A PCA-SMO Based Hybrid Classification Model for Predictions in Precision Agriculture

The human population is growing at an extremely rapid rate, the demand of food supplies for the survival and sustainability of life is a gleaming challenge. Each living being in the planet gets bestowed with the healthy food to remain active and healthy. Agriculture is a domain which is extremely important as it provides the fundamental resources for survival in terms of supplying food and thus the economy of the entire world is highly dependent on agricultural production. The agricultural production is often affected by various environmental and geographical factors which are difficult to avoid being part of nature. Thus, it requires proactive mitigation plans to reduce any detrimental effect caused by the imbalance of these factors. Precision agriculture is an approach that incorporates information technology in agriculture management, the needs of crops and farming fields are fulfilled to optimized crop health and resultant crop production. The proposed study involves an ambient intelligence-based implementation using machine learning to classify diseases in tomato plants based on the images of its leaf dataset. To analytically evaluate the performance of the framework, a publicly available plant-village dataset is used which is transformed to appropriate form using one-hot encoding technique to meet the needs of the machine learning algorithm. The transformed data is dimensionally reduced by Principal Component Analysis (PCA) technique and further the optimal parameters are selected using Spider Monkey Optimization (SMO) approach. The most relevant features as selected using the Hybrid PCA-SMO technique fed into a Deep Neural Networks (DNN) model to classify the tomato diseases. The optimal performance of the DNN model after implementing dimensionality reduction by Hybrid PCA-SMO technique reached at 99% accuracy was achieved in training and 94% accuracy was achieved after testing the model for 20 epochs. The proposed model is evaluated based on accuracy and loss rate metrics; it justifies the superiority of the approach

Clinical applications of resting coronary flow detection via transthoracic echocardiography

Beyond being a component of coronary flow velocity reserve, resting coronary blood flow is recognized as a clinically relevant measure, providing hemodynamic, diagnostic, and prognostic information across some cardiovascular diseases. With advancements in high-frequency transducers and imaging protocols, transthoracic coronary artery imaging has become increasingly non-invasive, practical, and useful. This review aims to summarize the clinical value and applications of transthoracic echocardiography (TTE) to assess coronary blood flow at rest

Lipid-lowering drugs affect lung cancer risk via sphingolipid metabolism: a drug-target Mendelian randomization study

Background: The causal relationship between lipid-lowering drug (LLD) use and lung cancer risk is controversial, and the role of sphingolipid metabolism in this effect remains unclear.Methods: Genome-wide association study data on low-density lipoprotein (LDL), apolipoprotein B (ApoB), and triglycerides (TG) were used to develop genetic instrumental variables (IVs) for LLDs. Two-step Mendelian randomization analyses were performed to examine the causal relationship between LLDs and lung cancer risk. The effects of ceramide, sphingosine-1-phosphate (S1P), and ceramidases on lung cancer risk were explored, and the proportions of the effects of LLDs on lung cancer risk mediated by sphingolipid metabolism were calculated.Results:APOB inhibition decreased the lung cancer risk in ever-smokers via ApoB (odds ratio [OR] 0.81, 95% confidence interval [CI] 0.70–0.92, p = 0.010), LDL (OR 0.82, 95% CI 0.71–0.96, p = 0.040), and TG (OR 0.63, 95% CI 0.46–0.83, p = 0.015) reduction by 1 standard deviation (SD), decreased small-cell lung cancer (SCLC) risk via LDL reduction by 1 SD (OR 0.71, 95% CI 0.56–0.90, p = 0.016), and decreased the plasma ceramide level and increased the neutral ceramidase level. APOC3 inhibition decreased the lung adenocarcinoma (LUAD) risk (OR 0.60, 95% CI 0.43–0.84, p = 0.039) but increased SCLC risk (OR 2.18, 95% CI 1.17–4.09, p = 0.029) via ApoB reduction by 1 SD. HMGCR inhibition increased SCLC risk via ApoB reduction by 1 SD (OR 3.04, 95% CI 1.38–6.70, p = 0.014). The LPL agonist decreased SCLC risk via ApoB (OR 0.20, 95% CI 0.07–0.58, p = 0.012) and TG reduction (OR 0.58, 95% CI 0.43–0.77, p = 0.003) while increased the plasma S1P level. PCSK9 inhibition decreased the ceramide level. Neutral ceramidase mediated 8.1% and 9.5% of the reduced lung cancer risk in ever-smokers via ApoB and TG reduction by APOB inhibition, respectively, and mediated 8.7% of the reduced LUAD risk via ApoB reduction by APOC3 inhibition.Conclusion: We elucidated the intricate interplay between LLDs, sphingolipid metabolites, and lung cancer risk. Associations of APOB, APOC3, and HMGCR inhibition and LPL agonist with distinct lung cancer risks underscore the multifaceted nature of these relationships. The observed mediation effects highlight the considerable influence of neutral ceramidase on the lung cancer risk reduction achieved by APOB and APOC3 inhibition

