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Crowdsourcing plays an essential role in the Internet of Things (IoT) for data collection, where a group of workers is equipped with Internet-connected geolocated devices to collect sensor data for marketing or research purpose. In this paper, we consider crowdsourcing these worker's hot travel path. Each worker is required to report his real-time location information, which is sensitive and has to be protected. Encryption-based methods are the most direct way to protect the location, but not suitable for resource-limited devices. Besides, local differential privacy is a strong privacy concept and has been deployed in many software systems. However, the local differential privacy technology needs a large number of participants to ensure the accuracy of the estimation, which is not always the case for crowdsourcing. To solve this problem, we proposed a trie-based iterative statistic method, which combines additive secret sharing and local differential privacy technologies. The proposed method has excellent performance even with a limited number of participants without the need of complex computation. Specifically, the proposed method contains three main components: iterative statistics, adaptive sampling, and secure reporting. We theoretically analyze the effectiveness of the proposed method and perform extensive experiments to show that the proposed method not only provides a strict privacy guarantee, but also significantly improves the performance from the previous existing solutions., This paper appears in IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2020.3039336

Recognizing traffic command gestures with high accuracy and quick response at a low computational cost is a requisite for driver assistance or autonomous driving. However, it has been understudied for a long time. Existing research takes advantage of increasing development in human action recognition but pays little attention to onboard conditions. In this article, we propose a simple but effective recognition model based on human upper-body geometric features and a long short-term memory (LSTM) network. The handcrafted geometric features can easily be calculated with estimated 2-D human keypoints at a low computational cost but are discriminative and sufficient in classification. Offline and online inferences are implemented to comprehensively evaluate the proposed model. For the sake of robustness required in the automotive domain, dual voting is designed to filter the output in online inference. On the recently published Chinese traffic police gesture (CTPG) dataset, the presented approach is the best with a remarkable improvement of approximately 8% compared to previous LSTM-based methods with handcrafted spatial features and is competitive with advanced GCN-based deep learning methods. The tradeoff pattern is explored to demonstrate how accuracy and response time alter with different training and inference strategies so that a balanced setup can be manually chosen under various application scenarios. Field tests are also carried out with an experimental vehicle, and the results uncover the present gap between research and practical application to some extent, moving a step closer to real-life traffic command gesture recognition.

Background. Renal replacement therapy (RRT), as a cornerstone of supportive treatment, has long been performed in critically ill patients with acute kidney injury (AKI). However, the majority of studies may have neglected the effect of the duration of RRT on the outcome of AKI patients. This paper is aiming to explore the effect of the long duration of RRT on the outcome of critically ill patients with AKI. Methods. This retrospective study was conducted by using the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database. The primary outcome measure of this study was the mortality at 28 days, 60 days, and 90 days in the long-duration RRT group and the non-long-duration RRT group. The secondary outcomes assessed the difference in clinical outcome in these two groups. Lastly, the effect of the duration of RRT on mortality in AKI patients was determined as the third outcome. Results. We selected 1,020 patients in total who received RRT according to the MIMIC-II database. According to the inclusion and exclusion criteria, we finally selected 506 patients with AKI: 286 AKI patients in the non-long-duration RRT group and 220 in the long-duration RRT group. After 28 days, there was a significant difference in all-cause mortality between the long-duration RRT group and the non-long-duration RRT group ( P = 0.001 ). However, the difference disappeared after 60 days and 90 days ( P = 0.803 and P = 0.925 , respectively). The length of ICU stay, length of hospital stay, and duration of mechanical ventilation were significantly longer in the long-duration RRT group than those in the non-long-duration RRT group. Considering 28-day mortality, the longer duration of RRT was shown to be a protective factor (HR = 0.995, 95% CI 0.993–0.997, P < 0.0001 ), while 60-day and 90-day mortality were not correlated with improved protection. Conclusions. The long duration of RRT can improve the short-term prognosis of AKI patients, but it does not affect the long-term prognosis of these patients. Prognosis is determined by the severity of the illness itself. This suggests that RRT can protect AKI patients through the most critical time; however, the final outcome cannot be altered.

Aims/Introduction Emerging evidence shows that epigenetic modifications occurring during fetal development in response to intrauterine exposures could be one of the mechanisms involved in the early determinants of adult metabolic disorders. This study aimed to investigate whether the placental maternally expressed gene 3 (MEG3) deoxyribonucleic acid (DNA) methylation profile is associated with maternal gestational diabetes mellitus status and newborn birthweight. Materials and Methods Samples for measurement were collected from 23 women with gestational diabetes mellitus and 23 healthy controls. MEG3 gene expression and DNA methylation levels were assessed using quantitative real‐time polymerase chain reaction and MethylTargetTM, respectively. Pearson correlation analyses were used to examine associations between placental DNA methylation levels and clinical variables of interest. The associated results were adjusted by multivariate linear regression for maternal age, body mass index, height, gestational age and newborn sex as confounders. Results We found that the DNA methylation levels in the MEG3 differentially methylated region were significantly different between the gestational diabetes mellitus and control groups on the maternal side of the placenta (40.64 ± 2.15 vs 38.33 ± 2.92; P = 0.004). Furthermore, the mean MEG3 DNA methylation levels were correlated positively with maternal fasting glucose concentrations (R = 0.603, P, The placental deoxyribonucleic acid methylation status in the maternally expressed gene 3 differentially methylated region was correlated with maternal fasting glucose concentrations and the newborn birthweight. These epigenetic adaptations might contribute to late‐onset obesity, underlining the adverse intrauterine environment.

Non-alcoholic fatty liver disease (NAFLD), an emerging risk factor for diabetes, is now recognized as the most common liver disease worldwide. Mesenchymal stem cells (MSCs), a promising tool in regenerative medicine, release abundant molecules into the conditioned medium (CM). Increasing evidence showed that MSC-CM is beneficial for diabetes-associated NAFLD. However, the mechanism of how MSC-CM improves NAFLD remains uncertain. In this study, to determine the effects of MSC-CM on NAFLD, streptozotocin (STZ) and high-fat diet (HFD) induced T2DM mice model and palmitic acid (PA)-stimulated L-O2 cells were used and treated with MSC-CM. Our results demonstrated that MSC-CM improved insulin resistance in diabetic mice, amended the pathological structure of the liver, enhanced the liver's total antioxidant capacity and mitochondrial function, reduced inflammation and cell apoptosis. We further verified that SIRT1 played a key role in mediating the protective effect of MSC-CM. These findings provide novel evidence that MSC-CM has the potential to treat T2DM patients with NAFLD clinically.

