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In recent years, rehabilitation robots have been developed and used in rehabilitation training for patients with hemiplegia. In this paper, a rehabilitation training robot with variable damping is designed to train patients with hemiplegia to recover upper limb function. Firstly, a magnetorheological joint damper (MR joint damper) is designed for the rehabilitation training robot, and its structural design and dynamic model are tested theoretically and experimentally. Secondly, the rehabilitation robot is simplified into a spring-damping system, and the rehabilitation training controller for human movement is designed. The rehabilitation robot dynamically adjusts the excitation current according to the feedback speed and human–machine interaction torque, so that the rehabilitation robot always outputs a stable torque. The magnetorheological joint damper acts as a clutch to transmit torque safely and stably to the robot joint. Finally, the upper limb rehabilitation device is tested. The expected torque is set to 20 N, and the average value of the output expected torque during operation is 20.02 N, and the standard deviation is 0.635 N. The output torque has good stability. A fast (0.5 s) response can be achieved in response to a sudden motor speed change, and the average expected output torque is 20.38 N and the standard deviation is 0.645 N, which can still maintain the stability of the output torque.

This study proposes a dual-coil magnetorheological torsional vibration damper (MRTVD) and verifies the effectiveness of semi-active damping control to suppress the shaft system’s torsional vibration via experimental research. Firstly, the mechanical model of the designed MRTVD and its coupling mechanical model with the rotating shaft system are established. Secondly, the torsional response of the shaft system is obtained via resonance experiments, and the influence of the current on the torsional characteristics of the magnetorheological torsional damper is analyzed. Finally, the MRTVD is controlled using the skyhook control approach. The experimental results demonstrate that when the main shaft passes through the critical speed range at various accelerations, the amplitude of the shaft’s torsional vibration decreases by more than 15%, and the amplitude of the shaft’s torsional angular acceleration decreases by more than 22%. These conclusions validate the inhibitory effect of MRTVD on the main shaft’s torsional vibrations under skyhook control.

Sports matches are very popular all over the world. The prediction of a sports match is helpful to grasp the team's state in time and adjust the strategy in the process of the match. It's a challenging effort to predict a sports match. Therefore, a method is proposed to predict the result of the next match by using teams' historical match data. We combined the Long Short-Term Memory (LSTM) model with the attention mechanism and put forward an AS-LSTM model for predicting match results. Furthermore, to ensure the timeliness of the prediction, we add the time sliding window to make the prediction have better timeliness. Taking the football match as an example, we carried out a case study and proposed the feasibility of this method.

Inspired by its highly efficient capability to deal with big data, the brain-like computational system has attracted a great amount of attention for its ability to outperform the von Neumann computation paradigm. As the core of the neuromorphic computing chip, an artificial synapse based on the memristor, with a high accuracy in processing images, is highly desired. We report, for the first time, that artificial synapse arrays with a high accuracy in image recognition can be obtained through the fabrication of a SiNz:H memristor with a gradient Si/N ratio. The training accuracy of SiNz:H synapse arrays for image learning can reach 93.65%. The temperature-dependent I–V characteristic reveals that the gradual Si dangling bond pathway makes the main contribution towards improving the linearity of the tunable conductance. The thinner diameter and fixed disconnection point in the gradual pathway are of benefit in enhancing the accuracy of visual identification. The artificial SiNz:H synapse arrays display stable and uniform biological functions, such as the short-term biosynaptic functions, including spike-duration-dependent plasticity, spike-number-dependent plasticity, and paired-pulse facilitation, as well as the long-term ones, such as long-term potentiation, long-term depression, and spike-time-dependent plasticity. The highly efficient visual learning capability of the artificial SiNz:H synapse with a gradual conductive pathway for neuromorphic systems hold great application potential in the age of artificial intelligence (AI).

