Search

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As natural populations continue to decline globally, direct forms of intervention are increasingly necessary to prevent extinction. One type of intervention, known as demographic rescue, occurs when individuals are added directly to a population to increase abundance and ultimately prevent population extinction. However, the role of infectious disease in demographic rescue remains unknown. To examine the effects of pathogens on demographic rescue, we used a host–pathogen system with the aquatic crustacean Daphnia dentifera as the host and the fungus Metschnikowia bicuspidata as the pathogen. We constructed a randomized 3 × 2 factorial experiment with three rescue treatments (none, low, high) and two pathogen treatments (unexposed, exposed), where the pathogen was introduced via infected individuals during rescue events. We found that adding more individuals to demographically depressed populations increased abundance over the short term; highly supplemented populations initially had 62% more individuals than populations that had no introduced individuals. However, by the end of the experiment, populations that did not have any individuals introduced averaged 640% higher abundance than populations where infected individuals had been added. Thus, the introduction of infected individuals can result in worse demographic outcomes for populations than if no rescue is attempted. [ABSTRACT FROM AUTHOR]

Background: The utilization of lateral extra-articular tenodesis (LET) augmentation for anterior cruciate ligament reconstruction has increased. Various fixation points have been recommended based on tactile and anatomic landmarks; however, there is limited reporting of the accuracy or precision of these techniques in clinical practice. Purpose/Hypothesis: The purpose of this study was to evaluate whether LET fixation points identified using anatomic landmarks and tactile techniques would fall within a predefined radiographic zone. It was hypothesized that the majority of LET fixation points would be inside the radiographic zone. Study Design: Cross-sectional study; Level of evidence, 4. Methods: Postoperative lateral knee radiographs of patients who underwent anterior cruciate ligament reconstruction with LET using a landmark-based technique without fluoroscopy between January 2018 and September 2023 were reviewed. Fixation points were measured by 2 raters based on their distance from an extension of the posterior femoral cortex line (PFCL) distally and a line perpendicular to the PFCL at the posterior condylar flare (PCF). Patients were excluded if the tunnel position could not be identified or if postoperative radiographs were malrotated. The mean LET position and percentage of points within the radiographic isometric zone, defined as 4 ± 4 mm posterior and 4 ± 3 mm anterior to the PFCL and 6 ± 4 mm distal and 20 ± 5 mm proximal to the PCF were calculated. Results: Complete data sets were obtained for 47 cases. The mean LET position was 6.4 ± 7.1 mm (range, –9 to 27.3 mm) anterior to the PFCL and 1.8 ± 7.6 mm (range, –16.7 to 12.6 mm) proximal to the PCF. Overall, 53% of LET fixation points were within the predefined radiographic zone. Of the malpositioned tunnels (n = 22), their locations relative to the radiographic zone were anterior (n = 18), posterior (n = 2), proximal (n = 1), and anterior and distal (n = 1). Conclusion: This study found large variation in the location of LET fixation points, and almost half of fixation points were outside the predefined radiographic zone. Accurate and precise tunnel placement is one of multiple factors that may be important to minimize the risk of lateral compartment overconstraint, anterior cruciate ligament graft failure, and anisometry leading to LET graft loosening. [ABSTRACT FROM AUTHOR]

Objective: The current study aims to evaluate the correlation between oxytocin augmentation and postpartum hemorrhage. Method: PubMed, Web of Science, and Scopus has been searched for studies assessing the correlation between oxytocin augmentation and postpartum hemorrhage up to January 24, 2024. The search strategy included relevant keywords related to PPH and oxytocin augmentation. The risk of bias assessment was conducted by two reviewers using the Newcastle-Ottawa Scale (NOS). To pool the effects sized of included studies odds ratios (OR) of interest outcome with their 95% confidence interval (CI) were used. Results: Eight studies were included in this meta-analysis. The pooled analysis of the included studies showed a statistically significant association between oxytocin augmentation and increased odds of PPH (pooled odds ratio [OR] = 1.27, 95% confidence interval [CI]: 1.05-1.53; I2 = 84.94%; p = 0.01). Publication bias was assessed using funnel plots, which appeared relatively asymmetrical, indicating significant publication bias. Galbraith plot and trim and fill plot were used for publication bias. Sensitivity analyses were performed by leave one out method. Conclusion: This meta-analysis suggests that using oxytocin for labor augmentation is linked to a significant increase in the risk of PPH. It highlights the need for careful monitoring and consideration when using oxytocin, especially in low and middle-income countries where guidelines and supervision are crucial. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this systematic review is to review the functional, clinical and radiological outcomes of patients undergoing large to massive rotator cuff repair with long head of biceps tendon (LHBT) autograft, as well as compare these to standard arthroscopic cuff repair. Methods: A review of the online Medline database was conducted on 20 October 2022 according to PRISMA guidelines and registered prospectively on the PROSPERO database. Clinical studies assessing patients with large to massive rotator cuff tears undergoing LHBT autograft repair were included. All studies reported on functional outcomes, range of movement (ROM) and radiological re-tear rates. The Methodological Index for Non-Randomised Studies (MINORS) tool was used to appraise all studies. Results: The search strategy identified ten studies for inclusion including a total of 594 patients. Five studies were comparable (346 patients), assessing LHBT autograft repair against arthroscopic rotator cuff repair without autograft. A variety of techniques of LHBT autograft were used across all studies, including bridging and augmentation styles. Radiographic comparison showed lower re-tear rates in the LHBT autograft group with two studies demonstrating statistically significant results. Pain scores, functional outcomes and ROM were significantly improved post-operatively in all studies for LHBT autograft patients, with no significant difference when compared to standard arthroscopic repair. Conclusion: LHBT autograft looks to significantly improve functional scoring and range of motion in patients with large to massive rotator cuff tears. When compared to standard arthroscopic cuff repair, LHBT autograft appears to significantly reduce the re-tear rate. Further randomised studies are needed to assess the efficacy of this technique. [ABSTRACT FROM AUTHOR]

