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Abstract Background Immune checkpoint inhibitor rechallenge has emerged as a prominent study area in non-small cell lung cancer (NSCLC). β-glucan was reported to reverse resistance to anti-PD-1/PD-L1 inhibitors by regulating the tumor microenvironment. In this self-initiated clinical trial (ChiCTR2100054796), NSCLC participants who have previously failed anti-PD-1 therapy received β-glucan (500 mg, bid, d1-21), Envafolimab (300 mg, d1) and Endostar (210 mg, civ72h) every 3 weeks until disease progression or unacceptable toxicity. The clinical efficacy and adverse events were observed, while serum samples were collected for proteomic analysis. Results Twenty Three patients were enrolled from January 2022 to March 2023 (median age, 65 years; male, n = 18 [78.3%]; squamous NSCLC, n = 9 [39.1%]; mutant type, n = 13 [56.5%]). The overall response rate (ORR) was 21.7% and disease control rate (DCR) was 73.9%. Median progression-free survival (mPFS) and median overall survival (mOS) was 4.3 months [95% CI: 2.0–6.6] and 9.8 months [95% CI: 7.2–12.4], respectively. The mPFS between PD-L1 positive and negative subgroup has significant difference (6.3 months vs. 2.3 months, p = 0.002). Treatment-related adverse events (TRAEs) occurred in 52.2% of patients. The most common TRAEs were hypothyroidism (26.1%) and fatigue (26.1%). 2 (8.7%) grade 3 adverse events were reported. No adverse reaction related deaths have been observed. Proteomic analysis revealed that the levels of CASP-8, ARG1, MMP12, CD28 and CXCL5 correlated with resistance to the treatment while the levels of CD40-L and EGF related to the favorable response. Conclusion β-glucan combined with Envafolimab and Endostar has considerable efficacy and safety for immune rechallenge in metastatic NSCLC patients who failed of anti-PD-1 treatment previously, especially for PD-L1 positive patients.

This study aims to enhance the accuracy of three-dimensional (3D) modeling in the planning, design, operation management, and risk assessment of 500 kV transmission lines. Addressing the limitations of traditional two-dimensional (2D) or low-precision 3D models, a new method based on airborne Light Detection and Ranging (LiDAR) technology is proposed. Initially, high-density, high-precision 3D point cloud data are collected using airborne LiDAR technology. This data is then processed using specialized software for noise removal, terrain classification extraction, and Geographic Information System (GIS) data integration. Subsequently, a Convolutional Neural Network (CNN) is employed to classify the 3D point cloud data to enhance the accuracy of complex structure recognition. Lastly, the point cloud data are converted into linear and planar entities using Computer-Aided Design (CAD) technology, achieving high-precision 3D reconstruction of the 500 kV transmission lines and their complex terrain environment. Experimental results show that the spatial resolution of the constructed model reaches 0.1 m per pixel, with overall geometric accuracy within ±5 cm. Compared to traditional aerial imagery and ground survey methods, the model based on airborne LiDAR technology achieves improvements of 30 % in detail richness and 35 % in terrain adaptability, with a 45 % increase in decision-making efficiency. These improvements significantly enhance the accuracy of line operations, safety assessments, and environmental impact analyses, providing solid data support for the modernization and intelligent development of the power industry.

BackgroundMultiple studies suggest a potential connection between the gut microbiome and asthma. Our objective is to use advanced genetic and metagenomic techniques to elucidate the causal relationships and underlying mechanisms between gut microbiota and asthma.MethodsThe study utilized comprehensive Linkage Disequilibrium Score Regression (LDSC) and Mendelian randomization (MR) analyses to examine the relationship between 119 gut microbiota genera and asthma, using publicly accessible genome-wide association studies (GWAS). The meta-analysis synthesized summary effect estimates obtained from LDSC, forward MR, and reverse MR. The MiBioGen collaboration, involving 18,340 individuals, identified genetic variations associated with gut bacteria. Asthma data were collected from the UK Biobank, FinnGen, and GERA, encompassing a total of 82,060 cases and 641,049 controls.ResultsLDSC analysis revealed significant negative genetic correlations between asthma and RuminococcaceaeUCG004 (Rg = −0.55, p = 7.66 × 10−5) and Subdoligranulum (Rg = −0.35, p = 3.61 × 10−4). Forward MR analysis suggested associations between Butyricicoccus (OR = 0.92, p = 0.01), Turicibacter (OR = 0.95, p = 0.025), Butyrivibrio (OR = 0.98, p = 0.047), and reduced asthma risk. Conversely, Coprococcus2 (OR = 1.10, p = 0.035) and Roseburia (OR = 1.07, p = 0.039) were associated with increased risk. Reverse MR analysis indicated significant associations between genetically predicted asthma and Eubacteriumxylanophilumgroup (Beta = −0.08, p = 9.25 × 10−7), LachnospiraceaeNK4A136group (Beta = −0.05, p = 1.26 × 10−4), and Eisenbergiella (Beta = 0.06, p = 0.015, Rg_P = 0.043).ConclusionThe findings underscore significant genetic correlations and causal relationships between specific gut microbiota and asthma. These insights highlight the potential of gut microbiota as both markers and modulators of asthma risk, offering new avenues for targeted therapeutic strategies.

