Automating Team Formation Using Machine Learning

Collaborative teams are the primary vehicle for coordinating experts with diverse skills for a particular project in academia, manufacturing, freelancing, and the healthcare sector. Forming a successful team whose members can effectively collaborate and deliver the outcomes within the specified constraints, such as planned budget and timeline, is challenging due to the immense number of candidates with various backgrounds, skills, and personality traits, as well as unknown synergistic balance among them; not all teams with best experts are necessarily successful. Historically, teams have been formed by relying on experience and instinct, resulting in suboptimal team composition due to the incomprehensive knowledge of candidates and hidden cognitive biases, among others. To automate forming optimum teams, current methods perform an exhaustive search over subgraphs of expert collaboration networks. They are not, however, scalable for large networks. In our research group, we propose machine learning models that learn relationships among experts and their social attributes through neural architectures. We aimed at bringing efficiency while maintaining efficacy by employing inherently iterative and online learning procedures in neural architectures. We also aimed at utilizing unsuccessful teams to convey complementary negative signals to neural models. Based on the closed-world assumption, we assume no currently known team of experts for the required skills is to be unsuccessful. Our experiments on two large-scale benchmark datasets, computer science research publications (DBLP) and movies (IMDB), show that neural models that take unsuccessful teams into account are faster and more accurate in forming collaborative teams

Flat Gain on C-band using Raman-EDFA Hybrid Optical Amplifier for DWDM System

An efficient optical link which provides flat gain over C-band  has been designed using a raman fibre amplifier and an Erbium doped fibre amplifier (EDFA) in a hybrid configuration for dense wave division multiplexing  system (DWDM). With an input power of 3 mW, gain of  > 40 dB is obtained  across the range of 1530 nm to 1565 nm with a gain variation of <1.2 dB without using any gain flattening techniques

Smart Grid Sensor Monitoring Based on Deep Learning Technique with Control System Management in Fault Detection

The smart grid environment comprises of the sensor for monitoring the environment for effective power supply, utilization and establishment of communication. However, the management of smart grid in the monitoring environment isa difficult process due to diversifieduser request in the sensor monitoring with the grid-connected devices. Presently, context-awaremonitoring incorporates effective management of data management and provision of services in two-way processing and computing. In a heterogeneous environment context-aware, smart grid exhibits significant performance characteristics with the grid-connected communication environment for effective data processing for sustainability and stability. Fault diagnoses in the automated system are formulated to diagnose the fault separately. This paper developed anoptimized power grid control model (OPGCM) model for fault detection in the control system model for grid-connected smart home appliances. OPGCM model uses the context-aware power-awarescheme for load management in grid-connected smart homes. Through the adaptive smart grid model,power-aware management is incorporated with the evolutionary programming model for context-awareness user communication. The OPGCM modelperforms fault diagnosis in the grid-connected control system initially, Fault diagnosis system comprises of a sequential process with the extraction of the statistical features to acquirea sustainable dataset with effective signal processing. Secondly, the features are extracted based on the sequential process for the acquired dataset with a reduction of dimensionality. Finally, the classification is performed with the deep learning model to predict or identify the fault pattern. With the OPGCM model, features are optimized with the whale optimization model to acquire features to perform fault diagnosis and classification. Simulation analysis expressed that the proposed OPGCM model exhibits ~16% improved classification accuracy compared with the ANN and HMM model

Cytotoxic Effect of Poly-Dispersed Single Walled Carbon Nanotubes on Erythrocytes In Vitro and In Vivo

Single wall Carbon Nanotubes (SWCNTs) are hydrophobic and do not disperse in aqueous solvents. Acid functionalization of SWCNTs results in attachment of carboxy and sulfonate groups to carbon atoms and the resulting acid functionalized product (AF-SWCNTs) is negatively charged and disperses easily in water and buffers. In the present study, effect of AF-SWCNTs on blood erythrocytes was examined. Incubation of mouse erythrocytes with AF-SWCNTs and not with control SWCNTs, resulted in a dose and time dependent lysis of erythrocyte. Using fluorescence tagged AF-SWCNTs, binding of AF-SWCNTs with erythrocytes could be demonstrated. Confocal microscopy results indicated that AF-SWCNTs could enter the erythrocytes. Treatment with AF-SWCNTs resulted in exposure of hydrophobic patches on erythrocyte membrane that is indicative of membrane damage. A time and dose dependent increase in externalization of phosphatidylserine on erythrocyte membrane bilayer was also found. Administration of AF-SWCNTs through intravenous route resulted in a transient anemia as seen by a sharp decline in blood erythrocyte count accompanied with a significant drop in blood haemoglobin level. Administration of AF-SWCNTs through intratracheal administration also showed significant decline in RBC count while administration through other routes (gavage and intra-peritoneal) was not effective. By using a recently developed technique of a two step in vivo biotinylation of erythrocytes that enables simultaneous enumeration of young (age <10 days) and old (age>40 days) erythrocytes in mouse blood, it was found that the in vivo toxic effect of AF-SWCNTs was more pronounced on older subpopulation of erythrocytes. Subpopulation of old erythrocytes fell after treatment with AF-SWCNTs but recovered by third day after the intravenous administration of AF-SWCNTs. Taken together our results indicate that treatment with AF-SWCNTs results in acute membrane damage and eventual lysis of erythrocytes. Intravenous administration of AF-SWCNTs resulted in a transient anemia in which older erythrocytes are preferably lysed

