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Abstract Introduction Intestinal obstruction is a common complication in patients with advanced malignancies, often attributed to the disease itself or as a side effect of opioid analgesics used for pain management. However, the occurrence of intestinal obstruction following antituberculosis therapy is rare. Case presentation We report a unique case of a 58-year-old Asian male diagnosed with stage IV pancreatic carcinoma and pulmonary tuberculosis. The patient was initiated on a regimen of ethambutol hydrochloride, pyrazinamide, rifampicin, and isoniazid tablets (II) for tuberculosis, alongside morphine for the management of severe cancer-related pain. Subsequently, he developed symptoms indicative of intestinal obstruction. Despite discontinuation of morphine, the patient’s symptoms persisted until he autonomously ceased all medications, leading to a rapid improvement in his condition. This unexpected resolution highlighted the antituberculosis drugs as the probable cause of his intestinal obstruction. Conclusions This case underscores the importance of considering antituberculosis drugs as a potential cause of intestinal obstruction, especially in patients who do not respond to conventional management strategies for drug-induced gastrointestinal side effects. It also emphasizes the need for heightened vigilance and monitoring when prescribing these medications to patients with advanced malignancies, to promptly identify and address rare but significant side effects.

In this article, a single-board integrated millimeter-wave (mm-Wave) asymmetric full-digital beamforming (AFDBF) array is developed for beyond-fifth-generation (B5G) and sixth-generation (6G) communications. The proposed integrated array effectively addresses the challenge of arranging a large number of ports in a full-digital array by designing vertical connections in a three-dimensional space and successfully integrating full-digital transmitting (Tx) and receiving (Rx) arrays independently in a single board. Unlike the traditional symmetric array, the proposed asymmetric array is composed of an 8 × 8 Tx array arranged in a square shape and an 8 + 8 Rx array arranged in an L shape. The center-to-center distance between two adjacent elements is 0.54λ0 for both the Tx and Rx arrays, where λ0 is the free-space wavelength at 27 GHz. The proposed AFDBF array possesses a more compact structure and lower system hardware cost and power consumption compared with conventional brick-type full-digital arrays. In addition, the energy efficiency of the proposed AFDBF array outperforms that of a hybrid beamforming array. The measurement results indicate that the operating frequency band of the proposed array is 24.25–29.50 GHz. An eight-element linear array within the Tx array can achieve a scanning angle ranging from −47° to +47° in both the azimuth and the elevation planes, and the measured scanning range of each eight-element Rx array is –45° to +45°. The measured maximum effective isotropic radiated power (EIRP) of the eight-element Tx array is 43.2 dBm at 28.0 GHz (considering the saturation point). Furthermore, the measured error vector magnitude (EVM) is less than 3% when 64-quadrature amplitude modulation (QAM) waveforms are used.

The existing roofing system in tropical climates is not adapting to the worsening climate, along with the demand for sustainable building design; this study introduced a pioneering cool roof system to reform the traditional roofing system. The pioneering cool roof system is composed of white reflective coating, hollow foamed concrete roof (HFCR) tile, and a sustainable active reflective insulation (RI) system. Despite the complex integrated roof technologies, the study adopted 3D CFD numerical simulation for an in-depth and frontier thermal distribution analysis and fluid flow presentation. The outcomes of the numerical model were compared with field measurements for validation purposes, which resulted in just a 3.31% error. The internal attic was 35.64 °C despite the roof exterior achieving 54.59 °C after receiving an average 680 W/m2 solar radiation source. After the heat flux was hugely terminated by the HFCR tile and sustainable active RI system, the heat flux magnitude attained in the internal attic was 1.0822 W/m2, and the active RI system removed 96.29% of heat flux excellently. Meanwhile, the proposed roofing design's energy-saving performance was 27.5%.

Abstract The anxiolytic effect of oxytocin (OXT) on psychosocial stress has been well documented, but the effectiveness under the interference of other factors still requires in-depth research. Previous studies have shown that nicotine addiction interacts with OXT on psychosocial stress on the behavioral level. However, the underlying neural mechanism of interaction between OXT and nicotine addiction on psychosocial stress has not been examined, and we conducted two experiments to reveal it. Firstly, after intranasal administration of randomized OXT or placebo (saline), a group of healthy participants (n = 27) and a group of smokers (n = 26) completed the Montreal Imaging Stress Task (MIST) in an MRI scanner. Secondly, a group of smokers (n = 22) was recruited to complete a transcranial direct current stimulation (tDCS) experiment, in which anodal tDCS was applied on subjects’ anterior right superior temporal gyrus (rSTG). In both experiment, subjective stress ratings, salivary cortisol samples and the amount of daily cigarette consumption were obtained from each participant. Analysis of variance were applied on both behavioral and neural data to examine the effects of OXT and nicotine addiction, and correlation analysis were used to examine relationships between neural and behavioral data. In first fMRI experiment, analysis of variance (ANOVA) revealed an interaction of OXT and nicotine addiction on subjective stress. In smokers, OXT failed to suppress the elevation of subjective stress and craving ratings after psychosocial stress. A voxel-wise ANOVA of fMRI data identified an interaction between OXT and nicotine addiction in anterior rSTG, and its functional connectivity with right middle frontal gyrus. Correlations between this functional connectivity and subjective psychosocial stress were also found abnormal in smokers. In second tDCS experiment, we found that under tDCS, OXT successfully suppressed the elevation of subjective stress and craving ratings after stress. In summary, we found that nicotine addiction blocked OXT’s anxiolytic on psychosocial stress, which was related to abnormalities in anterior rSTG. By applying anodal tDCS on anterior rSTG, OXT’s anxiolytic effect was restored in smokers. These findings will support further development on oxytocin’s intervention of psychosocial stress in nicotine addiction, and provides essential information for indicating OXT’s effectiveness.

Highlights Site-selective superassembly was used to synthesize a nanorobot with bionic rod-shaped head/hollow tail structure, exhibiting strong photothermal effect, high drug loading capacity, precise and controlled drug release, and excellent motility. Injectable regenerated silk fibroin (RSF) nanofibril hydrogels were developed via simple sonication, offering significant production, structural, and performance advantages. Nanorobot/thrombin (Thr)/RSF nanofibril hydrogels reduced intraoperative bleeding of hepatocellular carcinoma (HCC) spinal metastasis, provided starvation embolization, and prevented post-surgery recurrence. Nanorobot/Thr/RSF nanofibril hydrogels inhibited blood supply to residual tumors and reduced neovascularization, inhibiting post-surgery HCC spinal metastasis recurrence.

Abstract Background Chronic kidney disease (CKD) is a significant risk factor for pulmonary hypertension (PH), a complication that adversely affects patient prognosis. However, the mechanisms underlying this association remain poorly understood. A major obstacle to progress in this field is the lack of a reliable animal model replicating CKD-PH. Methods This study aimed to establish a stable rat model of CKD-PH. We employed a combined approach, inducing CKD through a 5/6 nephrectomy and concurrently exposing the rats to a high-salt diet. The model's hemodynamics were evaluated dynamically, alongside a comprehensive assessment of pathological changes in multiple organs. Lung tissues and serum samples were collected from the CKD-PH rats to analyze the expression of angiotensin-converting enzyme 2 (ACE2), evaluate the activity of key vascular components within the renin–angiotensin–aldosterone system (RAAS), and characterize alterations in the serum metabolic profile. Results At 14 weeks post-surgery, the CKD-PH rats displayed significant changes in hemodynamic parameters indicative of pulmonary arterial hypertension. Additionally, right ventricular hypertrophy was observed. Notably, no evidence of pulmonary vascular remodeling was found. Further analysis revealed RAAS dysregulation and downregulated ACE2 expression within the pulmonary vascular endothelium of CKD-PH rats. Moreover, the serum metabolic profile of these animals differed markedly from the sham surgery group. Conclusions Our findings suggest that the development of pulmonary arterial hypertension in CKD-PH rats is likely a consequence of a combined effect: RAAS dysregulation, decreased ACE2 expression in pulmonary vascular endothelial cells, and metabolic disturbances. Graphical Abstract

Abstract Mild sleep deprivation is widespread in many societies worldwide. Electroencephalography (EEG) microstate analysis provides information on spatial and temporal characteristics of resting brain network, serving as an indicator of neurophysiological activities at rest. This study seeks to investigate potential neural markers in EEG following mild sleep deprivation of a single night using EEG microstate analysis. Six-minute resting EEG was conducted on thirty healthy adults within 6 hours of waking in the morning and after at least 18 h of sleep deprivation. Translated and validated Malay language Karolinska Sleepiness Scale was used to assess the participants’ degree of sleepiness. Microstate characteristics analysis was conducted on the final 24 subjects based on four standard microstate maps. Microstate C shows a significant increase in mean duration, coverage and occurrence, while microstate D has significantly higher occurrence after sleep deprivation. This study demonstrates notable changes in resting state EEG microstates following mild sleep deprivation. Present findings deepen our understanding of the brain's spatiotemporal dynamics under this condition and suggest the potential utility of neural markers in this domain as components of composite markers for sleep deprivation.