Surface passivation for highly active, selective, stable, and scalable CO2 electroreduction

Electrochemical conversion of CO2 to formic acid using Bismuth catalysts is one the most promising pathways for industrialization. However, it is still difficult to achieve high formic acid production at wide voltage intervals and industrial current densities because the Bi catalysts are often poisoned by oxygenated species. Herein, we report a Bi3S2 nanowire-ascorbic acid hybrid catalyst that simultaneously improves formic acid selectivity, activity, and stability at high applied voltages. Specifically, a more than 95% faraday efficiency was achieved for the formate formation over a wide potential range above 1.0 V and at ampere-level current densities. The observed excellent catalytic performance was attributable to a unique reconstruction mechanism to form more defective sites while the ascorbic acid layer further stabilized the defective sites by trapping the poisoning hydroxyl groups. When used in an all-solid-state reactor system, the newly developed catalyst achieved efficient production of pure formic acid over 120 hours at 50 mA cm–2 (200 mA cell current)

Effects of dietary nitrate supplementation on isometric performance and physiological responses in college bodybuilders: a randomized, double-blind, crossover study

IntroductionIn bodybuilding competitions, athletes are required to hold static poses for extended periods. This study aimed to evaluate the effects of acute beetroot juice (BJ) supplementation on isometric muscle endurance in college bodybuilding athletes.MethodsSixteen male college bodybuilding athletes participated in a randomized, double-blind, crossover study conducted over three weeks with four laboratory visits. The first visit involved explaining the experimental protocol and performing the maximal voluntary isometric contraction (MVIC) test. The second visit familiarized participants with the testing procedures. During subsequent visits, participants consumed either BJ (250 ml,∼ 12.48 mmol of NO3−) or PL (250 ml,∼ 0.0005 mmol of NO3−), and blood samples were collected before testing to measure nitrate (NO3−) and nitrite (NO2−) concentrations. Participants then performed three rounds of isometric circuit endurance tests (ICET), during which heart rate (HR), ratings of perceived exertion (RPE), and blood lactate levels were recorded. Each round of ICET consisted of four subtests targeting the elbow flexors, core muscles, forearm muscles, and knee extensors, maintaining 70% of MVIC until fatigue. Additionally, surface electromyography (sEMG) was used to record and analyze muscle activity.ResultsCompared to PL, acute BJ supplementation resulted in a 10.87-fold and 1.57-fold increase in serum NO3− and NO2− levels, respectively (P < 0.001). No significant differences were observed in MVIC peak torque under different conditions (P > 0.05). In the third round of testing (ICET-3), endurance improved by 14.9, 25.4, and 25.2% for the elbow flexors, forearm muscles, and knee extensors, respectively. No significant differences in root mean square (RMS) values were observed between the BJ and PL groups (P > 0.05).DiscussionThese data suggest that acute beetroot juice supplementation had no significant effect on MVIC in college bodybuilding athletes but improved endurance in certain muscle groups during ICET. This suggests that nitrates may enhance endurance by optimizing intermittent recovery processes rather than directly increasing strength

Protocatechualdehyde induced tumor suppressive autophagy through AMPK/ULK1 signaling pathway in gastric cancer

