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Video-based person re-identification (Re-ID) aims at retrieving the person through the video sequences across non-overlapping cameras. Some characteristics of pedestrians are not consecutive across frames due to the variations of viewpoints, postures, and occlusions over time. However, existing methods ignore such data peculiarity and the networks tend to only learn those salient consecutive characteristics among frames in video sequences. As a result, the learned representations fail to cover all the characteristics of pedestrians, thus lacking integrity and discrimination. To tackle this problem, we present a novel deep architecture termed Hierarchical Mining Network (HMN), which mines as many pedestrians’ characteristics by referring to the temporal and intra-class knowledge. It consists of a novel Attentive Temporal Module (ATM) and a Dynamic Supervising Branch (DSB), with a Balancing Triplet Loss (BTL) assisting the training. The proposed ATM, with pedestrian perceiving capacity, is capable of evaluating each activation of features through temporal analysis, so that the temporally scattered characteristics of pedestrians can be better aggregated and the contaminated ones can be eliminated. Then, the DSB along with the BTL further enhances the integrity of representations by multiple supervision. Specifically, the DSB perceives the diversities of intra-class samples in each mini-batch and generates targeted supervising signals for them, in which process the BTL guarantees the signals with smaller intra-class variations and larger inter-class variations. Comprehensive experiments on two video-based datasets, i.e., MARS, and DukeMTMC-VideoReID, demonstrate the contribution of each component and the superiority of the proposed HMN over the state-of-the-arts. Benchmarking our model on three popular image-based datasets, i.e., Market1501, DukeMTMC-Reid, and MSMT17 additionally verifies the promising generalizability of the proposed DSB and BTL.

Infrared (IR) small target detection is one of the key technologies of infrared search and track (IRST) systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure (PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously. With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker (RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed.

Different from typical circuit breakers, breakers used to switch AC filters in convertor stations have to operate frequently according to the real-time operation mode and daily load changes. Therefore, the performance of filter breakers decreases very quickly, which leads to frequent accidents. Both preinsertion resistors and controlled switching devices can suppress switching transients. In this paper, a comparative analysis of whether a controlled switching device and preinsertion resistor should be adopted is carried out using PSCAD. The transient is calculated as the combined result of the switching angle and preinsertion resistor value, and the optimal configurations are proposed. After determining the resistor value and whether a controlled switching device should be included, the reference voltage of the arrester should be reevaluated.

Loop-closure detection (LCD), which aims to recognize a previously visited location, is a crucial component of the simultaneous localization and mapping system. In this paper, a novel appearance-based LCD method is presented. In particular, we propose a simple yet surprisingly useful feature matching algorithm for real-time geometrical verification of candidate loop-closures, termed as local relative orientation matching (LRO). It aims to efficiently establish reliable feature correspondences based on preserving local topological structures between the query image and candidate frame. To effectively retrieve candidate loop closures, we introduce the aggregated selective match kernel framework into the LCD task, which can effectively represent images and reduce the quantization noise of the traditional bag-of-words framework. In addition, the SuperPoint neural network is employed to extract reliable interest points and feature descriptors. Extensive experimental results demonstrate that our LRO can significantly improve the LCD performance, and the proposed overall LCD method can achieve much better performance over the current state-of-the-art on six publicly available datasets.

Visual-inertial odometry (VIO) is widely studied and used in autonomous robots. This article proposes a novel tightly coupled monocular VIO system based on point-line constraints (PLC-VIO). In the front end, PLC-VIO presents a line segment extraction and merging algorithm based on the EDLines method and achieves real-time feature tracking based on the geometric constraints between feature points and lines. In the back end, PLC-VIO reconstructs new 3-D landmarks of feature lines through points on the line and optimizes the states by minimizing a cost function that combines the preintegrated inertial measurement unit (IMU) error term together with the point and line reprojection error terms in a sliding window optimization framework. A loop closure module is also integrated, which enables relocalization and drift elimination. The corresponding experimental evaluations are conducted using public datasets to validate the effectiveness and robustness of the proposed system, and the results show that PLC-VIO can achieve good performance when compared with other state-of-the-art systems and, at the same time, with no compromise to real-time performance.

Background: Calibrated CT fat fraction (FFCT) measurements derived from non-enhanced abdominal CT reliably reflect liver fat content, allowing largescale population-level investigations of steatosis prevalence and associations. Objective: To compare prevalence of hepatic steatosis, assessed by calibrated CT measurements, between population-based Chinese and U.S. cohorts, and to investigate in these populations the relationship of steatosis with age, sex, and body mass index (BMI). Methods: This retrospective study included 3176 adults (1985 women, 1191 men) from seven Chinese provinces and 8748 adults (4834 women, 3914 men) from a single U.S. medical center, drawn from earlier studies. All participants were at least 40 years old and underwent unenhanced abdominal CT for the earlier studies. Liver fat content measurements on CT were cross-calibrated to MRI proton density fat fraction measurements using phantoms and expressed as adjusted FFCT. Mild, moderate, and severe steatosis were defined as adjusted FFCT of 5.0%-14.9%, 15.0%-24.9%, and ≥25.0%, respectively. The two cohorts were compared. Results: Median adjusted FFCT was for women 4.7% and 4.8%, and for men 5.8% and 6.2%, in the Chinese and U.S. cohorts, respectively. Steatosis prevalence was for women 46.3% and 48.7%, and for men 58.9% and 61.9%, in the Chinese and U.S. cohorts, respectively. Severe steatosis prevalence was for women 0.9% and 1.8%, and for men, 0.2% and 2.6%, in the Chinese and U.S. cohorts, respectively. Adjusted FFCT did not vary across age decades in women or men in the Chinese cohort, though increased across age decades in women and men in the U.S. cohort. Adjusted FFCT and BMI exhibited weak correlation (r=0.312-0.431). Among participants with normal BMI, 36.8% and 38.5% of those in the Chinese and U.S. cohorts had mild steatosis, and 3.0% and 1.5% had moderate or severe steatosis, respectively. Among U.S. participants with BMI ≥40.0, 17.7% had normal liver content. Conclusion: Steatosis and severe steatosis had higher prevalence in the U.S. than Chinese cohort in both women and men. BMI did not reliably predict steatosis. Clinical Impact: The findings provide new information on the dependence of hepatic steatosis on age, sex, and BMI.

Cloud gaming has been very popular in recent years, but issues relating to maintaining low interaction delay to guarantee satisfactory user experience are still prevalent. We observe that the server-side processing delay in cloud gaming system could be heavily influenced by how the resources are partitioned among processes. However, finding the optimal partitioning policy that minimizes the response delay faces several critical challenges. In this paper, we propose an online resource partitioning framework for reducing response delay in cloud gaming, which has several promising properties. First, we divide the processes into disjoint groups and partition resources among process groups, which greatly simplifies the resource partitioning problem while ensuring high partitioning effectiveness. Second, to tackle dynamic workload changes, we classify game workloads into several clusters and maintain separate process grouping plan for each cluster. Third, we leverage reinforcement learning to adaptively choose the best actions for minimizing response delay in real time. We evaluate the proposed framework in a real cloud gaming environment using several real games. The experimental results show that our approach can reduce the response delay by 22\% to 41\% compared to a system without resource partitioning, and outperforms other resource partitioning policies significantly.

