Case Report: Identification of a novel PRR12 variant in a Chinese boy with developmental delay and short stature.

Proline Rich 12 (PRR12) protein is primarily expressed in the brain and localized in the nucleus. The variants in the PRR12 gene were reported to be related to neuroocular syndrome. Patients with PRR12 gene presented with intellectual disability (ID), neuropsychiatric disorders, some congenital anomalies, and with or without eye abnormalities. Here, we report an 11-year-old boy with a novel PRR12 variant c.1549_1568del, p.(Pro517Alafs*35). He was the first PRR12 deficiency patient in China and presented with ID, short stature, and mild scoliosis. He could not concentrate on his studies and was diagnosed with attention deficit hyperactivity disorder (ADHD). The insulin-like growth factor 1 (IGH-1) was low in our patient, which may be the cause of his short stature. Patients with neuroocular syndrome are rare, and further exploration is needed to understand the reason for neurodevelopmental abnormalities caused by PRR12 variants. Our study further expands on the PRR12 variants and presents a new case involving PPR12 variants.

Reservoir Computing Based on Oxygen-Vacancy-Mediated X-ray Optical Synaptic Device for Medical CT Bone Diagnosis.

Recognition and judgment of X-ray computed tomography (CT) images play a crucial role in medical diagnosis and disease prevention. However, the storage and calculation of the X-ray imaging system applied in the traditional CT diagnosis is separate, and the pathological judgment is based on doctors' experience, which will affect the timeliness and accuracy of decision-making. In this paper, a simple-structured reservoir computing network (RC) is proposed based on Ga2O3 X-ray optical synaptic devices to recognize medical skeletal CT images with high accuracy. Through oxygen vacancy engineering, Ga2O3 X-ray optical synaptic devices with adjustable photocurrent gain and a persistent photoconductivity effect were obtained. By using the Ga2O3 X-ray optical synaptic device as a reservoir, we constructed an RC network for medical skeletal CT diagnosis and verified its image recognition capability using the MNIST data set with an accuracy of 78.08%. In the elbow skeletal CT image recognition task, the recognition rate is as high as 100%. This work constructs a simple-structured RC network for X-ray image recognition, which is of great significance in applications in medical fields.

Inferring gene regulatory networks from single-cell transcriptomics based on graph embedding.

MOTIVATION: Gene regulatory networks (GRNs) encode gene regulation in living organisms, and have become a critical tool to understand complex biological processes. However, due to the dynamic and complex nature of gene regulation, inferring GRNs from scRNA-seq data is still a challenging task. Existing computational methods usually focus on the close connections between genes, and ignore the global structure and distal regulatory relationships. RESULTS: In this study, we develop a supervised deep learning framework, IGEGRNS, to infer GRNs from scRNA-seq data based on graph embedding. In the framework, contextual information of genes is captured by GraphSAGE, which aggregates gene features and neighborhood structures to generate low-dimensional embedding for genes. Then, the k most influential nodes in the whole graph are filtered through Top-k pooling. Finally, potential regulatory relationships between genes are predicted by stacking CNNs. Compared with nine competing supervised and unsupervised methods, our method achieves better performance on six time-series scRNA-seq datasets. AVAILABILITY AND IMPLEMENTATION: Our method IGEGRNS is implemented in Python using the Pytorch machine learning library, and it is freely available at https://github.com/DHUDBlab/IGEGRNS.

Pyroelectric Photoconductive Diode for Highly Sensitive and Fast DUV Detection.

Detecting high-energy photons from the deep ultraviolet (DUV) to X-rays is vital in security, medicine, industry, and science. Wide bandgap (WBG) semiconductors exhibit great potential for detecting high-energy photons. However, the implementation of highly sensitive and high-speed detectors based on WBG semiconductors has been a huge challenge due to the inevitable deep level traps and the lack of appropriate device structure engineering. Here, a sensitive and fast pyroelectric photoconductive diode (PPD), which couples the interface pyroelectric effect with the photoconductive effect based on tailored polycrystal Ga-rich GaOx (PGR-GaOx) Schottky photodiode, is first proposed. The PPD device exhibits ultrahigh detection performance for DUV and X-ray light. The responsivity for DUV light and sensitivity for X-ray are up to 104 A W-1 and 105 µC Gyair -1 cm-2, respectively. Especially, the interface pyroelectric effect induced by polar symmetry in the depletion region of the PGR-GaOx can significantly improve the response speed of the device by 105 times. Furthermore, the potential of the device is demonstrated for imaging enhancement systems with low power consumption and high sensitivity. This work fully excavates the potential of the pyroelectric effect for detectors and provides a novel design strategy to achieve sensitive and high-speed detectors.

