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Abstract Accurate identification and localization of multiple abnormalities are crucial steps in the interpretation of chest X-rays (CXRs); however, the lack of a large CXR dataset with bounding boxes severely constrains accurate localization research based on deep learning. We created a large CXR dataset named CXR-AL14, containing 165,988 CXRs and 253,844 bounding boxes. On the basis of this dataset, a deep-learning-based framework was developed to identify and localize 14 common abnormalities and calculate the cardiothoracic ratio (CTR) simultaneously. The mean average precision values obtained by the model for 14 abnormalities reached 0.572-0.631 with an intersection-over-union threshold of 0.5, and the intraclass correlation coefficient of the CTR algorithm exceeded 0.95 on the held-out, multicentre and prospective test datasets. This framework shows an excellent performance, good generalization ability and strong clinical applicability, which is superior to senior radiologists and suitable for routine clinical settings.

Abstract Background Most studies support parafunctions play an important role in temporomandibular disorders (TMD), whereas the association between tooth wear and TMD remains controversial. Betel nut chewing as a parafunction is popular in South and Southeast Asia. We therefore investigated the association of severely worn dentition resulting from betel nut chewing with TMD. Methods A cross-sectional analysis of 408 control participants (male: 380, female: 28, 43.62 ± 9.54 years) and 408 participants with betel nut chewing related severely worn dentition (male: 380, female: 28, 43.73 ± 8.93 years) who received dental and TMD checkup according to Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) in Health Management Center, Xiangya Hospital was performed. Betel nut chewing related severely worn dentition meant all the natural teeth had moderate to severe tooth wear [Tooth Wear Index (TWI) ≥ 2)] including ≥ 2 severe wear teeth (TWI ≥ 3) due to betel nut chewing. Multivariable logistic regression analysis was used. Results After adjusting for age, gender, betel nut chewing related severely worn dentition, oral submucosal fibrosis, number of missing teeth, number of dental quadrants with missing teeth, visible third molar and orthodontic history, variables of age, gender and betel nut chewing related severely worn dentition were significant for overall TMD. Multivariable analysis showed betel nut chewing related severely worn dentition was significantly associated with intra-articular TMD [odds ratio and 95% confidence intervals: 1.689 (1.271–2.244), P = 0.001] in a betel nut chewing dose-dependent manner. Conclusion Betel nut chewing related severely worn dentition was associated with intra-articular TMD.

Compared with narrowband Doppler radar, ultrawideband radar can simultaneously acquire the range and Doppler information of targets, which is more beneficial for behavior recognition. To improve the recognition performance of fall behavior, frequency-modulated continuous-wave ultrawideband (UWB) radar was applied to collect daily behavior and fall data of 36 subjects in two real indoor complex scenes, and a multi-scene fall detection dataset was established with various action types; the range-time, time-Doppler, and range-Doppler spectrograms of the subjects were obtained after preprocessing radar data; based on the MobileNet-V3 lightweight network, three types of deep learning fusion networks at the data level, feature level, and decision level were designed for the radar spectrograms, respectively. A statistical analysis shows that the decision level fusion method proposed in this paper can improve fall detection performance compared with those using one type of spectrogram, the data level and the feature level fusion methods (all P values by significance test method are less than 0.003). The accuracies of 5-fold cross-validation and testing in the new scene of the decision level fusion method are 0.9956 and 0.9778, respectively, which indicates the good generalization ability of the proposed method.