BackgroundNumerous research studies have indicated a possible association between type 2 diabetes (T2DM) and gut microbiota. To explore specific metabolic pathways connecting gut microbiota and T2DM, we employed Mendelian randomization (MR) and linkage disequilibrium score regression (LDSC) techniques.MethodsThis research utilized data from genome-wide association studies (GWAS) that are publicly accessible. We evaluated the genetic correlation between gut microbiota and T2DM using LDSC. Causality was primarily determined through the inverse variance weighted (IVW) method. To verify the robustness of our results, we conducted sensitivity analyses using several approaches, including the weighted median, MR-Egger, and MR-PRESSO. We integrated summary effect estimates from LDSC, along with forward and reverse MR, into a meta-analysis for T2DM using various data sources. Additionally, mediation analysis was performed to explore the impact of plasma metabolites on the relationship between gut microbiota and T2DM.ResultsOur study indicated a significant genetic correlation between genus RuminococcaceaeUCG005 (Rg = −0.26, Rg_P = 2.07×10−4) and T2DM. Moreover, the forward MR analysis identified genus RuminococcaceaeUCG010 (OR = 0.857, 95% CI 0.795, 0.924; P = 6.33×10−5) and order Clostridiales (OR = 0.936, 95% CI 0.878, 0.997; P = 0.039) as being significantly associated with a decreased risk of T2DM. The analysis also highlighted several plasma metabolites as significant mediators in these relationships, with metabolites like octadecadienedioate (C18:2-DC) and branched chain 14:0 dicarboxylic acid being notably involved.ConclusionThe findings demonstrate a significant impact of gut microbiota on T2DM via plasma metabolites, suggesting potential metabolic pathways for therapeutic targeting. This study enhances our understanding of the microbiota’s role in T2DM pathogenesis and supports the development of microbiota-based interventions.

Due to the high gain, wide bandwidth, and directional radiation characteristics of Vivaldi antennas, this paper conducted relevant research on the feasibility of non-destructive blood glucose detection based on Vivaldi antennas. The research included finite element method (FEM) simulation and glucose concentration monitoring. In the simulation stage, the power transmission and reflection characteristics, radiation characteristics, and electric field distribution characteristics of the antenna were described in detail. In the test stage, the S11 response of the antenna to variation in glucose concentration in the range of 0–6.11 mg/mL was measured, including the S11 amplitude and phase. The experimental results show that there is a high linear correlation between the S11 response and glucose concentration, and the sensitivity of the S11 amplitude response to the variation in glucose concentration is close to 0.3445 (dB/(mg/mL)) at 14.2556 GHz, and the sensitivity of the S11 phase response to the variation in glucose concentration is about 0.5652 (degree/(mg/mL)) at 14.37 GHz. In addition, the predicted results of the glucose concentration based on linear regression are discussed.

BackgroundSeveral studies have suggested a potential link between allergic rhinitis (AR) and gut microbiota. In response, we conducted a meta-analysis of Linkage Disequilibrium Score Regression (LDSC) and Mendelian randomization (MR) to detect their genetic associations.MethodsSummary statistics for 211 gut microbiota taxa were gathered from the MiBioGen study, while data for AR were sourced from the Pan-UKB, the FinnGen, and the Genetic Epidemiology Research on Aging (GERA). The genetic correlation between gut microbiota and AR was assessed using LDSC. The principal estimate of causality was determined using the Inverse-Variance Weighted (IVW) method. To assess the robustness of these findings, sensitivity analyses were conducted employing methods such as the weighted median, MR-Egger, and MR-PRESSO. The summary effect estimates of LDSC, forward MR and reverse MR were combined using meta-analysis for AR from different data resources.ResultsOur study indicated a significant genetic correlation between genus Sellimonas (Rg = −0.64, p = 3.64 × 10−5, Adjust_P = 3.64 × 10−5) and AR, and a suggestive genetic correlation between seven bacterial taxa and AR. Moreover, the forward MR analysis identified genus Gordonibacter, genus Coprococcus2, genus LachnospiraceaeUCG010, genus Methanobrevibacter, and family Victivallaceae as being suggestively associated with an increased risk of AR. The reverse MR analysis indicated that AR was suggestively linked to an increased risk for genus Coprococcus2 and genus RuminococcaceaeUCG011.ConclusionOur findings indicate a causal relationship between specific gut microbiomes and AR. This enhances our understanding of the gut microbiota’s contribution to the pathophysiology of AR and lays the groundwork for innovative approaches and theoretical models for future prevention and treatment strategies in this patient population.

