Generation of drug-like molecules with high binding affinity to target proteins remains a difficult and resource-intensive task in drug discovery. Existing approaches primarily employ reinforcement learning, Markov sampling, or deep generative models guided by Gaussian processes, which can be prohibitively slow when generating molecules with high binding affinity calculated by computationally-expensive physicsbased methods. Researchers a UC San Diego have developed a new approach, named Latent Inceptionism on Molecules (LIMO), which significantly accelerates molecule generation with an inceptionism-like technique. LIMO employs a variational autoencoder-generated latent space and property prediction by two neural networks in sequence to enable faster gradient-based reverse-optimization of molecular properties.

As an important tool for electrophysiological recordings, neural electrodes implanted on the brain surface have been instrumental in basic neuroscience research to study large-scale neural dynamics in various cognitive processes, such as sensorimotor processing as well as learning and memory. In clinical settings, neural recordings have been adopted as a standard tool to monitor the brain activity in epilepsy patients before surgery for detection and localization of epileptogenic zones initiating seizures and functional cortical mapping. Neural activity recorded from the brain surface exhibits rich information content about the collective neural activities reflecting the cognitive states and brain functions. For the interpretation of surface potentials in terms of their neural correlates, most research has focused on local neural activities.   From basic neuroscience research to clinical treatments and neural engineering, electrocorticography (ECoG) has been widely used to record surface potentials to evaluate brain function and develop neuroprosthetic devices. However, the requirement of invasive surgeries for implanting ECoG arrays significantly limits the coverage of different cortical regions, preventing simultaneous recordings from spatially distributed cortical networks. However, this rich information content of surface potentials encoded for the large-scale cortical activity remains unexploited and little is known on how local surface potentials are correlated with the spontaneous neural activities of distributed large-scale cortical networks. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

Cardiac MRI is the clinical reference standard for visual and quantitative assessment of heart function. Specifically, cine balanced steady-state free precession (SSFP) can yield cardiac images with high myocardium–blood pool contrast for evaluation of left ventricular (LV) function. However, MRI suffers from long acquisition times, often requiring averaging across multiple heartbeats, and necessitates a trade-off among spatial resolution, temporal resolution, and scan time. Clinically, radiologists are forced to balance acquisition time with resolution to fit clinical needs, and certain applications such as real-time imaging may require small acquisition matrices. Image scaling is typically performed by using conventional upscaling methods, such as Fourier domain zero padding and bicubic interpolation. These methods, however, do not readily recover spatial detail, such as the myocardium–blood pool interface or delineation of papillary muscles.

Constructing antibody repertoires is an important error-correcting step in analyzing immunosequencing datasets that is important for reconstructing evolutionary (clonal) development of antibodies. However, the state-of-the-art repertoire construction tools typically miss low-abundance antibodies that often represent internal nodes in clonal trees and are crucially important for clonal tree reconstruction. Thus, although repertoire construction is a prerequisite for follow up clonal tree reconstruction, the existing repertoire reconstruction algorithms are not well suited for this task because they typically miss low-abundance antibodies that often represent internal nodes in clonal trees and are crucially important for clonal tree reconstruction.

The majority of state‐of‐the‐art lung segmentation algorithms in the literature do not simultaneously segment lungs, lung lobes, and airway in a single algorithm. Additionally, automated algorithms typically perform the segmentation task on a series of 2D slices, which can reduce segmentation accuracy of anatomical structures (i.e. lung lobes) that may require contextual information across all three spatial dimensions. Many existing algorithms also have not been validated on chest CTs across a wide variety of conditions to evaluate algorithm generalizability. Currently, quantification of respiratory measurements requires a radiologist, trained analyst, or technician to recognize, identify, and manually annotate anatomical landmarks such as the lung lobes or airway in the chest. A fully‐automated deep learning system may eliminate the need for manual analysis, thereby improving efficiency and expanding applicability to a large number of CTs.