With the fast development of semiconductor technology, self-driven devices have become an indispensable part of modern electronic and optoelectronic components. In this field, in addition to traditional Schottky and p–n junction devices, hybrid 2D/3D semiconductor heterostructures provide an alternative platform for optoelectronic applications. Herein we report the growth of 2D SnS1−xSex (x = 0, 0.5, 1) nanosheets and the construction of a hybrid SnS0.5Se0.5/GaAs heterostructure based self-driven photodetector. The strong anisotropy of 2D SnS1−xSex is demonstrated theoretically and experimentally. The self-driven photodetector shows high sensitivity to incident light from the visible to near-infrared regime. At the wavelength of 405 nm, the device enables maximum responsivity of 10.2 A W−1, high detectivity of 4.8 × 1012 Jones and fast response speed of 0.5/3.47 ms. Impressively, such a heterostructure device exhibits anisotropic photodetection characteristics with the dichroic ratio of ∼1.25 at 405 nm and ∼1.45 at 635 nm. These remarkable features can be attributed to the high-quality built-in potential at the SnS0.5Se0.5/GaAs interface and the alloy engineering, which effectively separates the photogenerated carriers and suppresses the deep-level defects, respectively. These results imply the great potential of our SnS0.5Se0.5/GaAs heterostructure for high-performance photodetection devices.

Because of the in-gap defect levels, high deep ultraviolet (DUV) light absorption and low leakage current, the incorporation of a 4H-silicon carbide (SiC) substrate has been confirmed to enhance the optoelectrical properties of two-dimensional (2D) layered materials in recent years. Tin disulfide (SnS2) is a post-transition metal dichalcogenide (PTMD) with high light absorption coefficient and thickness-dependent band gap modulation, leading to potential applications in solar cells, DUV-visible photodetectors (PDs), and flexible wearable devices. Here, we first transferred SnS2 nanosheets onto a SiO2 layer and epitaxial n−-type 4H-SiC substrate, respectively. The optoelectrical performance of the SnS2/4H-SiC structure is improved under the UV and visible light. In particular, under 325 nm illumination, the photocurrent density significantly increased by ∼44 times, the response time is shortened to 17 ms, and the specific detectivity (D*) increases to 7.3 × 1013 Jones. Moreover, the responsivity (R325 nm) of 2.42 × 104 A W−1 is increased by one order of magnitude, which outperforms most of 2D materials DUV PDs reported up to date. In particular, the enhancement of mobility, decreased trap density, and suppression of dark current can be also obtained in the SnS2/SiC structure compared with the SnS2/SiO2 structure. This work provides a facile route toward 2D materials/SiC-based PDs with high performance, facile processing, and simple architecture for future applications in industrial, environmental, and even biological fields.

One-dimensional (1D)–two-dimensional (2D) van der Waals (vdWs) mixed-dimensional heterostructures with advantages of an atomically sharp interface, high quality and good compatibility have attracted tremendous attention in recent years. Herein, a mixed-dimensional vertical heterostructure is constructed by transferring mechanically exfoliated 2D WS2 nanosheets on epitaxially grown 1D tellurium (Te) microwires. According to the theoretical type-II band alignment, the device exhibits a photovoltaic effect and serves as an excellent self-powered photodetector with a maximum open-circuit voltage (Voc) up to ∼0.2 V. Upon 635 nm light illumination, the photoresponsivity, external quantum efficiency and detectivity of the self-powered photodetector (SPPD) are calculated to be 471 mA W−1, 91% and 1.24 × 1012 Jones, respectively. Moreover, the dark current of the SPPD is highly suppressed to the sub-pA level due to the large lateral built-in electric field, which leads to a high Ilight/Idark ratio of 104 with a rise time of 25 ms and decay time of 14.7 ms. The abovementioned properties can be further enhanced under a negative bias of −2 V. In brief, the 1D Te–2D WS2 mixed-dimensional heterostructures have great application potential in high performance photodetectors and photovoltaics.

In recent years, two-dimensional material-based tunneling heterojunctions are emerging as a multi-functional architecture for logic circuits and photodetection owing to the flexible stacking, optical sensitivity, tunable detection band, and highly controllable conductivity behaviors. However, the existing structures are mainly focused on transition or post-transition metal chalcogenides and have been rarely investigated as topological insulator (such as Bi2Se3 or Bi2Te3)-based tunneling heterostructures. Meanwhile, it is challenging to mechanically exfoliate the topological insulator thin nanoflakes because of the strong layer-by-layer interaction with shorter interlayer spacing. Herein, we report Au-assisted exfoliation and non-destructive transfer method to fabricate large-scale Bi2Se3 thin nanosheets. Furthermore, a novel broken-gap tunneling heterostructure is designed by combing 2H-MoTe2 and Bi2Se3via the dry-transfer method. Thanks to the realized band alignment, this ambipolar-n device shows a clear rectifying behavior at Vds of 1 V. A built-in potential exceeding ∼0.7 eV is verified owing to the large band offsets by comparing the numerical solution of Poisson's equation and the experimental data. Carrier transport is governed by the majority carrier including thermionic emission and the tunneling process through the barrier height. At last, the device shows an ultralow dark current of ∼0.2 pA and a superior optoelectrical performance of Ilight/Idark ratio ≈106, a fast response time of 21 ms, and a specific detectivity of 7.2 × 1011 Jones for a visible light of 405 nm under zero-bias. Our work demonstrates a new universal method to fabricate a topological insulator and paves a new strategy for the construction of novel van der Waals tunneling structures.

Background charges or defects can substantially lower the photoluminescence (PL) in two-dimensional (2D) transition-metal dichalcogenides (TMDCs). Previous technologies such as chemical passivation...

