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Pectin is a structural polysaccharide and a major component of plant cell walls. Pectate lyases are a class of enzymes that degrade demethylated pectin by cleaving the α-1,4-glycosidic bond, and they play an important role in plant growth and development. Currently, little is known about the PL gene family members and their involvement in salt stress in potato. In this study, we utilized bioinformatics to identify members of the potato pectate lyase gene family and analyzed their gene and amino acid sequence characteristics. The results showed that a total of 27 members of the pectate lyase gene family were identified in potato. Phylogenetic tree analysis revealed that these genes were divided into eight groups. Analysis of their promoters indicated that several members’ promoter regions contained a significant number of hormone and stress response elements. Further, we found that several members responded positively to salt treatment under single salt and mixed salt stress. Since StPL18 exhibited a consistent expression pattern under both single and mixed salt stress conditions, its subcellular localization was determined. The results indicated that StPL18 is localized in the endoplasmic reticulum membrane. The results will establish a foundation for analyzing the functions of potato pectate lyase family members and their expression under salt stress.

Gait impairment, which is commonly observed in stroke survivors, underscores the imperative of rehabilitating walking function. Wearable inertial measurement units (IMUs) can capture gait parameters in stroke patients, becoming a promising tool for objective and quantifiable gait assessment. Optimal sensor placement for stroke assessment that involves optimal combinations of features (kinematics) is required to improve stroke assessment accuracy while reducing the number of sensors to achieve a convenient IMU scheme for both clinical and home assessment; however, previous studies lack comprehensive discussions on the optimal sensor placement and features. To obtain an optimal sensor placement for stroke assessment, this study investigated the impact of IMU placement on stroke assessment based on gait data and clinical scores of 16 stroke patients. Stepwise regression was performed to select the kinematics most correlated with stroke assessment (lower limb part of Fugl-Meyer assessment). Sensors at different locations were combined into 28 sensor groups and their stroke assessment was compared. First, the reduced number of gait features does not significantly impact the stroke assessment. Second, the selected gait parameters by stepwise regression are found all from sensors at the hip and bilateral thighs. Last, a three-sensor scheme–sensors at the hip and bilateral thighs was suggested, which achieved a high accuracy with an adjusted R 2  = 0.999, MAE = 0.07, and RMSE = 0.08. Further, the prediction error is zero if the predicted lower limb Fugl-Meyer scales are rounded to the nearest integer. These findings offer a convenient IMU solution for quantitatively assessing stroke patients. Therefore, the IMU-based stroke assessment provides a promising complementary tool for clinical assessment and home rehabilitation of stroke patients.

The concept of gait synergy provides novel human–machine interfaces and has been applied to the control of lower limb assistive devices, such as powered prostheses and exoskeletons. Specifically, on the basis of gait synergy, the assistive device can generate/predict the appropriate reference trajectories precisely for the affected or missing parts from the motions of sound parts of the patients. Optimal modeling for gait synergy methods that involves optimal combinations of features (inputs) is required to achieve synergic trajectories that improve human–machine interaction. However, previous studies lack thorough discussions on the optimal methods for synergy modeling. In addition, feature selection (FS) that is crucial for reducing data dimensionality and improving modeling quality has often been neglected in previous studies. Here, we comprehensively investigated modeling methods and FS using 4 up-to-date neural networks: sequence-to-sequence (Seq2Seq), long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrent unit (GRU). We also conducted complete FS using 3 commonly used methods: random forest, information gain, and Pearson correlation. Our findings reveal that Seq2Seq (mean absolute error: 0.404° and 0.596°, respectively) outperforms LSTM, RNN, and GRU for both interlimb and intralimb synergy modeling. Furthermore, FS is proven to significantly improve Seq2Seq’s modeling performance ( P < 0.05). FS-Seq2Seq even outperforms methods used in existing studies. Therefore, we propose FS-Seq2Seq as a 2-stage strategy for gait synergy modeling in lower limb assistive devices with the aim of achieving synergic and user-adaptive trajectories that improve human–machine interactions.

Epidural electrical stimulation (EES) has emerged as a promising treatment for spinal cord injury (SCI). However, the therapeutic potential of EES in functional recovery following incomplete SCI remains limited, with few studies of a large sample size. This study included 11 patients who received EES combined with physical therapy (PT) and 10 who received only PT. Follow-ups were conducted pre-surgery, post-surgery, and at 19 to 25 months postoperatively. After the surgery, patients in the EES + PT group showed significant improvements in sensory function ( P < 0.001) and muscle spasticity ( P < 0.001). Long-term follow-up indicated that the EES + PT group had significant improvements in sensory function ( P < 0.001), muscle spasticity ( P < 0.01), and urinary function ( P < 0.05). Among them, all 11 patients had improvements in sensory function and muscle spasticity, and 6 of 11 reported an improvement in urinary function. Moreover, of the 5 patients with neuropathic pain, 4 exhibited reduced pain scores. Compared with the PT-only group, the EES + PT group had significantly better recovery in sensory function ( P < 0.01), muscle spasticity ( P < 0.0001), muscle strength ( P < 0.01), and bowel function ( P < 0.01). Further analysis suggested that patients with less severe SCIs in the EES + PT group tend to achieve better functional recovery. With a relatively large sample size compared to those in previous studies, this study confirms the promising therapeutic effects of EES in SCI. EES combined with PT provides a potential approach for functional recovery in patients with incomplete SCI.

