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The bacterium Klebsiella pneumoniae (Kp) was the primary pathogen of hospital-acquired infection, but the current detection method could not rapidly and conveniently identify Kp. Recombinase polymerase amplification (RPA) was a fast and convenient isothermal amplification technology, and the clustered regularly interspaced short palindromic repeats (CRISPR) system could rapidly amplify the signal of RPA and improve its limit of detection (LOD). In this study, we designed three pairs of RPA primers for the rcsA gene of Kp, amplified the RPA signal through single-strand DNA reporter cleavage by CRISPR/Cas12a, and finally analyzed the cleavage signal using fluorescence detection (FD) and lateral flow test strips (LFTS). Our results indicated that the RPA-CRISPR/Cas12a platform could specifically identify Kp from eleven common clinical pathogens. The LOD of FD and LFTS were 1 fg/μL and 10 fg/μL, respectively. In clinical sample testing, the RPA-CRISPR/Cas12a platform was consistent with the culture method and qPCR method, and its sensitivity and specificity were 100% (16/16) and 100% (9/9), respectively. With the advantages of detection speed, simplicity, and accuracy, the RPA-CRISPR/Cas12a platform was expected to be a convenient tool for the early clinical detection of Kp.

Introduction:Salmonella was one of the most common bacteria that caused foodborne illness, with S. typhimurium (Salmonella typhimurium) and S. enteritidis (Salmonella enteritidis) infections accounting for more than 75% of human salmonella infections.Methods: In this study, we developed a method of dual recombinase polymerase amplification (RPA) combined with a lateral flow dipstick for the rapid detection of S. typhimurium and S. enteritidis in clinical specimens (stool).Results: The entire reaction process, including amplification and result reading, could be completed within 65 min. The detection limits of S. typhimurium and S. enteritidis in pure culture samples were 5.23 × 101 CFU/mL and 3.59 × 101 CFU/mL, respectively. The detection limits of S. typhimurium and S. enteritidis in artificially contaminated samples were 8.30 × 101 CFU/mL and 2.70 × 102 CFU/mL, respectively. In addition, the method had no cross-reaction with other pathogenic microorganisms. The results in clinical samples were fully consistent with those obtained using Bacterial Analysis Manual, with sensitivity and specificity were 100% (8/8) and 100% (17/17) for S. typhimurium and 100% (4/4) and 100% (21/21) for S. enteritidis, respectively.Discussion: The detection limits of S. typhimurium and S. enteritidis in artificially contaminated samples were higher than those in pure culture samples, which might be attributed to the inherent complex composition of artificially contaminated samples. In addition, the detection limits of S. typhimurium and S. enteritidis in the same sample were also different, which might be attributed to different amplification efficiency of two target genes in the same reaction system.Conclusion: This assay had potential application outdoors, as it could be performed within 1 h at 38°C without a complex instrument, and the results could be observed with the naked eye. In conclusion, the dual RPA-LFD assay established in this study had practical significance for the rapid detection of S. typhimurium and S. enteritidis in the future.

BackgroundCarbapenem-resistant Acinetobacter baumannii (CRAB) poses a severe nosocomial threat, prompting a need for efficient detection methods. Traditional approaches, such as bacterial culture and PCR, are time-consuming and cumbersome. The CRISPR-based gene editing system offered a potential approach for point-of-care testing of CRAB.MethodsWe integrated recombinase polymerase amplification (RPA) and CRISPR-Cas12a system to swiftly diagnose CRAB-associated genes, OXA-51 and OXA-23. This multiplex RPA-CRISPR-Cas12a system eliminates bulky instruments, ensuring a simplified UV lamp-based outcome interpretation.ResultsOperating at 37°C to 40°C, the entire process achieves CRAB diagnosis within 90 minutes. Detection limits for OXA-51 and OXA-23 genes are 1.3 × 10−6 ng/μL, exhibiting exclusive CRAB detection without cross-reactivity to common pathogens. Notably, the platform shows 100% concordance with PCR when testing 30 clinical Acinetobacter baumannii strains.ConclusionIn conclusion, our multiplex RPA coupled with the CRISPR-Cas12a system provides a fast and sensitive CRAB detection method, overcoming limitations of traditional approaches and holding promise for efficient point-of-care testing.