BackgroundHeat stroke (HS) is a serious, life-threatening disease. However, there is no scoring system for HS so far. This research is to establish a scoring system that can quantitatively assess the severity of exertional heat stroke (EHS).MethodsData were collected from a total of 170 exertional heat stroke (EHS) patients between 2005 and 2016 from 52 hospitals in China. Univariate statistical methods and comparison of the area under the receiver operating characteristic (ROC) curve (AUC) were used to screen exertional heat stroke score (EHSS) parameters, including but not limited body temperature (T), Glasgow Coma Scale (GCS) and others. By comparing the sizes of the AUCs of the APACHE II, SOFA and EHSS assessments, the effectiveness of EHSS in evaluating the prognosis of EHS patients was verified.ResultsThrough screening with a series of methods, as described above, the present study determined 12 parameters – body temperature (T), GCS, pH, lactate (Lac), platelet count (PLT), prothrombin time (PT), fibrinogen (Fib), troponin I (TnI), aspartate aminotransferase (AST), total bilirubin (TBIL), creatinine (Cr) and acute gastrointestinal injury (AGI) classification – as EHSS parameters. It is a 0–47 point system designed to reflect increasing severity of heat stroke. Low (EHSS 35) showed 100% survival and 100% mortality, respectively. We found that AUCEHSS > AUCSOFA > AUCAPACHE II.ConclusionA total of 12 parameters – T, GCS, pH, Lac, PLT, PT, Fib, TnI, AST, TBIL, Cr and gastrointestinal AGI classification – are the EHSS parameters with the best effectiveness in evaluating the prognosis of EHS patients. As EHSS score increases, the mortality rate of EHS patients gradually increases.

Heat stroke (HS) is considered to be a severe systemic inflammatory reaction disease that is caused by high fever. The mortality of HS is high worldwide due to the lack of effective treatments. Presently, mesenchymal stem cells (MSCs) have been demonstrated to serve roles in inflammation and immune regulation. Therefore, the current study aimed to investigate the protective effect and mechanism of MSCs against the HS-induced inflammatory response and organ dysfunction. A rat model of HS was induced by a high-temperature environment and treated with MSCs via tail veins. The levels of molecular markers of organ function, inflammatory factors and chemokines were examined at days 1, 7, 14 and 28. Histological staining was performed on the intestines of rats and control groups, and the Chiu's scores of the two groups were compared. The results revealed that MSCs injection significantly reduced the mortality and inhibited the circulatory inflammatory response. Additionally, main organ function, such as in the liver and kidney, were significantly improved following MSCs infusion in HS rats. Furthermore, MSCs treatment significantly improved edema, necrosis and villus exfoliation of intestinal mucosa, and reduced the inflammatory response of intestinal tissue. These results indicated that MSC infusion had therapeutic effects on HS of rats by regulating the circulatory and intestinal inflammatory response. Moreover, MSCs may be able to protect organ function and promote tissue repair in HS. The results of the current study indicated that MSCs may be used as a potential method to treat HS and the resulting organ dysfunction.

Coalbed methane is stored mainly in coal pores, and pore changes by fracturing fluid affect the extraction effect of coalbed methane. To select the optimal formulation of viscoelastic surfactant (VES) fracturing fluids, we treated coal samples of different hardness with deionized water (A) and three different fluids that contained cationic surfactants (B), mixed cationic and zwitterionic surfactants (C) and mixed anionic and zwitterionic surfactants (D) as the main agents. The effect of the three VES fracturing fluids on pores was compared quantitatively and on a multi-scale. The pore characteristics were tested by field emission-scanning electron microscopy, mercury intrusion capillary pressure and low-pressure nitrogen physisorption. Image-Pro Plus 6.0 software was used to digitize information from the obtained images. The Angulo and Frenkel–Halsey–Hill models were used to calculate the fractal dimension by using mercury intrusion capillary pressure and low-pressure nitrogen physisorption data, respectively. The effect of VES fracturing fluids B and D on pores from 14 nm to 180 μm was greater than that for VES fracturing fluid C as the main agent. The effect of VES fracturing fluid C on the pores (2–300 nm) was most obvious.

Sensor calibration is a prerequisite for autonomous driving and is vital for accurate perception, ensuring precise planning and control of the autonomous vehicle. The modern self-driving car considers data inputs from multiple sensors to construct an understanding of its surroundings. These sensors are inherently susceptible to errors arising from measurement uncertainty, and the risk is multiplied under sensor fusion, a critical function for vehicle localization and object detection. Existing works in this field of study largely focus on offline sensor calibration methods where the vehicle is assumed to have undergone the calibration process prior to vehicle operation on the road, typically using a known calibration target such as a checkerboard pattern target. This paper proposes a sensor calibration method for the LiDAR and the inertial navigation system (INS) using high definition (HD) maps as a readily available source of ground truth, giving potential to an online approach to calibration. To match the data between the LiDAR and the INS system, the trajectory from LiDAR odometry based on the normal distributions transform (NDT) and the trajectory from the INS are utilized. The HD map acts as an online calibration target and provides real positional coordinates which can correct for errors inherent to the sensor outputs and enhance the trajectory estimations. Upon the detection of miscalibration, the algorithm finds the optimum transformation matrix that aligns the LiDAR with the INS. Real-world data was used to verify the proposed approach and can be applied to find the correct extrinsic transformation matrix.

Nonimmune hydrops fetalis (NIHF) is a serious and complex fetal condition. Prenatal diagnosis of hydrops fetalis is not difficult by ultrasound. However, determining the underlying etiology of NIHF remains a challenge which is essential to address for prenatal counseling. We extracted DNA from a proband prenatally diagnosed unexplained NIHF. Trio-whole exome sequencing (WES) was performed to filter candidate causative variants. Two gene mutations were identified as a compound heterozygous state in the proband. Both variants located on the PIEZO1 gene: c.3895C > T, a missense mutation in exon 27 paternally inherited; c.4030_4032del, a maternally inherited in-frame deletion in exon 28. Both variants were first reported to be related to NIHF. PIEZO1 gene mutations, leading to an autosomal recessive congenital lymphatic dysplasia, which can present as NIHF and partial or complete resolution postnatally. In conclusion, WES can aid in the elucidation of the genetic cause of NIHF and has a positive effect on the assessment of prognosis.

Mesenchymal stem cells (MSCs) have been widely used in preclinical and clinical trials for various diseases and have shown great potential in the treatment of sepsis and coronavirus disease (COVID-19). Inflammatory factors play vital roles in the pathogenesis of diseases. The interaction between inflammatory factors is extremely complex. Once the dynamics of inflammatory factors are unbalanced, inflammatory responses and cytokine storm syndrome develop, leading to disease exacerbation and even death. Stem cells have become ideal candidates for the treatment of such diseases due to their immunosuppressive and anti-inflammatory properties. However, the mechanisms by which stem cells affect inflammation and immune regulation are still unclear. This article discusses the therapeutic mechanism and potential value of MSCs in the treatment of sepsis and the novel COVID-19, outlines how MSCs mediate innate and acquired immunity at both the cellular and molecular levels, and described the anti-inflammatory mechanisms and related molecular pathways. Finally, we review the safety and efficacy of stem cell therapy in these two diseases at the preclinical and clinical levels.