Electroencephalographic source imaging (a.k.a. magnetic/electric or M/EEG source imaging, ESI, or brain electrical tomography) usually depends upon sophisticated signal processing algorithms for data cleaning, source separation and imaging. Typically, these problems are addressed separately using a variety of heuristics, making it difficult to systematize a methodology for extracting robust brain source images on a wide range of applications.

Hemophilic arthropathy is a frequent and debilitating comorbidity. Point-of-care musculoskeletal ultrasound (MSKUS) with Power Doppler capacity has become critical during the past several years to evaluate progression of joint disease longitudinally, as well as to detect the presence or absence of joint bleeding associated with joint pains in a timely fashion. With the advent of emerging new treatment modalities the hemophilia population is aging, bringing hemophilic arthropathy rapidly into focus. Based on the increasing need to develop and validate a joint ultrasound imaging protocol that could easily be used in clinical practice as well as a research outcome tool UC San Diego Clinician-Investigators and Collaborators, specialized in Hemophilia, Ultrasound, Musculoskeletal Medicine and Radiology (Drs. Annette von Drygalski, Eric Chang and Randy Moore, as well as Lena Volland, DPT ) developed and validated a unique MSKUS protocol, specifically adept to assess the extent of hemophilic arthropathy in the acute and chronic setting.  This protocol is named JADE protocol (Joint Tissue Assessment and Damage Exam), as described below. The protocol is taught “hands on” during the CME accredited course “Musculoskeletal Ultrasound in Hemophilia”, and is also accessible through online modules. https://cme.ucsd.edu/muh/

Imaging machines used for radiology studies often export data (such as vascular velocities, bone densitometry, radiation dose, etc.) as characters stored in image format. Radiologists are expected to interpret this data and also store it in their text-based reports of the studies. This is usually accomplished by dictating the data into the text report or copying it by typing it. However, these methods are error-prone and time-intensive.

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Several study have suggested that fat composition and site of deposition can indicate the risk of many disorders, including cancer, type 2 diabetes, heart disease, and liver disease (NASH). In addition, regional differences in fat composition throughout the body suggest a depot-specific impact of stored fatty acids on adipocyte function and metabolism. Current diagnostic tools include MR spectroscopy, which has high spectral resolution but poor spatial resolution, and MRI IDEAL (iterative decomposition of water and fat with echo asymmetry and least squares estimation) gradient echo imaging, which can measure the amount but not the type of fat.

Bayesian networks are a popular class of graphical probabilistic models based on Bayes theorem. Bayesian networks represent a joint probability distribution over a set of variables. Once known, this joint distribution may be used to compute the probabilities of any configuration of the variables. Bayesian networks have been increasingly applied to various computation applications, such as computational biology and computer vision. The commonly used approach of modeling network behavior employs ordinary or partial differential equations (ODE or PDE), but this approach is limited to analyzing relatively small networks (10-20 nodes), as ODE or PDE approaches may consider only local effects in the network. There is need to overcome this limitation and provide a systematic way, based on biological networks, to evaluate the effects of inhibiting multiple drug targets on treating a disease.

Historically, molecular resonance imaging (MRI) has provided little or no signal for short transverse relaxation time (T2) tissues in the musculoskeletal system. Copyright/software provides a means to quantitatively measure the relaxation time for other tissues that have been difficult to image and may provide a means to assess change in structure and composition of the collagen matrix.

Volumetric modulated arc therapy (VMAT) is a new technique for radiation therapy treatment that provides superior conformal radiation treatment after just one or two arcs of gantry rotation. Compared to currently used intensity modulated radiation therapy (IMRT) techniques, VMAT reduces treatment time and the number of required monitor units. If well-designed, VMAT delivers a more conformal dose to targets and reduces dosage to organs at risk (OARs). However, the currently used optimization algorithms (such as heuristic simulated annealing) for VMAT planning are based on locating a good approximation to the global minimum across a large search space. Unfortunately, this computationally intensive approach typically requires anywhere from thirty to hundreds of minutes of processing time in order to optimize a single treatment plan, thus limiting its wide-spread use in clinical settings.

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