Three-dimensional CT angiography of the canine hepatic vasculature

Eight Beagle dogs were anesthetized and were imaged using a single channel helical CT scanner. The contrast medium used in this study was iohexol (300 mg I/ml) and doses were 0.5 ml/kg for a cine scan, 3 ml/kg for an enhanced scan. The flow rate for contrast material administration was 2 ml/sec for all scans. This study was divided into three steps, with unenhanced, cine and enhanced scans. The enhanced scan was subdivided into the arterial phase and the venous phase. All of the enhanced scans were reconstructed in 1 mm intervals and the scans were interpreted by the use of reformatted images, a cross sectional histogram, maximum intensity projection and shaded surface display. For the cine scans, optimal times were a 9-sec delay time post IV injection in the arterial phase, and an 18-sec delay time post IV injection in the venous phase. A nine-sec delay time was acceptable for the imaging of the canine hepatic arteries by CT angiography. After completion of arterial phase scanning, venous structures of the liver were well visualized as seen on the venous phase

Computed tomographic characteristics of acute thoracolumbar intervertebral disc disease in dogs

Forty canine patients with a presumptive diagnosis of the intervertebral disc herniation at the thoracolumbar region were imaged. A neurological examination was performed and all patients were classified under four grades by the examination. The degrees of attenuation of the herniated disc material were measured in Housefield units (HU) in each image. The ratio of the area to herniated disc material and the height to disc material were measured. The clinical grade was correlated with the area ratio of the herniated disc material to the spinal cord, but not correlated with the height ratio of that. In the patients with epidural hemorrhage at surgery, HUs of the herniated disc material was lower than those with no epidural hemorrhage at surgery. Non-contrast computed tomography scans of the spine can be useful in diagnosing acute intervertebral disc disease in chondrodystrophoid breeds, evaluating patient status and identifying concurrent epidural hemorrhage

Embedding of FDA Approved Drugs in Chemical Space Using Cascade Autoencoder with Metric Learning

© 2022 IEEE.Deep learning methods have been successfully used to predict characteristics of small molecules such as physicochemical properties and biological properties. Prediction is typically done by embedding compounds into a low-dimensional chemical space. The goal of our study is to create embedding space that can be used to distinguish approved and withdrawn drugs using compound information only. U.S. Food and Drug Administration (FDA) approved chemical drugs are validated substances in terms of therapeutic effect, toxicity, and side effects. Some of approved drugs are withdrawn due to various reasons, including toxic and disease-causing effects. Our study aims to propose a framework that embed FDA approved chemical drugs on chemical space by integrating representation of chemical structure from various encoding methods. Because withdrawn drugs were approved drugs, distinguishing them using compound information is quite challenging. Our proposed framework consists of three stacked deep autoencoder modules and effectively integrates the information of the chemical compounds by cascade modeling that continuously use latent representation learned from previous modules. Results showed that FDA approved chemical compounds have discriminative regions in the embedding space and complex representation information to understand the embedding of FDA drugs were incorporated well. Such results showed that our framework can be used as an embedding method for determining whether or not drug candidates will be approved.N

Probabilistic Trajectory Estimation based Leader Following for Multi-Robot Systems

The paper is concerned with the multi-robot leader-following problem in the presence of frequent dropouts in vision detection. In many scenarios, for instance a structured environment, it is inevitable to experience outage of vision detection due to reasons such as the target moving out of view, vision occlusion, motion blurring, etc. The paper proposes a Bayesian trajectory estimation based leader-following approach that can offer accurate path following given intermittent vision observations. The follower robot estimates the trajectory of the leader robot based on the noise-corrupted odometry information of both robots, and inter-robot relative observations based on detection of fiducial markers using an RGBD camera. A linear trajectory-following control method is employed to track a historical pose of the leader robot on the estimated trajectory. Results are obtained based on evaluating the proposed leader-following approach in tests with a zig-zag shaped trajectory and with a trajectory that contains sharp turns.Accepted versio

Visual Tracking via Random Partition Image Hashing

In this paper, we propose a discriminative and robust appearance model based on features extracted from a random partition image hashing algorithm to account for severe occlusion and disappearance. We divide the original image into multiple sub-blocks with random positions and scales. Hash functions are used to map blocks into compact binary codes, with which more effective target matching can be achieved. The tracking task is then formulated by producing a confidence map for the target and background, and obtaining the best samples using maximum a posteriori estimate. Experimental results demonstrate that our tracker can achieve more accurate tracking results in situations of occlusion, out-of-view, and violent motion blur when compared with most of state-of-the-art competing algorithms. Besides, the proposed tracking algorithm is able to run in real time.Accepted versio

