Pulse wave signal-driven machine learning for identifying left ventricular enlargement in heart failure patients.

BACKGROUND: Left ventricular enlargement (LVE) is a common manifestation of cardiac remodeling that is closely associated with cardiac dysfunction, heart failure (HF), and arrhythmias. This study aimed to propose a machine learning (ML)-based strategy to identify LVE in HF patients by means of pulse wave signals. METHOD: We constructed two high-quality pulse wave datasets comprising a non-LVE group and an LVE group based on the 264 HF patients. Fourier series calculations were employed to determine if significant frequency differences existed between the two datasets, thereby ensuring their validity. Then, the ML-based identification was undertaken by means of classification and regression models: a weighted random forest model was employed for binary classification of the datasets, and a densely connected convolutional network was utilized to directly estimate the left ventricular diastolic diameter index (LVDdI) through regression. Finally, the accuracy of the two models was validated by comparing their results with clinical measurements, using accuracy and the area under the receiver operating characteristic curve (AUC-ROC) to assess their capability for identifying LVE patients. RESULTS: The classification model exhibited superior performance with an accuracy of 0.91 and an AUC-ROC of 0.93. The regression model achieved an accuracy of 0.88 and an AUC-ROC of 0.89, indicating that both models can quickly and accurately identify LVE in HF patients. CONCLUSION: The proposed ML methods are verified to achieve effective classification and regression with good performance for identifying LVE in HF patients based on pulse wave signals. This study thus demonstrates the feasibility and potential of the ML-based strategy for clinical practice while offering an effective and robust tool for diagnosing and intervening ventricular remodeling.

Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis.

OBJECTIVE: This study seeks to construct a machine learning model that merges clinical characteristics with ultrasound radiomic analysis-encompassing both the intratumoral and peritumoral-to predict the status of axillary lymph nodes in patients with early-stage breast cancer. METHODS: The study employed retrospective methods, collecting clinical information, ultrasound data, and postoperative pathological results from 321 breast cancer patients (including 224 in the training group and 97 in the validation group). Through correlation analysis, univariate analysis, and Lasso regression analysis, independent risk factors related to axillary lymph node metastasis in breast cancer were identified from conventional ultrasound and immunohistochemical indicators, and a clinical feature model was constructed. Additionally, features were extracted from ultrasound images of the intratumoral and its 1-5 mm peritumoral to establish a radiomics feature formula. Furthermore, by combining clinical features and ultrasound radiomics features, six machine learning models (Logistic Regression, Decision Tree, Support Vector Machine, Extreme Gradient Boosting, Random Forest, and K-Nearest Neighbors) were compared for diagnostic efficacy, and constructing a joint prediction model based on the optimal ML algorithm. The use of Shapley Additive Explanations (SHAP) enhanced the visualization and interpretability of the model during the diagnostic process. RESULTS: Among the 321 breast cancer patients, 121 had axillary lymph node metastasis, and 200 did not. The clinical feature model had an AUC of 0.779 and 0.777 in the training and validation groups, respectively. Radiomics model analysis showed that the model including the Intratumor +3 mm peritumor area had the best diagnostic performance, with AUCs of 0.847 and 0.844 in the training and validation groups, respectively. The joint prediction model based on the XGBoost algorithm reached AUCs of 0.917 and 0.905 in the training and validation groups, respectively. SHAP analysis indicated that the Rad Score had the highest weight in the prediction model, playing a significant role in predicting axillary lymph node metastasis in breast cancer. CONCLUSION: The predictive model, which integrates clinical features and radiomic characteristics using the XGBoost algorithm, demonstrates significant diagnostic value for axillary lymph node metastasis in breast cancer. This model can provide significant references for preoperative surgical strategy selection and prognosis evaluation for breast cancer patients, helping to reduce postoperative complications and improve long-term survival rates. Additionally, the utilization of SHAP enhancing the global and local interpretability of the model.

Identification of critical links based on the optimal reliable path in stochastic traffic networks.