Abstract Background The P-stalk is a conserved and vital structural element of ribosome. The eukaryotic P-stalk exists as a P0-(P1-P2)2 pentameric complex, in which P0 function as a base structure for incorporating the stalk onto 60S pre-ribosome. Prior studies have suggested that P0 genes are indispensable for survival in yeast and animals. However, the functions of P0 genes in plants remain elusive. Results In the present study, we show that rice has three P0 genes predicted to encode highly conserved proteins OsP0A, OsP0B and OsP0C. All of these P0 proteins were localized both in cytoplasm and nucleus, and all interacted with OsP1. Intriguingly, the transcripts of OsP0A presented more than 90% of the total P0 transcripts. Moreover, knockout of OsP0A led to embryo lethality, while single or double knockout of OsP0B and OsP0C did not show any visible defects in rice. The genomic DNA of OsP0A could well complement the lethal phenotypes of osp0a mutant. Finally, sequence and syntenic analyses revealed that OsP0C evolved from OsP0A, and that duplication of genomic fragment harboring OsP0C further gave birth to OsP0B, and both of these duplication events might happen prior to the differentiation of indica and japonica subspecies in rice ancestor. Conclusion These data suggested that OsP0A functions as the predominant P0 gene, playing an essential role in embryo development in rice. Our findings highlighted the importance of P0 genes in plant development.

Metal–oxide–semiconductor (MOS) gas sensors are widely used for gas detection and monitoring. However, MOS gas sensors have always suffered from instability in the link between gas sensor data and the measured gas concentration. In this paper, we propose a novel deep learning approach that combines the surface state model and a Gated Recurrent Unit (GRU)-based regression to enhance the analysis of gas sensor data. The surface state model provides valuable insights into the microscopic surface processes underlying the conductivity response to pulse heating, while the GRU model effectively captures the temporal dependencies present in time-series data. The experimental results demonstrate that the theory guided model GRU+β outperforms the elementary GRU algorithm in terms of accuracy and astringent speed. The incorporation of the surface state model and the parameter rate enhances the model’s accuracy and provides valuable information for learning pulse-heated regression tasks with better generalization. This research exhibits superiority of integrating domain knowledge and deep learning techniques in the field of gas sensor data analysis. The proposed approach offers a practical framework for improving the understanding and prediction of gas concentrations, facilitating better decision-making in various practical applications.

In this paper, a sensor using a complementary split ring resonator (CSRR) is proposed for non-destructive testing of blood glucose. By depicting the complementary split ring structure on the ground, the electromagnetic field strength between the split rings can be enhanced effectively. The structure size of the sensor by CSRR is determined by simulation, so that the insertion loss curve of the device has a resonance point at the frequency of 3.419 GHz. With a special holder created by three-dimensional (3D) printing technology, the test platform was established when the concentration of the solution varied from 0 mg/mL to 20 mg/mL. The experimental results indicate that there is an obvious linear relationship between the insertion loss S21 and the glucose concentration at the resonant frequency. Similarly, the measured real part and imaginary part of the S21 both vary with glucose concentration linearly. Based on the above experimental results, the feasibility of the sensor using a CSRR proposed in this paper for non-destructive detection of blood glucose is preliminarily verified.

Objective. To conduct a systematic review and meta-analysis of the all-cause mortality associated with the most commonly used hemostatic treatments in patients with hemodynamically unstable pelvic fractures. Methods. Up to April 30, 2023, we searched PubMed, Embase, Web of Science, and Cochrane, including the references to qualified papers. A meta-analysis was performed on studies that reported odds ratios (ORs) or the number of events needed to calculate them. The PROSPERO registration number was CRD42023421137. Results. Of the 3452 titles identified in our original search, 29 met our criteria. Extraperitoneal packing (EPP) (OR = 0.626 and 95% CI = 0.413–0.949), external fixation (EF) (OR = 0.649 and 95% CI = 0.518–0.814), and arterial embolism (AE) (OR = 0.459 and 95% CI = 0.291–0.724) were associated with decreased mortality. Resuscitative endovascular balloon occlusion of the aorta (REBOA) (OR = 2.824 and 95% CI = 1.594–5.005) was associated with increased mortality. A random effect model meta-analysis of eight articles showed no difference in mortality between patients with AE and patients with EPP for the initial treatments for controlling blood loss (OR = 0.910 and 95% CI = 0.623–1.328). Conclusion. This meta-analysis collectively suggested EF, AE, or EPP as life-saving procedures for patients with hemodynamically unstable pelvic fractures.

Ammonia (NH3) is a harmful atmospheric pollutant and an important indicator of environment, health, and food safety conditions. Wearable devices with flexible gas sensors offer convenient real-time NH3 monitoring capabilities. A flexible ammonia gas sensing system to support the internet of things (IoT) is proposed. The flexible gas sensor in this system utilizes polyaniline (PANI) with multiwall carbon nanotubes (MWCNTs) decoration as a sensitive material, coated on a silver interdigital electrode on a polyethylene terephthalate (PET) substrate. Gas sensors are combined with other electronic components to form a flexible electronic system. The IoT functionality of the system comes from a microcontroller with Wi-Fi capability. The flexible gas sensor demonstrates commendable sensitivity, selectivity, humidity resistance, and long lifespan. The experimental data procured from the sensor reveal a remarkably low detection threshold of 0.3 ppm, aligning well with the required specifications for monitoring ammonia concentrations in exhaled breath gas, which typically range from 0.425 to 1.8 ppm. Furthermore, the sensor demonstrates a negligible reaction to the presence of interfering gases, such as ethanol, acetone, and methanol, thereby ensuring high selectivity for ammonia detection. In addition to these attributes, the sensor maintains consistent stability across a range of environmental conditions, including varying humidity levels, repeated bending cycles, and diverse angles of orientation. A portable, stable, and effective flexible IoT system solution for real-time ammonia sensing is demonstrated by collecting data at the edge end, processing the data in the cloud, and displaying the data at the user end.