Abstract Background Congenitally missing tooth is the most common dental abnormality which leaves spaces in the arch, leads to numerous forms of malocclusion due to the Bolton index discrepancy and is even associated with abnormal craniofacial morphology. Even though the roles of malocclusion and tooth loss in temporomandibular disorders (TMD) development remain controversial, basic researches have found some common molecules are involved in osteoarthritis and dental agenesis. However, the association of congenitally missing teeth with TMD is unknown. We hence investigated the association of congenitally missing teeth with TMD. Methods A cross-sectional analysis of 586 control participants (male: 287, female: 299, 38.33 ± 11.65 years) and 583 participants with non-third molar congenitally missing teeth (male: 238, female: 345, 39.13 ± 11.67 years) who consecutively received routine dental and TMD checkup according to Diagnostic Criteria for Temporomandibular Disorders Axis I in Health Management Center, Xiangya Hospital was performed. Logistic regression analysis was used to study the association of congenitally missing teeth with TMD. Results The congenitally missing teeth group included 581 hypodontia and 2 oligodontia participants. The congenitally missing anterior teeth participants, the congenitally missing posterior teeth participants and participants with both congenitally missing anterior and posterior teeth accounted for 88.34%, 8.40% and 3.26% of the congenitally missing teeth group respectively. Congenitally missing teeth group had greater ratios of females and orthodontic history. Participants with congenitally missing teeth had a significantly higher prevalence of overall TMD (67.24%) in comparison to control participants (45.90%). After adjusting age, gender, presence of congenitally missing teeth, number of congenitally missing teeth, number of non-congenitally missing teeth, number of dental quadrants with missing teeth, visible third molar and orthodontic history, the variables of age, gender, presence of congenitally missing teeth and number of dental quadrants with missing teeth were significant for overall TMD. Multivariable logistic regression analysis showed congenitally missing tooth was significantly related with overall TMD [odds ratio (OR):1.689(1.080–2.642), P = 0.022], intra-articular TMD [OR: 1.711(1.103–2.656), P = 0.017] and pain-related TMD [OR: 3.093(1.321–7.239), P = 0.009]. Conclusion Congenitally missing tooth is a risk factor for TMD. When treating the congenitally missing teeth population, TMJ evaluation and multidisciplinary strategies are necessary.

Voltage sags resulting from symmetrical or asymmetrical faults pose a significant threat to power quality. In response to this challenge, a voltage sag loss assessment method based on a two-stage Taguchi quality perspective approach is proposed to address the quantitative analysis of voltage sag economic losses. Initially, using the Taguchi quality perspective method, single-index quality loss functions are separately established for voltage sag magnitude and fault duration. Subsequently, by introducing a comprehensive load tolerance curve, sensitivity parameters within the quality loss function are accurately calculated. This yields a deterministic model for voltage sag assessment. Building upon this, the relative impact of the two indices on voltage sag loss is evaluated using the quality loss function. Consequently, a comprehensive loss model under the influence of multiple indices is formed by integrating two single-index evaluation models. The simulation results indicate that this method can effectively assess the economic losses of voltage sags under the combined influence of multiple factors. Compared to the original economic loss assessment method, it improves quantitative accuracy by approximately 3.72%. Moreover, the method reduces the computational complexity of loss assessment through the consolidation of intervals with similar sensitivity parameters.

Abstract Background The doctor/nurse and police officer population have some common typical characteristics of great professional pressure and night shift and past studies indicated oral mucosa lesions were closely associated with psychological factors and health-risking behaviors, however the prevalence of recurrent aphthous stomatitis (RAS) and the two commonly seen oral potentially malignant disorders of oral submucosal fibrosis (OSF) and oral leukoplakia in doctor/nurse and police officer in the Betel quid chewing city of Mainland China is unknown The cross-sectional study was to determine the prevalence differences of RAS, oral leukoplakia and OSF among doctor/nurse, police officer and non-doctor/nurse and non-police officer population aged 20–59 years. Methods RAS, OSF and oral leukoplakia were examined in doctor/nurse group (male: 659, female: 2439), police officer group (male: 839, female: 262) and non-doctor/nurse and non-police officer group (male: 7576, female: 8129) from 2020-11-01 to 2021-08-31 in Health Management Center, Xiangya Hospital in Changsha city, Hunan province. Results The prevalence rates of RAS, OSF, oral leukoplakia and oral leukoplakia combined with OSF in male and female non-doctor/nurse and non-police officer group are 8.32‰ and 10.83‰, 58.08‰ and 1.23‰, 11.75‰ and 0.25‰, 7.66‰ and 0.12‰ respectively. Compared with the non-doctor/nurse and non-police officer population, prevalence rates of RAS in male (24.27‰) and female (20.50‰) doctor/nurse population were significantly higher. Prevalence rates of OSF (21.24‰) and oral leukoplakia (3.03‰) in male doctor/nurse population were significantly less but prevalence rates of OSF (93.71‰), oral leukoplakia (20.17‰) and oral leukoplakia combined with OSF (15.42‰) for male police officer were significantly greater in comparison with male non-doctor/nurse and non-police officer group. OSF and oral leukoplakia prevalence rates were obvious lower for the female than the counterpart male group, but there were no significant differences of OSF and oral leukoplakia prevalence rates between the female non-doctor/nurse and non-police officer and female doctor/nurse group. Oral leukoplakia was not found in the female police officers. Conclusions Doctor/nurse population have higher prevalence of RAS. Male doctors/nurses have lower prevalence of OSF and oral leukoplakia, while male police officers have higher prevalence of OSF, oral leukoplakia and oral leukoplakia combined with OSF.

