Incobotulinum Toxin-A in Professional Musicians with Focal Task-Specific Dystonia: A Double Blind, Placebo Controlled, Cross-Over Study.

Background: Musician's focal task-specific dystonia is a complex disorder of fine motor control, with incomplete understanding of its etiology. There have been relatively few trials of botulinum toxin in upper limb task-specific dystonia, and prior studies have yielded variable results, leading to skepticism regarding the utility of this approach in elite performers. Methods: We conducted a double-blind, placebo-controlled, randomized, cross-over study of incobotulinum toxin-A in 21 professional musicians with focal upper extremity task-specific dystonia affecting performance on their instrument, using a novel paradigm of initial injections followed by booster injections at two- and four-week intervals. The primary outcome measure was the change in blinded dystonia rating of the active arm by two expert raters using a Clinical Global Impression numeric scale at week 8 compared to enrollment. Findings: 19 men and 2 women with musicians' dystonia were enrolled over a six-year period. Nineteen patients completed the study. Analysis of the primary outcome measure in comparison to baseline revealed a change in dystonia severity of P = 0.04 and an improvement in overall musical performance of P = 0.027. No clinically significant weakness was observed, and neutralizing antibodies to toxin were not found. Interpretation: Despite its small sample size, our study demonstrated a statistically significant benefit of incobotulinum toxin-A injections as a treatment for musicians' task-specific dystonia. Tailoring the use of toxin with booster injections allowed refinement of dosing strategy and outcomes, with benefits that were meaningful to patients clearly visible on videotaped evaluations. In addition to its application to musicians' dystonia, this approach may have relevance to optimize application of botulinum toxin in other forms of focal dystonia such as blepharospasm, cervical dystonia, writer's cramp, and spasmodic dysphonia.

Multi-Label Chest X-Ray Image Classification with Single Positive Labels.

Deep learning approaches for multi-label Chest X-ray (CXR) images classification usually require large-scale datasets. However, acquiring such datasets with full annotations is costly, time-consuming, and prone to noisy labels. Therefore, we introduce a weakly supervised learning problem called Single Positive Multi-label Learning (SPML) into CXR images classification (abbreviated as SPML-CXR), in which only one positive label is annotated per image. A simple solution to SPML-CXR problem is to assume that all the unannotated pathological labels are negative, however, it might introduce false negative labels and decrease the model performance. To this end, we present a Multi-level Pseudo-label Consistency (MPC) framework for SPML-CXR. First, inspired by the pseudo-labeling and consistency regularization in semi-supervised learning, we construct a weak-to-strong consistency framework, where the model prediction on weakly-augmented image is treated as the pseudo label for supervising the model prediction on a strongly-augmented version of the same image, and define an Image-level Perturbation-based Consistency (IPC) regularization to recover the potential mislabeled positive labels. Besides, we incorporate Random Elastic Deformation (RED) as an additional strong augmentation to enhance the perturbation. Second, aiming to expand the perturbation space, we design a perturbation stream to the consistency framework at the feature-level and introduce a Feature-level Perturbation-based Consistency (FPC) regularization as a supplement. Third, we design a Transformer-based encoder module to explore the sample relationship within each mini-batch by a Batch-level Transformer-based Correlation (BTC) regularization. Extensive experiments on the CheXpert and MIMIC-CXR datasets have shown the effectiveness of our MPC framework for solving the SPML-CXR problem.

Criteria2Query 3.0: Leveraging generative large language models for clinical trial eligibility query generation.

