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Type 2 diabetes mellitus (T2DM) is a metabolic disorder associated with an increased incidence of cognitive and emotional disorders. Previous studies have indicated that the frontostriatal circuits play a significant role in brain disorders. However, few studies have investigated functional connectivity (FC) abnormalities in the frontostriatal circuits in T2DM. We aimed to investigate the abnormal functional connectivity (FC) of the frontostriatal circuits in patients with T2DM and to explore the relationship between abnormal FC and diabetes-related variables. Twenty-seven patients with T2DM were selected as the patient group, and 27 healthy peoples were selected as the healthy controls (HCs). The two groups were matched for age and sex. In addition, all subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological evaluation. Seed-based FC analyses were performed by placing six bilateral pairs of seeds within defined subdivisions of the striatum. The functional connection strength of subdivisions of the striatum was compared between the two groups and correlated with each clinical variable. Patients with T2DM showed abnormalities in the FC of the frontostriatal circuits. Our findings show significantly reduced FC between the right caudate nucleus and left precentral gyrus (LPCG) in the patients with T2DM compared to the HCs. The FC between the prefrontal cortex (left inferior frontal gyrus, left frontal pole, right frontal pole, and right middle frontal gyrus) and the right caudate nucleus has a significant positive correlation with fasting blood glucose (FBG). The results showed abnormal FC of the frontostriatal circuits in T2DM patients, which might provide a new direction to investigate the neuropathological mechanisms of T2DM.

Visual tracking is an important branch in computer vision. In complex scenarios, there exist various interference factors, e.g . background clutter, similar objects etc ., making robust tracking based on correlation filter algorithm still a challenging task. In this paper, a correlation filter algorithm based on a novel adaptive multi-cue fusion strategy was proposed. First, a unified response map evaluation strategy was presented to assess the tracking reliability by combing the average peak correlation energy and the response peak historical information. Second, according to the cue reliability, an adaptive multi-cue fusion strategy was proposed to adaptively fuse two tracking cues, correlation filter and color histogram. The experimental results on OTB-2013 and UAV123 demonstrated that the proposed algorithm achieved competitive performance to the state-of the-art trackers.

Since early January 2020, after the outbreak of coronavirus infection in Wuhan, China, ≈365 confirmed cases have been reported in Shenzhen, China. The mode of community and intrafamily transmission is threatening residents in Shenzhen. Strategies to strengthen prevention and interruption of these transmissions should be urgently addressed.

We have surveyed avian influenza virus (AIV) genomes from live poultry markets within China since 2014. Here we present a total of 16,091 samples that were collected from May 2016 to February 2019 in 23 provinces and municipalities in China. We identify 2048 AIV-positive samples and perform next generation sequencing. AIV-positive rates (12.73%) from samples had decreased substantially since 2016, compared to that during 2014–2016 (26.90%). Additionally, H9N2 has replaced H5N6 and H7N9 as the dominant AIV subtype in both chickens and ducks. Notably, novel reassortants and variants continually emerged and disseminated in avian populations, including H7N3, H9N9, H9N6 and H5N6 variants. Importantly, almost all of the H9 AIVs and many H7N9 and H6N2 strains prefer human-type receptors, posing an increased risk for human infections. In summary, our nation-wide surveillance highlights substantial changes in the circulation of AIVs since 2016, which greatly impacts the prevention and control of AIVs in China and worldwide. In this study, the authors present a genomic surveillance of avian influenza genomes sampled from live poultry markets in China. They report that a number of variants have emerged since 2016 that pose an increased risk to humans. They highlight the importance of continuous genome surveillance of circulating influenza strains.

