Influence of formulated diets with varying protein levels on growth and ammonia excretion in the fry of Indian major carp, Cirrhina mrigala

The influence of formulated isocaloric diets of different protein levels (30, 35, 40, 45 and 50%) on the growth and ammonia excretion of the Indian major carp Cirrhina mrigala fry was studied for a rearing period of four weeks in the laboratory. Fishmeal, groundnut oilcake and silkworm pupae formed the source of protein in all the diets. As the dietary protein level increased from 30 to 40%, the growth and conversion efficiency increased significantly. Further increase in the protein level resulted in decrease in growth and conversion efficiency. Growth rate, weight gain (%), and gross and net feed conversion efficiencies were maximum at 40% dietary protein level. Ammonia excretion was directly proportional to the level of protein in the diet

A novel hybrid face recognition framework based on a low-resolution camera for biometric applications

In research work, human face recognition is an essential biometric symbol persistently continued so far due to its different levels of applications in society. Since the appearance of the human faces can have many variations due to issues like the effect of illumination, expression and face pose. These differences are correlated with one another, which results in a helpless ability to recognize a particular person's face. The motivation behind our work in this paper is to give a new framework for face recognition based on frequency analysis that contributes to solving the distinguishing proof issues with enormous varieties of boundaries like the effect of illumination, expression, and face pose. Here three algorithms combined for provable results: i) Difference of Gaussian filtered discrete wavelet transform (DDWT) for feature extraction; ii) Log Gabor (LG) filter for feature extraction; and iv) Multiclass support vector machine classifier, where feature coefficients of DDWT and LG filter are fused for classification and parameters evaluation. The evaluation of our experiment is carried out on a large database consisting of 15 persons of each 200-face image which are captured using a 5-megapixel low-resolution web camera and yielding satisfactory results on various parameters compared to existing methods

Performance Analysis on Deep Learning State of Art Algorithms for Object Recognition

The goal of computer vision, a subfield of computer science, is to replicate some of the intricacies of the human visual system so that machines can recognize and interpret images and videos in the same manner that humans do. Until recently, computer vision was only used in a restricted capacity. In the past few years, artificial intelligence has advanced significantly, outperforming humans in a number of tasks involving object detection, recognition, and classification. This has allowed computer vision to grow exponentially in terms of increasing the precision with which machines can recognize the objects in and around the surrounding environment. A computer vision technology called object recognition helps find and identify objects in a series of images and videos. Despite the fact that the image of the things varies in different viewpoints, different sizes and scales, or when they are translated or rotated, humans can recognise a large number of objects in images with minimal effort. Even when partially obscured from view, human vision system has the greatest capability to identify the objects. Whereas, for computer vision systems, this task is still a difficulty. Over the years, several different approaches and innovations in the algorithm have been tried to impose the human’s capability into a computer’s vision system. This paper provides a thorough investigation on the evolution of Object Recognition algorithms, datasets used and its performance metrics in a precise manner which will guide the future researchers a direction to proceed their research in innovating algorithms with better accuracy

Rule-based modeling of biochemical systems with BioNetGen

Totowa, NJ. Please cite this article when referencing BioNetGen in future publications. Rule-based modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects. The approach is notable in that it allows one to systematically incorporate site-specific details about proteinprotein interactions into a model for the dynamics of a signal-transduction system, but the method has other applications as well, such as following the fates of individual carbon atoms in metabolic reactions. The consequences of protein-protein interactions are difficult to specify and track with a conventional modeling approach because of the large number of protein phosphoforms and protein complexes that these interactions potentially generate. Here, we focus on how a rule-based model is specified in the BioNetGen language (BNGL) and how a model specification is analyzed using the BioNetGen software tool. We also discuss new developments in rule-based modeling that should enable the construction and analyses of comprehensive models for signal transduction pathways and similarly large-scale models for other biochemical systems. Key Words: Computational systems biology; mathematical modeling; combinatorial complexity; software; formal languages; stochastic simulation; ordinary differential equations; protein-protein interactions; signal transduction; metabolic networks. 1