OBJECTIVE: Automated identification of eligible patients is a bottleneck of clinical research. We propose Criteria2Query (C2Q) 3.0, a system that leverages GPT-4 for the semi-automatic transformation of clinical trial eligibility criteria text into executable clinical database queries. MATERIALS AND METHODS: C2Q 3.0 integrated three GPT-4 prompts for concept extraction, SQL query generation, and reasoning. Each prompt was designed and evaluated separately. The concept extraction prompt was benchmarked against manual annotations from 20 clinical trials by two evaluators, who later also measured SQL generation accuracy and identified errors in GPT-generated SQL queries from 5 clinical trials. The reasoning prompt was assessed by three evaluators on four metrics: readability, correctness, coherence, and usefulness, using corrected SQL queries and an open-ended feedback questionnaire. RESULTS: Out of 518 concepts from 20 clinical trials, GPT-4 achieved an F1-score of 0.891 in concept extraction. For SQL generation, 29 errors spanning seven categories were detected, with logic errors being the most common (n = 10; 34.48 %). Reasoning evaluations yielded a high coherence rating, with the mean score being 4.70 but relatively lower readability, with a mean of 3.95. Mean scores of correctness and usefulness were identified as 3.97 and 4.37, respectively. CONCLUSION: GPT-4 significantly improves the accuracy of extracting clinical trial eligibility criteria concepts in C2Q 3.0. Continued research is warranted to ensure the reliability of large language models.

Bone marrow-restricted aberrant myeloperoxidase expression in B-acute lymphoblastic leukemia: A diagnostic dilemma and mimicry of mixed phenotype acute leukemia.

Myeloperoxidase (MPO) is the most specific marker of the myeloid lineage, essential for diagnosing acute myeloid leukemia and mixed phenotype acute leukemia with myeloid components. In this regard, we present a unique case of B-acute lymphoblastic leukemia (B-ALL) with isolated MPO expression in bone marrow blasts detected by flow cytometry and immunohistochemistry, while peripheral blood blasts were negative for MPO expression. In this report, our discussion encompasses diagnostic pitfalls from a laboratory testing perspective in similar cases and includes a literature review. Furthermore, we emphasize the necessity of conducting a comprehensive analysis for the accurate diagnosis of MPO-positive B-ALL cases.

Structural basis for nuclear import of hepatitis B virus (HBV) nucleocapsid core.

Nuclear import of the hepatitis B virus (HBV) nucleocapsid is essential for replication that occurs in the nucleus. The ~360-angstrom HBV capsid translocates to the nuclear pore complex (NPC) as an intact particle, hijacking human importins in a reaction stimulated by host kinases. This paper describes the mechanisms of HBV capsid recognition by importins. We found that importin α1 binds a nuclear localization signal (NLS) at the far end of the HBV coat protein Cp183 carboxyl-terminal domain (CTD). This NLS is exposed to the capsid surface through a pore at the icosahedral quasi-sixfold vertex. Phosphorylation at serine-155, serine-162, and serine-170 promotes CTD compaction but does not affect the affinity for importin α1. The binding of 30 importin α1/ß1 augments HBV capsid diameter to ~620 angstroms, close to the maximum size trafficable through the NPC. We propose that phosphorylation favors CTD externalization and prompts its compaction at the capsid surface, exposing the NLS to importins.

Use of Localized Reconstruction to Visualize the Shigella Phage Sf6 Tail Apparatus.

Cryogenic electron microscopy (cryo-EM) single-particle analysis has revolutionized the structural analysis of icosahedral viruses, including tailed bacteriophages. In recent years, localized (or focused) reconstruction has emerged as a powerful data analysis method to capture symmetry mismatches and resolve asymmetric features in icosahedral viruses. Here, we describe the methods used to reconstruct the 2.65-MDa tail apparatus of the Shigella phage Sf6, a representative member of the Podoviridae superfamily.

Hyper-Connected Transformer Network for Multi-Modality PET-CT Segmentation.

