A prefrontal motor circuit initiates persistent movement.

Persistence reinforces continuous action, which benefits animals in many aspects. Diverse external or internal signals may trigger animals to start a persistent movement. However, it is unclear how the brain decides to persist with current actions by selecting specific information. Using single-unit extracellular recordings and opto-tagging in awake mice, we demonstrated that a group of dorsal mPFC (dmPFC) motor cortex projecting (MP) neurons initiate a persistent movement by selectively encoding contextual information rather than natural valence. Inactivation of dmPFC MP neurons impairs the initiation and reduces neuronal activity in the insular and motor cortex. After the persistent movement is initiated, the dmPFC MP neurons are not required to maintain it. Finally, a computational model suggests that a successive sensory stimulus acts as an input signal for the dmPFC MP neurons to initiate a persistent movement. These results reveal a neural initiation mechanism on the persistent movement.

Lithium-ion battery state of health and failure analysis with mixture weibull and equivalent circuit model.

Existing methods for interpreting Electrochemical Impedance Spectroscopy data involve various models, which face significant challenges in parameterization and physical interpretation and fail to comprehensively reflect the electrochemical behavior within batteries. To address these issues, this study proposes a Temperature-Controlled Second-Order R-CPE Equivalent Circuit Model to capture the non-ideal capacitive characteristics of electrode surfaces. Additionally, the study employs a Copula based Joint Mixture Weibull Model and multi-output Gaussian Process Regression to enhance the precision in capturing the distribution of battery electrochemical parameters and predict SoH curves. Experimental validation shows that the model used in this article has an average RMSE error of 8.5%, and the prediction of the SoH curve after the 100th cycle can achieve an average RMSE error of 9.2%. These findings provide insightful implications for understanding the electrochemical complexities and parameter interdependencies in the battery aging process, offering a robust framework for future research in battery diagnostics.

Compendium of 5810 genomes of sheep and goat gut microbiomes provides new insights into the glycan and mucin utilization.

BACKGROUND: Ruminant gut microbiota are critical in ecological adaptation, evolution, and nutrition utilization because it regulates energy metabolism, promotes nutrient absorption, and improves immune function. To study the functional roles of key gut microbiota in sheep and goats, it is essential to construct reference microbial gene catalogs and high-quality microbial genomes database. RESULTS: A total of 320 fecal samples were collected from 21 different sheep and goat breeds, originating from 32 distinct farms. Metagenomic deep sequencing and binning assembly were utilized to construct a comprehensive microbial genome information database for the gut microbiota. We successfully generated the largest reference gene catalogs for gut microbiota in sheep and goats, containing over 162 million and 82 million nonredundant predicted genes, respectively, with 49 million shared nonredundant predicted genes and 1138 shared species. We found that the rearing environment has a greater impact on microbial composition and function than the host's species effect. Through subsequent assembly, we obtained 5810 medium- and high-quality metagenome-assembled genomes (MAGs), out of which 2661 were yet unidentified species. Among these MAGs, we identified 91 bacterial taxa that specifically colonize the sheep gut, which encode polysaccharide utilization loci for glycan and mucin degradation. CONCLUSIONS: By shedding light on the co-symbiotic microbial communities in the gut of small ruminants, our study significantly enhances the understanding of their nutrient degradation and disease susceptibility. Our findings emphasize the vast potential of untapped resources in functional bacterial species within ruminants, further expanding our knowledge of how the ruminant gut microbiota recognizes and processes glycan and mucins. Video Abstract.

A prefrontal motor circuit initiates persistent movement.

Persistence reinforces continuous action, which benefits animals in many aspects. Diverse information may trigger animals to start a persistent movement. However, it is unclear how the brain decides to persist with current actions by selecting specific information. Using single-unit extracellular recordings and opto-tagging in awake mice, we demonstrated that a group of dorsal mPFC (dmPFC) motor cortex projecting (MP) neurons initiate a persistent movement selectively encoding contextual information rather than natural valence. Inactivation of dmPFC MP neurons impairs the initiation and reduces neuronal activity in the insular and motor cortex. Finally, a computational model suggests that a successive sensory stimulus acts as an input signal for the dmPFC MP neurons to initiate a persistent movement. These results reveal a neural initiation mechanism on the persistent movement.

Adaptive Intermediate Class-Wise Distribution Alignment: A Universal Domain Adaptation and Generalization Method for Machine Fault Diagnosis.