Summary: Plant clock function relies on precise timing of gene expression through complex regulatory networks consisting of activators and repressors at the core of oscillators. Although TIMING OF CAB EXPRESSION 1 (TOC1) has been recognized as a repressor involved in shaping oscillations and regulating clock-driven processes, its potential to directly activate gene expression remains unclear. In this study, we find that OsTOC1 primarily acts as a transcriptional repressor for core clock components, including OsLHY and OsGI. Here, we show that OsTOC1 possesses the ability to directly activate the expression of circadian target genes. Through binding to the promoters of OsTGAL3a/b, transient activation of OsTOC1 induces the expression of OsTGAL3a/b, indicating its role as an activator contributing to pathogen resistance. Moreover, TOC1 participates in regulating multiple yield-related traits in rice. These findings suggest that TOC1’s function as a transcriptional repressor is not inherent, providing flexibility to circadian regulations, particularly in outputs.

This study was conducted to investigate the effects of dietary supplementation with acidic calcium sulfate on growth, intestinal histology, and disease resistance of juvenile largemouth bass. Fish (19.88 ± 0.03 g) were fed on diets supplemented with 0 % (control), 1% acidic calcium sulfate (ACS1), 2 % acidic calcium sulfate (ACS2), 3 % acidic calcium sulfate (ACS3) and 0.03 % florfenicol (F) for 56 days. The results showed that the supplementation of 1 % acidic calcium sulfate to the diet significantly enhanced the growth performance (P

As computed tomography image (CTI) sequence can be regarded as a medical image set, which contains several neighboring and visually similar CTI s with temporal information, conducting an efficient similarity retrieval of the large CTI sequences poses great challenge. Finding the similar CTI sequences can assist diagnosis and treatment by allowing physicians to quickly locate pathological images of lesion tissues, accurately find the source of disease. The paper proposes a progressive Privacy-preserving similarity Retrieval scheme for the CTI Sequences in the mobile telemedicine network (MTN) called the P RS method. To better facilitate the P RS processing, five enabling techniques are devised: 1) key CTI (KCTI)-based similarity measure, 2) local privacy preserving scheme, 3) zero-distance (ZD)-based distributed storage scheme, 4) uniform distributed index framework, and 5) crowd-assisted verification. Extensive experiments demonstrate that the retrieval efficiency of our proposed P RS method is about 90% higher than that of the existing ones.

With the rapid advancement of medical imaging technologies, the high-resolution CT image data is becoming increasingly valuable for both medical research and clinical diagnosis. The paper takes lung CT image as an example. Retrieving images similar to the input one can help physicians with clinical diagnosis. In comparison to traditional content-based image retrieval, similarity retrieval of lung CT images requires higher retrieval accuracy, with similar requirements in external shape as well as internal vascular and lesion location similarity. In the state-of-the-art supervised deep learning networks, the learning of the network is based on labeling. The labeling of medical images, however, requires time and effort from professionals to label each image, which is prohibitively expensive. In this paper, we propose a weakly supervised deep learning network model for similarity analysis of lung CT images that is called a $W$ eakly $S$ upervised $s$ imilarity $A$ nalysis $N$ etwork ( $WSAN$ ). Extensive experiments show that the $WSAN$ model achieves satisfactory results in measuring the similarity between lung CT images and can be used for similarity retrieval tasks.

The in-depth combination and application of AI technology and medical imaging, especially high- definition CT imaging technology, make accurate diagnosis and treatment possible. Retrieving similar CT image(CI)s to an input one from the large-scale CI database of labeled diseases is helpful to realize a precise computer-aided diagnosis. In this paper, we take lung CI as an example and propose progressive content-based similarity retrieval(CBSR) method of the lung CIs based on a Weakly Supervised Similarity Learning Network (WSSLN) model. Two enabling techniques (i.e., the WSSLN model and the distance- based pruning scheme) are proposed to facilitate the CBSR processing of the large lung CIs. The main result of our paper is that, our approach is about 45% more effective than the state-of-the-art methods in terms of the mean average precision(mAP). Moreover, for the retrieval efficiency, the WSSLN-based CBSR method is about 150% more efficient than the sequential scan.