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Copyright of Arthroskopie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Generative Adversarial Network (GAN) is an exciting innovation in machine learning within the neural network field. These models are able to generate a realistic image, video or even voice output. One of the useful applications is its possibility to enrich data sets for better learning of neural network models. In the presented work, we focus on image augmentation with the use of several variations of GAN to improve the classification of convolutional neural network. Accordingly, to prove the advantage of GAN-based image augmentation in comparison with methods of classical augmentation, we used specifically three different degrees of image rotation and compared classification results of convolutional neural network that use images from these augmentation methods. Mentioned methods of image augmentation are applied to five datasets belonging to three different domains, specifically medical, astronomical and geological domain. The architecture and settings of the convolutional neural network are the same for all datasets. To evaluate classification results, we used confusion matrix, accuracy, precision, recall and F1-score. [ABSTRACT FROM AUTHOR]

Modern technology like Artificial Intelligence (AI) must be used in the agricultural sector if sustainable agricultural output is to be achieved. One of the most convenient strategies for resolving current and future issues is data-driven agriculture. For this, disease prediction is a major task for precise farming. For predictive analysis and precise agriculture monitoring systems, with the application of AI, Machine Learning (ML) and Deep Learning (DL) play vital roles in building a more robust system. In this work, we will design a DL-integrated rice disease prediction system to be implemented for precise farming. Improvisation of the developed model to detect rice plant diseases & pest attacks with a high level of precision. In this work, the Progressive Loss-Aware Fine-Tuning Stepwise Learning (PLAFTSL) model is proposed for disease detection. For step-wise learning fine-tuned ResNet50 model is used with the introduction of freezing and unfreezing layers. This reduces the training parameters and thus computational complexity. The introduction of the step-wise and progressive loss-aware layer will result in fast convergence and improved training efficiency during information exchange among layers respectively. Our proposed work uses a dataset from two sources. The result analysis is presented with an ablation study. Additionally, the baseline model, ResNet50, is used to display the outcomes of the ablation. The results demonstrate that the fine-tuned model results in better performance as compared to the transfer learning model. The Conditional Generative Adversarial Network (cGAN) augmentation is also added to the designed model which will improve detection effectiveness and can also manage the imbalance in input data. The model has achieved approx. 98% accuracy and outperforms better with comparative state-of-art models. [ABSTRACT FROM AUTHOR]

When an organism is exposed to environmental stimuli of varying intensity, the adaptive changes in the CNS can be explained by several conceptual provisions: the law of motivation–activation by Yerkes and Dodson, the laws of force and pessimal protective inhibition, and the theory of emotion activation. Later, reinforcement sensitivity theory was developed in the fields of psychology and psychophysics. At the same time, cortical prepulse inhibition (PPI), the prepulse inhibition of perceived stimulus intensity (PPIPSI), and the augmentation/reduction phenomenon were proposed in sensory neurophysiology, which expanded our understanding of consciousness. The aim of this study was to identify markers of levels of activity of cognitive processes under normal and in psychopathological conditions while amplifying the information stimulus. For this purpose, we changed the contrast level of reversible checkerboard patterns and mapped the visual evoked potentials (VEPs) in 19 monopolar channels among 52 healthy subjects and 39 patients with a mental illness without an active productive pathology. Their cognitive functions were assessed by presenting visual tests to assess invariant pattern recognition, short-term visual memory, and Gestalt perception. The personalities of the healthy subjects were assessed according to Cattell's 16-factor questionnaire, linking the data from neurophysiological and cognitive studies to factors Q4 (relaxation/tension) and C (emotional stability). According to the N70 and N150 VEP waves, the healthy subjects and the patients were divided into two groups. In some, there was a direct relationship between VEP amplitude and contrast intensity (21 patients and 29 healthy persons), while in the others, there was an inverse relationship, with a reduction in VEP amplitude (18 patients and 23 healthy persons). The relationship and mechanisms of subjects' cognitive abilities and personality traits are discussed based on the data acquired from the responses to information stimuli of varied intensity. [ABSTRACT FROM AUTHOR]