The purpose of this study was to solve the problem that the damping of rubber or silicone oil torsional dampers used in crankshafts is not adjustable and cannot effectively control torsional vibration at different resonant frequencies. Based on the controlled rheological properties of magnetorheological (MR) smart materials, this study designed a new type of variable damping MR torsional damper (MRTD) and proposed a semi-active control method to effectively control the torsional vibration of the crankshaft under multiple harmonic resonances. First, a mechanical model of the MRTD and a lumped parametric mass model of the crankshaft system were developed, and the resonance frequency harmonic range of the crankshaft system operation was determined by the torsional vibration characteristics analysis. Then a semi-active skyhook control method for the MRTD was proposed, and a joint control simulation analysis was performed using Amesim and Matlab software. The torsional vibration control effects of the crankshaft system with no damper, MRTD with different damping coefficients, and MRTD with skyhook control under acceleration and uniform speed conditions were comprehensively investigated. The simulation results indicated that the skyhook damping control significantly reduced the torsional vibration amplitude under both acceleration and uniform speed conditions, verifying the effectiveness of the skyhook-based control strategy for MRTD.

To reveal the transient temperature distribution pattern inside the magnetorheological grease (MRG) torsional vibration damper and explore the relationship between the current and internal temperature of the device, the transient temperature simulation analysis of the MRG device was conducted in this study. Firstly, a theoretical heat transfer model of MRG torsional vibration damper with dual heat source structural feature was established based on the Bingham constitutive model and the temperature-dependent viscosity characteristic of MRG. Then, the transient temperature field model of the MRG torsional vibration damper was developed by the finite element method, the temperature field distribution and temperature–time variation characteristics of the MRG torsional vibration damper at 0A, 1A, and 2A working conditions were analyzed, and the effects of viscosity and slip factors on the temperature rise of the device were investigated. The simulation results show that the temperature rise of MRG in the working domain is the fastest, but a gradual slowing of the temperature rise rate. The magnitude and rate of temperature rise are maximum when the 1A current is applied to the torsional vibration damper. Finally, the current–temperature curve is obtained by fitting the simulation results. The results of the analysis reveal the internal temperature distribution and temperature rise characteristics of the torsional vibration damper, which provide a theoretical basis for the structural optimization and control strategy design of the MRG torsional vibration damper considering temperature as a factor.

Three-dimensional NAND flash memory with high carrier injection efficiency has been of great interest to computing in memory for its stronger capability to deal with big data than that of conventional von Neumann architecture. Here, we first report the carrier injection efficiency of 3D NAND flash memory based on a nanocrystalline silicon floating gate, which can be controlled by a novel design of the control layer. The carrier injection efficiency in nanocrystalline Si can be monitored by the capacitance–voltage (C–V) hysteresis direction of an nc-Si floating-gate MOS structure. When the control layer thickness of the nanocrystalline silicon floating gate is 25 nm, the C–V hysteresis always maintains the counterclockwise direction under different step sizes of scanning bias. In contrast, the direction of the C–V hysteresis can be changed from counterclockwise to clockwise when the thickness of the control barrier is reduced to 22 nm. The clockwise direction of the C–V curve is due to the carrier injection from the top electrode into the defect state of the SiNx control layer. Our discovery illustrates that the thicker SiNx control layer can block the transfer of carriers from the top electrode to the SiNx, thereby improving the carrier injection efficiency from the Si substrate to the nc-Si layer. The relationship between the carrier injection and the C–V hysteresis direction is further revealed by using the energy band model, thus explaining the transition mechanism of the C–V hysteresis direction. Our report is conducive to optimizing the performance of 3D NAND flash memory based on an nc-Si floating gate, which will be better used in the field of in-memory computing.

Artificial neural networks, as a game-changer to break up the bottleneck of classical von Neumann architectures, have attracted great interest recently. As a unit of artificial neural networks, memristive devices play a key role due to their similarity to biological synapses in structure, dynamics, and electrical behaviors. To achieve highly accurate neuromorphic computing, memristive devices with a controllable memory window and high uniformity are vitally important. Here, we first report that the controllable memory window of an HfO2/TiOx memristive device can be obtained by tuning the thickness ratio of the sublayer. It was found the memory window increased with decreases in the thickness ratio of HfO2 and TiOx. Notably, the coefficients of variation of the high-resistance state and the low-resistance state of the nanocrystalline HfO2/TiOx memristor were reduced by 74% and 86% compared with the as-deposited HfO2/TiOx memristor. The position of the conductive pathway could be localized by the nanocrystalline HfO2 and TiO2 dot, leading to a substantial improvement in the switching uniformity. The nanocrystalline HfO2/TiOx memristive device showed stable, controllable biological functions, including long-term potentiation, long-term depression, and spike-time-dependent plasticity, as well as the visual learning capability, displaying the great potential application for neuromorphic computing in brain-inspired intelligent systems.