Abstract A dose-escalation and expansion, phase 1/2 study (ClinicalTrials.gov, NCT04818333) was conducted to assess the novel antibody-drug conjugate SHR-A1811 in pretreated HER2-altered advanced non-small cell lung cancer (NSCLC). Here, we report results from the phase 1 portion. Patients who had previously failed or were intolerant to platinum-based chemotherapy were enrolled and received SHR-A1811 intravenously at doses of 3.2 to 8.0 mg/kg every 3 weeks. Dose escalation followed a Bayesian logistic regression model that included overdose control, with subsequent selection of tolerable levels for dose expansion. Overall, 63 patients were enrolled, including 43 receiving a recommended dose for expansion of 4.8 mg/kg. All patients had HER2-mutant disease. Dose-limiting toxicity occurred in one patient in the 8.0 mg/kg dose cohort. Grade ≥ 3 treatment-related adverse events occurred in 29 (46.0%) patients. One patient in the 6.4 mg/kg cohort died due to interstitial lung disease. As of April 11, 2023, the 4.8 mg/kg cohort showed an objective response rate of 41.9% (95% CI 27.0–57.9), and a disease control rate of 95.3% (95% CI 84.2–99.4). The median duration of response was 13.7 months, with 13 of 18 responses ongoing. The median progression-free survival was 8.4 months (95% CI 7.1–15.0). SHR-A1811 demonstrated favourable safety and clinically meaningful efficacy in pretreated advanced HER2-mutant NSCLC.

The electricity consumption in the smart grids consists of an uncertainty feature. Also, an unstable atmosphere situation causes photovoltaic (PV) generation will be undefined output. With both of these problems, the net demand power of consumers can’t be a specific value. On the other hand, using consumption patterns, the consumptions and generations could be predicted for improving the operation of the power system. This paper reports the results of the differential performance of probabilistic forecasting of the residential electricity onsumption, PV power generation, and net demand related to smart buildings using the novel method of the Improved Deep Mixture Density Network (IDMDN). According to this, investigators used a strong Multi to Multi (M2M) mapping of the neural network model. They followed that they had used a kind of beta kernel to decrease the number of leakage issues. It is an attempt to generate random predictions by the method of end-to-end. It expressed a new performance of changed initiation and multiple procedures of educating to decrease or remove unsteady traits in the probabilistic problems of the beta kernel function. The results show good performance of the proposed method in comparison with other methods.

Abstract Objective The “Health” element is one of the elements in Significant Quality of Life Measure (SigQOLM) that measures quality of life and well-being of people. This study aims to evaluate the Health element (Health-SigQOLM) as a generic and dynamic scale to measure health-related quality of life (HRQOL) with a broader spectrum of coverage. This study used a secondary data that developed SigQOLM. Only the “Health” element with 33 items is used for analysis. Results The construct of Health-SigQOLM has a minimum factor loading of 0.425 with excellent model fit. The health status among healthcare workers is significantly associated with the Health-SigQOLM (p

The dorsomedial prefrontal cortex (dmPFC) plays a crucial role in social cognitive functions, including perspective-taking. Although perspective-taking has been linked to self-control, the mechanism by which the dmPFC might facilitate self-control remains unclear. Using the multimodal neuroimaging dataset from the Human Connectome Project (Study 1, N =978 adults), we established a reliable association between the dmPFC and self-control, as measured by discounting rate—the tendency to prefer smaller, immediate rewards over larger, delayed ones. Experiments (Study 2, N = 36 adults) involving high-definition transcranial direct current stimulation showed that anodal stimulation of the dmPFC reduces the discounting of delayed rewards and decreases the congruency effect in egocentric but not allocentric perspective in the visual perspective-taking tasks. These findings suggest that the dmPFC promotes self-control by inhibiting the egocentric perspective, offering new insights into the neural underpinnings of self-control and perspective-taking, and opening new avenues for interventions targeting disorders characterized by impaired self-regulation.

Abstract Under grid voltage sags, over current protection and exploiting the maximum capacity of the inverter are the two main goals of grid-connected PV inverters. To facilitate low-voltage ride-through (LVRT), it is imperative to ensure that inverter currents are sinusoidal and remain within permissible limits throughout the inverter operation. An improved LVRT control strategy for a two-stage three-phase grid-connected PV system is presented here to address these challenges. To provide over current limitation as well as to ensure maximum exploitation of the inverter capacity, a control strategy is proposed, and performance the strategy is evaluated based on the three generation scenarios on a 2-kW grid connected PV system. An active power curtailment (APC) loop is activated only in high power generation scenario to limit the current’s amplitude below the inverter’s rated current. The superior performance of the proposed strategy is established by comparison with two recent LVRT control strategies. The proposed method not only injects necessary active and reactive power but also minimizes overcurrent with increased exploitation of the inverter’s capacity under unbalanced grid voltage sag.

ObjectiveTo investigate the risk factors for early relapse after curative resection of Siewert type Ⅱ/Ⅲ adenocarcinoma of esophagogastric junction (AEG) and construct a visual predictive model.MethodsA retrospective analysis was conducted on the clinicopathological data of patients diagnosed with Siewert type Ⅱ/Ⅲ AEG who underwent curative resection at the Second Hospital of Lanzhou University from January 2016 to March 2021. The samples were randomly divided into a training group and a validation group in a 7∶3 ratio. The LASSO-Logistic regression method was used to select variables predictive of early recurrence of Siewert type Ⅱ/Ⅲ AEG and construct a predictive model for early recurrence. The model was validated through 1000 bootstrap resampling. Receiver operating characteristic (ROC) curves were drawn, and area under the curve (AUC), calibration curves, and decision curve analysis (DCA) were used to evaluate the model's stability.ResultsAccording to the inclusion and exclusion criteria of this study, a total of 320 Siewert type Ⅱ/Ⅲ AEG patients were included, with 122 experiencing recurrence within two years. LASSO-Logistic regression analysis revealed AJCC staging, degree of differentiation, CA199, CEA, NLR, and tumor maximum diameter as independent predictive factors for early recurrence of Siewert type Ⅱ/Ⅲ AEG. A predictive model was constructed with these factors and depicted as a nomogram. For the training group, the AUC of the ROC curve was 0.836(95% CI: 0.785-0.887), with a sensitivity of 81.4% and a specificity of 85.6%;for the validation group, the AUC was 0.812(95% CI: 0.711-0.912), with a sensitivity of 80.6% and a specificity of 87.7%. Calibration curves for both the training and validation groups displayed curves close to the reference line, indicating high model stability. The DCA curve showed that the model provided a good net benefit with threshold probabilities between 0.05 and 0.75.ConclusionsA multivariate model developed using LASSO-Logistic regression could predict early relapse in patients with Siewert type Ⅱ/Ⅲ AEG, which may be instrumental in assessing patient prognoses and in guiding postoperative surveillance and management for patients with Siewert type Ⅱ/Ⅲ AEG.

