yQTL Pipeline: A structured computational workflow for large scale quantitative trait loci discovery and downstream visualization.

Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. To facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to the association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. The options to run an ANOVA model or testing the interaction with a covariate are also available. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. It can generate Manhattan and Miami plots of phenotype traits, genotype-phenotype boxplots, and trait-QTL connection networks. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTLpipeline.

Balance recovery for lower limb exoskeleton in standing posture based on orbit energy analysis.

Introduction: The need for effective balance control in lower limb rehabilitation exoskeletons is critical for ensuring stability and safety during rehabilitation training. Current research into specialized balance recovery strategies is limited, highlighting a gap in biomechanics-inspired control methods. Methods: We introduce a new metric called "Orbit Energy" (OE), which assesses the balance state of the human-exoskeleton system based on the dynamics of the overall center of mass. Our control framework utilizes OE to choose appropriate balance recovery strategies, including torque controls at the ankle and hip joints. Results: The efficacy of our control algorithm was confirmed through Matlab Simulink simulations, which analyzed the recovery of balance under various disturbance forces and conditions. Further validation came from physical experiments with human subjects wearing the exoskeleton, where a significant reduction in muscle activation was observed during balance maintenance under external disturbances. Discussion: Our findings underscore the potential of biomechanics-inspired metrics like OE in enhancing exoskeleton functionality for rehabilitation purposes. The introduction of such metrics could lead to more targeted and effective balance recovery strategies, ultimately improving the safety and stability of exoskeleton use in rehabilitation settings.

L-Histidine attenuates NEFA-induced inflammatory responses by suppressing Gab2 expression.

Non-esterified fatty acids (NEFAs), key to energy metabolism, may become pathogenic at elevated levels, potentially eliciting immune reactions. Our laboratory's findings of reduced L-histidine in ketotic states, induced by heightened NEFA concentrations, suggest an interrelation with NEFA metabolism. This observation necessitates further investigation into the mitigating role of L-histidine on the deleterious effects of NEFAs. Our study unveiled that elevated NEFA concentrations hinder the proliferation of Bovine Mammary Epithelial Cells (BMECs) and provoke inflammation in a dose-responsive manner. Delving into L-histidine's influence on BMECs, RNA sequencing revealed 2124 genes differentially expressed between control and L-histidine-treated cells, with notable enrichment in pathways linked to proliferation and immunity, such as cell cycle and TNF signaling pathways. Further analysis showed that L-histidine treatment positively correlated with an increase in EdU-555-positive cell rate and significantly suppressed IL-6 and IL-8 levels (p < 0.05) compared to controls. Crucially, concurrent treatment with high NEFA and L-histidine normalized the number of EdU-555-positive cells and cytokine expression to control levels. Investigating the underlying mechanisms, Gab2 (Grb2-associated binder 2) emerged as a central player; L-histidine notably reduced Gab2 expression, while NEFA had the opposite effect (p < 0.05). Gab2 overexpression escalated nitric oxide (NO) production and IL6 and IL8 expression. However, L-histidine addition to Gab2-overexpressing cells resulted in NO concentrations indistinguishable from controls. Our findings collectively indicate that L-histidine can counteract NEFA-induced inflammation in BMECs by inhibiting Gab2 expression, highlighting its therapeutic potential against NEFA-related metabolic disturbances.

Comparative Metabolomic Profiling of L-Histidine and NEFA Treatments in Bovine Mammary Epithelial Cells.