Artificial senses like electronic nose, which ameliorates the problem of poor selectivity from single gas sensor, have elicited keen research interest to monitor hazardous gases. Herein, the doping effects of gallium on In2O3 nanotubes (NTs) are investigated and a four‐component sensor array for the detection of trimethylamine (TMA) is reported. All‐gallium‐doped/alloyed In2O3 (Ga‐In2O3) sensors show improved sensitivity and selectivity to TMA at an operating temperature of 240 °C, with 5 mol% Ga‐doped/alloyed one displaying the highest response in the range of 0.5–100 ppm and the lowest detection limit of 13.83 ppb. Based on the gas‐sensing properties, a four‐component sensor array is fabricated, which shows unique response patterns in variable‐gas backgrounds. Herein, back propagation neural network (BPNN), radial basis function neural network (RBFNN), and principal component analysis‐based linear regression (PCA‐LR) are trained with the gas‐sensing data to discriminate different gases with high accuracy, as well as to predict the concentrations of target gases in different gases and gas mixtures. Furthermore, accuracies of 92.85% and 99.14% can be achieved for the classification of six gases (three single gases and three binary gas mixtures) and for the prediction of TMA concentrations in the presence of different concentrations of TMA and acetone, respectively.

The collection of information about buildings and their colors is an important aspect of urban planning. The intelligent recognition of buildings using image information plays a significant role in the development of smart cities and urban planning. This thesis proposes a building color-recognition technique based on morphological features utilizing convolutional neural networks and the K-means clustering algorithm of image-recognition technology. The proposed method can identify buildings in images and classify them into two categories, buildings and panoramas, for color extraction and matching. This method involves training convolutional neural networks on deep learning so that the buildings can be differentiated and segmented. Subsequently, the K-means algorithm extracts colors from the segmented building images. The extracted building category, color, and text information were analyzed to obtain a comparison and analysis results of buildings and panoramas. The results demonstrated that the system is capable of accurately segmenting buildings, as well as extracting colors from both buildings and panoramas separately. It can also contribute to the extraction and presentation of color schemes in smart city planning and provide valuable insights for the future development of urban colors.

In the space environment, cosmic rays and high-energy particles may cause a single-event upset (SEU) during program execution, and further cause silent data corruption (SDC) errors in program outputs. After extensive research on SEU and SDC errors, it has been found that SDC errors in the routing program in satellite networks may lead to the emergence of Sinkhole (SH) and Grayhole (GH) nodes in the network, which may cause damage to satellite networks. To find and solve the problems in time, a digital-twin-based detection and protection framework for SDC-induced SH and GH nodes in satellite networks is proposed. First, the satellite network fault model under SEU and the generation mechanism of SH and GH nodes induced by SDC errors are described. Then, the data structure based on digital twins required by the proposed detection and protection framework is designed, and the detection methods of SH and GH nodes induced by SDC errors are proposed. SKT and LLFI simulation tools are used to build a simulated Iridium satellite network and carry out fault injection experiments. Experiment results show that the accuracy of the proposed detection method is 98–100%, and the additional time cost of routing convergence caused by the proposed framework is 3.1–28.2%. Compared with existing SH and GH detection methods, the proposed methods can timely and accurately detect faults during the routing update stage.

Higher accuracy in cluster failure prediction can ensure the long-term stable operation of cluster systems and effectively alleviate energy losses caused by system failures. Previous works have mostly employed BP neural networks (BPNNs) to predict system faults, but this approach suffers from reduced prediction accuracy due to the inappropriate initialization of weights and thresholds. To address these issues, this paper proposes an improved arithmetic optimization algorithm (AOA) to optimize the initial weights and thresholds in BPNNs. Specifically, we first introduced an improved AOA via multi-subpopulation and comprehensive learning strategies, called MCLAOA. This approach employed multi-subpopulations to effectively alleviate the poor global exploration performance caused by a single elite, and the comprehensive learning strategy enhanced the exploitation performance via information exchange among individuals. More importantly, a nonlinear strategy with a tangent function was designed to ensure a smooth balance and transition between exploration and exploitation. Secondly, the proposed MCLAOA was utilized to optimize the initial weights and thresholds of BPNNs in cluster fault prediction, which could enhance the accuracy of fault prediction models. Finally, the experimental results for 23 benchmark functions, CEC2020 benchmark problems, and two engineering examples demonstrated that the proposed MCLAOA outperformed other swarm intelligence algorithms. For the 23 benchmark functions, it improved the optimal solutions in 16 functions compared to the basic AOA. The proposed fault prediction model achieved comparable performance to other swarm-intelligence-based BPNN models. Compared to basic BPNNs and AOA-BPNNs, the MCLAOA-BPNN showed improvements of 2.0538 and 0.8762 in terms of mean absolute percentage error, respectively.

During long-distance flight, unmanned aerial vehicles (UAVs) need to perform cross-domain authentication to prove their identity and receive information from the ground control station (GCS). However, the GCS needs to verify all drones arriving at the area it is responsible for, which leads to the GCS being unable to complete authentication in time when facing cross-domain requests from a large number of drones. Additionally, due to potential threats from attackers, drones and GCSs are likely to be deceived. To improve the efficiency and security of cross-domain authentication, we propose an efficient blockchain-based cross-domain authentication scheme for the Internet of Drones (BCDAIoD). By using a consortium chain with a multi-chain architecture, the proposed method can query and update different types of data efficiently. By mutual authentication before cross-domain authentication, drones can compose drone groups to lighten the authentication workload of domain management nodes. BCDAIoD uses the notification mechanism between domains to enable path planning for drones in advance, which can further improve the efficiency of cross-domain authentication. The performance of BCDAIoD was evaluated through experiments. The results show that the cross-domain authentication time cost and computational overhead of BCDAIoD are significantly lower those of than existing methods when the number of drones is large.