In urban stochastic transportation networks, there are specific links that hold great importance. Disruptions or failures in these critical links can lead to reduced connectivity within the road network. Under this circumstance, this manuscript proposed a novel identification of critical links mathematical optimization model based on the optimal reliable path with consideration of link correlations under demand uncertainty. The method presented in this paper offers a solution to bypass the necessity of conducting a full scan of the entire road network. Due to the non-additive and non-linear properties of the proposed model, a modified heuristic algorithm based on K-shortest algorithm and inequality technical is presented. The numerical experiments are conducted to show that improve a certain road link may not necessarily improve the overall traffic conditions. Moreover, the results indicate that if the travel time reliability is not considered, it will bring errors to the identification of key links.

Near telomere-to-telomere genome assemblies of two Chlorella species unveil the composition and evolution of centromeres in green algae.

BACKGROUND: Centromeres play a crucial and conserved role in cell division, although their composition and evolutionary history in green algae, the evolutionary ancestors of land plants, remains largely unknown. RESULTS: We constructed near telomere-to-telomere (T2T) assemblies for two Trebouxiophyceae species, Chlorella sorokiniana NS4-2 and Chlorella pyrenoidosa DBH, with chromosome numbers of 12 and 13, and genome sizes of 58.11 Mb and 53.41 Mb, respectively. We identified and validated their centromere sequences using CENH3 ChIP-seq and found that, similar to humans and higher plants, the centromeric CENH3 signals of green algae display a pattern of hypomethylation. Interestingly, the centromeres of both species largely comprised transposable elements, although they differed significantly in their composition. Species within the Chlorella genus display a more diverse centromere composition, with major constituents including members of the LTR/Copia, LINE/L1, and LINE/RTEX families. This is in contrast to green algae including Chlamydomonas reinhardtii, Coccomyxa subellipsoidea, and Chromochloris zofingiensis, in which centromere composition instead has a pronounced single-element composition. Moreover, we observed significant differences in the composition and structure of centromeres among chromosomes with strong collinearity within the Chlorella genus, suggesting that centromeric sequence evolves more rapidly than sequence in non-centromeric regions. CONCLUSIONS: This study not only provides high-quality genome data for comparative genomics of green algae but gives insight into the composition and evolutionary history of centromeres in early plants, laying an important foundation for further research on their evolution.

Two new acorane-type sesquiterpenoids from an endophytic Trichoderma harzianum associated with Paeonia lactiflora Pall.

Two new acorane-type sesquiterpenoids, harzianes A and B (1 and 2), together with two known cyclonerodiol-type sesquiterpenoids (3-4) and four known sterols (5-8) were isolated from the endophytic Trichoderma harzianum, associated with the medicinal plant Paeonia lactiflora Pall. Compounds 1 and 2 were identified as a pair of heterotropic isomers by spectroscopic analysis (HR-ESI-MS, 1D and 2D NMR), and their absolute configurations were determined by ECD calculations. All compounds were tested for anti-inflammatory activity, however, none demonstrated such activity.

Behavioral responses of cave-roosting bats to artificial light of different spectra and intensities: Implications for lighting management strategy.