Metal halide perovskites quantum dots (QDs) stand at the forefront of multifarious photonic applications, including micro‐light‐emitting diode and further augmented reality, virtual reality, and other novel display, lighting technologies. Barriers to applications, however, lie in their toxicity of lead, instability to light, moisture and heat, and processability at the nanoscale‐particle level. Herein, a simple and versatile postprocessing approach is reported for the near‐single‐dot passivation and encapsulation of representative lead‐free double perovskite Cs2Ag0.4Na0.6InCl6:Bi through liquid‐phase processing of perhydropolysilazane and quantum dots colloid with controllable hydrolysis curing. The conventional unstable oleylamine and oleic acid ligands are replaced by ‐NCl bonding on the surface of nanocrystal, accompanied by the resulting compact and robust silica layer without compromising the optical properties of the quantum dots. With the near‐single‐dot protection, the quantum dots do not show fluorescence quenching even when stored for more than 90 days and exhibit remarkably improved stability against heat, ultraviolet irradiation and humidity compared to the raw quantum dots. The strategy offers a versatile way of creating nanoscale‐particle level protection of luminescent quantum dots, and can be universally compatible with solution‐based patterning techniques and photonics applications where quantum dots are used.

After the outbreak of SARS-CoV-2, nucleic acid testing quickly entered people’s lives. In addition to the polymerase chain reaction (PCR) which was commonly used in nucleic acid testing, isothermal amplification methods were also important nucleic acid testing methods. Among several common isothermal amplification methods like displaced amplification, rolling circle amplification, and so on, recombinase polymerase amplification (RPA) was recently paid more attention to. It had the advantages like a simple operation, fast amplification speed, and reaction at 37-42°C, et al. So it was very suitable for field detection. However, there were still some disadvantages to RPA. Herein, our review mainly summarized the principle, advantages, and disadvantages of RPA. The specific applications of RPA in bacterial detection, fungi detection, virus detection, parasite detection, drug resistance gene detection, genetically modified food detection, and SARS-CoV-2 detection were also described. It was hoped that the latest research progress on RPA could be better delivered to the readers who were interested in RPA.

Production line layout planning is closely related to robot structural parameter. For the intelligent production line of the headstock of a vertical machining center， in order to complete the work requirements of grabbing and flipping the workpiece， and reducing the operating space and structure size of the robot， a structural parameter optimization method for loading and unloading robots is proposed. The working space is a constraint condition， and the optimal section of the main section of the manipulator workspace is the objective function， and the structural parameter optimization model is established. Finally， the Monte Carlo method is used to verify the workspace. The results show that the size of the optimized robot workspace is significantly lower than other design schemes under the requirements of the loading and unloading workspace， which reduces the redundant waste of the workspace and further reduces the workload. The foundation of the design research of loading and unloading robot is laid.

The existing atmospheric mass density models (AMDM) would produce considerable errors in orbital prediction for Low Earth Orbit (LEO) satellites. In order to reduce these errors and correct the AMDM, this paper presents methods based on data mining with historical data of two-line element (TLE). Starting from a typical LEO satellite, TIANHUI, two orbital dynamical models are firstly proposed as the simulation environment to generate training data. The historical TLE data are regarded as actual space environment and used to generate application data. Secondly, three data mining methods, Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM), are combined with the training data to investigate their feasibility in recovering the known deviation of AMDM under simulation environment. Training results show that RF displays the best performance and achieves the accuracy of 99.99%, while the other two methods only achieve 86.83% and 71.90% respectively. Thirdly, under the actual space environment, this paper uses new training and application data to research the ability of the three methods in recovering the unknown deviation of the AMDM and improve the accuracy of orbital prediction. Numerical results are evidential to the accuracy of the proposed methods based on data mining. It is concluded that the capabilities of the data mining for correction for the atmospheric model are very promising, with great potential to advance practical applications on on-orbit propagation.

As humanity is faced with various changes in social-environmental systems, adaptation efforts have become increasingly important. Significant effort and resources have been spent on devising overarching, large-scale strategies in response to climate change and associated socioeconomic challenges. However, the mechanisms of adaptation employed by individuals and households in ordinary, daily life have received insufficient scholarly examination. This research aims to demonstrate the significance of everyday adaptation strategies of smallholder pastoralists who are on the front lines of changing socioeconomic regimes in East African drylands. We analyzed the daily spatiotemporal patterns of livestock behavior to understand everyday adaptation practices. Two different pastoralist communities in the Borana Zone in southern Ethiopia were compared, one of which keeps the traditional mobile pastoralism livelihood, while the other has sedentarized because of socio-environmental changes and policy incentives. The mobile pastoralists have larger grazing areas, and their cattle spend more time on foraging and resting. In contrast, the sedentarized pastoralists utilize the land near their settlements intensively and the cattle travel longer distances on average. Our findings suggest that sedentarization is a constrained adaptation strategy at best because it increases the recursive use of rangeland and its fragmentation and forces livestock to move more intensively on a daily and seasonal basis.