Comparative structural evolution under pressure of powder and single crystals of the layered antiferromagnet FePS3

FePS3 is a layered magnetic van der Waals compound that undergoes a Mott insulator-metal transition under applied pressure. The transition has an associated change in the crystal symmetry and magnetic structure. Understanding the underlying physics of these transitions requires a detailed understanding of the crystal structure as a function of pressure. Two conflicting models have previously been proposed for the evolution of the structure with pressure. To settle the disagreement, we present a study of the pressure-dependent crystal structures using both single-crystal and powder x-ray diffraction measurements. We show unambiguously that the highest-pressure transition involves a collapse of the interplanar spacing, along with an increase in symmetry from a monoclinic to a trigonal space group, to the exclusion of other models. Our collected results are crucial for understanding high-pressure behavior in these materials and demonstrate a clear and complete methodology for exploring complex two-dimensional material structures under pressure

Genetic diversity analysis in the Hypericum perforatum populations in the Kashmir valley by using inter-simple sequence repeats (ISSR) markers

Assessment of genetic variability among the Hypericum perforatum populations is critical to the development of effective conservation  strategies in the Kashmir valley. To obtain accurate estimates of genetic diversity among and within populations of H. perforatum, inter-simple sequence repeats (ISSR) markers were used. The study was aimed to check, whether ISSR fingerprinting may be a useful tool for studying genetic variations among H. perforatum populations in the Kashmir valley (India). A total of 15 ISSR primers were tested with the 20 genotypes of H. perforatum. The ten informative primers were selected and used to evaluate the degree of polymorphism and genetic relationships within and among all the H. perforatum populations. ISSR of 20 genotypes analysis yielded 98 fragments that could be scored, of which 71 were polymorphic, with an average of 7.1 polymorphic fragments per primer. Number of amplified fragments varied in size from 150 to 1650 bp. Percentage of polymorphism ranged from 60% to a maximum of 100%. Resolving power ranged from a minimum of 7.7 to a maximum of 14.3. Shannon indexes ranges from 0.166 to 0.389 with an average of 0.198 and Nei’s genetic diversity (h) ranges from 6.98 to 9.8. Estimated value of gene flow (Nm = 0.579) indicated that there was limited gene flow among the populations. The genetic diversity (Ht) within the population of 0.245 was clearly higher than that of among population genetic diversity (Hs= 0.115), indicating an out-crossing predominance in the studied populations. Analysis of molecular variance by ISSR markers indicated that over half of the total variation in the studied populations (58%) could be accounted for by differences among the 8 divisions, with a further 42% being accounted for by the variation among populations within a division.The dendrogram grouping the populations by unweighted pair-group method with arithmeticaverages (UPGMA) method revealed eight main clusters. In conclusion, combined analysis of ISSR markers and hypericin content is an optimal approach for further progress and breeding programs.Keywords: Hypericum perforatum (St. John's Wort), inter-simple sequence repeats (ISSR) markers, unweighted pair-group method with arithmetic averages (UPGMA), Nei’s genetic diversityAfrican Journal of Biotechnology, Vol. 13(1), pp. 18-31, 1 January, 201

Evaluation Of Mechanical and Biocompatibility Properties of Hydroxyapatite/Manganese Dioxide Nanocomposite Scaffolds for Bone Tissue Engineering Application