Abstract Epitaxial growth is a fundamental step required to create devices for the semiconductor industry, enabling different materials to be combined in layers with precise control of strain and defect structure. Patterning the growth substrate with a mask before performing epitaxial growth offers additional degrees of freedom to engineer the structure and hence function of the semiconductor device. Here, we demonstrate that conditions exist where such epitaxial lateral overgrowth can produce complex, three-dimensional structures that incorporate cavities of deterministic size. We grow germanium on silicon substrates patterned with a dielectric mask and show that fully-enclosed cavities can be created through an unexpected self-assembly process that is controlled by surface diffusion and surface energy minimization. The result is confined cavities enclosed by single crystalline Ge, with size and position tunable through the initial mask pattern. We present a model to account for the observed cavity symmetry, pinch-off and subsequent evolution, reflecting the dominant role of surface energy. Since dielectric mask patterning and epitaxial growth are compatible with conventional device processing steps, we suggest that this mechanism provides a strategy for developing electronic and photonic functionalities.

AIM: To evaluate the performance of three distinct large language models(LLM), including GPT-3.5, GPT-4, and PaLM2, in responding to queries within the field of ophthalmology, and to compare their performance with three different levels of medical professionals: medical undergraduates, master of medicine, and attending physicians.METHODS: A total of 100 ophthalmic multiple-choice tests, which covered ophthalmic basic knowledge, clinical knowledge, ophthalmic examination and diagnostic methods, and treatment for ocular disease, were conducted on three different kinds of LLM and three different levels of medical professionals(9 undergraduates, 6 postgraduates and 3 attending physicians), respectively. The performance of LLM was comprehensively evaluated from the aspects of mean scores, consistency and confidence of response, and it was compared with human.RESULTS: Notably, each LLM surpassed the average performance of undergraduate medical students(GPT-4:56, GPT-3.5:42, PaLM2:47, undergraduate students:40). Specifically, performance of GPT-3.5 and PaLM2 was slightly lower than those of master's students(51), while GPT-4 exhibited a performance comparable to attending physicians(62). Furthermore, GPT-4 showed significantly higher response consistency and self-confidence compared with GPT-3.5 and PaLM2.CONCLUSION: LLM represented by GPT-4 performs well in the field of ophthalmology, and the LLM model can provide clinical decision-making and teaching aids for clinicians and medical education.

There is a growing need for a more streamlined and automated method of refining biofuels, as there are currently no universally applicable process inspection instruments on the market. All process variables in bio-oil upgrading operations are maintained according to the offline specifications of the products and intermediates. Failure of the process and loss of resources can result from batch-wise monitoring not having real-time product standards. Consequently, in order to cut down on waste and lessen the chances of process failure, a quick and accurate tool for specifying intermediates and products is required. To resolve this issue, we created a model using response surface methodology and an artificial neural network that can increase the bio-oil yield involving parameters, i.e., biomass particle size (mm), temperature (°C), and residence time (min). The maximum bio-oil production (47.0883%) was achieved at 3 mm particle size, 523°C temperature, and 20 min residence time. All results are “better” for root mean squared error (∼1), and the highest coefficient of regression for bio-oil production is in the range of 0.97–0.99. Temperature is the most significant factor in bio-oil yield, followed by particle size and residence time. Based on physicochemical properties, bio-oil has the maximum kinematic viscosity (11.3 Cst) and water content (18.7%). Making bio-oil precious compounds allows it to be used as boiler feedstock and steam generation fuel.

Methicillin-resistant Staphylococcus aureus (MRSA), recognized for its resistance, poses a formidable challenge to public health, primarily attributable to its biochemical resistant mechanisms, capacity to form protective biofilms, and capacity to evade macrophage surveillance. In response to this threat, we constructed a multifunctional pH-responsive dimeric prodrug based on mannose (MAN) and cinnamaldehyde (CMA). This prodrug, designated as MACA@indocyanine green (ICG), incorporates the photosensitizer ICG. The nanoscale properties and MAN units integrated into the MACA@ICG prodrug could promote effective binding with infectious microbes or MAN residues on mammalian cells. This specific interaction, corroborated by co-localization studies using confocal microscopy and flow cytometry, is crucial for initial bacterial binding, biofilm penetration, and macrophage-specific uptake, all of which are strategic in surmounting the biological barriers posed by MRSA. Following its targeted accumulation, MACA@ICG was demonstrated responsiveness to the acidic microenvironment of bacteria to release free CMA, a natural compound with antimicrobial properties. The drug release, synergistically coupled with the effects of photothermal therapy (PTT) and photodynamic therapy (PDT) showcased superior synergistic antibacterial efficacy both in vitro and in vivo. Therefore, MACA@ICG was verified remarkable capabilities in surmounting bacterial biological barriers and exceptional antibacterial efficiency through the combinational treatments of herbal components, PDT and PTT.

BackgroundPulmonary hypertension (PH) is a progressive disease affecting the lung vasculature that is characterized by sustained vasoconstriction and leads to vascular remodeling. The lung microbiome contributes to PH progression, but the function of the gut microbiome and the correlation between the gut microbiome and metabolome remain unclear. We have analyzed whether chronic hypoxia-induced PH alters the rat fecal microbiota.PurposeWe explored hypoxia-induced pulmonary hypertension model rats to find out the characteristic changes of intestinal microorganisms and metabolites of hypoxia-induced pulmonary hypertension, and provide a theoretical basis for clinical treatment.MethodsIn the current study, a chronic hypoxia-induced PH rat model was used to investigate the role of the gut microbiome and metabolome as a potential mechanism contributing to the occurrence and development of PH. 16S ribosomal ribonucleic acid (16S rRNA), short-chain fatty acid (SCFA) measurements, mass spectrometry (MS) metabolomics analysis and metatranscriptome were performed to analyze stool samples. The datasets were analyzed individually and integrated for combined analysis using bioinformatics approaches.ResultsOur results suggest that the gut microbiome and metabolome of chronic hypoxia-induced PH rats are distinct from those of normoxic rats and may thus aid in the search for new therapeutic or diagnostic paradigms for PH.ConclusionThe gut microbiome and metabolome are altered as a result of chronic hypoxia-induced PH. This imbalanced bacterial ecosystem might play a pathophysiological role in PH by altering homeostasis.

Background: Although the changes of mitogen-activated protein kinase (MAPK) pathway in the central nervous system (CNS) induced by excessive fluoride has been confirmed by our previous findings, the underlying mechanism(s) of the action remains unclear. Here, we investigate the possibility that microRNAs (miRNAs) are involved in the aspect. Methods: As a model of chronic fluorosis, SD rats received different concentrations of fluoride in their drinking water for 3 or 6 months and SH-SY5Y cells were exposed to fluoride. Literature reviews and bioinformatics analyses were used to predict and real-time PCR to measure the expression of 12 miRNAs; an algorithm-based approach was applied to identify multiply potential target-genes and pathways; the dual-luciferase reporter system to detect the association of miR-132–3p with MAPK1; and fluorescence in situ hybridization to detect miR-132–3p localization. The miR-132–3p inhibitor or mimics or MAPK1 silencing RNA were transfected into cultured cells. Expression of protein components of the MAPK pathway was assessed by immunofluorescence or Western blotting. Results: In the rat hippocampus exposed with high fluoride, ten miRNAs were down-regulated and two up-regulated. Among these, miR-132–3p expression was down-regulated to the greatest extent and MAPK1 level (selected from the 220 genes predicted) was corelated with the alteration of miR-132–3p. Furthermore, miR-132–3p level was declined, whereas the protein levels MAPK pathway components were increased in the rat brains and SH-SY5Y cells exposed to high fluoride. MiR-132–3p up-regulated MAPK1 by binding directly to its 3′-untranslated region. Obviously, miR-132–3p mimics or MAPK1 silencing RNA attenuated the elevated expressions of the proteins components of the MAPK pathway induced by fluorosis in SH-SY5Y cells, whereas an inhibitor of miR-132–3p just played the opposite effect. Conclusion: MiR-132–3p appears to modulate the changes of MAPK signaling pathway in the CNS associated with chronic fluorosis.

