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Purpose: The objective of this systematic review and metaanalysis is to compare the efficacy and safety of decompression alone versus decompression plus fusion in single-level lumbar spinal stenosis with spondylolisthesis. Methods: A comprehensive search of the PubMed, Embase, Cochrane Library, and Ovid Medline databases was conducted to find randomized control trials (RCTs) or cohort studies that compared decompression alone and decompression plus fusion in single-level lumbar spinal stenosis with spondylolisthesis. Operation time; reoperation; postoperative complications; postoperative Oswestry disability index(ODI) scores and scores related to back and leg pain were collected from eligible studies for meta-analysis. Results: We included 3 randomized controlled trials and 9 cohort studies with 6182 patients. The decompression alone group showed less operative time(P < 0.001) and intraoperative blood loss(p = 0.000), and no significant difference in postoperative complications was observed in randomized controlled trials(p = 0.428) or cohort studies(p = 0.731). There was no significant difference between the other two groups in reoperation(P = 0.071), postoperative ODI scores and scores related to back and leg pain. Conclusions: In this study, we found that the decompression alone group performed better in terms of operation time and intraoperative blood loss, and there was no significant difference between the two surgical methods in rate of reoperation and postoperative complications, ODI, low back pain and leg pain. Therefore, we come to the conclusion that decompression alone is not inferior to decompression and fusion in patients with single-level lumbar spinal stenosis with spondylolisthesis. [ABSTRACT FROM AUTHOR]

Objective: Oblique lumbar interbody fusion (OLIF), performed using a retroperitoneal approach, can lead to complications related to the approach, such as lumbar sympathetic chain injury (LSCI). Although LSCI is a common complication of OLIF, its reported incidence varies across studies due to an absence of specific diagnostic criteria. Moreover, research on the risk factors of postoperative sympathetic chain injuries after OLIF remains limited. Therefore, this study aimed to describe the incidence, and identify independent risk factors for LSCI, in patients with degenerative lumbar spinal diseases who underwent OLIF. Methods: Between October 2020 and August 2023, a retrospective review was conducted at our institute on 200 patients who underwent OLIF at 1 to 4 consecutive spinal levels (L1–5) for degenerative spinal diseases including spinal stenosis, spondylolisthesis, degenerative scoliosis. We excluded those with infections, trauma, tumors, and lower extremity edema/warmth due to other causes. The patients were categorized into 2 groups: those with and without LSCI symptoms. Demographic data, operative data, and pre- and postoperative parameters were evaluated for their association with LSCI using a univariate logistic regression model. Variables with a p-value <0.1 in the univariate analysis were included in a multivariate model to identify the independent risk factors. Results: Thirty-five of 200 patients (17.5%) developed LSCI symptoms after OLIF. Multivariate logistic regression analysis indicated that prolonged retraction time, particularly exceeding 31.5 miniutes, remained an independent risk factor (adjusted odds ratio, 12.59; p<0.001). Conclusion: This study demonstrated that prolonged retraction time was an independent risk factor for LSCI following OLIF, particularly when it exceeded 31.5 minutes. Protecting the lumbar sympathetic chain during surgery and minimizing retraction time are crucial to avoiding LSCI following OLIF. [ABSTRACT FROM AUTHOR]

Backgroud: Supernatants from various cytological samples, including body cavity effusion, sputum, bronchoalveolar lavage fluid (BALF), and needle aspiration, have been validated for detecting genetic alterations using cell‐free DNA (cfDNA) in patients with non‐small cell lung cancer (NSCLC). However, the sensitivity of fusion variations detection remains challenging. The protection of cell‐free RNA (cfRNA) is critical for resolving the issue. Methods: A protective solution (PS) was applied for preserving cfRNA in cytological supernatant (CS), and the quality of protected cfRNA was assessed by cycle threshold (CT) values from reverse transcription quantitative polymerase chain reaction (RT‐qPCR). Furthermore, we collected an additional set of malignant cytological and matched tumor samples from 84 NSCLC patients, cfDNA & cfRNA extraction and double detection for driver gene mutations was validated using the multi‐gene mutations detection by RT‐qPCR. Results: Under the optimal protection system, 91.0% (101/111) of cfRNA were protected effectively. Among the 84 NSCLC patient samples, seven cytological samples failed the tests. In comparison with tumor samples, the overall sensitivity and specificity of detecting driver genes of supernatant cfDNA and cfRNA were 93.8% (74/77) and 100% (77/77), respectively. Notably, when focusing exclusively on patients with fusion gene changes, both sensitivity and specificity reached 100% (11/11) for EML4‐ALK, ROS1, RET fusions, and MET ex14 skipping. Conclusion: These findings suggest that cfDNA & cfRNA extraction and double detection strategy recommended in this study improve the accuracy of driver genes mutations test, especially for RNA‐based assay. [ABSTRACT FROM AUTHOR]

Study Design. Retrospective review of a prospectively collected multicenter registry. Objective. To evaluate health-related quality of life (HRQOL) measures in an operative cohort of patients (OP) and compare them with a matched nonoperative cohort (NON). Summary of Background Data. Historically, the surgical outcomes of adolescent idiopathic scoliosis (AIS) have been radiographically evaluated. However, the importance of HRQOL measures and their impact on surgical outcomes are increasingly being understood. Materials and Methods. We identified 90 NON patients with curves in the operative range who were observed for at least two years. These patients were matched with an OP cohort of 689 patients. All patients completed the Scoliosis Research Society-22 (SRS-22) questionnaire at the initial evaluation and at a minimum of two-year follow-up. Subgroup comparisons were based on curve type: primary thoracic (Th), primary thoracolumbar/lumbar (TL/L), and double major (DM) curves. Results. The preoperative major curves in the Th, TL/L, andDMOP subgroups averaged 50.4°, 45.4°, and 51.5°, respectively, and 49.4°, 43.7°, and 48.9° in the NON cohort (P>0.05). At two years postoperatively, the major curve in the Th, TL/L, and DM OP subgroups improved to 19.0°, 19.2°, and 19.3°, respectively, compared with the progression to 51.3°, 44.5°, and 49.7° in the NON group at two-year follow-up (P<0.05). The SRS-22 self-image, mental health, satisfaction, and total scores at the two-year follow-up were significantly better in all OP subgroups (P<0.001) but remained largely unchanged in the NON group. A significant percentage of patients (P<0.001) in the OP cohort reported better SRS-22 scores at the two-year follow-up in the self-image, mental health, and satisfaction domains than the NON group at two years. Conclusions. Surgically treated patients with AIS have improved HRQOL outcomes in several domains compared with agematched and curve magnitude-matched nonoperatively treated patients at two-year follow-up. [ABSTRACT FROM AUTHOR]

As value-based health care arrangements gain traction in spine care, understanding the true cost of care becomes critical. Historically, inaccurate cost proxies have been used, including negotiated reimbursement rates or list prices. However, time-driven activity-based costing (TDABC) allows for a more accurate cost assessment, including a better understanding of the primary drivers of cost in 1-level lumbar fusion. To determine the variation of total hospital cost, differences in characteristics between high-cost and nonhigh-cost patients, and to identify the primary drivers of total hospital cost in a sample of patients undergoing 1-level lumbar fusion. Retrospective, multicenter (one academic medical center, one community-based hospital), observational study. A total of 383 patients undergoing elective 1-level lumbar fusion for degenerative spine conditions between November 2, 2021 and December 2, 2022. Total hospital cost of care (normalized); preoperative, intraoperative, and postoperative cost of care (normalized); ratio of most to least expensive 1-level lumbar fusion. Patients undergoing a 1-level lumbar fusion between November 2, 2021 and December 2, 2022 were identified at two hospitals (one quaternary referral academic medical center and one community-based hospital) within our health system. TDABC was used to calculate total hospital cost, which was also broken up into: pre-, intra-, and postoperative timeframes. Operating surgeon and patient characteristics were also collected and compared between high- and nonhigh-cost patients. The correlation of surgical time and cost was determined. Multivariable linear regression was used to determine factors associated with total hospital cost. The most expensive 1-level lumbar fusion was 6.8x more expensive than the least expensive 1-level lumbar fusion, with the intraoperative period accounting for 88% of total cost. On average. the implant cost accounted for 30% of the total, but across the patient sample, the implant cost accounted for a range of 6% to 44% of the total cost. High-cost patients were younger (55 years [SD: 13 years] vs 63 years [SD: 13 years], p=.0002), more likely to have commercial health insurance (24 out of 38 (63%) vs 181 out of 345 (52%), p=.003). There was a poor correlation between time of surgery (ie, incision to close) and total overall cost (ρ:.26, p<.0001). Increase age (RC: -0.003 [95% CI: -0.006 to -0.000007], p=.049) was associated with decreased cost. Surgery by certain surgeons was associated with decreased total cost when accounting for other factors (p<.05). A large variation exists in the total hospital cost for patients undergoing 1-level lumbar fusion, which is primarily driven by surgeon-level decisions and preferences (eg, implant and technology use). Also, being a "fast" surgeon intraoperatively does not mean your total cost is meaningfully lower. As efforts continue to optimize patient value through ensuring appropriate clinical outcomes while also reducing cost, spine surgeons must use this knowledge to lead, or at least be active participants in, any discussions that could impact patient care. [ABSTRACT FROM AUTHOR]

