No association between serum cholesterol and death by suicide in patients with schizophrenia, bipolar affective disorder, or major depressive disorder

BACKGROUND: Previous research on serum total cholesterol and suicidality has yielded conflicting results. Several studies have reported a link between low serum total cholesterol and suicidality, whereas others have failed to replicate these findings, particularly in patients with major affective disorders. These discordant findings may reflect the fact that studies often do not distinguish between patients with bipolar and unipolar depression; moreover, definitions and classification schemes for suicide attempts in the literature vary widely. METHODS: Subjects were patients with one of the three major psychiatric disorders commonly associated with suicide: schizophrenia, bipolar affective disorder, and major depressive disorder (MDD). We compared serum lipid levels in patients who died by suicide (82 schizophrenia, 23 bipolar affective disorder, and 67 MDD) and non-suicide controls (200 schizophrenia, 49 bipolar affective disorder, and 175 MDD). RESULTS: Serum lipid profiles did not differ between patients who died by suicide and control patients in any diagnostic group. CONCLUSIONS: Our results do not support the use of biological indicators such as serum total cholesterol to predict suicide risk among patients with a major psychiatric disorder

Semantic segmentation of large-scale outdoor point clouds by encoder–decoder shared mlps with multiple losses

Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand the scene environment in various applications, such as geometry mapping, autonomous driving, and more. With an advantage of being a 3D metric space, 3D LiDAR point clouds, on the other hand, pose a challenge for a deep learning approach, due to their unstructured, unorder, irregular, and large-scale characteristics. Therefore, this paper presents an encoder–decoder shared multi-layer perceptron (MLP) with multiple losses, to address an issue of this semantic segmentation. The challenge rises a trade-off between efficiency and effectiveness in performance. To balance this trade-off, we proposed common mechanisms, which is simple and yet effective, by defining a random point sampling layer, an attention-based pooling layer, and a summation of multiple losses integrated with the encoder–decoder shared MLPs method for the large-scale outdoor point clouds semantic segmentation. We conducted our experiments on the following two large-scale benchmark datasets: Toronto-3D and DALES dataset. Our experimental results achieved an overall accuracy (OA) and a mean intersection over union (mIoU) of both the Toronto-3D dataset, with 83.60% and 71.03%, and the DALES dataset, with 76.43% and 59.52%, respectively. Additionally, our proposed method performed a few numbers of parameters of the model, and faster than PointNet++ by about three times during inferencing

Synthesis of UV/blue light-emitting aluminum hydroxide with oxygen vacancy and their application to electrically driven light-emitting diodes

Aluminum hydroxide nanoparticles, one of the essential luminescent materials for display technology, bio-imaging, and sensors due to their non-toxicity, affordable pricing, and rare-earth-free phosphors, are synthesized via a simple method at a reaction time of 10 min at a low temperature of 200 degrees C. By controlling the precursor's ratio of aluminum acetylacetonate to oleic acid, UV or blue light-emitting aluminum hydroxides with oxygen defects and carbonyl radicals can be synthesized. As a result, aluminum hydroxide (Al(OH)(3-x)) nanoparticles overwhelmingly emit UVA light (390 nm) because of the oxygen defects in nanoparticles, and carbon-related radicals on the nanoparticles are responsible for the blue-light emission at 465 nm. Electrically driven light-emitting devices are applied using luminescent aluminum hydroxide as an emissive layer, that consists of a cost-efficient inverted bottom-emission structure as [ITO (cathode)/ZnO/emissive layers/2,2 '-bis(4-(carbazol-9-yl)phenyl)-biphenyl (BCBP)/MoO3/Al (anode)]. The device with aluminum hydroxide as an emissive layer shows a maximum luminance of 215.48 cd m(-2) and external quantum efficiency (EQE) of 0.12%. The new method for synthesizing UV-blue emitting aluminum hydroxides and their application to LEDs will contribute to developing the field of non-toxic optoelectronic material or UV-blue emitting devices.N

wear-UCAM: A toolkit for mobile user interactions in smart environments

Abstract. In this paper, we propose a toolkit, wear-UCAM, which is a smart user interface to support mobile user interactions in ubiquitous computing environments through utilizing user’s context. With the rapid developments of ubiquitous computing and its relevant technologies, the interest in contextaware applications for mobile/wearable computing also becomes popular in both academic and industrial fields. In such smart environments, furthermore, it is crucial for a user to manage personal information (health, preferences, activities, etc) for the personalized services. However, there are only a few research activities on frameworks or toolkits which support smart user interfaces on mobile/wearable computers regarding reflection of user’s context to context-aware applications. In the proposed wear-UCAM, therefore, we focus on a framework of smart user interface for context-aware applications by taking account of how to acquire contextual information relevant to a user from sensors, how to integrate and manage it, and how to control the user context in smart environments. For an experimental evaluation on the proposed toolkit, we utilize physiological sensors and PDA with wireless LAN and Bluetooth for the smart user interface, and experiment with it in ubiHome, a smart home environment test-bed.