The conventional pneumatic soft robot has the problem of insufficient stiffness, while in the magnetorheological soft robot, the magnetic field provided by electromagnet has the disadvantage of oversized structure and poor flexibility. This paper presents a variable stiffness pneumatic soft robot based on magnetorheological grease (MRG) to solve these problems. Its three soft fingers cooperate with the adjustable gripper to adjust the gripping range for the robot hand, and it is used to provide gripping driving force through the bending drive. The MRG layer is designed on the gripping surface to provide adaptivity and rigid support for the gripped objects. A magnetic-air structure consisting of a Halbach array and Halbach array actuator is designed inside the soft fingers to provide a flexible magnetic field for the MRG layer. Theoretical and simulation analysis is carried out, and the results show that the state of the MRG changes and the stiffness of the clamping surface changes under the working pressure of 30 kPa. Finally, the experiment further proves the variable and high adaptivity of the surface stiffness of the gripping surface to reduce the damage to the gripped objects.

To enable a-SiCx:H-based memristors to be integrated into brain-inspired chips, and to efficiently deal with the massive and diverse data, high switching uniformity of the a-SiC0.11:H memristor is urgently needed. In this study, we introduced a TiSbTe layer into an a-SiC0.11:H memristor, and successfully observed the ultra-high uniformity of the TiSbTe/a-SiC0.11:H memristor device. Compared with the a-SiC0.11:H memristor, the cycle-to-cycle coefficient of variation in the high resistance state and the low resistance state of TiSbTe/a-SiC0.11:H memristors was reduced by 92.5% and 66.4%, respectively. Moreover, the device-to-device coefficient of variation in the high resistance state and the low resistance state of TiSbTe/a-SiC0.11:H memristors decreased by 93.6% and 86.3%, respectively. A high-resolution transmission electron microscope revealed that a permanent TiSbTe nanocrystalline conductive nanofilament was formed in the TiSbTe layer during the DC sweeping process. The localized electric field of the TiSbTe nanocrystalline was beneficial for confining the position of the conductive filaments in the a-SiC0.11:H film, which contributed to improving the uniformity of the device. The temperature-dependent I-V characteristic further confirmed that the bridge and rupture of the Si dangling bond nanopathway was responsible for the resistive switching of the TiSbTe/a-SiC0.11:H device. The ultra-high uniformity of the TiSbTe/a-SiC0.11:H device ensured the successful implementation of biosynaptic functions such as spike-duration-dependent plasticity, long-term potentiation, long-term depression, and spike-timing-dependent plasticity. Furthermore, visual learning capability could be simulated through changing the conductance of the TiSbTe/a-SiC0.11:H device. Our discovery of the ultra-high uniformity of TiSbTe/a-SiC0.11:H memristor devices provides an avenue for their integration into the next generation of AI chips.

Magnetorheological (MR) absorbers in the artillery recoil systems are usually used to dissipate the impact energy as much as possible and reduce the recoil force transmitted to the artillery carriage, while the firing stability of artillery during the buffer process is rarely considered. In this paper, we analyzed the firing stability characteristics of the fixed artillery and the field artillery systems and established corresponding mechanical models. Then, we proposed the ideal recoil F–v curves of these two kinds of artillery, respectively. The “platform effect” of recoil curve was taken as the recoil force control target of the fixed artillery, while based on the firing stability, the linear segmented recoil curve was drawn up as the ideal recoil buffer control target of the field artillery. To verify the feasibility and controllability of the designed multi-stage MR absorber in two kinds of recoil buffer system, the impact tests were conducted under different current loadings. The test results show that the designed MR absorber can realize different buffer control effects by changing the input current, but the ideal “platform effect” recoil curve of the fixed artillery cannot be completely realized due to small controllable damping force output. In the field artillery recoil system, the MR absorber can realize ideal recoil buffer control in the range of 0°–25° firing angles.

