Comparison of tremor induced by valproate and lithium in bipolar disorder using a hand steadiness tester

Background: Quantitative measurement of valproate and lithium induced tremor using hand steadiness tester and their comparison in bipolar disorder.Methods: 200 newly diagnosed patients of bipolar disorder were randomly allocated into two equal groups receiving lithium (300mg twice daily) and sodium valproate (500 mg twice daily) after they fulfilled the inclusion / exclusion criteria of the study. 87 patients from Lithium group and 93 from Valproate completed the study.  Hand Tremor was assessed quantitatively at 0, 6, 12, 18 and 24 weeks using hand steadiness tester. Anxiety level of the study subjects was assessed to be insignificant using a standardized anxiety scale.  Final data was assessed after 24 weeks by using Stat Calc and Z test. P value <0.05 was considered to be significant.Results: No significant difference was found in terms of the development and transition of tremor induced by valproate and lithium (p=0.22).  However more men developed tremor with lithium when compared with females (p<0.05) and the mean age of patients who developed tremor appeared to be significantly higher in lithium group (54.7±3.9) than valproate (39.6±5.1).Conclusions: Tremor of hands is a common side effect of lithium and valproate treatment. Timely, objective assessment of onset and extent of tremor has always remained a challenge to the clinicians. Hand Steadiness tester is a simple, portable, inexpensive, non-invasive instrument that can be used to ascertain the development and transition of tremor in a quantitative manner. This would guide the clinicians as when to intervene for better management of such tremors

Formulation and Evaluation of Sustained Release Matrix Tablet of Atenolol Based on Natural Polymer

ABSTRACT The purpose of the present investigation was to develop sustained release matrix tablets of Atenolol (ATL) using Xanthan gum (XG) and Guar gum (GG) as matrix former. Different ratios of XG and GG were selected and their suitability was tested as drug carrier. A natural gum Jeol (JG) was used as binder and its effect on hardness and drug release profile of prepared tablets were examined. The in-vitro drug release studies were performed in 0.1N HCl for 2 h followed by phosphate buffer at pH 6.8. The drug release profiles reveal that the release is dependent upon the nature and concentration of the polymer. The matrix tablets composed of XG showed 20.64% drug release during 2h in the acid stage, whereas for XG-GG mixture tablets, it was 27.96% to 39.26%. The addition of JG was found to increase the hardness of the tablets. The dissolution data demonstrated that JG has significant influence on drug release from XG matrix whereas insignificant effect was observed in XG-GG mixture. Statistical analysis of the drug release data at 2h indicated that the drug release is significantly ( *** p&lt;0.0001) affected by the nature and concentration of the polymers as compared to marketed product Aten®

Simulation of location-specific severe thunderstorm events using high-resolution land data assimilation

In this study, the impact of different initial land conditions on the simulation of thunderstorms and monsoon depressions is investigated using the Weather Research and Forecasting (WRF) model. A control run (CNTL) and a simulation with an improved land state (soil moisture and temperature) using the High Resolution Land Data Assimilation System (HRLDAS) are compared for three different rainfall cases in order to examine the robustness of the assimilation system. The study comprises two thunderstorm cases (one in the pre-monsoon and one during the monsoon) and one monsoon depression case that occurred during the Interaction of Convective Organisation, Atmosphere, Surface and Sea (INCOMPASS) field campaign of the 2016 Indian monsoon. HRLDAS is shown to yield improvements in the representation of location-specific rainfall, particularly over land. Further, it is found that the surface fluxes as well as the convective indices are better captured for the pre-monsoon thunderstorm case in HRLDAS. By analysing components of the vorticity tendency equation, it is found that the vertical advection term is the major contributor towards the positive vorticity tendency in HRLDAS compared to CNTL, hence improving localised convection and consequently facilitating rainfall. Significant improvements in the simulation of the pre-monsoon thunderstorm are noted, as seen using Automatic Weather Station (AWS) validation, whereas improvements in the monsoon depression is minimal. Further, it is found that vertical advection (moisture flux convergence) is the major driver modulating the convective circulation in localised thunderstorm (monsoon depression) cases and these dynamics are better represented by HRLDAS compared to CNTL. These findings underline the importance of accurate and high resolution land-state conditions in model initial conditions for forecasting severe weather systems, particularly the simulation of localised thunderstorms over India

Deep Learning Algorithms for Predicting Histone Post-Translational Modificationsand Single Guide RNA CRISPR Efficiency