Structure-Based Virtual Screening and De Novo Design to Identify Submicromolar Inhibitors of G2019S Mutant of Leucine-Rich Repeat Kinase 2

Missense mutations of leucine-rich repeat kinase 2 (LRRK2), including the G2019S mutant, are responsible for the pathogenesis of Parkinson's disease. In this work, structure-based virtual screening of a large chemical library was carried out to identify a number of novel inhibitors of the G2019S mutant of LRRK2, the biochemical potencies of which ranged from the low micromolar to the submicromolar level. The discovery of these potent inhibitors was made possible due to the modification of the original protein-ligand binding energy function in order to include an accurate ligand dehydration energy term. The results of extensive molecular docking simulations indicated that the newly identified inhibitors were bound to the ATP-binding site of the G2019S mutant of LRRK2 through the multiple hydrogen bonds with backbone amide groups in the hinge region as well as the hydrophobic interactions with the nonpolar residues in the P-loop, hinge region, and interdomain region. Among 18 inhibitors derived from virtual screening, 4-(2-amino-5-phenylpyrimidin-4-yl)benzene-1,3-diol (Inhibitor 2) is most likely to serve as a new molecular scaffold to optimize the biochemical potency, because it revealed submicromolar inhibitory activity in spite of its low molecular weight (279.3 amu). Indeed, a highly potent inhibitor (Inhibitor 2n) of the G2019S mutant was derived via the structure-based de novo design using the structure of Inhibitor 2 as the molecular core. The biochemical potency of Inhibitor 2n surged to the nanomolar level due to the strengthening of hydrophobic interactions in the ATP-binding site, which were presumably caused by the substitutions of small nonpolar moieties. Due to the high biochemical potency against the G2019S mutant of LRRK2 and the putatively good physicochemical properties, Inhibitor 2n is anticipated to serve as a new lead compound for the discovery of antiparkinsonian medicines.11Nsciescopu

Semantic Cardiac Segmentation in Chest CT Images Using K-Means Clustering and the Mathematical Morphology Method

Whole cardiac segmentation in chest CT images is important to identify functional abnormalities that occur in cardiovascular diseases, such as coronary artery disease (CAD) detection. However, manual efforts are time-consuming and labor intensive. Additionally, labeling the ground truth for cardiac segmentation requires the extensive manual annotation of images by the radiologist. Due to the difficulty in obtaining the annotated data and the required expertise as an annotator, an unsupervised approach is proposed. In this paper, we introduce a semantic whole-heart segmentation combining K-Means clustering as a threshold criterion of the mean-thresholding method and mathematical morphology method as a threshold shifting enhancer. The experiment was conducted on 500 subjects in two cases: (1) 56 slices per volume containing full heart scans, and (2) 30 slices per volume containing about half of the top of heart scans before the liver appears. In both cases, the results showed an average silhouette score of the K-Means method of 0.4130. Additionally, the experiment on 56 slices per volume achieved an overall accuracy (OA) and mean intersection over union (mIoU) of 34.90% and 41.26%, respectively, while the performance for the first 30 slices per volume achieved an OA and mIoU of 55.10% and 71.46%, respectively

Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images

Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems’ (CADs’) diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively

Hybridizing germanium anodes with polysaccharide-derived nitrogen-doped carbon for high volumetric capacity of Li-ion batteries

Achieving high volumetric energy Ge anodes leaves behind a big challenge such as a huge volume expansion upon Li-ion uptake. Among various strategies, the introduction of conductive and buffering carbon layers can resolve the typical problems (such as a large volume change and poor electrical conductivity) of alloy-type anodes to some extent. On the other hand, a cost-effective and scalable synthesis method has yet to be revealed. In this study, a highly conductive carbon (ANHC) layer derived from polysaccharide with a high nitrogen-doping level (>10%) effectively mitigates the structural deformation of Ge anodes, which is also independently involved in the reversible redox reaction with an improved electrochemical performance compared to typical graphite anodes. The ANHC/Ge self-assembled by a carbothermal reduction process has remarkable anode performance in a half cell, including a stable cycle life (95% capacity retention after 500 cycles at a rate of 1C) with a high volumetric capacity of >1500 mA h cm(-3) and a significant suppression of electrode swelling (<21%). In addition, the full cell consisting of the ANHC/Ge anode and LiCoO2 cathode shows excellent cyclability corresponding to a capacity retention of 73% over 300 cycles at a rate of 1C, which offers ultra-high volumetric energy applicable in various energy storage applications

Deep-Learning-Based Coronary Artery Calcium Detection from CT Image

One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible