Studies on nutritional and anti-​nutritional composition of Bambusa multiplex (lour.) raeusch. Ex schult

Bambusa multiplex, a multipurpose ornamental and com. bamboo species used for hedges, construction, basketing and as handicraft material. The shoots are edible and consumed in Southeast Asia and in North eastern regions of India. As earlier investigations does not emphasize on finding out the harvesting time, an attempt has been made to find out the harvesting time to obtain quality shoots. The bamboo shoots were harvested on different days (7-​30 days after emergence from ground) and analyzed for chem., nutritional and anti-​nutritional components. The shoots harvested at various time intervals showed variation in nutritional compn. with an overall decrease in protein and increase in dietary fiber and carbohydrate content. All the nutritional elements except calcium showed decreased content with shoot maturity. The optimum harvesting age for B. multiplex shoots were found to be 7- 10 days with high nutritional content and antinutritional component, cyanide was found to be completely absent

Aldo-keto reductase-1 (AKR1) protect cellular enzymes from salt stress by detoxifying reactive cytotoxic compounds

Cytotoxic compounds like reactive carbonyl compounds such as methylglyoxal (MG), melandialdehyde (MDA), besides the ROS accumulate significantly at higher levels under salinity stress conditions and affect lipids and proteins that inhibit plant growth and productivity. The detoxification of these cytotoxic compounds by overexpression of NADPH-dependent Aldo-ketoreductase (AKR1) enzyme enhances the salinity stress tolerance in tobacco. The PsAKR1 overexpression plants showed higher survival and chlorophyll content and reduced MDA, H2O2, and MG levels under NaCl stress. The transgenic plants showed reduced levels of Na+ levels in both root and shoot due to reduced reactive carbonyl compounds (RCCs) and showed enhanced membrane stability resulted in higher root growth and biomass. The increased levels of antioxidant glutathione and enhanced activity of superoxide dismutase (SOD), ascorbate peroxidase (APX) and glutathione reductase (GR) suggest AKR1 could protect these enzymes from the RCC induced protein carbonylation by detoxification process. The transgenics also showed higher activity of delta 1-pyrroline-5- carboxylate synthase (P5CS) enzyme resulted in increasedproline levels to maintain osmotic homeostasis. The results demonstrates that the AKR1 protects proteins or enzymes that are involved in scavenging of cytotoxic compounds by detoxifying RCCs generated under salinity stress. © 2017 Elsevier Masson SA

Evaluation of claw development in giant freshwater prawn, Macrobrachium rosenbergii (de Man, 1879)

Dynamics of claw development in Macrobrachium rosenbergii (de Man, 1879) was evaluated through monosex culture. The segregated males and females were stocked separately in two earthen ponds of 200 m2 area, at a density of 2.5 m-2 and reared for 3 months. Percentage contribution of claw weight to body weight (PCB) increased from 8.96 to 14.4% in the first month, but the change was minimal (14.4 to 17.19%) during the rest of the culture period. In order to delineate the relationship further, the data obtained was pooled together and classified into different classes based on the body weight (class interval 10 g). Interestingly, PCB in males decreased gradually upto 30 - 40 g weight class and then increased considerably for higher weight classes. But for females, the increase in PCB was marginal

Water‐soluble red pigments from Isaria farinosa and structural characterization of the main colored component

International audienceThe present study describes the red pigment synthesized by the filamentous fungi Isaria farinosa under submerged culture conditions. The pigment production was optimal under the following conditions: pH 5, agitation speed 150 rpm, temperature 27 °C, incubation time 192 h, light source total darkness, sucrose and glucose as carbon source, yeast extract, meat peptone and monosodium glutamate at a fixed concentration of 3% as nitrogen source. The addition of 10 mM CaCl2 to the culture medium increased the biomass and pigment production. Structural elucidation of the pigment using gas chromatography-mass spectrometry, Fourier transform infrared spectroscopy and 1H nuclear magnetic resonance spectroscopy revealed that the red pigment contains an anthraquinone-related compound. In addition, the isolated pigment was water soluble, and was stable when exposed to salt solution (96.1% of stability after treatment with sodium chloride), acid (72.1% with citric acid), heat (86.2% at 60 °C), and sunlight (99.4%). These results are promising to further exploit the fungal culture of Isaria farinosa for producing the red pigment and, subsequently, to considerably increase its yield. The study has commercial importance in the production of Isaria farinosa pigment for industrial application after considerable toxicological examination. (© 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Computational study targeting anti-fungal Tavaborole analogs and anti-cancer BRACO19