Ferroptosis, a new form of non-apoptotic, regulated cell death characterized by iron dependency and lipid peroxidation, is involved in many pathological conditions such as neurodegenerative diseases, heart ischemia/reperfusion injury, acute renal failure, and cancer. While metabolic dysfunctions can lead to excessive lipid peroxidation culminating in ferroptotic cell death, glutathione peroxidase 4 (GPX4) resides in the center of a network that functions to prevent lipid hydroperoxides from accumulation, thereby suppressing ferroptosis. Indeed, RSL3 and other small-molecule GPX4 inhibitors can induce ferroptosis in not only cultured cancer cells but also tumor xenografts implanted in mice. Similarly, erastin and other system Xc− inhibitors can deplete intracellular glutathione required for GPX4 function, leading to lipid peroxidation and ferroptosis. As therapy-resistant cancer cells are sensitive to GPX4-targeted therapeutic regimens, the agents capable of inducing ferroptosis hold great promises to improve current cancer therapy. This review will outline the molecular basis of ferroptosis, but focus on the strategies and the agents developed in recent years for therapeutic induction of ferroptosis. The potentials of these ferroptosis-inducing agents, which include system Xc− inhibitors, GPX4 inhibitors, and iron-based nanoparticles, in cancer therapy will be subsequently discussed.

In this work, we report the resistive switching behavior of an amorphous La2Ti2O7 (LTO) film as sandwiched between two Pt electrodes. The resistive switching is forming-free and highly uniform. Furthermore, it exhibits self-rectifying resistive switching behaviors owing to the Schottky contact and quasi-ohmic contact formed at the top and bottom interfaces, respectively. The mechanism of switching behavior in the device is attributed to the trapping/detrapping-mediated electronic bipolar resistance switching. By fitting the current-voltage characteristics, it indicates the coexistent conduction mechanisms of Schottky emission and space-charge-limited-conduction (SCLC), while the Schottky barrier modified by electron trapping/detrapping plays a dominating role in the resistive switching process.

The swivel clevis (SC) is a vulnerable part of the Overhead Catenary System (OCS). Regular inspection using computer vision technology is an effective way to detect SC defects and improve the OCS operation safety. However, achieving full automation of SC defects detection is still a difficult task due to defective sample scarcity and data distribution shift. To overcome these problems, this paper proposes a novel defects detection method that combines an adaptive SC segmentation network (Adaptive SSN) and local operators. During the inspection process, an unreliability index defined by the model uncertainty and prior knowledge is used to monitor the reliability of the Adaptive SSN. When data distribution shift causes the Adaptive SSN to be unreliable, human annotator will be asked to update the training set and retrain the Adaptive SSN to adapt to the new data distribution. Then the geometric features obtained from segmentation masks and the local features extracted by local operators are used to detect the SC defects. Effectiveness of the proposed method is demonstrated by the experimental results on the data from several high-speed railway lines.

Anomaly detection of hyperspectral data has been gaining particular attention for its ability in detecting targets in an unsupervised manner. Autoencoder (AE), together with its variants can not only extract intrinsic features automatically but also detect anomalies that differ dramatically from others. Many AE-driven algorithms are, thus, proposed for anomaly detection in hyperspectral imagery (HSI), but they suffer from two problems: 1) when there exist anomalies in the training set, AE can generalize so well that it can also learn the abnormal patterns well, thereby reducing the ability to distinguish anomalies from the background and 2) geometric structure among samples are lost in latent space of AE, which is vital in hyperspectral anomaly detection. To tackle these problems, we propose a robust anomaly detector based on the AE framework, named robust graph AE (RGAE) detector, in this article. To be specific, we propose a robust AE framework with l $_{2,1}$ -norm that is robust to noise and anomalies during training. Meanwhile, we embed a superpixel segmentation-based graph regularization term (SuperGraph) into AE. This strategy can preserve the geometric structure and the local spatial consistency of HSI simultaneously and also effectively reduce the searching space and execution time for each pixel. Extensive experiments are conducted on five datasets, and the results demonstrate that our method has a better detection performance, after comparing with other state-of-the-art hyperspectral anomaly detectors.

Ferroelectric tunnel junctions (FTJs) as the artificial synaptic devices have been considered promising for constructing brain-inspired neuromorphic computing systems. However, the memristive synapses based on the flexible FTJs have been rarely studied. Here, we report a flexible FTJ memristor grown on a mica substrate, which consists of an ultrathin ferroelectric barrier of BiFeO3, a semiconducting layer of ZnO, and an electrode of SrRuO3. The obtained flexible FTJ memristor exhibits stable voltage-tuned multi-states, and the resistive switchings are robust after 103 bending cycles. The capability of the FTJ as a flexible synaptic device is demonstrated by the functionality of the spike-timing-dependent plasticity with bending, and the accurate conductance manipulation with small nonlinearity (−0.24) and low cycle-to-cycle variation (1.77%) is also realized. Especially, artificial neural network simulations based on experimental device behaviors reveal that the high recognition accuracies up to 92.8% and 86.2% are obtained for handwritten digits and images, respectively, which are close to the performances for ideal memristors. This work highlights the potential applications of FTJ as flexible electronics for data storage and processing.

This paper provides new evidence on the dynamics of social capital across the life course and over different periods in urban China. Using three cross-sectional datasets from the Job Search and Social Network survey (1999, 2009, and 2014), this study distinguishes family-centered and work-centered social capital and then constructs age-period-cohort models of social capital. The age effects indicate that social capital stock shows an inverse U shape across the individual life course, with the peak occurring between ages 38 and 42 years old. Period effects suggest a significant increase in social capital stock from 1999 to 2014, during which time family-centered social capital declined, while work-centered social capital was invigorated and became the primary source of social capital for urban residents. Cohort effects indicate that political events and institutional reforms exert profound effects on the accumulation of social capital for different birth cohorts. The birth cohort growing up in 1930–1948 stands out as having relatively high social capital, while social capital underwent a significant decline among those who grew up from 1949 to 1978, followed by rapid growth again among those born after 1979. These conclusions enhance the nuanced understanding of the dynamics of social capital across the micro life course and macro social processes.