[18F]-Fluorodeoxyglucose (FDG) positron emission tomography - computed tomography (PET-CT) has become the imaging modality of choice for diagnosing many cancers. Co-learning complementary PET-CT imaging features is a fundamental requirement for automatic tumor segmentation and for developing computer aided cancer diagnosis systems. In this study, we propose a hyper-connected transformer (HCT) network that integrates a transformer network (TN) with a hyper connected fusion for multi-modality PET-CT images. The TN was leveraged for its ability to provide global dependencies in image feature learning, which was achieved by using image patch embeddings with a self-attention mechanism to capture image-wide contextual information. We extended the single-modality definition of TN with multiple TN based branches to separately extract image features. We also introduced a hyper connected fusion to fuse the contextual and complementary image features across multiple transformers in an iterative manner. Our results with two clinical datasets show that HCT achieved better performance in segmentation accuracy when compared to the existing methods.Clinical Relevance-We anticipate that our approach can be an effective and supportive tool to aid physicians in tumor quantification and in identifying image biomarkers for cancer treatment.

Investigation of the wake region behind a hemispherical turret via laser Rayleigh scattering.

Planar and volumetric density measurements in the wake region behind a mounted hemispherical turret are obtained using laser Rayleigh scattering. The measurements are conducted in a Mach 2 wind tunnel facility at the Kirtland Air Force Base. Quantitative measurements of density and contour plots with lines of constant density are computed, thus enabling visualization of the turret wake's fluid dynamics. A new, to the best of our knowledge, laser diagnostic methodology and configuration for capturing laser images is also presented. This methodology enables further suppression of background light scattering. Multi-dimensional single-shot and time-average measurements are recorded at multiple axial locations behind the turret. The images acquired reveal turbulent regions of the wake flow, and a discussion of the observed phenomena is presented.

Compression strategies for large-scale electrophysiology data.

Objective.With the rapid adoption of high-density electrode arrays for recording neural activity, electrophysiology data volumes within labs and across the field are growing at unprecedented rates. For example, a one-hour recording with a 384-channel Neuropixels probe generates over 80 GB of raw data. These large data volumes carry a high cost, especially if researchers plan to store and analyze their data in the cloud. Thus, there is a pressing need for strategies that can reduce the data footprint of each experiment.Approach.Here, we establish a set of benchmarks for comparing the performance of various compression algorithms on experimental and simulated recordings from Neuropixels 1.0 (NP1) and 2.0 (NP2) probes.Main results.For lossless compression, audio codecs (FLACandWavPack) achieve compression ratios (CRs) 6% higher for NP1 and 10% higher for NP2 than the best general-purpose codecs, at the expense of decompression speed. For lossy compression, theWavPackalgorithm in 'hybrid mode' increases the CR from 3.59 to 7.08 for NP1 and from 2.27 to 7.04 for NP2 (compressed file size of ∼14% for both types of probes), without adverse effects on spike sorting accuracy or spike waveforms.Significance.Along with the tools we have developed to make compression easier to deploy, these results should encourage all electrophysiologists to apply compression as part of their standard analysis workflows.

Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma.

PURPOSE: Prognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in various cancers, resulting in promising performance. This study aims to evaluate the clinical value of multi-task deep learning for prognostic prediction in LA-NPC patients. METHODS: A total of 886 LA-NPC patients acquired from two medical centers were enrolled including clinical data, [18F]FDG PET/CT images, and follow-up of progression-free survival (PFS). We adopted a deep multi-task survival model (DeepMTS) to jointly perform prognostic prediction (DeepMTS-Score) and tumor segmentation from FDG-PET/CT images. The DeepMTS-derived segmentation masks were leveraged to extract handcrafted radiomics features, which were also used for prognostic prediction (AutoRadio-Score). Finally, we developed a multi-task deep learning-based radiomic (MTDLR) nomogram by integrating DeepMTS-Score, AutoRadio-Score, and clinical data. Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were used to evaluate the discriminative ability of the proposed MTDLR nomogram. For patient stratification, the PFS rates of high- and low-risk patients were calculated using Kaplan-Meier method and compared with the observed PFS probability. RESULTS: Our MTDLR nomogram achieved C-index of 0.818 (95% confidence interval (CI): 0.785-0.851), 0.752 (95% CI: 0.638-0.865), and 0.717 (95% CI: 0.641-0.793) and area under curve (AUC) of 0.859 (95% CI: 0.822-0.895), 0.769 (95% CI: 0.642-0.896), and 0.730 (95% CI: 0.634-0.826) in the training, internal validation, and external validation cohorts, which showed a statistically significant improvement over conventional radiomic nomograms. Our nomogram also divided patients into significantly different high- and low-risk groups. CONCLUSION: Our study demonstrated that MTDLR nomogram can perform reliable and accurate prognostic prediction in LA-NPC patients, and also enabled better patient stratification, which could facilitate personalized treatment planning.