Segmentation of nuclei in histology images is key in analyzing and quantifying morphology changes of nuclei features and tissue structures. Conventional diagnosis, segmenting, and detection methods have relied heavily on the manual-visual inspection of histology images. These methods are only effective on clearly visible cancerous lesions on histology images thus limited in their performance due to the complexity of tissue structures in histology images. Hence, early detection of breast cancer is key for treatment and profits from Computer-Aided-Diagnostic (CAD) systems introduced to efficiently and automatically segment and detect nuclei cells in pathology. This paper proposes, an automatic watershed segmentation method of cancerous lesions in unsupervised human breast histology images. Firstly, this approach pre-processes data through various augmentation methods to increase the size of dataset images, then a stain normalization technique is applied to these augmented images to isolate nuclei features from tissue structures. Secondly, data enhancement techniques namely; erosion, dilation, and distance transform are used to highlight foreground and background pixels while removing unwanted regions from the highlighted nuclei objects on the image. Consequently, the connected components method groups these highlighted pixel components with similar intensity values and, assigns them to their relevant labeled component binary mask. Once all binary masked groups have been determined, a deep-learning recurrent neural network from the Keras architecture uses this information to automatically segment nuclei objects with cancerous lesions and their edges on the image via watershed filling. This segmentation method is evaluated on an unsupervised, augmented human breast cancer histology dataset of 11,151 images. This proposed method produced a significant evaluation result of 98 % F1-accuracy score. [ABSTRACT FROM AUTHOR]

Background: The fusarium wilt disease of chickpea leaves is a common illness that leads to economic problems for farmers due decreased crop yield. Early disease detection and the implementation of suitable precautions can help to increase the yield of chickpeas. This study offers an improved method for Fusarium wilt disease prediction based on severity level using a convolutional neural learning algorithm. Methods: The Convolutional Neural Network (CNN) model is utilized in this work to identify leaf disease due to wilting. The dataset contains 4,339 images of chickpea leaves that were obtained from Kaggle. After preprocessing, the data is sent into the network model for training. The model shows acceptable classification and accuracy metrics. Result: Deep learning methods are very useful tools for tracking leaf diseases at their early stages and can help farmers with the use of controlling methods. The proposed work looks for changes in the shape and color of chickpea leaves in order to predict severe fusarium disease. Training and validation accuracies show a balanced trade-off by giving satisfactory outcomes. The model shows an overall accuracy of 74.79%. The confusion matrix and classification parameters increase the model's performance. [ABSTRACT FROM AUTHOR]

Visible-infrared person re-identification aims to match the identity of the same person from different modalities. The main challenge is the modality difference between visible and infrared images. Most existing methods mainly use generative adversarial networks to generate compensatory images of the corresponding modality to reduce the modality difference, or design diverse two-stream networks to learn global feature representations and extract globally shared features. However, due to the substantial difference between visible and infrared modalities, the created pseudo-modalities often struggle to effectively bridge the gap between modalities and tend to introduce noise. The extracted modality-shared features typically exhibit weak discriminative capability, inevitably leading to the loss of critical discriminative features related to person identity and a lack of robustness to noisy images. To tackle these challenges, we introduce a modality synergy alignment learning network. This network incorporates a novel data augmentation technique known as SliceMix, which mixes random sections of cross-modality images to synthesize a new sample that exhibits both discriminative to identity and robust to noise, thereby facilitating the learning of modality-invariant feature representations. By adjusting the mixing ratio, mixed modalities can be generated flexibly to minimize the impact of modality imbalance. Additionally, a modality alignment module is introduced to ensure similarity within the modality class and accentuate the differences between modalities. Moreover, we propose a data augmentation method called random channel grayscale, which enhances the network’s robustness to color changes and expands data diversity. Comprehensive experiments on mainstream datasets, including SYSU-MM01 and RegDB, demonstrated that our method significantly improves the performance of cross-modality retrieval. [ABSTRACT FROM AUTHOR]