Data clustering analysis is proposed to detect the orbital maneuvers of satellites at different scales. In this study, the unsupervised classification methods of K-means, hierarchical, and fuzzy C-means clustering are used to handle the two-line element (TLE) historical data. The K-means-based contour map method is applied to the characteristic variable selection and cluster number determination. The TLE data of large-, medium-, and small-scale orbital maneuvers are clustered by the aforementioned three methods. Through a series of numerical experiments, it is found that for different scales of orbital maneuvers, the clustering methods have different performances and that they can essentially fulfill the functional requirements of orbital detection. By data mining, the orbital maneuvers of the remote sensing satellites “YAOGAN-9”, “TIANHUI-1”, and “Envisat” can be easily detected, which will provide useful information for further orbital supervision and prediction.

Urban green space has significant social, ecological, cultural and economic value. This study uses social media data to examine the spatiotemporal utilization of major parks in Shanghai and explore the determinants of their recreational attraction. Methods: Based on microblog check-in data between 2012 and 2018 across 17 parks in Shanghai, we investigated the patterns at different temporal scales (weekly, seasonal and annual) and across workdays and weekends by using log-linear regression models. Results: Our findings indicate that both internal and external factors affect park utilization. In particular, the presence of sports facilities significantly contributes to higher visit frequency. Factors such as the number of subway stations nearby, scenic quality and popularity have a positive impact on check-in numbers, while negative factors affecting park use are number of roads, ticket price and average surrounding housing price. Across different temporal scales, the use patterns of visitors have obvious seasonal and monthly tendencies, and the differences of workday and weekend models lie in external factors’ impacts. Conclusions: In order to achieve the goal of better serving the visitors, renewal of urban green spaces in megacities should consider these influential factors, increase sports facilities, subway stations nearby and improve scenic quality, popularity and water quality. This study on spatiotemporal utilization of urban parks can help enhance comprehensive functions of urban parks and be helpful for urban renewal strategies.

Large-scale land transactions (LSLTs) can precipitate dramatic changes in land systems. Ethiopia has experienced one of the largest amounts of LSLTs in Africa, yet their effects on local land systems are poorly understood. In this study, we quantify the direct and indirect land use and land cover (LULC) changes associated with LSLTs at eight socio-environmentally diverse sites in central and western Ethiopia. To estimate these effects, we employ a novel, two-stage counterfactual analysis. We first use a region-growing procedure to identify a "control" site with comparable landscape-level characteristics to each LSLT. Then, we sample and reweight points within each control site to further improve covariate balance. This two-stage approach both controls for potential confounding factors at multiple spatial levels and reduces the costs of extensive LULC data classification. Our results show that the majority of the reported transacted area (62%) remained unconverted to large-scale agriculture. Most of the land that was developed into large-scale agriculture displaced smallholder agriculture (53%), followed by conversion of woodland/shrubland (35%) and forest (9%). Beyond their boundaries, LSLTs indirectly influenced rates of smallholder agricultural expansion and abandonment, pointing to site dependence in how LSLTs affect adjacent land systems. In particular, the low prevalence of forest within and around these LSLTs underscores a need to move beyond measures of deforestation as proxies for LSLT effects on land systems. Our two-stage approach shows promise as an efficient method for generating robust counterfactuals and thereby LULC change estimates in systems lacking wall-to-wall LULC data.

Interactions between neighboring cells often occur in a semiconductor silicon detector array. That is, there are high-energy particles incident on one cell of the detector that generate an electric signal for the cell, while weak signals are also generated in other neighboring cells. Therefore, isolation of the detector cells is an important factor in the performance of the detector, particularly for its energy resolution. To eliminate the dead space in the 3D-trench-electrode detector proposed by Brookhaven National Laboratory, a novel three-dimensional (3D) detector, the 3D-Open-Shell-Electrode Detector (3DOSED), was proposed in 2018, in which good isolation between cells was achieved. To verify this and determine the optimal structure design, a Silvaco technology computer-aided design simulation tool was used to simulate the electrical characteristics of the detector in 3D. The distributions of the electric field and the weighting field of 3DOSEDs were obtained with different opening gap angles. A single minimum ionizing particle was used that was incident vertically on the middle of the central collection electrode and peripheral trench electrode of the detector cell, according to the Ramo theorem of the principle of induced-current generation, and the mathematical software matlab was employed to calculate the induced current and carrier drift time. The interference current between the neighboring cells of a 3DOSED was obtained. Good isolation was found between neighboring cells in the 3DOSED.