Artificial light at night has become an emerging environmental pollutant, posing a serious threat to biodiversity. Cave-roosting animals are vulnerable to light pollution due to long-term adaptation to nocturnal niches, and the problem is especially severe in the context of cave tourism and limestone mining. Mitigating the adverse impacts of artificial light on cave-dwelling animals presents a challenge. This study aimed to assess the relative contributions of spectral parameters and light intensity to the emergence behavior of nine cave-roosting bat species: Rhinolophus macrotis, Rhinolophus pearsonii, Rhinolophus rex, Rhinolophus pusillus, Rhinolophus siamensis, Rhinolophus sinicus, Hipposideros armiger, Myotis davidii, and Miniopterus fuliginosus. We manipulated light spectra and intensities through light-emitting diode (LED) lighting and gel filters at the entrance of bat roost. We monitored nightly passes per species to quantify bat emergence under the dark control and ten lighting conditions (blue, green, yellow, red, and white light at high and low intensities) using ultrasonic recording. Our analyses showed that the number of bat passes tended to be reduced in the presence of white, green, and yellow light, independent of light intensity. In contrast, the number of bat passes showed no pronounced differences under the dark control, blue light, and red light. The number of bat passes was primarily affected by LED light's blue component, red component, peak wavelength, and half-width instead of light intensity. These results demonstrate that spectral parameters of LED light can significantly affect emergence behavior of cave-dwelling bats. Our findings highlight the importance of manipulating light colors to reduce the negative impacts of light pollution on cave-roosting bats as a function of their spectral sensitivity. We recommend the use of gel filters to manage existing artificial lighting systems at the entrance of bat-inhabited caves.

Pulse wave-based evaluation of the blood-supply capability of patients with heart failure via machine learning.

Pulse wave, as a message carrier in the cardiovascular system (CVS), enables inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Heart failure (HF) is a major CVD, typically requiring expensive and time-consuming treatments for health monitoring and disease deterioration; it would be an effective and patient-friendly tool to facilitate rapid and precise non-invasive evaluation of the heart's blood-supply capability by means of powerful feature-abstraction capability of machine learning (ML) based on pulse wave, which remains untouched yet. Here we present an ML-based methodology, which is verified to accurately evaluate the blood-supply capability of patients with HF based on clinical data of 237 patients, enabling fast prediction of five representative cardiovascular function parameters comprising left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVDd), left ventricular end-systolic diameter (LVDs), left atrial dimension (LAD), and peripheral oxygen saturation (SpO2). Two ML networks were employed and optimized based on high-quality pulse wave datasets, and they were validated consistently through statistical analysis based on the summary independent-samples t-test (p > 0.05), the Bland-Altman analysis with clinical measurements, and the error-function analysis. It is proven that evaluation of the SpO2, LAD, and LVDd performance can be achieved with the maximum error < 15%. While our findings thus demonstrate the potential of pulse wave-based, non-invasive evaluation of the blood-supply capability of patients with HF, they also set the stage for further refinements in health monitoring and deterioration prevention applications.

Study on diagnosing thyroid nodules of ACR TI-RADS 4-5 with multimodal ultrasound radiomics technology.

BACKGROUND: Explore the feasibility of using the multimodal ultrasound (US) radiomics technology to diagnose American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) 4-5 thyroid nodules. METHOD: This study prospectively collected the clinical characteristics, conventional, and US elastography images of 100 patients diagnosed with ACR TI-RADS 4-5 nodules from May 2022 to 2023. Independent risk factors for malignant thyroid nodules were extracted and screened using methods such as the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) model, and a multimodal US radiomics combined diagnostic model was established. Using a multifactorial LR analysis and a Rad-score rating, the predictive performance was validated and evaluated, and the final threshold range was determined to assess the clinical net benefit of the model. RESULTS: In the training set, the US radiomics combined predictive model area under curve (AUC = 0.928) had higher diagnostic performance compared with clinical characteristics (AUC = 0.779), conventional US (AUC = 0.794), and US elastography model (AUC = 0.852). In the validation set, the multimodal US radiomics combined diagnostic model (AUC = 0.829) also had higher diagnostic performance compared with clinical characteristics (AUC = 0.799), conventional US (AUC = 0.802), and US elastography model (AUC = 0.718). CONCLUSION: Multi-modal US radiomics technology can effectively diagnose thyroid nodules of ACR TI-RADS 4-5, and the combination of radiomics signature and conventional US features can further improve the diagnostic performance.

Comparing the efficacies of transcranial magnetic stimulation treatments using different targeting methods in major depressive disorder: protocol for a network meta-analysis.