Non-esterified fatty acids (NEFAs) are pivotal in energy metabolism, yet high concentrations can lead to ketosis, a common metabolic disorder in cattle. Our laboratory observed lower levels of L-histidine in cattle suffering from ketosis, indicating a potential interaction between L-histidine and NEFA metabolism. This relationship prompted us to investigate the metabolomic alterations in bovine mammary epithelial cells (BMECs) induced by elevated NEFA levels and to explore L-histidine's potential mitigating effects. Our untargeted metabolomic analysis revealed 893 and 160 metabolite changes in positive and negative models, respectively, with VIP scores greater than 1 and p-values below 0.05. Notable metabolites like 9,10-epoxy-12-octadecenoic acid were upregulated, while 9-Ethylguanine was downregulated. A pathway analysis suggested disruptions in fatty acid and steroid biosynthesis pathways. Furthermore, L-histidine treatment altered 61 metabolites in the positive model and 34 in the negative model, with implications for similar pathways affected by NEFA. Overlaying differential metabolites from both conditions uncovered a potential key mediator, 1-Linoleoylglycerophosphocholine, which was regulated in opposite directions by NEFA and L-histidine. Our study uncovered that both NEFA L- and histidine metabolomics analyses pinpoint similar lipid biosynthesis pathways, with 1-Linoleoylglycerophosphocholine emerging as a potential key metabolite mediating their interaction, a discovery that may offer insights for therapeutic strategies in metabolic diseases.

Cross-platform proteomics signatures of extreme old age.

In previous work we used a Somalogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals.

Underestimated Dry Season Methane Emissions from Wetlands in the Pantanal.

Tropical wetlands contribute ∼30% of the global methane (CH4) budget. Limited observational constraints on tropical wetland CH4 emissions lead to large uncertainties and disparities in representing emissions. In this work, we combine remote sensing observations with atmospheric and wetland models to investigate dry season wetland CH4 emissions from the Pantanal region of South America. We incorporate inundation maps generated from the Cyclone Global Navigation Satellite System (CYGNSS) satellite constellation together with traditional inundation maps to generate an ensemble of wetland CH4 emission realizations. We challenge these realizations with daily satellite observations for May-July when wetland CH4 emission predictions diverge. We find that the CYGNSS inundation products predict larger emissions in May, in better agreement with observations. We use the model ensemble to generate an empirical observational constraint on CH4 emissions independent of choice of inundation map, finding large dry season wetland CH4 emissions (31.7 ± 13.6 and 32.0 ± 20.2 mg CH4/m2/day in May and June/July during 2018/2019, respectively). These May/June/July emissions are 2-3 times higher than current models, suggesting that annual wetland emissions may be higher than traditionally simulated. Observed trends in the early dry season indicate that dynamics during this period are of importance in representing tropical wetland CH4 behaviors.

Risk and mechanisms of phosphorus release at the sediment-water interface of lakes in cold and arid regions during non-frozen seasons.

In recent years, the eutrophication of lakes has accelerated in cold arid regions; the release of nutrients from sediments is an important contributor. The sequential extraction method, high-resolution peeper (HR-Peeper), and diffusive gradients in thin films (DGT) techniques were used to study the occurrence characteristics, release risk, and release mechanism of phosphorus (P) at the sediment-water interface (SWI) of Ulanor Wetland in the Hulun Lake Basin, Inner Mongolia, China. The mean total P concentration in overlying water was lower in August than that in May. Dissolved organic P (DOP) or particulate P (PP) was the main form of P in the overlying water. PP dominates in May and DOP in August. Refractory P was the main form of P in sediments. The concentrations of soluble reactive P and DGT-active P in the pore water of the sediment column were higher than those in the overlying water, and the concentrations were higher in August than those in May. Release of P in the wetland sediments occurred during the non-frozen seasons, with a higher risk in August than in May. The good linear correlation between dissolved P, Fe, and Mn in the DGT profiles verified their co-release due to the anaerobic reduction of Fe/Mn oxides. Moreover, alkaline sediments are also conducive to the release of sediment P. This study can provide data and theoretical support for eutrophication control in Ulanor Wetland and other similar water bodies in cold and arid regions.

Bidirectional two-sample Mendelian randomization analysis identifies causal associations between cardiovascular diseases and frozen shoulder.