Deep coal bed methane generally has a higher formation temperature than shallower coal, which can have important consequences for hydraulic fracturing. In this study, the pore characteristics of coal samples treated by viscoelastic surfactant (VES) fracturing fluid as a function of temperature (293.15–353.15 K) were qualitatively and quantitatively analyzed using mercury injection capillary pressure tests (MICP) and low-temperature N2 gas adsorption (N2GA). The Angulo model and Frenkel–Halsey–Hill (FHH) model were used to describe the fractal characteristics of the coal pores. The results indicated that VES fracturing fluid was most effective for pore changes at temperatures below 323.15 K. The mechanism of VES fracturing fluid on coal pores at variable temperatures was analyzed and addressed from two aspects: (1) the ability of fracturing fluid to enter pores and contact the pore surface; and (2) the ability of fracturing fluid to scavenge minerals on the pore surface. The wettability of VES fracturing fluid on pore surface was analyzed from the surface tension, carboxyl and hydroxyl groups of the coal. The influence of temperature on the solubility of minerals (e.g., calcite, kaolinite, and other inorganic minerals) in VES fracturing fluid was analyzed.

In this paper, we provide a precision prediction method for high-definition map elements extraction based on sequential images taken by crowd-sourcing cameras. Generating the updated elements through crowd-sourcing images using multiple view geometry technique is preferred because of low manufacture cost and easy access of image data. Constrained by the angles of images taken, the reconstructed location precision of elements by vision are inferior to lidar, and could not satisfy the requirements of high-definition map updating. Hence, we propose the concept of position dilution of precision which reveals the theoretical triangulation error of 3D point triggered by 2D pixel error. As long as the predicated precision is accurate, even if the location precision of updating element not so precisely, we could still update the high-definition map by providing the confidence level of the element. Simulations and field images data collected are provided for the validation of our theoretical analysis. It is shown that the predicated variances are almost aligned with the actual measured results.

Cognitive dysfunction often occurs in diabetes mellitus patients. This study aimed to investigate the efficacy of melatonin (MLT) in improving diabetes-associated cognitive decline and the underlying mechanism involved. Type 2 diabetic mice and palmitic acid (PA)-stimulated BV-2 cells were treated by MLT, and the potential mechanisms among MLT, cognition, and autophagy were explored. The results showed that type 2 diabetic mice showed obvious learning and memory impairments in the Morris water maze test compared with normal controls, which could be ameliorated by MLT treatment. Meanwhile, MLT administration significantly improved neuroinflammation and regulated microglial apoptosis. Furthermore, autophagy inhibitor 3-methyladenine (3-MA) increased the microglial inflammation and apoptosis, indicating that the treatment effect of MLT was mediated by autophagy. Lastly, MLT treatment significantly decreased the levels of toll-like receptors 4 (TLR4), phosphorylated-protein kinase B (Akt), and phosphorylated-mechanistic target of rapamycin (mTOR), indicating that blocking TLR4/Akt/mTOR pathway might be an underlying basis for the anti-inflammatory and anti-apoptosis effects of MLT. Collectively, our study suggested that MLT could improve learning and memory in type 2 diabetic mice by activating autophagy via the TLR4/Akt/mTOR pathway, thereby inhibiting neuroinflammation and microglial apoptosis.

Objective This study aimed to examine whether addition of cervical elastographic parameters measured by ElastoScan for the cervix (E-cervix) improves the predictive value of cervical length (CL) in induction of labor at term by dinoprostone. Methods We conducted a prospective, observational study between January 2020 and June 2020 in term primiparous women (n = 73) who were scheduled for labor induction by a 10-mg dinoprostone vaginal insert. The time intervals from the start of labor induction to regular uterine contractions and to vaginal delivery were calculated as the primary outcomes. We divided subjects into two groups using a threshold of 24 hours. Ultrasound measurements were compared between the two groups and the area under the curve (AUC) of the prediction model was calculated. Results Women who delivered vaginally within 24 hours had a shorter CL and softer cervix than those who delivered after 24 hours. The combination of CL and elastographic parameters increased the AUC to 0.672 compared with CL alone (AUC = 0.637). Conclusions Measurement by E-cervix is relatively reproducible. Addition of cervical strain elastography slightly improves the predictive performance of CL in vaginal delivery within 24 hours. This technique is a promising ancillary tool for use with ultrasound.

The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users’ location information, traffic information, motor vehicle information, environmental information, etc., which raises severe sensitive personal information privacy concerns of the users. In addition, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs unexpected amount of communication cost. To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model. Specifically, we propose four LDP mechanisms to perturb gradients generated by vehicles. The proposed Three-Outputs mechanism introduces three different output possibilities to deliver a high accuracy when the privacy budget is small. The output possibilities of Three-Outputs can be encoded with two bits to reduce the communication cost. Besides, to maximize the performance when the privacy budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We further propose a suboptimal mechanism (PM-SUB) with a simple formula and comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by combining Three-Outputs and PM-SUB. Finally, an LDP-FedSGD algorithm is proposed to coordinate the cloud server and vehicles to train the model collaboratively. Extensive experimental results on real-world datasets validate that our proposed algorithms are capable of protecting privacy while guaranteeing utility. Info-communications Media Development Authority (IMDA) National Research Foundation (NRF) Accepted version

Gestational diabetes mellitus (GDM) is a common pregnancy complication which is normally diagnosed in the second trimester of gestation. With an increasing incidence, GDM poses a significant threat to maternal and offspring health. Therefore, we need a deeper understanding of GDM pathophysiology and novel investigation on the diagnosis and treatment for GDM. MicroRNAs (miRNAs), a class of endogenic small noncoding RNAs with a length of approximately 19-24 nucleotides, have been reported to exert their function in gene expression by binding to proteins or being enclosed in membranous vesicles, such as exosomes. Studies have investigated the roles of miRNAs in the pathophysiological mechanism of GDM and their potential as noninvasive biological candidates for the management of GDM, including diagnosis and treatment. This review is aimed at summarizing the pathophysiological significance of miRNAs in GDM development and their potential function in GDM clinical diagnosis and therapeutic approach. In this review, we summarized an integrated expressional profile and the pathophysiological significance of placental exosomes and associated miRNAs, as well as other plasma miRNAs such as exo-AT. Furthermore, we also discussed the practical application of exosomes in GDM postpartum outcomes and the potential function of several miRNAs as therapeutic target in the GDM pathological pathway, thus providing a novel clinical insight of these biological signatures into GDM therapeutic approach.