Video sequences exhibit significant nuisance variations (undesired effects) of speed of actions, temporal locations, and subjects' poses, leading to temporal-viewpoint misalignment when comparing two sets of frames or evaluating the similarity of two sequences. Thus, we propose Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE) for sequence pairs. In particular, we focus on 3D skeleton sequences whose camera and subjects' poses can be easily manipulated in 3D. We evaluate JEANIE on skeletal Few-shot Action Recognition (FSAR), where matching well temporal blocks (temporal chunks that make up a sequence) of support-query sequence pairs (by factoring out nuisance variations) is essential due to limited samples of novel classes. Given a query sequence, we create its several views by simulating several camera locations. For a support sequence, we match it with view-simulated query sequences, as in the popular Dynamic Time Warping (DTW). Specifically, each support temporal block can be matched to the query temporal block with the same or adjacent (next) temporal index, and adjacent camera views to achieve joint local temporal-viewpoint warping. JEANIE selects the smallest distance among matching paths with different temporal-viewpoint warping patterns, an advantage over DTW which only performs temporal alignment. We also propose an unsupervised FSAR akin to clustering of sequences with JEANIE as a distance measure. JEANIE achieves state-of-the-art results on NTU-60, NTU-120, Kinetics-skeleton and UWA3D Multiview Activity II on supervised and unsupervised FSAR, and their meta-learning inspired fusion. [ABSTRACT FROM AUTHOR]

Objective: This study assesses whether fusion or cognitive magnetic resonance imaging (MRI)-guided prostate targeted and systematic transperineal biopsies (TPB) increase detection of clinically significant prostate cancer (csPCa). Materials and Methods: A retrospective analysis was completed of patients (2018–2020) undergoing 3-Tesla multiparametric prostate MRI informing targeted (either cognitive or MIM software fusion approach) and systematic TPB. ISUP (International Society of Urological Pathology) grade group ⩾ 2 was considered csPCa. Results: A total of 355 cases from 4 urologists were included; 131 were fusion and 224 were cognitive MRI-guided biopsies. Of all csPCa found, 86.8% (n = 171) of cases were confirmed to be at the MRI-indicated location and 11.6% were found as part of active surveillance. In all, 45.0% of the fusion group were found to have csPCa, compared to 62.05% (n = 139) in the cognitive group (p = 0.002). csPCa detection rates varied between urologists (41% to 78%, p < 0.001), so a subgroup analysis was performed on Urologist A; 45.0% of fusion and 41.3% of cognitive biopsies had csPCa (p = 0.644). Multinomial logistic regression analysis showed that biopsy type, being on active surveillance, number of biopsy cores, iPSA (initial Prostate Specific Antigen) value or PIRADS (Prostate Imaging-Reporting and Data System) score made no significant difference in whether csPCa was found. Conclusion: Cognitive and fusion targeting had similar csPCa detection rates. Further prospective studies would be beneficial to validate these findings. Level of evidence: 2b (according to Oxford Centre for Evidence-Based Medicine) [ABSTRACT FROM AUTHOR]

Images and text have become essential parts of the multimodal machine learning (MMML) framework in today's world because data are always available, and technological breakthroughs bring disparate forms together, and while text adds semantic richness and narrative to images, images capture visual subtleties and emotions. Together, these two media improve knowledge beyond what would be possible with just one revolutionary application. This paper investigates feature extraction and advancement from text and image data using pre-trained models in MMML. It offers a thorough analysis of fusion architectures, outlining text and image data integration and evaluating their overall advantages and effects. Furthermore, it draws attention to the shortcomings and difficulties that MMML currently faces and guides areas that need more research and development. We have gathered 341 research articles from five digital library databases to accomplish this. Following a thorough assessment procedure, we have 88 research papers that enable us to evaluate MMML in detail. Our findings demonstrate that pre-trained models, such as BERT for text and ResNet for images, are predominantly employed for feature extraction due to their robust performance in diverse applications. Fusion techniques, ranging from simple concatenation to advanced attention mechanisms, are extensively adopted to enhance the representation of multimodal data. Despite these advancements, MMML models face significant challenges, including handling noisy data, optimizing dataset size, and ensuring robustness against adversarial attacks. Our findings highlight the necessity for further research to address these challenges, particularly in developing methods to improve the robustness of MMML models. [ABSTRACT FROM AUTHOR]

Study Design: Retrospective cohort. Objective: Billing and coding-related administrative tasks are a major source of healthcare expenditure in the United States. We aim to show that a second-iteration Natural Language Processing (NLP) machine learning algorithm, XLNet, can automate the generation of CPT codes from operative notes in ACDF, PCDF, and CDA procedures. Methods: We collected 922 operative notes from patients who underwent ACDF, PCDF, or CDA from 2015 to 2020 and included CPT codes generated by the billing code department. We trained XLNet, a generalized autoregressive pretraining method, on this dataset and tested its performance by calculating AUROC and AUPRC. Results: The performance of the model approached human accuracy. Trial 1 (ACDF) achieved an AUROC of.82 (range:.48-.93), an AUPRC of.81 (range:.45-.97), and class-by-class accuracy of 77% (range: 34%-91%); trial 2 (PCDF) achieved an AUROC of.83 (.44-.94), an AUPRC of.70 (.45-.96), and class-by-class accuracy of 71% (42%-93%); trial 3 (ACDF and CDA) achieved an AUROC of.95 (.68-.99), an AUPRC of.91 (.56-.98), and class-by-class accuracy of 87% (63%-99%); trial 4 (ACDF, PCDF, CDA) achieved an AUROC of.95 (.76-.99), an AUPRC of.84 (.49-.99), and class-by-class accuracy of 88% (70%-99%). Conclusions: We show that the XLNet model can be successfully applied to orthopedic surgeon's operative notes to generate CPT billing codes. As NLP models as a whole continue to improve, billing can be greatly augmented with artificial intelligence assisted generation of CPT billing codes which will help minimize error and promote standardization in the process. [ABSTRACT FROM AUTHOR]

Study Design: Retrospective comparative study. Objectives: This study compares supine vs bending flexibility radiographs and evaluates their ability to predict residual postoperative lumbar curvature after selective thoracic fusion for Lenke 1 and 2 curves across different lumbar modifiers (A, B, and C) in adolescent idiopathic scoliosis (AIS). Methods: This was a retrospective review of AIS Lenke 1 and 2 patients who underwent posterior fusion. All patients had preoperative flexibility radiographs including side-bending and supine posteroanterior (PA) films, in addition to pre- and post-operative standing PA and lateral radiographs. We used SurgiMap 2.0 software for all radiographic measurements. Pearson correlations and linear regression models were developed in SAS. Results: A total of 86 patients were included mean age 14.9 years and follow-up 72.3 months. Preoperative supine lumbar Cobb angle and preoperative side-bending Cobb angles had similar, positive correlations with postoperative lumbar Cobb angle, r =.55 (P <.001) and r =.54 (P <.001), respectively. Three regression models were built to predict postoperative lumbar Cobb angles from preoperative information: Model S (R2 =.39) uses preoperative supine lumbar curve; Model B (R2 =.44) uses preoperative side-bending lumbar curve; Model SB (R2 =.49) uses both preoperative supine and side-bending lumbar curves. Model S and B performed just as well as Model SB. Conclusion: Either supine or side-bending radiographs alone may be used to estimate mean residual postoperative lumbar curvature after selective posterior thoracic fusion, but little is to be gained by taking both supine and side-bending radiographs. [ABSTRACT FROM AUTHOR]