BACKGROUND: Although prior observational studies indicate an association between cardiovascular diseases (CVDs) and frozen shoulder (FS), the potential causal relationship between them remains uncertain. This study aims to explore the genetic causal relationship between CVDs and FS using Mendelian randomization (MR). METHODS: Genetic variations closely associated with FS were obtained from the FinnGen Consortium. Summary data for CVD, including atrial fibrillation (AF), coronary artery disease (CAD), heart failure (HF), myocardial infarction (MI), stroke, and ischemic stroke (IS), were sourced from several large-scale genome-wide association studies (GWAS). MR analysis was performed using inverse variance weighting (IVW), MR Egger, and weighted median methods. IVW, as the primary MR analysis method, complemented by other sensitivity analyses, was utilized to validate the robustness of the results. Further reverse MR analysis was conducted to explore the presence of reverse causal relationships. RESULTS: In the forward MR analysis, genetically determined risk of stroke and IS was positively associated with FS (OR [95% CI] = 1.58 (1.23-2.03), P < 0.01; OR [95% CI] = 1.46 (1.16-1.85), P < 0.01, respectively). There was no strong evidence of an effect of genetically predicted other CVDs on FS risk. Sensitivity analyses confirmed the robustness of the results. In the reverse MR analysis, no causal relationships were observed between FS and various CVDs. CONCLUSION: The study suggests that stroke increases the risk of developing FS. However, further basic and clinical research is needed to substantiate our findings.

yQTL Pipeline: a structured computational workflow for large scale quantitative trait loci discovery and downstream visualization.

Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. In order to facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to genome-wide association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. Upon uploading the result file, it can generate Manhattan plots of user-selected phenotype traits and trait-QTL connection networks based on user-specified p-value thresholds. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs cumulatively associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTL-Pipeline.

miR-196a Promotes Proliferation of Mammary Epithelial Cells by Targeting CDKN1B.

Heat stress (HS) has become one of the key challenges faced by the dairy industry due to global warming. Studies have reported that miR-196a may exert a role in the organism's response to HS, enhancing cell proliferation and mitigating cellular stress. However, its specific role in bovine mammary epithelial cells (BMECs) remains to be elucidated. In this study, we aimed to investigate whether miR-196a could protect BMECs against proliferation arrest induced by HS and explore its potential underlying mechanism. In this research, we developed an HS model for BMECs and observed a significant suppression of cell proliferation as well as a significant decrease in miR-196a expression when BMECs were exposed to HS. Importantly, when miR-196a was overexpressed, it alleviated the inhibitory effect of HS on cell proliferation. We conducted RNA-seq and identified 105 differentially expressed genes (DEGs). Some of these DEGs were associated with pathways related to thermogenesis and proliferation. Through RT-qPCR, Western blotting, and dual-luciferase reporter assays, we identified CDKN1B as a target gene of miR-196a. In summary, our findings highlight that miR-196a may promote BMEC proliferation by inhibiting CDKN1B and suggest that the miR-196a/CDKN1B axis may be a potential pathway by which miR-196a alleviates heat-stress-induced proliferation arrest in BMECs.

Analysis of Cytological Misdiagnosis and Oversight of Adenoid Cystic Carcinoma of Salivary Gland.

OBJECTIVE: In this article on adenoid cystic carcinoma (ACC) of salivary gland, we intend to summarize the causes of misdiagnosis and oversight of ACC hoping to improve cytological diagnostic accuracy, clinical management and patient treatment. METHODS: The study retrospectively reviewed 32 patients with ACC of salivary gland, registered at the Affiliated Hospital of Southwest Medical University from July 2014 to June 2021. These cases were diagnosed by FNA and surgical excision biopsy. All cytopathological results were retrospectively categorized according to Milan system for reporting salivary gland cytopathology (MSRSGC). The accuracy of FNA was verified by surgical excision biopsy. RESULTS: Of these 32 patients, 16 (50.0%) cases were male, and 16 (50.0%) were female. Their age ranged from 21 to 79 years, with an average age of 50.32 years. The highest incidence (15/32, 46.9%) of ACC was observed in patients between 41 and 50 years of age. 10 cases (31.3%) occurred in the parotid gland, 9 cases (28.1%) in the submandibular gland, 9 cases (28.1%) in the sublingual gland, 3 cases (9.4%) in the palate, and 1 case (3.1%) in the lip. Among the 32 cases of ACC, 23 cases (71.9%) were classified to VI, 4 cases (12.5%) to IVa, and 5 cases (15.6%) to II by MSRSGC. A comparison of the FNA results with biopsy showed that the accuracy of FNA in ACC of salivary gland is 71.9%. Being able to identify the cytomorphological features is the key factor for accurate diagnosis of ACC of the salivary gland. CONCLUSION: Our results confirm that FNA is an important initial screening in the diagnosis of ACC of salivary gland. Increased study of the cytomorphology of ACC is beneficial for more accurate diagnosis of ACC, to reduce misdiagnosis and oversight.