Circulating microparticles: square the circle

Background: The present review summarizes current knowledge about microparticles (MPs) and provides a systematic overview of last 20 years of research on circulating MPs, with particular focus on their clinical relevance. Results: MPs are a heterogeneous population of cell-derived vesicles, with sizes ranging between 50 and 1000 nm. MPs are capable of transferring peptides, proteins, lipid components, microRNA, mRNA, and DNA from one cell to another without direct cell-to-cell contact. Growing evidence suggests that MPs present in peripheral blood and body fluids contribute to the development and progression of cancer, and are of pathophysiological relevance for autoimmune, inflammatory, infectious, cardiovascular, hematological, and other diseases. MPs have large diagnostic potential as biomarkers; however, due to current technological limitations in purification of MPs and an absence of standardized methods of MP detection, challenges remain in validating the potential of MPs as a non-invasive and early diagnostic platform. Conclusions: Improvements in the effective deciphering of MP molecular signatures will be critical not only for diagnostics, but also for the evaluation of treatment regimens and predicting disease outcomes

A potent betulinic acid analogue ascertains an antagonistic mechanism between autophagy and proteasomal degradation pathway in HT-29 cells

Betulinic acid (BA), a member of pentacyclic triterpenes has shown important biological activities like anti-bacterial, anti-malarial, anti-inflammatory and most interestingly anticancer property. To overcome its poor aqueous solubility and low bioavailability, structural modifications of its functional groups are made to generate novel lead(s) having better efficacy and less toxicity than the parent compound. BA analogue, 2c was found most potent inhibitor of colon cancer cell line, HT-29 cells with IC50 value 14.9 μM which is significantly lower than standard drug 5-fluorouracil as well as parent compound, Betulinic acid. We have studied another mode of PCD, autophagy which is one of the important constituent of cellular catabolic system as well as we also studied proteasomal degradation pathway to investigate whole catabolic pathway after exploration of 2c on HT-29 cells. Mechanism of autophagic cell death was studied using fluorescent dye like acridine orange (AO) and monodansylcadaverin (MDC) staining by using fluorescence microscopy. Various autophagic protein expression levels were determined by Western Blotting, qRT-PCR and Immunostaining. Confocal Laser Scanning Microscopy (CLSM) was used to study the colocalization of various autophagic proteins. These were accompanied by formation of autophagic vacuoles as revealed by FACS and transmission electron microscopy (TEM). Proteasomal degradation pathway was studied by proteasome-Glo™ assay systems using luminometer.The formation of autophagic vacuoles in HT-29 cells after 2c treatment was determined by fluorescence staining – confirming the occurrence of autophagy. In addition, 2c was found to alter expression levels of different autophagic proteins like Beclin-1, Atg 5, Atg 7, Atg 5-Atg 12, LC3B and autophagic adapter protein, p62. Furthermore we found the formation of autophagolysosome by colocalization of LAMP-1 with LC3B, LC3B with Lysosome, p62 with lysosome. Finally, as proteasomal degradation pathway downregulated after 2c treatment colocalization of ubiquitin with lysosome and LC3B with p62 was studied to confirm that protein degradation in autophagy induced HT-29 cells follows autolysosomal pathway. In summary, betulinic acid analogue, 2c was able to induce autophagy in HT-29 cells and as proteasomal degradation pathway downregulated after 2c treatment so protein degradation in autophagy induced HT-29 cell

Phytochemistry of Chukrasia tabularis A. Juss: A reservoir of bioactive compounds with ecological and economic importance