INTRODUCTION: Transcranial magnetic stimulation (TMS) over the left dorsolateral prefrontal cortex (lDLPFC) has been widely used as a treatment for major depressive disorder (MDD) in the past two decades. Different methods for localising the lDLPFC target include the '5 cm' method, the F3 method and the neuro-navigational method. However, whether TMS efficacies differ between the three targeting methods remains unclear. We present a protocol for a systematic review and network meta-analysis (NMA) to compare the efficacies of TMS treatments using these three targeting methods in MDD. METHODS AND ANALYSIS: Relevant studies reported in English or Chinese and published up to May 2023 will be identified from searches of the following databases: PubMed, Cochrane Central Register of Controlled Trials, Embase, PsycINFO, China National Knowledge Infrastructure, Wan Fang Database, Chinese BioMedical Literature Database, and China Science and Technology Journal Database. We will include all randomised controlled trials assessing the efficacy of an active TMS treatment using any one of the three targeting methods compared with sham TMS treatment or comparing efficacies between active TMS treatments using different targeting methods. Interventions must include a minimum of 10 sessions of high-frequency TMS over the lDLPFC. The primary outcome is the reduction score of the 17-item Hamilton Depression Rating Scale, 24-item Hamilton Depression Rating Scale or Montgomery-Asberg Depression Rating Scale. The dropout rate is a secondary outcome representing the TMS treatment's acceptability. Pairwise meta-analyses and a random-effects NMA will be conducted using Stata. We will use the surface under the cumulative ranking curve to rank the different targeting methods in terms of efficacy and acceptability. ETHICS AND DISSEMINATION: This systematic review and NMA does not require ethics approval. The results will be submitted for publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42023410273.

Specialization in the marketplace for ideas.

Organizations that compete for attention in the marketplace face a strategic decision: whether to target a specialized niche or diversify to reach a broader market. Previous research has extensively analyzed the specialization dilemma faced by for-profit firms. We extend the analysis to knowledge-sharing groups in the marketplace of ideas. Using data on over 1,500 technology groups collected from an online event-organizing platform over a fifteen-year period, we measure the effect of topical focus, rarity, novelty, and technical exclusivity on audience growth, retention, and sustained engagement. We find that knowledge-sharing groups benefit marginally by specializing in rare topics but not in new topics. The strongest predictor of growth and survival is whether the group is associated with technically sophisticated topics, regardless of the breadth of focus, even though technical topics are less widely accessible. We conclude that what matters is not how specialized the organization, but how the organization is specialized.

miR-141-3p Regulates the Proliferation and Apoptosis of Endometrial-Myometrial Interface Smooth Muscle Cells in Adenomyosis Via JAK2/STAT3 Pathway.

Adenomyosis (ADS) is a common benign gynecological disease. Abnormal proliferation at the endometrial-myometrial interface (EMI) plays a crucial role in the occurrence and progression of ADS. miR-141-3p is associated with cell proliferation and apoptosis. However, the specific mechanism of miR-141-3p in the etiology of ADS is still unknown. In this study, we explored the effects of miR-141-3p on the proliferation and apoptosis of ADS EMI smooth muscle cells (SMCs). We collected EMI tissues for the primary culture of SMCs from 25 patients diagnosed with ADS and 20 without ADS. Real-time quantitative polymerase chain reaction and western blot were used to measure the mRNA and protein expression levels of miR-141-3p, JAK2, STAT3, phospho-JAK2, and phospho-STAT3 in ADS EMI SMCs. The cell counting kit 8 assay and flow cytometry analysis were used to evaluate the proliferation and apoptosis of EMI SMCs. The miR-141-3p mimic/inhibitor was used to increase or decrease the expression level of miR-141-3p. We added WP1066 to block the phosphorylation of JAK2/STAT3 pathway components. The miR-141-3p levels were decreased, while JAK2 and STAT3 levels were increased in ADS EMI SMCs. miR-141-3p overexpression significantly inhibited the proliferation and enhanced the apoptosis of EMI SMCs, whereas a decrease in miR-141-3p expression level was connected to the opposite results. Meanwhile, inactivated JAK2/STAT3 pathway decreased proliferation and enhanced apoptosis of EMI SMCs after WP1066 treatment. Furthermore, rescue experiments confirmed that the JAK2/STAT3 pathway was the downstream pathway of miR-141-3p and reduced the effect of miR-141-3p on the proliferation and apoptosis of EMI SMCs. These results demonstrate that miR-141-3p regulates the proliferation and apoptosis of ADS EMI SMCs by modulating the JAK2/STAT3 pathway.