The impact resistance of the steel fiber reinforced concrete (SFRC) beams after exposure to fire was studied. The SFRC beams were subjected to open flame for 120 min. After cooling to the ambient temperature, the drop-weight impact tests were conducted on the SFRC specimens. The influence of the steel fiber type and fire temperature on the impact-resistant properties was examined. The temperature time history, force time history, and reaction force-deflection curve were obtained. The peak force is discussed and the energy absorption is calculated. The results showed that the influence of steel fiber type on the impact resistance of SFRC was negligible, while the impact resistance and ductility of SFRC decreased significantly after exposure to fire. Compared with the partial fracture of the specimen at room temperature, the specimen after fire showed a more complete fracture under impact load, indicating a larger deflection and more pullout of steel fibers. This means that more friction is needed during the damage process, resulting a higher energy absorption.

Abstract Background Patients with pulmonary hypertension (PH) and chronic obstructive pulmonary disease (COPD) have an increased risk of disease exacerbation and decreased survival. We aimed to develop and validate a non‐invasive nomogram for predicting COPD associated with severe PH and a prognostic nomogram for patients with COPD and concurrent PH (COPD–PH). Methods This study included 535 patients with COPD–PH from six hospitals. A multivariate logistic regression analysis was used to analyse the risk factors for severe PH in patients with COPD and a multivariate Cox regression was used for the prognostic factors of COPD–PH. Performance was assessed using calibration, the area under the receiver operating characteristic curve and decision analysis curves. Kaplan–Meier curves were used for a survival analysis. The nomograms were developed as online network software. Results Tricuspid regurgitation velocity, right ventricular diameter, N‐terminal pro‐brain natriuretic peptide (NT‐proBNP), the red blood cell count, New York Heart Association functional class and sex were non‐invasive independent variables of severe PH in patients with COPD. These variables were used to construct a risk assessment nomogram with good discrimination. NT‐proBNP, mean pulmonary arterial pressure, partial pressure of arterial oxygen, the platelet count and albumin were independent prognostic factors for COPD–PH and were used to create a predictive nomogram of overall survival rates. Conclusions The proposed nomograms based on a large sample size of patients with COPD–PH could be used as non‐invasive clinical tools to enhance the risk assessment of severe PH in patients with COPD and for the prognosis of COPD–PH. Additionally, the online network has the potential to provide artificial intelligence‐assisted diagnosis and treatment. Highlights A multicentre study with a large sample of chronic obstructive pulmonary disease (COPD) patients diagnosed with PH through right heart catheterisation. A non‐invasive online clinical tool for assessing severe pulmonary hypertension (PH) in COPD. The first risk assessment tool was established for Chinese patients with COPD–PH.

Abstract In this work, 2D ferromagnetic M3GeTe2 (MGT, M = Ni/Fe) nanosheets with rich atomic Te vacancies (2D‐MGTv) are demonstrated as efficient OER electrocatalyst via a general mechanical exfoliation strategy. X‐ray absorption spectra (XAS) and scanning transmission electron microscope (STEM) results validate the dominant presence of metal‐O moieties and rich Te vacancies, respectively. The formed Te vacancies are active for the adsorption of OH* and O* species while the metal‐O moieties promote the O* and OOH* adsorption, contributing synergistically to the faster oxygen evolution kinetics. Consequently, 2D‐Ni3GeTe2v exhibits superior OER activity with only 370 mV overpotential to reach the current density of 100 mA cm−2 and turnover frequency (TOF) value of 101.6 s−1 at the overpotential of 200 mV in alkaline media. Furthermore, a 2D‐Ni3GeTe2v‐based anion‐exchange membrane (AEM) water electrolysis cell (1 cm2) delivers a current density of 1.02 and 1.32 A cm−2 at the voltage of 3 V feeding with 0.1 and 1 m KOH solution, respectively. The demonstrated metal‐O coordination with abundant atomic vacancies for ferromagnetic M3GeTe2 and the easily extended preparation strategy would enlighten the rational design and fabrication of other ferromagnetic materials for wider electrocatalytic applications.

ABSTRACT Candida albicans (C. albicans) and Lactiplantibacillus plantarum subsp. plantarum (L. plantarum) are frequently identified in various niches, but their dual-species interaction, especially with C. albicans in yeast form, remains unclear. This study aimed to investigate the dual-species interaction of L. plantarum and C. albicans, including proliferation, morphology, and transcriptomes examined by selective agar plate counting, microscopy, and polymicrobial RNA-seq, respectively. Maintaining a stable and unchanged growth rate, L. plantarum inhibited C. albicans yeast cell proliferation but not hyphal growth. Combining optical microscopy and atomic force microscopy, cell-to-cell direct contact and co-aggregation with L. plantarum cells surrounding C. albicans yeast cells were observed during dual-species interaction. Reduced C. albicans yeast cell proliferation in mixed culture was partially due to L. plantarum cell-free culture supernatant but not the acidic environment. Upon polymicrobial transcriptomics analysis, interesting changes were identified in both L. plantarum and C. albicans gene expression. First, two L. plantarum quorum-sensing systems showed contrary changes, with the activation of lamBDCA and repression of luxS. Second, the upregulation of stress response-related genes and downregulation of cell cycle, cell survival, and cell integrity-related pathways were identified in C. albicans, possibly connected to the stress posed by L. plantarum and the reduced yeast cell proliferation. Third, a large scale of pathogenesis and virulence factors were downregulated in C. albicans, indicating the potential interruption of pathogenic activities by L. plantarum. Fourth, partial metabolism and transport pathways were changed in L. plantarum and C. albicans. The information in this study might aid in understanding the behavior of L. plantarum and C. albicans in dual-species interaction.IMPORTANCEThe anti-Candida albicans activity of Lactiplantibacillus plantarum has been explored in the past decades. However, the importance of C. albicans yeast form and the effect of C. albicans on L. plantarum had also been omitted. In this study, the dual-species interaction of L. plantarum and C. albicans was investigated with a focus on the transcriptomes. Cell-to-cell direct contact and co-aggregation with L. plantarum cells surrounding C. albicans yeast cells were observed. Upon polymicrobial transcriptomics analysis, interesting changes were identified, including contrary changes in two L. plantarum quorum-sensing systems and reduced cell survival-related pathways and pathogenesis determinants in C. albicans.

Abstract Malignant pleural mesothelioma (MPM) is a rare but lethal pleural cancer with high intratumor heterogeneity (ITH). A recent study in lung adenocarcinoma has developed a clonal gene signature (ORACLE) from multiregional transcriptomic data and demonstrated high prognostic values and reproducibility. However, such a strategy has not been tested in other types of cancer with high ITH. We aimed to identify biomarkers from multi-regional data to prognostically stratify MPM patients. We generated a multiregional RNA-seq dataset for 78 tumor samples obtained from 26 MPM patients, each with one sample collected from a superior, lateral, and inferior region of the tumor. By integrating this dataset with the Cancer Genome Atlas MPM RNA-seq data, we selected 29 prognostic genes displaying high variability across different tumors but low ITH, which named PRACME (Prognostic Risk Associated Clonal Mesothelioma Expression). We evaluated PRACME in two independent MPM datasets and demonstrated its prognostic values. Patients with high signature scores are associated with poor prognosis after adjusting established clinical factors. Interestingly, the PRACME and the ORACLE signatures defined respectively from MPM and lung adenocarcinoma cross-predict prognosis between the two cancer types. Further investigation indicated that the cross-prediction ability might be explained by the high similarity between the two cancer types in their genomic regions with copy number variation, which host many clonal genes. Overall, our clonal signature PRACME provided prognostic stratification in MPM and this study emphasized the importance of multi-regional transcriptomic data for prognostic stratification based on clonal genes.