Purpose: Oral squamous cell carcinoma (OSCC) is one of the most common malignant tumors of the head and neck, while metastasis is the main cause of OSCC-related death. There is an urgent need to explore novel prognostic biomarkers and identify biological targets related to metastasis in OSCC treatment.Methods: Analysis of differential expression was performed using datasets in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Immunohistochemistry (IHC) was conducted to assess the expression of betacellulin (BTC) in OSCC. SCC4 and CAL27 cells were used for in vitro experiments, in which CCK-8, transwell assays, and wounding healing assays were performed to verify the biological functions of BTC. The role of BTC in EMT was analyzed by EMT score and Western blot.Results: Through the analysis of the mRNA expression profile data from TCGA database in OSCC, we found that only low expression of BTC was significantly correlated with a poor prognosis in OSCC patients. The results of IHC assays and TCGA databases showed that the expression level of BTC was related to the tumor stage, histological grade, and metastasis status. In vitro analysis showed that overexpression of BTC significantly suppressed the proliferation and migration of OSCC cells. Furthermore, we confirmed that BTC could affect EMT through the PI3K-AKT signaling pathway.Conclusion: The overexpression of BTC suppresses the proliferation, migration, and EMT of OSCC cells via the PI3K-AKT pathways, leading to a better prognosis in OSCC. BTC may be used as a novel molecular marker to assess the prognosis of OSCC patients.

Background and AimThe identification of ulcerative colitis (UC) and Crohn's disease (CD) is a key element interfering with therapeutic response, but it is often difficult for less experienced endoscopists to identify UC and CD. Therefore, we aimed to develop and validate a deep learning diagnostic system trained on a large number of colonoscopy images to distinguish UC and CD.MethodsThis multicenter, diagnostic study was performed in 5 hospitals in China. Normal individuals and active patients with inflammatory bowel disease (IBD) were enrolled. A dataset of 1,772 participants with 49,154 colonoscopy images was obtained between January 2018 and November 2020. We developed a deep learning model based on a deep convolutional neural network (CNN) in the examination. To generalize the applicability of the deep learning model in clinical practice, we compared the deep model with 10 endoscopists and applied it in 3 hospitals across China.ResultsThe identification accuracy obtained by the deep model was superior to that of experienced endoscopists per patient (deep model vs. trainee endoscopist, 99.1% vs. 78.0%; deep model vs. competent endoscopist, 99.1% vs. 92.2%, P < 0.001) and per lesion (deep model vs. trainee endoscopist, 90.4% vs. 59.7%; deep model vs. competent endoscopist 90.4% vs. 69.9%, P < 0.001). In addition, the mean reading time was reduced by the deep model (deep model vs. endoscopists, 6.20 s vs. 2,425.00 s, P < 0.001).ConclusionWe developed a deep model to assist with the clinical diagnosis of IBD. This provides a diagnostic device for medical education and clinicians to improve the efficiency of diagnosis and treatment.