Study Design: Technical Report Objective: Cerebrospinal fluid (CSF) leak secondary to anterior osteophytes at the cervico-thoracic junction is a rare cause of intracranial hypotension. In this article we describe a technique for anterior repair of spontaneous ventral cerebrospinal fluid leaks in the upper thoracic spine. Methods: In this technical report and operative video, we describe a 23-year-old male who presented with positional headaches and bilateral subdural hematoma. Dynamic CT myelography demonstrated a high flow ventral cerebrospinal fluid leak associated with a ventral osteophyte at the level of the T1-T2 disc space. Targeted blood patch provided only temporary improvement in symptoms. An anterior approach was chosen to remove the offending spur and micro-surgically repair the dural defect. Results: The patient had complete resolution of his preoperative symptoms after primary repair. Conclusions: In select cases, an anterior approach to the upper thoracic spine is effective to repair Type 1 cerebrospinal fluid leaks. [ABSTRACT FROM AUTHOR]

Early diagnosis is paramount to preventing skin diseases and reducing mortality, given their global prevalence. Visual detection by experts using dermoscopy images has become the gold standard for detecting skin cancer. However, a significant challenge in skin cancer detection and classification lies in the similarity of appearance among skin disease lesions and the complexity of dermoscopic images. In response, we developed multi-model late feature fusion network (MLFF-Net), a multi-model late feature fusion network tailored for skin disease detection. Our approach begins with image pre-processing techniques to enhance image quality. We then employ a twostream network comprising an enhanced densely linked network (DenseNet121) and a vision transformer (ViTb16). We leverage shallow and deep feature fusion, late fusion, and an attention module to enhance the model's feature extraction efficiency. The subsequent feature fusion module constructs multi-receptive fields to capture disease information across various scales and uses generalized mean pooling (GeM) pooling to reduce the spatial dimensions of lesion characteristics. Finally, we implement and test our skin lesion categorization model, demonstrating its effectiveness. Despite the combination, convolutional neural network (CNN) outperforms ViT approaches, with our model enhancing the accuracy of the best model by 6.1%. [ABSTRACT FROM AUTHOR]

Globally, the COVID-19 pandemic has aggravated the problem of depression. In the literature on depression detection, predictive capacity of multi-modal biomarkers like speech, facial and language characteristics were combined to improve the performance of Machine Learning (ML) models. The existing techniques have the limitation of not using the feature based weighting on multimodal data items in supervised learning models. The aim of the study is to use correlation based weighting mechanism between the feature and label in supervised learning to build an automatic multimodal depression detection system. We constructed a dataset of audio-visual recordings that consists of depressed and non-depressed subjects, to address this limitation by proposing a novel two-stage multimodal method for classification. The first stage attention weight is found using the Pearson correlation coefficients. Then these attention weights are combined with the multimodal feature vector, to use in the second stage. The resultant feature vector is experimented with three state-of-the-art classifiers i.e. Support Vector Machine (SVM), Decision Tree (DT) and K-Nearest Neighbour (KNN) classifiers, for predictions. Overall performance measures of proposed model are: accuracy 87.3%, precision 83.3%, recall 77%, and f1-score 80% which showed its effectiveness. Also outcomes of this proposed model were compared with the outcomes of existing state-of-the-art methods, such as baseline base classifiers and feature-level fusion method. Further, the proposed model is also evaluated applying to a widely used benchmarking dataset i.e. Distress Analysis Interview Corpus (DAIC) in depression detection. Results showed that combination of heterogeneous classifiers improves performance when classifying depressed and non-depressed subjects. [ABSTRACT FROM AUTHOR]

The availability of raw Global Navigation Satellites System (GNSS) measurements in Android smartphones fosters advancements in high-precision positioning for mass-market devices. However, challenges like inconsistent pseudo-range and carrier phase observations, limited dual-frequency data integrity, and unidentified hardware biases on the receiver side prevent the ambiguity resolution of smartphone GNSS. Consequently, relying solely on GNSS for high-precision positioning may result in frequent cycle slips in complex conditions such as deep urban canyons, underpasses, forests, and indoor areas due to non-line-of-sight (NLOS) and multipath conditions. Inertial/GNSS fusion is the traditional common solution to tackle these challenges because of their complementary capabilities. For pedestrians and smartphones with low-cost inertial sensors, the usual architecture is Pedestrian Dead Reckoning (PDR)+ GNSS. In addition to this, different GNSS processing techniques like Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) have also been integrated with INS. However, integration with PDR has been limited and only with Kalman Filter (KF) and its variants being the main fusion techniques. Recently, Factor Graph Optimization (FGO) has started to be used as a fusion technique due to its superior accuracy. To the best of our knowledge, on the one hand, no work has tested the fusion of GNSS Post-Processed Kinematics (PPK) and PDR on smartphones. And, on the other hand, the works that have evaluated the fusion of GNSS and PDR employing FGO have always performed it using the GNSS Single-Point Positioning (SPP) technique. Therefore, this work aims to combine the use of the GNSS PPK technique and the FGO fusion technique to evaluate the improvement in accuracy that can be obtained on a smartphone compared with the usual GNSS SPP and KF fusion strategies. We improved the Google Pixel 4 smartphone GNSS using Post-Processed Kinematics (PPK) with the open-source RTKLIB 2.4.3 software, then fused it with PDR via KF and FGO for comparison in offline mode. Our findings indicate that FGO-based PDR+GNSS–PPK improves accuracy by 22.5% compared with FGO-based PDR+GNSS–SPP, which shows smartphones obtain high-precision positioning with the implementation of GNSS–PPK via FGO. [ABSTRACT FROM AUTHOR]

In this Crada we designed and fabricated plasma discharge devices and operated them with deuterium and hydrogen gases. We studied the dynamics of plasma discharged in the glow discharge regime. We quantified rates of fusion reactions between deuterium atoms and ions that we accelerated from plasma discharges into palladium targets. The results showed enhanced fusion reaction rates in metal hydrides compared to know rates form gas phase reaction, likely due to modifications of the electron screening potentials and associated tunnel barrier probability increases. We modeled this effect and published it in the peer reviewed publications listed above

Abstract This research introduces a Computer-Aided Diagnosis-system designed aimed at automated detections & classification of tomato leaf diseases, combining traditional handcrafted features with advanced deep learning techniques. The system’s process encompasses preprocessing, feature extraction, feature fusion, and classification. It utilizes enhancement filters and segmentation algorithms to isolate with Regions-of-Interests (ROI) in images tomato leaves. These features based arranged in ABCD rule (Asymmetry, Borders, Colors, and Diameter) are integrated with outputs from a Convolutional Neural Network (CNN) pretrained on ImageNet. To address data imbalance, we introduced a novel evaluation method that has shown to improve classification accuracy by 15% compared to traditional methods, achieving an overall accuracy rate of 92% in field tests. By merging classical feature engineering with modern machine learning techniques under mutual information-based feature fusion, our system sets a new standard for precision in agricultural diagnostics. Specific performance metrics showcasing the effectiveness of our approach in automated detection and classifying of tomato leaf disease.

Objective Oblique lumbar interbody fusion (OLIF), performed using a retroperitoneal approach, can lead to complications related to the approach, such as lumbar sympathetic chain injury (LSCI). Although LSCI is a common complication of OLIF, its reported incidence varies across studies due to an absence of specific diagnostic criteria. Moreover, research on the risk factors of postoperative sympathetic chain injuries after OLIF remains limited. Therefore, this study aimed to describe the incidence, and identify independent risk factors for LSCI, in patients with degenerative lumbar spinal diseases who underwent OLIF. Methods Between October 2020 and August 2023, a retrospective review was conducted at our institute on 200 patients who underwent OLIF at 1 to 4 consecutive spinal levels (L1–5) for degenerative spinal diseases including spinal stenosis, spondylolisthesis, degenerative scoliosis. We excluded those with infections, trauma, tumors, and lower extremity edema/warmth due to other causes. The patients were categorized into 2 groups: those with and without LSCI symptoms. Demographic data, operative data, and pre- and postoperative parameters were evaluated for their association with LSCI using a univariate logistic regression model. Variables with a p-value

Abstract Parkinson’s disease (PD) is classified as a neurological, progressive illness brought on by cell death in the posterior midbrain. Early PD detection will assist doctors in reducing the disease’s consequences. A collection of skilled models that may be applied to regression as well as classification is known as artificial intelligence (AI). PD can be detected using a variety of dataset formats, including text, speech, and picture datasets. For the purpose of classifying Parkinson’s disease, this study suggests merging deep with machine learning recognition approaches. The three primary components of the suggested approach are designed to enhance the accuracy of Parkinson’s disease early diagnosis. These sections cover the topics of categorising, combining, and separating. Convolutional Neural Networks (CNN) as well as attention procedures are used to create feature extractors. The related motion signals are fed to a combination of convolutional neural network and long-short-memory model for feature extraction. Besides, for the classification of patients from non-suffers of Parkinson’s disease, Random Forest, Logistic Regression, Support Vector Machine, Extreme Boot Classifier, and voting classifier were used. Our result shows that for the PD handwriting and related motion datasets, using the proposed CNN with an attention and voting classifier yields 99.95% accuracy, 99.99% precision, 99.98% sensitivity, and 99.95% F1-score. Based on these results, it is warranted to conclude that the proposed methodology of feature extraction from photos of handwriting and relating motor symptoms, fusing of those features, and following it with a voting classifier yields excellent results for PD classification.

