Facial Recognition in Public Areas

The security of information nowadays is very significant and difficult, so there are a number of ways to improve security. Especially in public areas like airports, railway sta-tions, Universities, ATMs, etc. and security cameras are presently common in these areas. So, in this paper, we are presenting how Facial recognition can be used in public areas like airports, toll gates, offices, etc. We are comparing or matching a face of a person who we want to detect, with the video which is recorded through CCTV. There are certain algorithms to detect faces from video like through HAAR cascades, eigenface, fisher face, etc. open-source computer vision library is used for facial recognition

Development of a novel HPTLC fingerprint method for simultaneous estimation of berberine and rutin in medicinal plants and their pharmaceutical preparations followed by its application in antioxidant assay

The present study was designed to develop and validate a high-performance thin-layer chromatography (HPTLC) system for the simultaneous quantitative determination of berberine and rutin in Tinospora cordifolia extract and their pharmaceutical preparations. Chromatographic development was done using a blend of n-hexane, ethyl acetate, glacial acetic acid and methanol (10:1.1:1.1:2.5, v/v) as the mobile phase. Detection was completed densitometrically at 254 nm. The RF estimation of berberine and rutin was observed to be 0.67 ± 0.02 and 0.47 ± 0.02, respectively. The developed HPTLC method was validated according to ICH guidelines; the method was specific, linear and accurate and can be used to determine berberine and rutin in marketed herbal preparations. The Tinospora cordifolia plant extract was further evaluated for antioxidant activity using HPTLC, and berberine was found to be more active than rutin during DPPH (2,2-diphenyl-1-picrylhydrazyl) scavenging activity. The method was found simple, rapid, accurate, specific and robust for the analysis of berberine and rutin in crude drug using the same method

Tests for Genetic Interactions in Type 1 Diabetes: Linkage and Stratification Analyses of 4,422 Affected Sib-Pairs

OBJECTIVE - Interactions between genetic and environmental factors lead to immune dysregulation causing type 1 diabetes and other autoimmune disorders. Recently, many common genetic variants have been associated with type 1 diabetes risk, but each has modest individual effects. Familial clustering of type 1 diabetes has not been explained fully and could arise from many factors, including undetected genetic variation and gene interactions. RESEARCH DESIGN AND METHODS - To address this issue, the Type 1 Diabetes Genetics Consortium recruited 3,892 families, including 4,422 affected sib-pairs. After genotyping 6,090 markers, linkage analyses of these families were performed, using a novel method and taking into account factors such as genotype at known susceptibility loci. RESULTS - Evidence for linkage was robust at the HLA and INS loci, with logarithm of odds (LOD) scores of 398.6 and 5.5, respectively. There was suggestive support for five other loci. Stratification by other risk factors (including HLA and age at diagnosis) identified one convincing region on chromosome 6q14 showing linkage in male subjects (corrected LOD = 4.49; replication P = 0.0002), a locus on chromosome 19q in HLA identical siblings (replication P = 0.006), and four other suggestive loci. CONCLUSIONS - This is the largest linkage study reported for any disease. Our data indicate there are no major type 1 diabetes subtypes definable by linkage analyses; susceptibility is caused by actions of HLA and an apparently random selection from a large number of modest-effect loci; and apart from HLA and INS, there is no important susceptibility factor discoverable by linkage methods

Neurodevelopmental disorders in children aged 2-9 years: Population-based burden estimates across five regions in India.

BACKGROUND: Neurodevelopmental disorders (NDDs) compromise the development and attainment of full social and economic potential at individual, family, community, and country levels. Paucity of data on NDDs slows down policy and programmatic action in most developing countries despite perceived high burden. METHODS AND FINDINGS: We assessed 3,964 children (with almost equal number of boys and girls distributed in 2-<6 and 6-9 year age categories) identified from five geographically diverse populations in India using cluster sampling technique (probability proportionate to population size). These were from the North-Central, i.e., Palwal (N = 998; all rural, 16.4% non-Hindu, 25.3% from scheduled caste/tribe [SC-ST] [these are considered underserved communities who are eligible for affirmative action]); North, i.e., Kangra (N = 997; 91.6% rural, 3.7% non-Hindu, 25.3% SC-ST); East, i.e., Dhenkanal (N = 981; 89.8% rural, 1.2% non-Hindu, 38.0% SC-ST); South, i.e., Hyderabad (N = 495; all urban, 25.7% non-Hindu, 27.3% SC-ST) and West, i.e., North Goa (N = 493; 68.0% rural, 11.4% non-Hindu, 18.5% SC-ST). All children were assessed for vision impairment (VI), epilepsy (Epi), neuromotor impairments including cerebral palsy (NMI-CP), hearing impairment (HI), speech and language disorders, autism spectrum disorders (ASDs), and intellectual disability (ID). Furthermore, 6-9-year-old children were also assessed for attention deficit hyperactivity disorder (ADHD) and learning disorders (LDs). We standardized sample characteristics as per Census of India 2011 to arrive at district level and all-sites-pooled estimates. Site-specific prevalence of any of seven NDDs in 2-<6 year olds ranged from 2.9% (95% CI 1.6-5.5) to 18.7% (95% CI 14.7-23.6), and for any of nine NDDs in the 6-9-year-old children, from 6.5% (95% CI 4.6-9.1) to 18.5% (95% CI 15.3-22.3). Two or more NDDs were present in 0.4% (95% CI 0.1-1.7) to 4.3% (95% CI 2.2-8.2) in the younger age category and 0.7% (95% CI 0.2-2.0) to 5.3% (95% CI 3.3-8.2) in the older age category. All-site-pooled estimates for NDDs were 9.2% (95% CI 7.5-11.2) and 13.6% (95% CI 11.3-16.2) in children of 2-<6 and 6-9 year age categories, respectively, without significant difference according to gender, rural/urban residence, or religion; almost one-fifth of these children had more than one NDD. The pooled estimates for prevalence increased by up to three percentage points when these were adjusted for national rates of stunting or low birth weight (LBW). HI, ID, speech and language disorders, Epi, and LDs were the common NDDs across sites. Upon risk modelling, noninstitutional delivery, history of perinatal asphyxia, neonatal illness, postnatal neurological/brain infections, stunting, LBW/prematurity, and older age category (6-9 year) were significantly associated with NDDs. The study sample was underrepresentative of stunting and LBW and had a 15.6% refusal. These factors could be contributing to underestimation of the true NDD burden in our population. CONCLUSIONS: The study identifies NDDs in children aged 2-9 years as a significant public health burden for India. HI was higher than and ASD prevalence comparable to the published global literature. Most risk factors of NDDs were modifiable and amenable to public health interventions

