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Breast cancer is a significant global health concern with rising incidence and mortality rates. Current diagnostic methods face challenges, necessitating improved approaches. This study employs various machine learning (ML) algorithms, including KNN, SVM, ANN, RF, XGBoost, ensemble models, AutoML, and deep learning (DL) techniques, to enhance breast cancer diagnosis. The objective is to compare the efficiency and accuracy of these models using original and synthetic datasets, contributing to the advancement of breast cancer diagnosis. The methodology comprises three phases, each with two stages. In the first stage of each phase, stratified K-fold cross-validation was performed to train and evaluate multiple ML models. The second stage involved DL-based and AutoML-based ensemble strategies to improve prediction accuracy. In the second and third phases, synthetic data generation methods, such as Gaussian Copula and TVAE, were utilized. The KNN model outperformed others on the original dataset, while the AutoML approach using H2OXGBoost using synthetic data also showed high accuracy. These findings underscore the effectiveness of traditional ML models and AutoML in predicting breast cancer. Additionally, the study demonstrated the potential of synthetic data generation methods to improve prediction performance, aiding decision-making in the diagnosis and treatment of breast cancer.

The neuropathological hallmarks of Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal lobar degeneration (FTLD), and amyotrophic lateral sclerosis (ALS) are present in urban children exposed to fine particulate matter (PM 2.5 ), combustion and friction ultrafine PM (UFPM), and industrial nanoparticles (NPs). Metropolitan Mexico City (MMC) forensic autopsies strongly suggest that anthropogenic UFPM and industrial NPs reach the brain through the nasal/olfactory, lung, gastrointestinal tract, skin, and placental barriers. Diesel-heavy unregulated vehicles are a key UFPM source for 21.8 million MMC residents. We found that hyperphosphorylated tau, beta amyloid 1-42 , α-synuclein, and TAR DNA-binding protein-43 were associated with NPs in 186 forensic autopsies (mean age 27.45 ± 11.89 years). The neurovascular unit is an early NPs anatomical target, and the first two decades of life are critical: 100% of 57 children aged 14.8 ± 5.2 years had AD pathology; 25 (43.9%) AD+TDP-43; 11 (19.3%) AD + PD + TDP-43; and 2 (3.56%) AD +PD. Fe, Ti, Hg, Ni, Co, Cu, Zn, Cd, Al, Mg, Ag, Ce, La, Pr, W, Ca, Cl, K, Si, S, Na, and C NPs are seen in frontal and temporal lobes, olfactory bulb, caudate, substantia nigra, locus coeruleus, medulla, cerebellum, and/or motor cortical and spinal regions. Endothelial, neuronal, and glial damages are extensive, with NPs in mitochondria, rough endoplasmic reticulum, the Golgi apparatus, and lysosomes. Autophagy, cell and nuclear membrane damage, disruption of nuclear pores and heterochromatin, and cell death are present. Metals associated with abrasion and deterioration of automobile catalysts and electronic waste and rare earth elements, i.e., lanthanum, cerium, and praseodymium, are entering young brains. Exposure to environmental UFPM and industrial NPs in the first two decades of life are prime candidates for initiating the early stages of fatal neurodegenerative diseases. MMC children and young adults—surrogates for children in polluted areas around the world—exhibit early AD, PD, FTLD, and ALS neuropathological hallmarks forecasting serious health, social, economic, academic, and judicial societal detrimental impact. Neurodegeneration prevention should be a public health priority as the problem of human exposure to particle pollution is solvable. We are knowledgeable of the main emission sources and the technological options to control them. What are we waiting for?

A new carbazole-coupled tetrakis -(1 H-pyrrole-2-carbaldehyde) anion receptor 1 has been designed and synthesized. Anion binding studies in organic media using fluorescence and UV-vis spectroscopy revealed that receptor 1 is capable of sensing HP 2 O 7 3- with high selectivity. Addition of HP 2 O 7 3- to THF solution of 1 resulted in the emergence of a new broad band at longer wavelength along with quenching of the original emission band forming a ratiometric response. Based on dynamic light scattering (DLS) experiment and fluorescence lifetime measurement, we propose that the emergence of new emission band in the presence of HP 2 O 7 3- ion is due to the aggregation-induced excimer formation.