The objectives of fusion surgery for spinal deformities include decompressing neural elements and achieving balanced spinal alignment. Particularly, in cases where spinal deformities coexist with osteoporosis, successful surgery requires careful consideration due to the susceptibility to fixation failure and non-union. Various efforts are being made to restore spinal alignment through surgery in osteoporotic patients. The administration of osteoporosis medications before and after surgery is effective for bony union. Additionally, appropriate selection of fusion range, rigid internal fixation, and utilization of bone substitutes play significant roles in successful fusion surgery. Although surgical treatment for spinal deformities accompanied by osteoporosis remains still challenging, we can anticipate successful outcomes with effective strategies and ongoing advancements in the future.

Abstract Purpose The objective of this systematic review and metaanalysis is to compare the efficacy and safety of decompression alone versus decompression plus fusion in single-level lumbar spinal stenosis with spondylolisthesis. Methods A comprehensive search of the PubMed, Embase, Cochrane Library, and Ovid Medline databases was conducted to find randomized control trials (RCTs) or cohort studies that compared decompression alone and decompression plus fusion in single-level lumbar spinal stenosis with spondylolisthesis. Operation time; reoperation; postoperative complications; postoperative Oswestry disability index(ODI) scores and scores related to back and leg pain were collected from eligible studies for meta-analysis. Results We included 3 randomized controlled trials and 9 cohort studies with 6182 patients. The decompression alone group showed less operative time(P

We propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity within a multi-subject electroencephalogram dataset to boost representation learning and classification accuracy. The WaveFusion framework demonstrates high accuracy in classifying confidence levels by achieving a classification accuracy of 95.7% while also identifying influential brain regions.

Peripheral nerve injuries are potentially devastating injuries leading to pain and impairment in motor and sensory functions. Since the first published use of microsurgical epineural repair of peripheral nerves in 1964, a wide variety of adjuncts have been studied. Polyethylene glycol is a fusogen that has been shown to restore axolemmal membranes. The use of polyethylene glycol in nerve injuries was first described in 1986, and animal studies have shown fusion of transected sensory and motor nerves following early application at the time of surgical repair with improved motor and sensory outcomes. Early human clinical trials have shown promising results, although more data are needed to provide specific indications and protocols. This article summarizes the background, current evidence, and future directions as well as potential applications of polyethylene glycol–mediated nerve fusion.

Currently, the oil sands industry is producing millions of tons of tailings by‐products from the tailings solvent recovery unit (TSRU) into the tailings ponds. TSRU tailings (TTs) consist of water, asphaltene, and minerals, including silica and alumina mixtures. The oil sands sector has expanded efforts to discover solutions to remove the tailings ponds due to growing public concern about the environmental effects of these ponds and stiffer government rules on their disposal. Therefore, this report studied the effect of the different methods for the conversion of the TTs into the activated carbon‐zeolite composite. The TTs were treated with activation followed by hydrothermal, either with or without fusion with NaOH at 800°C for 1 h as a pre‐treatment. Zeolite Na‐P, zeolite A, and zeolite X were identified during the different characterizations, depending on the pre‐treatment of the fusion. The result showed that the fusion with NaOH before the hydrothermal reaction was effective as it increased the porosity and adsorption of the composite. The CO2 capture capacity of the product before fusion was 0.19 mmol/g, and after fusion, it was improved to 0.486 mmol/g. [ABSTRACT FROM AUTHOR]

Given the importance of input data, particularly precipitation, in hydrologic modeling for streamflow simulation, there has been growing emphasis on developing frameworks that harness multiple data sources concurrently to achieve more precise results. In the proposed framework of this study, which relies on the integrated capabilities of the multiobjective optimization model [nondominated sorting genetic algorithm-II (NSGA-II)], the ensemble Kalman filter data assimilation method, and data fusion, rainfall data from multiple sources are incorporated. The utilized framework leads to an improvement in the mean absolute relative error (MARE) index of streamflow simulation results. The innovation of the proposed methodology is the calculation of optimal weights corresponding to the simulated runoff time-series in the fusion model. This is accomplished through a competitive process among a multitude of optimized scenarios simulated within the framework provided. MARE as the main index identified in the objective functions and standard deviation, centered root mean square distance, and the correlation coefficient as auxiliary indices have been considered in this process. In this framework, satellite-based and in situ precipitation data sets are used as the forcing data. The main challenge has been to choose the greatest scenario for fusion among the selected scenarios, which the proposed methodology has overcome. The performance of the suggested methodology is demonstrated for the Siakh-Darengon catchment located in the Fars Province of Iran. According to the results, an average of 14.07% improvement in the MARE index has been achieved after applying the proposed methodology. By utilizing the proposed method, satellite-based rainfall data are integrated alongside ground-based rainfall data in the flood modeling process, resulting in enhanced accuracy in simulation outcomes within the utilized watersheds. Practical Applications: Today, influenced by factors such as climate change and anthropogenic alterations to the environment, the issue of flooding and its associated hazards has garnered unprecedented attention from researchers. One of the crucial components in flood modeling is rainfall data, which are collected through various means such as ground stations and satellite sensing instruments. In the past, the primary focus in the process of flood modeling has been on rainfall data recorded at ground stations; nowadays, efforts are being made to further enhance the role of satellite-derived rainfall data in flood modeling, aiming at enhancing their precision. In this study, a fusion model has been developed using the data fusion method and simultaneous utilization of ground-based and satellite rainfall data. Various flood simulation scenarios have been generated using a multiobjective optimization model, and the best scenario is selected through a competitive process. By implementing the proposed methodology in the Siakh-Darengon watershed located in Fars Province, Iran, improvements in simulation results have been achieved, resulting in based on the calculated performance indicators. [ABSTRACT FROM AUTHOR]

In breast cancer research, diverse data types and formats, such as radiological images, clinical records, histological data, and expression analysis, are employed. Given the intricate nature of natural phenomena, relying on the features of a single modality is seldom sufficient for comprehensive analysis. Therefore, it is possible to guarantee medical relevance and achieve improved clinical outcomes by combining several modalities. The presen study carefully maps and reviews 47 primary articles from six well-known digital libraries that were published between 2018 and 2023 for breast cancer classification based on multimodal deep learning fusion (MDLF) techniques. This systematic literature review encompasses various aspects, including the medical modalities combined, the datasets utilized in these studies, the techniques, models, and architectures used in MDLF and it also discusses the advantages and limitations of each approach. The analysis of selected papers has revealed a compelling trend: the emergence of new modalities and combinations that were previously unexplored in the context of breast cancer classification. This exploration has not only expanded the scope of predictive models but also introduced fresh perspectives for addressing diverse targets, ranging from screening to diagnosis and prognosis. The practical advantages of MDLF are evident in its ability to enhance the predictive capabilities of machine learning models, resulting in improved accuracy across diverse applications. The prevalence of deep learning models underscores their success in autonomously discerning complex patterns, offering a substantial departure from traditional machine learning approaches. Furthermore, the paper explores the challenges and future directions in this field, including the need for larger datasets, the use of ensemble learning methods, and the interpretation of multimodal models. [ABSTRACT FROM AUTHOR]