Effect of different waste plastic modifiers on conventional asphalt performance: optimal preparation parameters determination and mechanism analysis.

The typical treatment of waste plastics has become a global environmental problem. In light of recent developments, waste plastics used as asphalt modifiers offer an efficient approach to solve this problem. This paper studied the effects of three kinds of waste plastic-modified asphalts (WPMA), with polypropylene (PP), polyethylene (PE) and ethylene-vinyl acetate copolymer (EVA) as their respective modifiers, on the conventional asphalt performance. Furthermore, an orthogonal experimental design (OED) was used to determine the preparation parameters of WPMA. Thereafter, thermogravimetric-differential scanning calorimetry (TG-DSC) and Fourier transform infrared spectroscopy (FTIR) were employed to expound the mechanism of WPMA. It was then subsequently ascertained that the optimum preparation parameters of PP-modified asphalt (PPMA) and PE-modified asphalt (PEMA) were 170 °C, 3000 rpm, and 30 min, while the optimum preparation parameters of EVA-modified asphalt (EVAMA) were 180 °C, 3000 rpm, and 30 min. In addition, WPMA displayed better high-temperature performance and are inherently more suitable for pavement in high-temperature regions. Ultimately, this study will effectively solve the disposal of waste plastic and promote the research and application of WPMA in the future.

Temporal and spatial changes in rhizosphere bacterial diversity of mountain Rhododendron mucronulatum.

To better conserve the ecology of the wild Rhododendron mucronulatum range, we studied the rhizosphere microenvironment of R. mucronulatum in Beijing's Yunmeng Mountain National Forest Park. R. mucronulatum rhizosphere soil physicochemical properties and enzyme activities changed significantly with temporal and elevational gradients. The correlations between soil water content (SWC), electrical conductivity (EC), organic matter content (OM), total nitrogen content (TN), catalase activity (CAT), sucrose-converting enzyme activity (INV), and urease activity (URE) were significant and positive in the flowering and deciduous periods. The alpha diversity of the rhizosphere bacterial community was significantly higher in the flowering period than in the deciduous period, and the effect of elevation was insignificant. The diversity of the R. mucronulatum rhizosphere bacterial community changed significantly with the change in the growing period. A network analysis of the correlations revealed stronger linkages between the rhizosphere bacterial communities in the deciduous period than in the flowering period. Rhizomicrobium was the dominant genus in both periods, but its relative abundance decreased in the deciduous period. Changes in the relative abundance of Rhizomicrobium may be the main factor influencing the changes in the R. mucronulatum rhizosphere bacterial community. Moreover, the R. mucronulatum rhizosphere bacterial community and soil characteristics were significantly correlated. Additionally, the influence of soil physicochemical properties on the rhizosphere bacterial community was larger than that of enzyme activity on the bacterial community. We mainly analyzed the change patterns in the rhizosphere soil properties and rhizosphere bacterial diversity of R. mucronulatum during temporal and spatial variation, laying the foundation for further understanding of the ecology of wild R. mucronulatum.

A theory of emergence: Knowledge, rewiring and innovation.

