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The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities. [ABSTRACT FROM AUTHOR]

Most methanotrophs produce Mb to facilitate copper uptake, maintain Cu homeostasis, and protect against the toxic effects of high Cu concentration ( I M. trichosporium i OB3b on 10 µM Cu) [[98]]. The chelation of copper forms the Cu-Mb complex, which is a copper uptaking strategy of MOB in copper-limited conditions [[97]]. In the culture of I Methylococcus capsulatus i Bath, pMMO facilitates CH SB 4 sb oxidation in the cytoplasm when Cu/cell ratios are high (increased from 0 to 55 µM of Cu), while sMMO is activated in the periplasmic membrane space when Cu/cell ratios are low (decreased to 0 µM of Cu) because of different expression patterns (see above). Keywords: ammonia and methane oxidation; monooxygenase; copper; iron; methanobactin; siderophore EN ammonia and methane oxidation monooxygenase copper iron methanobactin siderophore 1806 18 11/17/22 20221101 NES 221101 1. On the other hand, Cu-chelating Mb also played a critical role in maintaining Cu homeostasis and protection against the potentially toxic effects of a high Cu concentration ( I M. trichosporium i OB3b on 10 µM copper) (see Figure 2) [[98]]. [Extracted from the article]

Prostate cancer (PCa) is a non-cutaneous malignancy in males with wide variation in incidence rates across the globe. It is the second most reported cause of cancer death. Its etiology may have been linked to genetic polymorphisms, which are not only dominating cause of malignancy casualties but also exerts significant effects on pharmacotherapy outcomes. Although many therapeutic options are available, but suitable candidates identified by useful biomarkers can exhibit maximum therapeutic efficacy. The single-nucleotide polymorphisms (SNPs) reported in androgen receptor signaling genes influence the effectiveness of androgen receptor pathway inhibitors and androgen deprivation therapy. Furthermore, SNPs located in genes involved in transport, drug metabolism, and efflux pumps also influence the efficacy of pharmacotherapy. Hence, SNPs biomarkers provide the basis for individualized pharmacotherapy. The pharmacotherapeutic options for PCa include hormonal therapy, chemotherapy (Docetaxel, Mitoxantrone, Cabazitaxel, and Estramustine, etc.), and radiotherapy. Here, we overview the impact of SNPs reported in various genes on the pharmacotherapy for PCa and evaluate current genetic biomarkers with an emphasis on early diagnosis and individualized treatment strategy in PCa. [ABSTRACT FROM AUTHOR]

Abstract: A consideration of the economic viability of hydrogen fuel production is important in the STEP (Solar Thermal Electrochemical Photo) production of hydrogen fuel. STEP is an innovative way to decrease costs and increase the efficiency of hydrogen fuel production, which is a synergistic process that can use concentrating photovoltaics (CPV) and solar thermal energy to drive a high temperature, low voltage, electrolysis (water-splitting), resulting in H2 at decreased energy and higher solar efficiency. This study provides evidence that the STEP system is an economically viable solution for the production of hydrogen. STEP occurs at both higher electrolysis and solar conversion efficiencies than conventional room temperature photovoltaic (PV) generation of hydrogen. This paper probes the economic viability of this process, by comparing four different systems: (1) 10% or (2) 14% flat plate PV driven aqueous alkaline electrolysis H2 production, (3) 25% CPV driven molten electrolysis H2 production, and (4) 35% CPV driven solid oxide electrolysis H2 production. The molten and solid oxide electrolysers are high temperature systems that can make use of light, normally discarded, for heating. This significantly increases system efficiency. Using levelized cost analysis, this study shows significant cost reduction using the STEP system. The total price per kg of hydrogen is shown to decrease from $5.74 to $4.96 to $3.01 to $2.61 with the four alternative systems. The advanced STEP plant requires less than one seventh of the land area of the 10% flat cell plant. To generate the 216 million kg H2/year required by 1 million fuel cell vehicles, the 35% CPV driven solid oxide electrolysis requires a plant only 9.6mi2 in area. While PV and electrolysis components dominate the cost of conventional PV generated hydrogen, they do not dominate the cost of the STEP-generated hydrogen. The lower cost of STEP hydrogen is driven by residual distribution and gate costs. [Copyright &y& Elsevier]

We determine the fundamental iron oxide high solubility mechanism that drives a new electrolytic pathway to iron production, and eliminates a major CO2emission source, for example it is produced using wind and solar energy, in a molten carbonate electrolyte, at a high rate and a low electrolysis energy. [ABSTRACT FROM AUTHOR]