Potential Application of Ovalbumin Gel Nanoparticles Loaded with Carvacrol in the Preservation of Fresh Pork.

Plant essential oil has attracted much attention in delaying pork spoilage due to its safety, but its low antibacterial efficiency needs to be solved by encapsulation. Our previous research had fabricated a type of ovalbumin gel nanoparticles loaded with carvacrol (OCGn-2) using the gel-embedding method, which had a high encapsulation rate and antibacterial activity. The main purpose of this study was to further evaluate the stability and slow-release characteristics of OCGn-2 and potential quality effects of the nanoparticles on the preservation of fresh pork pieces during 4 °C storage. The particle test showed that the nanoparticles had better heat stability below 85 °C and salt stability below 90 mM. The in vitro release study indicated that the carvacrol in OCGn-2 followed a Fickian release mechanism. The pork preservation test suggested that the OCGn-2 coating treatments could remarkably restrict the quality decay of pork slices compared to free carvacrol or a physical mixture of ovalbumin and carvacrol treatment. Nano-encapsulation of ovalbumin is beneficial to the sustained release, enhanced oxidation resistance, and improved antibacterial activity of carvacrol. The study suggested that ovalbumin gel nanoparticles embedded with carvacrol could be applied as an efficient bacterial active packaging to extend the storage life of pork.

DMFDDI: deep multimodal fusion for drug-drug interaction prediction.

Drug combination therapy has gradually become a promising treatment strategy for complex or co-existing diseases. As drug-drug interactions (DDIs) may cause unexpected adverse drug reactions, DDI prediction is an important task in pharmacology and clinical applications. Recently, researchers have proposed several deep learning methods to predict DDIs. However, these methods mainly exploit the chemical or biological features of drugs, which is insufficient and limits the performances of DDI prediction. Here, we propose a new deep multimodal feature fusion framework for DDI prediction, DMFDDI, which fuses drug molecular graph, DDI network and the biochemical similarity features of drugs to predict DDIs. To fully extract drug molecular structure, we introduce an attention-gated graph neural network for capturing the global features of the molecular graph and the local features of each atom. A sparse graph convolution network is introduced to learn the topological structure information of the DDI network. In the multimodal feature fusion module, an attention mechanism is used to efficiently fuse different features. To validate the performance of DMFDDI, we compare it with 10 state-of-the-art methods. The comparison results demonstrate that DMFDDI achieves better performance in DDI prediction. Our method DMFDDI is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDEBLab/DMFDDI.git.

Changes in mineral fraction and pore morphology of coal with acidification treatment: contribution of clay minerals to methane adsorption.

The accurate calculation of the contribution which provided by clay minerals in coal on methane adsorption not only bares a significant importance for evaluating the effectiveness of acid stimulation in improving permeability and estimating the coalbed methane reserves but also serves a guide for the governance and utilization of methane resources. In this study, hydrochloric acid (HCl) and hydrofluoric acid (HF) were used to remove specific minerals in Qingdong coal samples. We firstly analyzed the mineral compositions of coal samples with different acidification treatments based on the X-ray diffraction (XRD) experiments, together with analysis of the changes in pore morphology and adsorption capacity. The results showed that acidification did not significantly change the shape of the pores, which remained slit-/plate-like pore. However, the altered adsorption capacity of the coal samples was attributed to changes in pore structure and mineral distribution. Acid erosion of mesopores promoted the transition from mesopores to macropores, contributing to an increase of 8.4% and 24.36% in the percentage of macropores in coal samples treated with HCl and HF, respectively. Fractal dimension D1 grew from 2.2193 to 2.3888 and 2.2572, respectively, but D2 decreased from 2.6146 to 2.5814 and 2.5433, indicating an increment in pore surface roughness and a simplification of the pore structure. The mineral richness of the coal seams should be taken into consideration when applying acid stimulation to increase permeability due to that the acidification products may block the passage of gas migration when the mineral content is slight, which can hinder gas extraction. The aim of this study is to quantitatively determine the contribution rate of clay minerals in coal to methane adsorption with a calculation method is provided by combining pore parameters and limit adsorption capacity, resulting in a contribution rate of 15%.

