Effect of electrical stimulation on the fusion rate after spinal surgery: a systematic review and meta-analysis.

Electrical stimulation is an important adjuvant therapy for spinal surgery, but whether receiving electrical stimulation can improve the fusion rate after spinal surgery is still controversial. The purpose of this study was to analyse and evaluate the effect of electrical stimulation on the fusion rate after spinal surgery. We systematically searched for related articles published in the PubMed, Embase and Cochrane Library databases on or before September 30, 2023.

The impact of sarcopenia on the incidence of postoperative outcomes following spine surgery: Systematic review and meta-analysis.

Purpose: Sarcopenia is considered to be an important predictor of adverse outcomes following spinal surgery, but the specific relationship between the two is not clear. The purpose of this meta-analysis is to systematically review all relevant studies to evaluate the impact of sarcopenia on spinal surgery outcomes.

Methods: We systematically searched PubMed, Embase and the Cochrane Library for relevant articles published on or before January 9, 2023.

Ask Questions With Double Hints: Visual Question Generation With Answer-Awareness and Region-Reference.

The visual question generation (VQG) task aims to generate human-like questions from an image and potentially other side information (e.g., answer type).

Risk factors of postoperative delirium following spine surgery: A meta-analysis of 50 cohort studies with 1.1 million participants.

Objectives: Postoperative delirium (POD) is considered to be a common complication of spine surgery. Although many studies have reported the risk factors associated with POD, the results remain unclear. Therefore, we performed a meta-analysis to identify risk factors for POD among patients following spinal surgery.

FADngs: Federated Learning for Anomaly Detection.

With the increasing demand for data privacy, federated learning (FL) has gained popularity for various applications. Most existing FL works focus on the classification task, overlooking those scenarios where anomaly detection may also require privacy-preserving. Traditional anomaly detection algorithms cannot be directly applied to the FL setting due to false and missing detection issues.

The impact of perioperative opioid use on postoperative outcomes following spinal surgery: a meta-analysis of 60 cohort studies with 13 million participants.

Background Context: An important factor for the prognosis of spinal surgery is the perioperative use of opioids. However, the relationship is not clear.

Purpose: The purpose of this study was to evaluate the effect of perioperative opioid use on the prognosis of patients following spinal surgery.

Attribute-driven streaming edge partitioning with reconciliations for distributed graph neural network training.

Current distributed graph training frameworks evenly partition a large graph into small chunks to suit distributed storage, leverage a uniform interface to access neighbors, and train graph neural networks in a cluster of machines to update weights. Nevertheless, they consider a separate design of storage and training, resulting in huge communication costs for retrieving neighborhoods. During the storage phase, traditional heuristic graph partitioning not only suffers from memory overhead because of loading the full graph into the memory but also damages semantically related structures because of its neglecting meaningful node attributes.

Risk factors for lumbar disc herniation recurrence after percutaneous endoscopic lumbar discectomy: a meta-analysis of 58 cohort studies.

Recurrent lumbar disc herniation (rLDH) is one of the most serious complications and major causes of surgical failure and paralysis following percutaneous endoscopic lumbar discectomy (PELD). There are reports in the literature on the identification of risk factors associated with rLDH; however, the results are controversial. Therefore, we conducted a meta-analysis to identify risk factors for rLDH among patients following spinal surgery.

Risk factors of epidural hematoma in patients undergoing spinal surgery: a meta-analysis of 29 cohort studies.

Objective: The authors conducted this meta-analysis to identify risk factors for spinal epidural haematoma (SEH) among patients following spinal surgery.

Methods: The authors systematically searched Pub: Med, Embase, and the Cochrane Library for articles that reported risk factors associated with the development of SEH in patients undergoing spinal surgery from inception to 2 July 2022. The pooled odds ratio (OR) was estimated using a random-effects model for each investigated factor.

Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding.

Temporal grounding is the task of locating a specific segment from an untrimmed video according to a query sentence. This task has achieved significant momentum in the computer vision community as it enables activity grounding beyond pre-defined activity classes by utilizing the semantic diversity of natural language descriptions. The semantic diversity is rooted in the principle of compositionality in linguistics, where novel semantics can be systematically described by combining known words in novel ways (compositional generalization).

Environmental polycyclic aromatic hydrocarbon exposure is associated with low back pain.

Several studies have assessed the influence of several often-ignored environmental factors on low back pain (LBP), but the effects of environmental polycyclic aromatic hydrocarbon (PAH) exposure on LBP are unclear. During the 2001-2004 cycle of the National Health and Nutrition Examination Survey (NHANES), our study was given to a representative sample of US participants older than 20 (N = 2743). Environmental PAH exposure was calculated using urinary PAH metabolite concentrations.

Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images.

Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. However, RGB-D data is not easily acquired, which limits the development of RGB-D SOD techniques. To alleviate this issue, we present a Dual-Semi RGB-D Salient Object Detection Network (DS-Net) to leverage unlabeled RGB images for boosting RGB-D saliency detection.

Local-Global Memory Neural Network for Medication Prediction.

Electronic medical records (EMRs) play an important role in medical data mining and sequential data learning. In this article, we propose to use a sequential neural network with dynamic content-based memories to predict future medications, given EMRs. The local-global memory neural network contains two layers of memories: the local memory and the global memory.

VPModel: High-Fidelity Product Simulation in a Virtual-Physical Environment.

In the development of a new product, the design team must describe the expected effects of the final products to potential users and stakeholders. However, existing prototyping tools can only present a product imperfectly, due to limitations at different levels. Specifically, the physical product model, which may be the product of 3D printing, could lack a visual interface; the presentation of the product through modeling software such as Rhinoceros 3D does not provide good realistic tactile perception; or the interface platforms, such as Axure RP, used to display the interactive effects differ from those to be used in the actual operation.

Media and public reactions toward vaccination during the 'hepatitis B vaccine crisis' in China.

Background: Public disputations affected vaccine confidence and vaccine rates particularly when adverse events occur. The vigorous development of Internet in China provides an opportunity to observe public reaction and sentiment toward vaccination when Kangtai Hepatitis B vaccine crisis happened and evolved to a widespread debate on the internet from December 12, 2013 to January 3, 2014.

Methods: This study conducted Internet surveillance by examining three daily indicators including the daily number of relevant online news article, Sina Weibo posts and Baidu search index during the crisis.

Cross-modal learning to rank via latent joint representation.

Cross-modal ranking is a research topic that is imperative to many applications involving multimodal data. Discovering a joint representation for multimodal data and learning a ranking function are essential in order to boost the cross-media retrieval (i.e.

EyeMap: a software system for visualizing and analyzing eye movement data in reading.

We have developed EyeMap, a freely available software system for visualizing and analyzing eye movement data specifically in the area of reading research. As compared with similar systems, including commercial ones, EyeMap has more advanced features for text stimulus presentation, interest area extraction, eye movement data visualization, and experimental variable calculation. It is unique in supporting binocular data analysis for unicode, proportional, and nonproportional fonts and spaced and unspaced scripts.