Giant cell tumor of tendon sheath: A report of 216 cases.

BACKGROUND: In this article on giant cell tumor of tendon sheath (GCTTS), we intend to summarize and analyze the clinical and pathological features of GCTTS hoping to improve clinical management and patient treatment. METHODS: The study retrospectively reviewed 216 patients of GCTTS, registered at the Affiliated Hospital of Southwest Medical University from January 2010 to December 2020. These cases were diagnosed by surgical excision. The clinicopathological features and the prognosis were reviewed in the light of the current literature. RESULTS: Of these 216 GCTTS patients, 72 were males (33.3%) and 144 females (66.7%), with a ratio male-to-female of 1:2. The patients' age ranged from 5 to 82, the average being 41.5 years at diagnosis. A total of 96 cases (44.4%) occurred in the hand region, followed by 35 cases (16.2%) in the knee, 32 cases (14.8%) in the foot, 25 cases (11.6%) in the ankle, 12 cases (5.6%) in the wrist, 12 cases (5.6%) in the leg, 2 cases (0.9%) in the head, 1 case (0.5%) in the forearm, and 1 case (0.5%) inside and outside the spinal channel. Histopathology mainly revealed large synovial-like monocytes, small monocytes, and osteoclast-like giant cells. CONCLUSION: Our results confirm that GCTTS predominantly occurs in the hands of young women. Complete surgical resection with long-term follow-up is the preferred management.

A metabolomic signature of the APOE2 allele.

With the goal of identifying metabolites that significantly correlate with the protective e2 allele of the apolipoprotein E (APOE) gene, we established a consortium of five studies of healthy aging and extreme human longevity with 3545 participants. This consortium includes the New England Centenarian Study, the Baltimore Longitudinal Study of Aging, the Arivale study, the Longevity Genes Project/LonGenity studies, and the Long Life Family Study. We analyzed the association between APOE genotype groups E2 (e2e2 and e2e3 genotypes, N = 544), E3 (e3e3 genotypes, N = 2299), and E4 (e3e4 and e4e4 genotypes, N = 702) with metabolite profiles in the five studies and used fixed effect meta-analysis to aggregate the results. Our meta-analysis identified a signature of 19 metabolites that are significantly associated with the E2 genotype group at FDR < 10%. The group includes 10 glycerolipids and 4 glycerophospholipids that were all higher in E2 carriers compared to E3, with fold change ranging from 1.08 to 1.25. The organic acid 6-hydroxyindole sulfate, previously linked to changes in gut microbiome that were reflective of healthy aging and longevity, was also higher in E2 carriers compared to E3 carriers. Three sterol lipids and one sphingolipid species were significantly lower in carriers of the E2 genotype group. For some of these metabolites, the effect of the E2 genotype opposed the age effect. No metabolites reached a statistically significant association with the E4 group. This work confirms and expands previous results connecting the APOE gene to lipid regulation and suggests new links between the e2 allele, lipid metabolism, aging, and the gut-brain axis.

Vicarious experiences of touch (mirror touch) in a Chinese sample: Cross-cultural and individual differences.