In this dissertation, we investigate two problems in computational biologythat can be solved using machine learning methods, specifically using deep learning architectures.In this first, we study the problem of predicting histonepost-translational modifications (PTMs) from transcription factor binding data and the primary DNA sequence. Histone PTMs are involved in a variety of essential regulatory processes in the cell, including transcription control. Here we introduce a deep learning architecture called DeepPTM for predicting histone PTMs. Extensive experimental results show that DeepPTM outperforms the prediction accuracy of the model proposed in Benveniste et al. (PNAS, 2014) and DeepHistone (BMC Genomics, 2019). The competitive advantage of our framework lies in the synergistic use of deep learning combined with an effective pre-processing step. Our classification framework has also enabled the discovery that the knowledge of a small subset of transcription factors (which are histone-PTM and cell-type-specific) can provide almost the same prediction accuracy that can be obtained using all the transcription factors data.In the second, we investigate the problem of predicting single guide RNA(sgRNA) CRISPR-Cas9 and CRISPR-Cas12a activity from the primary sequence of the sgRNA. A negative selection screen in the absence of non-homologous end-joining (the dominant DNA repair mechanism) is used to generate single guide RNA (sgRNA) activity profiles for both SpCas9 and LbCas12a for the non-conventional yeasts \emph{Yarrowia lipolytica} and \emph{Kluyveromyces marxianus}. This genome-wide data serves as input to a deep learning algorithm, DeepGuide, that is able to accurately predict guide activity. DeepGuide uses unsupervised learning to obtain a compressed representation of the genome, followed by supervised learning to map sgRNA sequence, genomic context, and epigenetic features with guide activity. Experimental validation, both genome-wide and with a subset of selected genes, confirms DeepGuide’s ability to accurately predict high activity sgRNAs. We also show that the prediction accuracy of DeepGuide can be further improved by incorporating sgRNA samples from different screening conditions of the genome-wide library based on carbon source (glucose, xylose, and lactose) and the temperature at which the non-conventional yeast is grown. To the best of our knowledge, our method is the first sgRNA predictive tool that employ guides from different screening conditions to improve the prediction performance

Prediction of histone post-translational modifications using deep learning

MotivationHistone post-translational modifications (PTMs) are involved in a variety of essential regulatory processes in the cell, including transcription control. Recent studies have shown that histone PTMs can be accurately predicted from the knowledge of transcription factor binding or DNase hypersensitivity data. Similarly, it has been shown that one can predict PTMs from the underlying DNA primary sequence.ResultsIn this study, we introduce a deep learning architecture called DeepPTM for predicting histone PTMs from transcription factor binding data and the primary DNA sequence. Extensive experimental results show that our deep learning model outperforms the prediction accuracy of the model proposed in Benveniste et al. (PNAS 2014) and DeepHistone (BMC Genomics 2019). The competitive advantage of our framework lies in the synergistic use of deep learning combined with an effective pre-processing step. Our classification framework has also enabled the discovery that the knowledge of a small subset of transcription factors (which are histone-PTM and cell-type-specific) can provide almost the same prediction accuracy that can be obtained using all the transcription factors data.Availabilityand implementationhttps://github.com/dDipankar/DeepPTM.Supplementary informationSupplementary data are available at Bioinformatics online

Land surface‐precipitation feedback analysis for a landfalling monsoon depression in the Indian region

Abstract A series of numerical experiments are carried out to investigate the sensitivity of a landfalling monsoon depression to land surface conditions using the Weather Research and Forecasting (WRF) model. Results suggest that precipitation is largely modulated by moisture influx and precipitation efficiency. Three cloud microphysical schemes (WSM6, WDM6, and Morrison) are examined, and Morrison is chosen for assessing the land surface‐precipitation feedback analysis, owing to better precipitation forecast skills. It is found that increased soil moisture facilitates Moisture Flux Convergence (MFC) with reduced moisture influx, whereas a reduced soil moisture condition facilitates moisture influx but not MFC. A higher Moist Static Energy (MSE) is noted due to increased evapotranspiration in an elevated moisture scenario which enhances moist convection. As opposed to moist surface, sensible heat dominates in a reduced moisture scenario, ensued by an overall reduction in MSE throughout the Planetary Boundary Layer (PBL). Stability analysis shows that Convective Available Potential Energy (CAPE) is comparable in magnitude for both increased and decreased moisture scenarios, whereas Convective Inhibition (CIN) shows increased values for the reduced moisture scenario as a consequence of drier atmosphere leading to suppression of convection. Simulations carried out with various fixed soil moisture levels indicate that the overall precipitation features of the storm are characterized by initial soil moisture condition, but precipitation intensity at any instant is modulated by soil moisture availability. Overall results based on this case study suggest that antecedent soil moisture plays a crucial role in modulating precipitation distribution and intensity of a monsoon depression

(2-Amino-7-methyl-4-oxidopteridine-6-carboxylato-&#954;3O4,N5,O6)aqua(ethane-1,2-diamine-&#954;2N,N&#8242;)nickel(II) dihydrate

The NiII atom in the title complex, [Ni(C8H5N5O3)(C2H8N2)(H2O)]&#183;2H2O, is six-coordinated in a distorted octahedral geometry by a tridentate 2-amino-7-methyl-4-oxidopteridine-6-carboxylate (pterin) ligand, a bidentate ancillary ethane-1,2-diamine (en) ligand and a water molecule. The pterin ligand forms two chelate rings. The en and pterin ligands are arranged nearly orthogonally [dihedral angle between the mean plane of the en molecule and the pterin ring = 77.1&#8197;(1)&#176;]. N&#8212;H...O, O&#8212;H...N and O&#8212;H...O hydrogen bonds link the complex molecules and lattice water molecules into a three-dimensional network. &#960;&#8211;&#960; interactions are observed between the pyrazine and pyrimidine rings [centroid&#8211;centroid distance = 3.437&#8197;(2)&#8197;&#197;]