Silencing of UCA1 Protects Against MPP+-Induced Cytotoxicity in SK-N-SH Cells via Modulating KCTD20 Expression by Sponging miR-423-5p.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder. Long noncoding RNA (lncRNA) urothelial carcinoma-associated 1 (UCA1) has been implicated in PD development. Nevertheless, little insight has been gained on the mechanisms of UCA1 in PD pathogenesis. The levels of UCA1, miR-423-5p and potassium channel tetramerization domain containing 20 (KCTD20) were assessed by qRT-PCR and western blot. Cell viability was gauged by the CCK-8 assay, and cell apoptosis was detected by flow cytometry. Targeted relationships among UCA1, miR-423-5p and KCTD20 were verified by dual-luciferase reporter and RNA immunoprecipitation assays. Our data showed that MPP+ induced UCA1 expression in SK-N-SH cells. UCA1 silencing protected against MPP+-evoked cytotoxicity in SK-N-SH cells. UCA1 functioned as a miR-423-5p sponge, and the protective impact of UCA1 silencing on MPP+-evoked cytotoxicity was mediated by miR-423-5p. KCTD20 was a direct target of miR-423-5p, and miR-423-5p overexpression mitigated MPP+-triggered cell injury by down-regulating KCTD20. Furthermore, UCA1 regulated KCTD20 expression by acting as a sponge of miR-423-5p in SK-N-SH cells. Our study first identified that the silencing of UCA1 protected SK-N-SH cells from MPP+-evoked cytotoxicity at least in part by targeting the miR-423-5p/KCTD20 axis.

Cerebellar contribution to auditory feedback control of speech production: Evidence from patients with spinocerebellar ataxia.

The cerebellum has been implicated in the feedforward control of speech production. However, the role of the cerebellum in the feedback control of speech production remains unclear. To address this question, the present event-related potential study examined the behavioral and neural correlates of auditory feedback control of vocal production in patients with spinocerebellar ataxia (SCA) and healthy controls. All participants were instructed to produce sustained vowels while hearing their voice unexpectedly pitch-shifted -200 or -500 cents. The behavioral results revealed significantly larger vocal compensations for pitch perturbations in patients with SCA relative to healthy controls. At the cortical level, patients with SCA exhibited significantly smaller cortical P2 responses that were source localized in the right superior temporal gyrus, primary auditory cortex, and supramarginal gyrus than healthy controls. These findings indicate that reduced brain activity in the right temporal and parietal regions are significant neural contributors to abnormal auditory-motor processing of vocal pitch regulation as a consequence of cerebellar degeneration, which may be related to disrupted reciprocal interactions between the cerebellum and cortical regions that support the top-down modulation of auditory-vocal integration. These differences in behavior and cortical activity between healthy controls and patients with SCA demonstrate that the cerebellum is not only essential for feedforward control but also plays a crucial role in the feedback-based control of speech production.

Lychee Fruit Detection Based on Monocular Machine Vision in Orchard Environment.

Due to the change of illumination environment and overlapping conditions caused by the neighboring fruits and other background objects, the simple application of the traditional machine vision method limits the detection accuracy of lychee fruits in natural orchard environments. Therefore, this research presented a detection method based on monocular machine vision to detect lychee fruits growing in overlapped conditions. Specifically, a combination of contrast limited adaptive histogram equalization (CLAHE), red/blue chromatic mapping, Otsu thresholding and morphology operations were adopted to segment the foreground regions of the lychees. A stepwise method was proposed for extracting individual lychee fruit from the lychee foreground region. The first step in this process was based on the relative position relation of the Hough circle and an equivalent area circle (equal to the area of the potential lychee foreground region) and was designed to distinguish lychee fruits growing in isolated or overlapped states. Then, a process based on the three-point definite circle theorem was performed to extract individual lychee fruits from the foreground regions of overlapped lychee fruit clusters. Finally, to enhance the robustness of the detection method, a local binary pattern support vector machine (LBP-SVM) was adopted to filter out the false positive detections generated by background chaff interferences. The performance of the presented method was evaluated using 485 images captured in a natural lychee orchard in Conghua (Area), Guangzhou. The detection results showed that the recall rate was 86.66%, the precision rate was greater than 87% and the F1-score was 87.07%.

Assessment of External Properties for Identifying Banana Fruit Maturity Stages Using Optical Imaging Techniques.