Distributed machine learning (ML) has been extensively studied to meet the explosive growth of training data. A wide range of machine learning models are trained by a family of first-order optimization algorithms, i.e., stochastic gradient descent (SGD). The core operation of SGD is the calculation of gradients. When executing SGD in a distributed environment, the workers need to exchange local gradients through the network. In order to reduce the communication cost, a category of quantification-based compression algorithms are used to transform the gradients to binary format, at the expense of a low precision loss. Although the existing approaches work fine for dense gradients, we find that these methods are ill-suited for many cases where the gradients are sparse and nonuniformly distributed. In this paper, we study is there a compression framework that can efficiently handle sparse and nonuniform gradients? We propose a general compression framework, called SKCompress, to compress both gradient values and gradient keys in sparse gradients. Our first contribution is a sketch-based method that compresses the gradient values. Sketch is a class of algorithm that approximates the distribution of a data stream with a probabilistic data structure. We first use a quantile sketch to generate splits, sort gradient values into buckets, and encode them with the bucket indexes. Our second contribution is a new sketch algorithm, namely MinMaxSketch, which compresses the bucket indexes. MinMaxSketch builds a set of hash tables and solves hash collisions with a MinMax strategy. Since the bucket indexes are nonuniform, we further adopt Huffman coding to compress MinMaxSketch. To compress the keys of sparse gradients, the third contribution of this paper is a delta-binary encoding method that calculates the increment of the gradient keys and encode them with binary format. An adaptive prefix is proposed to assign different sizes to different gradient keys, so that we can save more space. We also theoretically discuss the correctness and the error bound of our proposed methods. To the best of our knowledge, this is the first effort utilizing data sketch to compress gradients in ML. We implement a prototype system in a real cluster of our industrial partner Tencent Inc. and show that our method is up to 12× faster than the existing methods.

The objective of this study is to expound the CT features of COVID-19 patients whose throat swab samples were negative for two consecutive nucleic acid tests after treatment. We retrospectively reviewed 46 COVID-19 patients with two consecutive negative RT-PCR tests after treatment. The cases were divided into moderate group and severe/critical group according to disease severity. Clinical and CT scanning data were collected. CT signs of pulmonary lesions and the score of lung involvement were expounded. Thirty-nine moderate cases and seven severe/critical cases were included. Residual pulmonary lesions were visible in CT images. Moderate patients showed peripheral lesions while severe/critical cases exhibited both central and peripheral lesions with all lobes involvement. Mixed ground glass opacity (GGO) and pulmonary consolidation were noted. A larger proportion of severe patients showed reticular pulmonary interstitium thickening. Air bronchogram, pleural effusion, vascular enlargement, bronchial wall thickening, bronchiectasis, pleural thickening and pleural adhesion were more frequently observed in severe/critical group. The severe/critical group showed higher CT score. Pulmonary lesions persisted even after twice consecutive negative nucleic acid tests. We strongly recommended regular follow-up of CT scans after nucleic acid tests conversion. Evaluation of complete remission should base on chest CT.

Sulfonamide antibiotics (SAs) are ubiquitous and persistent organic contaminants in aquatic and soil ecosystems due to their extensive application and high structural stability, causing rising environmental hazards. Conventional treatment approaches, generally based on physical adsorption or biological processes, remain limited in achieving efficient and stable removal as well as deep molecular modification of SAs. In recent years, modified biochar has developed as a flexible environmental functional material incorporating adsorption and reaction regulation capabilities, owing to its customizable pore structure, surface chemistry, and electronic characteristics. This study comprehensively highlights current achievements in the adsorption and degradation of sulfonamide antibiotics by modified biochar, with specific emphasis on modification techniques, structural modulation, structure–performance connections, and interfacial reaction processes. Through physical activation, heteroatom doping, defect engineering, and metal integration, biochar has developed from a traditional adsorbent into a carbon-based interfacial reactor capable of pollutant adsorption, molecular activation, and directed transformation. Surface-confined reaction interfaces, where π–π interactions, hydrogen bonding, electrostatic interactions, and metal coordination cooperatively control adsorption and transformation processes, are primarily responsible for the elimination of SAs. Moreover, the dual functions of modified biochar in driving both radical and non-radical pathways are explored, showing the vital importance of interfacial electronic structure modulation and electron-transfer mechanisms in influencing reaction efficiency and selectivity. The impact of sulfonamide molecular configurations, ambient circumstances, and concomitant chemicals on removal performance are also explored. Unlike previous reviews that mainly summarize adsorption efficiency or oxidant activation systems separately, this work integrates structural modulation, interfacial electronic regulation, and bond-selective transformation mechanisms into a unified structure–chemistry–reactivity framework. By correlating sulfonamide molecular configuration with biochar electronic structure, this review provides a mechanistic roadmap for the rational design of next-generation catalytic biochar systems. Finally, key challenges related to structural controllability, long-term stability, and engineering scalability are identified, and future research directions are proposed to support the rational design of high-performance biochar materials and the practical control of sulfonamide antibiotic pollution.

