Miracle-dz/UniFAR-FTUs huggingface dataset:
Tetrahydrocannabinol-Rich Extracts From Cannabis Sativa L. Improve Glucose Consumption and Modulate Metabolic Complications Linked to Neurodegenerative Diseases in Isolated Rat Brains

Reduced brain glucose consumption arising from impaired glucose uptake and utilization has been linked to the pathogenesis and complications of neurodegenerative diseases. The ability of Cannabis sativa L. tetrahydrocannabinol (THC)-rich extracts to stimulate brain glucose uptake and utilization as well as its modulatory effect on gluconeogenesis, antioxidative, purinergic and cholinergic activities were investigated in isolated rats’ brains. C. sativa leaves were sequentially extracted to yield the hexane and dichloromethane extracts. The extracts were incubated at 37°C with freshly harvested brains in the presence of glucose for 2 h. The control consisted of incubation without the extracts, while brains without the extracts and glucose served as the normal control. Metformin was used as the standard drug. C. sativa extracts caused a significant (p < 0.05) increase in brain glucose uptake, with concomitant elevation of glutathione level, superoxide dismutase, catalase, and ecto-nucleoside triphosphate diphosphohydrolase activities compared to the controls. Incubation with C. sativa extracts also led to depletion in malondialdehyde and nitric oxide levels, acetylcholinesterase, butyrylcholinesterase, glucose 6-phosphatase and fructose-1,6-biphosphatase activities. GC-MS analysis of the extracts revealed the presence of THC. In silico analysis predicted THC to be permeable across the blood-brain-barrier. THC was also predicted to have an oral LD50 and toxicity class values of 482 mg/kg and 4 respectively. These results indicate that C. sativa improves glucose consumption with concomitant suppression of oxidative stress and cholinergic dysfunction, and modulation of purinergic and gluconeogenic activities in brain tissues
Glutamate in Male and Female Sexual Behavior: Receptors, Transporters, and Steroid Independence

The survival of animal species predicates on the success of sexual reproduction. Neurotransmitters play an integral role in the expression of these sexual behaviors in the brain. Here, we review the role of glutamate in sexual behavior in rodents and non-rodent species for both males and females. These encompass the release of glutamate and correlations with glutamate receptor expression during sexual behavior. We then present the effects of glutamate on sexual behavior, as well as the effects of antagonists and agonists on different glutamate transporters and receptors. Following that, we discuss the potential role of glutamate on steroid-independent sexual behavior. Finally, we demonstrate the interaction of glutamate with other neurotransmitters to impact sexual behavior. These sexual behavior studies are crucial in the development of novel treatments of sexual dysfunction and in furthering our understanding of the complexity of sexual diversity. In the past decade, we have witnessed the burgeoning of novel techniques to study and manipulate neuron activity, to decode molecular events at the single-cell level, and to analyze behavioral data. They pose exciting avenues to gain further insight into future sexual behavior research. Taken together, this work conveys the essential role of glutamate in sexual behavior.
Early Intervention Services for First Episode of Psychosis in South London and the Maudsley (SLaM): 20 Years of Care and Research for Young People