BackgroundGastric cancer (GC) is one of the primary causes of cancer-related fatalities, which requires novel treatment including traditional Chinese medicine (TCM) to prolong survival. Protocatechualdehyde (PCA), a monomer from Chinese herbs, exhibits an anti-cancer effect by inhibiting proliferation and migration, or inducing apoptosis in various types of tumors. However, the anti-cancer effect and underlying mechanism of PCA in gastric cancer are still unclear.MethodsThe cell proliferation ability was detected by the cell counting kit-8 (CCK-8) and colony formation. The occurrence of autophagy was observed by TEM (Tansmission electron microscopy) and immunofluorescence. The expression of proteins involved in AMPK/mTOC1 signaling pathway was detected by western blotting. Apoptosis and cell cycle analysis were determined through flow cytometry. A xenograft mouse model was employed to validate the anticancer effect of PCA in vivo.ResultsPCA was first identified as a specific inhibitor to gastric cancer cells that significantly inhibited the proliferation of human gastric cancer cells MKN45 and AGS in a dose- and time-dependent manner, but not that of human gastric epithelial cells. Furthermore, PCA induced tumor suppressive autophagy in both gastric cancer cells, and blockage of the autophagy by silencing ATG5 can partially reverse the proliferation inhibition of PCA. Mechanistically, PCA induced-autophagy was largely dependent on the activation of the AMPK/ULK1 signaling pathway, and blockage of the pathway through AMPK specific inhibitor Compound C (Com C) or siRNAs targeting ULK1 prevented the occurrence of autophagy and partially reversed the proliferation inhibition induced by PCA. In addition, PCA significantly suppressed the growth of gastric cancer in the gastric cancer xenograft mouse model by activating key proteins related to the AMPK/ULK1 signaling pathway of autophagy.ConclusionThese findings demonstrated that PCA inhibited gastric cancer by inducing tumor suppressive autophagy through the AMPK/ULK1 signaling pathway. PCA may serve as a novel candidate for the treatment of gastric cancer

Natural Coevolution of Tumor and Immunoenvironment in Glioblastoma.

Isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) has a dismal prognosis. A better understanding of tumor evolution holds the key to developing more effective treatment. Here we study GBM\u27s natural evolutionary trajectory by using rare multifocal samples. We sequenced 61,062 single cells from eight multifocal IDH wild-type primary GBMs and defined a natural evolution signature (NES) of the tumor. We show that the NES significantly associates with the activation of transcription factors that regulate brain development, including MYBL2 and FOSL2. Hypoxia is involved in inducing NES transition potentially via activation of the HIF1A-FOSL2 axis. High-NES tumor cells could recruit and polarize bone marrow-derived macrophages through activation of the FOSL2-ANXA1-FPR1/3 axis. These polarized macrophages can efficiently suppress T-cell activity and accelerate NES transition in tumor cells. Moreover, the polarized macrophages could upregulate CCL2 to induce tumor cell migration. SIGNIFICANCE: GBM progression could be induced by hypoxia via the HIF1A-FOSL2 axis. Tumor-derived ANXA1 is associated with recruitment and polarization of bone marrow-derived macrophages to suppress the immunoenvironment. The polarized macrophages promote tumor cell NES transition and migration. This article is highlighted in the In This Issue feature, p. 2711

Aggregation-Induced Emission (AIE), Life and Health

Light has profoundly impacted modern medicine and healthcare, with numerous luminescent agents and imaging techniques currently being used to assess health and treat diseases. As an emerging concept in luminescence, aggregation-induced emission (AIE) has shown great potential in biological applications due to its advantages in terms of brightness, biocompatibility, photostability, and positive correlation with concentration. This review provides a comprehensive summary of AIE luminogens applied in imaging of biological structure and dynamic physiological processes, disease diagnosis and treatment, and detection and monitoring of specific analytes, followed by representative works. Discussions on critical issues and perspectives on future directions are also included. This review aims to stimulate the interest of researchers from different fields, including chemistry, biology, materials science, medicine, etc., thus promoting the development of AIE in the fields of life and health

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io