Compressed sensing is one of the key technologies to reduce the volume of hyperspectral image for real-time storage and transmission. Reconstruction based on spectral unmixing show tremendous potential in hyperspectral compressed sensing compared with other conventional algorithms that directly reconstruct images. In this article, a joint spatial-spectral joint compressed sensing scheme is proposed. In this scheme, compressed hyperspectral data are collected by spatial-spectral hybrid compressed sampling. As for the reconstruction, an objective function is developed by introducing the fidelity constraints of spatial and spectral measurements and the row sparsity constraint of abundance that guarantee the precise reconstruction of hyperspectral images and obtain endmembers and abundances as by-products accurately. An augmented Lagrangian type algorithm is meticulously elaborated to solve the above optimization problem. Extensive experimental results on several real hyperspectral datasets indicate that the proposed approach can achieve a reconstruction accuracy higher than those of other state-of-the-art methods. The efficiency and feasibility of the proposed scheme give it great potential in hyperspectral compressed sensing.

Hyperspectral compressed sensing (HCS) is a new imaging method that effectively reduces the power consumption of data acquisition. In this article, we present a novel HCS algorithm by incorporating spatial-spectral hybrid compressed sensing, followed by a reconstruction based on spectral unmixing. At the sampling stage, the measurements are acquired by a spatial-spectral hybrid compressive sampling scheme to preserve the necessary information for the following spectral unmixing, where spatial compressive sampling mainly retains the endmember information, and spectral compressive sampling mainly retains the abundance information. Due to the limitations of the traditional linear mixed model, an improved mixed model is proposed for HCS reconstruction, which considers spectral variability, nonlinear mixing, and other factors. At the reconstruction stage, based on the improved mixed model, semi-nonnegative matrix factorization is introduced to realize spectral unmixing on the measurements to achieve the final reconstruction by using an alternate iteration manner. The proposed algorithm is tested on real hyperspectral data, and the selection of parameters is fully analyzed. Experimental results demonstrate that the proposed algorithm can significantly outperform state-of-the-art HCS algorithms in terms of reconstruction performance.

Hyperspectral compressed sensing (HCS) based on spectral unmixing technique has shown great reconstruction performance. In particular, the linear mixed model (LMM) has been widely used in HCS reconstruction. However, due to the complexity of environmental conditions, instrumental configurations, and material nonlinear mixing effects, LMM cannot accurately represent the hyperspectral images, which limits the improvement of reconstruction quality. In this article, first, by introducing spectral variability, nonlinear mixing, and residuals, a multitype mixed model (MMM) is proposed to establish a more accurate hyperspectral image model. Then, a novel MMM-based HCS is proposed, which performs spectral compressed sampling at the sampling stage only, and at the reconstruction stage, by using spectral unmixing, an MMM-based HCS super-resolution reconstruction algorithm from spectral compressed sensing data is developed, and the alternating direction multiplier method is employed to estimate each component of the MMM, furthermore, reasonable prior knowledge of each component is introduced to improve the estimation accuracy. Experimental results on hyperspectral datasets demonstrate that the proposed model outperforms those state-of-the-art methods based on the LMM in terms of HCS reconstruction quality.

For any power system, the reliability of measurement data is essential in operation, management and also in planning. However, it is inevitable that the measurement data are prone to outliers, which may impact the results of data-based applications. In order to improve the data quality, the outliers cleaning method for measurement data in the distribution network is studied in this paper. The method is based on a set of association rules (AR) that are automatically generated form historical measurement data. First, the association rules are mining in conjunction with the density-based spatial clustering of application with noise (DBSCAN), k-means and Apriori technique to detect outliers. Then, for the outliers repairing process after outliers detection, the proposed method uses a distance-based model to calculate the repairing cost of outliers, which describes the similarity between outlier and normal data. Besides, the Mahalanobis distance is employed in the repairing cost function to reduce the errors, which could implement precise outliers cleaning of measurement data in the distribution network. The test results for the simulated datasets with artificial errors verify that the superiority of the proposed outliers cleaning method for outliers detection and repairing.