Expansion-assisted selective plane illumination microscopy for nanoscale imaging of centimeter-scale tissues.

Recent advances in tissue processing, labeling, and fluorescence microscopy are providing unprecedented views of the structure of cells and tissues at sub-diffraction resolutions and near single molecule sensitivity, driving discoveries in diverse fields of biology, including neuroscience. Biological tissue is organized over scales of nanometers to centimeters. Harnessing molecular imaging across three-dimensional samples on this scale requires new types of microscopes with larger fields of view and working distance, as well as higher imaging throughput. We present a new expansion-assisted selective plane illumination microscope (ExA-SPIM) with diffraction-limited and aberration-free performance over a large field of view (85 mm 2 ) and working distance (35 mm). Combined with new tissue clearing and expansion methods, the microscope allows nanoscale imaging of centimeter-scale samples, including entire mouse brains, with diffraction-limited resolutions and high contrast without sectioning. We illustrate ExA-SPIM by reconstructing individual neurons across the mouse brain, imaging cortico-spinal neurons in the macaque motor cortex, and tracing axons in human white matter.

High-resolution cryo-EM structure of the Pseudomonas bacteriophage E217.

E217 is a Pseudomonas phage used in an experimental cocktail to eradicate cystic fibrosis-associated Pseudomonas aeruginosa. Here, we describe the structure of the whole E217 virion before and after DNA ejection at 3.1 Å and 4.5 Å resolution, respectively, determined using cryogenic electron microscopy (cryo-EM). We identify and build de novo structures for 19 unique E217 gene products, resolve the tail genome-ejection machine in both extended and contracted states, and decipher the complete architecture of the baseplate formed by 66 polypeptide chains. We also determine that E217 recognizes the host O-antigen as a receptor, and we resolve the N-terminal portion of the O-antigen-binding tail fiber. We propose that E217 design principles presented in this paper are conserved across PB1-like Myoviridae phages of the Pbunavirus genus that encode a ~1.4 MDa baseplate, dramatically smaller than the coliphage T4.

Multi-Level Adversarial Spatio-Temporal Learning for Footstep Pressure Based FoG Detection.

Freezing of gait (FoG) is one of the most common symptoms of Parkinson's disease, which is a neurodegenerative disorder of the central nervous system impacting millions of people around the world. To address the pressing need to improve the quality of treatment for FoG, devising a computer-aided detection and quantification tool for FoG has been increasingly important. As a non-invasive technique for collecting motion patterns, the footstep pressure sequences obtained from pressure sensitive gait mats provide a great opportunity for evaluating FoG in the clinic and potentially in the home environment. In this study, FoG detection is formulated as a sequential modelling task and a novel deep learning architecture, namely Adversarial Spatio-temporal Network (ASTN), is proposed to learn FoG patterns across multiple levels. ASTN introduces a novel adversarial training scheme with a multi-level subject discriminator to obtain subject-independent FoG representations, which helps to reduce the over-fitting risk due to the high inter-subject variance. As a result, robust FoG detection can be achieved for unseen subjects. The proposed scheme also sheds light on improving subject-level clinical studies from other scenarios as it can be integrated with many existing deep architectures. To the best of our knowledge, this is one of the first studies of footstep pressure-based FoG detection and the approach of utilizing ASTN is the first deep neural network architecture in pursuit of subject-independent representations. In our experiments on 393 trials collected from 21 subjects, the proposed ASTN achieved an AUC 0.85, clearly outperforming conventional learning methods.

