Outer Membrane Vesicles Transmitting blaNDM-1 Mediate the Emergence of Carbapenem-Resistant Hypervirulent Klebsiella pneumoniae.

Dissemination of hypervirulent and carbapenem-resistant Klebsiella pneumoniae (CRKP) has been reported worldwide, posing a serious threat to antimicrobial therapy and public health. Outer membrane vesicles (OMVs) act as vectors for the horizontal transfer of virulence and resistance genes. However, K. pneumoniae OMVs that transfer carbapenem resistance genes into hypervirulent K. pneumoniae (hvKP) have been insufficiently investigated. Therefore, this study investigates the transmission of the blaNDM-1 gene encoding resistance via OMVs released from CRKP and the potential mechanism responsible for the carbapenem-resistant hypervirulent K. pneumoniae (CR-hvKP) emergence. OMVs were isolated via ultracentrifugation from CRKP with or without meropenem selective pressure. OMVs were then used to transform classical K. pneumoniae (ckp) ATCC 10031, extended-spectrum ß-lactamase (ESBL)-producing K. pneumoniae ATCC 700603, and hvKP NTUH-K2044. Our results showed that meropenem treatment resulted in changes in the number and diameter of OMVs secreted by CRKP. OMVs derived from CRKP mediated the transfer of blaNDM-1 to ckp and hvKP, thereby increasing the carbapenem MIC of transformants. Further experiments confirmed that NTUH-K2044 transformants exhibited hypervirulence. Our study demonstrates, for the first time, that OMVs derived from CRKP can carry blaNDM-1 and deliver resistance genes to other K. pneumoniae strains, even hvKP. The transfer of carbapenem genes into hypervirulent strains may promote the emergence and dissemination of CR-hvKP. This study elucidates a new mechanism underlying the formation of CR-hvKP.

Association between polymorphisms in connexin 40 gene (Cx40) and risk of atrial fibrillation: a meta-analysis based on 3,452 subjects.

INTRODUCTION: Atrial fibrillation (AF) is a common cardiac arrhythmia that is associated with heart failure and stroke, leading sometimes to death. But the pathogenesis of AF remains unclear. Numerous studies have investigated whether the connexin 40 (Cx40) polymorphisms influences the risk of AF, but the results are controversial. METHODS: We searched English and Chinese databases and calculated the odds ratio (OR) and 95% confidence interval (CI) to examine the existence of genetic associations between the Cx40 polymorphisms and the risk of AF. All relevant studies were screened and meta-analyzed using Review Manager 5.0. RESULTS: A total of 12 studies, including 10 studies for -44 polymorphism (rs35594137) and 4 studies for -26 polymorphism (rs10465885), were identified for the meta-analysis. For -44 polymorphism, the results showed a significantly increased risk of AF in the five genetic models in the overall analysis. Furthermore, in subgroup analysis, increased AF risks were also observed in Asian and non-Asian populations. For -26 polymorphism, the overall OR revealed an increased risk of AF in dominant model. In subgroup analysis, increased AF risk was only found in recessive genetic model of the Asian population. CONCLUSIONS: The Cx40 polymorphisms were positively associated with AF in both populations, especially on -44 polymorphism.

Seasonal Variation in Neurological Severity and Clinical Outcomes in Ischemic Stroke Patients　- A 9-Year Study of 5,238 Patients.

BACKGROUND: Because the effects of extreme weather conditions on stroke severity and outcomes are unclear, we evaluated seasonal variations in stroke severity and clinical outcomes.Methods and Results: Between 2012 and 2020 we enrolled 5,238 patients with acute ischemic stroke, who were divided into 4 seasons according to stroke onset: spring, summer, autumn and winter. We analyzed the effect of season on the severity and outcomes of all subjects. Multivariable analysis showed that the winter group had 1.234-fold increased risk of moderate-to-severe neurological deficits than the summer group (95% confidence interval (CI): 1.034-1.472, P=0.020). Compared with the summer group, the winter and the spring groups experienced 1.243- and 1.251-fold the risk of suffering from worse outcomes among all patients at 6-month follow-up (95% CI 1.008-1.534, P=0.042, 95% CI 1.013-1.544, P=0.037). The 1-year follow-up revealed similar results. Further comparison of each season in the 2012-2015 and 2016-2020 periods found that the proportion of poor outcomes in the latter autumn group was lower than that in the former time period, with significant differences in both 6-month and 1-year follow-up. CONCLUSIONS: The onset season was related to the severity and clinical outcomes of ischemic stroke. Patients with winter onset had more severe neurological deficits and worse outcomes than those with summer onset.

