Serum alkaline phosphatase and high sensitivity C-reactive protein in type II diabetes mellitus: a risk of cardio vascular disease in South Indian population

Background: Diabetes mellitus (DM) is a clinical syndrome characterized by abnormal metabolism of carbohydrate, protein and fat resulting in hyperglycemia due to absolute or relative deficiency of insulin ending up in vascular complications leading to retinopathy, neuropathy and nephropathy. The aim of the study was to examine the relationship between alkaline phosphatase (ALP) and high sensitive C reactive protein (hsCRP), in type 2 diabetic patients. We assessed the association of ALP and hsCRP levels with CVD complication and determined its utility for CVD risk prediction in type 2 DM subjects with good and poor glycemic control. Further, we investigated correlation between serum ALP and hsCRP level with glycemic control (FBS, PP2BS, HbA1c) in subjects.Methods: A cross sectional study consists of 390 patients out of which 100 normal healthy control (Group I) , 120 patients having type 2 DM with good control (Group II), 170 patients with type 2 DM with poor control (Group III) were selected. Serum ALP, serum hsCRP, FBS, PP2BS, HbA1c, and other biochemical investigations including serum liver enzymes and lipid profile were measured.Results: In Study I Mean serum ALP(145.17±23.91) and hsCRP (2.53±0.76) concentration in group II patients when compared to group I serum ALP(142.17±16.48) and Hscrp (1.51±0.15) shows a significance of ALP (p<0.05) and Hscrp (p<0.001).Study II Mean serum ALP(145.17±23.91) and hsCRP (2.53±0.76) concentration in group II patients when compared to group III serum ALP (147.79±28.95) and Hscrp (3.848±0.47) group shows a significance of ALP (p<0.001) and Hscrp (p<0.05). Study III Mean serum ALP (147.79±28.95) and hsCRP (3.848± 0.47) concentration in group III patients when compared to group I serum ALP (142.17±16.48) and Hscrp (1.51±0.15) shows a high significance of both ALP and Hscrp (p<0.001). Further significant positive correlation was observed between ALP and hsCRP concentration as well as with HbA1c, FBS, and PP2BS.Conclusions: Inflammation along with the poor glycemic control in diabetes play a role in diabetic macrovascular complication like CVD. All these finding are showing a link between CVD, inflammation and glycemic control in patient with type 2 diabetes mellitus

A MEMORY EFFICIENT HARDWARE BASED PATTERN MATCHING AND PROTEIN ALIGNMENT SCHEMES FOR HIGHLY COMPLEX DATABASES

Protein sequence alignment to find correlation between different species, or genetic mutations etc. is the most computational intensive task when performing protein comparison. To speed-up the alignment, Systolic Arrays (SAs) have been used. In order to avoid the internal-loop problem which reduces the performance, pipeline interleaving strategy has been presented. This strategy is applied to an SA for Smith Waterman (SW) algorithm which is an alignment algorithm to locally align two proteins. In the proposed system, the above methodology has been extended to implement a memory efficient FPGA-hardware based Network Intrusion Detection System (NIDS) to speed up network processing. The pattern matching in Intrusion Detection Systems (IDS) is done using SNORT to find the pattern of intrusions. A Finite State Machine (FSM) based Processing Elements (PE) unit to achieve minimum number of states for pattern matching and bit wise early intrusion detection to increase the throughput by pipelining is presented

Smart Multi-Model Emotion Recognition System with Deep learning

Emotion recognition is added a new dimension to the sentiment analysis. This paper presents a multi-modal human emotion recognition web application by considering of three traits includes speech, text, facial expressions, to extract and analyze emotions of people who are giving interviews. Now a days there is a rapid development of Machine Learning, Artificial Intelligence and deep learning, this emotion recognition is getting more attention from researchers. These machines are said to be intelligent only if they are able to do human recognition or sentiment analysis. Emotion recognition helps in spam call detection, blackmailing calls, customer services, lie detectors, audience engagement, suspicious behavior. In this paper focus on facial expression analysis is carried out by using deep learning approaches with speech signals and input text

Encryption and Decryption of Images with Pixel Data Modification Using Hand Gesture Passcodes

To ensure data security and safeguard sensitive information in society, image encryption and decryption as well as pixel data modifications, are essential. To avoid misuse and preserve trust in our digital environment, it is crucial to use these technologies responsibly and ethically. So, to overcome some of the issues, the authors designed a way to modify pixel data that would hold the hidden information. The objective of this work is to change the pixel values in a way that can be used to store information about black and white image pixel data. Prior to encryption and decryption, by using Python we were able to construct a passcode with hand gestures in the air, then encrypt it without any data loss. It concentrates on keeping track of simply two pixel values. Thus, pixel values are slightly changed to ensure the masked image is not misleading. Considering that the RGB values are at their border values of 254, 255 the test cases of masking overcome issues with the corner values susceptibility