The demand for medicinal plants and their derived substances is increasing day by day due to their relevance in the context of drug discovery and development. The goal of this investigation is to assess the pharmacological and phytochemical potentials of the grossly underexplored Antidesma montanum Blume (Family: Phyllanthaceae). The methanolic extract of the leave of this plant was fractionated and then followed by initial screening of phytochemical. The investigation of the pharmacological potential, which includes antioxidant, antidiarrheal, anti-inflammatory, analgesic, anti-pyretic, and anxiolytic evaluations, was accomplished using an in vitro free radical scavenging assay with 2,2-diphenyl-1-picrylhydrazyl (DPPH), castor oil-induced diarrheal test, egg albumin test, acetic acid-induced writhing model, brewer’s yeast induced fever test, swing test, open field, and light-dark test, respectively. The investigation o phytochemicals proposes that the methanol extract of A. montanum possesses flavonoids, tannins, terpenoids, saponins, amino acids, fixed oils, and sterols. Pharmacological evaluation suggests that A. montanum possesses significant antioxidant, anti-diarrheal, anti-inflammatory, and analgesic effects. The methanol and chloroform fractions exhibited better DPPH radical scavenging activities with an IC50: 103 ± 0.05 and 108.7 ± 0.05 µg/ml, respectively. The methanol and chloroform fractions also showed anti-inflammatory capacities in the egg albumin (IC50 values: 89.10 ± 0.07 and 92.85 ± 0.07 µg/ml, respectively) model. The plant also showed anti-pyretic and anxiolytic activities in a dose-dependent manner. One of the possible sources of phytotherapeutic lead compounds is A. montanum. To extract and analyze the key bioactive components of this essential therapeutic plant, more research is required.

A new carbazole-coupled tetrakis-(1 H-pyrrole-2-carbaldehyde) anion receptor 1 has been designed and synthesized. Anion binding studies in organic media using fluorescence and UV-vis spectroscopy revealed that receptor 1 is capable of sensing HP O with high selectivity. Addition of HP O to THF solution of 1 resulted in the emergence of a new broad band at longer wavelength along with quenching of the original emission band forming a ratiometric response. Based on dynamic light scattering (DLS) experiment and fluorescence lifetime measurement, we propose that the emergence of new emission band in the presence of HP O ion is due to the aggregation-induced excimer formation.

Multi-criteria decision-making (MCDM) methods are used to deal with multiple properties of products to classify them precisely instead of traditional ABC analysis. However, to remain competitive, companies must often introduce newly manufactured products and reclassify existing inventory, which is time-consuming. To reduce time consumption and disruption, machine learning (ML) methods are employed to forecast the class of newly added inventory items. The goal of this research is to compare support vector machine (SVM), and K-nearest neighbours (KNN) with the MCDM method Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) to determine the most accurate ML model for multicriteria inventory item classification. Initially, ABC analysis is used to categorize existing inventory items based on TOPSIS performance parameters, and then KNN and SVM are implemented to forecast the class of newly added inventory items. Following this, performance measures for each algorithm are calculated. From our case studies, the average training and test accuracy of the KNN model is 98.575 and 97.17% respectively. On the other hand, the average training and test accuracy of the SVM model is 69.143 and 82.5% respectively. The findings demonstrates that the KNN model had greater training and test accuracy than the SVM model.

Breast cancer is a significant global health concern with rising incidence and mortality rates. Current diagnostic methods face challenges, necessitating improved approaches. This study employs various machine learning (ML) algorithms, including KNN, SVM, ANN, RF, XGBoost, ensemble models, AutoML, and deep learning (DL) techniques, to enhance breast cancer diagnosis. The objective is to compare the efficiency and accuracy of these models using original and synthetic datasets, contributing to the advancement of breast cancer diagnosis. The methodology comprises three phases, each with two stages. In the first stage of each phase, stratified K-fold cross-validation was performed to train and evaluate multiple ML models. The second stage involved DL-based and AutoML-based ensemble strategies to improve prediction accuracy. In the second and third phases, synthetic data generation methods, such as Gaussian Copula and TVAE, were utilized. The KNN model outperformed others on the original dataset, while the AutoML approach using H2OXGBoost using synthetic data also showed high accuracy. These findings underscore the effectiveness of traditional ML models and AutoML in predicting breast cancer. Additionally, the study demonstrated the potential of synthetic data generation methods to improve prediction performance, aiding decision-making in the diagnosis and treatment of breast cancer.