[TrkB receptor-dependent PV neurons regulate visual orientation discrimination in mice].

The neurotrophin-tyrosine receptor kinase B (TrkB) signaling pathway plays an important role in regulating the balance of excitation and inhibition in the primary visual cortex (V1). Previous studies have revealed its mechanism of regulating the level of cortical excitability by increasing the efficiency of excitatory transmission, but it has not been elucidated how TrkB receptors regulate the balance of excitation and inhibition through the inhibitory system, which in turn affects visual cortex function. Therefore, the objective of this study was to investigate how the TrkB signaling pathway specifically regulates the most important inhibitory neuron-PV neurons affects the visual cortex function of mice. The expression of TrkB receptor on PV neurons in the V1 region was specifically reduced by the virus, the functional changes of inhibitory and excitatory neurons in the primary visual cortex were recorded by multi-channel electrophysiological in vivo. The orientation discrimination ability of mice was tested by behavioral experiments, and altered orientation discrimination ability of mice was tested by behavioral experiments. The results showed that reduced expression of TrkB receptors on PV inhibitory neurons in primary visual cortex significantly increased the response intensity of excitatory neurons, reduced the orientation discrimination ability of inhibitory and excitatory neurons, and increased the signal-to-noise ratio, but the orientation discrimination ability at the individual level in mice showed a decrease. These results suggest that the TrkB signaling pathway does not modulate the function of PV neurons solely by increasing excitatory transmission targeting PV neurons, and its effect on neuronal signal-to-noise ratio is not due to enhancement of the inhibitory system.

Combing experimental methods and molecular simulations to study self-healing behaviors of polyurethane elastomers containing multiple hydrogen bond networks and flexible blocks.

The preparation of polymers with high self-healing ability is conducive to environmental protection and resource conservation. In the present work, two kinds of polyurethane (PU) elastomers were prepared: the one containing flexible end blocks (polypropylene glycol) and the other containing flexible end blocks and 2-ureido-4[1H]-pyrimidinone (UPy) groups that can form reversible quadruple hydrogen bonds. Both of the two PU elastomers have self-healing ability. At low temperatures the PU without UPy groups exhibits stronger self-healing ability, while at high temperatures the PU with UPy groups has better self-healing function. The difference can be attributed to the combined effect of segmental mobility and reversible network strength. Based on molecular simulations, we further observed that the self-healing behaviors are affected by four factors: healing temperature, reversible interaction strength, reversible interaction site density and segment diffusion ability.

Enhanced grading methods for lumbar paraspinal fat infiltration and its prognostic value in predicting lumbar disc herniation.

BACKGROUND: The simplified 3-grade system for measuring fat infiltration in the paraspinal muscles is widely utilized. In comparing our proposed 4-grade system to the existing 3-grade system, we evaluated its impact on results and particularly its ability to predict disc herniation, ultimately highlighting deficiencies in the latter. The objective of this investigation was to validate the efficacy of our newly proposed semi-quantitative simplified 4-grade system for assessing fat infiltration, as compared to the existing literature-based simplified 3-grade system, in terms of their predictive value for lumbar disc herniation. METHODS: Infiltration of the right and left lumbar multifidus and erector spinae muscles were assessed using a semi-quantitative 3- and 4-grade fat infiltration system on axial magnetic resonance imaging sections at the L3-S1 level in all subjects, with comparison of results between groups. The correlation between these grading systems and lumbar disc herniation was investigated. RESULTS: The simplified 3-degree system for measuring fat infiltration was not effective in predicting lumbar disc herniation (p > 0.05), while the 4-degree system proved to be useful in predicting it (p < 0.05). In both grading systems, females were found to have a higher risk of lumbar disc herniation than males (p < 0.05), and the risk increased with age and body mass index (BMI) (p < 0.001). CONCLUSIONS: It was observed that using the 4-grade fat infiltration system to determine the level of fat infiltration in the paraspinal muscles is more effective in predicting lumbar disc herniation compared to the 3-grade system. The 4-grade fat infiltration grading system proves to be an efficient semi-quantitative method that can replace the simplified 3-grade system.