Many applications of wireless power transmission (WPT) require high misalignment tolerance. However, the transmission power of WPT is highly susceptible to the misalignment. To solve this problem, the magnetic resonant WPT control method with high misalignment tolerance is proposed here. First, based on the general mutual model, the complex ratio transformer model is established. This model gives an intuitive physical picture in power transmission process and frequency splitting phenomenon. Second, approximately constant voltage gain at frequency splitting point in a wide range of misalignment is analysed. Considering this frequency splitting characteristics of Magnetic resonant WPT, the maximum peak point tracking control methods for wide misalignment, maintaining constant output power is proposed. Finally, the corresponding simulation and experimental validation is given, demonstrating the feasibility of the control method. When the misalignment reached 40%, the fluctuation of the output voltage will be

In order to increase the power rating of electric vehicles, insulated gate bipolar translator (IGBT) modules with multiple power terminals are usually adopted. The transient current sharing of the same polarity power terminals is related to the stray inductance in the branches of the bus-bar. Based on the laminated bus-bar of a three-phase inverter in the electric vehicles that consists of asymmetrical parallel branches, this paper investigates the transient current imbalance sharing caused by the asymmetrical stray inductance in the parallel branches of the bus-bar from the view of energy storing and releasing of stray inductance for the first time. Besides, the partial self-inductance and mutual-inductance model of the parallel branches is set up. Finally, a high-precision partial stray inductance measurement method is proposed, and the accuracy of the partial stray inductance model for asymmetrical parallel branches is verified by experimental tests.

The Lower Cretaceous Shahezi Formation glutenite reservoirs in the Xujiaweizi Fault Depression are important tight gas reservoirs in the deep strata of the Songliao Basin. Their complex porosity and permeability relationships pose challenges to determining reservoir boundaries and evaluating reservoir grades. For the Shahezi Formation glutenite reservoirs, the water film thickness method, charge dynamics method, gas testing productivity method, and buoyancy balance method were used to determine theoretical lower limits, gas accumulation lower limits, effective flow lower limits, and reservoir-forming upper limits. The petrophysical values of the reservoir boundaries were determined based on the porosity and permeability relationships under different diagenetic controlling factors. On this basis, reservoir types were classified according to differences in microscopic pore structures, and a grading evaluation standard for tight reservoirs was established. This standard was then applied using logging data to provide a basis for selecting sweet spots in the tight gas exploration areas. The Shahezi Formation glutenite was divided into conventional reservoirs, Class Ⅰ-Ⅳ tight glutenite reservoirs, and non-reservoirs. The reservoir boundaries and classification evaluation results were well matched. Conventional reservoirs had porosity greater than 9% and permeability greater than 0.05×10-3 μm2. Class Ⅰ tight reservoirs had porosity of 8%-9% and permeability of (0.01-0.05)×10-3 μm2. Class Ⅱ tight reservoirs had porosity of 5%-8% and permeability of (0.001-0.01)×10-3 μm2. Class Ⅲ tight reservoirs had porosity of 3.5%-5% and permeability of (0.2-1)×10-3 μm2. Class Ⅳ tight reservoirs had porosity of 2%-3.5% and permeability of (0.05-0.2)×10-6 μm2. Non-reservoirs had porosity less than 2% and permeability less than 0.05×10-6 μm2. The gas production of tight glutenite was controlled by the reservoir type. Class Ⅰ and Class Ⅱ tight reservoirs were favorable high-yield layers. The thickness of favorable reservoirs in the Anda-Songzhan area of the northern Xujiaweizi Fault Depression was large, making it a sweet spot for tight gas exploration and development.

This paper presents a novel fully automated approach for generating structured 3D synthetic tree models, addressing the limitations of existing datasets used in applications like digital twin construction, carbon stock calculation, and environmental assessments. The method allows for the automated creation of a large-scale dataset containing 13,000 tree models of ten common species, each featuring a detailed 3D point cloud with hierarchical structures, precise parameters, and separate branch and leaf information. The dataset includes both original and noise-added point clouds to enhance method testing and evaluation. It stands out by providing comprehensive structural data, including branch numbering and detailed tree skeleton information with node hierarchies and radius data. Furthermore, this study introduces randomly distributed batches of tree models within specific terrains. It provides results from airborne laser scanning simulations, which facilitate the individualized segmentation of these tree models. This first-of-its-kind, extensive synthetic dataset is designed for accurate algorithm evaluation in tasks such as branch-leaf separation, 3D reconstruction, individual tree segmentation and carbon stock estimation. The paper validates the dataset’s utility by applying state-of-the-art algorithms to demonstrate its effectiveness in various applications, marking a significant advancement in 3D tree modeling research. The datasets are publicly available, accessible via the ‘‘TreeNet3D Dataset’’ link.

