This paper proposes a new theory regarding energy regulation in man. Current theory states that similar adults have similar energy requirements when engaged in similar activities. As a corollary, if activities remain constant and energy intake is altered, weight will change. This theory has been unable to explain the repeated observations that individuals of the same sex and age and engaged in similar work show a mean weekly coefficient of variation in energy intake of about 16% without significant fluctuations in body weight. Furthermore, repeated studies have failed to show any individual "pattern" relating energy intake to output. This lack of pattern has been attributed either to methodological error or to the fact that human energy requirements cannot be determined by current methods. This paper shows that neither case is correct. The explanation lies in the stochastic stationary nature of energy requirements. Because of the nature of significant intraindividual variations noted in all experiments, "requirement" is a dynamic concept, and energy balance will vary as a matter of course about zero. The implications of this for the individual, society, and policy are enormous and are discussed herein.
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Sci Rep
January 2025
Department of Biology, Faculty of Sciences and Techniques (FST), Dan Dicko Dankoulodo University of Maradi, Maradi, Niger.
Climate change affects peri-urban agricultural systems. However, most studies on Climate-Smart Agriculture (CSA) often focused on climate-smart villages in the Sahel region. This study investigated peri-urban farming systems in West African Sahel cities.
View Article and Find Full Text PDFAAPS PharmSciTech
January 2025
University of Maryland, School of Pharmacy, Department of Pharmaceutical Sciences, 20 N Pine Street, Baltimore, Maryland, 21201, USA.
Dosage forms containing Ivermectin (IVER) and Praziquantel (PRAZ) are important combination drug products in animal health. Understanding the relationship between products with differing in vitro release characteristics and bioequivalence could facilitate generics. The goal of this study was to create granulations for each active ingredient, with similar release mechanisms, but substantially different in vitro release rates, and then compressing these granulations into tablets with differing release rates.
View Article and Find Full Text PDFNPJ Antimicrob Resist
June 2024
William Brookshire Chemical and Biomolecular Engineering Department, University of Houston, Houston, TX, USA.
Metabolic inhibitors are known to exhibit complex interactions with antibiotics in bacteria, potentially acting as antagonists by inducing cell dormancy and promoting cell survival. However, the specific synergistic or antagonistic effects of these inhibitors depend on factors like their mechanisms of action, concentrations, and treatment timings, which require further investigation. In our study, we systematically explored the synergistic interactions of various metabolic inhibitors-such as chloramphenicol (a translation inhibitor), rifampicin (a transcription inhibitor), arsenate (an ATP production inhibitor), and thioridazine (a PMF inhibitor)-in combination with ofloxacin.
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January 2025
Zhejiang Ansheng Science & Technology Stock Co.,Ltd, Yongkang, 321314, China.
To address the limitations of the original algorithm, several optimization techniques are proposed. This article presents an original RRT*-Connect algorithm for the planning of obstacle avoidance paths on robotic arms. These strategies include implementing a target biasing algorithm, using elliptic space sampling to enhance the sampling process, the revision of the cost function to better guide path planning, and implementing an artificial potential field and gradient descent strategy to design adaptive step sizes.
View Article and Find Full Text PDFNat Commun
January 2025
Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece.
Artificial neural networks (ANNs) are at the core of most Deep Learning (DL) algorithms that successfully tackle complex problems like image recognition, autonomous driving, and natural language processing. However, unlike biological brains who tackle similar problems in a very efficient manner, DL algorithms require a large number of trainable parameters, making them energy-intensive and prone to overfitting. Here, we show that a new ANN architecture that incorporates the structured connectivity and restricted sampling properties of biological dendrites counteracts these limitations.
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