Facultative parthenocarpy is of great practical value. However, the molecular mechanism underlying facultative parthenocarpy remains elusive. Transcriptional co-repressors (TPL) act as a central regulatory hub controlling all nine phytohormone pathways. Previously, we proved that SlTPLs participate in the auxin signaling pathway by interacting with auxin/indole acetic acid (Aux/IAAs) in tomato; however, their function in fruit development has not been studied. In addition to their high expression levels during flower development, the interaction between SlTPL1 and SlIAA9 stimulated the investigation of its functional significance via RNA interference (RNAi) technology, whereby the translation of a protein is prevented by selective degradation of its encoded mRNA. Down-regulation of SlTPL1 resulted in facultative parthenocarpy. Plants of SlTPL1-RNAi transgenic lines produced similar fruits which did not show any pleiotropic effects under normal conditions. However, they produced seedless fruits upon emasculation and under heat stress conditions. Furthermore, SlTPL1-RNAi flower buds contained higher levels of cytokinins and lower levels of abscisic acid. To reveal how SlTPL1 regulates facultative parthenocarpy, RNA-seq was performed to identify genes regulated by SlTPL1 in ovaries before and after fruit set. The results showed that down-regulation of SlTPL1 resulted in reduced expression levels of cytokinin metabolism-related genes, and all transcription factors such as MYB, CDF, and ERFs. Conversely, down-regulation of SlTPL1 induced the expression of genes related to cell wall and cytoskeleton organization. These data provide novel insights into the molecular mechanism of facultative tomato parthenocarpy and identify SlTPL1 as a key factor regulating these processes.

Background. Multiple organ failure (MOF) may lead to an increased mortality rate of moderately severe (MSAP) or severe acute pancreatitis (SAP). This study is aimed to use machine learning to predict the risk of MOF in the course of disease. Methods. Clinical and laboratory features with significant differences between patients with and without MOF were screened out by univariate analysis. Prediction models were developed for selected features through six machine learning methods. The models were internally validated with a five-fold cross-validation, and a series of optimal feature subsets were generated in corresponding models. A test set was used to evaluate the predictive performance of the six models. Results. 305 (68%) of 455 patients with MSAP or SAP developed MOF. Eighteen features with significant differences between the group with MOF and without it in the training and validation set were used for modeling. Interleukin-6 levels, creatinine levels, and the kinetic time were the three most important features in the optimal feature subsets selected by K-fold cross-validation. The adaptive boosting algorithm (AdaBoost) showed the best predictive performance with the highest AUC value (0.826; 95% confidence interval: 0.740 to 0.888). The sensitivity of AdaBoost (80.49%) and specificity of logistic regression analysis (93.33%) were the best scores among the six models in the test set. Conclusions. A predictive model of MOF complicated by MSAP or SAP was successfully developed based on machine learning. The predictive performance was evaluated by a test set, for which AdaBoost showed a satisfactory predictive performance. The study is registered with the China Clinical Trial Registry (Identifier: ChiCTR1800016079).

The non-contact monitoring of vital signs by radar has great prospects in clinical monitoring. However, the accuracy of separated respiratory and heartbeat signals has not satisfied the clinical limits of agreement. This paper presents a study for automated separation of respiratory and heartbeat signals based on empirical wavelet transform (EWT) for multiple people. The initial boundary of the EWT was set according to the limited prior information of vital signs. Using the initial boundary, empirical wavelets with a tight frame were constructed to adaptively separate the respiratory signal, the heartbeat signal and interference due to unconscious body movement. To verify the validity of the proposed method, the vital signs of three volunteers were simultaneously measured by a stepped-frequency continuous wave ultra-wideband (UWB) radar and contact physiological sensors. Compared with the vital signs from contact sensors, the proposed method can separate the respiratory and heartbeat signals among multiple people and obtain the precise rate that satisfies clinical monitoring requirements using a UWB radar. The detection errors of respiratory and heartbeat rates by the proposed method were within ±0.3 bpm and ±2 bpm, respectively, which are much smaller than those obtained by the bandpass filtering, empirical mode decomposition (EMD) and wavelet transform (WT) methods. The proposed method is unsupervised and does not require reference signals. Moreover, the proposed method can obtain accurate respiratory and heartbeat signal rates even when the persons unconsciously move their bodies.