Mirror-touch synaesthesia (MTS) refers to tactile sensations people have on their own body when they see another person being touched. This trait has been linked to individual differences in computing body awareness and ownership (e.g., on questionnaires, cognitive tests) as well as differences in the brain. Here it is assessed for the first time in a non-Western (Chinese) population. Study 1 shows that reports of mirror-touch are elevated in a Chinese sample (N = 298) relative to comparable Western samples shown identical stimuli. In other respects, they are qualitatively similar (e.g., showing a difference between whether humans or inanimate objects are touched) and, overall, these differences could not be attributed to an acquiescence bias. The Chinese sample also completed a battery of questionnaires relating to body awareness and social-emotional functioning including mental health (Study 2) and had participated in brain imaging (the structural scans were analysed using voxel-based morphometry in Study 3). Participants reporting higher levels of mirror touch reported higher levels of anxiety. There were no reliable differences in the VBM analysis. It is suggested instead that cross-cultural differences in embodied cognition can manifest themselves in different rates of vicarious experience such as mirror touch.

Evaluation of GENESIS, SAIGE, REGENIE and fastGWA-GLMM for genome-wide association studies of binary traits in correlated data.

Performing a genome-wide association study (GWAS) with a binary phenotype using family data is a challenging task. Using linear mixed effects models is typically unsuitable for binary traits, and numerical approximations of the likelihood function may not work well with rare genetic variants with small counts. Additionally, imbalance in the case-control ratios poses challenges as traditional statistical methods such as the Score test or Wald test perform poorly in this setting. In the last couple of years, several methods have been proposed to better approximate the likelihood function of a mixed effects logistic regression model that uses Saddle Point Approximation (SPA). SPA adjustment has recently been implemented in multiple software, including GENESIS, SAIGE, REGENIE and fastGWA-GLMM: four increasingly popular tools to perform GWAS of binary traits. We compare Score and SPA tests using real family data to evaluate computational efficiency and the agreement of the results. Additionally, we compare various ways to adjust for family relatedness, such as sparse and full genetic relationship matrices (GRM) and polygenic effect estimates. We use the New England Centenarian Study imputed genotype data and the Long Life Family Study whole-genome sequencing data and the binary phenotype of human extreme longevity to compare the agreement of the results and tools' computational performance. The evaluation suggests that REGENIE might not be a good choice when analyzing correlated data of a small size. fastGWA-GLMM is the most computationally efficient compared to the other three tools, but it appears to be overly conservative when applied to family-based data. GENESIS, SAIGE and fastGWA-GLMM produced similar, although not identical, results, with SPA adjustment performing better than Score tests. Our evaluation also demonstrates the importance of adjusting by full GRM in highly correlated datasets when using GENESIS or SAIGE.

Attribute-aware interpretation learning for thyroid ultrasound diagnosis.

Thyroid nodule diagnosis from ultrasound images is a critical computer-aided diagnosis task. Previous works tried to imitate the doctor's diagnosis logic by considering the key attributes to improve the diagnosis performance and explaining the conclusion. However, their clinical feasibilities are still ambiguous because of the ignorance of the correlation between attribute features and global characteristics, as well as the lack of clinical effectiveness evaluation of result interpretations. Following the common logic of ultrasonic investigation, we design a novel Attribute-Aware Interpretation Learning (AAIL) model, consisting of attribute properties discovery module and attribute-global feature fusion module. Adequate result interpretation ensures reliability and transparency of diagnostic conclusions, including the visualization of attribute features and the relationship between attributes and the global feature. Extensive experiments on a practical dataset demonstrate the model's effectiveness, and an innovative human-computer collaborative experiment demonstrates the auxiliary diagnostic ability of the interpretations that can benefit professional doctors.

The human oxidation field.

Hydroxyl (OH) radicals are highly reactive species that can oxidize most pollutant gases. In this study, high concentrations of OH radicals were found when people were exposed to ozone in a climate-controlled chamber. OH concentrations calculated by two methods using measurements of total OH reactivity, speciated alkenes, and oxidation products were consistent with those obtained from a chemically explicit model. Key to establishing this human-induced oxidation field is 6-methyl-5-hepten-2-one (6-MHO), which forms when ozone reacts with the skin-oil squalene and subsequently generates OH efficiently through gas-phase reaction with ozone. A dynamic model was used to show the spatial extent of the human-generated OH oxidation field and its dependency on ozone influx through ventilation. This finding has implications for the oxidation, lifetime, and perception of chemicals indoors and, ultimately, human health.

Isolation of Sphingomonas sp. AJ-1 and its enantioselective S-methylation of the triazole fungicide prothioconazole.