Abstract Steering soft robots in a self-regulated manner remains a grand challenge, which often requires continuous symmetry breaking and recovery steps for persistent motion. Although structural morphology is found significant for robotic functions, geometric topology has rarely been considered and appreciated. Here we demonstrate a series of knotbots, namely hydrogel-based robots with knotted structures, capable of autonomous rolling and spinning/rotating motions. With symmetry broken by external stimuli and restored by self-regulation, the coupling between self-constraint-induced prestress and photothermal strain animates the knotbots continuously. Experiments and simulations reveal that nonequilibrium processes are regulated dynamically and cooperatively by self-constraints, active deformations, and self-shadowing effect of the photo-responsive gel. The active motions enable the knotbots to execute tasks including gear rotation and rod climbing. This work paves the way to devise advanced soft robots with self-regulated sustainable motions by harnessing the topology.

Abstract Curved displays can adjust their shape to accommodate different objects and are used in electronics and decorative lighting. Due to the immutable pixel spacing, existing commercial curved displays are flexible but not compatible with undevelopable surfaces. Inspired by kirigami and auxetic structures, we propose an approach that combines luminescent elements and rotating square tessellations to create a stretchable, arbitrary curve adaptive display. We connect square islands by vertical interconnects to relieve the stress concentration and provide extra deformation patterns. The vertical interconnects are patterned on a flexible printed circuit board (FPCB) using laser cutting and folded up via specially designed molds. Further, the freed-up space by folded interconnects allows the structure to be compressed. A prototype stretchable display is demonstrated that it can maintain electrical performance under biaxial strain and adapt to different Gaussian curvature surfaces, including cylindrical, spherical, saddle and arbitrary surfaces. Theoretical models and finite element calculations are established to describe the tensile behavior of the structures under different boundary conditions and agree with the experimental results. This proposed technology paves a feasible solution of mass production of adaptive curved displays and sets the trend for the next-generation display.

With the development of a new generation of information technology, smart elderly care plays an important role in promoting the construction of elderly care services. The emerging application tools provide door-to-door meal service for urban elderly groups, solving meal problems for special and ordinary elderly with different priority levels and penalty costs of violating time windows. Based on this, considering the personalized needs of the elderly group, this study examines the route optimization problem of cooperative delivery of elderly meals by trucks and robots, and builds a mixed integer programming model to minimize the total cost of the system. For large-scale problems, this study designs an improved adaptive large neighbourhood search algorithm that incorporates simulated annealing algorithm and artificial bee colony algorithm to avoid falling into local optimality. Experiments have proved feasibility and effectiveness of the algorithm and proposed the corresponding management insights from the aspects of delivery efficiency and service quality.

This paper presents a systematic investigation and demonstration of a K-band circularly polarized liquid-crystal-based phased array (LCPA), including the design, over-the-air (OTA) in-array calibration, and experimental validation. The LCPA contains 16 phase-shifting radiating channels, each consisting of a circularly polarized stacked patch antenna and a liquid-crystal-based phase shifter (LCPS) based on a loaded differential line structure. Thanks to its slow-wave properties, the LCPS exhibits a maximum phase-shifting range of more than 360° with a figure of merit of 78.3(° )·dB−1 based on a liquid crystal layer with a thickness of only 5 μm. Furthermore, an automatic OTA calibration based on a state ergodic method is proposed, which enables the extraction of the phase–voltage curve of every individual LCPA channel. The proposed LCPA is manufactured and characterized with a total profile of only 1.76 mm, experimentally demonstrating a scanned circularly polarized beam from −40° to +40° with a measured peak gain of 12.5 dBic and a scanning loss of less than 2.5 dB. The bandwidth of the LCPA, which satisfies the requirements of port reflection (|S11|) < −15 dB, an axial ratio (AR) < 3 dB, beam squinting < 3°, and a gain variation < 2.2 dB, spans from 25.5 to 26.0 GHz. The total efficiency is about 34%, which represents a new state of the art. The use of the demonstrated low-profile LCPA to support circularly polarized scanning beams, along with the systematic design and calibration methodology, holds potential promise for a variety of millimeter-wave applications.

A broadband multimode circularly polarized (CP) stacked antenna with pattern diversity is proposed in this paper. The antenna consists of three layers, two substrates with annular-ring patches and a parasitic metal patch. The antenna can radiate different patterns by exciting different modes of annular-ring micro-strip antenna (ARMSA). When port 1 is excited, the antenna is excited at the second-order mode(TM21) to generate the conical radiation pattern, whereas the antenna is excited at the fundamental mode(TM11) to generate the broad beam radiation pattern when port 2 is excited. In order to generate circular polarization, ARMSAs are fed by two feeding points and both of the annular-ring patches are fed with the network in the same layer. The two-port antenna achieves the broadband property by parasitic patch. The parasitic top metal layer can broaden the impedance bandwidth of TM11 mode while the ground plane of TM11 mode can act as the parasitic patch to broaden the impedance bandwidth of TM21 mode. To verify the design, the antenna is fabricated and measured. The measured results show that the antenna proposed in this paper has an impedance bandwidth of 0.22GHz (1.92-2.14GHz) and 3dB axial ratio (AR) bandwidth of about 0.25GHz (1.90-2.15GHz).

Conventional perturb and observe (P&O) algorithm fails to track global maximum power point (GMPP) under complex partial shading conditions (PSC) in photovoltaic (PV) system. While many of the latest maximum power point tracking (MPPT) algorithms are designed to handle simpler PSCs with fewer peaks, their capability to handle highly complex PSCs remains uncertain. This study presented more practical, challenging, and complex PSCs that have over five peaks and extremely close peak values. A new deterministic peak hopping (PH)-based MPPT algorithm with simple mechanisms is proposed to address these complex PSCs. An agent is utilized to scan and hop between the lower and higher duty cycle regions of P-V curve with optimum step size, thereby effectively narrowing down the tracking region, moving towards the GMPP. Additionally, the proposed algorithm utilizes an adjustable sampling time during scanning and hopping process to accelerate the convergence. Extensive simulation studies have revealed the effectiveness of the proposed algorithm in tracking GMPP. Moreover, the proposed algorithm outperforms five of the latest MPPT algorithms. In experimental setup, the proposed algorithm is successfully implemented into real-time TI C2000 microcontroller and performed robustly using ITECH IT6012C-800-40 PV simulator, achieving tracking time shorter than 0.83s and tracking accuracy over 98.70%.

A new taper-shaped linearly polarized (LP) magneto-electric (ME) dipole, excited by the U-shaped microstrip line through a coupling slot, is proposed in this paper. An ultra-wide simulated impedance bandwidth of 85% (20.5-50.6 GHz) and 1.5-dB gain bandwidth of 73% (22.3-47.9 GHz) with a peak gain up to 7.7 dBi are achieved, combining the advantages of simple and compact structure, high integration, easy assembling and stable radiation characteristics. For proof-of-concept demonstration, a $4\times4$ LP array antenna is then implemented with an ultra-wide measured impedance bandwidth of 91.8% (18.8-50.7 GHz) and 3-dB gain bandwidth of 69.3% (19.8 to 40.8 GHz) as well as a peak gain of 18.0 dBi. Subsequently, the LP element is conveniently extended to a dual-polarized (DP) one due to its 90° rotationally symmetrical structure, exhibiting a wide simulated overlapped bandwidth of 61.4% (23.7-44.7 GHz), port isolation exceeding 17 dB and a stable gain response with a peak gain of 7.5 dBi. Furthermore, two $1\times4$ DP active phased array antennas (APAAs) are designed by utilizing the integrated circuits (ICs) for 5G/6G millimeter-wave (mmWave) mobile terminal applications. Meanwhile, the beam-scanning performances at 28- and 38-GHz are measured for both polarizations. The validated advantages render the proposed antenna designs competitive candidates for diverse 5G/6G mmWave applications.