Direct-drive laser inertial fusion is a potential producer of baseline power that has increased credibility following the achievement at the National Ignition Facility of ignition and net gain using indirect-drive via laser-produced X-rays. Ultraviolet broad band lasers such as argon fluoride, at 193 nm and 10 THz, are predicted by hydrocode simulations to enable energy gains greater than 100 with laser energies less than 0.5 MJ, stimulating renewed reactor design effort in anticipation of experimental verification. The present study attempts to create a reactor design with very few unknowns in materials, corrosion, first wall viability, tritium breeding and ease of servicing. A new variant of magnetic intervention has an increased ion dump surface area combined with a simple structure. Around an inner vacuum vessel an all-ceramic tritium breeder blanket is possible in an unconstrained volume, allowing helium coolant to be used without excessive pressure or flow power. The case is made for development of a lead (Pb) ceramic as the neutron multiplier. [ABSTRACT FROM AUTHOR]

Protoplast fusion technology originated from cell-cell fusion technology in the 1950s and was initially applied to animal cells. The advantage of applying it in the field of edible fungi breeding is that compared with conventional natural selection and hybrid breeding methods, it could more effectively overcome certain incompatibility obstacles to achieve distant hybridization, and more purposefully select parents to obtain new varieties with outstanding characteristics. For these reasons protoplast fusion technology is becoming of great significance for the improvement of edible fungi strains, the breeding of new varieties, and the taming of difficult-to-cultivate species. At present, relatively stable and capable-of-fruiting fusants are limited to several genus and species, and using protoplast fusion for distant hybridization requires new breakthroughs overcoming vegetative incompatibilities. Technically, the acquisition and identification of fusants could also be improved to a more controllable approach. This article focuses on the longitudinal discussion of the role of protoplast fusion technology in the genetic breeding of edible fungi. It explains: i) the preparation and regeneration of protoplasts in the process, ii) the specific methods of fusion, iii) the identification, mutagenesis, and rejuvenation of fusants, and iv) possible impacts of two incompatible systems. Certain application practice cases are also introduced to summarize its promotion prospects.

Incidences of dengue-virus infection are increasing globally. Around 3.90 billion population is at high risk of infection. There is no cure/ prevention available. Hence aim of the current review was the evaluation of current development and prospects for the production of safe anti-dengue drugs. The data of review was synthesized by PRISMA. Grading of Recommendation Assessment, Development, and Evaluation criteria were employed to establish the quality of the literature. Many drugs/compounds have shown promising prospects. Target site of these drugs on dengue virus were inhibition of replication (N-sulfonyl peptide-hybrids, milk-exosomes, sunitinib, schisandrin-A, cavinafungin, minocycline, 4-hydroxyphenyl retinamide, Goniothalamus umbrosus, Dryopteris crassirhizoma, Morus alba, statins), protease-enzyme (Thiazolidinone-peptide, nelfinavir, carnosine, 1,2-benzisothiazol-3(2H)- one and 1,3,4-oxadiazole), capsid-protein (Juglanin and silymarin), NS4B-gene (Desatinib), entry-in-host-cell (Duramycin, glycodendrimers, curdlan sulfate), RNA-capping (Lanatoside-C), fusion (Chloroquine) and NS3-helicase (Suramina). Many compounds/drugs are found effective by targeting the structural proteins of the dengue virus to use as a therapy in the future.

The PION code has been integrated into the European Transport Solver (ETS) transport workflow, and we present the first application to model Ion Cyclotron Resonance Frequency (ICRF) heating scenarios in the next-step fusion reactor ITER. We present results of predictive, self-consistent and time-dependent simulations where the resonant ion concentration is varied to study its effects on the performance, with a special emphasis on the resulting bulk ion heating and thermal ion temperature. We focus on two ICRF heating schemes, i.e., fundamental H minority heating in a 4He plasma at 2.65 T/7.5 MA and a three-ion ICRF scheme consisting of fundamental 3He heating in a H-4He plasma at 3.3 T/ 8.8 MA. The H minority heating scenario is found to result in strong absorption by resonant H ions as compared to competing absorption mechanisms and dominant background electron heating for H concentrations up to 10%. The highest H absorption of ∼80% of the applied ICRF power and highest ion temperature of ∼15 keV are obtained with an H concentration of 10%. For the three-ion scheme in 85%:15% H:4He plasma, PION+ETS predicts 3He absorption in the range of 21–65% for 3He concentrations in the range of 0.01–0.20%, with the highest 3He absorption at a 3He concentration of 0.20%.

This paper proposes a penalized method for high-dimensional variable selection and subgroup identification in the Tobit model. Based on Olsen's [(1978). Note on the uniqueness of the maximum likelihood estimator for the Tobit model. Econometrica: Journal of the Econometric Society, 46(5), 1211–1215. https://doi.org/10.2307/1911445] convex reparameterization of the Tobit negative log-likelihood, we develop an efficient algorithm for minimizing the objective function by combining the alternating direction method of multipliers (ADMM) and generalised coordinate descent (GCD). We also establish the oracle properties of our proposed estimator under some mild regularity conditions. Furthermore, extensive simulations and an empirical data study are conducted to demonstrate the performance of the proposed approach.

Introduction: Anterior cervical discectomy and fusion (ACDF) has proven to be a clinically efficient and cost-effective method for treating patients with degenerative cervical spine conditions. New intervertebral implant products are being developed to improve fusion and stability while decreasing complications. This study assesses the effectiveness of Tritanium C (Tri-C) Anterior Cervical Cage (Stryker) in the treatment of degenerative disk disease (DDD) of the cervical spine compared with polyetheretherketone (PEEK) cages. Methods: A retrospective cohort analysis was conducted using data prospectively collected from two institutions. Patients who underwent ACDFs for DDD using either the Tri-C cage or PEEK cage were identified. The patients' demographics, comorbidities, operative variables, and baseline patient-reported outcomes (PROs) were collected. PROs included the Neck Disability Index (NDI) and numeric rating scale (NRS) for neck and arm pain. The primary outcomes included 3- and 12-month PROs as well as the rates of 90-day readmission, 90-day reoperation, and perioperative complication. The radiographic outcomes included rates of subsidence, cage movement, and successful fusion within 12 months. Multivariate linear regression models were run to identify variables predictive of 12-month PROs. Results: A total of 275 patients who underwent ACDF were included in this study and were divided into two groups: PEEK (n=213) and Tri-C (n=62). Both groups showed improvement in neck and arm pain and NDI postoperatively. When Tri-C and PEEK were compared, no significant differences were observed in the 3- or 12-month changes in neck or arm pain or NDI. Furthermore, there were no differences in the rates of 90-day readmission, 90-day reoperation, and perioperative complication. Regression analysis revealed that Tri-C vs. PEEK was not a significant predictor of any outcome. Conclusions: Our results indicate that the use of porous titanium Tri-C cage during ACDFs is an effective method for managing cervical DDD in terms of PROs, perioperative morbidity, and radiologic parameters. No significant difference was observed in any clinical outcome between patients undergoing ACDF using the Tri-C cage and those in whom the PEEK cage was used. Level of Evidence: III

Introduction: This study aimed to evaluate the added diagnostic value of systematic biopsies (SBx) after magnetic resonance imaging (MRI)-targeted biopsies (TBx) and the presence of prostate cancer (PCa) outside MRI targets, in a prospective, contemporary, multicentric series of fusion biopsy patients. Methods: We collected data on 962 consecutive patients who underwent fusion biopsy between 2022 and 2024. Prostate cancer was considered clinically significant (csPCa) in the case of grade ≥ 2. Median test and Fisher exact chi-square tests were used. To identify predictors of out-field positivity, univariate and multivariable logistic regression analyses were performed. Results: Prostate cancer and csPCa were detected by TBx only in 56% and 50%, respectively, and by SBx only in 55% and 45%, respectively (p < 0.001). Prostate cancer and csPCa were diagnosed by TBx in 100 (10%) and 82 (8%) SBx-negative cases and by SBx in 86 (9%) and 54 (6%) TBx-negative cases (p < 0.001). Tumors outside MRI targets were found in 213 (33%) cases in the same lobe and 208 (32%) in the contralateral lobe, most of them being csPCa. Predictors of out-field contralateral PCa were positive DRE (HR 1.50, p 0.03), PSA density ≥ 0.15 (HR 2.20, p < 0.001), and PI-RADS score 5 (HR 2.04, p 0.01). Conclusions: Both TBx and SBx identify a non-negligible proportion of csPCa when the other modality is negative. SBx after TBx should always be considered given the risk of missing other csPCa foci within the prostate, especially in patients with positive DRE, PSA density ≥ 0.15, and PIRADS 5 lesions.