Prothioconazole is a widely used chiral triazole fungicide, and its residue pollution has attracted wide attention in recent years. However, little is known about microbial metabolic processes of prothioconazole enantiomers. In this study, a prothioconazole-degrading strain, Sphingomonas sp. AJ-1, was isolated from activated sludge. The optimal temperature and pH for prothioconazole degradation by strain AJ-1 were 30 °C and 6.0, respectively, and the degradation rate of prothioconazole by strain AJ-1 was negatively correlated with the initial concentration. When supplemented with additional carbon source, the degradation rates of 10 mg/L (Rac)-/(S)-/(R)-prothioconazole by strain AJ-1 were 76.0 %, 100.0 % and 64.8 % within 6 d, respectively. The CS bond of prothioconazole was methylated to produce (S)-/(R)-prothioconazole-S-methyl by strain AJ-1, but the degradation rate of prothioconazole by strain AJ-1 with (S)-enantiomer was 2.54-fold of that with (R)-enantiomer. Moreover, the toxicity of (Rac)-prothioconazole-S-methyl was 5.57 times lower than that of (Rac)-prothioconazole to Pseudokirchneriella subcapitata. The results showed that strain AJ-1 had obvious enantioselective metabolism for prothioconazole, and this metabolism was a detoxification process. This study provides new insights into the enantioselective metabolism of the chiral fungicide prothioconazole in microorganisms.

The Empirical Analysis of Bitcoin Price Prediction Based on Deep Learning Integration Method.

As a new type of electronic currency, bitcoin is more and more recognized and sought after by people, but its price fluctuation is more intense, the market has certain risks, and the price is difficult to be accurately predicted. The main purpose of this study is to use a deep learning integration method (SDAE-B) to predict the price of bitcoin. This method combines two technologies: one is an advanced deep neural network model, which is called stacking denoising autoencoders (SDAE). The SDAE method is used to simulate the nonlinear complex relationship between the bitcoin price and its influencing factors. The other is a powerful integration method called bootstrap aggregation (Bagging), which generates multiple datasets for training a set of basic models (SDAES). In the empirical study, this study compares the price sequence of bitcoin and selects the block size, hash rate, mining difficulty, number of transactions, market capitalization, Baidu and Google search volume, gold price, dollar index, and relevant major events as exogenous variables uses SDAE-B method to compare the price of bitcoin for prediction and uses the traditional machine learning method LSSVM and BP to compare the price of bitcoin for prediction. The prediction results are as follows: the MAPE of the SDAE-B prediction price is 0.016, the RMSE is 131.643, and the DA is 0.817. Compared with the other two methods, it has higher accuracy and lower error, and can well track the randomness and nonlinear characteristics of bitcoin price.

Human metabolic emissions of carbon dioxide and methane and their implications for carbon emissions.

Carbon dioxide (CO2) and methane (CH4) are important greenhouse gases in the atmosphere and have large impacts on Earth's radiative forcing and climate. Their natural and anthropogenic emissions have often been in focus, while the role of human metabolic emissions has received less attention. In this study, exhaled, dermal and whole-body CO2 and CH4 emission rates from a total of 20 volunteers were quantified under various controlled environmental conditions in a climate chamber. The whole-body CO2 emissions increased with temperature. Individual differences were the most important factor for the whole-body CH4 emissions. Dermal emissions of CO2 and CH4 only contributed ~3.5% and ~5.5% to the whole-body emissions, respectively. Breath measurements conducted on 24 volunteers in a companion study identified one third of the volunteers as CH4 producers (exhaled CH4 exceeded 1 ppm above ambient level). The exhaled CH4 emission rate of these CH4 producers (4.03 ± 0.71 mg/h/person, mean ± one standard deviation) was ten times higher than that of the rest of the volunteers (non-CH4 producers; 0.41 ± 0.45 mg/h/person). With increasing global population and the expected large reduction in global anthropogenic carbon emissions in the next decades, metabolic emissions of CH4 (although not CO2) from humans may play an increasing role in regional and global carbon budgets.