Research on Directional Hydraulic Coal Cutting and Outburst Prevention Technology in Soft and Hard Composite Coal Seams.

Affected by tectonics, soft and hard composite coal seams are widely distributed in China; the soft stratification in the soft and hard composite coal seam is the key to controlling the occurrence of coal and gas outburst accidents. Based on this, for soft and hard composite coal seams, in order to accurately extract soft layers, a directional hydraulic coal mining equipment has been developed, including a drilling rig pump truck system, a directional coal wireless measurement system, and a cutter drill pipe system. By constructing a mathematical model and conducting numerical simulations, it was found that the vertical stress, horizontal stress, and gas pressure of the coal body around the borehole after coal extraction decreased significantly compared to normal borehole conditions; the on-site test results indicate that the hydraulic coal extraction volume of directional hydraulic coal extraction boreholes reaches 0.25 m3 per meter. The total amount of coal extracted accounts for more than 3‰ of the total amount of coal within the coverage area. The average concentration of gas extraction in the coal extraction area is 80.15%, and the net amount of gas extraction from 100 m boreholes reaches 0.17 m3/(min·hm). After extraction, the measured residual gas content in the coal extraction and non-extraction areas decreased by 59.27 and 40.38%, respectively. Directional hydraulic coal mining technology can effectively solve the problem of coal and gas outburst prevention in soft and hard composite coal seams and has good application prospects.

Deep enhanced constraint clustering based on contrastive learning for scRNA-seq data.

Single-cell RNA sequencing (scRNA-seq) measures transcriptome-wide gene expression at single-cell resolution. Clustering analysis of scRNA-seq data enables researchers to characterize cell types and states, shedding new light on cell-to-cell heterogeneity in complex tissues. Recently, self-supervised contrastive learning has become a prominent technique for underlying feature representation learning. However, for the noisy, high-dimensional and sparse scRNA-seq data, existing methods still encounter difficulties in capturing the intrinsic patterns and structures of cells, and seldom utilize prior knowledge, resulting in clusters that mismatch with the real situation. To this end, we propose scDECL, a novel deep enhanced constraint clustering algorithm for scRNA-seq data analysis based on contrastive learning and pairwise constraints. Specifically, based on interpolated contrastive learning, a pre-training model is trained to learn the feature embedding, and then perform clustering according to the constructed enhanced pairwise constraint. In the pre-training stage, a mixup data augmentation strategy and interpolation loss is introduced to improve the diversity of the dataset and the robustness of the model. In the clustering stage, the prior information is converted into enhanced pairwise constraints to guide the clustering. To validate the performance of scDECL, we compare it with six state-of-the-art algorithms on six real scRNA-seq datasets. The experimental results demonstrate the proposed algorithm outperforms the six competing methods. In addition, the ablation studies on each module of the algorithm indicate that these modules are complementary to each other and effective in improving the performance of the proposed algorithm. Our method scDECL is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DBLABDHU/scDECL.

In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array.

Detection and recognition of latent fingerprints play crucial roles in identification and security. However, the separation of sensor, memory, and processor in conventional ex-situ fingerprint recognition system seriously deteriorates the latency of decision-making and inevitably increases the overall computing power. In this work, a photoelectronic reservoir computing (RC) system, consisting of DUV photo-synapses and nonvolatile memristor array, is developed to detect and recognize the latent fingerprint with in-sensor and parallel in-memory computing. Through the Ga-rich design, we achieve amorphous GaOx (a-GaOx) photo-synapses with an enhanced persistent photoconductivity (PPC) effect. The PPC effect, which induces nonlinearly tunable conductivity, renders the a-GaOx photo-synapses an ideal deep ultraviolet (DUV) photoelectronic reservoir, thus mapping the complex input vector into a dimensionality-reduced output vector. Connecting the reservoirs and a memristor array, we further construct an in-sensor RC system for latent fingerprint identification. The system maintains over 90% recognition accuracy for latent fingerprint within 15% stochastic noise level via the proposed dual-feature strategy. This work provides a subversive prototype system of DUV in-sensor RC for highly efficient recognition of latent fingerprints.