Background Bone marrow (BM) is the most common site of metastatic disease at diagnosis and a frequent site of relapse in neuroblastoma. Digital cytology images of BM smears offer a rich data source for artificial intelligence models, which may potentially facilitate more cost-effective risk stratification within the diagnostic workflow. This study aims to develop an interpretable cytology model for detecting BM metastasis in pediatric neuroblastoma. Methods This retrospective diagnostic study used Wright-Giemsa–stained BM cytology images from 359 neuroblastoma patients who underwent BM screening between January 2019 and June 2024 across multiple centers in China. After the quality evaluation, we generated 1384,007 patches from BM digital cytology to develop and validate the cytology model. In the model construction, we integrated a multiple-instance learning framework with convolutional neural networks to extract cytology features, referred to as cMIL. The cytology model was trained for BM metastasis detection and risk stratification with interpretability. Results For metastasis detection, the cytology model achieved an AUC of 0.924 (95% CI, 0.775–1.000) in the training cohort. Performance remained strong in external validation, with AUCs of 0.826 (95% CI, 0.741–0.911) in Cohort A and 0.795 (95% CI, 0.684–0.906) in Cohort B, indicating consistent performance across independent multicenter cohorts. The cMIL score also successfully stratified patients in terms of survival outcomes (log-rank p  < 0.05). Interpretability analyses further demonstrated that the model’s predictions were associated with clinically relevant cytological features. Conclusions In this retrospective diagnostic study, the developed cytology model demonstrated high discriminative performance in detecting BM metastasis and captured the underlying complexity and heterogeneity of BM. These findings suggest that the cytology model could serve as a promising tool for improving metastasis detection and risk stratification in patients with neuroblastoma, potentially contributing to personalized treatment strategies and enhanced disease monitoring. Trial registration This retrospective study was registered with ClinicalTrials.gov (NCT06703944) on November 21, 2024. Study title: bone marrow cytology-based artificial intelligence model for detection and prognosis of neuroblastoma. ( https://register.clinicaltrials.gov ).

Background To evaluate the prevalence of myopia in school students in Urumqi, China, and explore the influence of the interaction between parental myopia and poor reading and writing habits on myopia to identify the at-risk population and provide evidence to help school students avoid developing myopia. Methods A cross-sectional survey was conducted with 6,883 school students aged 7–20 years in Urumqi in December 2019. The Standard Eye Chart and mydriatic optometry were used to determine whether students had myopia. Falconer’s method was used to calculate the heritability of parental myopia. Multivariate unconditional logistic regression models were used to analyze the risk factors for myopia and the additive and multiplicative interaction of parental myopia and poor reading and writing habits. Results After standardizing the age of the 6,883 students, the overall prevalence rate of myopia was 47.50 %. The heritability of parental myopia was 66.57 % for boys, 67.82 % for girls, 65.02 % for the Han group, and 52.71 % for other ethnicities. There were additive interactions between parental myopia and poor reading and writing habits; among them, parental myopia and poor eye habits when reading and writing (the distance between the eyes and book is less than 30 cm when reading and writing, fingers block the sight of one eye while holding the pen, and leaning one’s body when reading and writing; habit 1) increased the risk of myopia by 10.99 times (odds ratio [ OR ] = 10.99, 95 % confidence interval [ CI ] = 8.33–14.68), parental myopia and poor reading posture (reading while lying down, walking, or in the car; habit 2) increased the risk of myopia by 5.92 times ( OR  = 5.92, 95 % CI  = 4.84–7.27). There was no multiplicative interaction between parental myopia and habit 1 or habit 2 ( OR  = 0.69, 95 % CI  = 0.44–1.08; OR  = 0.89, 95 % CI  = 0.66–1.21, respectively). Conclusion The prevalence of myopia among students in Urumqi, Xinjiang is relatively high. The risk of developing myopia is affected by parental myopia and poor reading and writing habits. In addition, parental myopia amplifies the harm caused by poor reading and writing habits, thereby increasing the risk of myopia. Students with parents who have myopia should be targeted during myopia prevention efforts.