Many transfer learning methods have been proposed to implement fault transfer diagnosis, and their loss functions are usually composed of task-related losses, distribution distance losses, and correlation regularization losses. The intrinsic parameters and trade-off parameters between losses, however, need to be tuned according to the specific diagnosis tasks; thus, the generalization abilities of these methods in multiple tasks are limited. Besides, the alignment goal of most domain adaptation (DA) mechanisms dynamically changes during the training process, which will result in loss oscillation, slow convergence and poor robustness. To overcome the above-mentioned issues, a novel and simple transfer learning diagnosis method named adaptive intermediate class-wise distribution alignment (AICDA) model is proposed, and it is established via the proposed AICDA mechanism, dynamic intermediate alignment (DIA) adaptive layer and AdaSoftmax loss. The AICDA mechanism develops an adaptive intermediate distribution as the alignment goal of multiple source domains and target domains, and it can simultaneously align the global and class-wise distributions of these domains. The DIA layer is designed to adaptively achieve domain confusion without the distribution distance loss and the correlation regularization loss. Meanwhile, to ensure the classification performance of the AICDA mechanism, AdaSoftmax loss is proposed for boosting the separability of Softmax loss. Finally, in order to evaluate the effectiveness and universality of the AICDA diagnosis model to the most degree, various multisource mixed fault transfer diagnosis tasks of wind turbine planetary gearboxes, including DA and domain generalization (DG), are implemented, and the experimental results indicate that our proposed AICDA model has a higher diagnosis accuracy and a stronger generalization ability than other state-of-the-art transfer learning methods.

Digestive fluid components affect speciation and bioaccessibility and the subsequent exposure risk of soil chromium from stomach to intestinal phase in in-vitro gastrointestinal digestion.

The simulated in-vitro gastrointestinal method provides a simple way to evaluate the health risk of human body exposed to soil contaminants. Several in-vitro methods have been successfully established for soil As, Pb, and Cd. However, the method development for soil Cr failed up to now, which could be resulted from alteration in the species of Cr (e.g., Cr(VI)/Cr(III)) caused by the gastrointestinal digestion components, ultimately affecting the accessibility of Cr. This study explored the transformation and bioaccessibility of Cr in two Cr-contaminated soils during the physiologically based extraction test. The water-soluble and exchangeable Cr in soil was dissolved in gastrointestinal tract, accompanied with reduction of Cr(VI) into Cr(III), and the reduction occurred after the chemical extraction in two soils rather than during the extraction. Pepsin and organic acids in gastric phase could reduce Cr(VI) into Cr(III) and reduction efficiency were 20.4%- 53.0%, while in intestinal phase, pancreatin and bile salt had little effect on the Cr(VI) reduction, instead, more Cr(VI) was released from soil. In the gastric solution, Cr(VI) was mainly present as HCrO4- and Cr(III) as free Cr3+ ion. In the intestinal phase, Cr(VI) mainly occurred as CrO42- and Cr(III) as Cr(OH)3 (aq). Cr in the soil solid phase was dominated as the precipitates of Cr-Fe oxide, which was hardly extracted. Bioaccessibility of Cr in gastric phase increased as extraction duration increased and decreased in the intestinal phase, the contrary trend was observed for the hazard quotient of Cr in two phases due to Cr(VI)/Cr(III) transformation. This study indicates that the gastrointestinal components could influence the Cr transformation and subsequently affect the Cr bioaccessibility, which would help for a successful establishment of in vitro determination method for soil Cr bioaccessibility.

Learning induced neuronal identity switch in the superficial layers of the primary somatosensory cortex.