Tuning Bienenstock-Cooper-Munro learning rules in a two-terminal memristor for neuromorphic computing.

In memristors, the implementation of the Bienenstock-Cooper-Munro (BCM) learning rule plays a significant role in the modulation balance of artificial synapses and the reduction of energy consumption owing to their sliding frequency threshold. At present, the BCM learning rule is mostly achieved by adjusting gating voltage or channel current in field effect transistors. However, owing to the lack of the tunable degrees of freedom, the progress of two-terminal memristors is limited to simulating the BCM learning rule. In this study, by adjusting the series resistance, three types of BCM-like learning rules are found in a two-terminal BaTiO3 memristor. Specifically, the abnormal BCM learning rule with high-frequency depression and low-frequency potentiation is obtained for a small series resistance, the monotonous BCM learning rule with high-frequency potentiation and low-frequency depression is achieved for a large series resistance, and the type of BCM learning rule with the enhanced depression effect is obtained for a moderate series resistance. These three BCM learning rules are related to the non-monotonous conductance modulation caused by the migration of ionized oxygen vacancies and are proved by X-ray photoelectron spectroscopy. Moreover, spike rate-dependent plasticity (SRDP) and history-dependent plasticity are achieved. This study offers promising prospects for neuromorphic computing.

Extracting laboratory test information from paper-based reports.

BACKGROUND: In the healthcare domain today, despite the substantial adoption of electronic health information systems, a significant proportion of medical reports still exist in paper-based formats. As a result, there is a significant demand for the digitization of information from these paper-based reports. However, the digitization of paper-based laboratory reports into a structured data format can be challenging due to their non-standard layouts, which includes various data types such as text, numeric values, reference ranges, and units. Therefore, it is crucial to develop a highly scalable and lightweight technique that can effectively identify and extract information from laboratory test reports and convert them into a structured data format for downstream tasks. METHODS: We developed an end-to-end Natural Language Processing (NLP)-based pipeline for extracting information from paper-based laboratory test reports. Our pipeline consists of two main modules: an optical character recognition (OCR) module and an information extraction (IE) module. The OCR module is applied to locate and identify text from scanned laboratory test reports using state-of-the-art OCR algorithms. The IE module is then used to extract meaningful information from the OCR results to form digitalized tables of the test reports. The IE module consists of five sub-modules, which are time detection, headline position, line normalization, Named Entity Recognition (NER) with a Conditional Random Fields (CRF)-based method, and step detection for multi-column. Finally, we evaluated the performance of the proposed pipeline on 153 laboratory test reports collected from Peking University First Hospital (PKU1). RESULTS: In the OCR module, we evaluate the accuracy of text detection and recognition results at three different levels and achieved an averaged accuracy of 0.93. In the IE module, we extracted four laboratory test entities, including test item name, test result, test unit, and reference value range. The overall F1 score is 0.86 on the 153 laboratory test reports collected from PKU1. With a single CPU, the average inference time of each report is only 0.78 s. CONCLUSION: In this study, we developed a practical lightweight pipeline to digitalize and extract information from paper-based laboratory test reports in diverse types and with different layouts that can be adopted in real clinical environments with the lowest possible computing resources requirements. The high evaluation performance on the real-world hospital dataset validated the feasibility of the proposed pipeline.

Multi-Phase Fusion for Pedestrian Localization Using Mass-Market GNSS and MEMS Sensors.

