Local Industrialization Based Lucrative Farming Using Machine Learning Technique

In recent times, agriculture have gained lot of attention of researchers. More precisely, crop prediction is trending topic for research as it leads agri-business to success or failure. Crop prediction totally rest on climatic and chemical changes. In the past which crop to promote was elected by rancher. All the decisions related to its cultivation, fertilizing, harvesting and farm maintenance was taken by rancher himself with his experience. But as we can see because of constant fluctuations in atmospheric conditions coming to any conclusion have become very tough. Picking correct crop to grow at right times under right circumstances can help rancher to make more business. To achieve what we cannot do manually we have started building machine learning models for it nowadays. To predict the crop deciding which parameters to consider and whose impact will be more on final decision is also equally important. For this we use feature selection models. This will alter the underdone data into more precise one. Though there have been various techniques to resolve this problem better performance is still desirable. In this research we have provided more precise & optimum solution for crop prediction keeping Satara, Sangli, Kolhapur region of Maharashtra. Along with crop & composts to increase harvest we are offering industrialization around so rancher can trade the yield & earn more profit. The proposed solution is using machine learning algorithms like KNN, Random Forest, Naïve Bayes where Random Forest outperforms others so we are using it to build our final framework to predict crop

Role of Drug Repurposing in Cancer Treatment and Liposomal Approach of Drug Targeting

Cancer is the leading cause of death, and incidences are increasing significantly and patients suffering from it desperately need a complete cure from it. The science of using an already-invented drug that has been approved by the FDA for a new application is known as “drug repurposing.” Currently, scientists are drawn to drug repositioning science in order to investigate existing drugs for newer therapeutic uses and cancer treatment. Because of their unique ability to target cancer cells, recently repurposed drugs and the liposomal approach are effective in the treatment of cancer. Liposomes are nanovesicles that are drastically flexible, rapidly penetrate deeper layers of cells, and enhance intracellular uptake. More importantly, liposomes are biocompatible, biodegradable; entrap both hydrophobic and hydrophilic drugs. This chapter summarizes various approaches to drug repurposing, as well as drug repurposing methods, advantages and limitations of drug repurposing, and a liposomal approach to using repurposed drugs in cancer targeting. This chapter also summarizes liposomal structure, drug loading, and the mechanism of liposomes in targeted cancer treatment. The lipid-based liposomal approach is emerging as a powerful technique for improving drug solubility, bioavailability, reducing side effects, and improving the therapeutic efficacy of repurposed drugs for cancer treatment

Structure-Function Studies of DNA Binding Domain of Response Regulator KdpE Reveals Equal Affinity Interactions at DNA Half-Sites

Expression of KdpFABC, a K+ pump that restores osmotic balance, is controlled by binding of the response regulator KdpE to a specific DNA sequence (kdpFABCBS) via the winged helix-turn-helix type DNA binding domain (KdpEDBD). Exploration of E. coli KdpEDBD and kdpFABCBS interaction resulted in the identification of two conserved, AT-rich 6 bp direct repeats that form half-sites. Despite binding to these half-sites, KdpEDBD was incapable of promoting gene expression in vivo. Structure-function studies guided by our 2.5 Å X-ray structure of KdpEDBD revealed the importance of residues R193 and R200 in the α-8 DNA recognition helix and T215 in the wing region for DNA binding. Mutation of these residues renders KdpE incapable of inducing expression of the kdpFABC operon. Detailed biophysical analysis of interactions using analytical ultracentrifugation revealed a 2∶1 stoichiometry of protein to DNA with dissociation constants of 200±100 and 350±100 nM at half-sites. Inactivation of one half-site does not influence binding at the other, indicating that KdpEDBD binds independently to the half-sites with approximately equal affinity and no discernable cooperativity. To our knowledge, these data are the first to describe in quantitative terms the binding at half-sites under equilibrium conditions for a member of the ubiquitous OmpR/PhoB family of proteins

Insights into <i>Mycobacterium leprae</i> Proteomics and Biomarkers—An Overview

Although leprosy is curable, the identification of biomarkers for the early diagnosis of leprosy would play a pivotal role in reducing transmission and the overall prevalence of the disease. Leprosy-specific biomarkers for diagnosis, particularly for the paucibacillary disease, are not well defined. Therefore, the identification of new biomarkers for leprosy is one of the prime themes of leprosy research. Studying Mycobacterium leprae, the causative agent of leprosy, at the proteomic level may facilitate the identification, quantification, and characterization of proteins that could be potential diagnostics or targets for drugs and can help in better understanding the pathogenesis. This review aims to shed light on the knowledge gained to understand leprosy or its pathogen employing proteomics and its role in diagnosis

<i>Binding analysis of the half-sites of kdpFABC_BS</i>.

<p>SE analysis of binding of KdpE_DBD to S1 (<i>kdpFABC_BS—7</i>) (<b>A</b>)and S2 (<i>kdpFABC_BS</i>—<i>1</i>) (<b>B</b>) half-sites revealed a 1∶1 stoichiometry. Mixtures of KdpE_DBD and DNA were spun at 9,000 (•), 19,800 (□) and 34,000 (Δ) rpm. The <i>K_d</i>s obtained for KdpE_DBD binding at half-sites S1 was 350±100 nM and for S2 was 200±100 nM using a one site binding model (AB) in SEDPHAT. The molecular weights calculated from the SE data were 30,000±1,500 for <i>kdpFABC_BS—1</i> and 30,000±2,500 for <i>kdpFABC_BS—7</i>.</p

Biochemical and functional characterization of KdpE_DBD.