During learning, multi-dimensional inputs are integrated within the sensory cortices. However, the strategies by which the sensory cortex employs to achieve learning remains poorly understood. We studied the sensory cortical neuronal coding of trace eyeblink conditioning (TEC) in head-fixed, freely running mice, where whisker deflection was used as a conditioned stimulus (CS) and an air puff to the cornea delivered after an interval was used as unconditioned stimulus (US). After training, mice learned the task with a set of stereotypical behavioral changes, most prominent ones include prolonged closure of eyelids, and increased reverse running between CS and US onset. The local blockade of the primary somatosensory cortex (S1) activities with muscimol abolished the behavior learning suggesting that S1 is required for the TEC. In naive animals, based on the response properties to the CS and US, identities of the small proportion (~20%) of responsive primary neurons (PNs) were divided into two subtypes: CR (i.e. CS-responsive) and UR neurons (i.e. US-responsive). After animals learned the task, identity of CR and UR neurons changed: while the CR neurons are less responsive to CS, UR neurons gain responsiveness to CS, a new phenomenon we defined as 'learning induced neuronal identity switch (LINIS)'. To explore the potential mechanisms underlying LINIS, we found that systemic and local (i.e. in S1) administration of the nicotinic receptor antagonist during TEC training blocked the LINIS, and concomitantly disrupted the behavior learning. Additionally, we monitored responses of two types of cortical interneurons (INs) and observed that the responses of the somatostatin-expressing (SST), but not parvalbumin-expressing (PV) INs are negatively correlated with the learning performance, suggesting that SST-INs contribute to the LINIS. Thus, we conclude that L2/3 PNs in S1 encode perceptual learning by LINIS like mechanisms, and cholinergic pathways and cortical SST interneurons are involved in the formation of LINIS.

Persistence is driven by a prefrontal motor circuit.

Persistence provides a long-lasting effect on actions, including avoiding predators and storing energy, and hence is crucial for the survival (Adolphs and Anderson, 2018). However, how the brain loads persistence on movements is unknown. Here, we demonstrate that being persistent is determined at the initial phase of movement, and this persistency will be sustained until the terminal signaling. The neural coding of persistent movement phases (initial or terminal) is independent from the judgement (i.e. valence) (Li et al., 2022; Wang et al., 2018) upon the external stimuli. Next, we identify a group of dorsal medial prefrontal cortex (dmPFC) motor cortex projecting (MP) neurons (Wang and Sun, 2021), which encodes the initial phase of a persistent movement rather than the valence. Inactivation of dmPFC MP neurons impairs the initiation of persistency and reduce the neural activity in the insular and motor cortex. Finally, a MP network-based computational model suggests that an intact, successive sensory stimulus acts as a triggering signal to direct the initiation of persistent movements. These findings reveal a neural mechanism that transforms the brain state from neutral to persistent during a movement.

Federated Active Learning for Multicenter Collaborative Disease Diagnosis.

Current computer-aided diagnosis system with deep learning method plays an important role in the field of medical imaging. The collaborative diagnosis of diseases by multiple medical institutions has become a popular trend. However, large scale annotations put heavy burdens on medical experts. Furthermore, the centralized learning system has defects in privacy protection and model generalization. To meet these challenges, we propose two federated active learning methods for multicenter collaborative diagnosis of diseases: the Labeling Efficient Federated Active Learning (LEFAL) and the Training Efficient Federated Active Learning (TEFAL). The proposed LEFAL applies a task-agnostic hybrid sampling strategy considering data uncertainty and diversity simultaneously to improve data efficiency. The proposed TEFAL evaluates the client informativeness with a discriminator to improve client efficiency. On the Hyper-Kvasir dataset for gastrointestinal disease diagnosis, with only 65% of labeled data, the LEFAL achieves 95% performance on the segmentation task with whole labeled data. Moreover, on the CC-CCII dataset for COVID-19 diagnosis, with only 50 iterations, the accuracy and F1-score of TEFAL are 0.90 and 0.95, respectively on the classification task. Extensive experimental results demonstrate that the proposed federated active learning methods outperform state-of-the-art methods on segmentation and classification tasks for multicenter collaborative disease diagnosis.

Using ecological niche modeling to predict the potential distribution of scrub typhus in Fujian Province, China.