Background: Sepsis is the leading cause of death among ICU patients, especially elderly patients. We designed this study to confirm the protective effect of MSCs against sepsis in aged rats. Methods: We used naturally aged SD rats to establish sepsis model and injected 5×106 umbilical cord-derived MSCs via the tail vein. Each group of rats was analyzed for survival, examined for biochemical parameters, stained for organ histology, and analyzed for the Th cell subpopulation ratio and inflammatory cytokine levels by flow cytometry. Western blotting was performed to detect the activity of the JAK-STAT signaling pathway. Vitro experiments were designed to confirm the effect of MSCs, and verified the possible mechanism using JAK/STAT inhibitors. Findings: The 72 h survival rate of sepsis rats treated with MSCs was significantly increased, organ damage and inflammatory infiltration were reduced, the levels of organ damage indicators were decreased, the ratios of Th1/Th2 and Th17/Treg in peripheral blood and spleen were significantly decreased, the levels of pro-inflammatory cytokines were decreased, the levels of anti-inflammatory cytokines were increased, and the levels of STAT1 and STAT3 phosphorylation were reduced. These results were validated in in vitro experiments. Interpretation: This study confirms that MSCs can control the inflammatory response induced by sepsis by regulating Th cells and inflammatory factors, and that this leads to reduction of tissue damage, protection of organ functions and ultimately improvement of survival in aged sepsis model rats. Inhibition of the JAK-STAT signaling pathway may be an important mechanism for their action. Funding Information: Not applicable. Declaration of Interests: All authors declare no competing interests. Ethics Approval Statement: This study was approved by the Ethics Committee of the Chinese PLA General Hospital. Animal experiments were approved by the Experimental Animal Welfare Ethics Committee of Chinese PLA General Hospital. Umbilical cords were obtained from the Department of Obstetrics and Gynecology, the First Medical Center, Chinese PLA General Hospital, China, and the mothers signed an informed consent.

Engineering of defects in semiconductors provides an effective protocol for improving photocatalytic N2 conversion efficiency. This review focuses on the state-of-the-art progress in defect engineering of photocatalysts for the N2 reduction toward ammonia. The basic principles and mechanisms of thermal catalyzed and photon-induced N2 reduction are first concisely recapped, including relevant properties of the N2 molecule, reaction pathways, and NH3 quantification methods. Subsequently, defect classification, synthesis strategies, and identification techniques are compendiously summarized. Advances of in situ characterization techniques for monitoring defect state during the N2 reduction process are also described. Especially, various surface defect strategies and their critical roles in improving the N2 photoreduction performance are highlighted, including surface vacancies (i.e., anionic vacancies and cationic vacancies), heteroatom doping (i.e., metal element doping and nonmetal element doping), and atomically defined surface sites. Finally, future opportunities and challenges as well as perspectives on further development of defect-engineered photocatalysts for the nitrogen reduction to ammonia are presented. It is expected that this review can provide a profound guidance for more specialized design of defect-engineered catalysts with high activity and stability for nitrogen photochemical fixation.

Background: The purpose of this research was to establish prediction model of fetal distress risk and admission to neonatal intensive care unit(NICU) risk in a Chinese population of patients with fetal growth restriction(FGR), to provide a reliable basis for clinical management. Methods: A retrospective analysis of 930 patients were delivered with FGR in the obstetrics and gynecology hospital affiliated from January 1, 2014 to December 30, 2019 at Women’s Hospital, School of Medicine, Zhejiang University, in Hangzhou, and screened out the independent predictors affecting fetal distress and admission to NICU using lasso regression. Multivariable logistic regression analysis was used to establish the nomogram prediction model of fetal distress risk and admission to NICU risk. Discrimination of the predicting model was assessed by the C-index and receiver operating characteristic (ROC) curve, using the bootstrapping validation and the ROC curve to evaluate the Internal validation. Calibration and clinical usability of the predicting model were respectively adopted by calibration curves and decision curve analysis. Internal validation was assessed using the bootstrapping validation. Results: We found that six identified factors associated with fetal distress of patients with FGR: age, gestational week, delivery method, placenta previa, abnormal cord blood flow, and using low molecular weight heparin(LMWH). The application of LMWH during pregnancy could reduce the incidence of fetal distress. Four independent predictors were selected for admission to NICU of patients with FGR: gestational week, delivery method, hypertensive disorders of pregnancy, and intrahepatic cholestasis of pregnancy. The delivery method of cesarean section increased the above risks. Two nomograms were developed and verified accordingly. The two models had good discrimination and good calibration respectively. The decision curve analysis performed that the clinical usability and benefits of the nomograms were the range of 3%-75% and 17%-95%. Conclusions: Two nomograms were the first to predict fetal distress and admission to NICU of patients with FGR. Establishing effective predictive models based on independent predictors could help early diagnosis and evaluation of fetal distress and admission to NICU in patients with FGR.

Background: Dyslipidemia in pregnancy are associated with risk of adverse outcomes, such as gestational diabetes mellitus, preeclampsia, preterm birth, and small-for-gestational-age. As an adverse pregnancy outcome, small-for-gestational-age has been extensively studied in Western countries. However, similar studies have rarely been conducted in Asian countries.Methods: Data were derived from 5695 pairs of non-diabetic mothers and neonates between 1 Jan 2014 and 31 Dec 2014. Serum samples were collected during second and third trimesters for evaluation on fasting total cholesterol, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol levels. The present study intended to explore the associations between maternal lipid profile and small-for-gestational-age neonates. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated and adjusted via logistic regression analysis.Results: After adjustments for confounders, third-trimester total cholesterol levels were associated with a decreased risk for small-for-gestational-age (aOR = 0.622, 95% CI: 0.458–0.848), and third-trimester high-density lipoprotein cholesterol and low-density lipoprotein cholesterol levels were associated with an increased risk for small-for-gestational-age (aOR = 1.955, 95% CI: 1.465–2.578; aOR = 1.403, 95% CI: 1.014–1.944). The effect of third-trimester high-density lipoprotein cholesterol levels on the risk for small-for-gestational-age rose across different gestational weight gain strata.Conclusions: High high-density lipoprotein cholesterol level during third trimester could be considered as indicators of a high-risk of small-for-gestational-age, regardless of gestational weight gain. Lowering high-density lipoprotein cholesterol levels may help to reduce the incidence of small-for-gestational-age.