Background: Acute bilirubin encephalopathy (ABE) is a significant cause of neonatal mortality and disability. Early detection and treatment of ABE can prevent the further development of ABE and its long-term complications. Due to the limited classification ability of single-modal magnetic resonance imaging (MRI), this study aimed to validate the classification performance of a new deep learning model based on multimodal MRI images. Additionally, the study evaluated the effect of a spatial attention module (SAM) on improving the model’s diagnostic performance in distinguishing ABE. Methods: This study enrolled a total of 97 neonates diagnosed with ABE and 80 neonates diagnosed with hyperbilirubinemia (HB, non-ABE). Each patient underwent three types of multimodal imaging, which included T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and an apparent diffusion coefficient (ADC) map. A multimodal MRI classification model based on the ResNet18 network with spatial attention modules was built to distinguish ABE from non-ABE. All combinations of the three types of images were used as inputs to test the model’s classification performance, and we also analyzed the prediction performance of models with SAMs through comparative experiments. Results: The results indicated that the diagnostic performance of the multimodal image combination was better than any single-modal image, and the combination of T1WI and T2WI achieved the best classification performance (accuracy = 0.808 ± 0.069, area under the curve = 0.808 ± 0.057). The ADC images performed the worst among the three modalities’ images. Adding spatial attention modules significantly improved the model’s classification performance. Conclusion: Our experiment showed that a multimodal image classification network with spatial attention modules significantly improved the accuracy of ABE classification.

BackgroundSmoking is a global health threat. Attentional bias influences smoking behaviors. Although attentional bias retraining has shown benefits and recent advances in technology suggest that attentional bias retraining can be delivered via smartphone apps, there is a paucity of research on this topic. ObjectiveThis study aims to address this gap by exploring the use of attentional bias retraining via a novel smartphone app using a mixed methods pilot study. In the quantitative phase, it is hypothesized that participants in the training group who undertake attentional bias retraining via the app should have decreased levels of attentional bias, subjective craving, and smoking frequency, compared with those in the control group who do not undertake attentional bias retraining. The qualitative phase explores how the participants perceive and experience the novel app. MethodsIn all, 10 adult smokers (3 females and 7 males) between the ages of 26 and 56 years (mean 34.4 years, SD 9.97 years) were recruited. The participants were randomly allocated to the training and control groups. In weeks 1 and 3, participants from both groups attempted the standard visual probe task and rated their smoking frequency and subjective craving. In week 2, the participants in the training group attempted the modified visual probe task. After week 3, participants from both groups were interviewed about their views and experiences of the novel app. ResultsThe results of the quantitative analysis did not support this study’s hypothesis. The qualitative data were analyzed using thematic analysis. The results yielded 5 themes: ease, helpfulness, unhelpful aspects, barriers, and refinement. ConclusionsFindings from the qualitative study were consistent with those from previous studies on health-related smartphone apps. The qualitative results were helpful in understanding the user perspectives and experiences of the novel app, indicating that future research in this innovative area is necessary.

Background A computer tomography image (CI) sequence can be regarded as a time-series data that is composed of a great deal of nearby and similar CIs. Since the computational and I/O costs of similarity measure, encryption, and decryption calculation during a similarity retrieval of the large CI sequences (CIS) are extremely high, deploying all retrieval tasks in the cloud, however, will lead to excessive computing load on the cloud, which will greatly and negatively affect the retrieval performance. Methodologies To tackle the above challenges, the paper proposes a progressive privacy-preserving Batch Retrieval scheme for the lung CISs based on edge-cloud collaborative computation called the BRS method. There are four supporting techniques to enable the BRS method, such as: 1) batch similarity measure for CISs, 2) CIB-based privacy preserving scheme, 3) uniform edge-cloud index framework, and 4) edge buffering. Results The experimental results reveal that our method outperforms the state-of-the-art approaches in terms of efficiency and scalability, drastically reducing response time by lowering network communication costs while enhancing retrieval safety and accuracy.

Polycaprolactone (PCL) has been widely used as a scaffold material for tissue engineering. Reliable applications of the PCL scaffolds require overcoming their native hydrophobicity and obtaining the sustained release of signaling factors to modulate cell growth and differentiation. Here, we report a surface modification strategy for electrospun PCL nanofibers using an azide-terminated amphiphilic graft polymer. With multiple alkylation and pegylation on the side chains of poly-L-lysine, stable coating of the graft polymer on the PCL nanofibers was achieved in one step. Using the azide-alkyne “click chemistry”, we functionalized the azide-pegylated PCL nanofibers with dibenzocyclooctyne-modified nanocapsules containing growth factor, which rendered the nanofiber scaffold with satisfied cell adhesion and growth property. Moreover, by specific immobilization of pH-responsive nanocapsules containing bone morphogenetic protein 2 (BMP-2), controlled release of active BMP-2 from the PCL nanofibers was achieved within 21 days. When bone mesenchyme stem cells were cultured on this nanofiber scaffold, enhanced ossification was observed in correlation with the time-dependent release of BMP-2. The established surface modification can be extended as a generic approach to hydrophobic nanomaterials for longtime sustainable release of multiplex signaling proteins for tissue engineering.