BACKGROUND: Despite the increasing number of cases of scrub typhus and its expanding geographical distribution in China, its potential distribution in Fujian Province, which is endemic for the disease, has yet to be investigated. METHODS: A negative binomial regression model for panel data mainly comprising meteorological, socioeconomic and land cover variables was used to determine the risk factors for the occurrence of scrub typhus. Maximum entropy modeling was used to identify the key predictive variables of scrub typhus and their ranges, map the suitability of different environments for the disease, and estimate the proportion of the population at different levels of infection risk. RESULTS: The final multivariate negative binomial regression model for panel data showed that the annual mean normalized difference vegetation index had the strongest correlation with the number of scrub typhus cases. With each 0.1% rise in shrubland and 1% rise in barren land there was a 75.0% and 37.0% increase in monthly scrub typhus cases, respectively. In contrast, each unit rise in mean wind speed in the previous 2 months and each 1% increase in water bodies corresponded to a decrease of 40.0% and 4.0% in monthly scrub typhus cases, respectively. The predictions of the maximum entropy model were robust, and the average area under the curve value was as high as 0.864. The best predictive variables for scrub typhus occurrence were population density, annual mean normalized difference vegetation index, and land cover types. The projected potentially most suitable areas for scrub typhus were widely distributed across the eastern coastal area of Fujian Province, with highly suitable and moderately suitable areas accounting for 16.14% and 9.42%, respectively. Of the total human population of the province, 81.63% reside in highly suitable areas for scrub typhus. CONCLUSIONS: These findings could help deepen our understanding of the risk factors of scrub typhus, and provide information for public health authorities in Fujian Province to develop more effective surveillance and control strategies in identified high risk areas in Fujian Province.

Deep Joint Distribution Alignment: A Novel Enhanced-Domain Adaptation Mechanism for Fault Transfer Diagnosis.

Various domain adaptation (DA) methods have been proposed to address distribution discrepancy and knowledge transfer between the source and target domains. However, many DA models focus on matching the marginal distributions of two domains and cannot satisfy fault-diagnosed-task requirements. To enhance the ability of DA, a new DA mechanism, called deep joint distribution alignment (DJDA), is proposed to simultaneously reduce the discrepancy in marginal and conditional distributions between two domains. A new statistical metric that can align the means and covariances of two domains is designed to match the marginal distributions of the source and target domains. To align the class conditional distributions, a Gaussian mixture model is used to obtain the distribution of each category in the target domain. Then, the conditional distributions of the source domain are computed via maximum-likelihood estimation, and information entropy and Wasserstein distance are employed to reduce class conditional distribution discrepancy between the two domains. With joint distribution alignment, DJDA can achieve domain confusion to the highest degree. DJDA is applied to the fault transfer diagnosis of a wind turbine gearbox and cross-bearing with unlabeled target-domain samples. Experimental results verify that DJDA outperforms other typical DA models.

Epidemiological characteristics and spatiotemporal patterns of scrub typhus in Fujian province during 2012-2020.

BACKGROUND: Scrub typhus has become a serious public health concern in the Asia-Pacific region including China. There were new natural foci continuously recognized and dramatically increased reported cases in mainland China. However, the epidemiological characteristics and spatiotemporal patterns of scrub typhus in Fujian province have yet to be investigated. OBJECTIVE: This study proposes to explore demographic characteristics and spatiotemporal dynamics of scrub typhus cases in Fujian province, and to detect high-risk regions between January 2012 and December 2020 at county/district scale and thereby help in devising public health strategies to improve scrub typhus prevention and control measures. METHOD: Monthly cases of scrub typhus reported at the county level in Fujian province during 2012-2020 were collected from the National Notifiable Disease Surveillance System. Time-series analyses, spatial autocorrelation analyses and space-time scan statistics were applied to identify and visualize the spatiotemporal patterns of scrub typhus cases in Fujian province. The demographic differences of scrub typhus cases from high-risk and low-risk counties in Fujian province were also compared. RESULTS: A total of 11,859 scrub typhus cases reported in 87 counties from Fujian province were analyzed and the incidence showed an increasing trend from 2012 (2.31 per 100,000) to 2020 (3.20 per 100,000) with a peak in 2018 (4.59 per 100,000). There existed two seasonal peaks in June-July and September-October every year in Fujian province. A significant positive spatial autocorrelation of scrub typhus incidence in Fujian province was observed with Moran's I values ranging from 0.258 to 0.471 (P<0.001). Several distinct spatiotemporal clusters mainly concentrated in north and southern parts of Fujian province. Compared to low-risk regions, a greater proportion of cases were female, farmer, and older residents in high-risk counties. CONCLUSIONS: These results demonstrate a clear spatiotemporal heterogeneity of scrub typhus cases in Fujian province, and provide the evidence in directing future researches on risk factors and effectively assist local health authorities in the refinement of public health interventions against scrub typhus transmission in the high risk regions.

Machine Learning-Assisted Design of Yttria-Stabilized Zirconia Thermal Barrier Coatings with High Bonding Strength.