Introduction: Early Intervention for a first episode of Psychosis (EI) is essential to improve outcomes. There is limited research describing real-world implementation of EI services. Method: Analysis of service characteristics, outcomes (described through a retrospective 2007–2017 Electronic Health Record (EHR) cohort study) and clinical research relating to the first 20 years of implementation of EI services in South London and Maudsley (SLaM) Trust. Results: SLaM EI are standalone services serving 443,050 young individuals in South-London, where (2017) incidence of psychosis (58.3–71.9 cases per 100,000 person-years) is greater than the national average. From 2007–2017 (when the EHR was established), 1,200 individuals (62.67% male, mean age 24.38 years, 88.17% single; two-thirds of non-white ethnicity) received NICE-compliant EI care. Pathways to EI services came mainly (75.26%) through inpatient (39.83%) or community (19.33%) mental health services or Accident and Emergency departments (A&E) (16%). At 6 year follow-up 34.92% of patients were still being prescribed antipsychotics. The 3 month and 6 year cumulative proportions of those receiving clozapine were 0.75 and 7.33%; those compulsorily admitted to psychiatric hospitals 26.92 and 57.25%; those admitted to physical health hospitals 6.83 and 31.17%, respectively. Average 3 months and 6 year days spent in hospital were 0.82 and 1.85, respectively; mean 6 year attendance at A&E was 3.01. SLaM EI clinical research attracted £58 million grant income and numerous high-impact scientific publications. Conclusions: SLaM EI services represent one of the largest, most established services of its kind, and are a leading model for development of similar services in the UK and worldwide.
Embracing the Social Nature of Recovery: A Qualitative Study on the Resource Group Method for People With Severe Mental Illness

Objective: The resource group method for people with severe mental illness might provide a useful framework to facilitate patient's empowerment and systematically engage significant others. However, no research has explored the perspectives and experiences of patients and their significant others. This is crucial for better adjustment to the needs of the people using the method. The aim of this study was to develop a useful framework for a deeper understanding of the resource group method and its outcomes. Method: The study used a longitudinal, qualitative multiple case-study design based on grounded theory methodology. During a period of 2 years, the developments and processes in eight resource groups were explored by conducting a total of 74 interviews (e.g., with patients, significant others, and mental health professionals) and 26 observations of resource group meetings. Results: Analysis showed that a well-functioning resource group set the stage for five processes to unfold: (i) experience of support; (ii) acknowledgment of significant others; (iii) activation; (iv) openness; and (v) integration. These processes facilitated recovery both in terms of an arousing curiosity within the patient as well as increasing reciprocity and equality in their social relations. In addition, the method emphasized the uniqueness of each recovery journey, thereby providing a framework to shape recovery-oriented care. The analysis also revealed three hindering factors: (i) embedding and implementation issues; (ii) predominant network; and (iii) tensions inherent in the resource group setting. Conclusion: Working according to the resource group method involves that the person's recovery work becomes a social process that takes place in relation to the social environment and everyday life in which it is important to acknowledge and integrate the needs of significant others in treatment and care. This study provides a first step toward a multidimensional comprehension of the resource group method, the working mechanisms and its influence on recovery for people with severe mental illness.
SRF‐Net: Spatial Relationship Feature Network for Tooth Point Cloud Classification

3D scanned point cloud data of teeth is popular used in digital orthodontics. The classification and semantic labelling for point cloud of each tooth is a key and challenging task for planning dental treatment. Utilizing the priori ordered position information of tooth arrangement, we propose an effective network for tooth model classification in this paper. The relative position and the adjacency similarity feature vectors are calculated for tooth 3D model, and combine the geometric feature into the fully connected layers of the classification training task. For the classification of dental anomalies, we present a dental anomalies processing method to improve the classification accuracy. We also use FocalLoss as the loss function to solve the sample imbalance of wisdom teeth. The extensive evaluations, ablation studies and comparisons demonstrate that the proposed network can classify tooth models accurately and automatically and outperforms state‐of‐the‐art point cloud classification methods.
Coexistence of Pseudomonas aeruginosa With Candida albicans Enhances Biofilm Thickness Through Alginate-Related Extracellular Matrix but Is Attenuated by N-acetyl-l-cysteine