BackgroundNumerous studies showed that insulin resistance (IR) was associated with cancer risk. However, few studies investigated the relationship between IR and non-small cell lung cancer (NSCLC). The aim of this study is to explore the association of triglyceride glucose (TyG) index, a simple surrogate marker of IR, with NSCLC risk.Methods791 histologically confirmed NSCLC cases and 787 controls were enrolled in the present study. Fasting blood glucose and triglyceride were measured. The TyG index was calculated as ln [fasting triglycerides (mg/dl) ×fasting glucose (mg/dl)/2]. Logistic regression analysis was performed to estimate the relationship between NSCLC risk and the TyG index.ResultsThe TyG index was significantly higher in patients with NSCLC than that in controls (8.42 ± 0.55 vs 8.00 ± 0.45, P < 0.01). Logistic regression analysis showed that the TyG index (OR = 3.651, 95%CI 2.461–5.417, P < 0.001) was independently associated with NSCLC risk after adjusting for conventional risk factors. In addition, a continuous rise in the incidence of NSCLC was observed along the tertiles of the TyG index (29.4 vs 53.8 vs 67.2%, P < 0.001). However, there were no differences of the TyG index in different pathological or TNM stages. In receiver operating characteristic (ROC) curve analysis, the optimal cut-off level for the TyG index to predict incident NSCLC was 8.18, and the area under the ROC curve (AUROC) was 0.713(95% CI 0.688–0.738).ConclusionsThe TyG index is significantly correlated with NSCLC risk, and it may be suitable as a predictor for NSCLC.

Background: Lipids are known to accumulate abnormally in the alveoli and circulate during pulmonary alveolar proteinosis (PAP). However, the relationship between lipid ratios and PAP is not clear. In this study, we investigated the lipid ratios in PAP patients and explored the relationships between lipid ratios and the severity of PAP.Methods: A total of 122 PAP patients were diagnosed and divided the mild- moderate PAP group (n = 61) and the severe PAP group (n = 61) according to the value of disease severity score (DSS). One hundred thirty healthy volunteers were classified as the control group. Routine blood examination and pulmonary function tests were performed and lipid profile were measured.Results: Compared with the control group, patients with PAP had significantly higher TG, TC/HDL-C, TG/HDL-C, and non-HDL-C, while lower HDL-C (all P < 0.05). Patients with the severe PAP had higher TC, TG, LDL-C, TC/HDL-C, and non-HDL-C, while lower HDL-C than patients with the mild- moderate PAP (all P < 0.05). Binary logistic regression analysis indicated that TC/HDL-C (OR = 2.322, 95% CI 1.621–3.713, P = 0.024) and non-HDL-C (OR = 1.797, 95% CI 1.239–3.109, P = 0.036) were all significantly correlated with the severity of PAP after adjustment for other risk factors. The AUC value of TC/HDL-C for predicting the severity of PAP was larger than that of non-HDL-C. The AUROC for TC/HDL-C was 0.741 (0.654–0.828), and the optimal cut-off point for TC/HDL-C was 5.05 (sensitivity: 73.6%, specificity: 68.1%).Conclusions: Lipid ratios, including TC-HDL-C and non-HDL-C, were independent risk factors for the severity of PAP. TC/HDL-C is a promising biomarker for the severity of PAP.

Objective: To develop predictive models for contrast induced acute kidney injury (CI-AKI) among acute myocardial infarction (AMI) patients treated invasively.Methods: Patients with AMI who underwent angiography therapy were enrolled and randomly divided into training cohort (75%) and validation cohort (25%). Machine learning algorithms were used to construct predictive models for CI-AKI. The predictive models were tested in a validation cohort.Results: A total of 1,495 patients with AMI were included. Of all the patients, 226 (15.1%) cases developed CI-AKI. In the validation cohort, Random Forest (RF) model with top 15 variables reached an area under the curve (AUC) of 0.82 (95% CI: 0.76–0.87), while the best logistic model had an AUC of 0.69 (95% CI: 0.62–0.76). ACEF (age, creatinine, and ejection fraction) model reached an AUC of 0.62 (95% CI: 0.53–0.71). RF model with top 15 variables achieved a high recall rate of 71.9% and an accuracy of 73.5% in the validation group. Random Forest model significantly outperformed logistic regression in every comparison.Conclusions: Machine learning algorithms especially Random Forest algorithm improves the accuracy of risk stratifying patients with AMI and should be used to accurately identify the risk of CI-AKI in AMI patients.