As a high-quality thermal barrier coating material, yttria-stabilized zirconia (YSZ) can effectively reduce the temperature of the collective materials to be used on the surface of gas turbine hot-end components. The bonding strength between YSZ and the substrate is also one of the most important factors for the applications. Herein, the Gaussian mixture model (GMM) and support vector regression (SVR) were used to construct a machine learning model between YSZ coating bonding strength and atmospheric plasma spraying (APS) process parameters. First, GMM was used to expand the original 8 data points to 400 with the R value of leave-one-out cross-validation improved from 0.690 to 0.990. Then, the specific effects of APS process parameters were explored through Shapley additive explanations and sensitivity analysis. Principal component analysis was used to explain the constructed model and obtain the optimized area with a high bonding strength. After experimental validation, the results showed that under the APS process parameters of a current of 617 A, a voltage of 65 V, a H2 flow of 3 L min-1, and a thickness of 200 µm, the bonding strength increased by more than 19% to 55.5 MPa compared with the original maximum value of 46.6 MPa, indicating that the constructed GMM-SVR model can accurately predict the bonding strength of YSZ coating.

A Characterization of Maximally Entangled Two-Qubit States.

As already known by Rana's result, all eigenvalues of any partial-transposed bipartite state fall within the closed interval [-12,1]. In this note, we study a family of bipartite quantum states where the minimal eigenvalues of partial-transposed states are -12. For a two-qubit system, we find that the minimal eigenvalue of its partial-transposed state is -12 if and only if such a two-qubit state is maximally entangled. However this result does not hold in general for a two-qudit system when the dimensions of the underlying space are larger than two.

A long-range, recurrent neuronal network linking the emotion regions with the somatic motor cortex.

Recurrent neural networks (RNNs) are designed to learn sequential patterns in silico, but it is unclear whether and how an RNN forms in the native networks of the mammalian brain. Here, we report an innate RNN, which is formed by the unidirectional connections from three basic units: input units arriving from emotion regions, a hidden unit in the medial prefrontal cortex (mPFC), and output units located at the somatic motor cortex (sMO). Specifically, the neurons from basal lateral amygdala (BLA) and the insular cortex (IC) project to the mPFC motor-cortex-projecting (MP) neurons. These MP neurons form a local self-feedback loop and target major projecting neurons of the sMO. Within the sMO, the neurons in the infragranular layers receive stronger input than the neurons in supragranular layers. Finally, we show in vivo evidence that the communications from the emotion regions to the sMO are abolished when MP neurons are chemogenetically silenced.

Dense Packing of Acetylene in a Stable and Low-Cost Metal-Organic Framework for Efficient C2 H2 /CO2 Separation.

Porous materials for C2 H2 /CO2 separation mostly suffer from high regeneration energy, poor stability, or high cost that largely dampen their industrial implementation. A desired adsorbent should have an optimal balance between excellent separation performance, high stability, and low cost. We herein report a stable, low-cost, and easily scaled-up aluminum MOF (CAU-10-H) for highly efficient C2 H2 /CO2 separation. The suitable pore confinement in CAU-10-H can not only provide multipoint binding interactions with C2 H2 but also enable the dense packing of C2 H2 inside the pores. This material exhibits one of the highest C2 H2 storage densities of 392 g L-1 and highly selective adsorption of C2 H2 over CO2 at ambient conditions, achieved by a low C2 H2 adsorption enthalpy (27 kJ mol-1 ). Breakthrough experiments confirm its exceptional separation performance for C2 H2 /CO2 mixtures, affording both large C2 H2 uptake of 3.3 mmol g-1 and high separation factor of 3.4. CAU-10-H achieves the benchmark balance between separation performance, stability, and cost for C2 H2 /CO2 separation.

FXR1 regulation of parvalbumin interneurons in the prefrontal cortex is critical for schizophrenia-like behaviors.

Parvalbumin interneurons (PVIs) are affected in many psychiatric disorders including schizophrenia (SCZ), however the mechanism remains unclear. FXR1, a high confident risk gene for SCZ, is indispensable but its role in the brain is largely unknown. We show that deleting FXR1 from PVIs of medial prefrontal cortex (mPFC) leads to reduced PVI excitability, impaired mPFC gamma oscillation, and SCZ-like behaviors. PVI-specific translational profiling reveals that FXR1 regulates the expression of Cacna1h/Cav3.2 a T-type calcium channel implicated in autism and epilepsy. Inhibition of Cav3.2 in PVIs of mPFC phenocopies whereas elevation of Cav3.2 in PVIs of mPFC rescues behavioral deficits resulted from FXR1 deficiency. Stimulation of PVIs using a gamma oscillation-enhancing light flicker rescues behavioral abnormalities caused by FXR1 deficiency in PVIs. This work unveils the function of a newly identified SCZ risk gene in SCZ-relevant neurons and identifies a therapeutic target and a potential noninvasive treatment for psychiatric disorders.