As the expert gear of the emergency rescue system, drones are frequently utilized to distribute supplies following a calamity. The cost and effectiveness of rescue efforts as well as equitable distribution should be taken into account when allocating emergency supplies to disaster-affected areas. This work explores the emergency material allocation problem for truck-drone joint transportation with dynamic energy restrictions based on taking the fairness of emergency material allocation into consideration. In order to guarantee the equitable distribution of materials, the psychological stress experienced by the victims at each catastrophe site is measured using the relative deprivation cost. An adaptive large-scale neighborhood search method serves as the foundation for the creation of a two-stage heuristic algorithm, which reduces the overall cost of the system. The integer programming model MIP is built for this purpose. The research findings can serve as a useful guide for developing a just and effective emergency drone rescue system, and the testing results demonstrate the viability and effectiveness of the two-stage heuristic algorithm.

Abstract Gastrointestinal (GI) cancers account for a significant incidence and mortality rates of cancers globally. Utilization of a phenomic data approach allows researchers to reveal the mechanisms and molecular pathogenesis of these conditions. We aimed to investigate the association between the phenomic features and GI cancers in a large cohort study. We included 502,369 subjects aged 37–73 years in the UK Biobank recruited since 2006, followed until the date of the first cancer diagnosis, date of death, or the end of follow-up on December 31st, 2016, whichever occurred first. Socio-demographic factors, blood chemistry, anthropometric measurements and lifestyle factors of participants collected at baseline assessment were analysed. Unvariable and multivariable logistic regression were conducted to determine the significant risk factors for the outcomes of interest, based on the odds ratio (OR) and 95% confidence intervals (CI). The analysis included a total of 441,141 participants, of which 7952 (1.8%) were incident GI cancer cases and 433,189 were healthy controls. A marker, cystatin C was associated with total and each gastrointestinal cancer (adjusted OR 2.43; 95% CI 2.23–2.64). In this cohort, compared to Asians, the Whites appeared to have a higher risk of developing gastrointestinal cancers. Several other factors were associated with distinct GI cancers. Cystatin C and race appear to be important features in GI cancers, suggesting some overlap in the molecular pathogenesis of GI cancers. Given the small proportion of Asians within the UK Biobank, the association between race and GI cancers requires further confirmation.

The fifth generation (5G) mobile communication systems employ technologies such as massive MIMO, millimeter-wave (mmWave), and ultra-dense networks to support higher transmission data rate and lower latency, thereby enabling commercial deployment. Furthermore, the forthcoming sixth generation (6G) networks will integrate terrestrial and non-terrestrial networks, aiming to achieve full spectrum, full applications, and global coverage. Whether in the context of 5G or 6G networks, base transceiver stations (BTS) require a substantial number of radio frequency (RF) transceiver chains and antenna array, particularly in mmWave frequency bands. It is known that bandpass RF filters between antenna elements and transceivers are key components for suppressing out-of-band spurs and interference. The single board seamless integration of transceivers and antennas has become a growing trend. It means there is no extra room for a large number of filters at mmWave bands, leading to the emergence of integrated designs that combine filtering circuitry with antennas, known as filtenna or filtering antenna. With illustrated examples, the design methodologies, operational principles, and implementation strategies of filtennas are reviewed in this paper.

Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

Objective To summarize the clinical characteristics of children with functional neurological disorder (FND) presented with motor symptoms. Methods and Results Total 18 children with FND mainly manifested by motor symptoms were admitted to Department of Pediatrics in Peking University Third Hospital from July 2017 to June 2023. The clinical phenotypes included 8 cases of myoclonus, 3 of tremor, 3 of myasthenia, 2 of dystonia, and 2 of psychogenic non⁃epileptic seizure (PNES). There was no overlap of movement disorder symptoms in all patients. Of the 8 cases with clonus, 5 showed bilateral symmetry myoclonus, and 3 involved only the right upper limb or right hand. Three cases of tremor presented with rapid tremor on one or both upper limbs or hands. Three patients with myasthenia showed weakness of both lower limbs and inability to walk. Of the 2 patients with dystonia, one had paroxysmal upper limb and trunk torsion postural abnormalities, and one had intermittent muscle tone and hip flexion of the lower limb. Two cases of PNES were characterized by gaze, neck tilt, limb extension and flexion tremors, and tapping. One case had mild abnormal EEG and repeated small chromosome fragments. Tirteen cases had mild symptoms and good prognosis after treatment. Five cases with severe symptoms combined with mental symptoms were transferred to Department of Psychiatry for further treatment. Conclusions The most common manifestations of children with FND presented with motor symptoms are myoclonus, tremor and myasthenia, which can be traced to related causes, and may be accompanied by a variety of other physical or psychiatric symptoms. Some children need to be referred to Department of Psychiatric for further treatment.

Abstract Objectives We aimed to develop a combined model based on clinical and radiomic features to classify fracture age. Methods We included 1219 rib fractures from 239 patients from our center between March 2016 and September 2022. We created an external dataset using 120 rib fractures from 32 patients from another center between October 2019 and August 2023. According to tasks (fracture age between 12 weeks), the internal dataset was randomly divided into training and internal test sets. A radiomic model was built using radiomic features. A combined model was constructed using clinical features and radiomic signatures by multivariate logistic regression, visualized as a nomogram. Internal and external test sets were used to validate model performance. Results For classifying fracture age between 12 weeks, the combined model had higher AUCs than the radiomic model in the training model (0.848 vs 0.837, p = 0.12) and internal test sets (0.818 vs 0.793, p

The increasing complexity of modern mechanical systems, especially rotating machinery, demands effective condition monitoring techniques, particularly deep learning, to predict potential failures in a timely manner and enable preventative maintenance strategies. Health monitoring data analysis, a widely used approach, faces challenges due to data randomness and interpretation difficulties, highlighting the importance of robust data quality analysis for reliable monitoring. This paper presents a two-part approach to address these challenges. The first part focuses on comprehensive data preprocessing using only feature scaling and selection via random forest (RF) algorithm, streamlining the process by minimizing human intervention while managing data complexity. The second part introduces a Recurrent Expansion Network (RexNet) composed of multiple layers built on recursive expansion theories from multi-model deep learning. Unlike traditional Rex architectures, this unified framework allows fine tuning of RexNet hyperparameters, simplifying their application. By combining data quality analysis with RexNet, this methodology explores multi-model behaviors and deeper interactions between dependent (e.g., health and condition indicators) and independent variables (e.g., Remaining Useful Life (RUL)), offering richer insights than conventional methods. Both RF and RexNet undergo hyperparameter optimization using Bayesian methods under variability reduction (i.e., standard deviation) of residuals, allowing the algorithms to reach optimal solutions and enabling fair comparisons with state-of-the-art approaches. Applied to high-speed bearings using a large wind turbine dataset, this approach achieves a coefficient of determination of 0.9504, enhancing RUL prediction. This allows for more precise maintenance scheduling from imperfect predictions, reducing downtime and operational costs while improving system reliability under varying conditions.

Arrhythmia is the main cause of sudden cardiac death, and ECG signal analysis is a common method for the noninvasive diagnosis of arrhythmia. In this paper, we propose an arrhythmia classification model based on the combination of a channel attention mechanism (SE module), convolutional neural network (CNN), and long short-term memory neural network (LSTM). The data of this model use the MIT-BIH arrhythmia database, and after noise reduction of raw ECG data by the EEMD denoising algorithm, a CNN-LSTM is used to learn features from the data, and the fusion channel attention mechanism is used to adjust the weight of the feature map. The CNN-LSTM-SE model is compared with the LSTM, CNN-LSTM, and LSTM-attention models, and the models are evaluated using Precision, Recall, and F1-Score. The classification performance of the tested CNN-LSTM-SE classification prediction model is better, with a classification accuracy of 98.5%, a classification precision rate of more than 97% for each label, a recall rate of more than 98%, and an F1-score of more than 0.98. It meets the requirements of arrhythmia classification prediction and has a certain practical value.