Simple Summary: Knowledge about the metabolic landscape of cancer cells may provide groundbreaking discoveries in the field of new methods for the diagnosis, prognosis, and treatment of lung cancer. The aim of our study was to assess mitochondrial alterations in the blood of lung cancer patients. We confirmed that fusion and fission protein blood expressions varied between 47 lung cancer patients and 21 healthy people. In the blood of lung cancer patients, fission protein expression is promoted only at an early stage of the disease. In locally advanced and metastatic stages of lung cancer, there is an increase in fusion protein expression. The results of this study provide hope for mitochondrial dynamics understanding in patients with lung cancer, which in the future may contribute to the discovery of new predictive factors for personalized therapy or diagnosis. In lung cancer patients, two complementary abnormalities were found that can cause disruption of the mitochondrial network: increased fusion and impaired fission, manifested by reduced levels of FIS1, a mitochondrial division regulator, and increased expression of MFN1, a mitochondrial fusion mediator. Immunoexpression studies of MFN1 and FIS1 proteins were performed in serum samples obtained from 47 patients with non-small cell lung cancer (NSCLC) and 21 controls. In the NSCLC patients, the immunoexpression of the MFN1 protein was significantly higher, and the FIS1 protein level was significantly lower than in the control group (p < 0.01; p < 0.001; UMW test). Patients with early, operable lung cancer had significantly lower levels of MFN1 immunoexpression compared to patients with advanced, metastatic lung cancer (p < 0.05; UMW test). This suggests that early stages of the disease are characterized by greater fragmentation of damaged mitochondria and apoptosis. In contrast, lower FIS1 protein levels were associated with a worse prognosis. Increased mitochondrial fusion in the blood of lung cancer patients may suggest an increase in protective and repair mechanisms. This opens up questions about why these mechanisms fail in the context of existing advanced cancer disease and is a starting point for further research into why protective mechanisms fail in lung cancer patients. [ABSTRACT FROM AUTHOR]

Introduction: Using a non-human primate (NHP) model of maternal Westernstyle diet (mWSD) feeding during pregnancy and lactation, we previously reported altered offspring beta:alpha cell ratio in vivo and insulin hypersecretion ex vivo. Mitochondria are known to maintain beta-cell function by producing ATP for insulin secretion. In response to nutrient stress, the mitochondrial network within beta cells undergoes morphological changes to maintain respiration and metabolic adaptability. Given that mitochondrial dynamics have also been associated with cellular fate transitions, we assessed whether mWSD exposure was associated with changes in markers of beta-cell maturity and/or mitochondrial morphology that might explain the offspring islet phenotype. Methods: We evaluated the expression of beta-cell identity/maturity markers (NKX6.1, MAFB, UCN3) via florescence microscopy in islets of Japanese macaque pre-adolescent (1 year old) and peri-adolescent (3-year-old) offspring born to dams fed either a control diet or WSD during pregnancy and lactation and weaned onto WSD. Mitochondrial morphology in NHP offspring beta cells was analyzed in 2D by transmission electron microscopy and in 3D using super resolution microscopy to deconvolve the beta-cell mitochondrial network. Results: There was no difference in the percent of beta cells expressing key maturity markers in NHP offspring from WSD-fed dams at 1 or 3 years of age; however, beta cells of WSD-exposed 3 year old offspring showed increased levels of NKX6.1 per beta cell at 3 years of age. Regardless of maternal diet, the beta-cell mitochondrial network was found to be primarily short and fragmented at both ages in NHP; overall mitochondrial volume increased with age. In utero and lactational exposure to maternal WSD consumption may increase mitochondrial fragmentation. Discussion: Despite mWSD consumption having clear developmental effects on offspring beta:alpha cell ratio and insulin secretory response to glucose, this does not appear to be mediated by changes to beta-cell maturity or the beta-cell mitochondrial network. In general, the more fragmented mitochondrial network in NHP beta cells suggests greater ability for metabolic flexibility. [ABSTRACT FROM AUTHOR]

Predictions of the scrape‐off layer (SOL) plasma conditions from the 2D multi‐fluid code EDGE2D‐EIRENE are compared to the OEDGE 1D fluid code for JET low‐confinement mode (L‐mode) plasmas. In the low‐recycling divertor conditions (divertor target plasma collisionality νe≈30$$ {\nu}_e\approx 30 $$), OEDGE and EDGE2D‐EIRENE agree on the electron temperature, the ion temperature, and the electron density within 10% in the low‐field side (LFS) divertor X‐point region (divertor SOL) and within 25% in the LFS midplane region. In high‐recycling conditions (νe≈55,000$$ {\nu}_e\approx 55,000 $$), the predictions of both codes agree on the electron temperature within 20%, while differences in the ion temperature and electron density increase to as high as 50% in the divertor SOL. [ABSTRACT FROM AUTHOR]

Purpose: The study evaluated the efficacy of SC arthrodesis with lunate preservation for treating patients diagnosed with stage IIIB or IIIC Kienböck's disease, who also exhibit neutral ulnar variance. The study further aimed to explore potential variations in outcomes between patients diagnosed with stage IIIB and IIIC Kienböck's disease. Methods: Thirty-two patients diagnosed with stage IIIB (n = 19) and stage IIIC (n = 13) Kienböck's disease underwent SC arthrodesis with distal radius bone grafting stabilised by Herbert compression screws. All participants underwent pre- and post-operative assessments including VAS score for pain, ROM, grip strength, MMWS, and the Quick DASH score. Additionally, RS angle, LHI ratio, and CHI ratio were assessed. Results: For all patients, the mean operative time was 73 min, follow-up was 45.6 months, time to union was 14 weeks, and time to full return to work was 24 weeks. The rate of union at the arthrodesis site was 91% (29 out of 32 patients) whilst the incidence of postoperative degenerative arthritis was 36% (8 out of 32 patients). Regarding changes in the means of outcomes from pre- to post-operatively, the VAS score decreased from 8.2 to 1.3 and grip strength improved from 36 to 79%. The RS angle was corrected from 59° to 50°. Significant improvements were noted in the mean MMWS from 45 to 75 and QuickDASH score from 78 to 21. However, no significant changes were observed in ROM, LHI, and CHI. There were no significant differences between patients with stage IIIB and stage IIIC in terms of these parameters, except for differences observed in the RS angle, LHI, and CHI preoperatively and in LHI and CHI postoperatively. Conclusion: Evidence level: II. Our research demonstrates that SC arthrodesis is a valuable approach for reducing pain, improving grip strength, and enhancing overall function in individuals with advanced Kienböck's disease. Importantly, our results indicate no notable differences in outcomes between patients diagnosed with stage IIIB or IIIC Kienböck's disease. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the effect of mecobalamin dispersible tablets and surgical treatment on nerve conduction velocity and skin sympathetic reflex in patients with cervical spondylotic radiculopathy (CSR). Methods: 82 patients diagnosed with CSR between February 2019 and January 2022 in Inner Mongolia Medical University School of Basic Medicine, Hohhot City, China were randomized into control and study groups comprising 41 patients each. The control group was treated with anterior cervical diskectomy and fusion, while the study group was treated with mecobalamin dispersible tablets in addition to surgery. Efficacy was assessed 3 months after surgery. Results: Efficacy in the study group was significantly higher than in the control group (p < 0.05). Furthermore, the study group had significantly greater motor and sensory nerve conduction velocity of the median and ulnar nerve as well as reduced initial wave latency (p < 0.05). Scores of subjective symptoms, clinical signs, and life-work-social adaptability were significantly higher in the study group than in the control group (p < 0.05). Range of motion of the neck significantly improved and brainderived neurotrophic factor and glial neurotrophic factor were significantly increased in the study group compared to control group (p < 0.05). Conclusion: Mecobalamin dispersible tablets in combination with surgery improve nerve conduction velocity, skin sympathetic reflex function, neurotrophic factor level, cervical motion, and cervical spine function in patients with CSR. Multicenter long-term controlled studies with larger sample sizes are required in future studies. [ABSTRACT FROM AUTHOR]

Public opinion significantly influences discussions on the selection of technologies for future energy systems. Our goal was to support the development of energy scenarios by evaluating decision-making criteria and energy-production alternatives at a national level. The importance of the criteria and the determination of energy-production preferences were based on a social survey conducted in European countries. However, public opinions on specific technologies are frequently based on limited knowledge and can vary depending on the context. In our study, we analyzed the comparative data sets from an international survey on energy sources using three approaches: direct ranking, a comparison using various criteria, and classifying the knowledge of technologies. The evaluated results not only provide an excellent opportunity for the development of energy scenarios but also enable policymakers to develop more effective communication strategies to inform the public about technological choices and raise awareness of the need for modifications to the energy system. [ABSTRACT FROM AUTHOR]