Referring expression comprehension and segmentation aim to locate and segment a referred instance in an image according to a natural language expression. However, existing methods tend to ignore the interaction between visual and language modalities for visual feature learning, and establishing a synergy between the visual and language modalities remains a considerable challenge. To tackle the above problems, we propose a novel end-to-end framework, Cross-Modality Synergy Network (CMS-Net), to address the two tasks jointly. In this work, we propose an attention-aware representation learning module to learn modal representations for both images and expressions. A language self-attention submodule is proposed in this module to learn expression representations by leveraging the intra-modality relations, and a language-guided channel-spatial attention submodule is introduced to obtain the language-aware visual representations under language guidance, which helps the model pay more attention to the referent-relevant regions in the images and relieve background interference. Then, we design a cross-modality synergy module to establish the inter-modality relations for modality fusion. Specifically, a language-visual similarity is obtained at each position of the visual feature map, and the synergy is achieved between the two modalities in both semantic and spatial dimensions. Furthermore, we propose a multi-scale feature fusion module with a selective strategy to aggregate the important information from multi-scale features, yielding target results. We conduct extensive experiments on four challenging benchmarks, and our framework achieves significant performance gains over state-of-the-art methods.

BACKGROUND Basal ganglia germ cell tumors (BGGCTs) are rare intracranial germ cell tumors (iGCTs) that often presents with cognitive impairment. OBJECTIVE To assess structural brain plasticity in the presence of unilateral basal ganglia germ cell tumors (BGGCTs), and the correlation between gray matter volume (GMV) changes and cognitive tests. MATERIALS AND METHODS We applied voxel-based morphometry (VBM) to structural magnetic resonance imaging (MRI) scans to compare a sample of 41 patients with BGGCTs in the left (n = 22) or right (n = 19) and a sample of 16 patients as control group using a two-sample t-test, correcting for family-wise-errors. A battery of cognitive tests was administered to all BGGCTs patients prior to MRI. We used Pearson correlation analysis to assess the correlation between cognitive test scores and GMV changes. RESULTS In patients with left BGGCTs, whole-brain VBM analysis revealed a large cluster of voxels reflecting an increase in GMV in the left parahippocampal region (k = 529 voxels, T = 4.18, p

Excessive use of pesticides can cause contamination of the environment and agricultural products that are directly threatening human life and health. Therefore, in the process of food safety supervision, it is crucial to conduct sensitive and rapid detection of pesticide residues. The recognition element is the vital component of sensors and methods for fast testing pesticide residues in food. Improper recognition elements may lead to defects of testing methods, such as poor stability, low sensitivity, high economic costs, and waste of time. We can use the molecular biological technique to address these challenges as a good strategy for recognition element production and modification. Herein, we review the molecular biological methods of five specific recognition elements, including aptamers, genetic engineering antibodies, DNAzymes, genetically engineered enzymes, and whole-cell-based biosensors. In addition, the application of these identification elements combined with biosensor and immunoassay methods in actual detection was also discussed. The purpose of this review was to provide a valuable reference for further development of rapid detection methods for pesticide residues.

Unmanned Aerial Vehicles (UAVs) play a vital role in military warfare. In a variety of battlefield mission scenarios, UAVs are required to safely fly to designated locations without human intervention. Therefore, finding a suitable method to solve the UAV Autonomous Motion Planning (AMP) problem can improve the success rate of UAV missions to a certain extent. In recent years, many studies have used Deep Reinforcement Learning (DRL) methods to address the AMP problem and have achieved good results. From the perspective of sampling, this paper designs a sampling method with double-screening, combines it with the Deep Deterministic Policy Gradient (DDPG) algorithm, and proposes the Relevant Experience Learning-DDPG (REL-DDPG) algorithm. The REL-DDPG algorithm uses a Prioritized Experience Replay (PER) mechanism to break the correlation of continuous experiences in the experience pool, finds the experiences most similar to the current state to learn according to the theory in human education, and expands the influence of the learning process on action selection at the current state. All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV. The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm, while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.

Background: Despite the implementation of the universal salt iodization (USI) program for correction of iodine deficiency in China for ∼20 years, the actual iodine nutrition status of Chinese residents and the prevalence of iodine deficiency and iodine excess are issues that need to be addressed. This nationally representative cross-sectional study was conducted across all 31 provinces of mainland China to gather extensive data on iodine nutrition status and the influential factors. Methods: This study included 78,470 participants, aged 18 years or older, who were interviewed and asked to answer a questionnaire. Urine iodine concentration (UIC) was measured by the inductively coupled plasma mass spectrometry method, and goiter was examined by thyroid ultrasonography. In addition, sixty 9-11 years old school children in each province were randomly selected to evaluate the UIC and thyroid ultrasonography. The iodine nutrition status was determined according to the World Health Organization guidelines. Results: The iodized salt coverage was 95.37%. The median urine iodine (MUI) was 177.89 μg/L (interquartile range [IQR], 117.89-263.90 μg/L) and goiter prevalence was 1.17% (confidence interval [95% CI 0.95-1.43]) in the adult population. The MUI was 199.75 μg/L (IQR, 128.41-303.37 μg/L) in school-age children, and goiter prevalence was 3.50% [95% CI, 2.93-4.13]. The percentage of individuals with UIC

Fast and accurate perception of crop plants is critical for robotic weeding. However, due to the similar appearance, it remains challenging to precisely and efficiently segment crop plants from weeds, especially when the weed density is high. To tackle this problem, we devise an ultra-fast bi-phase advanced network (BA-Net) architecture that advances both the feature extraction phase and the model training phase. A bi-path fusion tree structure and a bi-polar distributed loss are introduced for the two phases, respectively. The bi-path fusion tree merges features from two adjacent convolutional stages at each step and efficiently models the scene through a tree-like structure. The bi-polar distributed loss strengthens the training at ambiguous areas, which can greatly enhance the distinctness at detailed structures like the crop plant boundaries. Experiments demonstrate the superiority of the proposed BA-Net to other state-of-the-art methods in terms of both accuracy and efficiency. BA-Net can accurately segment the crop plants from the weedy background, achieving an intersection over union score of 0.963, a F-measure score of 0.981, and a mean absolute error of 0.00376. Also, BA-Net is lightweight with only 0.55 M trainable parameters and achieves ultra-fast speed, i.e., over 340 FPS on a desktop and over 26 FPS on a Jetson TX2 embedded computer. The code of BA-Net is available at https://github.com/ZhangXG001/BA-Net .

The low voltage DC distribution networks are emerging as a great development trend for their flexible access to photovoltaic energy. However, there is no mature DC circuit breaker to protect DC fault. T-source circuit breakers can quickly and automatically cut off a fault current in a very short time. Compared with Z-source circuit breakers, T-source circuit breakers have greater advantages of zero reflected current, common ground, and capability of low-pass filtering. However, as a result of the high power loss of semiconductor devices, transmission efficiency is reduced. Therefore, this paper proposes a new efficient bidirectional T-source circuit breaker. The number of semiconductor devices that conduct in steadystate is reduced and therefore the on-state loss is reduced by more than 40% compared to the existing bidirectional T-source circuit breakers. The new circuit breaker can be manually turned off by reusing the capacitors. The soft starting circuit is also integrated into the circuit breaker. Finally, it is verified by simulation and experiment that the new circuit breaker has the capabilities of fault clearing, manual tripping, and soft starting.