Precise pedestrian positioning based on smartphone-grade sensors has been a research hotspot for several years. Due to the poor performance of the mass-market Micro-Electro-Mechanical Systems (MEMS) Magnetic, Angular Rate, and Gravity (MARG) sensors, the standalone pedestrian dead reckoning (PDR) module cannot avoid long-time heading drift, which leads to the failure of the entire positioning system. In outdoor scenes, the Global Navigation Satellite System (GNSS) is one of the most popular positioning systems, and smartphone users can use it to acquire absolute coordinates. However, the smartphone's ultra-low-cost GNSS module is limited by some components such as the antenna, and so it is susceptible to serious interference from the multipath effect, which is a main error source of smartphone-based GNSS positioning. In this paper, we propose a multi-phase GNSS/PDR fusion framework to overcome the limitations of standalone modules. The first phase is to build a pseudorange double-difference based on smartphone and reference stations, the second phase proposes a novel multipath mitigation method based on multipath partial parameters estimation (MPPE) and a Double-Difference Code-Minus-Carrier (DDCMC) filter, and the third phase is to propose the joint stride lengths and heading estimations of the two standalone modules, to reduce the long-time drift and noise. The experimental results demonstrate that the proposed multipath error estimation can effectively suppress the double-difference multipath error exceeding 4 m, and compared to other methods, our fusion method achieves a minimum error RMSE of 1.63 m in positioning accuracy, and a minimum error RMSE of 4.71 m in long-time robustness for 20 min of continuous walking.

EdgeMap: An Optimized Mapping Toolchain for Spiking Neural Network in Edge Computing.

Spiking neural networks (SNNs) have attracted considerable attention as third-generation artificial neural networks, known for their powerful, intelligent features and energy-efficiency advantages. These characteristics render them ideally suited for edge computing scenarios. Nevertheless, the current mapping schemes for deploying SNNs onto neuromorphic hardware face limitations such as extended execution times, low throughput, and insufficient consideration of energy consumption and connectivity, which undermine their suitability for edge computing applications. To address these challenges, we introduce EdgeMap, an optimized mapping toolchain specifically designed for deploying SNNs onto edge devices without compromising performance. EdgeMap consists of two main stages. The first stage involves partitioning the SNN graph into small neuron clusters based on the streaming graph partition algorithm, with the sizes of neuron clusters limited by the physical neuron cores. In the subsequent mapping stage, we adopt a multi-objective optimization algorithm specifically geared towards mitigating energy costs and communication costs for efficient deployment. EdgeMap-evaluated across four typical SNN applications-substantially outperforms other state-of-the-art mapping schemes. The performance improvements include a reduction in average latency by up to 19.8%, energy consumption by 57%, and communication cost by 58%. Moreover, EdgeMap exhibits an impressive enhancement in execution time by a factor of 1225.44×, alongside a throughput increase of up to 4.02×. These results highlight EdgeMap's efficiency and effectiveness, emphasizing its utility for deploying SNN applications in edge computing scenarios.

Excess dietary zinc drives a Cushing's-like syndrome in ovariectomized mice - Implications for postmenopausal obesity.

Postmenopausal women have an increased risk of obesity, but the underlying cause is not clear. We unexpectedly found that excess dietary zinc induced severe obesity and a Cushing's-like syndrome without increased food intake in ovariectomized (Ovx) but not in sham-operated mice. Zinc accumulated in the adrenal glands and inhibited adrenal 17,20-lyase activity and steroid synthesis. As adrenal steroids are the only source of estrogen in Ovx mice, estrogen deficiency induced adrenal hyperplasia, glucocorticoid overproduction, and consequent development of a Cushing's-like syndrome. Adrenal steroid supplementation prevented the effects of zinc. Plasma zinc was positively correlated with cortisol level and negatively correlated with the levels of adrenal steroids and estrogen in obese postmenopausal women. The finding of a link between dietary zinc, estrogen deficiency, and postmenopausal obesity, implies that postmenopausal obesity might be prevented by supplementation with a adrenal steroid and avoiding excess dietary zinc.

Living on the border of the CNS: Dural immune cells in health and disease.

With the discovery of functional lymphatic vessels and numerous immune cells in the dura mater, people have gradually realized that the dura mater is not only a biophysical barrier, but also seems to have become a newly emerging immune center that plays an important role in immune defense, immune surveillance, and immune homeostasis. This article will introduce in detail the composition and source of dural immune cells; as well as the changes in the dural immune landscape under various central nervous system (CNS) diseases (such as aging and neurodegeneration, autoimmune diseases, tumor, infection, stroke and migraine). Our final goal is to shed light on the immune function of the dura mater, and ultimately provide more possibilities for the diagnosis and treatment of CNS diseases from the perspective of regulating dura mater immunity.