This thesis comprises of three computer aided drug design studies utilizing molecular docking and molecular dynamic simulations: (i) a lead optimization study virtually screening an initial library of ~120000 lead compounds targeting fungal leucyl tRNA synthetase, (ii) an exploratory study to understand the binding pathway of BRACO19 to a parallel telomeric DNA G-quadruplex by MD simulations and compare with experimentally solved X-ray crystal structure (iii) a comparative study to understand the lack of selectivity of BRACO19 to various topologies of human telomeric DNA G-quadruplex over DNA duplex. The first chapter provides the background information required to understand the molecular docking studies and molecular dynamics simulation (MD) studies conducted and discussed in this thesis. This introductory chapter is organized as follows: the first section is an introduction to molecular recognition in protein-ligand interactions, the second section introduces computer-aided drug design, the third section introduces homology modelling, the fourth section discusses molecular docking and virtual screening, the fifth section introduces methods for binding affinity prediction and the sixth section explains MD simulations. The second chapter of this thesis proposes a library of compounds with enhanced activity compared to the parent molecule it had been modified from. Tavaborole, the recently approved topological anti-fungal drug, inhibits leucyl tRNA synthetase by irreversible covalent bonding and hinders protein synthesis. The benzo-boroxole pharmacophore of tavaborole is responsible for its unique activity. This study theoretically proposes molecules with improved anti-fungal affinity. The third chapter of this thesis explores the binding pathway of anti-cancer drug, BRACO19 and human telomeric DNA G-quadruplex. G-quadruplex specific ligands that stabilizes the G-quadruplex, have great potential to be developed as anticancer agents. A free human telomeric DNA G-quadruplex and an unbound BRACO19 are simulated and the resulting structure is then compared with an experimentally solved X-ray structure of human telomeric G-quadruplex with a bound BRACO19 intercalated within the G-quadruplex. Three binding modes have been identified: top end stacking, bottom intercalation and groove binding. Bottom intercalation mode (51% of the population) is identical to the binding pose in the X-ray solved crystal structure. The fourth chapter of this thesis compares different topological folds of human telomeric DNA G-quadruplexes (parallel, antiparallel and hybrid) that have been experimentally solved using molecular dynamic simulation to understand the 62-fold preferential selectivity of BRACO19 towards human telomeric DNA G-quadruplex over DNA duplex. Groove binding mode was found to be the most stable binding mode for the duplex and top stacking mode for the G-quadruplexes. The non-existential binding selectivity of BRACO19 can be accounted to the similar groove binding to both the duplex and the G-quadruplex. For that reason, a modification should be induced such that this prospective ligand destabilizes binding to the duplex but stabilizes the G-quadruplex binding

Region Based Data Mining on Agriculture Data

Spatial Data Mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. Most relationships in spatial datasets are regional and there is a great need for regional regression methods that derive regional reflects different spatial characteristics of different regions. A central challenge in spatial data mining is the efficiency of spatial data mining algorithms, due to the often huge amount of spatial data and the complexity of spatial data types and spatial accessing methods. This paper proposes a regional regression technique for regions that are defined by a categorical attribute, in particular soil type. The result is a series of hierarchically grouped regions according to their similarity

Re-sequencing of the APOAI promoter region and the genetic association of the -75G > A polymorphism with increased cholesterol and low density lipoprotein levels among a sample of the Kuwaiti population

BACKGROUND: APOAI, a member of the APOAI/CIII/IV/V gene cluster on chromosome 11q23-24, encodes a major protein component of HDL that has been associated with serum lipid levels. The aim of this study was to determine the genetic association of polymorphisms in the APOAI promoter region with plasma lipid levels in a cohort of healthy Kuwaiti volunteers. METHODS: A 435 bp region of the APOAI promoter was analyzed by re-sequencing in 549 Kuwaiti samples. DNA was extracted from blood taken from 549 healthy Kuwaiti volunteers who had fasted for the previous 12 h. Univariate and multivariate analysis was used to determine allele association with serum lipid levels. RESULTS: The target sequence included a partial segment of the promoter region, 5’UTR and exon 1 located between nucleotides −141 to +294 upstream of the APOAI gene on chromosome 11. No novel single nucleotide polymorphisms (SNPs) were observed. The sequences obtained were deposited with the NCBI GenBank with accession number [GenBank: JX438706]. The allelic frequencies for the three SNPs were as follows: APOAI rs670G = 0.807; rs5069C = 0.964; rs1799837G = 0.997 and found to be in HWE. A significant association (p < 0.05) was observed for the APOAI rs670 polymorphism with increased serum LDL-C. Multivariate analysis showed that APOAI rs670 was an independent predictive factor when controlling for age, sex and BMI for both LDL-C (OR: 1.66, p = 0.014) and TC (OR: 1.77, p = 0.006) levels. CONCLUSION: This study is the first to report sequence analysis of the APOAI promoter in an Arab population. The unexpected positive association found between the APOAI rs670 polymorphism and increased levels of LDL-C and TC may be due to linkage disequilibrium with other polymorphisms in candidate and neighboring genes known to be associated with lipid metabolism and transport