Machine learning (ML) models have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that ML models are vulnerable to membership inference attacks (MIAs), which aim to infer whether a data record was used to train a target model or not. MIAs on ML models can directly lead to a privacy breach. For example, via identifying the fact that a clinical record that has been used to train a model associated with a certain disease, an attacker can infer that the owner of the clinical record has the disease with a high chance. In recent years, MIAs have been shown to be effective on various ML models, e.g., classification models and generative models.Meanwhile, many defense methods have been proposed to mitigate MIAs. Although MIAs on ML models form a newly emerging and rapidly growing research area, there has been no systematic survey on this topic yet. In this article, we conduct the first comprehensive survey on membership inference attacks and defenses. We provide the taxonomies for both attacks and defenses, based on their characterizations, and discuss their pros and cons. Based on the limitations and gaps identified in this survey, we point out several promising future research directions to inspire the researchers who wish to follow this area. This survey not only serves as a reference for the research community but also provides a clear description for researchers outside this research domain. [ABSTRACT FROM AUTHOR]

Accurate individual tree reconstruction based on laser point clouds is vital for precise biomass estimation, virtual geographic environment modeling, and simulation. However, mobile laser 3D scanning systems often capture tree point clouds obscured by leaves, resulting in missing or incomplete branch data, posing significant challenges to detailed tree reconstruction. In this paper, a novel method for fine-grained 3D tree model reconstruction from incomplete point clouds is proposed. This approach addresses the structural reconstruction of trees, tackling two main problems: reconstructing the main trunk and branches based on data completeness. Initially, a 3D morphological algorithm is employed to separate the main trunk and branches in the point cloud. Next, branch point clouds are node-aggregated using clustering, and the trunk’s skeleton point is computed using a multi-scale curve fitting method. To account for incomplete branch point clouds, the Alpha shape is calculated and used as a constraint for the growth model of the L-system, enabling the automatic generation of branch structures. Finally, a morphologically constrained multi-level trunk fusion method is utilized to achieve complete tree model reconstruction. To validate the effectiveness of this method, five trees with varying structures and levels of complexity were selected for structural reconstruction and compared with state-of-the-art (SOTA) methods. Experimental results evince that the method delineated in this study exhibits an aptitude for adeptly approximating tree nodes, even in scenarios characterized by incomplete point cloud data. This approach transcends the reconstruction accuracy manifested by SOTA algorithms specific to three-dimensional tree modeling, simultaneously demonstrating a pronounced resilience to noise and enhanced robustness.

Despite offshore aquaculture brings great economic benefits, it also has destructive effects on the ecological environment of coastal regions. Therefore, the accurate monitoring of offshore aquaculture areas is vital. Existing methods for extracting aquaculture ponds are still limited by the lack of high-resolution hyperspectral imagery and inadequate feature extraction. In this paper, we proposed an unsupervised aquaculture ponds extraction method based on hyperspectral imagery super-resolution, feature fusion and stepwise extraction strategy. First, the resolution of the original hyperspectral imagery is enhanced via a deep learning-based super-resolution method. Then we introduce a feature fusion method by combining multi-dimensional features including elevation, reflectance, biochemistry, principal component analysis, and fast-Fourier transform features to enhance the feature sensitivity to the aquaculture ponds. The aquaculture ponds are finally extracted via a step-wise extraction strategy. Experiments on ZH-1 and GF-7 imageries are conducted, and the experimental results show that our method achieves an OA of 97.9% on the aquaculture pond extraction and that the method can be generalized for the object extraction on multispectral datasets. Ablation experiments show that all three of our innovative modules can effectively improve the extraction accuracy and confirm the generalization of the method.