Why do social interactions linked to sharing knowledge drive the emergence of a regional technology economy? We proffer a positive theory and explanation-sketch identifying mechanisms and initial conditions in an explanation of emergence of a knowledge economy. We trace the emergence of a knowledge economy, from a small group of founding members to a regional technology economy. With the rapid influx of new people, knowledge spillover motivates technologists and entrepreneurs to reach out beyond existing contacts to explore the expanding knowledge economy and interact with new acquaintances in the search for novelty. In the course of network rewiring in knowledge clusters, individuals share knowledge and cooperate in innovation, and move to more central positions when they interact. Mirroring the trends of increased knowledge exploration and innovative activity at the individual level, new startup firms founded during this time period come to span a greater number of industry groups. Endogenous dynamics of overlapping knowledge networks lie behind the rapid morphogenesis of new regional technology economies in New York City and Los Angeles.

Impacts of respiratory fluctuations on cerebral circulation: a machine-learning-integrated 0-1D multiscale hemodynamic model.

Objective. This study aims to accurately identify the effects of respiration on the hemodynamics of the human cardiovascular system, especially the cerebral circulation.Approach: we have developed a machine learning (ML)-integrated zero-one-dimensional (0-1D) multiscale hemodynamic model combining a lumped-parameter 0D model for the peripheral vascular bed and a one-dimensional (1D) hemodynamic model for the vascular network.In vivomeasurement data of 21 patients were retrieved and partitioned into 8000 data samples in which respiratory fluctuation (RF) of intrathoracic pressure (ITP) was fitted by the Fourier series. ML-based classification and regression algorithms were used to examine the influencing factors and variation trends of the key parameters in the ITP equations and the mean arterial pressure. These parameters were employed as the initial conditions of the 0-1D model to calculate the radial artery blood pressure and the vertebral artery blood flow volume (VAFV).Main results: during stable spontaneous respiration, the VAFV can be augmented at the inhalation endpoints by approximately 0.1 ml s-1for infants and 0.5 ml s-1for adolescents or adults, compared to those without RF effects. It is verified that deep respiration can further increase the ranges up to 0.25 ml s-1and 1 ml s-1, respectively.Significance. This study reveals that reasonable adjustment of respiratory patterns, i.e. in deep breathing, enhances the VAFV and promotes cerebral circulation.

Non-invasive hemodynamic diagnosis based on non-linear pulse wave theory applied to four limbs.

Introduction: Hemodynamic diagnosis indexes (HDIs) can comprehensively evaluate the health status of the cardiovascular system (CVS), particularly for people older than 50 years and prone to cardiovascular disease (CVDs). However, the accuracy of non-invasive detection remains unsatisfactory. We propose a non-invasive HDIs model based on the non-linear pulse wave theory (NonPWT) applied to four limbs. Methods: This algorithm establishes mathematical models, including pulse wave velocity and pressure information of the brachial and ankle arteries, pressure gradient, and blood flow. Blood flow is key to calculating HDIs. Herein, we derive blood flow equation for different times of the cardiac cycle considering the four different distributions of blood pressure and pulse wave of four limbs, then obtain the average blood flow in a cardiac cycle, and finally calculate the HDIs. Results: The results of the blood flow calculations reveal that the average blood flow in the upper extremity arteries is 10.78 ml/s (clinically: 2.5-12.67 ml/s), and the blood flow in the lower extremity arteries is higher than that in the upper extremity. To verify model accuracy, the consistency between the clinical and calculated values is verified with no statistically significant differences (p < 0.05). Model IV or higher-order fitting is the closest. To verify the model generalizability, considering the risk factors of cardiovascular diseases, the HDIs are recalculated using model IV, and thus, consistency is verified (p < 0.05 and Bland-Altman plot). Conclusion: We conclude our proposed algorithmic model based on NonPWT can facilitate the non-invasive hemodynamic diagnosis with simpler operational procedures and reduced medical costs.

Disturbed functional connectivity and topological properties of the frontal lobe in minimally conscious state based on resting-state fNIRS.