Improving Anti-Humidity Property of a SnO2-Based Chemiresistive Hydrogen Sensor by a Breathable and Hydrophobic Fluoropolymer Coating.

Metal-oxide-based chemiresistive hydrogen sensors exhibit high sensitivity, long-term stability, and low cost and have been extensively applied in safety monitoring of H2. However, the sensing performances are dramatically affected by the water vapor, resulting in reduced response value and increased response/recovery time. To improve the anti-humidity property of sensors, coating the breathable and hydrophobic membrane on the surface of the sensing film is an effective strategy. In this work, the poly[4,5-difluoro-2,2-bis(trifluoromethyl)-1,3-dioxole-co-tetrafluoroethylene] (Teflon AF-2400) was dip-coated on the surface of SnO2 in a commercial hydrogen sensor (TGS2615) as a breathable and hydrophobic membrane. For safety, He instead of H2 was used to test the gas permeability of membranes. The Teflon membrane shows a high He permeability of up to 40,700 Barrer and an excellent He/H2O selectivity of 99. Moreover, Teflon shows high processability to form a defect-free coating on the rough surface of the sensing film and high chemical stability under the operando condition of the sensor. Thus, the Teflon-modified sensor possesses excellent selectivity with a value of 5, and the resistance is stable at 10,554 ± 3% Ω for 20 days in 80% RH. The modified sensor shows an improved anti-humidity property with a 75% response to 200 ppm H2 at 80% RH and has a low coefficient of variation value of 7.23% that shows advances than other reported sensors modified by coatings. The commercially available Teflon and the simple coating technology make the strategy easily scale up and show promising applications.

Molecular epidemiology and clinical characteristics of Clostridioides difficile infection in patients with inflammatory bowel disease from a teaching hospital.

BACKGROUND: Clostridioides difficile infection (CDI) in patients with inflammatory bowel disease (IBD) is of increasing concern. This study aimed to investigate the molecular epidemiology and antimicrobial susceptibilities of toxigenic C. difficile isolated from IBD patients and to evaluate the risk factors for CDI in IBD population. METHODS: Loose or watery stools from IBD patients were tested for glutamate dehydrogenase, C. difficile toxins A&B and anaerobic culture. Toxigenic C. difficile isolates were characterized by multi-locus sequence typing, ribotyping and antimicrobial susceptibility testing. RESULTS: The prevalence of CDI in IBD patients was 13.6% (43/317). The dominant sequence types (STs) were ST35 (20.9%), ST2 (18.6%) and ST37 (16.3%). The most common ribotypes (RTs) were RT 017 (18.6%), RT 012 (14.0%), and RT 220 (14.0%), whereas RT 027 and RT 078 were not detected in this study. All the isolates were susceptible to vancomycin and metronidazole. The multidrug resistance rate of C. difficile RT 017 was higher (p < 0.01) than that of other RT strains. Recent hospitalization, use of corticosteroids and proton pump inhibitors were related to increased risk of CDI in IBD patients; of these, recent hospitalization and proton pump inhibitors use were independent risk factors. CONCLUSION: Patients with IBD have a relatively high incidence rate of CDI. C. difficile RT 017 is most frequently isolated from IBD patients in this region and warrants more attention to its high resistance rate. Clinicians should pay greater attention to CDI testing in IBD patients with diarrhea to ensure early diagnosis and initiation of effective treatment.

Autonomous 6-DOF Manipulator Operation for Moving Target by a Capture and Placement Control System.

The robot control technology combined with a machine vision system provides a feasible method for the autonomous operation of moving target. However, designing an effective visual servo control system is a great challenge. For the autonomous operation of the objects moving on the pipeline, this article is dedicated to developing a capture and placement control system for the six degrees of freedom (6-DOF) manipulator equipped with an eye-in-hand camera. Firstly, a path planning strategy of online capture and offline placement is proposed for real-time capture and efficient placement. Subsequently, to achieve the fast, stable, and robust capture for a moving target, a position-based visual servo (PBVS) controller is developed by combining estimated velocity feedforward and refined PID control. Feedforward control is designed using the estimated velocity by a proposed motion estimation method for high response speed. PID control is refined by dead zone constraint to reduce the manipulator's jitter caused by the frequent adjustment of manipulator control system. Besides, the proportional, integral, and differential coefficients of PID controller are adaptively tuned by fuzzy control to reject the noise, disturbance, and dynamic variation in the capture process. Finally, validation experiments are performed on the constructed ROS-Gazebo simulation platform, demonstrating the effectiveness of the developed control system.

Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior.

By means of compressive sampling (CS), a sparse signal can be efficiently recovered from its far fewer samples than that required by the Nyquist-Shannon sampling theorem. However, recovering a speech signal from its CS samples is a challenging problem, as it is not sparse enough on any existing canonical basis. To solve this problem, we propose a method which combines the approximate message passing (AMP) and Markov chain that exploits the dependence between the modified discrete cosine transform (MDCT) coefficients of a speech signal. To reconstruct the speech signal from CS samples, a turbo framework, which alternately iterates AMP and belief propagation along the Markov chain, is utilized. In addtion, a constrain is set to the turbo iteration to prevent the new method from divergence. Extensive experiments show that, compared to other traditional CS methods, the new method achieves a higher signal-to-noise ratio, and a higher perceptual evaluation of speech quality (PESQ) score. At the same time, it maintaines a better similarity of the energy distribution to the original speech spectrogram. The new method also achieves a comparable speech enhancement effect to the state-of-the-art method.

A State Optimization Model Based on Kalman Filtering and Robust Estimation Theory for Fusion of Multi-Source Information in Highly Non-linear Systems.

Consistent state estimation is a vital requirement in numerous real life applications from localization to multi-source information fusion. The Kalman filter and its variants have been successfully used for solving state estimation problems. Kalman filtering-based estimators are dependent upon system model assumptions. A deviation from defined assumptions may lead to divergence or failure of the system. In this work, we propose a Kalman filtering-based robust state estimation model using statistical estimation theory. Its primary intention is for multiple source information fusion, although it is applicable to most non-linear systems. First, we propose a robust state prediction model to maintain state constancy over time. Secondly, we derive an error covariance estimation model to accept deviations in the system error assumptions. Afterward, an optimal state is attained in an iterative process using system observations. A modified robust MM estimation model is executed within every iteration to minimize the impact of outlying observation and approximation errors by reducing their weights. For systems having a large number of observations, a subsampling process is introduced to intensify the optimized solution redundancy. Performance is evaluated for numerical simulation and real multi sensor data. Results show high precision and robustness of proposed scheme in state estimation.

A High-Computational Efficiency Human Detection and Flow Estimation Method Based on TOF Measurements.

State-of-the-art human detection methods focus on deep network architectures to achieve higher recognition performance, at the expense of huge computation. However, computational efficiency and real-time performance are also important evaluation indicators. This paper presents a fast real-time human detection and flow estimation method using depth images captured by a top-view TOF camera. The proposed algorithm mainly consists of head detection based on local pooling and searching, classification refinement based on human morphological features, and tracking assignment filter based on dynamic multi-dimensional feature. A depth image dataset record with more than 10k entries and departure events with detailed human location annotations is established. Taking full advantage of the distance information implied in the depth image, we achieve high-accuracy human detection and people counting with accuracy of 97.73% and significantly reduce the running time. Experiments demonstrate that our algorithm can run at 23.10 ms per frame on a CPU platform. In addition, the proposed robust approach is effective in complex situations such as fast walking, occlusion, crowded scenes, etc.

[Improving the Surveillance Alarm Response Rate Among Nurses in the Internal Medicine Intensive Care Unit].

BACKGROUND & PROBLEMS: According to the Emergency Care Research Institute, "not responding to alarms" is a top-ten health-technology hazard that ranked first between 2008 and 2014. The failure of clinical nurses to respond to alarms in time due to lack of awareness, fatigue, or other cause represents a great threat to patient safety. Between August 2014 and August 2015, two patients in this unit died because the red alert on the physiological alarm surveillance system was not answered and dealt with promptly. PURPOSE: To raise the 10-second response rate to red alerts from 22% to 100% in order to enhance inpatient safety. METHODS: Establish standard operating procedures for alarms and for the handling of physiologic monitor devices when alarms sound; form a gatekeeper system; and arrange on-the-job training. RESULTS: The 10-second response rate to red alerts increased from 22% to 100% between November 2016 and November 2017. CONCLUSIONS: By following standard operating procedures, personnel now have a guide to respond to and handle red alerts comprehensively. Implementing the gatekeeper system also increased the team spirit of the unit and helped personnel appreciate the importance of cooperation in handling alarms. In addition, the functions of the physiologic monitor devices and the 10-second response rate for red alerts will be included in the annual quality control checklist of the unit for follow up, review, and further improvement.