Exploring chemical space for lead identification by propagating on chemical similarity network

Motivation: Lead identification is a fundamental step to prioritize candidate compounds for downstream drug discovery process. Machine learning (ML) and deep learning (DL) approaches are widely used to identify lead compounds using both chemical property and experimental information. However, ML or DL methods rarely consider compound similarity information directly since ML and DL models use abstract representation of molecules for model construction. Alternatively, data mining approaches are also used to explore chemical space with drug candidates by screening undesirable compounds. A major challenge for data mining approaches is to develop efficient data mining methods that search large chemical space for desirable lead compounds with low false positive rate. Results: In this work, we developed a network propagation (NP) based data mining method for lead identification that performs search on an ensemble of chemical similarity networks. We compiled 14 fingerprint-based similarity networks. Given a target protein of interest, we use a deep learning-based drug target interaction model to narrow down compound candidates and then we use network propagation to prioritize drug candidates that are highly correlated with drug activity score such as IC50. In an extensive experiment with BindingDB, we showed that our approach successfully discovered intentionally unlabeled compounds for given targets. To further demonstrate the prediction power of our approach, we identified 24 candidate leads for CLK1. Two out of five synthesizable candidates were experimentally validated in binding assays. In conclusion, our framework can be very useful for lead identification from very large compound databases such as ZINC

Improvement of CH₃NH₃PbI₃ Formation for Efficient and Better Reproducible Mesoscopic Perovskite Solar Cells

High-performance perovskite solar cells (PSCs) are obtained through optimization of the formation of CH₃NH₃PbI₃ nanocrystals on mesoporous TiO₂ film, using a two-step sequential deposition process by first spin-coating a PbI₂ film and then submerging it into CH₃NH₃I solution for perovskite conversion (PbI₂ + CH₃NH₃I → CH₃NH₃PbI₃). It is found that the PbI₂ morphology from different film formation process (thermal drying, solvent extraction, and as-deposited) has a profound effect on the CH₃NH₃PbI₃ active layer formation and its nanocrystalline composition. The residual PbI₂ in the active layer contributes to substantial photocurrent losses, thus resulting in low and inconsistent PSC performances. The PbI₂ film dried by solvent extraction shows enhanced CH₃NH₃PbI₃ conversion as the loosely packed disk-like PbI₂ crystals allow better CH₃NH₃I penetration and reaction in comparison to the multicrystal aggregates that are commonly obtained in the thermally dried PbI₂ film. The as-deposited PbI₂ wet film, without any further drying, exhibits complete conversion to CH₃NH₃PbI₃ in MAI solution. The resulting PSCs reveal high power conversion efficiency of 15.60% with a batch-to-batch consistency of 14.60 ± 0.55%, whereas a lower efficiency of 13.80% with a poorer consistency of 11.20 ± 3.10% are obtained from the PSCs using thermally dried PbI₂ films

SREBP1c-PARP1 axis tunes anti-senescence activity of adipocytes and ameliorates metabolic imbalance in obesity

Emerging evidence indicates that the accretion of senescent cells is linked to metabolic disorders. However, the underlying mechanisms and metabolic consequences of cellular senescence in obesity remain obscure. In this study, we found that obese adipocytes are senescence-susceptible cells accompanied with genome instability. Additionally, we discovered that SREBP1c may play a key role in genome stability and senescence in adipocytes by modulating DNA-damage responses. Unexpectedly, SREBP1c interacted with PARP1 and potentiated PARP1 activity during DNA repair, independent of its canonical lipogenic function. The genetic depletion of SREBP1c accelerated adipocyte senescence, leading to immune cell recruitment into obese adipose tissue. These deleterious effects provoked unhealthy adipose tissue remodeling and insulin resistance in obesity. In contrast, the elimination of senescent adipocytes alleviated adipose tissue inflammation and improved insulin resistance. These findings revealed distinctive roles of SREBP1c-PARP1 axis in the regulation of adipocyte senescence and will help decipher the metabolic significance of senescence in obesity.N