The input-independent and output-series (IIOS) structure is often used in the high step-up ratio converters required for medium-voltage DC (MVDC) grid interface of photovoltaic (PV) systems, which achieves high voltage gain and ensures high efficiency. However, due to the characteristics of IIOS structure, when some PV arrays are shadowed or malfunction, the generated power of these modules will be mismatched. Mismatched power will result in output capacitor voltages deviating from the rated value, which is not conducive to the standardized multi-module design. Besides, voltages of some PV arrays will deviate from their maximum power point (MPP) value, leading to a decrease in the total generated power. In order to balance the module output voltages of the IIOS structure, this paper proposes bidirectional buck-boost and series LC based power balancing units for PV DC collection systems. Compared with a traditional solution, nearly half of the switches are saved and half of the inductors are displaced by small inductors. Power balance can be achieved through the LC power balancing units (LC-PBU) by controlling the duty ratio and the phase of the pulse-width modulation (PWM) signal. Simulations and experiments are carried out to verify the effectiveness of LC-PBU under power sags.

Hepatitis B virus (HBV) is a kind of hepatotropic DNA virus. The main target organ is liver, except for liver, HBV has been found in a variety of extrahepatic tissues, such as kidney, thyroid, pancreas, bone marrow, etc. HBV can cause severe complications by invading these tissues. Among them, pancytopenia is one of the common complications, especially thrombocytopenia that causes life-threatening bleeding. However, the mechanism of thrombocytopenia is unclear and the treatment is extremely difficult. It has been confirmed that HBV has a close relationship with platelets. HBV can directly infect bone marrow, inhibit platelet production, and accelerate platelet destruction by activating monocyte-macrophage system and immune system. While platelets act as a double-edged sword to HBV. On one hand, the activated platelets can degranulate and release inflammatory mediators to help clear the viruses. Furthermore, platelets can provide anti-fibrotic molecules to improve liver functions and reduce hepatic fibrosis. On the other hand, platelets can also cause negative effects. The infected platelets collect HBV-specific CD8+ T cells and nonspecific inflammatory cells into liver parenchyma, inducing chronic inflammation, liver fibrosis and hepatic carcinoma. This article explores the interaction between HBV infection and platelets, providing a theoretical basis for clinical treatment of thrombocytopenia and severe hemorrhage caused by HBV infection.

Arbitrage opportunity exploration is important to ensure the profitability of statistical arbitrage. Prior studies that concentrate on cointegration model and other predictive models suffer from various problems in both prediction and transaction. To prevent these problems, we propose a novel strategy based on machine learning to explore arbitrage opportunities and further predict whether they will make a profit or not. The experiment is conducted in the context of Chinese financial markets with high-frequency data of CSI 300 exchange traded fund (ETF) and CSI 300 index futures (IF) from 2012 to 2020. We find that machine learning strategy can explore more arbitrage opportunities with lower risks, which outperforms cointegration strategy in different aspects. Besides, we compare different algorithms and find that LSTM achieve better performance in predicting the positive arbitrage samples and obtaining higher ROI and Sharpe ratio. The profitability of machine learning strategy validate the mean reversion and price discovery function of asset price between spot market and futures market, which further substantiate the market efficiency. Our empirical results provide practical significance to the development of quantitative finance.

Liquid storage tanks are lifeline structures and strategically very important. Heavy damages or even collapse of these facilities subjected to strong earthquakes may cause disastrous consequences. In this paper, the seismic response of a multistage series liquid storage tank was simulated by a finite element method and verified by a scaled-down experiment. The structural flexibility of the tank and the liquid-structure coupling characteristics between the liquid and tank wall were considered in the research. A multimass-block and spring model was employed to be equivalent to the longitudinal vibration of the liquid in the storage tank. The relationships between the connection springs and the elements of the stiffness matrix were explicitly deduced. The seismic response analysis of a four-stage series liquid tank was carried out, and the acceleration response, the stress response of the tank, and the vertical vibration of the liquid were obtained. The experimental results are in good agreement with the simulation results, which verifies the effectiveness of the modeling method in this paper.

Many studies have associated altered intestinal bacterial communities and non-alcoholic fatty liver disease, but the putative effects are inconclusive. The purpose of this network meta-analysis (NMA) was to evaluate the effects of probiotics, prebiotics, and synbiotics on non-alcoholic fatty liver disease through randomised intervention trials. Literature searches were performed until March 2020. For each outcome, a random NMA was performed, the surface under the cumulative ranking curve (SUCRA) was determined. A total of 22 randomised trials comparing prebiotic, probiotic, and synbiotic treatments included 1301 participants. Considering all seven results (aspartate aminotransferase, alanine aminotransferase, body mass index, weight, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol) together, the highest SUCRA values are probiotics (94%), synbiotics (61%) and prebiotics (56%), respectively. NMA results provide evidence that probiotics, prebiotics, and synbiotics can alleviate non-alcoholic fatty liver disease. However, due to the lack of high-quality randomised trials, this research also has some limitations.

Context Metabolic disorders and cardiovascular disease (CVD) threaten human health. Many studies have assessed the phenomenon of metabolic disorders and CVD in patients with diabetes. However, in euglycemic individuals, the relationships between glucose regulation, metabolism, and CVD remain unclear. Objective This work aimed to explore the associations between postprandial glucose dips, metabolic disorders, and CVD risk. Methods We analyzed data from the Thyroid disorders, Iodine status and Diabetes Epidemiological survey (TIDE study), which included 38 878 euglycemic individuals from all 31 provinces of mainland China. The prevalence of metabolic disorders and their related components and CVD risk were calculated according to postprandial glucose dips. Logistic regression models of quartiles of postprandial glucose dips were used to further explore whether the prevalence of these disorders was associated with postprandial glucose dips. Results Odds ratios for the fourth vs the first quartile of glucose dips were 0.59 (95% CI, 0.55-0.63) (P Conclusion Postprandial glucose dips are associated with metabolic disorders, MetS and its related component diseases, and CVD risk. Glucose dips may be a marker of underlying metabolic abnormalities.