In order to reduce dead zone of Closed Shell-Electrode Silicon Detector (CSESD), this paper introduces an optimal scheme of 3D shell electrode type detector– a novel type of Honeycomb Shell Electrode Detector– HSED (Chinese patent #zl 201710 676008.3). In this paper, we first study dead zone of the new structure and find dead zone of non-elongated HSED cell is smaller than that of CSESD. An array arrangement method of CSESD is also proposed, which makes virtually no dead zone in the array comprised by elongated detector cells. Detector electrical simulations with SILVACO TCAD, including I-V and C-V characteristic, will be given. Then, the structure with different nesting depth and dbot is simulated with SILVACO TCAD. It has been found that the closer nesting position is to the surface, the smaller dead zone is, and the deeper the nesting is, the larger the dead zone is. However, through the simulation of the weighting field, it is found that the deeper the nesting depth, the smaller the influence of the weighting field by neighboring cell.

Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer’s accuracy of the Urban/Built class with the cost of decreasing its user’s accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion.

In order to improve the radiation resistance of semiconductor detector, 3D trench electrode Si detector structures have been proposed by the Brookhaven National Laboratory (BNL) in 2009. In this paper, we propose a 3D trench electrode Si detector with adjustable central collection electrode, which integrates advantages of cylindrical units and parallel units. For those combined units, we use 1D Poisson equation as the first order approximation to study the changing of depletion in different regions. These results have been compared to those obtained from the full 3D simulation using a 3D-TCAD tool. According to calculations, we design a 3D trench electrode Si detector with adjustable central collection electrode. Furthermore, electrical characteristics of this type of detectors with different lengths of central collection electrode (lp+) have been simulated using the 3D-TCAD. Regarded as the main factor of signal-noise ratio, the geometric capacitance obtained from Silvaco TCAD simulations is compared with which is calculated by physical models when detectors size varies. Finally, the physic model of charge collection is established using Ramo theorem. The CCE (Charge Collection Efficiency) of detector unit is studied with this model (after Φ=1016 neq/cm2, average CCE is 38%).

Tracking environmental change is important to ensure efficient and sustainable natural resources management. East Africa is dominated by arid and semi-arid rangeland systems, where extensive grazing of livestock represents the primary livelihood for most of the human population. Despite several mapping efforts, East Africa lacks accurate and reliable high-resolution rangeland health maps necessary for management, policy, and research purposes. Earth Observations offer the opportunity to assess spatiotemporal dynamics in rangeland health conditions at much higher spatial and temporal coverage than conventional approaches that rely on in-situ methods, while complimenting their certainty. Using machine learning-based classification and linear unmixing, this paper produced Landsat-based time series at 30 m spatial resolution for mapping of land cover classes (LCC) and vegetation fractional cover (VFC, including photosynthetic vegetation PV, non-photosynthetic vegetation NPV, and bare ground BG), two major data assets to derive metrics for rangeland health in East Africa. Due to scarcity of in-situ measurements in a large, remote and highly heterogeneous landscape, an algorithm was developed to combine very high-resolution WorldView-2 and -3 satellite imagery at < 2 m resolutions with a limited set of ground observations to generate reference labels across the study region. The LCC analysis yielded an overall accuracy of 0.856 using our validation dataset, with Kappa of 0.832; VFC, yielded R² = 0.801, p < 2.2e-16, normalized root mean squared error (nRMSE) = 0.123. Our products represent the first multi-decadal high-resolution dataset specifically designed for mapping and monitoring rangelands health in East Africa including Kenya, Ethiopia and Somalia, covering a total area of 745,840 km², dominated by arid and semi-arid extensive rangeland systems. These data can be valuable to a wide range of development, humanitarian, and ecological conservation efforts and are available at https://doi.org/10.5281/zenodo.7106166 (Soto et al., 2023) and Google Earth Engine (GEE; details in data availability section). [ABSTRACT FROM AUTHOR]