Vehicle platooning is a cooperative driving technology that can be supported by 5G enhanced Vehicle-to-Everything (eV2X) communication to improve road safety, traffic efficiency, and reduce fuel consumption. eV2X communication among the platoon vehicles involves the periodic exchange of Cooperative Awareness Messages (CAMs) containing vehicle information under strict latency and reliability requirements. These requirements can be maintained by administering the assignment of resources, in terms of time slots and frequency bands, for CAM exchanges in a platoon, with the help of a resource allocation mechanism. State-of-the-art on control and communication design for vehicle platoons either consider a simplified platoon model with a detailed communication architecture or consider a simplified communication delay model with a detailed platoon control system. Departing from existing works, we have developed a comprehensive vehicle platoon communication and control framework using OMNET++, the benchmarking network simulation tool. We have carried out an inclusive and comparative study of three different platoon Information Flow Topologies (IFTs), namely Car-to-Server, Multi-Hop, and One-Hop over 5G using the Predecessor-leader following platoon control law to arrive at the best-suited IFT for platooning. Secondly, for the best-suited 5G eV2X platooning IFT selected, we have analyzed the performance of three different resource allocation algorithms, namely Maximum of Carrier to Interference Ratio (MaxC/I), Proportional Fair (PF), and Deficit Round Robin (DRR). Exhaustive system-level simulations show that the One-Hop information flow strategy along with the MaxC/I resource allocation yields the best Quality of Service (QoS) performance, in terms of latency, reliability, Age of Information (AoI), and throughput.

The next-generation wireless network, 6G and beyond, envisions to integrate communication and sensing to overcome interference, improve spectrum efficiency, and reduce hardware and power consumption. Massive Multiple-Input Multiple Output (mMIMO)-based Joint Communication and Sensing (JCAS) systems realize this integration for 6G applications such as autonomous driving, as it requires accurate environmental sensing and time-critical communication with neighboring vehicles. Reinforcement Learning (RL) is used for mMIMO antenna beamforming in the existing literature. However, the huge search space for actions associated with antenna beamforming causes the learning process for the RL agent to be inefficient due to high beam training overhead. The learning process does not consider the causal relationship between action space and the reward, and gives all actions equal importance. In this work, we explore a causally-aware RL agent which can intervene and discover causal relationships for mMIMO-based JCAS environments, during the training phase. We use a state dependent action dimension selection strategy to realize causal discovery for RL-based JCAS. Evaluation of the causally-aware RL framework in different JCAS scenarios shows the benefit of our proposed framework over baseline methods in terms of the beamforming gain.

The genome of Mycobacterium smegmatis encodes numerous penicillin-interacting enzymes, and analysing their functions provides insights into the evolutionary mechanisms behind beta-lactam resistance in mycobacteria. In this study, we characterised one such enzyme, MSMEG_6194, annotated as a putative beta-lactamase. Although MSMEG_6194 shares structural similarity with class A beta-lactamases, it showed no detectable beta-lactamase activity under the tested conditions. Heterologous expression of MSMEG_6194 in Escherichia coli and Δmsmeg_6194 deleted strains of M. smegmatis did not confer significant resistance to beta-lactams, and the purified protein failed to hydrolyse nitrocefin either. However, ectopic expression of MSMEG_6194 partly restores the morphological defects in seven PBP-deleted E. coli strains, and the purified enzyme successfully cleaves the terminal D-alanine from a pentapeptide substrate, confirming its DD-carboxypeptidase activity. Structural analysis revealed the absence of a conserved glutamic acid residue in the omega-loop, which is critical for beta-lactamase catalysis in class A beta-lactamase. Substituting this residue (V139E mutant) imparts beta-lactamase activity though significantly reduces DD-carboxypeptidase function. Overall, these findings establish MSMEG_6194 as a DD-carboxypeptidase and demonstrate how a single amino acid change can alter catalytic preference, shedding light on the evolutionary transition from DD-Carboxypeptidases to beta-lactamases in mycobacteria.