Abstract Tainan, a city that prospered early in Taiwan, has a hot and humid atmosphere. Hence, the grilled doors in numerous old buildings for ventilation and lighting to conserve energy. This study analyzed a fire incident that occurred during the late night of March 17, 2019 in a 38-year-old dwelling, where three residents were severely covered with soot. The site investigation showed that eight staircases lead to the same basement, which apparently created a stack effect and a makeup air phenomenon. Numerical simulations have been performed in this study to reconstruct the fire scene, whose results were consistent with the actual fire scene. In particular, the results showed that some staircases in the fire were blackened by smoke, while others acted as makeup air inlets. The temperature at the households’ doors on all floors of Staircase 2, which was closest to the fire, exceeded 60 °C after four minutes. Furthermore, two immediately feasible improvement strategies according to the control volume theory of fluid mechanics were proposed in this study. Firstly, changing the grilled doors in the basement to a closed flat door style could effectively prevent smoke from flowing up in the staircases. Secondly, residents may consider closing the windows of the stairs at night to improve fire safety. The results showed that the chimney effect can be significantly reduced. These improvements could be a reference for other old dwellings to enhance their fire safety.

Many studies have identified the influences of PM2.5. However, very little research has addressed the spatiotemporal dependence and heterogeneity in the relationships between impact factors and PM2.5. This study firstly utilizes spatial statistics and time series analysis to investigate the spatial and temporal dependence of PM2.5 at the city level in China using a three-year (2015–2017) dataset. Then, a new local regression model, multiscale geographically weighted regression (MGWR), is introduced, based on which we measure the influence of PM2.5. A spatiotemporal lag is constructed and included in MGWR to account for spatiotemporal dependence and spatial heterogeneity simultaneously. Results of MGWR are comprehensively compared with those of ordinary least square (OLS) and geographically weighted regression (GWR). Experimental results show that PM2.5 is autocorrelated in both space and time. Compared with existing approaches, MGWR with a spatiotemporal lag (MGWRL) achieves a higher goodness-of-fit and a more significant effect on eliminating residual spatial autocorrelation. Parameter estimates from MGWR demonstrate significant spatial heterogeneity, which traditional global models fail to detect. Results also indicate the use of MGWR for generating local spatiotemporal dependence evaluations which are conditioned on various covariates rather than being simple descriptions of a pattern. This study offers a more accurate method to model geographic events.

The rapid detection of foodborne microbial pathogens contaminating fresh fruits and vegetables during the intervening period between harvest and consumption could revolutionize microbial quality assurance of food usually consumed raw and those with a limited shelf life. We have developed a sensitive, shotgun whole genome sequencing protocol capable of detecting as few as 1 colony forming unit (cfu) of Salmonella enterica serovar Typhimurium spiked on 25 g of lettuce. The Ion Torrent sequencing platform was used to generate reads of globally amplified DNA from microbes recovered from the surface of lettuce followed by bioinformatic analyses of the nucleotide sequences to detect the presence of Salmonella. The test is rapid and sensitive, and appropriate for testing perishable foods, and those consumed raw, for Salmonella contamination. The test has the potential to be universally applicable to any microbial contaminant on lettuce as long as a suitable bioinformatics pipeline is available and validated. A universal test is expected to pave the way for preventive and precision food safety and the re-shaping of the entire spectrum of food safety investigations from the current disease-limiting, reactive procedure to a proactive, disease prevention process.

We have developed a targeted, amplicon-based next-generation sequencing method to detect and analyze 227 virulence genes (VG) of Salmonella (AmpliSeqSalm_227VG) for assessing the pathogenicity potential of Salmonella. The procedure was developed using 80 reference genomes representing 75 epidemiologically-relevant serovars associated with human salmonellosis. We applied the AmpliSeqSalm_227VG assay to (a) 35 previously characterized field strains of Salmonella consisting of serovars commonly incriminated in foodborne illnesses and (b) 34 Salmonella strains with undisclosed serological or virulence attributes, and were able to divide Salmonella VGs into two groups: core VGs and variable VGs. The commonest serovars causing foodborne illnesses such as Enteritidis, Typhimurium, Heidelberg and Newport had a high number of VGs (217–227). In contrast, serovars of subspecies not commonly associated with human illnesses, such as houtenae, arizonae and salame, tended to have fewer VGs (177–195). Variable VGs were not only infrequent but, when present, displayed considerable sequence variation: safC, sseL, sseD, sseE, ssaK and stdB showed the highest variation and were linked to strain pathogenicity. In a chicken infection model, VGs belonging to rfb and sse operons showed differences and were linked with pathogenicity. The high-throughput, targeted NGS-based AmpliSeqSalm_227VG procedure provided previously unknown information about variation in select virulence genes that can now be applied to a much larger population of Salmonella for evaluating pathogenicity of various serovars of Salmonella and for risk assessment of foodborne salmonellosis.