Highly Linear and Symmetric Synaptic Memtransistors Based on Polarization Switching in Two-Dimensional Ferroelectric Semiconductors.

Brain-inspired neuromorphic computing hardware based on artificial synapses offers efficient solutions to perform computational tasks. However, the nonlinearity and asymmetry of synaptic weight updates in reported artificial synapses have impeded achieving high accuracy in neural networks. Here, this work develops a synaptic memtransistor based on polarization switching in a two-dimensional (2D) ferroelectric semiconductor (FES) of α-In2 Se3 for neuromorphic computing. The α-In2 Se3 memtransistor exhibits outstanding synaptic characteristics, including near-ideal linearity and symmetry and a large number of programmable conductance states, by taking the advantages of both memtransistor configuration and electrically configurable polarization states in the FES channel. As a result, the α-In2 Se3 memtransistor-type synapse reaches high accuracy of 97.76% for digit patterns recognition task in simulated artificial neural networks. This work opens new opportunities for using multiterminal FES memtransistors in advanced neuromorphic electronics.

The Role of Exosome MicroRNA-103 in the Proliferation and Invasion of Hepatoma Cells and Its Mechanism.

Liver cancer (HCC) is a common malignant tumor whose incidence is increasing worldwide, but existing chemotherapeutic agents are not ideally effective drugs and have considerable resistance to chemotherapy. Exosome microRNA-103 plays an important role in the proliferation and invasion of liver cancer cells. The purpose of this article is to investigate the role and mechanism of exosome microRNA-103 in hepatocellular carcinoma cell proliferation and invasion. 84 patients with hepatocellular carcinoma diagnosed in a hospital from June 2017 to June 2020 were selected. The average age was 60.13±6.99 years. When the patient was fasting, 3 mL of peripheral venous blood was taken. And 50 healthy control groups were established, with an average age of 59.98±8.18 years old. 3 ml of peripheral venous blood was collected on an empty stomach to compare the cell proliferation rate and invasion rate. The results of the study showed that the number of stage III hepatocellular carcinoma cells invaded at 6h was 68.9, and it changed to 89.4 at 12h, and 106.4 at 24h; compared with that, the cell invasion rate in stage IV was higher. The number of stage IV hepatocellular carcinoma invasions at 6h was 68, which changed to 94.5 at 12h and 112.4 at 24h.

Anatomic measurement of osseous parameters of the glenoid.

The angle and position of the scapular glenoid are important in shoulder mechanics, the interpretation of diseases, and planning shoulder replacement surgery. In total shoulder replacement, understanding the bony parameters of the glenoid is also of considerable guiding significance for designing implant size and improving material adaptability. To compare glenoid parameters measured from skeletal scapula specimens with those measured by 3D modeling of CT scanning images, analyze correlations between these data, and draw conclusions to guide clinical treatment of shoulder joint injury and total shoulder joint replacement. The data of manual and CT measurements from the same Chinese dry glenoid was compared. Three-dimensional measurement data were collected from the Japanese population and compared with the Chinese population data generated in this study. There were no significant differences between manual measurement and CT measurement in the inclination angle, glenopolar angle, anteroposterior transverse diameter, upper to lower vertical diameter, and depth of the glenoid (P = 0.288, 0.524, 0.111, 0.194, and 0.055, respectively). Further, there were no significant differences between Japanese and Chinese glenoid bones in the upper and lower vertical diameters or anteroposterior transverse diameters (P > 0.05). There were no significant differences between CT and manual measurements, suggesting that the CT method may provide measurements very close to the actual specimen size. This result, however, indicated that the measurer should be careful when measuring the depth of the glenoid.

Inferring Gene Regulatory Networks From Single-Cell Transcriptomic Data Using Bidirectional RNN.