A systematic review: Virtual-reality-based techniques for human exercises and health improvement.

Virtual Reality (VR) has emerged as a new safe and efficient tool for the rehabilitation of many childhood and adulthood illnesses. VR-based therapies have the potential to improve both motor and functional skills in a wide range of age groups through cortical reorganization and the activation of various neuronal connections. Recently, the potential for using serious VR-based games that combine perceptual learning and dichoptic stimulation has been explored for the rehabilitation of ophthalmological and neurological disorders. In ophthalmology, several clinical studies have demonstrated the ability to use VR training to enhance stereopsis, contrast sensitivity, and visual acuity. The use of VR technology provides a significant advantage in training each eye individually without requiring occlusion or penalty. In neurological disorders, the majority of patients undergo recurrent episodes (relapses) of neurological impairment, however, in a few cases (60-80%), the illness progresses over time and becomes chronic, consequential in cumulated motor disability and cognitive deficits. Current research on memory restoration has been spurred by theories about brain plasticity and findings concerning the nervous system's capacity to reconstruct cellular synapses as a result of interaction with enriched environments. Therefore, the use of VR training can play an important role in the improvement of cognitive function and motor disability. Although there are several reviews in the community employing relevant Artificial Intelligence in healthcare, VR has not yet been thoroughly examined in this regard. In this systematic review, we examine the key ideas of VR-based training for prevention and control measurements in ocular diseases such as Myopia, Amblyopia, Presbyopia, and Age-related Macular Degeneration (AMD), and neurological disorders such as Alzheimer, Multiple Sclerosis (MS) Epilepsy and Autism spectrum disorder. This review highlights the fundamentals of VR technologies regarding their clinical research in healthcare. Moreover, these findings will raise community awareness of using VR training and help researchers to learn new techniques to prevent and cure different diseases. We further discuss the current challenges of using VR devices, as well as the future prospects of human training.

A Shortened Model for Logan Reference Plot Implemented via the Self-Supervised Neural Network for Parametric PET Imaging.

Dynamic PET imaging provides superior physiological information than conventional static PET imaging. However, the dynamic information is gained at the cost of a long scanning protocol; this limits the clinical application of dynamic PET imaging. We developed a modified Logan reference plot model to shorten the acquisition procedure in dynamic PET imaging by omitting the early-time information necessary for the conventional reference Logan model. The proposed model is accurate theoretically, but the straightforward approach raises the sampling problem in implementation and results in noisy parametric images. We then designed a self-supervised convolutional neural network to increase the noise performance of parametric imaging, with dynamic images of only a single subject for training. The proposed method was validated via simulated and real dynamic [Formula: see text]-fallypride PET data. Results showed that it accurately estimated the distribution volume ratio (DVR) in dynamic PET with a shortened scanning protocol, e.g., 20 minutes, where the estimations were comparable with those obtained from a standard dynamic PET study of 120 minutes of acquisition. Further comparisons illustrated that our method outperformed the shortened Logan model implemented with Gaussian filtering, regularization, BM4D and the 4D deep image prior methods in terms of the trade-off between bias and variance. Since the proposed method uses data acquired in a short period of time upon the equilibrium, it has the potential to add clinical values by providing both DVR and Standard Uptake Value (SUV) simultaneously. It thus promotes clinical applications of dynamic PET studies when neuronal receptor functions are studied.

Fine-Grained and Multiple Classification for Alzheimer's Disease With Wavelet Convolution Unit Network.