Non-typhoidal Salmonella is a leading cause of foodborne illness worldwide. Prompt and accurate identification of the sources of Salmonella responsible for disease outbreaks is crucial to minimize infections and eliminate ongoing sources of contamination. Current subtyping tools including single nucleotide polymorphism (SNP) typing may be inadequate, in some instances, to provide the required discrimination among epidemiologically unrelated Salmonella strains. Prophage genes represent the majority of the accessory genes in bacteria genomes and have potential to be used as high discrimination markers in Salmonella. In this study, the prophage sequence diversity in different Salmonella serovars and genetically related strains was investigated. Using whole genome sequences of 1,760 isolates of S. enterica representing 151 Salmonella serovars and 66 closely related bacteria, prophage sequences were identified from assembled contigs using PHASTER. We detected 154 different prophages in S. enterica genomes. Prophage sequences were highly variable among S. enterica serovars with a median ± interquartile range (IQR) of 5 ± 3 prophage regions per genome. While some prophage sequences were highly conserved among the strains of specific serovars, few regions were lineage specific. Therefore, strains belonging to each serovar could be clustered separately based on their prophage content. Analysis of S. Enteritidis isolates from seven outbreaks generated distinct prophage profiles for each outbreak. Taken altogether, the diversity of the prophage sequences correlates with genome diversity. Prophage repertoires provide an additional marker for differentiating S. enterica subtypes during foodborne outbreaks.

The Salmonella Syst-OMICS consortium is sequencing 4,500 Salmonella genomes and building an analysis pipeline for the study of Salmonella genome evolution, antibiotic resistance and virulence genes. Metadata, including phenotypic as well as genomic data, for isolates of the collection are provided through the Salmonella Foodborne Syst-OMICS database (SalFoS), at https://salfos.ibis.ulaval.ca/. Here, we present our strategy and the analysis of the first 3,377 genomes. Our data will be used to draw potential links between strains found in fresh produce, humans, animals and the environment. The ultimate goals are to understand how Salmonella evolves over time, improve the accuracy of diagnostic methods, develop control methods in the field, and identify prognostic markers for evidence-based decisions in epidemiology and surveillance.

The use of treated municipal wastewater residues (biosolids) as fertilizers is an attractive, inexpensive option for growers and farmers. Various regulatory bodies typically employ indicator organisms (fecal coliforms, E. coli and Salmonella) to assess the adequacy and efficiency of the wastewater treatment process in reducing pathogen loads in the final product. Molecular detection approaches can offer some advantages over culture-based methods as they can simultaneously detect a wider microbial species range, including non-cultivable microorganisms. However, they cannot directly assess the viability of the pathogens. Here, we used bacterial enumeration methods together with molecular methods including qPCR, 16S rRNA and cpn60 gene amplicon sequencing and shotgun metagenomic sequencing to compare pre- and post-treatment biosolids from two Canadian wastewater treatment plants (WWTPs). Our results show that an anaerobic digestion WWTP was unsuccessful at reducing the live indicator organism load (coliforms, generic E. coli and Salmonella) below acceptable regulatory criteria, while biosolids from a dewatering/pelletization WWTP met these criteria. DNA from other pathogens was detected by the molecular methods, but these species were considered less abundant. Clostridium DNA increased significantly following anaerobic digestion treatments. In addition to pathogen DNA, genes related to virulence and antibiotic resistance were identified in treated biosolids. Shotgun metagenomics revealed the widest range of pathogen DNA and, among the approaches used here, was the only approach that could access functional gene information in treated biosolids. Overall, our results highlight the potential usefulness of amplicon sequencing and shotgun metagenomics as complementary screening methods that could be used in parallel with culture-based methods, although more detailed comparisons across a wider range of sites would be needed.