The maturity stage of bananas has a considerable influence on the fruit postharvest quality and the shelf life. In this study, an optical imaging based method was formulated to assess the importance of different external properties on the identification of four successive banana maturity stages. External optical properties, including the peel color and the local textural and local shape information, were extracted from the stalk, middle and tip of the bananas. Specifically, the peel color attributes were calculated from individual channels in the hue-saturation-value (HSV), the International Commission on Illumination (CIE) L*a*b* and the CIE L*ch color spaces; the textural information was encoded using a local binary pattern with uniform patterns (UP-LBP); and the local shape features were described by histogram of oriented gradients (HOG). Three classifiers based on the naïve Bayes (NB), linear discriminant analysis (LDA) and support vector machine (SVM) algorithms were adopted to evaluate the performance of identifying banana fruit maturity stages using the different optical appearance features. The experimental results demonstrate that overall identification accuracies of 99.2%, 100% and 99.2% were achieved using color appearance features with the NB, LDA and SVM classifiers, respectively; overall accuracies of 92.6%, 86.8% and 93.4% were obtained using local textural features for the three classifiers, respectively; and overall accuracies of only 84.3%, 83.5% and 82.6% were obtained using local shape features with the three classifiers, respectively. Compared to the complicated calculation of both the local textural and local shape properties, the simplicity and high accuracy of the peel color property make it more appropriate for identifying banana fruit maturity stages using optical imaging techniques.

Citrus Tree Segmentation from UAV Images Based on Monocular Machine Vision in a Natural Orchard Environment.

The segmentation of citrus trees in a natural orchard environment is a key technology for achieving the fully autonomous operation of agricultural unmanned aerial vehicles (UAVs). Therefore, a tree segmentation method based on monocular machine vision technology and a support vector machine (SVM) algorithm are proposed in this paper to segment citrus trees precisely under different brightness and weed coverage conditions. To reduce the sensitivity to environmental brightness, a selective illumination histogram equalization method was developed to compensate for the illumination, thereby improving the brightness contrast for the foreground without changing its hue and saturation. To accurately differentiate fruit trees from different weed coverage backgrounds, a chromatic aberration segmentation algorithm and the Otsu threshold method were combined to extract potential fruit tree regions. Then, 14 color features, five statistical texture features, and local binary pattern features of those regions were calculated to establish an SVM segmentation model. The proposed method was verified on a dataset with different brightness and weed coverage conditions, and the results show that the citrus tree segmentation accuracy reached 85.27% ± 9.43%; thus, the proposed method achieved better performance than two similar methods.

Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis.

Variety classification is an important step in seed quality testing. This study introduces t-distributed stochastic neighbourhood embedding (t-SNE), a manifold learning algorithm, into the field of hyperspectral imaging (HSI) and proposes a method for classifying seed varieties. Images of 800 maize kernels of eight varieties (100 kernels per variety, 50 kernels for each side of the seed) were imaged in the visible- near infrared (386.7⁻1016.7 nm) wavelength range. The images were pre-processed by Procrustes analysis (PA) to improve the classification accuracy, and then these data were reduced to low-dimensional space using t-SNE. Finally, Fisher's discriminant analysis (FDA) was used for classification of the low-dimensional data. To compare the effect of t-SNE, principal component analysis (PCA), kernel principal component analysis (KPCA) and locally linear embedding (LLE) were used as comparative methods in this study, and the results demonstrated that the t-SNE model with PA pre-processing has obtained better classification results. The highest classification accuracy of the t-SNE model was up to 97.5%, which was much more satisfactory than the results of the other models (up to 75% for PCA, 85% for KPCA, 76.25% for LLE). The overall results indicated that the t-SNE model with PA pre-processing can be used for variety classification of waxy maize seeds and be considered as a new method for hyperspectral image analysis.

Data augmentation based on multiple oversampling fusion for medical image segmentation.

A high-performance medical image segmentation model based on deep learning depends on the availability of large amounts of annotated training data. However, it is not trivial to obtain sufficient annotated medical images. Generally, the small size of most tissue lesions, e.g., pulmonary nodules and liver tumours, could worsen the class imbalance problem in medical image segmentation. In this study, we propose a multidimensional data augmentation method combining affine transform and random oversampling. The training data is first expanded by affine transformation combined with random oversampling to improve the prior data distribution of small objects and the diversity of samples. Secondly, class weight balancing is used to avoid having biased networks since the number of background pixels is much higher than the lesion pixels. The class imbalance problem is solved by utilizing weighted cross-entropy loss function during the training of the CNN model. The LUNA16 and LiTS17 datasets were introduced to evaluate the performance of our works, where four deep neural network models, Mask-RCNN, U-Net, SegNet and DeepLabv3+, were adopted for small tissue lesion segmentation in CT images. In addition, the small tissue segmentation performance of the four different deep learning architectures on both datasets could be greatly improved by incorporating the data augmentation strategy. The best pixelwise segmentation performance for both pulmonary nodules and liver tumours was obtained by the Mask-RCNN model, with DSC values of 0.829 and 0.879, respectively, which were similar to those of state-of-the-art methods.

Detection and localization of citrus fruit based on improved You Only Look Once v5s and binocular vision in the orchard.