OBJECTIVE:Quantitative ventricular fibrillation (VF) waveform analysis is a potentially powerful tool to optimize defibrillation. However, whether combining VF features with additional attributes that related to the previous shock could enhance the prediction performance for subsequent shocks is still uncertain. METHODS:A total of 528 defibrillation shocks from 199 patients experienced out-of-hospital cardiac arrest were analyzed in this study. VF waveform was quantified using amplitude spectrum area (AMSA) from defibrillator's ECG recordings prior to each shock. Combinations of AMSA with previous shock index (PSI) or/and change of AMSA (ΔAMSA) between successive shocks were exercised through a training dataset including 255shocks from 99patientswith neural networks. Performance of the combination methods were compared with AMSA based single feature prediction by area under receiver operating characteristic curve(AUC), sensitivity, positive predictive value (PPV), negative predictive value (NPV) and prediction accuracy (PA) through a validation dataset that was consisted of 273 shocks from 100patients. RESULTS:A total of61 (61.0%) patients required subsequent shocks (N = 173) in the validation dataset. Combining AMSA with PSI and ΔAMSA obtained highest AUC (0.904 vs. 0.819, p

We present THz conductivity of LaAlO3 (LAO) as a function of temperature and annealing, using terahertz time-domain spectroscopy (THz-TDS). We observed that, after annealing, spectral weight redistribution occurs, such that the real conductivity σ1(ω) changed from a featureless and almost frequency-independent spectrum, into one where peaks occur near the phonon frequencies. These phonon frequencies increase with increasing temperature. We attribute the appearance of these absorption peaks to the diffusion and relocation of oxygen vacancies. The dielectric functions of annealed LAO are well fitted with the Drude-Lorentz model.

This study presents a comprehensive analysis of global, regional, and national trends in the burden of hearing loss (HL) among children and adolescents from 1990 to 2019, using data from the Global Burden of Disease study. Over this period, there was a general decline in HL prevalence and years lived with disability (YLDs) globally, with average annual percentage changes (AAPCs) of −0.03% (95% uncertainty interval [UI], −0.04% to −0.01%; p = 0.001) and −0.23% (95% UI, −0.25% to −0.20%; p < 0.001). Males exhibited higher rates of HL prevalence and YLDs than females. Mild and moderate HL were the most common categories across all age groups, but the highest proportion of YLDs was associated with profound HL [22.23% (95% UI, 8.63%−57.53%)]. Among females aged 15−19 years, the prevalence and YLD rates for moderate HL rose, with AAPCs of 0.14% (95% UI, 0.06%−0.22%; p = 0.001) and 0.13% (95% UI, 0.08%−0.18%; p < 0.001). This increase is primarily attributed to age-related and other HL (such as environmental, lifestyle factors, and occupational noise exposure) and otitis media, highlighting the need for targeted research and interventions for this demographic. Southeast Asia and Western Sub-Saharan Africa bore the heaviest HL burden, while High-income North America showed lower HL prevalence and YLD rates but a slight increasing trend in recent years, with AAPCs of 0.13% (95% UI, 0.1%−0.16%; p < 0.001) and 0.08% (95% UI, 0.04% to 0.12%; p < 0.001). Additionally, the analysis revealed a significant negative correlation between sociodemographic index (SDI) and both HL prevalence (r = −0.74; p < 0.001) and YLD (r = −0.76; p < 0.001) rates. However, the changes in HL trends were not significantly correlated with SDI, suggesting that factors beyond economic development, such as policies and cultural practices, also affect HL. Despite the overall optimistic trend, this study emphasizes the continued need to focus on specific high-risk groups and regions to further reduce the HL burden and enhance the quality of life for affected children and adolescents. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is the commonest neurodegenerative disorder. It reduces motor and cognitive function in patients. Vinpocetine (Vinp) has the effects of anti‐inflammatory and antioxidant, and could improve cognitive function in patients. This study was aimed to investigating the therapeutic effects of Vinp on dyskinesia in a 6‐Hydroxydopamine hydrobromide (6‐OHDA)‐induced PD rat model. We constructed a PD rat model by injecting 6‐OHDA, and intervened with Vinp for 7 days. The motor function of the rats was evaluated by an open‐field test and rotation test. Besides, H&E staining was applied to observe the changes of dopaminergic neurons in the striatum. The levels of superoxide dismutase (SOD) and malondialdehyde (MDA) in the rat striatum were detected. We assessed the impact of Vinp on α‐synuclein (α‐Syn) and Wnt/β‐catenin signaling pathway‐related molecules by western blot and qRT‐PCR. Rats in the PD group showed reduced horizontal movement frequency and number of squares crossed, increased contact time and rotation frequency, and reduced number of dopaminergic neurons accompanied by severe morphological damage. Vinp treatment increased the horizontal movement frequency and number of squares crossed, reduced the contact time, and rotation frequency in PD rats. Also, Vinp downregulated α‐Syn protein expression and MDA level, while upregulated SOD activity in the striatum of PD rats. Furthermore, Vinp treatment activated the Wnt/β‐catenin signaling pathway in the striatum of PD rats. In conclusion, Vinp improved the dyskinesia in 6‐OHDA‐induced PD rats by alleviating oxidative stress, and these effects may be associated with activating the Wnt/β‐catenin signaling pathway. [ABSTRACT FROM AUTHOR]