Bacteria and Candida albicans are prominent gut microbiota, and the translocation of these organisms into blood circulation might induce mixed-organism biofilms, which warrants the exploration of mixed- versus single-organism biofilms in vitro and in vivo. In single-organism biofilms, Acinetobacter baumannii and Pseudomonas aeruginosa (PA) produced the least and the most prominent biofilms, respectively. C. albicans with P. aeruginosa (PA+CA) induced the highest biofilms among mixed-organism groups as determined by crystal violet straining. The sessile form of PA+CA induced higher macrophage responses than sessile PA, which supports enhanced immune activation toward mixed-organism biofilms. In addition, Candida incubated in pre-formed Pseudomonas biofilms (PA>CA) produced even higher biofilms than PA+CA (simultaneous incubation of both organisms) as determined by fluorescent staining on biofilm matrix (AF647 color). Despite the initially lower bacteria during preparation, bacterial burdens by culture in mixed-organism biofilms (PA+CA and PA>CA) were not different from biofilms of PA alone, supporting Candida-enhanced Pseudomonas growth. Moreover, proteomic analysis in PA>CA biofilms demonstrated high AlgU and mucA with low mucB when compared with PA alone or PA+CA, implying an alginate-related mucoid phenotype in PA>CA biofilms. Furthermore, mice with PA>CA biofilms demonstrated higher bacteremia with more severe sepsis compared with mice with PA+CA biofilms. This is possibly due to the different structures. Interestingly, l-cysteine, a biofilm matrix inhibitor, attenuated mixed-organism biofilms both in vitro and in mice. In conclusion, Candida enhanced Pseudomonas alginate–related biofilm production, and Candida presentation in pre-formed Pseudomonas biofilms might alter biofilm structures that affect clinical manifestations but was attenuated by l-cysteine.
Organotypic Modeling of the Tumor Landscape

Cancer is a complex disease and it is now clear that not only epithelial tumor cells play a role in carcinogenesis. The tumor microenvironment is composed of non-stromal cells, including endothelial cells, adipocytes, immune and nerve cells, and a stromal compartment composed of extracellular matrix, cancer-associated fibroblasts and mesenchymal cells. Tumorigenesis is a dynamic process with constant interactions occurring between the tumor cells and their surroundings. Even though all connections have not yet been discovered, it is now known that crosstalk between actors of the microenvironment drives cancer progression. Taking into account this complexity, it is important to develop relevant models to study carcinogenesis. Conventional 2D culture models fail to represent the entire tumor microenvironment properly and the use of animal models should be decreased with respect to the 3Rs rule. To this aim, in vitro organotypic models have been significantly developed these past few years. These models have different levels of complexity and allow the study of tumor cells alone or in interaction with the microenvironment actors during the multiple stages of carcinogenesis. This review depicts recent insights into organotypic modeling of the tumor and its microenvironment all throughout cancer progression. It offers an overview of the crosstalk between epithelial cancer cells and their microenvironment during the different phases of carcinogenesis, from the early cell autonomous events to the late metastatic stages. The advantages of 3D over classical 2D or in vivo models are presented as well as the most promising organotypic models. A particular focus is made on organotypic models used for studying cancer progression, from the less complex spheroids to the more sophisticated body-on-a-chip. Last but not least, we address the potential benefits of these models in personalized medicine which is undoubtedly a domain paving the path to new hopes in terms of cancer care and cure.
Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering

Superior human pose and shape reconstruction from monocular images depends on removing the ambiguities caused by occlusions and shape variance. Recent works succeed in regression‐based methods which estimate parametric models directly through a deep neural network supervised by 3D ground truth. However, 3D ground truth is neither in abundance nor can efficiently be obtained. In this paper, we introduce body part segmentation as critical supervision. Part segmentation not only indicates the shape of each body part but helps to infer the occlusions among parts as well. To improve the reconstruction with part segmentation, we propose a part‐level differentiable renderer that enables part‐based models to be supervised by part segmentation in neural networks or optimization loops. We also introduce a general parametric model engaged in the rendering pipeline as an intermediate representation between skeletons and detailed shapes, which consists of primitive geometries for better interpretability. The proposed approach combines parameter regression, body model optimization, and detailed model registration altogether. Experimental results demonstrate that the proposed method achieves balanced evaluation on pose and shape, and outperforms the state‐of‐the‐art approaches on Human3.6M, UP‐3D and LSP datasets.
Analysis of visible/infrared polarization characteristics of small UAV with complex background of buildings