Matching low-cost camera and vector HD map is proven to be a practical and effective way of estimating the location and orientation of intelligent vehicles. However, map-based approach is viable only when the landmark observation is adequate and precise. In some areas with sparse and noisy observation, or even non-existent map matching features, the localization results may be unstable. In this paper, we introduce a novel algorithm by fusing visual odometry and vector HD map in a tightly-coupled optimization framework to tackle these problems. Our algorithm exploits the observation of visual feature points and vector HD map landmarks in the sliding window manner and optimize their residuals in a tightly-coupled approach. In this way, the system is more robust against the noisy HD map landmark observations. In addition, our method is able to accurately estimate vehicle pose even when landmarks are sparse. Experiments under two challenging scenarios with noisy and sparse landmark observations show that our method can achieve the Mean Absolute Error (MAE) at 0.1473m and 0.2496m respectively.

Choosing a suitable programming language during the development of the project can get twice the result with half the effort. The open source software community Gitee was taken as an example to explore the popularity of programming languages. We use pycharm to capture the corresponding number of followers, collections and forks of the projects and programming languages in the community. Then we use cluster analysis to classify programming languages and construct a binary matching network of programming languages and projects to verify the results of language popularity division. The study found that programming languages can be divided into 5 types according to their popularity. Java, c and python belong to the first level, with higher popularity. Although the Go language is not widely used, it has attracted the attention of the majority of developers, and it is bound to develop into a mainstream language in the future.

Background: Heat stroke (HS) is a severe systemic inflammatory response disease caused by high fever, mainly with nervous system damage. Currently, mesenchymal stem cells (MSCs) have inflammation and immunomodulatory effects. Therefore, we aimed to explore the protective effect and mechanism of MSCs on HS-induced excessive inflammation and neurological dysfunction.Methods: A heat stroke Sprague-Dawley (SD) rat model was established at continuous high temperature (42 °C) and high humidity (70%-80%). After modeling, the rats were randomly divided into a heat stroke model group (HS group), a MSCs treatment group (HS+MSCs group), and a Control group in which the rats were kept at room temperature without any treatment. Survival analysis, neurological deficit scoring, pathological staining of hippocampus and cerebellum, immunofluorescence staining of microglia cells, and tissue level detection of inflammatory cytokines were performed in the three groups on day 1, 3, 7, 14 and 28 separately.Results: After heat stroke modeling, the rats were severely paralyzed and had a high mortality. MSCs treatment significantly reduced the mortality in both early stage (day 3) and late stage (day 28). MSCs treatment also significantly reduced the neurological impairment of heat stroke rats, and improved hippocampal and cerebellar pathology and neuronal cell damage. In addition, MSCs treatment significantly inhibited the overactivation of microglia in the hippocampus of HS rats as well as the levels of pro-inflammatory factors and chemokines in the hippocampus. In the early stage (day 1) of MSCs treatment, the activation of cerebellar microglia in the heat stroke rats was significantly promoted. Meanwhile, MSCs treatment had no significant inhibitory effect on the levels of pro-inflammatory factors in the cerebellar tissues of heat stroke rats, but can inhibit the levels of chemokines in the early stage.Conclusions: The application of MSCs for heat stroke treatment in rats can significantly reduce the mortality and neurological defects and improve the hippocampal injury. Meanwhile, MSCs can inhibit the over-activation of microglia cells in the hippocampus of heat stroke rats, which may be a mechanism of MSCs in protecting heat-stroke-caused hippocampal injury.

Heat stroke (HS) is the most acute type of heat illness accompanied with serious central nervous system (CNS) dysfunction. Despite the pathological process being clearly studied, effective treatment is deficient. Currently, mesenchymal stem cells (MSCs) have been demonstrated to have neuroprotective effects as there are no old ones. Thus, the purpose of the present study was to explore the neuroprotective effects and mechanisms of MSCs against HS-induced CNS injury. HS in rat models was induced by a high-temperature environment and treated with MSCs via the tail vein. The results demonstrated that MSC injection significantly reduced the mortality and inhibited the circulation inflammatory response. Moreover, the HS-induced neurological deficit and neuronic damage of the hippocampus were significantly ameliorated by MSC administration. In addition, MSC administration significantly restored astrocytes and inhibited cerebral inflammatory response. These results indicate that MSC infusion has therapeutic effects in HS of rats by regulating the circulation and cerebral inflammatory response. Moreover, astrocytes increased in MSC-treated HS rats when compared with the untreated ones. This may suggest a potential mechanism for HS prevention and therapy through MSC administration.

Spintronic elements based on spin transfer torque have emerged with potential for on-chip memory, but they suffer from large energy dissipation due to the large current densities required. In contrast, an electric-field-driven magneto-electric storage element can operate with capacitive displacement charge and potentially reach 1-10 µJ cm-2 switching operation. Here, magneto-electric switching of a magnetoresistive element is shown, operating at or below 200 mV, with a pathway to get down to 100 mV. A combination of phase detuning is utilized via isovalent La substitution and thickness scaling in multiferroic BiFeO3 to scale the switching energy density to ≈10 µJ cm-2 . This work provides a template to achieve attojoule-class nonvolatile memories.

Mobile LiDAR Systems (MLSs) have recently been recognized as an effective way to extract road markings. Although existing studies have achieved good accuracy (about 90%) in road marking extraction, the majority of them are based on image processing methods; only a few researchers directly use laser points, especially for the recognition and assessment of road markings. This study introduces a three-step automated method for the extraction, recognition, and maintenance of road markings based on the intensity information from the MLS data: (1) an automated mechanism to filter the ground surface in laser data, (2) an adaptive block and multi-threshold method to detect road markings, (3) an automated method to achieve the classification, recognition, and assessment of road markings. Qualitative and quantitative analyses based on experimental datasets with eight types of road markings were used to evaluate the feasibility and robustness of the proposed method. Experimental results show that the average values of completeness (CPT), correctness (CRT), and F-measure of the road marking detection results are 94.35%, 98.35%, and 95.7% and the average values of CPT, CRT, and quality (QUA) of recognition results are 99.0%, 93.2%, and 92.3%, respectively, indicating that the proposed method is feasible and effective. The detection and recognition results were used to reconstruct, improve, and update the road features database; provide guidelines for road applications and maximum assistance for road maintenance; and deliver a valuable solution for maintenance and management of constructed facilities.

SUMMARY: Aquaporins (AQPs) are members of the aquaporin water channel family that play an important role in reabsorption of water from the renal tubular fluid to concentrate urine. Using immunohistochemical staining on paraffin sections, We studied expression of AQP2, AQP3 and AQP4 in renal medulla of Bactrian camel (Camelus bactrianus). The renal medulla of cattle (Bos taurus) acted as the control. Compared with the control, strong expression of AQP2 was observed at the apical plasma membrane and intracellular vesicles, in both the outer medullary collecting duct (OMCD) and the inner medullary collecting duct (IMCD) of camel. Strong expression of AQP3 was observed at the basolateral plasma membrane of the IMCD of camel. Strong AQP4 expression, however, was observed at the basolateral plasma membrane in the OMCD of camel. Moreover, moderate AQP4 expression was detected in endothelium of capillary in medullary region of camels, whereas very weak/absent expression was detected in endothelium of capillary of cattle. We concluded that expression of AQP2, AQP3 and AQP4 in the camel kidney showed some differences from cattle in renal trans-epithelial water transport. It may enhance our better understanding of special water metabolism mechanisms that enable camels to survive in extreme environments.

© 2018 IEEE. Data aggregation plays an important role in the Internet of Things, and its study and analysis has resulted in a range of innovative services and benefits for people. However, the privacy issues associated with raw sensory data raise significant concerns due to the sensitive nature of the user information it often contains. Thus, numerous schemes have been proposed over the last few decades to preserve the privacy of users' data. Most methods are based on encryption technology, which is computationally and communicationally expensive. In addition, most methods can only handle a single aggregation function. Therefore, in this paper, we propose a multifunctional data aggregation method with differential privacy. The method is based on machine learning and can support a wide range of statistical aggregation functions, including additive and non-additive aggregation. It operates within a fog computing architecture, which extends cloud computing to the edge of the network, alleviating much of the computational burden on the cloud server. And, by only reporting the results of the aggregation to the server, communication efficiency is improved. Extensive experimental results show that the proposed method not only answers flexible aggregation queries that meet diversified aggregation goals, but also produces aggregation results with high accuracy.

© 2013 IEEE. In recent years, the widespread prevalence of smart devices has created a new class of mobile Internet of Thing applications. Called mobile crowdsensing, these techniques use workers with mobile devices to collect data and send it to task requester for rewards. However, to ensure the optimal allocation of tasks, a centralized server needs to know the precise location of each user, but exposing the workers' exact locations raises privacy concerns. In this paper, we propose a data release mechanism for crowdsensing techniques that satisfies differential privacy, providing rigorous protection of worker locations. The partitioning method is based on worker density and considers non-uniform worker distribution. In addition, we propose a geocast region selection method for task assignment that effectively balances the task assignment success rate with worker travel distances and system overheads. Extensive experiments prove that the proposed method not only provides a strict privacy guarantee but also significantly improves performance.

Recent studies have shown that radiofrequency (RF) ablation therapy is a safe, feasible, and effective procedure for hepatic hemangiomas, even huge hepatic hemangiomas. RF ablation has the following advantages in the treatment of hepatic hemangiomas: minimal invasiveness, definite efficacy, high safety, fast recovery, relatively simple operation, and wide applicability. It is necessary to formulate a widely accepted consensus among the experts in China who have extensive expertise and experience in the treatment of hepatic hemangiomas using RF ablation, which is important to standardize the application of RF ablation for the management of hepatic hemangiomas, regarding the selection of patients with suitable indications to receive RF ablation treatment, the technical details of the techniques, therapeutic effect evaluations, management of complications, etc. A final consensus by a Chinese panel of experts who have the expertise of using RF ablation to treat hepatic hemangiomas was reached by means of literature review, comprehensive discussion, and draft approval.

High-quality (0 1 0) VO2 thin films were epitaxially grown on functional ferroelectric (1 1 1)-oriented Pb(Mg1/3Nb2/3)0.7Ti0.3O3 (PMN-0.3PT) substrates by reactive magnetron sputtering. The VO2/PMN-0.3PT heterostructures demonstrated metal–insulator transition (MIT) hysteresis with a resistance change of the order of ∼350. Structural characterization of the heterostructures at varying temperatures confirmed that a structural phase transition accompanies the MIT. Moreover, the dynamic strain induced by the converse piezoelectric effect lowers the critical temperature of the MIT from 341.9 K at 0 kV/cm to 339.1 K at 6 kV/cm in the VO2/PMN-0.3PT heterostructures. The resistance of the VO2 thin films could be dynamically modulated by electric field-induced strain, with a change ratio of up to 9.8% near the ferroelectric coercive field. Moreover, the heterostructures displayed non-volatile resistance switching, providing the potential to encode binary information at room temperature by proper electric-field cycling. These functional heterostructures based on correlated electron materials may realize dynamic and non-volatile manipulation of the MIT and resistance switching, thus demonstrating great potential for use in energy-efficient and non-volatile oxide electronic devices.

Due to the rapid development of the cloud computing environment, it is widely accepted that cloud servers are important for users to improve work efficiency. Users need to know servers’ capabilities and make optimal decisions on selecting the best available servers for users’ tasks. We consider the process that users learn servers’ capabilities as a multi-agent Reinforcement learning process. The learning speed and efficiency in Reinforcement learning can be improved by transferring the learning experience among learning agents which is defined as advising. However, existing advising frameworks are limited by a requirement during experience transfer, which all learning agents in a Reinforcement learning environment must have the completely same available choices, also called actions. To address the above limit, this paper proposes a novel differential privacy agent advising approach in Reinforcement learning. Our proposed approach can significantly improve the conventional advising frameworks’ application when agents’ choices are not the completely same. The approach can also speed up the Reinforcement learning by the increase of possibility of experience transfer among agents with different available choices.

The development of major scientific instruments has indispensable impacts on the modern science and technology, however, it is a complex and difficult mission. Comparing to common scientific projects, the development of major scientific instruments not only requires progresses in relevant theories and technologies, but also requires efficient projects management. In this chapter, quoting the Special Fund for Research on National Major Research Instruments as an example, we will discuss the applications of the information for the program of developing major scientific instruments, and comment on their importance and necessity. Our discussion and comments are made on the basis of following five parts, including sources of project ideas, the process of developing instruments, the integration of instrument systems, the implementation of the projects management, and sharing information after the completion of instruments.

Accurate road information is important for applications involving road maintenance, intelligent transportation, and road network updates. Mobile laser scanning (MLS) can effectively extract road information. However, accurately extracting road edges based on large-scale data for complex road conditions, including both structural and non-structural road types, remains difficult. In this study, a robust method to automatically extract structural and non-structural road edges based on a topological network of laser points between adjacent scan lines and auxiliary surfaces is proposed. The extraction of road and curb points was achieved mainly from the roughness of the extracted surface, without considering traditional thresholds (e.g., height jump, slope, and density). Five large-scale road datasets, containing different types of road curbs and complex road scenes, were used to evaluate the practicality, stability, and validity of the proposed method via qualitative and quantitative analyses. Measured values of the correctness, completeness, and quality of extracted road edges were over 95.5%, 91.7%, and 90.9%, respectively. These results confirm that the proposed method can extract road edges from large-scale MLS datasets without the need for auxiliary information on intensity, image, or geographic data. The proposed method is effective regardless of whether the road width is fixed, the road is regular, and the existence of pedestrians and vehicles. Most importantly, the proposed method provides a valuable solution for road edge extraction that is useful for road authorities when developing intelligent transportation systems, such as those required by self-driving vehicles.

Application of human pose estimation in intelligent driving has been gradually popular in recent years, as human pose could be informative in depiction of their actions and intentions. The focus of this paper is pose estimation for vulnerable road users with relatively small amounts of data. We present an improved framework, transferring general human pose estimation algorithms based on convolutional neural network to intelligent vehicle domain. Limb detection is introduced as an auxiliary task to allow explicit appearance-based modeling of spatial relationships among human keypoints. The network for keypoint detection is initialized with the parameters in limb detection task. Besides, the produced limb heatmaps are incorporated with keypoint heatmaps as extra source to gain the final keypoint locations. Concerning practical application, we adapt the widely used pose estimation networks to our proposed framework and evaluate them on the newly introduced TDCB-Pose dataset. The experiment results validate our method, and demonstrate the leverage of limb detection in human pose estimation without abundant data.

Vehicle-road cooperative perception system can greatly improve the perception ability of intelligent vehicles by making use of perception information from road side units (RSU). This paper focuses on the target positioning of static camera for vehicle-road cooperation. A low-cost camera calibration method is proposed to complete the accurate mapping between the image plane and the 3D world space. Precise location of interested targets are achieved by an efficient tracking strategy. Real test scenarios show that our algorithm can effectively locate vehicles, pedestrians, non-motor vehicles and other targets with high accuracy. Our algorithm won the Monocular Static Camera Positioning and Ranging Competition for Autonomous Driving championship in 2019.

Due to the rapid growth of computers and the technology that is capable of capturing data, the data is increasing exponentially; they are collected from the everyday interactions with digital products or services, including mobile devices, information sensing, social media and so on. Big data gives us unprecedented insights and opportunities, but the collected big data contains a large amount of personal or sensitive information, which raises big privacy concerns. This chapter provides an extensive literature review on privacy attack models, privacy-preserving technologies and privacy metrics. We systematically analysis how the data privacy can be disclosed, what kind of privacy technology has been developed and how to evaluate the privacy provided by the proposed privacy-preserving method.

Manhole covers, which are a key element of urban infrastructure management, have a direct impact on travel safety. At present, there is no automatic, safe, and efficient system specially used for the intelligent detection, identification, and assessment of manhole covers. In this work, we developed an automatic detection, identification, and assessment system for manhole covers. First, we developed a sequential exposure system via the addition of multiple cameras in a symmetrical arrangement to realize the joint acquisition of high-precision laser data and ultra-high-resolution ground images. Second, we proposed an improved histogram of an oriented gradient with symmetry features and a support vector machine method to detect manhole covers effectively and accurately, by using the intensity images and ground orthophotos that are derived from the laser points and images, respectively, and apply the graph segmentation and statistical analysis to achieve the detection, identification, and assessment of manhole covers. Qualitative and quantitative analyses are performed using large experimental datasets that were acquired with the modified manhole-cover detection system. The detected results yield an average accuracy of 96.18%, completeness of 94.27%, and F-measure value of 95.22% in manhole cover detection. Defective manhole-cover monitoring and manhole-cover ownership information are achieved from these detection results. The results not only provide strong support for road administration works, such as data acquisition, manhole cover inquiry and inspection, and statistical analysis of resources, but also demonstrate the feasibility and effectiveness of the proposed method, which reduces the risk involved in performing manual inspections, improves the manhole-cover detection accuracy, and serves as a powerful tool in intelligent road administration.

Intestinal barrier dysfunction plays a pivotal role in multiorgan dysfunction during heatstroke (HS). Neutrophils are involved in intestinal inflammation and thus dampen the mucosal integrity. Regulatory T cells (Tregs) have been shown to orchestrate neutrophils and thus sustain mucosal integrity in miscellaneous inflammation-related diseases. However, whether Tregs are involved in HS-induced intestinal barrier dysfunction remains unknown. Thus, we investigated whether Tregs could alleviate intestinal barrier dysfunction in mice. We found that HS could induce intestinal injury and mucosal barrier dysfunction 0, 24, and 72 h after heat stress. Flow cytometry revealed an increase of neutrophil infiltration and a decrease of Treg frequencies in the small intestinal epithelium 72 h after heat stress. Treg depletion starting 2 days before HS exacerbated intestinal damage and mucosal barrier dysfunction. Adoptive transfer of Tregs at 0 h improved intestinal injury and mucosal barrier dysfunction at 72 h. The manipulation of Tregs affected the neutrophil frequencies in the small intestinal epithelium 72 h after heat stress. Our study demonstrated that Tregs could improve HS-induced intestinal barrier dysfunction, probably via modulation of neutrophils in the intestine of mice during HS.

A highly efficient single-photon detector with strong polarization sensitivity is an important device in the field of quantum communication. Here, we present the design of a kind of superconducting-nanowire single-photon detector (SNSPD), in which the superconducting nanowire is embedded in a resonant cavity covered by a metasurface made of a coupled asymmetric split-ring resonator (CASRR). When illuminated by an incident light with different polarizations, CASRR will show totally different optical responses, which is used to obtain high polarization contrast in SNSPDs. Our simulation results show that an NbN nanowire meander with a filling factor of 0.2 can achieve an absorption efficiency of 84.3% at a wavelength of 1550 nm, when the electric field of the incident light is parallel to the NbN nanowire. Moreover, the polarization extinction ratio, i.e., the ratio of absorption rates between cases, where the electric fields are parallel and perpendicular to the NbN nanowire, is 522. Comparing with the results reported before, our SNSPD design demonstrates a much better performance.