Two new species of Trechispora indigenous to southern China, T. laxa and T. tongdaoensis, are described and illustrated, and the first record of T. khokpasiensis in China is reported. Molecular phylogenetic analyses of the concatenated nuclear rDNA ITS1–5.8S– ITS2 and nuclear large subunit sequences supported the inclusion of the three species within the Trechispora clade, together with species formerly classified in Scytinopogon. The new species are similar in micromorphology to species of Trechispora (as traditionally circumscribed) but are distinguished by having coralloid basidiomata. A key to the known coralloid Trechispora species in China is provided. [ABSTRACT FROM AUTHOR]

This article is concerned with the following Hamiltonian elliptic system: −ε2Δu+εb→⋅∇u+u+V(x)v=Hv(u,v)inRN,−ε2Δv−εb→⋅∇v+v+V(x)u=Hu(u,v)inRN,\left\{\begin{array}{l}-{\varepsilon }^{2}\Delta u+\varepsilon \overrightarrow{b}\cdot \nabla u+u+V\left(x)v={H}_{v}\left(u,v)\hspace{1em}\hspace{0.1em}\text{in}\hspace{0.1em}\hspace{0.33em}{{\mathbb{R}}}^{N},\\ -{\varepsilon }^{2}\Delta v-\varepsilon \overrightarrow{b}\cdot \nabla v+v+V\left(x)u={H}_{u}\left(u,v)\hspace{1em}\hspace{0.1em}\text{in}\hspace{0.1em}\hspace{0.33em}{{\mathbb{R}}}^{N},\end{array}\right. where ε>0\varepsilon \gt 0 is a small parameter, VV is a potential function, and HH is a super-quadratic sub-critical Hamiltonian. Applying suitable variational arguments and refined analysis techniques, we construct a new multiplicity result of semiclassical solutions which depends on the number of global minimum points of VV. This result indicates how the shape of the graph of VV affects the number of semiclassical solutions.

This study focuses on the Yeosu Coast, a critical ecological and economic zone on the Korean Peninsula. We conducted a comprehensive analysis of fish data obtained from daily set nets for the period of 2008–2022 (excluding 2017) to investigate variations in fish composition and community structure over a 15-year period. The catches were characterized by a prevalence of warm-water fish species, reflecting the temperate and subtropical character of the Yeosu Coast. The total catch during the study period was 3210.8 tonnes, with 2010 and 2016 registering the highest and lowest catches, respectively. Our analysis identified sea surface temperature as a primary factor correlated with these changes in species composition. The two dominant species, Scomberomorus niphonius (Cuvier, 1832) and Engraulis japonicus Temminck et Schlegel, 1846 together accounted for a remarkable 78.9% of the total catch. Notably, the Shannon–Weaver diversity index values were lower in 2015 than in 2008, indicating potential shifts in the community structure. Our analysis identified sea surface temperature as the most dominant factor that influenced these changes in species composition. Furthermore, we highlight the significant threats posed by climate change and the increasing number of jellyfish challenging fisheries on the Yeosu Coast. These threats have the potential to result in diminished set-net catches and declining biodiversity within this valuable ecosystem. Our findings underscore the importance of monitoring and addressing these ecological shifts to ensure the long-term sustainability of Yeosu Coast fisheries.