The polarization phenomenon of the surface of an object and its changing in different spectra include its surface spatial geometric information and material information. Based on Kirchhoff law and Jones vector , the polarization model of the emission and reflection on the surface of the object is established, and the polarization phenomenon in infrared(IR) and visible light with different materials and incident angles are simulated. The IR and visible binocular polarization imaging system was constructed and the actual polarization data of small unmanned aerial vehicle(SUAV) and buildings were obtained. Two types of characteristic parameters, the degree of polarization and the angle of polarization, were extracted and analyzed, and the results proved that the SUAV and the background of the buildings had obvious differences in IR and visible. This research provides a basis for SUAV target detection and tracking using IR and visible polarization imaging in complex backgrounds.
Based on CycleGAN infrared ship image expansion method

To solve the problem of poor sample diversity of infrared ship image data, a method of infrared ship image data expansion is implemented based on CycleGAN. The method using the ideas of circulating, iterative approximation, there can be no noise of ship target infrared simulation image dataset maps to conform to the actual infrared detection scenario of ship target image dataset, according to the demand of the subsequent target detection identification, on the premise of the ship target itself form unchanged, injected with appropriate clutter interference, so as to realize the effective expansion of the image data, the method fully considers the target and background infrared characteristics, and are not influenced by whether the scale of the target alignment issues, which can effectively increase the image sample of diversification, for subsequent ship target detection identification algorithm, provides rich data support. The comparison experiment of image structure similarity and object detection accuracy verifies the effectiveness of the algorithm.
Visual-inertial odometry based on point and line segments

In low textured environments, visual Simultaneous localization and mapping (SLAM) only using point features is challenging in tracking sufficient point features, and this leads to poor accuracy and robustness. For this problem, this paper proposed an improved visual-inertial odometry (VIO), which can make better use of the advantage of point and line segment features to achieve fast and robust results. Visual features including points and lines are extracted and tracked using optical flow method. For line segment features, Plücker coordinates are employed for 3D spatial line and optimized using orthonormal representation. Furthermore, point features and line endpoints are expressed using inverse depth. To fuse the data from visual sensors and inertial measurement units (IMU), the states including IMU states and 3D landmarks are optimized by minimizing the measurement residuals including visual re-projection error and pre-integrated IMU error in a sliding window. Our proposal is evaluated on EuRoc datasets and compared with state of the art methods. Our method can achieve more robust performance in most of the experiments while maintaining its speed and accuracy.
Research on Beidou/GNSS wide area real-time positioning for automatic driving

In view of the technical problems of Beidou / GNSS multi-frequency multi-mode navigation signal compatibility and interoperability and wide area real-time high-precision Lane level positioning, the physical characteristics and difference characteristics of the new generation Beidou navigation satellite signal are analyzed, the deviation identification, modeling expression and correction compensation methods of Beidou/GNSS multi-frequency multi-mode signal are studied, and Beidou / GNSS is established Based on the real-time precise positioning model of wide area, the adaptive fusion and optimization of multi-source and heterogeneous constellation data are studied, and the solution of Beidou / GNSS rapid precise positioning aided by enhanced information of reference station network is proposed. The experimental results show that the scheme can improve the navigation and positioning in the complex urban environment, realize the real-time and fast decimeter level positioning, and the positioning accuracy in the open road is better than 10 cm.
Mitochondrial DNA Polymorphism in the Leaf Beetle Ophraella communa, an Introduced Biocontrol Agent of the Common Ragweed Ambrosia artemisiifolia (Asteraceae) Invading East Asia