BACKGROUND The radiation dose for patients with low-grade gliomas (LGGs) is controversial. The objective of this study was to investigate the impact of the radiation dose on survival for patients with LGGs and especially for molecularly defined subgroups. METHODS Three hundred fifty-one patients with newly diagnosed LGGs from the multicenter Chinese Glioma Cooperative Group received postoperative radiotherapy (RT) in 2005-2018. The RT dose, as a continuous variable, was entered into a Cox regression model using penalized spline regression to allow for a nonlinear relationship between the RT dose and overall survival (OS) or progression-free survival (PFS). Inverse probability of treatment weighting (IPTW)-adjusted propensity scores were used to correct for potential confounders. Dose effects on survival within IDH mutation and 1p/19q codeletion defined subgroups were analyzed. RESULTS The risk of mortality and disease progression decreased sharply until 54 Gy. High-dose RT (≥54 Gy) was associated with significantly better 5-year OS (81.7% vs 64.0%; hazard ratio [HR], 0.33; P < .001) and PFS (77.4% vs 54.5%; HR, 0.46; P < .001) than low-dose RT (

Purpose. The previous lab and clinical studies of the correlation between the ultraviolet B and age-related cataract (ARC) did not reach in the universal agreement, especially in different morphological types of ARC. It is important to systemically summarize those previous data of epidemiological studies, which might penetrate the relevance between three morphological types of ARC, cortical, nuclear, and posterior capsular (PSC), with sunlight exposure. Methods. PubMed, Web of Science, CNKI, Embase, and Cochrane were searched online. Data were extracted and recalculated, and quality check was performed by hand. Review Manager was used to perform the fixed effects meta-analysis on ARC and its morphological types. The highest exposed dose group was defined as the exposed group, and the lowest dose group as the control group as possible. Results. Finally, the number of analyzed studies was 31: 20 for ARC and twelve, eleven, and nine for the morphological types cortical, nuclear, and PSC, respectively. The pooled OR for ARC was 1.15 (range 1.00~43.78, 95% CI 1.09 to 1.21). The cortical cataract revealed a slightly higher risk, and pooled OR was 1.03 (range 0.67~2.91, 95% CI 1.02 to 1.03). But the pooled OR for nuclear and PSC were 1.00 (range 0.50~5.35, 95% CI 1.00 to 1.00) and 0.99 (range 0.57~1.87, 95% CI 0.95 to 1.01), respectively. Conclusions. The systemic analysis of epidemiological articles reported till now reveals a significantly increased risk of ARC for those exposed with more sunlight, especially the morphological type of cortical cataract.

This paper presents a novel voltage self‐balancing converter (VBC) applied to DC solid‐state transformer (DCSST), analyzes the operating principle of the proposed VBC, and proves the voltage balance performance. Based on this converter, a novel DCSST topology is proposed, which can be used in medium and low voltage DC distribution application, microgrid and medium and low voltage energy storage system. Compared with the previous work, under the same voltage level, it can effectively reduce the number of transformers and switches, reduce the voltage level of switches, and realize the soft switching of the VBC switches, so as to improve the system efficiency and power density, and then improve the system reliability. Meanwhile, the fault handling ability and fault tolerance ability of DCSST are improved, which makes DCSST more reliable when multiple isolated bi‐directional DC/DC Converters (IBDC) are connected in series. In addition, with this topology, the input voltage self‐balancing control strategy of the IBDC is not needed because of the voltage self‐balancing characteristic of VBC, which simplifies the control complexity and greatly improves the control accuracy of the control system. Experimental results agree well with the accuracy and correctness of the proposed topology.

In human-human interaction, body language is one of the most important emotional expressions. However, each emotion category contains abundant emotional body gestures, and basic emotions used in most researches are difficult to describe complex and diverse emotional states. It is costly to collect sufficient samples of all emotional expressions, and new emotions or new body gestures that are not included in the training set may appear during testing. To address the above problems, we design a novel mechanism that treats each emotion category as a collection of multiple body gesture categories to make better use of gesture information for emotion recognition. A Generalized Zero-Shot Learning (GZSL) framework is introduced to recognize both seen and unseen body gesture categories with the help of semantic information, and emotion predictions are further provided based on the relationship between gestures and emotions. This framework consists of two branches. The first branch is a Hierarchical Prototype Network (HPN) which learns the prototypes of body gestures and uses them to calculate the emotion attentive prototypes. This branch aims to obtain predictions on samples of the seen gesture categories. The second branch is a Semantic Auto-Encoder (SAE) which utilizes semantic representations to predict samples of unseen gesture categories. Thresholds are further trained to determine which branch result will be used during testing, and the emotion labels are finally obtained from these results. Comprehensive experiments are conducted on an emotion recognition dataset which contains skeleton data of multiple body gestures, and the performance of our framework is superior to both the traditional emotion classifier and state-of-the-art zero-shot learning methods.

Existing state-of-the-art methods for salient object detection mainly achieve significant progress by integrating multi-level features. However, most of them simply resize the multi-level features to the same spatial resolution and aggregate them by channel-wise concatenation or element-wise addition. Thus, they are limited to integrating features of adjacent layers due to large discrepancy between these multi-level features. Besides, most existing methods adopt the U-shape architecture where high-level semantic information is gradually transmitted to shallower layers to suppress the background noises. Nonetheless, little attention has been paid to refine high-level features. In this paper, we propose the BINet to solve the above problems. Specifically, we design the feature interaction module (FIM) to exploit complementary information between multiple features, which is then utilized to simultaneously refine these features. Besides, we propose the cascaded bidirectional interaction decoder (CBID) to further refine multi-level features iteratively. Our CBID progressively refines multi-level features with feedback mechanisms and employs a channel attention module (CAM) to assign larger weights to channels showing higher response to salient regions. Equipped with these modules, our BINet is capable of segmenting salient regions accurately and quickly. Experimental results on six widely used benchmark datasets validate that our BINet outperforms 16 other state-of-the-art methods in terms of 7 standard evaluation metrics. Our code will be publicly available at https://github.com/clelouch/BINet .

Simultaneous multitarget vital signs measurement has become a hot issue for noncontact vital signs perception. However, there is still challenge in the multitarget heartbeat measurement due to the weakness of heartbeat signal and interference from complex environment. In this article, a new multiple-input–multiple-output (MIMO) continuous-wave (CW) radar system equipped with 2-D digital beamforming (DBF) is presented to measure the respiration and heartbeat of multiple human subjects at unknown positions simultaneously. Through 2-D beam scanning of the whole scene, a 2-D radar image is generated. From the image, the chest motion of multiple targets is accurately located. Then, the vital signs of targets are obtained through forming individual beams focusing on the chests of targets. Moreover, the low intermediate frequency (low-IF) architecture is adopted to minimize the impact of flicker noise in low-frequency amplifier stages. The experimental results demonstrate that the proposed MIMO 2-D imaging radar system can locate chest areas of multiple targets, suppress the clutters, and make vital signs measurement, heartbeat measurement in particular, more robust compared with single-input–multiple-output (SIMO) radar system in complex environment.

The resonant modes and their coupling effect of metamaterials is a rapidly growing research topic with potential for applications in optoelectronics. In this work, a strong absorption coupling between the spoof surface plasmon polariton (SSPP) modes and the intrinsic modes has been investigated in a novel terahertz metamaterial. The anticrossing effects of the absorption-mode coupling show a splitting frequency difference that can be described based on a Rabi-splitting-like model. The theoretical and experimental results demonstrate that the splitting phenomenon is induced by an energy exchange between the intrinsic mode and the SSPP mode. This coupling process is mediated by the SSPP mode and can be controlled efficiently by directionally varying the lattice constant of the metamaterial. Furthermore, this absorption process involving hybrid modes can lead to resonances with ultra-high quality factors, which will provide a strong platform for the applications in the field of sensing and detection.

