Enhancers activation by CRISPR/Cas9-based acetyltransferase rescues loss of function in CREBBP point mutant

https://openworks.mdanderson.org/sumexp23/1029/thumbnail.jp

The Evaluation of the Clinical, Laboratory, and Radiological Findings of 16 Cases of Brucellar Spondylitis

Objective. To evaluate the clinical, laboratory, and radiological presentation of 16 cases of brucellar spondylitis.Methods. The clinical manifestations, laboratory tests, and imaging findings of 16 patients (aged from 24 to 66 years) with brucellar spondylitis treated between September 2012 and September 2014 at the Second Affiliated Hospital of Xi’an Jiaotong University (Xi’an, China) were retrospectively analyzed.Results. Clinical manifestations included high fever, severe pain, sweating, and fatigue. One patient had epididymitis, and two showed clear signs of spinal nerve damage. Laboratory tests showed elevated erythrocyte sedimentation rate (ESR) and C-reactive protein content. Serum brucella agglutination tests were positive, and 11 brucella blood cultures were positive. Imaging manifestations mainly consisted of abnormal signals in the intervertebral space or abnormal signals in the adjacent vertebral bodies (16/16, 100%) in magnetic resonance imaging (MRI), disc space narrowing (14/16, 88%) in X-ray and MRI, or bone destruction and sclerosis around the damaged zone (13/16, 81%) in computed tomography, with rare cases of psoas abscess (2/16, 13%) and sequestrum (1/16, 6%).Conclusion. Since brucellar spondylitis exhibited characteristic clinical and imaging manifestations, it could be diagnosed with specific laboratory tests. Early MRI examination of suspected cases could improve rapid diagnosis.</jats:p

Crustal Structure of the Indochina Peninsula From Ambient Noise Tomography

The collision between the Indian and Eurasian plates promotes the southeastward extrusion of the Indochina Peninsula while the internal dynamics of its crustal deformation remain enigmatic. Here, we make use of seismic data from 38 stations and employ the ambient noise tomography to construct a 3‐D crustal shear‐wave velocity (Vs) model beneath the Indochina Peninsula. A low‐Vs anomaly is revealed in the mid‐lower crust of the Shan‐Thai Block and probably corresponds to the southern extension of the crustal flow from SE Tibet. Although the Khorat Plateau behaves as a rigid block, the observed low‐Vs anomalies in the lower crust and also below the Moho indicate that the crust may have been partially modified by mantle‐derived melts. The strike‐slip shearing motions of the Red River Fault may have dominantly developed crustal deformation at its western flank where a low‐Vs anomaly is observed at the upper‐middle crust

Single-Session Combined Anterior-Posterior Approach for Treatment of Ankylosing Spondylitis with Obvious Displaced Lower Cervical Spine Fractures and Dislocations

For patients with AS and lower cervical spine fractures, surgical methods have mainly included the single anterior approach, single posterior approach, and combined anterior-posterior approach. However, various surgical procedures were utilized because the fractures have not been clearly classified according to presence of displacement in these previous studies. Consequently, controversies have been raised regarding the selection of the surgical procedure. This study retrospective analysis was conducted in 12 patients with AS and lower cervical spine fractures and dislocations and explored single-session combined anterior-posterior approach for the treatment of AS with obvious displaced lower cervical spine fractures and dislocations which has demonstrated advantages such as good stabilization, satisfied fracture healing, and easy postoperative cares. However, to some extent, the difficulty and risk of this approach should be considered. Attention should be paid to the prevention of perioperative complications.</jats:p

A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval

The computer-aided diagnosis (CAD) system can provide a reference basis for the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs) can not only extract visual elements such as colors and shapes but also semantic features. As such they have made great improvements in many tasks of dermoscopy images. The imaging of dermoscopy has no principal orientation, indicating that there are a large number of skin lesion rotations in the datasets. However, CNNs lack rotation invariance, which is bound to affect the robustness of CNNs against rotations. To tackle this issue, we propose a rotation meanout (RM) network to extract rotation-invariant features from dermoscopy images. In RM, each set of rotated feature maps corresponds to a set of outputs of the weight-sharing convolutions and they are fused using meanout strategy to obtain the final feature maps. Through theoretical derivation, the proposed RM network is rotation-equivariant and can extract rotation-invariant features when followed by the global average pooling (GAP) operation. The extracted rotation-invariant features can better represent the original data in classification and retrieval tasks for dermoscopy images. The RM is a general operation, which does not change the network structure or increase any parameter, and can be flexibly embedded in any part of CNNs. Extensive experiments are conducted on a dermoscopy image dataset. The results show our method outperforms other anti-rotation methods and achieves great improvements in dermoscopy image classification and retrieval tasks, indicating the potential of rotation invariance in the field of dermoscopy images