Intelligent detection and localization of mature citrus fruits is a critical challenge in developing an automatic harvesting robot. Variable illumination conditions and different occlusion states are some of the essential issues that must be addressed for the accurate detection and localization of citrus in the orchard environment. In this paper, a novel method for the detection and localization of mature citrus using improved You Only Look Once (YOLO) v5s with binocular vision is proposed. First, a new loss function (polarity binary cross-entropy with logit loss) for YOLO v5s is designed to calculate the loss value of class probability and objectness score, so that a large penalty for false and missing detection is applied during the training process. Second, to recover the missing depth information caused by randomly overlapping background participants, Cr-Cb chromatic mapping, the Otsu thresholding algorithm, and morphological processing are successively used to extract the complete shape of the citrus, and the kriging method is applied to obtain the best linear unbiased estimator for the missing depth value. Finally, the citrus spatial position and posture information are obtained according to the camera imaging model and the geometric features of the citrus. The experimental results show that the recall rates of citrus detection under non-uniform illumination conditions, weak illumination, and well illumination are 99.55%, 98.47%, and 98.48%, respectively, approximately 2-9% higher than those of the original YOLO v5s network. The average error of the distance between the citrus fruit and the camera is 3.98 mm, and the average errors of the citrus diameters in the 3D direction are less than 2.75 mm. The average detection time per frame is 78.96 ms. The results indicate that our method can detect and localize citrus fruits in the complex environment of orchards with high accuracy and speed. Our dataset and codes are available at https://github.com/AshesBen/citrus-detection-localization.

The serum galectin-3 levels are associated with the severity and prognosis of ischemic stroke.

Galectin-3, a microglia/macrophage-derived inflammatory mediator, plays a role in the stroke progression. In this single-center prospective study, we included 288 consecutive patients with a first-ever acute ischemic stroke to assess the association between galectin-3 serum level and clinical severity at admission and outcome at discharge by univariate and multivariate logistic regression. The results were presented as odds ratios (OR) and 95% confidence intervals (CI). Patients with high severity and poor outcomes had higher serum levels of galectin-3 (P<0.001 and P<0.001). Multivariate analysis suggested that a galectin-3 serum level in the highest quartile (The lowest three quartiles[Q1-3] as the reference) was associated with poor functional outcome (OR, 3.15; 95% CI, 2.44-3.87). The AUC (standard error) for the NIHSS and the combined model were 0.764 (0.031) and 0.823 (0.027), corresponding to a difference of 0.059 (0.004). This study shows that higher serum levels of galectin-3 are associated with stroke severity at admission and stroke prognosis at discharge in ischemic stroke.

Identification of modules of hepatic encephalopathy based on protein-protein network and gene expression data.

Hepatic encephalopathy (HE) is regarded as a complication of liver cirrhosis, and 50-75% of patients who have been diagnosed with cirrhosis have HE syndrome. The aim of this study was to identify genes and pathways associated with HE alcoholics. Human protein-protein interactions were downloaded from the STRING database. Gene expression data were downloaded from EMBL-EBI. Combined score and Pearson's correlation coefficient were calculated to construct differential co-expression networks. Graph-theoretical measure was used to calculate the module connectivity dynamic score of multiple differential modules. In total, 11,134 genes were obtained after mapping between probes and genes. Then, 501,736 pairs and 16,496 genes were obtained to form background protein-protein interaction networks, 1,435 edges and 460 nodes were obtained constituting differential co-expression networks. Twenty-three seed genes and 10 significantly differential modules were identified. Four significantly differential modules which had larger connectivity alternation were observed. The identified seed genes and significantly differential modules offer novel understanding and molecular targets for the treatment of HE alcoholics.

[Clinical manifestations and molecular genetics of spinal bulbar muscular atrophy: report of 5 cases].

OBJECTIVE: To study the clinical and molecular genetic characteristics of spinal bulbar muscular atrophy (SBMA). METHODS: The clinical data, including case history, physical examination, biochemical analyses of blood, EMG, and muscle biopsy, of 5 Chinese patients with SBMA, all males, aged 29 - 58, with the onset age of 36 (17 - 49), were collected the information of in 5 cases. Four patients underwent PCR to examine the number of copies of CAG repeat region in androgen receptor (AR) gene. RESULTS: The clinical characteristics of the 5 patients included atrophy of lingualis, dysarthria, weakness and waste of the limbs, especially in the hands, and elevated creatine kinase (CK), fasting glucose, testosterone, and progesterone in the blood. EMG showed denervation motor potentials in all cases. The muscle biopsy in one case showed neurogenic atrophy. The number of (CAG) n repeat in AR gene was 50 - 62 in the, remarkably from that of 13 normal controls (19 - 20) without overlapping. CONCLUSION: SBMA affects the middle age males, shows a slowly progressing muscular atrophy in spinal and bulbar muscles. The different number of (CAG) n repeat of AR gene between the SBMA patients and the normal controls may be an important identification to differentiate SBMA from other motor neuron diseases.