Circuit Mechanisms Underlying Epileptogenesis in a Mouse Model of Focal Cortical Malformation.

The way in which aberrant neural circuits contribute to epilepsy remains unclear. To elucidate this question, we dissected the circuit mechanisms underlying epileptogenesis using a mouse model of focal cortical malformation with spontaneous epileptiform discharges. We found that spontaneous spike-wave discharges and optogenetically induced hyperexcitable bursts in vivo were present in a cortical region distal to (>0.7 mm) freeze-lesion-induced microgyrus, instead of near the microgyrus. ChR2-assisted circuit mapping revealed ectopic inter-laminar excitatory input from infragranular layers to layers 2/3 pyramidal neurons as the key component of hyperexcitable circuitry. This hyperactivity disrupted the balance between excitation and inhibition and was more prominent in the cortical region distal to the microgyrus. Consistently, the inhibition from both parvalbumin-positive interneurons (PV) and somatostatin-positive interneurons (SOM) to pyramidal neurons were altered in a layer- and site-specific fashion. Finally, closed-loop optogenetic stimulation of SOM, but not PV, terminated spontaneous spike-wave discharges. Together, these results demonstrate the occurrence of highly site- and cell-type-specific synaptic reorganization underlying epileptic cortical circuits and provide new insights into potential treatment strategies.

The Impacts of Climatic Factors and Vegetation on Hemorrhagic Fever with Renal Syndrome Transmission in China: A Study of 109 Counties.

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne infectious disease caused by hantaviruses. About 90% of global cases were reported in China. We collected monthly data on counts of HFRS cases, climatic factors (mean temperature, rainfall, and relative humidity), and vegetation (normalized difference vegetation index (NDVI)) in 109 Chinese counties from January 2002 to December 2013. First, we used a quasi-Poisson regression with a distributed lag non-linear model to assess the impacts of these four factors on HFRS in 109 counties, separately. Then we conducted a multivariate meta-analysis to pool the results at the national level. The results of our study showed that there were non-linear associations between the four factors and HFRS. Specifically, the highest risks of HFRS occurred at the 45th, 30th, 20th, and 80th percentiles (with mean and standard deviations of 10.58 ± 4.52 °C, 18.81 ± 17.82 mm, 58.61 ± 6.33%, 198.20 ± 22.23 at the 109 counties, respectively) of mean temperature, rainfall, relative humidity, and NDVI, respectively. HFRS case estimates were most sensitive to mean temperature amongst the four factors, and the lag patterns of the impacts of these factors on HFRS were heterogeneous. Our findings provide rigorous scientific support to current HFRS monitoring and the development of early warning systems.

Epidemiological Characteristics and Spatial Analysis of Tick-Borne Encephalitis in Jilin Province, China.

Tick-borne encephalitis (TBE) is a viral infectious disease and has become a reemerging public health threat in recent years in northeastern China. However, no studies has characterized the epidemiologic features and explored the spatial dynamics and environmental factors of TBE cases in Jilin Province. In this study, we have described the epidemiological features of 846 reported human TBE cases from 2006 to 2016 in Jilin Province. There was an obvious single peak pattern of TBE cases from May to July in Jilin Province. More than 60% of TBE cases occurred in farmers, and the people in 50- to 59-year-old group had the high incidence of the disease. The results of Getis-Ord Gi* statistics demonstrated that the human TBE cases were more clustered in the northeastern border including Dunhua and Yanji cities and Antu and Wangqing counties, and southern areas including Huinan, Jingyu, Jiangyuan, and Liuhe counties in Jilin Province. We demonstrated that the temporal dynamics of TBE in Jilin was significantly associated with the dynamics of meteorological factors especially after 2009. The results from the auto-logistic regression analysis showed that the percentage coverage of forest, temperature, and autoregressive term were significantly associated with the occurrence of human TBE cases in Jilin Province. Our findings will provide a scientific evidence for the targeted prevention and control programs.