In this article, we propose a novel wavelet convolution unit for the image-oriented neural network to integrate wavelet analysis with a vanilla convolution operator to extract deep abstract features more efficiently. On one hand, in order to acquire non-local receptive fields and avoid information loss, we define a new convolution operation by composing a traditional convolution function and approximate and detailed representations after single-scale wavelet decomposition of source images. On the other hand, multi-scale wavelet decomposition is introduced to obtain more comprehensive multi-scale feature information. Then, we fuse all these cross-scale features to improve the problem of inaccurate localization of singular points. Given the novel wavelet convolution unit, we further design a network based on it for fine-grained Alzheimer's disease classifications (i.e., Alzheimer's disease, Normal controls, early mild cognitive impairment, late mild cognitive impairment). Up to now, only a few methods have studied one or several fine-grained classifications, and even fewer methods can achieve both fine-grained and multi-class classifications. We adopt the novel network and diffuse tensor images to achieve fine-grained classifications, which achieved state-of-the-art accuracy for all eight kinds of fine-grained classifications, up to 97.30%, 95.78%, 95.00%, 94.00%, 97.89%, 95.71%, 95.07%, 93.79%. In order to build a reference standard for Alzheimer's disease classifications, we actually implemented all twelve coarse-grained and fine-grained classifications. The results show that the proposed method achieves solidly high accuracy for them. Its classification ability greatly exceeds any kind of existing Alzheimer's disease classification method.

Deep multimodal graph-based network for survival prediction from highly multiplexed images and patient variables.

The spatial architecture of the tumour microenvironment and phenotypic heterogeneity of tumour cells have been shown to be associated with cancer prognosis and clinical outcomes, including survival. Recent advances in highly multiplexed imaging, including imaging mass cytometry (IMC), capture spatially resolved, high-dimensional maps that quantify dozens of disease-relevant biomarkers at single-cell resolution, that contain potential to inform patient-specific prognosis. Existing automated methods for predicting survival, on the other hand, typically do not leverage spatial phenotype information captured at the single-cell level. Furthermore, there is no end-to-end method designed to leverage the rich information in whole IMC images and all marker channels, and aggregate this information with clinical data in a complementary manner to predict survival with enhanced accuracy. To that end, we present a deep multimodal graph-based network (DMGN) with two modules: (1) a multimodal graph-based module that considers relationships between spatial phenotype information in all image regions and all clinical variables adaptively, and (2) a clinical embedding module that automatically generates embeddings specialised for each clinical variable to enhance multimodal aggregation. We demonstrate that our modules are consistently effective at improving survival prediction performance using two public breast cancer datasets, and that our new approach can outperform state-of-the-art methods in survival prediction.

High-resolution cryo-EM structure of the Shigella virus Sf6 genome delivery tail machine.

Sf6 is a bacterial virus that infects the human pathogen Shigella flexneri. Here, we describe the cryo-electron microscopy structure of the Sf6 tail machine before DNA ejection, which we determined at a 2.7-angstrom resolution. We built de novo structures of all tail components and resolved four symmetry-mismatched interfaces. Unexpectedly, we found that the tail exists in two conformations, rotated by ~6° with respect to the capsid. The two tail conformers are identical in structure but differ solely in how the portal and head-to-tail adaptor carboxyl termini bond with the capsid at the fivefold vertex, similar to a diamond held over a five-pronged ring in two nonidentical states. Thus, in the mature Sf6 tail, the portal structure does not morph locally to accommodate the symmetry mismatch but exists in two energetic minima rotated by a discrete angle. We propose that the design principles of the Sf6 tail are conserved across P22-like Podoviridae.

Real-time high-spectral-resolution mid-infrared spectroscopy with a signal-to-noise ratio of ten thousand.

We developed a mid-infrared spectroscopy system with high spectral resolution and a high signal-to-noise ratio using an extremely high-order germanium immersion grating. The spectroscopic system covers wavelengths from 3 to 5 µm and has a spectral resolution of 1 GHz with a single-shot bandwidth of 2 THz. We proposed a method of improving the signal-to-noise ratio and achieved a ratio of over 3000 with a data acquisition rate of 125 Hz in the presence of fluctuations in the light source and environment. A signal-to-noise ratio of 10,000 was achieved with 0.1-s integration for 100-µW mid-infrared light.