The aim of this research was to evaluate the mechanical properties, biocompatibility, and degradation behavior of scaffolds made of pure hydroxyapatite (HA) and HA‐modified by MnO2 for bone tissue engineering applications. HA and MnO2 were developed using sol‐gel and precipitation methods, respectively. The scaffolds properties were characterized using X‐ray diffraction (XRD), Fourier transform spectroscopy (FTIR), scanning electron microcopy (SEM), energy dispersive spectroscopy (EDS), and transmission electron microscopy (TEM). The interaction of scaffold with cells was assessed using in vitro cell proliferation and alkaline phosphatase (ALP) assays. The obtained results indicate that the HA/ MnO2 scaffolds possess higher compressive strength, toughness, hardness, and density when compared to the pure HA scaffolds. After immersing the scaffold in the SBF solution, more deposited apatite appeared on the HA/MnO2, which results in the rougher surface on this scaffold compared to the pure HA scaffold. Finally, the in vitro biological analysis using human osteoblast cells reveals that scaffolds are biocompatible with adequate ALP activit

Morphological characteristics of motor neurons do not determine their relative susceptibility to degeneration in a mouse model of severe spinal muscular atrophy

Spinal muscular atrophy (SMA) is a leading genetic cause of infant mortality, resulting primarily from the degeneration and loss of lower motor neurons. Studies using mouse models of SMA have revealed widespread heterogeneity in the susceptibility of individual motor neurons to neurodegeneration, but the underlying reasons remain unclear. Data from related motor neuron diseases, such as amyotrophic lateral sclerosis (ALS), suggest that morphological properties of motor neurons may regulate susceptibility: in ALS larger motor units innervating fast-twitch muscles degenerate first. We therefore set out to determine whether intrinsic morphological characteristics of motor neurons influenced their relative vulnerability to SMA. Motor neuron vulnerability was mapped across 10 muscle groups in SMA mice. Neither the position of the muscle in the body, nor the fibre type of the muscle innervated, influenced susceptibility. Morphological properties of vulnerable and disease-resistant motor neurons were then determined from single motor units reconstructed in Thy.1-YFP-H mice. None of the parameters we investigated in healthy young adult mice - including motor unit size, motor unit arbor length, branching patterns, motor endplate size, developmental pruning and numbers of terminal Schwann cells at neuromuscular junctions - correlated with vulnerability. We conclude that morphological characteristics of motor neurons are not a major determinant of disease-susceptibility in SMA, in stark contrast to related forms of motor neuron disease such as ALS. This suggests that subtle molecular differences between motor neurons, or extrinsic factors arising from other cell types, are more likely to determine relative susceptibility in SMA

Single photon emission computed tomography (SPECT) of anxiety disorders before and after treatment with citalopram

BACKGROUND: Several studies have now examined the effects of selective serotonin reuptake inhibitor (SSRI) treatment on brain function in a variety of anxiety disorders including obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), and social anxiety disorder (social phobia) (SAD). Regional changes in cerebral perfusion following SSRI treatment have been shown for all three disorders. The orbitofrontal cortex (OFC) (OCD), caudate (OCD), medial pre-frontal/cingulate (OCD, SAD, PTSD), temporal (OCD, SAD, PTSD) and, thalamic regions (OCD, SAD) are some of those implicated. Some data also suggests that higher perfusion pre-treatment in the anterior cingulate (PTSD), OFC, caudate (OCD) and antero-lateral temporal region (SAD) predicts subsequent treatment response. This paper further examines the notion of overlap in the neurocircuitry of treatment and indeed treatment response across anxiety disorders with SSRI treatment. METHODS: Single photon emission computed tomography (SPECT) using Tc-(99 m )HMPAO to assess brain perfusion was performed on subjects with OCD, PTSD, and SAD before and after 8 weeks (SAD) and 12 weeks (OCD and PTSD) treatment with the SSRI citalopram. Statistical parametric mapping (SPM) was used to compare scans (pre- vs post-medication, and responders vs non-responders) in the combined group of subjects. RESULTS: Citalopram treatment resulted in significant deactivation (p = 0.001) for the entire group in the superior (t = 4.78) and anterior (t = 4.04) cingulate, right thalamus (t = 4.66) and left hippocampus (t = 3.96). Deactivation (p = 0.001) within the left precentral (t = 4.26), right mid-frontal (t = 4.03), right inferior frontal (t = 3.99), left prefrontal (3.81) and right precuneus (t= 3.85) was more marked in treatment responders. No pattern of baseline activation distinguished responders from non-responders to subsequent pharmacotherapy. CONCLUSIONS: Although each of the anxiety disorders may be mediated by different neurocircuits, there is some overlap in the functional neuro-anatomy of their response to SSRI treatment. The current data are consistent with previous work demonstrating the importance of limbic circuits in this spectrum of disorders. These play a crucial role in cognitive-affective processing, are innervated by serotonergic neurons, and changes in their activity during serotonergic pharmacotherapy seem crucial