Modeling, Simulation and Optimization of Agricultural Tillage Process Vibrations using an Interactive Active Control System

Tractor operators have to work for long hours to perform different tasks. During tillage, implements are attached to the tractor for soil preparations for crop production. The attached implements also have direct effect on vibrations of tractors. Drivers of tractors are exposed to different frequency vibrations which effect their health badly. In this study, the vibrations transmitted during tillage to seat of operator were measured using accelerometer and root mean square (RMS) accelerations was analysed according to ISO standard. Tests were carried out with different type of implements (Cultivator and Disc Harrow) and at different forward speeds of tractor. The dynamic model of tractor and tillage implements was created in MATLAB-Simulink software. An active seat suspension was designed with PID and Fuzzy Logic Controllers and was analysed for improvement in ride comfort level of driver. The results reveal that designed controllers reduce the RMS accelerations at operator’s seat base by 69% as compared to passive seat

Rapid identification of major-effect genes using the collaborative cross.

The Collaborative Cross (CC) was designed to facilitate rapid gene mapping and consists of hundreds of recombinant inbred lines descended from eight diverse inbred founder strains. A decade in production, it can now be applied to mapping projects. Here, we provide a proof of principle for rapid identification of major-effect genes using the CC. To do so, we chose coat color traits since the location and identity of many relevant genes are known. We ascertained in 110 CC lines six different coat phenotypes: albino, agouti, black, cinnamon, and chocolate coat colors and the white-belly trait. We developed a pipeline employing modifications of existing mapping tools suitable for analyzing the complex genetic architecture of the CC. Together with analysis of the founders\u27 genome sequences, mapping was successfully achieved with sufficient resolution to identify the causative genes for five traits. Anticipating the application of the CC to complex traits, we also developed strategies to detect interacting genes, testing joint effects of three loci. Our results illustrate the power of the CC and provide confidence that this resource can be applied to complex traits for detection of both qualitative and quantitative trait loci. Genetics 2014 Sep; 198(1):75-86

Rapid Identification Of Major-effect Genes Using The Collaborative Cross

The Collaborative Cross (CC) was designed to facilitate rapid gene mapping and consists of hundreds of recombinant inbred lines descended from eight diverse inbred founder strains. A decade in production, it can now be applied to mapping projects. Here, we provide a proof of principle for rapid identification of major-effect genes using the CC. To do so, we chose coat color traits since the location and identity of many relevant genes are known. We ascertained in 110 CC lines six different coat phenotypes: albino, agouti, black, cinnamon, and chocolate coat colors and the white-belly trait. We developed a pipeline employing modifications of existing mapping tools suitable for analyzing the complex genetic architecture of the CC. Together with analysis of the founders’ genome sequences, mapping was successfully achieved with sufficient resolution to identify the causative genes for five traits. Anticipating the application of the CC to complex traits, we also developed strategies to detect interacting genes, testing joint effects of three loci. Our results illustrate the power of the CC and provide confidence that this resource can be applied to complex traits for detection of both qualitative and quantitative trait loci

Systematic evaluation of genes and genetic variants associated with type 1 diabetes susceptibility

Genome-wide association studies have found >60 loci that confer genetic susceptibility to type 1 diabetes (T1D). Many of these are defined only by anonymous single nucleotide polymorphisms: the underlying causative genes, as well as the molecular bases by which they mediate susceptibility, are not known. Identification of how these variants affect the complex mechanisms contributing to the loss of tolerance is a challenge. In this study, we performed systematic analyses to characterize these variants. First, all known genes in strong linkage disequilibrium (r2 > 0.8) with the reported single nucleotide polymorphisms for each locus were tested for commonly occurring nonsynonymous variations. We found only a total of 22 candidate genes at 16 T1D loci with common nonsynonymous alleles. Next, we performed functional studies to examine the effect of non-HLA T1D risk alleles on regulating expression levels of genes in four different cell types: EBV-transformed B cell lines (resting and 6 h PMA stimulated) and purified CD4+ and CD8+ T cells. We mapped cis-acting expression quantitative trait loci and found 24 non-HLA loci that affected the expression of 31 transcripts significantly in at least one cell type. Additionally, we observed 25 loci that affected 38 transcripts in trans. In summary, our systems genetics analyses defined the effect of T1D risk alleles on levels of gene expression and provide novel insights into the complex genetics of T1D, suggesting that most of the T1D risk alleles mediate their effect by influencing expression of multiple nearby genes