PREDICTIVE MODELLING OF ROAD DETERIORATION USING AN ARTIFICIALLY INTELLIGENT BAYESIAN BELIEF NETWORKS APPROACH

The ability to predict road deterioration is the cornerstone for developing a reliable Pavement Management System (PMS) that optimizes pavement maintenance programs. Such prediction capacity becomes increasingly important, especially when highway agency funds are confined. This research focuses on the development of prediction models based on an artificial intelligence technique, Bayesian Belief Networks (BBN), that aid decision-makers in forecasting expected road distress curves on the lights of various (e.g., environmental, traffic, and road-specific) factors and maintenance decisions. The novelty of this research revolves around deploying BBNs which allow analysts to yield Markovian predictions of annual road deterioration based on incomplete and/or uncertain historical data, which is probabilistically inferred based on the interrelations of factors modelled in the Bayesian Networks. Such probabilistic inferences not only tackle a gap in current road deterioration modelling literature, but are also deemed to provide a reasonable alternative over costly data collection campaigns and assist in road condition diagnoses and assessment efforts in cases where data are only partially available. The major objectives of the study are to: (1) Estimate the correlations between various deterioration factors to optimize the data collection efforts using machine learning algorithms (Correlation analysis model), (2) Develop a prediction model to estimate the probabilistic values of deterioration factors using Dynamic BBN analysis based on Markov chain process, to aid in the development of temporal graphs representing the pattern of deterioration factors in the future years (Time-series prediction model), (3) Develop a decision-support system which generates suitable alerts whenever the deterioration factors cross the safe limits, enabling the practitioners to conduct appropriate repair and maintenance activities at the right time to increase the service life of the pavements (Decision-support system). The BBN models were trained using a collection of 3,272 road sections, representing a variety of 32 arterial, collector, freeway, and expressway roads in UAE from 2013 to 2019. The BBN models developed in this study show high accuracy with a contingency table fit of over 85% for the correlation analysis models and over 80% of overall precision and reliability rate for the performance prediction models. The proposed BBN approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers to establish a decision support system that is aimed not only at prioritizing maintenance during the operation stage, but also to design pavements during the design stage, with an upfront foresight into the life-cycle implications of their design, ultimately improving and/or extending their deterioration curves

A clinical study of feto-maternal outcome in pregnancies with oligohydramnios

Background: The amniotic fluid that surrounds the fetus serves several roles during pregnancy. Oligohydramnios is diagnosed when ultrasonographically the AFI is less than 5cm/5th percentile. It affects 3-5% of all pregnancies. Assessment of amniotic fluid volume is a helpful tool in determining who is at risk for potentially adverse obstetric and perinatal outcome.Methods: Pregnant women with oligohydramnios reporting to Cheluvamba Hospital, attached to Mysore Medical College and Research Institute, Mysore from December 2012- June 2014 were included in the clinical study of maternal and fetal outcome. All singleton, non-anomalous, low risk pregnancies with AFI≤5cm with intact membranes and gestational age between 28-42 weeks were included in the study. Various outcomes such as mode of delivery, meconium staining, Apgar at 1 and 5 minutes, birth weight and NICU admissions were assessed.Results: A total of 130 cases of isolated oligohydramnios were assessed. 55.4% had vaginal delivery. 13.8% underwent elective LSCS and 30.8% had emergency LSCS. 18.5% had meconium stained liquor, 4.6% babies had APGAR of <7 at 5 minutes. 17.7% had birth weight of <2.5 kg and 6.9% of babies required NICU admission.Conclusions: The present study was conducted to know the feto-maternal outcome in pregnancies with oligohydramnios. The study showed that isolated oligohydramnios had no adverse maternal or perinatal outcome

The Use of a Large Language Model for Cyberbullying Detection

The dominance of social media has added to the channels of bullying for perpetrators. Unfortunately, cyberbullying (CB) is the most prevalent phenomenon in today’s cyber world, and is a severe threat to the mental and physical health of citizens. This opens the need to develop a robust system to prevent bullying content from online forums, blogs, and social media platforms to manage the impact in our society. Several machine learning (ML) algorithms have been proposed for this purpose. However, their performances are not consistent due to high class imbalance and generalisation issues. In recent years, large language models (LLMs) like BERT and RoBERTa have achieved state-of-the-art (SOTA) results in several natural language processing (NLP) tasks. Unfortunately, the LLMs have not been applied extensively for CB detection. In our paper, we explored the use of these models for cyberbullying (CB) detection. We have prepared a new dataset (D2) from existing studies (Formspring and Twitter). Our experimental results for dataset D1 and D2 showed that RoBERTa outperformed other models