ABSTRACT: The accidentally introduced leaf beetle Ophraella communa LeSage has become widely dispersed over regions with different climates in East Asia, following its host plant—the alien invasive ragweed Ambrosia artemisiifolia. During this range expansion, some of its life history traits have changed in response to environmental conditions in newly-colonized habitats. In this study we examined genetic variation in mtCOI gene among nine geographical populations of O. communa in East Asia, including one from Japan, one from Korea and seven from China. Our analysis identified two distinct clades (C1 and C2) among these populations, C2 including only the Fujian population from China and C1 including the other populations; haplotype sequence divergence was as large as ≥ 3% between the two clades yet negligible within each of them. Our results suggest that the Fujian population of O. communa is distinctly diverged at mtCOI genetic level from the other populations.
UGN: U-shape network based on graph convolution for 3D point cloud semantic segmentation

With the rapid development of 3D acquisition technology, 3D point cloud data has become a fast and accurate means to obtain 3D spatial information. However, as a kind of non-Euclidean data, the point cloud's disorder and sparseness make it difficult to train directly using conventional CNN. We propose a 3D point cloud semantic segmentation network based on graph convolution and U-shaped network structure. Among them, the graph convolution module with attention mechanism is used to aggregate the local features of the point cloud data. The U-shaped network with random sampling is used for semantic segmentation to make the network more accurate and more robust. Finally, we evaluated our model on S3DIS indoor data set and got competitive results compared with the benchmark method.
Imitation of ultra-sharp light focusing within turbid tissue-like scattering medium by using time-independent Helmholtz equation and method Monte Carlo

Based on time-independent Helmholtz equation and its solution in frame of inhomogeneous approximation a hybrid computational method for imitation of propagation of bounded laser beam focused into biological tissue is introduced. The biological tissue is simulated as a semi-infinite randomly inhomogeneous medium. The developed approach is intended to model laser beams in the super-sharp focusing mode. The results of modeling of laser light focusing into the turbid tissue-like scattering medium with lenses of various shapes are presented.
Escalate and De-Escalate Therapies for Intracranial Pressure Control in Traumatic Brain Injury

Severe traumatic brain injury (TBI) is frequently associated with an elevation of intracranial pressure (ICP), followed by cerebral perfusion pressure (CPP) reduction. Invasive monitoring of ICP is recommended to guide a step-by-step “staircase approach” which aims to normalize ICP values and reduce the risks of secondary damage. However, if such monitoring is not available clinical examination and radiological criteria should be used. A major concern is how to taper the therapies employed for ICP control. The aim of this manuscript is to review the criteria for escalating and withdrawing therapies in TBI patients. Each step of the staircase approach carries a risk of adverse effects related to the duration of treatment. Tapering of barbiturates should start once ICP control has been achieved for at least 24 h, although a period of 2–12 days is often required. Administration of hyperosmolar fluids should be avoided if ICP is normal. Sedation should be reduced after at least 24 h of controlled ICP to allow neurological examination. Removal of invasive ICP monitoring is suggested after 72 h of normal ICP. For patients who have undergone surgical decompression, cranioplasty represents the final step, and an earlier cranioplasty (15–90 days after decompression) seems to reduce the rate of infection, seizures, and hydrocephalus.
PointSkelCNN: Deep Learning‐Based 3D Human Skeleton Extraction from Point Clouds

A 3D human skeleton plays important roles in human shape reconstruction and human animation. Remarkable advances have been achieved recently in 3D human skeleton estimation from color and depth images via a powerful deep convolutional neural network. However, applying deep learning frameworks to 3D human skeleton extraction from point clouds remains challenging because of the sparsity of point clouds and the high nonlinearity of human skeleton regression. In this study, we develop a deep learning‐based approach for 3D human skeleton extraction from point clouds. We convert 3D human skeleton extraction into offset vector regression and human body segmentation via deep learning‐based point cloud contraction. Furthermore, a disambiguation strategy is adopted to improve the robustness of joint points regression. Experiments on the public human pose dataset UBC3V and the human point cloud skeleton dataset 3DHumanSkeleton compiled by the authors show that the proposed approach outperforms the state‐of‐the‐art methods.