Multi-Scale Heterogeneity-Aware Hypergraph Representation for Histopathology Whole Slide Images

Survival prediction is a complex ordinal regression task that aims to predict the survival coefficient ranking among a cohort of patients, typically achieved by analyzing patients' whole slide images. Existing deep learning approaches mainly adopt multiple instance learning or graph neural networks under weak supervision. Most of them are unable to uncover the diverse interactions between different types of biological entities(\textit{e.g.}, cell cluster and tissue block) across multiple scales, while such interactions are crucial for patient survival prediction. In light of this, we propose a novel multi-scale heterogeneity-aware hypergraph representation framework. Specifically, our framework first constructs a multi-scale heterogeneity-aware hypergraph and assigns each node with its biological entity type. It then mines diverse interactions between nodes on the graph structure to obtain a global representation. Experimental results demonstrate that our method outperforms state-of-the-art approaches on three benchmark datasets. Code is publicly available at \href{https://github.com/Hanminghao/H2GT}{https://github.com/Hanminghao/H2GT}.Comment: 9 pages, 6 figures, accepted by ICME202

Faster Diffusion Action Segmentation

Temporal Action Segmentation (TAS) is an essential task in video analysis, aiming to segment and classify continuous frames into distinct action segments. However, the ambiguous boundaries between actions pose a significant challenge for high-precision segmentation. Recent advances in diffusion models have demonstrated substantial success in TAS tasks due to their stable training process and high-quality generation capabilities. However, the heavy sampling steps required by diffusion models pose a substantial computational burden, limiting their practicality in real-time applications. Additionally, most related works utilize Transformer-based encoder architectures. Although these architectures excel at capturing long-range dependencies, they incur high computational costs and face feature-smoothing issues when processing long video sequences. To address these challenges, we propose EffiDiffAct, an efficient and high-performance TAS algorithm. Specifically, we develop a lightweight temporal feature encoder that reduces computational overhead and mitigates the rank collapse phenomenon associated with traditional self-attention mechanisms. Furthermore, we introduce an adaptive skip strategy that allows for dynamic adjustment of timestep lengths based on computed similarity metrics during inference, thereby further enhancing computational efficiency. Comprehensive experiments on the 50Salads, Breakfast, and GTEA datasets demonstrated the effectiveness of the proposed algorithm.Comment: 25 pages, 6 figure

SynAsk: Unleashing the Power of Large Language Models in Organic Synthesis

The field of natural language processing (NLP) has witnessed a transformative shift with the emergence of large language models (LLMs), revolutionizing various language tasks and applications, and the integration of LLM into specialized domains enhances their capabilities for domain-specific applications. Notably, NLP has made significant strides in organic chemistry, particularly in predicting synthetic tasks, paving the way for the development of LLMs tailored to the organic chemistry field. In this work, we introduce SynAsk, a comprehensive organic chemistry domain-specific LLM platform developed by AIChemEco Inc. By finetuning an LLM with domain-specific data and integrating it with a chain of thought approach, SynAsk seamlessly accesses our knowledge base and advanced chemistry tools in a question-and-answer format. This includes functionalities such as a basic chemistry knowledge base, molecular information retrieval, reaction performance prediction, retrosynthesis prediction, chemical literature acquisition, and more. This novel methodology synergizes fine-tuning techniques with external resource integration, resulting in an organic chemistry-specific model poised to facilitate research and discovery in the field. Accessible via http://synask.aichemeco.com, SynAsk represents a significant advancement in leveraging NLP for synthetic applications

Prediction of DNA i-motifs via machine learning

i-Motifs (iMs), are secondary structures formed in cytosine-rich DNA sequences and are involved in multiple functions in the genome. Although putative iM forming sequences are widely distributed in the human genome, the folding status and strength of putative iMs vary dramatically. Much previous research on iM has focused on assessing the iM folding properties using biophysical experiments. However, there are no dedicated computational tools for predicting the folding status and strength of iM structures. Here, we introduce a machine learning pipeline, iM-Seeker, to predict both folding status and structural stability of DNA iMs. The programme iM-Seeker incorporates a Balanced Random Forest classifier trained on genome-wide iMab antibody-based CUT&Tag sequencing data to predict the folding status and an Extreme Gradient Boosting regressor to estimate the folding strength according to both literature biophysical data and our in-house biophysical experiments. iM-Seeker predicts DNA iM folding status with a classification accuracy of 81% and estimates the folding strength with coefficient of determination (R2) of 0.642 on the test set. Model interpretation confirms that the nucleotide composition of the C-rich sequence significantly affects iM stability, with a positive correlation with sequences containing cytosine and thymine and a negative correlation with guanine and adenine