Well isolated 72 nm pitch hexagonal honeycomb magnetic tunneling junction (MTJ) array with about 45 nm MgO CD was successfully fabricated on a DRAM platform by using mature DRAM array patterning solution with self-aligned double patterning technology and proposed triple hard mask scheme. The 72 nm pitch MTJ array had an equivalent cell size as small as $0.0044~\mu \text{m}$ 2, the world’s smallest pitch size and cell size for industrial MTJ array to our knowledge. The hexagonal array geometry offered considerably merit for storage density enhancement and MTJ etch convenience. A reactive ion etch (RIE) and ion beam etch (IBE) combined MTJ etching solution was adopted to optimize pillar isolation and mitigate metallic re-deposition and etch damage. Perpendicular magnetic anisotropy was maintained in MTJ array; however, free layer coercivity was smaller than expected, indicating the existence of etch damage or fringe magnetic field interaction. Potential challenges for the chip integration of high-density hexagonal MTJ array are also discussed.

Background This study aimed to retrospectively explore the safety and feasibility of computed tomography (CT)-guided synchronous microwave ablation (MWA) followed by core-needle biopsy (CNB) via a coaxial cannula for highly suspected malignant lung ground-glass opacities (GGOs). Methods The clinical data of 66 patients (66 GGOs) treated with CT-guided synchronous MWA followed by CNB via a coaxial cannula from January 2019 to January 2021 were included in this study. The technical success rate, curative effect, and complications were evaluated. Results Technical success rates were 100%. The pneumothorax rate was 36.4% (24/66). 72.7% (48/66) patients had the bronchopulmonary hemorrhage, 81.3% of hemorrhage was attributable to CNB. 24.2% (16/66) patients had varying degrees of pleural effusion. The pathological results were adenocarcinomas (n = 44), atypical adenomatous hyperplasia (n = 2), chronic inflammation (n = 3) and indeterminate pathological diagnosis (n = 17) with a 69.7% (46/66) positive diagnosis rate. The therapeutic response rate was 100.0% (66/66). Conclusions Synchronous MWA followed by CNB via a coaxial cannula has a satisfactory ablation effectiveness and an acceptable biopsy positive rate, which is an alternative treatment for highly suspected malignant GGOs.

OBJECTIVES/HYPOTHESIS To develop a deep-learning-based automatic diagnosis system for identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and hyperplasia), using both white light imaging (WLI) and narrow-band imaging (NBI) nasopharyngoscopy images. STUDY DESIGN Retrospective study. METHODS A total of 4,783 nasopharyngoscopy images (2,898 WLI and 1,885 NBI) of 671 patients were collected and a novel deep convolutional neural network (DCNN) framework was developed named Siamese deep convolutional neural network (S-DCNN), which can simultaneously utilize WLI and NBI images to improve the classification performance. To verify the effectiveness of combining the above-mentioned two modal images for prediction, we compared the proposed S-DCNN with two baseline models, namely DCNN-1 (only considering WLI images) and DCNN-2 (only considering NBI images). RESULTS In the threefold cross-validation, an overall accuracy and area under the curve of the three DCNNs achieved 94.9% (95% confidence interval [CI] 93.3%-96.5%) and 0.986 (95% CI 0.982-0.992), 87.0% (95% CI 84.2%-89.7%) and 0.930 (95% CI 0.906-0.961), and 92.8% (95% CI 90.4%-95.3%) and 0.971 (95% CI 0.953-0.992), respectively. The accuracy of S-DCNN is significantly improved compared with DCNN-1 (P-value

Urolithiasis is a common urological disease, and treatment strategy options vary between different stone types. However, accurate detection of stone composition can only be performed in vitro. The management of infection stones is particularly challenging with yet no effective approach to pre-operatively identify infection stones from non-infection stones. Therefore, we aimed to develop a radiomic model for preoperatively identifying infection stones with multicenter validation. In total, 1198 eligible patients with urolithiasis from three centers were divided into a training set, an internal validation set, and two external validation sets. Stone composition was determined by Fourier transform infrared spectroscopy. A total of 1316 radiomic features were extracted from the pre-treatment Computer Tomography images of each patient. Using the least absolute shrinkage and selection operator algorithm, we identified a radiomic signature that achieved favorable discrimination in the training set, which was confirmed in the validation sets. Moreover, we then developed a radiomic model incorporating the radiomic signature, urease-producing bacteria in urine, and urine pH based on multivariate logistic regression analysis. The nomogram showed favorable calibration and discrimination in the training and three validation sets (area under the curve [95% confidence interval], 0.898 [0.840-0.956], 0.832 [0.742-0.923], 0.825 [0.783-0.866], and 0.812 [0.710-0.914], respectively). Decision curve analysis demonstrated the clinical utility of the radiomic model. Thus, our proposed radiomic model can serve as a non-invasive tool to identify urinary infection stones in vivo, which may optimize disease management in urolithiasis and improve patient prognosis.

BACKGROUND Cerebral infarction is an extremely rare postoperative complication of anterior cervical discectomy and fusion (ACDF), particularly in the delayed setting. We present a case who had a sudden stroke on day 18 after surgery. By sharing our experience with this case, we hope to provide new information about stroke after anterior cervical surgery. CASE SUMMARY We present the case of a 61-year-old man with more than 20 years of hypertension and 14 years of coronary heart disease who had suffered a stroke 11 years ago. The patient was admitted for a multiple ACDF due to symptoms of cervical spondylotic myelopathy and had a sudden stroke on day 18 after surgery. Imaging findings showed a large-area infarct of his left cerebral hemisphere and thrombosis in his left common carotid artery. With the consent of his family, the thrombus was removed and a vascular stent was implanted through an interventional operation. Forty days later, the patient was transferred to a rehabilitation hospital for further treatment. He had normal consciousness but slurred speech at the 1-year follow-up evaluation. The motor and sensory functions of his hemiplegic limbs partially recovered. CONCLUSION This case illustrated that a postoperative stroke related to anterior cervical surgery may be attributed to prolonged carotid retraction and might have a long silent period. Preventive measures include careful preoperative and postoperative examination for high-risk patients as well as gentle and intermittent retraction of carotid artery sheath during operation.