Introduction This study investigates how surgery for pituitary adenoma (PA) affects the visual pathway, examining changes in the retina, blood vessel density, and nerve function. Since PAs often impair vision as a result of their location near visual structures, this research is key to understanding and improving vision recovery after surgery. Methods Our study is based on a retrospective analysis of the historical data of 28 patients diagnosed with pituitary adenomas. We conducted assessments by reviewing preoperative and postoperative imaging records. These included optical coherence tomography (OCT) for retinal structure analysis, diffusion tensor imaging (DTI) for neural transmission evaluation, and optical coherence tomography angiography for assessing blood vessel density. These tools allowed for a detailed understanding of the structural and functional changes within the visual pathway following PA surgery. Results OCT findings show postoperative changes in the eye: thinning in average and nasal circumpapillary retinal nerve fiber layer, thickening in macular central 1 mm inner plexus layer, ganglion cell complex, and nasal retinal nerve fiber layer. DTI reveals increased fractional anisotropy (FA) in the left optic chiasm and posterior optic nerve, decreased mid-segment optic nerve FA, and increased apparent diffusion coefficient (ADC) in the right optic chiasm and nerve segments. Early postoperative reduction in radial peripapillary capillaries plexus density is noted. Preoperative ganglion cell layer (GCL) thickness correlates with postoperative visual radiation FA and ADC values, especially in the inferior quadrant. A negative correlation exists between preoperative GCL thickness and postoperative visual field mean defect values, particularly on the temporal side and superior inner ring. All changes are statistically significant ( P  < 0.05). Conclusions The study finds that surgery for PA has varied effects on vision. Early post surgery, there are changes in the retina and nerve signals. Macular GCL thickness before surgery might predict early visual recovery, influencing future research and treatment for vision issues related to PA.

Aiming at the fault diagnosis of rolling element bearings, propose a method for fine diagnosis of bearings based on wavelet transform and one-dimensional convolutional neural network. First use wavelet transform to decompose the experimental data; Use the resulting low-frequency signal as a one-dimensional convolutional neural network input, bearing fault identification. The experiment uses the deep groove ball bearing of Case Western Reserve University as the research object, Use this method to identify the normal and outer ring faults of the bearing. the result shows: This method can be effectively applied to the precise identification of bearings.

The concept of gait synergy provides novel human–machine interfaces and has been applied to the control of lower limb assistive devices, such as powered prostheses and exoskeletons. Specifically, on the basis of gait synergy, the assistive device can generate/predict the appropriate reference trajectories precisely for the affected or missing parts from the motions of sound parts of the patients. Optimal modeling for gait synergy methods that involves optimal combinations of features (inputs) is required to achieve synergic trajectories that improve human–machine interaction. However, previous studies lack thorough discussions on the optimal methods for synergy modeling. In addition, feature selection (FS) that is crucial for reducing data dimensionality and improving modeling quality has often been neglected in previous studies. Here, we comprehensively investigated modeling methods and FS using 4 up-to-date neural networks: sequence-to-sequence (Seq2Seq), long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrent unit (GRU). We also conducted complete FS using 3 commonly used methods: random forest, information gain, and Pearson correlation. Our findings reveal that Seq2Seq (mean absolute error: 0.404° and 0.596°, respectively) outperforms LSTM, RNN, and GRU for both interlimb and intralimb synergy modeling. Furthermore, FS is proven to significantly improve Seq2Seq’s modeling performance ( P < 0.05). FS-Seq2Seq even outperforms methods used in existing studies. Therefore, we propose FS-Seq2Seq as a 2-stage strategy for gait synergy modeling in lower limb assistive devices with the aim of achieving synergic and user-adaptive trajectories that improve human–machine interactions.

目的比较SD、E3与DA1U大鼠视网膜光损伤敏感性,为构建不同品系大鼠视网膜光损伤模型和研究眼底色素抗光损伤保护视网膜的相关机制研究提供实验依据。方法成年SD、E3、DA1U大鼠,雌雄随机选取,采用视网膜电图与HE染色分别检测正常情况下与光损伤后不同品系大鼠视网膜感光功能与组织形态变化,采用TUNEL染色观察光损伤后视网膜凋亡细胞的分布与数量。结果正常状态下,SD大鼠色素上皮层、脉络膜层及E3大鼠色素上皮层均无色素分布,而E3大鼠脉络膜层、DA1U大鼠色素上皮层与脉络膜层有褐色色素分布;HE结果显示,在正常状态下,3种品系大鼠视网膜外核层厚度没有显著差异,而在光损伤条件下,SD大鼠视网膜各层细胞均严重受损;视网膜电图结果显示,在正常状态下,不同品系大鼠间视网膜电图视杆细胞反应b波振幅存在显著差异,E3大鼠最大混合反应a波及b波振幅与SD大鼠相比有显著差异,而在光损伤条件下,与SD大鼠相比,E3和DA1U大鼠视网膜电图a波与b波的波幅降幅显著减弱;TUNEL染色结果显示,与SD大鼠相比,E3与DA1U大鼠视网膜内核层及外核层凋亡细胞分布范围与数量均显著下降。结论 SD、E3与DA1U大鼠间存在视网膜色素分布与光...