This retrospective study investigated the clinical and radiographic outcomes following temporary transpedicular posterior instrumentation between two cohorts of patients with thoracolumbar fractures (TLF) who underwent selective or bi-segments intervertebral articular process fusion. Patients with TLF who underwent the temporary posterior fixation with selective fusion (Group SF), or bi-segments fusion (Group BF) were studied. Superior intervertebral articular process and interlaminar fusion were performed in Group SF, whereas in Group BF, the patients underwent bi-segments fusion in both superior and inferior articular processes, as well as interlaminar fusion. We measured the distal and proximal intervertebral mobility, regional kyphotic angle, and vertebral height before and after surgery in both groups. Greenough Low-Back Outcome Score was used to assess the clinical outcomes. Sixty-five patients with TLF from T12 to L2 fractures were enrolled in the study period: 33 patients in the Group SF and 32 patients in the Group BF. All the patients experienced fracture healing (mean follow-up time: 19.7 months). The mean postoperative functional outcomes were 65.0 ± 2.0 points for the Low-Back Outcome Score in the Group SF and 65.2 ± 1.8 for the Group BF. A progressive regional kyphotic angle was observed with time regardless of fusion but was not significantly different between the two groups. There was a statistical difference between unfused inferior proximal adjacent and inferior distal adjacent segment regardless of fracture segments. The strategy of selective fusion is reported to be useful for the treatment of patients with TLF. The motion in the un-fused and adjacent segment could be better regained after instrumentation removal in the selective fusion group. Level 3. [ABSTRACT FROM AUTHOR]

Left-digit bias is a behavioral heuristic or cognitive "shortcut" in which the leftmost digit of a number, such as patient age, disproportionately influences surgical decisions. To determine if left-digit bias in patient age influences the decision to perform arthrodesis with instrumentation vs decompression in lumbar spinal stenosis (LSS). Retrospective cohort. Patients with an ICD-10 diagnosis of lumbar stenosis or spondylolisthesis identified in the 2017-2021 National Surgical Quality Improvement Program (NSQIP) database. The primary outcome was the percent of patients who underwent arthrodesis with instrumentation (AwI). Matched age group comparisons without left-digit differences (ie, 76/77 vs 78/79, 80/81 vs 82/83, etc.) were performed to isolate the effect of the heuristic. Secondary outcomes including peri-operative events and complications were also compared within AwI and decompression cohorts. Using CPT codes, procedures were classified as either AwI or decompression. Patients were grouped into 6 cohorts based on 2-year age windows (74/75, 76/77, 78/79, 80/81, 82/83, 84/85). The cohorts were propensity matched with neighboring age groups based on the presence of spondylolisthesis, demographics, and comorbidities. The primary comparison was between those aged 78/79 vs 80/81. After matching, the primary cohort consisted of two groups of 1,550 patients (aged 78/79 and 80/81). Patients aged 80/81 were less likely to undergo AwI than patients aged 78/79 (23.5% vs 27.2%, p=.021). AwI procedures occurred at similar rates between age groups with the same left digit. Within the decompression and AwI cohorts, there were no differences in secondary outcomes between patients aged 78/79 and 80/81. LSS patients aged 80/81 are less likely to undergo AwI than patients aged 78/79, regardless of comorbidities. This was not seen when comparing patients with similar left digits in age. Until objective measures of physiologic capacity are established, left-digit bias may influence clinical decisions. [ABSTRACT FROM AUTHOR]

Background: Incongruity between irregularly shaped vertebral endplates and the uniform surfaces of stock interbody fusion cages has been identified as contributing to cage subsidence, pseudarthrosis, and unpredictable alignment. Advances in manufacturing techniques have driven the development of personalized interbody cages (PICs) that can match individual endplate morphology and provide the exact shape and size needed to fill the disc space and achieve the planned correction. This study used computed tomography (CT) imaging to evaluate the implant-endplate contact area, fusion, subsidence, and achievement of planned alignment correction in patients receiving PIC devices. Methods: This retrospective study included patients treated for adult spinal deformity at a single site and implanted with PIC devices at L4 to L5 or L5 to S1 for segmental stabilization and alignment correction, who received 1-year postoperative CT images as part of their standard of care. An evaluation using 3-dimensional thin-section scans was conducted. Implant-endplate contact and signs of fusion were assessed in each CT slice across both endplates. The degree of subsidence as well as measures of segmental and global lumbar alignment were also assessed. Results: Fifteen patients were included in the study, with a mean age of 68.2 years. Follow-up ranged between 9 and 14 months. Twenty-six total lumbar levels were implanted; 20 with PIC devices via the anterior lumbar interbody fusion approach, 2 with stock cages via the anterior lumbar interbody fusion approach, and 4 with PIC devices via the transforaminal lumbar interbody fusion approach. CT analysis of PIC-implanted levels found an overall implant-endplate contact area ratio of 93.9%, a subsidence rate of 4.5%, a fusion rate of 100%, and satisfactory segmental and global lumbar correction compared with the preoperative plan. Conclusions: PIC implants can provide nearly complete contact with endplate surfaces regardless of the individual endplate morphology. Subsidence, fusion, and alignment assessments in this tomographic study illustrated results consistent with the benefits of a personalized interbody implant. [ABSTRACT FROM AUTHOR]

Background: Emerging data have highlighted the significance of planning and aligning total and segmental lumbar lordosis with pelvic morphology when performing short-segment fusion with the goal of reducing the risk of adjacent segment disease while also decreasing spine-related disability. This study evaluates the impact of personalized interbody implants in restoring pelvic incidence-lumbar lordosis (PI-LL) mismatch compared with a similar study using stock interbody implants. Methods: This multicenter retrospective analysis assessed radiographic pre- and postoperative spinopelvic alignment (PI-LL) in patients who underwent 1- or 2-level lumbar fusions with personalized interbody implants for degenerative (nondeformity) indications. The aim was to assess the incidence of malalignment (PI-LL = 10°) both before and after fusion surgery and to determine the rate of alignment preservation and/or correction in this population. Results: There were 135 patients included in this study. Of 83 patients who were aligned preoperatively, alignment was preserved in 76 (91.6%) and worsened in 7 (8.4%). Among the 52 preoperatively malaligned patients, alignment was restored in 23 (44.2%), and 29 (55.8%) were not fully corrected. Among patients who were preoperatively aligned, there was no statistically significant difference in either the "preserved" or "worsened" groups between stock devices and personalized interbody devices. In contrast, among patients who were preoperatively malaligned, there was a statistically significant increase in the "restored" group (P = 0.046) and a statistically significant decrease in the "worsened" groups in patients with personalized interbodies compared with historical stock device data (P < 0.05). Conclusions: Compared with a historical cohort with stock implants, personalized interbody implants in short-segment fusions have shown a statistically significant improvement in restoring patients to normative PI-LL. Using 3-dimensional preoperative planning combined with personalized implants provides an important tool for planning and achieving improvement in spinopelvic parameters. [ABSTRACT FROM AUTHOR]

Background: Percutaneous pedicle screw (PPS) placement has become a pivotal technique in spinal surgery, increasing surgical efficiency and limiting the invasiveness of surgical procedures. The aim of this study was to analyze the accuracy of computer-assisted PPS placement with a standardized technique in the lateral decubitus position. Methods: A retrospective review of prospectively collected data was performed on 44 consecutive patients treated between 2021 and 2023 with lateral decubitus single-position surgery. PPS placement was assessed by computed tomography scans, and breaches were graded based on the magnitude and direction of the breach. Facet joint violations were assessed. Variables collected included patient demographics, indication, intraoperative complications, operative time, fluoroscopy time, estimated blood loss, and length of stay. Results: Forty-four patients, with 220 PPSs were identified. About 79.5% of all patients underwent anterior lumbar interbody fusion only, 13.6% underwent lateral lumbar interbody fusion only, and 6.8% received a combination of both anterior lumbar interbody fusion and lateral lumbar interbody fusion. Eleven screw breaches (5%) were identified: 10 were Grade II breaches (<2 mm), and 1 was a Grade IV breach (>4 mm). All breaches were lateral. About 63.6% involved down-side screws indicating a trend toward the laterality of breaches for down-side pedicles. When analyzing breaches by level, 1.2% of screws at L5, 13% at L4, and 11.1% at L3 demonstrated Grade II breaches. No facet joint violations were noted. Conclusion: PPS placement utilizing computer-assisted navigation in lateral decubitus single-position surgery is both safe and accurate. An overall breach rate of 5% was found; considering a safe zone of 2 mm, only 1 screw (0.5%) demonstrated a relevant breach. Clinical Relevance: PPS placement is both safe and accurate. Breaches are rare, and when breaches do occur, they are lateral. [ABSTRACT FROM AUTHOR]