Background: Patients in minimally conscious state (MCS) exist measurable evidence of consciousness. The frontal lobe is a crucial part of the brain that encodes abstract information and is closely related to the conscious state. We hypothesized that the disturbance of the frontal functional network exists in MCS patients. Methods: We collected the resting-state functional near-infrared spectroscopy (fNIRS) data of fifteen MCS patients and sixteen age- and gender-matched healthy controls (HC). The Coma Recovery Scale-Revised (CRS-R) scale of MCS patients was also composed. The topology of the frontal functional network was analyzed in two groups. Results: Compared with HC, the MCS patients showed widely disrupted functional connectivity in the frontal lobe, especially in the frontopolar area and right dorsolateral prefrontal cortex. Moreover, the MCS patients displayed lower clustering coefficient, global efficiency, local efficiency, and higher characteristic path length. In addition, the nodal clustering coefficient and nodal local efficiency in the left frontopolar area and right dorsolateral prefrontal cortex were significantly reduced in MCS patients. Furthermore, the nodal clustering coefficient and nodal local efficiency in the right dorsolateral prefrontal cortex were positively correlated to auditory subscale scores. Conclusion: This study reveals that MCS patients' frontal functional network is synergistically dysfunctional. And the balance between information separation and integration in the frontal lobe is broken, especially the local information transmission in the prefrontal cortex. These findings help us to understand the pathological mechanism of MCS patients better.

Endometrial microbiota in women with and without adenomyosis: A pilot study.

Introduction: The endometrial microbiota plays an essential role in the health of the female reproductive system. However, the interactions between the microbes in the endometrium and their effects on adenomyosis remain obscure. Materials and methods: We profile endometrial samples from 38 women with (n=21) or without (n=17) adenomyosis to characterize the composition of the microbial community and its potential function in adenomyosis using 5R 16S rRNA gene sequencing. Results: The microbiota profiles of patients with adenomyosis were different from the control group without adenomyosis. Furthermore, analysis identified Lactobacillus zeae, Burkholderia cepacia, Weissella confusa, Prevotella copri, and Citrobacter freundii as potential biomarkers for adenomyosis. In addition, Citrobacter freundii, Prevotella copri, and Burkholderia cepacia had the most significant diagnostic value for adenomyosis. PICRUSt results identified 30 differentially regulated pathways between the two groups of patients. In particular, we found that protein export, glycolysis/gluconeogenesis, alanine, aspartate, and glutamate metabolism were upregulated in adenomyosis. Our results clarify the relationship between the endometrial microbiota and adenomyosis. Discussion: The endometrial microbiota of adenomyosis exhibits a unique structure and Citrobacter freundii, Prevotella copri, and Burkholderia cepacia were identified as potential pathogenic microorganisms associated with adenomyosis. Our findings suggest that changes in the endometrial microbiota of patients with adenomyosis are of potential value for determining the occurrence, progression, early of diagnosis, and treatment oadenomyosis.

Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers.

Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid-liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells.

Fast and non-destructive discriminating the geographical origin of Hangbaiju by hyperspectral imaging combined with chemometrics.

Hangbaiju is highly appreciated flower tea for its health benefits, and its quality and price are affected by geographical origin. Fast and accurate identification of the geographical origin of Hangbaiju is very significant for producers, consumers and market regulators. In this work, hyperspectral imaging combined with chemometrics, was used, for the first time, to explore and implement the geographical origin classification of Hangbaiju. The hyperspectral images in the spectral range of 410-2500 nm for 75 samples of five different origins were collected. As a versatile chemometrics tool, bagging classification tree-radial basis function (BAGCT-RBFN), compared with classification tree (CT), radial basis function network (RBFN), was applied to discriminate Hangbaiju samples from different origins. The results showed that BAGCT-RBFN based on optimal wavelengths yielded superior classification performances to CT and RBFN with full wavelengths. The recognition rates (RR) of the training and prediction sets by BAGCT-RBFN were 96.0 % and 92.0 %, respectively. Hyperspectral imaging combined with chemometric can be considered as a powerful, feasible and convenient tool for the classification of Hangbaiju samples from different origins. It promises to be a potential way for origin discriminant analysis and quality monitor in food fields.