Chukrasia tabularis A. Juss, a lesser-known species from the Meliaceae family, is gaining attention for its rich phyto-pharmaceutical potential conferred by its bioactive compounds, namely, limonoids, flavonoids, tannins and essential oils. Widely used in traditional medicine systems like Ayurveda, Siddha and Chinese medicine, because this plant possesses numerous therapeutic properties like that of antimicrobial, anti-inflammatory, antimalarial and cytotoxic. It is historically used and its phytochemical composition and pharmacological properties have not been explored fairly in science. Studies in recent years have demonstrated that C. tabularis bark, leaves, seeds and wood are rich in bioactivity especially in the fight against microbial infections and cancer. The plant extracts were found to have antimicrobial activity against a wide range of bacterial and fungal strains; hence the plant has potential for natural antimicrobial agent. In addition, its limonoid rich extracts are cytotoxic properties and could be used as anticancer agents targeting different cancer cell lines and overcoming multidrug resistance (MDR). In addition, these therapeutic benefits may include chronic inflammatory and oxidative stress-related diseases due to the plant’s anti-inflammatory and antioxidant properties. C. tabularis has a wide range of medicinal uses, but it is also important in agriculture and forestry due to its use as a biopesticide, soil enhancer and natural dye supplier. It also has high quality timber and serves the country’s ecological and economic importance. Nevertheless, more complex toxicity assessments and clinical trials are needed to verify them safety and therapeutic potential. This review highlights the importance of further research on combining C. tabularis into current pharmaceutical and sustainable mechanisms and the in-depth studies on C. tabularis to support its integration into modern medicine, pharmaceuticals and sustainable industries

Enhancing Heart Disease Prediction With Reinforcement Learning and Data Augmentation

The study presents a novel method to improve the prediction accuracy of cardiac disease by combining data augmentation techniques with reinforcement learning. The complex nature of cardiac data frequently presents challenges for traditional machine learning models, which results in subpar performance. In response, our fusion methodology improves predictive capabilities by augmenting data and utilizing reinforcement learning\u27s skill at sequential decision-making. Our method predicts cardiac disease with an astounding 94 % accuracy rate, which is an outstanding result. This significant improvement outperforms existing techniques and shows a deeper comprehension of intricate data relationships. The amalgamation of reinforcement learning and data augmentation not only yields superior predictive accuracy but also bears noteworthy consequences for patient care and accurate cardiac diagnosis. Through the efficient combination of these approaches, our method provides a powerful response to the difficulties presented by complicated cardiac data. The potential to transform illness prediction and prevention techniques and ultimately improve patient outcomes is demonstrated by this integration\u27s success

Optimizing in vitro micropropagation strategies in Santalum album L. through different explants

This study focuses on optimization of in vitro propagation protocols for Santalum album L. (Indian sandalwood) to address the increasing global demand and to aid in conservation aspects. This investigation evaluated various micropropagation parameters including explant types, basal media, disinfection methods and plant growth regulators (PGR). Murashige and Skoog medium consistently outperformed other media, with shoot tips showing the highest morphogenetic response (66.66%). Surface disinfection with 4% NaOCl for 10 min was the most effective and resulted in the explant survival rate of 86%. For shoot induction, 2 mg/L kinetin in MS medium resulted in the highest number of shoots (3.81) and longest shoots (5.39 cm) after 60 days. An effect was observed when PGR was combined with 5.0 mg/L kinetin + 2.0 mg/L BAP, corresponding to a shoot induction rate of 65.75%. Root induction was recorded at 14.79% after 10 days with the best treatment (MS + 1 mg/L IBA). Callus culture showed limited success as only one treatment (MS + 1 mg/L BAP + 25% coconut water) showed a regenerative response of 7.14%. These results provide a foundation for micropropagation of Sandal while highlighting areas that require further optimization, particularly in root induction and acclimation phases

Impact of PGPR inoculation on photosynthetic pigment and protein contents in Arachis hypogaea L.

The impact of microbial consortium comprising plant development advancing rhizobacteria (PGPR) like Rhizobium, Pseudomonas and Bacillus were tried independently and in blend of Arachis hypogaea. The mixes of previously mentioned PGPR strains essentially expanded photosynthetic color (chlorophyll an and b, add up to chlorophyll and carotenoid) and protein content in  A. hypogaea, when contrasted with the un-inoculated control. The consequences of this study propose that PGPR connected in mix can possibly build the photosynthetic colors and protein substance of A. hypogaea which can be a potential tool in increasing the yield in this economically important crop in sustainable way