Existing learning-based dehazing algorithms struggle to deal with real world hazy images for lack of paired clean data. Moreover, most dehazing methods require significant computation and memory. To address the above problems, we propose a joint dual-teacher knowledge distillation and unsupervised fusion framework for single image dehazing in this paper. First, considering the complex degradation factors in real-world hazy images, two synthetic-to-real dehazing networks are explored to generate two preliminary dehazing results with the heterogeneous distillation strategy. Second, to get more qualified ground truth, an unsupervised adversarial fusion network is proposed to refine the preliminary outputs of teachers with unpaired clean images. In particular, the unpaired clean images are enhanced to deal with the dim artifacts. Furthermore, to alleviate the structure distortion in the unsupervised adversarial training, we constructed an intermediate image to constrain the output of the fusion network. Finally, considering the memory storage and computation overhead, an end-to-end lightweight student network is trained to learn the mapping from the original hazy image to the output of the fusion network. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on real-world hazy images in terms of no-reference image quality assessment and the parameters. [ABSTRACT FROM AUTHOR]

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The analysis of ground-penetrating radar (GPR) data is of vital importance for detecting various subsurface features that might manifest as hyperbolic peaks, which are indicators of a buried object or grayscale variation in the case of contrast in the soil texture. This method focuses on identifying exaggerated patterns through a series of image-processing steps. Two GPR images are initially read and preprocessed by extracting channels, flipping, and resizing. Then, specific regions of interest (ROIs) are cropped, and the Fourier transform is further applied to turn them into the frequency domain. With the help of their frequency signatures, these patterns are extracted from the images, and binary masks are constructed to obtain features of interest. These masked images were reconstructed and merged to make hyperbolic features visible. Finally, Local Binary Pattern (LBP) analysis is used to emphasize these hyperbolic peaks, thereby facilitating their recognition across the whole image. The proposed approach improves the detection of performance subsurface features in GPR data; hence, it is an important tool for geophysical surveys and other related applications. The results prove the high performance of the proposed procedure in improving GPR image characteristics. [ABSTRACT FROM AUTHOR]

Radiology narrative reports often describe characteristics of a patient's disease, including its location, size, and shape. Motivated by the recent success of multimodal learning, we hypothesized that this descriptive text could guide medical image analysis algorithms. We proposed a novel vision-language model, ConTEXTual Net, for the task of pneumothorax segmentation on chest radiographs. ConTEXTual Net extracts language features from physician-generated free-form radiology reports using a pre-trained language model. We then introduced cross-attention between the language features and the intermediate embeddings of an encoder-decoder convolutional neural network to enable language guidance for image analysis. ConTEXTual Net was trained on the CANDID-PTX dataset consisting of 3196 positive cases of pneumothorax with segmentation annotations from 6 different physicians as well as clinical radiology reports. Using cross-validation, ConTEXTual Net achieved a Dice score of 0.716±0.016, which was similar to the degree of inter-reader variability (0.712±0.044) computed on a subset of the data. It outperformed vision-only models (Swin UNETR: 0.670±0.015, ResNet50 U-Net: 0.677±0.015, GLoRIA: 0.686±0.014, and nnUNet 0.694±0.016) and a competing vision-language model (LAVT: 0.706±0.009). Ablation studies confirmed that it was the text information that led to the performance gains. Additionally, we show that certain augmentation methods degraded ConTEXTual Net's segmentation performance by breaking the image-text concordance. We also evaluated the effects of using different language models and activation functions in the cross-attention module, highlighting the efficacy of our chosen architectural design. [ABSTRACT FROM AUTHOR]

Introduction: Hallux valgus is a commonly treated condition by foot and ankle surgeons with more than 200 different described correction techniques. Recurrence rates range from 5% to 50%, with increasing support of the theory that arthrodesis procedures may have a lower recurrence rate than osteotomies. Arthrodesis procedures to the first metatarsophalangeal (MTP) joint or tarsometatarsal (TMT) joint for correction of hallux valgus deformity are becoming more commonly utilized. The purpose of this study is to investigate the surgical incidence and revision rates of hallux valgus deformities corrected by arthrodesis compared to osteotomy in the state of South Carolina. Methods: The South Carolina Revenue and Fiscal Affairs Office was queried from 2000 to 2017 to identify all surgically treated hallux valgus deformities. Data extraction included patient demographics, ICD-9 diagnoses, CPT procedure codes, and dates of surgery. A logistic regression model was used for statistical inference. Results: A total of 22 199 feet had surgical treatment for hallux valgus during this time period, with 20 422 (92.0%), 592 (2.7%), and 1185(5.3%) receiving an osteotomy, arthrodesis, or other procedure at initial treatment, respectively. There was an all-cause revision rate of 5.6% in the osteotomy group and 6.4% in the arthrodesis group. Demographic factors such as female sex, white race, and surgery pre-2010 were associated with higher revision rates. Multiple comorbidities were correlated with higher revision rates such as tobacco use, hypothyroidism, osteoarthritis, recurrent dislocations, hallux rigidus, lesser toe deformities, metatarsus varus, and talipes cavus. Conclusion: Despite the recent increase in arthrodesis procedures for the treatment of hallux valgus deformity, our results suggest that osteotomy procedures are more commonly performed and there is no difference in all-cause revision surgery. However, there are multiple patient demographics and comorbidities that are associated with higher rates of revision surgery and should be considered and discussed during the preoperative planning period. Level of Evidence: Level IV [ABSTRACT FROM AUTHOR]

Fusion techniques on MRI for brain tumors can provide comprehensive visualization by combining Axial T2-Flair and Axial T1-GD (T1-weighted post-contrast) sequence images. Fusion MRI in brain tumors is able to clearly display the location, size and characteristics of the tumor. However, not all institutions can install such additional fusion software due to significant additional costs. Therefore, this study aims to prove that the Stacks feature on ImageJ as an alternative can be optimal in visualizing brain tumor image information through MRI fusion techniques. This study used 17 image samples with a quasi experimental design post test only without control group design to compare three analysis methods, namely fusion maximum intensity, minimum intensity and average intensity so that the most suitable projection can be determined. The evaluation of image quality was carried out through a histogram which was then analyzed with a crucal-wallis and the Mann Whitney u test, while the analysis of pathological information used a crucal-wallis, followed by a post hoc test and continued with Mann Whitney u for further analysis. The results show that the stacks feature on ImageJ can be used in the application of fusion techniques so that it will improve the contrast and sharpness of MRI images, especially in areas with high tumor activity. MRI images of brain tumors with maximum fusion intensity produced images with the highest average gray level and the best pathological information. [ABSTRACT FROM AUTHOR]

Due to its widespread applications, human action recognition is one of the most widely studied research problems in Computer Vision. Recent studies have shown that addressing it using multimodal data leads to superior performance as compared to relying on a single data modality. During the adoption of deep learning for visual modelling in the past decade, action recognition approaches have mainly relied on Convolutional Neural Networks (CNNs). However, the recent rise of Transformers in visual modelling is now also causing a paradigm shift for the action recognition task. This survey captures this transition while focusing on Multimodal Human Action Recognition (MHAR). Unique to the induction of multimodal computational models is the process of 'fusing' the features of the individual data modalities. Hence, we specifically focus on the fusion design aspects of the MHAR approaches. We analyze the classic and emerging techniques in this regard, while also highlighting the popular trends in the adaption of CNN and Transformer building blocks for the overall problem. In particular, we emphasize on recent design choices that have led to more efficient MHAR models. Unlike existing reviews, which discuss Human Action Recognition from a broad perspective, this survey is specifically aimed at pushing the boundaries of MHAR research by identifying promising architectural and fusion design choices to train practicable models. We also provide an outlook of the multimodal datasets from their scale and evaluation viewpoint. Finally, building on the reviewed literature, we discuss the challenges and future avenues for MHAR. [ABSTRACT FROM AUTHOR]