The imbalance between parking availability and demand has led to a rise in traffic challenges in many cities. The adoption of technologies like the Internet of Things and deep learning algorithms has been extensively explored to build automated smart parking systems in urban environments. Non-human-mediated, scalable smart parking systems that are built on decentralized blockchain systems will further enhance transparency and trust in this domain. The presented work, PARKTag, is an integration of a blockchain-based system and computer vision models to detect on-field free parking slots, efficiently navigate vehicles to those slots, and automate the computation of parking fees. This innovative approach aims to enhance the efficiency, scalability, and convenience of parking management by leveraging and integrating advanced technologies for real-time slot detection, navigation, and secure, transparent fee calculation with blockchain smart contracts. PARKTag was evaluated through implementation and emulation in selected areas of the MIT Art Design Technology University campus, with a customized built-in dataset of over 2000 images collected on-field in different conditions. The fine-tuned parking slot detection model leverages pre-trained algorithms and achieves significant performance metrics with a validation accuracy of 92.9% in free slot detection. With the Solidity smart contract deployed on the Ethereum test network, PARKTag achieved a significant throughput of 10 user requests per second in peak traffic hours. PARKTag is implemented as a mobile application and deployed in the mobile application store. Its beta version has undergone user validation for feedback and acceptance, marking a significant step toward the development of the final product.

As the economic level of individuals rises, so too does the demand for mutton. Enhancing the breeds of mutton sheep not only boosts production efficiency and economic benefits but also fosters the sustainable growth of the mutton sheep breeding industry. Thus, this study examines the early growth and reproductive traits of Tianmu Sainuo sheep, analyzing the genetic interactions among these traits to furnish a theoretical foundation for refining breeding strategies and expediting the genetic advancement of this breed. The investigation compiled 29,966 data entries, involving 111 sires for birth weight (BWT) and 113 for other metrics. The data encompassed 10,415 BWT records from 1,633 dams, 12,753 weaning weight (WWT) records from 1,570 dams, 12,793 average daily gain (ADG) records from 1,597 dams, and 13,594 litter size (LS) records from 1,499 dams. Utilizing the GLM procedure in SAS 9.2 software, the study analyzed the non-genetic influences on lamb BWT, WWT, ADG, and LS. Concurrently, DMU software estimated the variance components across various animal models for each trait. Employing the Akaike Information Criterion (AIC) and likelihood ratio test (LRT), six models were tested, incorporating or excluding maternal inheritance and environmental impacts, to identify the optimal model for deriving genetic parameters. The findings reveal that birth year (BY), birth quarter (BQ), birth type (BT), age of mother (AM), and birth sex (BS) exerted significant impacts on BWT, WWT, and ADG (p < 0.01). Additionally, BQ and AM significantly influenced LS (p < 0.01). The most accurate genetic evaluation model determined the heritability of BWT, WWT, ADG, and LS to be 0.0695, 0.0849, 0.0777, and 0.1252, respectively.

Probucol has been utilized as a cholesterol-lowering drug with antioxidative properties. However, the impact and fundamental mechanisms of probucol in obesity-related cognitive decline are unclear. In this study, male C57BL/6J mice were allocated to a normal chow diet (NCD) group or a high-fat diet (HFD) group, followed by administration of probucol to half of the mice on the HFD regimen. Subsequently, the mice were subjected to a series of behavioral assessments, alongside the measurement of metabolic and redox parameters. Notably, probucol treatment effectively alleviates cognitive and social impairments induced by HFD in mice, while exhibiting no discernible influence on mood-related behaviors. Notably, the beneficial effects of probucol arise independently of rectifying obesity or restoring systemic glucose and lipid homeostasis, as evidenced by the lack of changes in body weight, serum cholesterol levels, blood glucose, hyperinsulinemia, systemic insulin resistance, and oxidative stress. Instead, probucol could regulate the levels of nitric oxide and superoxide-generating proteins, and it could specifically alleviate HFD-induced hippocampal insulin resistance. These findings shed light on the potential role of probucol in modulating obesity-related cognitive decline and urge reevaluation of the underlying mechanisms by which probucol exerts its beneficial effects.

Objective: This study aimed to develop an automated detection schema for otosclerosis with interpretable deep learning using temporal bone computed tomography images. Methods: With approval from the institutional review board, we retrospectively analyzed high-resolution computed tomography scans of the temporal bone of 182 participants with otosclerosis (67 male subjects and 115 female subjects; average age, 36.42 years) and 157 participants without otosclerosis (52 male subjects and 102 female subjects; average age, 30.61 years) using deep learning. Transfer learning with the pretrained VGG19, Mask RCNN, and EfficientNet models was used. In addition, 3 clinical experts compared the system's performance by reading the same computed tomography images for a subset of 35 unseen subjects. An area under the receiver operating characteristic curve and a saliency map were used to further evaluate the diagnostic performance. Results: In prospective unseen test data, the diagnostic performance of the automatically interpretable otosclerosis detection system at the optimal threshold was 0.97 and 0.98 for sensitivity and specificity, respectively. In comparison with the clinical acumen of otolaryngologists at P

Several studies have shown that activation of unfolded protein response and endoplasmic reticulum (ER) stress plays a crucial role in severe cerebral ischemia/reperfusion injury. Autophagy occurs within hours after cerebral ischemia, but the relationship between ER stress and autophagy remains unclear. In this study, we established experimental models using oxygen-glucose deprivation/reoxygenation in PC12 cells and primary neurons to simulate cerebral ischemia/reperfusion injury. We found that prolongation of oxygen-glucose deprivation activated the ER stress pathway protein kinase-like endoplasmic reticulum kinase (PERK)/eukaryotic translation initiation factor 2 subunit alpha (eIF2α)-activating transcription factor 4 (ATF4)-C/EBP homologous protein (CHOP), increased neuronal apoptosis, and induced autophagy. Furthermore, inhibition of ER stress using inhibitors or by siRNA knockdown of the PERK gene significantly attenuated excessive autophagy and neuronal apoptosis, indicating an interaction between autophagy and ER stress and suggesting PERK as an essential target for regulating autophagy. Blocking autophagy with chloroquine exacerbated ER stress-induced apoptosis, indicating that normal levels of autophagy play a protective role in neuronal injury following cerebral ischemia/reperfusion injury. Findings from this study indicate that cerebral ischemia/reperfusion injury can trigger neuronal ER stress and promote autophagy, and suggest that PERK is a possible target for inhibiting excessive autophagy in cerebral ischemia/reperfusion injury.

Accurate and reliable relative humidity forecasting is important when evaluating the impacts of climate change on humans and ecosystems. However, the complex interactions among geophysical parameters are challenging and may result in inaccurate weather forecasting. This study combines long short-term memory (LSTM) and extreme learning machines (ELM) to create a hybrid model-based forecasting technique to predict relative humidity to improve the accuracy of forecasts. Detailed experiments with univariate and multivariate problems were conducted, and the results show that LSTM-ELM and ELM-LSTM have the lowest MAE and RMSE results compared to stand-alone LSTM and ELM for the univariate problem. In addition, LSTM-ELM and ELM-LSTM result in lower computation time than stand-alone LSTM. The experiment results demonstrate that the proposed hybrid models outperform the comparative methods in relative humidity forecasting. We employed the recursive feature elimination (RFE) method and showed that dewpoint temperature, temperature, and wind speed are the factors that most affect relative humidity. A higher dewpoint temperature indicates more air moisture, equating to high relative humidity. Humidity levels also rise as the temperature rises.