<p><b>A.</b> Sedimentation velocity analysis of the KdpE_DBD to detect self-association. The c(s) distribution of the KdpE_DBD at 21 (dots), 42 (solid line), and 84 µM (dashes) shows a single species of 1.4 S. No concentration-dependent formation of higher-order species was observed. <b>B.</b> Interaction of KdpE_DBD protein with <i>kdpFABC_BS</i> and <i>ompF_Pro</i> DNA sequences analyzed by EMSA. The triangles represent increasing molar ratios of 1∶0, 1∶1, 1∶2, and 1∶3 of DNA to purified KdpE_DBD. The lower and upper bands represent free DNA and DNA-KdpE_DBD complex, respectively. <b>C. </b><i>In vivo</i> analysis of expression of the β-galactosidase gene fused to <i>kdpFABC_Pro</i>. <i>E. coli</i> RH003 cells lacking the histidine kinase (<i>kdpD</i>) and RR (<i>kdpE</i>) were used to express full-length KdpD alone as well as KdpD combined with KdpE or KdpE_DBD. As described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030102#s2" target="_blank">methods</a>, the cells were grown in K0 (▪) and K10 (□) media prior to analysis of gene expression. Growth in K0 medium mimics stresses resulting from external K⁺ depletion. The β-galactosidase activity expressed as Miller units represents the mean of three independent experiments; error bars represent standard error.</p

Structure of KdpE_DBD.

<p><b>A.</b> A cartoon representation of a molecule showing the wHTH motif in progressive coloring; the rest is in gray. To maintain continuity with the structure of the N-terminal receiver domain of KdpE <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030102#pone.0030102-ToroRoman1" target="_blank">[25]</a>, the β-strands and α-helices of KdpE_DBD are labeled starting with β-6 and α-6. The side chains shown in stick representation are residues R193 and R200 in α8 and T215 in β11 targeted for mutagenesis. N and C refer to the amino- and carboxyl- termini. <b>B.</b> Conservation of the sequence in the wHTH motif across members of the OmpR/PhoB family (upper panel) and between KdpE orthologs (lower panel) presented in logo format derived from multiple sequence alignments <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030102#pone.0030102-Crooks1" target="_blank">[61]</a>. The Y-axis represents sequence conservation in bits. The residues targeted for mutagenesis in KdpE are boxed, the triangles represent residues involved in base specific interactions in PhoB-DNA complex (PDB code: 1GXP), and the residue numbering is that of KdpE sequence. Shown below the logo representation are the sequences of the wHTH motif of KdpE and PhoB (upper panel) and that of KdpE in the lower panel. The gap in the lower panel represents a three residue insertion in few of the KdpE orthologs used in sequence alignment. The schematic of the secondary structure was derived from the structure of KdpE_DBD. <b>C.</b> Superposition of KdpE_DBD onto the structure of PhoB bound to DNA (PDB code: 1GXP). Only wHTH motifs of KdpE_DBD and chain A of PhoB in 1GXP and part of the DNA are shown. The coloring scheme: green, KdpE_DBD; purple, PhoB and yellow/orange, DNA strands. The following side chains of residues of PhoB (and in parenthesis equivalent residues in KdpE_DBD labeled in blue) are shown as sticks: T194 (Y191), V197 (I194), R201 (H198) and R219 (T217, not shown), R203 (R200) and T217 (T215) and D196 (R193). Residues T194, V197, R201 and R219 (that penetrates the minor groove is labeled in red) of PhoB have been shown to be form base specific interactions.</p

Identification and characterization of half-sites S1 and S2 on DNA that interacts with KdpE_DBD.

<p><b>A.</b> Sequence logo representation to highlight conserved sequences in a 24 bp stretch of <i>kdpFABC_BS</i>. In the logo, the height of the letter represents its frequency of occurrence in a multiple sequence alignment (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030102#pone.0030102.s003" target="_blank">Fig. S3</a>) and the error bars indicate the sampling error at individual positions. Two 6 bp imperfect direct repeats (TTTATA and TTTACA) separated by a 5 bp sequence are shown in dashed boxes below the logo. <b>B.</b> Identification of the minimal length of DNA required for binding KdpE. For EMSA, double-stranded DNA molecules with progressive deletions (indicated by Δ) at either 5′, 3′, or both ends were used (the nomenclature for oligonucleotides: 5′Δ2, 3′Δ8 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030102#pone-0030102-g004" target="_blank">Fig. 4B</a>, lane 9) refers to deletion of 2 and 8 bp from the 5′ and 3′ ends respectively of the wild-type (30 bp) DNA molecule; oligonucleotides used are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030102#pone.0030102.s005" target="_blank">Table S2</a>). The interpretation of EMSA was qualitative: discreet band shifts as observed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030102#pone-0030102-g004" target="_blank">Fig. 4B</a>, lane 1 were considered a positive reaction (+), whereas no shift (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030102#pone-0030102-g004" target="_blank">Fig. 4B</a>, lane 3) was scored negative (−) and smeared bands as exemplified by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030102#pone-0030102-g004" target="_blank">Fig. 4B</a>, lane 2 were considered partial binding. <b>C.</b> Effects of changes in DNA sequence on the KdpE_DBD-DNA interaction. A summary of EMSA data (data not shown) using the 30 bp <i>kdpFABC_BS</i> sequence and modified oligonucleotides (only specific two or one nucleotide substitutions are noted) are presented. The scoring of EMSA analysis was as described above. The dashed boxes represent the 6 bp direct repeats that form half-sites S1 and S2.</p