Due to the centralization of communication in the management of data generated by diverse Internet of Thing (IoT) devices, there is a lack of reliability when data is being transferred and stored. Among errors caused by various behaviors, Silent Data Corruption (SDC) error, owing to stealthy destruction without error prompt, is one of the most difficult data consistency problems in the storage system, whether it is a traditional multi-control, distributed storage, or public cloud storage. Nowadays, for SDC error detection, extracting instruction features to analyze vulnerabilities of programs or instructions has still not been fully explored. Literature generally just count the number of possible features, without mining the inter-characteristic of the instruction and correlation between them. Thus, we propose a method of SDC-causing Error Detection based on Support Vector Regression (SED-SVR) for fully exploiting the correlation between data features. Specifically, firstly, we extract instruction features based on the SDC vulnerability of program instructions by analyzing results of fault injections. Secondly, we establish the instruction SDC vulnerability prediction model based on SVR and propose our SED-SVR model. Thirdly, according to the predicted values of SDC vulnerability, we develop some solutions for faults tolerance of target programs by different granularity of instruction redundancy. The experimental results show that our SED-SVR has higher fault detection rate with lower performance overhead.

The wideband spectrum estimation is an essential step in the wireless network. In order to avoid employing power-hungry high-rate analog-to-digital converters (ADCs), the CS-based sub-Nyquist sampling approaches are used to estimate the wideband spectrum. In this paper, we propose a sub-Nyquist sampling system based on the analog to information converter (AIC), and the proposed system is constructed by multiple parallel channels with a banks of low pass filters. The system model is constructed in the time domain. To estimate the power spectrum, we define a new power spectrum of samples with a finite length, called the circular power spectrum (CPS), served as the aim we strive to estimate. The defined CPS can clearly reflect the power of the signal varying with frequency and is also with the same length as the equivalent digital samples. The experimental results indicate that the defined CPS can be successfully estimated from samples captured by the proposed sub-Nyquist sampling system whose overall sampling rate is much lower than the Nyquist rate.

As a key component of building management and security, occupancy inference through smart sensing has attracted a lot of research attention for nearly two decades. Recently, a cutting edge technique visible light sensing (VLS) that utilizes the LED luminaires as light sensors has shown its promising application potentials in occupancy inference as it piggybacks on pervasive lighting infrastructure without extra equipment deployment. Although existing inference algorithms based on the VLS data set can achieve high accuracy, the performance degrades when the occupants are moving. This paper focuses on the occupancy inference issue and presents an ensemble learning algorithm to improve the inference accuracy. We use heterogeneous learning algorithms to generate diverse learners. Consequently, we adopt forward sequential pruning to enhance the ensemble that pursues inference error minimization. We conduct extensive experiments based on the field data. The experiment results show that the proposed algorithm is able to improve inference accuracy, especially for highly dynamic occupancy data set.

To address the problem of detecting malicious codes in malware and extracting the corresponding evidences in mobile devices, we construct a consortium blockchain framework, which is composed of a detecting consortium chain shared by test members and a public chain shared by users. Specifically, in view of different malware families in Android-based system, we perform feature modeling by utilizing statistical analysis method, so as to extract malware family features, including software package feature, permission and application feature, and function call feature. Moreover, for reducing false-positive rate and improving the detecting ability of malware variants, we design a multi-feature detection method of Android-based system for detecting and classifying malware. In addition, we establish a fact-base of distributed Android malicious codes by blockchain technology. The experimental results show that, compared with the previously published algorithms, the new proposed method can achieve higher detection accuracy in limited time with lower false-positive and false-negative rates.

Abstract This paper is concerned with a non-zero sum differential game problem of an anticipated forward-backward stochastic differential delayed equation under partial information. We establish a maximum principle and a verification theorem for the Nash equilibrium point by virtue of the duality and convex variation approach. We study a linear-quadratic system under partial information and present an explicit form of the Nash equilibrium point. We derive the filtering equations and prove the existence and uniqueness for the Nash equilibrium point. As an application, we solve a time-delayed pension fund management problem with nonlinear expectation to measure the risk and obtain the explicit solution.

Abstract ATP-binding cassette A3 (ABCA3) is a phospholipid carrier that is mainly expressed in the alveolar epithelium. Biallelic mutations of ABCA3 has been associated with fatal respiratory distress syndrome and interstitial lung disease (ILD) in children. However, whether variations in ABCA3 have a role in the development of adult ILD, including idiopathic pulmonary fibrosis (IPF), remains to be addressed. In this study, we screened for germline variants of ABCA3 by exons-sequencing in 30 patients with sporadic IPF and in 30 matched healthy controls. Eleven missense variants, predominantly in heterozygous, were found in 13 of these patients, but only two missenses in 2 healthy controls. We then selected four of the detected missense variants (p.L39V, p.S828F, p.V968M and p.G1205R) to performed cohort analysis in 1,024 ILD patients, containing 250 IPF and 774 connective tissue disease-ILD (CTD-ILD) patients, and 1,054 healthy individuals. Our results showed that the allele frequency of p.G1205R, but not p.L39V, was significantly higher in ILD patients than in healthy controls. However, no additional subject carrying the variant p.S828F or p.V968M was detected in the cohort analysis. These results indicate that the heterozygous ABCA3 gene variants may contribute to susceptibility to diseases in the Chinese population.