Indian Immunologicals (India)

Artificial Intelligence (AI) has become one of the most transformative foundational technologies of our time cutting across industry and function, while machine learning has become its engine of maturation. The rapid advancement of AI applications in different domains including computer vision, speech recognition, and natural language processing (NLP) has led to a need for highly accurate, efficient, scalable, and deployable systems in the real world. The paper discusses advances needed to advance AI applicability in these three major areas, especially using advanced machine-learning technologies to improve AI efficiency in terms of the performance, energy requirements, and computational scalability of AI systems In order to seek out the accuracy and speed of the computer vision systems, the study’s investigation studies the works related to the modernization of machine learning techniques namely deep learning, reinforcement learning, and transfer learning. Such advances are critical for real-time applications including ampere nous driving, facial recognizance, and medical image diagnosis. In parallel, studies on the architecture optimization for speech recognition models are being conducted to enhance feature extraction, reduce computation and latency while maintaining performance. Such improvements are crucial for voice assistants, transcription services and multilingual communication tools. For NLP, these translations shed light on the latest in transformer-based architectures, better pre-trained language models, and methods for reducing the size and inference time of models, all so that these AI services can be reasonable in chatbots, content summarization, and sentiment analysis, etc. This work presents a detailed insight into how machine learning techniques can offer massive energy savings that can be derived from the analysis of state-of-the-art architectures, finding bottlenecks, and comparing performance. In addition, it underscores recent advances in lightweight models, distributed training, model compression and hardware-aware optimization engage computational burden while preserving or improving predictive performance. This brings these approaches together to maintain that AI technologies can be consolidated more sustainably across edge devices, cloud platforms, and embedded systems.

Abstract We examine the long-term impact factor over the past decade (2015–2024) for seven flagship journals widely used in astrophysics: ApJ, AJ, ApJL, ApJS, A&A, JCAP, and MNRAS. We checked the variation of both the traditional impact factor and the median-based impact factor, which we had studied in a previous work. We find that ApJS exhibits the largest drop in impact factor from 14.9 to 9.0, while ApJ and MNRAS show a steady decline from 6.7 to 5.2 and 5.6 to 5.0, respectively. However, the impact factors for AJ and ApJL have shown a steady increase from 4.55 to 5.35 and 6.4 to 9.7, respectively. The median-based impact factors are much more stable and show variations of at most ±1 within the last decade.

Abstract Earlier models of stress reactions did not consider the concurrent coupling of intracellular Ca² + levels with the concomitant release of ATP and the production of reactive oxygen species (ROS). In such conditions, extracellular ATP acting on purinergic type 2 (P2) receptors can trigger the release of various mediator molecules that support cellular defence. Additionally, extracellular adenosine (Ado), formed from ATP, acts on P1 receptors to moderate and control mediator release, preventing cellular hyperactivity, supporting survival, replenishing ATP pools, or, in certain cases, inducing a pathological state. In this study, we present three integrated models, based on earlier publications and complemented by new original observations: a) “Model of the extracellular ATP–adenosine danger cycle coupled to stress hormones”; b) “Model of cellular homeostasis, apoptosis, and necrosis in various danger/stress situations related to Ca² + , ATP, and adenosine”; c) “Model of intracellular, extracellular, and systemic elements of stress responses related to the ATP-adenosine danger cycle, ROS, and hormones.”

The present study deals with the separation and identification of the photodegradation products formed when a commercial soft drink containing Carmoisine (E122) dye was exposed to natural sunlight. An ultra high performance liquid chromatography with quadrupole time-of-flight tandem mass spectrometry method was developed and validated to identify the unknown species of E122. During the study, it was observed that the dye decolourizes rapidly in beverage when compared to model standard solutions. The sunlight irradiation of beverage containing E122 resulted in four photodegradation products as identified by nontarget screening using high-resolution tandem mass spectrometry. Accurate mass measurements were used to identify the elemental composition, and to elucidate the structures of degradation products a software tool was employed. The degradation products (P1-P4) were formed from the interactions of the dye with other ingredients present in the beverage. The toxicity of the degradation products was evaluated on five bacterial strains (TA98, TA100, TA1535, TA1537, and WP2 uvrA pKM101) through an in vitro bacterial reverse mutation assay. The photodegradation products showed strong mutagenic potential in strain TA 100 (without S9) as detected by the Ames assay.

In the present study, Bactec MGIT 960 ParaTB system, a liquid culture system has been used to culture the Mycobacterium avium paratuberculosis (Map). Faecal, milk and serum samples were collected in separate sterile containers from 20 milking crossbred cows, aged above 4 years with a history of chronic diarrhea, weak body condition and weight loss. Five (25.0%, 5/20) animals reacted positively to single intra-dermal Johnin test. Four (20.0%, 4/20) samples each of serum and milk were positive by ELISA. The positive milk and serum samples belonged to the same individuals. Direct PCR could not detect Map DNA in any of the faeces/milk samples. However, 1 out of 20 faecal samples was tagged positive in system on day 19 post culture. Z-N staining, conventional IS900 PCR and real-time PCR confirmed these to be Map.

Phage display technology is a powerful in vitro method for the identification of specific monoclonal antibodies (antibody fragments) to an antigenic target and allows the rapid generation and selection of high affinity, fully human antibodies directed toward any disease target appropriate for antibody therapy. In the present study, we exploited the phage display technology for the selection of an antigen binding fragment (Fabs) toward tetanus toxoid using human naïve phage antibody library constructed from peripheral blood lymphocytes of naïve human donors. The phages displaying Fab were subjected to three rounds of bio-panning with tetanus toxoid as antigen on a solid phase. The high affinity antibody fragments were expressed in HB2151 strain of Escherichia coli and purified by immobilized metal affinity chromatography. The binding activity and specificity of the antibody fragment was established by its reactivity toward tetanus toxoid and non-reactivity toward other related toxins as determined by enzyme-linked immunosorbent assay and immunoblot analysis. The selected Fab fragment forming the antigen-binding complexes with the toxoid in flocculation assay indicates that the Fab may have a potential neutralizing ability toward antigen.