Digestibility of Proteins in Legumes

Legume proteins have recently attracted interest from the food industry. Indeed, they are economical and have good nutritional and functional attributes. In addition to being important for growth and maintenance, they also provide antioxidant peptides, and are hence gaining importance for these additional health benefits. The nutritional benefits of leguminous seeds, are linked to the digestibility of the proteins into peptides and amino acids. Seed proteins have a complex structure. Coexisting with these proteins in the seed matrix, are other components that interfere with protein digestibility. Among them, are the antinutritional factors (ANFs), like trypsin inhibitors, which are also significant in animal nutrition. Thus, improving access to legume proteins, often depends on the removal of these inhibitors. Therefore, this chapter focuses on the factors affecting the efficient digestion of proteins, with emphasis on ANFs and methods to eliminate them. Enzymatic treatment is an effective method to solve the problems encountered. Exogenous enzymes, act as digestive aids and help improve protein digestibility in vivo, where digestion is impaired due to insufficient digestive enzymes. Enzymes provide an environment-friendly alternative to energy-intensive processes in the food industry. Complete digestion of legumes will prevent wastage and enhance food security, besides contributing to sustainability

Development of Sorghum Genotypes for Improved Yield and Resistance to Grain Mold Using Population Breeding Approach

The infection caused by grain mold in rainy season grown sorghum deteriorates the physical and chemical quality of the grain, which causes a reduction in grain size, blackening, and making them unfit for human consumption. Therefore, the breeding for grain mold resistance has become a necessity. Pedigree breeding has been widely used across the globe to tackle the problem of grain mold. In the present study, a population breeding approach was employed to develop genotypes resistant to grain mold. The complex genotype × environment interactions (GEIs) make the task of identifying stable grain mold-resistant lines with good grain yield (GY) challenging. In this study, the performance of the 33 population breeding derivatives selected from the four-location evaluation of 150 genotypes in 2017 was in turn evaluated over four locations during the rainy season of 2018. The Genotype plus genotype-by-environment interaction (GGE) biplot analysis was used to analyze a significant GEI observed for GY, grain mold resistance, and all other associated traits. For GY, the location explained a higher proportion of variation (51.7%) while genotype (G) × location (L) contributed to 21.9% and the genotype contributed to 11.2% of the total variation. For grain mold resistance, G × L contributed to a higher proportion of variation (30.7%). A graphical biplot approach helped in identifying promising genotypes for GY and grain mold resistance. Among the test locations, Dharwad was an ideal location for both GY and grain mold resistance. The test locations were partitioned into three clusters for GY and two clusters for grain mold resistance through a “which-won-where” study. Best genotypes in each of these clusters were selected. The breeding for a specific cluster is suggested. Genotype-bytrait biplots indicated that GY is influenced by flowering time, 100-grain weight (HGW), and plant height (PH), whereas grain mold resistance is influenced by glume coverage and PH. Because GY and grain mold score were independent of each other, there is a scope to improve both yield and resistance together

Highly time-resolved chemical speciation and source apportionment of organic aerosol components in Delhi, India, using extractive electrospray ionization mass spectrometry

In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution, especially during winter. Comprehensive knowledge of the composition and sources of the organic aerosol (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary OA (POA) and showed that secondary OA (SOA) played a major role but were unable to resolve specific SOA sources. We address the latter through the first field deployment of an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer (AMS). Measurements were conducted during the winter of 2018/19, and positive matrix factorization (PMF) was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning OA (BBOA-1 and BBOA-2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LO-OOA). On average, 40 % of the total OA mass was apportioned to the secondary factors. The SOA contribution to total OA mass varied greatly between the daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %, 21:00–04:00 LT). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin: aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA-dominated factors were related to the total AMS SOA (i.e. MO-OOA + LO-OOA) by multiple linear regression (MLR). Aromatic SOA was the major SOA component during the daytime, with a 55.2 % contribution to total SOA mass (42.4 % contribution to total OA). Its contribution to total SOA, however, decreased to 25.4 % (7.9 % of total OA) during the nighttime. This factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA accounted for 18.4 % of total SOA mass (14.2 % of total OA) during the daytime and 36.1 % of total SOA mass (11.2 % of total OA) during the nighttime. Aged biomass burning and mixed urban SOA accounted for 15.2 % and 11.0 % of total SOA mass (11.7 % and 8.5 % of total OA mass), respectively, during the daytime and 15.4 % and 22.9 % of total SOA mass (4.8 % and 7.1 % of total OA mass), respectively, during the nighttime. A simple dilution–partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed daytime concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production, likely from the oxidation of locally emitted volatile organic compounds (VOCs). In contrast, biogenic SOA was related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study show that in Delhi, the nighttime high concentrations are caused by POA emissions led by traffic and biomass burning and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction. Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the daytime suggests an increased OA toxicity and health-related consequences for the general public.</p