Radiation therapy (RT) modulates immune cells and cytokines, resulting in both clinically beneficial and detrimental effects. The changes in peripheral blood T lymphocyte subsets and cytokines during RT for pediatric brain tumors and the association of these changes with therapeutic outcomes have not been well described.The study population consisted of children (n = 83, aged 3~18) with primary brain tumors (medulloblastoma, glioma, germ cell tumors (GCT), and central nervous system embryonal tumor-not otherwise specified), with or without residual or disseminated (R/D) diseases who were starting standard postoperative focal or craniospinal irradiation (CSI). Peripheral blood T lymphocyte subsets collected before and 4 weeks after RT were enumerated by flow cytometry. Plasma levels of interleukin (IL)-2, IL-4, IL-6, IL-10, tumor necrosis factor-α, interferon-γ, and IL-17A were measured by cytometric bead array.Patients with R/D lesions receiving CSI (n = 32) had a post-RT increase in the frequency of CD3+T and CD8+T cells, a decrease in CD4+T cells, and an increase in regulatory T cells (Tregs) and CD8+CD28- suppressor cells, which was more predominantly seen in these patients than in other groups. In the CSI group with such R/D lesions, consisting of patients with medulloblastoma and germ cell tumors, 19 experienced a complete response (CR) and 13 experienced a partial response (PR) on imaging at 4 weeks after RT. The post/pre-RT ratio of Tregs (P = .0493), IL-6 (P = .0111), and IL-10 (P = .0070) was lower in the CR group than in the PR group. Multivariate analysis revealed that the post/pre-RT ratios of Treg, IL-6, and IL-10 were independent predictors of CR (P.0001, P = .018, P.0001, respectively). The areas under the receiver operating curves and confidence intervals were 0.7652 (0.5831-0.8964), 0.7794 (0.5980-0.9067), and 0.7085 (0.5223-0.8552) for IL-6, IL-10, and Treg, respectively. The sensitivities of IL-6, IL-10, and Treg to predict radiotherapeutic responses were 100%, 92.3%, and 61.5%, and specificity was 52.6%, 57.9%, and 84.2%, respectively.CSI treatment to those with R/D lesions predominantly exerted an effect on antitumor immune response compared with both R/D lesion-free but exposed to focal or CSI RT and with R/D lesions and exposed to focal RT. Such CSI with R/D lesions group experiencing CR is more likely to have a decrease in immunoinhibitory molecules and cells than patients who only achieve PR. Measuring peripheral blood Treg, IL-6, and IL-10 levels could be valuable for predicting radiotherapeutic responses of pediatric brain tumors with R/D lesions to CSI for medulloblastoma and intracranial germ cell tumors.

We have recently proposed a new approach to evaluate 2D femoral neck (FN) structure, named the Minimal Model (MM), that comprised FN areal bone mineral density (FNaBMD) and FNWidth and 2 new internal structural measures; (1) the standard deviation of normalized mineral mass projection profile distribution (FNSigma), and (2) the displacement between center-of-mineral mass and geometric center of mineral mass projection profile (FNDelta). The contralateral hip of 67 FN fracture Chinese male patients had a QCT scan shortly after fracture and was compared to 156 community participants without hip fracture. The QCT scans were analyzed using Mindways software to enable DXA-equivalent 2D images to be obtained; MM variables were calculated from these images. In FN fracture and nonfracture participants, the 4 MM variables as well as age, weight and height were compared. Compared to nonfracture, fracture participants were older, weighed less and were taller. After adjustment for these differences FN fracture participants compared to nonfracture had mean ± SD lower FNaBMD 0.54 ± 0.11 vs 0.70 ± 0.11 g/cm2 (p

Objective: To present our preliminary experiences of robot-assisted laparoscopic radical or partial cystectomy for bladder/prostate rhabdomyosarcoma (B/P RMS) in children. Methods: A retrospective study was conducted with children who underwent robot-assisted laparoscopic surgery for B/P RMS between July 2018 and March 2020 under the approval of IRB of our center. The patient characteristics, tumor position, perioperative information, short time oncologic survival outcomes, and urinary function were recorded and evaluated. Results: We identified eight children who underwent robot-assisted laparoscopic surgery for B/P RMS. Partial cystectomy was performed on all, but one, patients, who underwent radical cystectomy and sigmoid neobladder reconstruction. Preoperative chemotherapy was administered to all eight patients, while preoperative radiotherapy was conducted in three cases, including one patient with a history of pelvic RMS. Unilateral/bilateral ureteral reimplantation was done in four cases in which the patients' ureteral orifices were involved. The mean follow-up time was 13.3 months, and no local recurrence or metastasis was observed. No patient experienced urinary incontinence eventually. Conclusion: Robot-assisted laparoscopic resection for bladder RMS in children is safe and feasible. Preoperative radiotherapy could decrease the tumor volume so that the membranous urethra could be preserved for as long as possible. The oncologic efficacy and the overall survival rate require further investigation and longer follow-up.

Objective To compare the safety and effectiveness of robot‐assisted minimally invasive transforaminal lumbar interbody fusion (Mis‐TLIF) and oblique lumbar interbody fusion (OLIF) for the treatment of single‐level lumbar degenerative spondylolisthesis (LDS). Methods This is a retrospective study. Between April 2018 and April 2020, a total of 61 patients with single‐level lumbar degenerative spondylolisthesis and treated with robot‐assisted OLIF (28 cases, 16 females, 12 males, mean age 50.4 years) or robot‐assisted Mis‐TLIF (33 cases, 18 females, 15 males, mean age 53.6 years) were enrolled and evaluated. All the pedicle screws were implanted percutaneously assisted by the TiRobot system. Surgical data included the operation time, blood loss, and length of postoperative hospital stay. The clinical and functional outcomes included Oswestry Disability Index (ODI), Visual Analog scores (VAS) for back and leg pain, complication, and patient's satisfaction. Radiographic outcomes include pedicle screw accuracy, fusion status, and disc height. These data were collected before surgery, at 1 week, 3 months, 6 months, and 12 months postoperatively. Results There were no significantly different results in preoperative measurement between the two groups. There was significantly less blood loss (142.4 ± 89.4 vs 291.5 ± 72.3 mL, P, Robot‐assisted OLIF and Mis‐TLIF both have similar good clinical outcomes, but OLIF has the additional benefits of less blood loss, less hospital stays, higher disc height, and higher fusion rates. Robots can improve the safety of the minimally invasive spine surgery.

Action recognition is one of the important computer vision tasks , which has many applications. This paper proposes a Multi-cue based Four-stream 3D ResNets (MF3D) model for action recognition. The proposed MF3D model contains four streams: a video saliency stream, an appearance stream, a motion stream and an audio stream . Four cues ( i.e. the appearance cue, the motion cue, the video saliency cue and audio cue) are captured by the four streams of our proposed MF3D model. In addition, three different connections between different streams are injected, which can transfer different cues between different streams to obtain more effective spatiotemporal features. Experiments are conducted on the Kinetics and Kinetics-Sounds datasets, and the results verify that our MF3D model is effective and outperforms current existing models.