A Pilot Study for a Better Visibility in the 3D Laparoscopic Right Colectomy Surgery.

OBJECTIVE: The aim of this study was to investigate the feasibility of digital defog technique in 3D laparoscopic surgery for right colon cancer. METHODS: Fifty patients with right colon cancer were divided into digital defogging group and control group. The intraoperative image clarity, the surgeon's anxiety, the time of operation and the time of fog nursing were compared. RESULTS: The clarity of the video screen of the digital defogging group was significantly higher than that of the control group, and the degree of anxiety was significantly lower than that of the control group. The operative time was (136.4 ± 30.4) min in the digital defogging group, the operation time of the control group was (168.7 ± 32.7) min, and the difference was statistically significant (P < 0.05). The time of dehumidification was (4.8 ± 1.3) min in the digital defogging group and (16.3 ± 4.6) min in the control group, and the difference was statistically significant (P < 0.05). CONCLUSION: Digital defogging technology in the 3D laparoscopic right colon cancer surgery significantly improves the clarity of video images, reduces the surgeon due to screen clarity caused by anxiety, reduces the operation of right colon cancer time and reduces the time to fog care.

Statistical Multipath Model Based on Experimental GNSS Data in Static Urban Canyon Environment.

A deep understanding of multipath characteristics is essential to design signal simulators and receivers in global navigation satellite system applications. As a new constellation is deployed and more applications occur in the urban environment, the statistical multipath models of navigation signal need further study. In this paper, we present statistical distribution models of multipath time delay, multipath power attenuation, and multipath fading frequency based on the experimental data in the urban canyon environment. The raw data of multipath characteristics are obtained by processing real navigation signal to study the statistical distribution. By fitting the statistical data, it shows that the probability distribution of time delay follows a gamma distribution which is related to the waiting time of Poisson distributed events. The fading frequency follows an exponential distribution, and the mean of multipath power attenuation decreases linearly with an increasing time delay. In addition, the detailed statistical characteristics for different elevations and orbits satellites is studied, and the parameters of each distribution are quite different. The research results give useful guidance for navigation simulator and receiver designers.

Compact see-through 3D head-mounted display based on wavefront modulation with holographic grating filter.

A compact see-through three-dimensional head-mounted display (3D-HMD) is proposed and investigated in this paper. Two phase holograms are analytically extracted from the object wavefront and uploaded on different zones of the spatial light modulator (SLM). A holographic grating is further used as the frequency filter to couple the separated holograms together for wavefront modulation. The developed preliminary prototype has a simple optical facility and a compact structure (133.8mm × 40.4mm × 35.4mm with a 47.7mm length viewing accessory). Optical experiments demonstrated that the proposed system can present 3D images to the human eye with full depth cues. Therefore, it is free of the accommodation-vergence conflict and visual fatigue problem. The dynamic display ability is also tested in the experiments, which provides a promising potential for the true 3D interactive display.

Design and fabrication of DOEs on multi- freeform surfaces via complex amplitude modulation.

A non-iterative design and precise fabrication method of diffractive optical elements (DOEs) on multiple freeform surfaces is proposed and investigated in this paper. Complex amplitude modulation (CAM) technology is applied to design complicated DOEs. The wave-front for desired DOEs fabrication is interfered with a plane wave and then be encoded to a pure phase hologram. Simulations for different DOEs (binary and gray scales) on freeform surfaces are performed and the relative errors are 0.56% and 0.78%, respectively. Since the reconstructed optical fields generated by spatial light modulator (SLM) can be recorded into light-sensitive materials (photopolymer), the DOEs fabrication is realized by optical exposure. The results show that the proposed method can design and fabricate DOEs on multi-freeform surfaces at one time with high quality. Since the CAM method ensures precise reconstruction without iterations, the fabrication is accurate as well as the design is fast. It is expected that the proposed method could be applied in the precise 3D optical fabrication and processing in the future.