Accurate inference of gene regulatory rules is critical to understanding cellular processes. Existing computational methods usually decompose the inference of gene regulatory networks (GRNs) into multiple subproblems, rather than detecting potential causal relationships simultaneously, which limits the application to data with a small number of genes. Here, we propose BiRGRN, a novel computational algorithm for inferring GRNs from time-series single-cell RNA-seq (scRNA-seq) data. BiRGRN utilizes a bidirectional recurrent neural network to infer GRNs. The recurrent neural network is a complex deep neural network that can capture complex, non-linear, and dynamic relationships among variables. It maps neurons to genes, and maps the connections between neural network layers to the regulatory relationship between genes, providing an intuitive solution to model GRNs with biological closeness and mathematical flexibility. Based on the deep network, we transform the inference of GRNs into a regression problem, using the gene expression data at previous time points to predict the gene expression data at the later time point. Furthermore, we adopt two strategies to improve the accuracy and stability of the algorithm. Specifically, we utilize a bidirectional structure to integrate the forward and reverse inference results and exploit an incomplete set of prior knowledge to filter out some candidate inferences of low confidence. BiRGRN is applied to four simulated datasets and three real scRNA-seq datasets to verify the proposed method. We perform comprehensive comparisons between our proposed method with other state-of-the-art techniques. These experimental results indicate that BiRGRN is capable of inferring GRN simultaneously from time-series scRNA-seq data. Our method BiRGRN is implemented in Python using the TensorFlow machine-learning library, and it is freely available at https://gitee.com/DHUDBLab/bi-rgrn.

Entropy-based inference of transition states and cellular trajectory for single-cell transcriptomics.

The development of single-cell RNA-seq (scRNA-seq) technology allows researchers to characterize the cell types, states and transitions during dynamic biological processes at single-cell resolution. One of the critical tasks is to infer pseudo-time trajectory. However, the existence of transition cells in the intermediate state of complex biological processes poses a challenge for the trajectory inference. Here, we propose a new single-cell trajectory inference method based on transition entropy, named scTite, to identify transitional states and reconstruct cell trajectory from scRNA-seq data. Taking into account the continuity of cellular processes, we introduce a new metric called transition entropy to measure the uncertainty of a cell belonging to different cell clusters, and then identify cell states and transition cells. Specifically, we adopt different strategies to infer the trajectory for the identified cell states and transition cells, and combine them to obtain a detailed cell trajectory. For the identified cell clusters, we utilize the Wasserstein distance based on the probability distribution to calculate distance between clusters, and construct the minimum spanning tree. Meanwhile, we adopt the signaling entropy and partial correlation coefficient to determine transition paths, which contain a group of transition cells with the largest similarity. Then the transitional paths and the MST are combined to infer a refined cell trajectory. We apply scTite to four real scRNA-seq datasets and an integrated dataset, and conduct extensive performance comparison with nine existing trajectory inference methods. The experimental results demonstrate that the proposed method can reconstruct the cell trajectory more accurately than the compared algorithms. The scTite software package is available at https://github.com/dblab2022/scTite.

New Evidence of Central Nervous System Damage in Diabetes: Impairment of Fine Visual Discrimination.

Diabetes can damage both the peripheral sensory organs, causing retinopathy, and the central visual system, leading to contrast sensitivity and impaired color vision in patients without retinopathy. Orientation discrimination is important for shape recognition by the visual system. Our psychophysical findings in this study show diminished orientation discrimination in patients with diabetes without retinopathy. To reveal the underlying mechanism, we established a diabetic mouse model and recorded in vivo electrophysiological data in the dorsal lateral geniculate nucleus (dLGN) and primary visual cortex (V1). Reduced orientation selectivity was observed in both individual and populations of neurons in V1 and dLGN, which increased in severity with disease duration. This diabetes-associated neuronal dysfunction appeared earlier in the V1 than dLGN. Additionally, neuronal activity and signal-to-noise ratio are reduced in V1 neurons of diabetic mice, leading to a decreased capacity for information processing by V1 neurons. Notably, the V1 in diabetic mice exhibits reduced excitatory neuronal activity and lower levels of phosphorylated mammalian target of rapamycin (mTOR). Our findings show that altered responses of both populations of and single V1 neurons may impair fine vision, thus expanding our understanding of the underlying causes of diabetes-related impairment of the central nervous system.