data, CausNet inferred regulatory interactions among soybean flowering genes. Published reports in the literature provided empirical verification for several of CausNet's inferred regulatory interactions. We further confirmed the inferred regulatory roles of the flowering suppressors GmCOL1a and GmCOL1b using GmCOL1 RNAi transgenic soybean plants. Combinations of the alleles of GmCOL1 and the major maturity locus E1 demonstrated positive interaction between these genes, leading to enhanced suppression of flowering transition. Our work provides novel insights and testable hypotheses in the complex molecular mechanisms of photoperiodic flowering control in soybean and lays a framework for de novo prediction of biological networks controlling important agronomic traits in crops.

Knowledge, attitude & practices (KAPs) regarding rabies endemicity among the community members, Pakistan.

INTRODUCTION: Rabies is one of the most neglected tropical diseases in numerous regions of the world. Annually, 60,000 deaths have been reported, mostly in Asia and Africa. Dogs are responsible for approximately 99% of human deaths due to rabies. Consequently, incidences of canine bites are increased yet rabies continues to remain a neglected disease in Pakistan. The objective of the study was to investigate the Knowledge, Attitude and Practices (KAP) for rabies, among the participants from urban and rural population of Rawalpindi and Islamabad, Pakistan. METHODS: Data was collected by conducting household based cross-sectional survey over a period of 5 months (from January 2018 to May 2018). Quantitative data was collected in the form of questionnaires to investigate awareness and knowledge of rabies among the participants. The questionnaire compromised of socio-demographic features and degree of KAP with respect to rabies management and control. RESULTS: A total sample size of 434 participants responded in the study. Among them, 89.4% were found to have heard about rabies, 38.7% have no knowledge of signs and symptoms, 93.3% knew that infected dogs are the major cause of spreading rabies and 77.6% considered that the vaccination of animals is important for prevention of rabies. However, only 39.8% actively seek medical treatment at a hospital if bitten by a dog. From the 434 participants 61 reported dog bites, of which 68.8% (42/61) were male and 49.1% (30/61) belonged to age group of 6-25 years. CONCLUSION: This study concludes that respondents have relatively limited knowledge, inconsistent attitude and poor practices towards rabies prevention and control. There is a high need of imparting knowledge to the population from multidisciplinary programs for effective management and prevention of rabies.

Functionalized graphene oxide coating on Ti6Al4V alloy for improved biocompatibility and corrosion resistance.

The present study focused on the development of magnesium-functionalized graphene oxide (FGO) coating on titanium alloy (Ti6Al4V) by electrophoretic deposition. Graphene oxide (GO) was synthesized by modified Hummers' method and functionalized with magnesium ions. X-ray diffraction, infrared spectroscopy (IR) and Raman spectroscopy were employed to confirm the synthesis of GO and GO-coatings on Ti6Al4V. Functionalization of GO with Mg ions was confirmed by energy dispersive X-ray spectroscopy. The surface morphology of coated samples was examined through scanning electron microscopy. Reduction of FGO coating (labelled as rFGO) by heating at 200 °C was confirmed by IR. The rFGO coated Ti6Al4V was found to be hydrophilic in nature as determined by contact angle measurement which showed reduction in the contact angle of Ti6Al4V from 95.4° to 42.1°. The percent cell viability over the coated sample was appreciably improved compared to as-received Ti6Al4V sample owing to hydrophilicity of the former. The positive shift in open circuit potential and increase in polarization resistance was observed after coating Ti6Al4V samples with FGO. The significant decrease in the corrosion current density and negative polarization loop in the reverse scan of samples also confirmed the improved corrosion resistance of rFGO-coated Ti6Al4V over uncoated Ti6Al4V in the PBS solution. Furthermore, the impedance spectroscopy revealed that the preferential adsorption of ionic species (indicated by large Rads) at the surface improved the barrier characteristics of rFGO coated samples and exhibited an order of magnitude higher Rct compared to as-received samples.