Tuberculosis (TB) is a major health issue with high mortality rates worldwide. Recently, tremendous researches of artificial intelligence (AI) have been conducted targeting at TB to reduce the diagnostic burden. However, most researches are conducted in the developed urban areas. The feasibility of applying AI in low-resource settings remains unexplored. In this study, we apply an automated detection (AI) system to screen a large population in an underdeveloped area and evaluate feasibility and contribution of applying AI to help local radiologists detect and diagnose TB using chest X-ray (CXR) images. First, we divide image data into one training dataset including 2627 TB-positive cases and 7375 TB-negative cases and one testing dataset containing 276 TB-positive cases and 619 TB-negative cases, respectively. Next, in building AI system, the experiment includes image labeling and preprocessing, model training and testing. A segmentation model named TB-UNet is also built to detect diseased regions, which uses ResNeXt as the encoder of U-Net. We use AI-generated confidence score to predict the likelihood of each testing case being TB-positive. Then, we conduct two experiments to compare results between the AI system and radiologists with and without AI assistance. Study results show that AI system yields TB detection accuracy of 85%, which is much higher than detection accuracy of radiologists (62%) without AI assistance. In addition, with AI assistance, the TB diagnostic sensitivity of local radiologists is improved by 11.8%. Therefore, this study demonstrates that AI has great potential to help detection, prevention, and control of TB in low-resource settings, particularly in areas with more scant doctors and higher rates of the infected population.

OBJECTIVE: To describe and quantify patients’ self-reported experiences of receiving healthcare from Pakistan’s Programmatic Management of Drug-Resistant Tuberculosis (PMDT) model of care, and to understand these experiences within the broader context of Pakistan’s health system.METHOD: This was a cross-sectional survey of patients attending three PMDT clinics in Khyber-Pakhtunkhwa Province in Pakistan.RESULTS: The median consultation time at the PMDT clinics was 10 minutes. In their most recent visit to the PMDT clinic, 34.9% of patients spent >40% of their monthly income to access treatment. To specify, 71% of patients reported spending out-of-pocket for ancillary medicines and 44.7% for laboratory tests. In 10.5% of cases, medicines for drug-resistant TB (DR-TB) were dispensed without the patient attending the clinic. Only 43.7% of treatment supporters regularly accompanied patients to the clinic, and 6% supervised the patient’s intake of medicines. Disbursement of financial support was irregular in 98.6% of cases. Only 6.2% of patients received their daily injections from a public facility, the rest went elsewhere.CONCLUSION: Several shortcomings in PMDT services, including hurried consultations, irregularities in financial support, and gaps in Pakistan’s broader health system undermined healthcare experience of patients with DR-TB. To improve health outcomes and patients’ care experience these service gaps need to be addressed.

Focal fibrocartilaginous dysplasia is a rare benign bone lesion of young children that causes deformities in the extremities. However, the pathogenesis and treatments have not been defined and the MR manifestations have been less well described. To describe the MR manifestations of focal fibrocartilaginous dysplasia, especially on the T1-W three-dimensional (3-D) volumetric interpolated breath-hold examination (VIBE) sequence. In this retrospective study, the authors reviewed the MR and radiographic images, pathology and medical records of 21 cases of focal fibrocartilaginous dysplasia. All cases were evaluated by spin-echo MRI sequence. Among them, 17 cases were evaluated by T1-W 3-D VIBE sequence. The cohort consisted of 13 boys and 8 girls ages 4–75 months. In 14 cases, focal fibrocartilaginous dysplasia was located in the tibia, 3 in the femur and 4 in the ulna. MRI 3-D VIBE sequence findings showed all cases had hypointense fiber band structures in the bone defect areas. The fibrous bands in the lower extremities ended in the epiphysis or epiphyseal plate, and in the upper extremities the epiphysis or carpal bone. Ten cases had hyperintensities that might represent cartilage composition. Four cases had cartilage signals that were continuous with the epiphyseal cartilage. MR spin-echo sequence findings showed that bone marrow edema of the adjacent joint was observed in eight cases, enlargement of the epiphyseal plate in three cases and medial meniscus injury in five cases. The 3-D VIBE sequence reveals useful details in focal fibrocartilaginous dysplasia.

Background Microsurgical clipping is effective for treating early rupture hemorrhage in intracranial aneurysm (IA) patients. We aimed to evaluate the therapeutic effects of microsurgical clipping at different time points on IA and to explore prognostic factors. Methods A total of 102 eligible patients were divided into good prognosis group (n = 87) and poor prognosis group (n = 15) according to Glasgow Outcome Scale (GOS) scores at discharge. The effects of microsurgical clipping at different time points (within 24 h, 48 h and 72 h) were compared. The incidence rates of postoperative complications in patients with different Hunt–Hess grades were compared. Prognostic factors were determined by multivariate logistic regression analysis. The nomogram prediction model was established based on independent risk factors and validated. Results The good recovery and success rates of complete aneurysm clipping were significantly higher in patients undergoing surgery within 24 h after rupture. The incidence rate of complications was significantly higher in patients with Hunt–Hess grade IV. Good and poor prognosis groups had significantly different age, history of hypertension, preoperative intracranial hematoma volume, aneurysm size, preoperative Hunt–Hess grade, later surgery, postoperative complications and National Institute of Health Stroke Scale (NIHSS) score, as independent risk factors for prognosis. The nomogram model predicted that poor prognosis rate was 14.71%. Conclusion Timing (within 24 h after rupture) microsurgical clipping benefits the prognosis of IA patients. Age, history of hypertension, preoperative intracranial hematoma volume, aneurysm size, preoperative Hunt–Hess grade, later surgery, postoperative complications and NIHSS score are independent risk factors for poor prognosis.

Objective: Multiple brain metastases are a severe condition for cancer patients. To date, no general consensus exists regarding the optimal treatment procedure for multiple brain metastases. Radiotherapy is the most commonly used treatment option. The role of surgical resection for multiple brain metastases is unclear. The aim of this study was to compare the outcomes of patients with multiple brain metastases treated with either surgery or stereotactic radiosurgery (SRS). Methods: The medical records of 279 consecutive adult patients with multiple brain metastases treated with either surgery (26 patients) or SRS (253 patients) were retrospectively reviewed. Propensity score matching was conducted to correct for discrepancies in the baseline characteristics, and 78 patients (26 receiving surgery and 52 receiving SRS) were chosen for comparison of outcomes, such as overall survival, local tumor control rate, and symptom improvement. Results: The tumor size in the surgery group was significantly greater than that in the SRS group after propensity score matching. However, the neurological recovery rate, incidence of leptomeningeal metastasis after surgery, 1-year local tumor control rate, and overall survival were not significantly different between groups. Conclusions: Our data demonstrate that surgery and radiosurgery have identical overall survival and local tumor control rates in patients with 2 to 4 brain metastases. Although SRS remains the primary and standard option for patients with brain metastasis, surgery offers several distinct advantages, such as establishing a diagnosis or relieving mass effects, and may additionally be beneficial in carefully selected patients with 2–4 brain metastases.