Abstract Background G-protein coupled receptors (GPCRs) are recognized as attractive targets for drug therapy. However, it remains poorly understood how GPCRs, except for a few chemokine receptors, regulate the progression of liver fibrosis. Here, we aimed to reveal the role of GPR65, a proton-sensing receptor, in liver fibrosis and to elucidate the underlying mechanism. Methods The expression level of GPR65 was evaluated in both human and mouse fibrotic livers. Furthermore, Gpr65-deficient mice were treated with either bile duct ligation (BDL) for 21 d or carbon tetrachloride (CCl4) for 8 weeks to investigate the role of GPR65 in liver fibrosis. A combination of experimental approaches, including Western blotting, quantitative real-time reverse transcription‑polymerase chain reaction (qRT-PCR), and enzyme-linked immunosorbent assay (ELISA), confocal microscopy and rescue studies, were used to explore the underlying mechanisms of GPR65’s action in liver fibrosis. Additionally, the therapeutic potential of GPR65 inhibitor in the development of liver fibrosis was investigated. Results We found that hepatic macrophages (HMs)-enriched GPR65 was upregulated in both human and mouse fibrotic livers. Moreover, knockout of Gpr65 significantly alleviated BDL- and CCl4-induced liver inflammation, injury and fibrosis in vivo, and mouse bone marrow transplantation (BMT) experiments further demonstrated that the protective effect of Gpr65 knockout is primarily mediated by bone marrow-derived macrophages (BMMs). Additionally, in vitro data demonstrated that Gpr65 silencing and GPR65 antagonist inhibited, while GPR65 overexpression and application of GPR65 endogenous and exogenous agonists enhanced the expression and release of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and transforming growth factor-β (TGF-β), all of which subsequently promoted the activation of hepatic stellate cells (HSCs) and the damage of hepatocytes (HCs). Mechanistically, GPR65 overexpression, the acidic pH and GPR65 exogenous agonist induced up-regulation of TNF-α and IL-6 via the Gαq-Ca2+-JNK/NF-κB pathways, while promoted the expression of TGF-β through the Gαq-Ca2+-MLK3-MKK7-JNK pathway. Notably, pharmacological GPR65 inhibition retarded the development of inflammation, HCs injury and fibrosis in vivo. Conclusions GPR65 is a major regulator that modulates the progression of liver fibrosis. Thus, targeting GPR65 could be an effective therapeutic strategy for the prevention of liver fibrosis.

The performance evaluation of a Photovoltaic (PV) system heavily relies on accurately estimating the parameters based on its current—voltage relationships. However, due to the PV model’s inherent complexity, obtaining these parameters with precision and efficiency is a challenging task. In this study, a new variant known as teaching learning-based optimization with unique exemplar generation schemes (TLBO-UEGS) is proposed to address PV module parameter estimation problems with robustness and effectiveness. To enhance the performance of TLBO-UEGS, a modified initialization scheme that leverages the strengths of chaotic maps and dynamic oppositional based learning is introduced. This scheme ensures the generation of an initial population with improved solution quality. Furthermore, both the modified teacher phase and modified learner phase are integrated within the TLBO-UEGS optimization framework. This integration allows for different learning strategies to be employed based on the fitness values of each learner, effectively updating their search trajectories. Within the modified teacher phase, two unique exemplar generation schemes are designed to facilitate more effective guidance for learners in the first half of the population while maintaining population diversity. Meanwhile, the modified learner phase emulates a realistic knowledge acquisition process by enabling learners in the second half of the population to engage in collaborative learning with multiple peer learners or retain valuable knowledge from previous learning processes. Extensive simulations demonstrate that TLBO-UEGS achieves superior results, with the minimum root mean square error (RMSE) values of 3.5644 × 10−04 ± 0.0014, 1.3237 × 10−04 ± 0.0043, and 6.6016 × 10−06 ± 0.00011 obtained for Photowatt-PWP201, Leibold Solar (LSM 20), and Leybold Solar (STE 4/100) PV modules, respectively.

As one of the core components of Advanced Driver Assistance Systems (ADAS), automotive millimeter-wave radar has become the focus of scholars and manufacturers at home and abroad because it has the advantages of all-day and all-weather operation, miniaturization, high integration, and key sensing capabilities. The core performance indicators of the automotive millimeter-wave radar are distance, speed, angular resolution, and field of view. Accuracy, cost, real-time and detection performance, and volume are the key issues to be considered. The increasing performance requirements pose several challenges for the signal processing of millimeter-wave radar systems. Radar signal processing technology is crucial for improving radar performance to meet more stringent requirements. Obtaining dense radar point clouds, generating accurate radar imaging results, and mitigating mutual interference among multiple radar systems are the key points and the foundation for subsequent tracking, recognition, and other applications. Therefore, this paper discusses the practical application of the automotive millimeter-wave radar system based on the key technologies of signal processing, summarizes relevant research results, and mainly discusses the topics of point cloud imaging processing, synthetic aperture radar imaging processing, and interference suppression. Finally, herein, we summarize the research status at home and abroad. Moreover, future development trends for automotive millimeter-wave radar systems are forecast with the hope of enlightening readers in related fields.

Abstract Background Patients with non‐small‐cell lung cancer (NSCLC) and uncommon EGFR alterations typically have worse treatment outcomes than patients with classically EGFR‐mutated NSCLC. This study aimed to investigate the efficacy and safety of PD‐1 blockade with sintilimab plus anti‐angiogenic treatment with anlotinib in patients with NSCLC harboring uncommon EGFR mutations. Methods Patients with metastatic NSCLC harboring uncommon EGFR mutations after two previous treatments, including a platinum‐based chemotherapy regimen and a targeted treatment (or chemotherapy only for patients harboring EGFR ex20ins), received sintilimab combined with anlotinib. The primary endpoint was objective response rate (ORR). Results At data cutoff (September 27, 2022), median follow‐up was 22.3 months (range, 1.2–37.6). Among 21 enrolled patients, 12 had EGFR ex20ins and nine had other uncommon EGFR mutations such as L861Q, G719A, and G709X. Overall, eight patients (38.1%) achieved an objective response, and 18 (85.7%) achieved disease control. Median (95% CI) progression‐free survival (PFS) was 7.0 (5.4–8.6) months, and median overall survival (OS) was 20.0 (15.6–24.4) months. The 12‐month PFS rate (95% CI) was 22.2% (7.4–42.0), and the 12‐month OS rate was 66.7% (42.5–82.5). Patients harboring EGFR ex20ins had similar ORR and PFS to those with other mutations. Six patients (28.6%) experienced grade 3 treatment‐related adverse events (TRAEs); hand‐foot syndrome was the most common grade 3 TRAE (2 patients; 9.5%). No grade ≥4 TRAEs were observed. Conclusions The combination of sintilimab and anlotinib demonstrated durable efficacy and was generally well tolerated in patients with NSCLC and uncommon EGFR mutations who had received prior standard‐of‐care treatments. (ClinicalTrials.gov identifier: NCT04790409).

Abstract The CRB (coconut rhinoceros beetle) haplotype was classified into CRB-S and CRB-G, based on the presence of single nucleotide polymorphisms (SNPs) in the mitochondrial cox1 gene. Mitochondrial genomes (mitogenomes) are the most widely used genetic resources for molecular evolution, phylogenetics, and population genetics in relation to insects. This study presents the mitogenome CRB-G and CRB-S which were collected in Johor, Malaysia. The mitogenome of CRB-G collected from oil palm plantations in 2020 and 2021, and wild coconut palms in 2021 was 15,315 bp, 15,475 bp, and 17,275 bp, respectively. The CRB-S was discovered in coconut and oil palms in 2021, and its mitogenome was 15,484 bp and 17,142 bp, respectively. All the mitogenomes have 37 genes with more than 99% nucleotide sequence homology, except the CRB-G haplotype collected from oil palm in 2021 with 89.24% nucleotide sequence homology. The mitogenome of Johor CRBs was variable in the natural population due to its elevated mutation rate. Substitutions and indels in cox1, cox2, nad2 and atp6 genes were able to distinguish the Johor CRBs into two haplotypes. The mitogenome data generated in the present study may provide baseline information to study the infection and relationship between the two haplotypes of Johor CRB and OrNV in the field. This study is the first report on the mitogenomes of mixed haplotypes of CRB in the field.