Background: Patellar tendon ruptures are infrequent but disabling injuries. Surgical treatment is the gold standard to obtain good outcomes, and numerous techniques have been described. Biomechanical studies report better results when augmented techniques are used. However, there is a lack of consensus regarding the best standard technique. Purpose/Hypothesis: The purpose of this study was to assess a cohort of patients with acute patellar tendon rupture that was surgically treated and to compare the clinical outcomes of 2 groups: isolated repair and biological augmentation techniques with autograft or allograft. It was hypothesized that the biological augmentation group would have better clinical outcomes than the isolated repair group. Study Design: Cohort study; Level of evidence, 3. Methods: All patients with acute patellar tendon rupture surgically treated in our center between 2016 and 2022 were retrospectively reviewed. Patient and rupture characteristics, surgical technique, clinical outcomes, and complications were recorded. The primary outcome was rerupture rate, and secondary outcomes were infection rate, stiffness >15° of knee flexion deficit, and extensor mechanism lag >5°. Results: The study included 34 patients with 36 operated knees (2 patients with bilateral rupture). The mean age was 44.9 years. Isolated repair was performed in 20 knees (55.6%), and 16 knees underwent repair and biological augmentation with autograft or allograft. Both groups were comparable in terms of their demographic characteristics. A statistically significant association was found between the type of surgery and tendon rerupture. Of the patients in the isolated repair group, 5 of 20 (25%) experienced a failure, whereas in the biological augmentation group, no reruptures were recorded (P =.031). However, no statistically significant associations were found between the type of surgery and other complications, such as the development of stiffness (P =.54), residual extension lag >5° (P =.87), or the development of infection (P =.25). Conclusion: In this cohort of patients, biological augmentation reduced the rate of surgical failure for acute patellar tendon rupture without being associated with a higher risk of complications such as stiffness, residual extension lag, or the development of infection. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced approach for classifying Bengali songs into different genres by leveraging feature importance analysis and deep learning techniques. The research addresses the challenge of limited data points in the Bengali Song Dataset by employing strategies, including audio segmentation and feature importance analysis, to enhance model performance. Multiple machine learning and deep learning architectures are evaluated to identify the most effective models for Bengali song classification. Additionally, this research conducts feature importance analysis to identify significant audio features contributing to classification accuracy. The best-performing deep learning model achieves an impressive validation accuracy of 94.17%, showcasing the project efficacy of the proposed methodology. Our findings highlight the effectiveness of our proposed methodology, demonstrating significant improvements in classification accuracy and contributing to advancements in Bengali music classification research. [ABSTRACT FROM AUTHOR]

Lumpy skin disease virus (LSDV) is an extremely infectious, viral, and chronic skin disease that is caused by the Capripox virus. This viral disease is predominantly found in cows. Mosquitoes and ticks are the primary transmitters for the spread of this virus. Recently, LSDV has been rapidly spreading all over the world, especially in several areas of Pakistan, India, and Iran. Thousands of cows have died due to this infectious virus in Pakistan and early detection of LSDV is needed to avoid further loss. The prediction and classification of LSDV are hindered by the lack of publicly available datasets. Despite a few studies using LSDV datasets, such datasets are often small, which may lead to model overfitting. In this regard, we collect the dataset from several online sources, as well as, collecting images from veterinary farms in different areas of Pakistan. Deep learning has been widely used in the medical field for disease detection and classification. Therefore, this study leverages DenseNet deep learning models for LSDV detection and classification. Experiments are performed using VGG-16, ResNet-50, MobileNet-V2, custom-designed convolutional neural network, and Inception-V3. The DenseNet architecture presents a Convolutional Block Attention Module (CBAM) and Spatial Attention (SA) for the prediction and classification of LSD. Results demonstrate that a 99.11% accuracy can be obtained on the augmented dataset while a 94.23% accuracy can be achieved with the original dataset for chicken pox, monkey pox, and LSDV. Comparison with state-of-the-art studies corroborates the superior performance of the proposed model. [ABSTRACT FROM AUTHOR]

Spasticity management should be provided within the context of a comprehensive person-centered rehabilitation program. Furthermore, active goal setting for specific spasticity interventions is also important, with a well-established "more is better" approach. It is critical to consider adjunctive therapy and multimodal approaches if patients are not attaining their treatment goals. Often used interchangeably, there may be confusion between the terms adjunctive and multimodal therapy. Yet it is imperative to understand the differences between these approaches to achieve treatment goals in spasticity management. Addition of a secondary pharmacologic or non-pharmacologic treatment to optimize the efficacy of the initial modality, such as adding electrical stimulation or casting to BoNT-A, is considered an adjunctive therapy. Adjunctive therapy is time-specific and requires the added therapy be initiated within a specific period to enhance the primary treatment; usually within 2 weeks. Multimodal therapy is an integrated, patient-centric program of pharmacologic and nonpharmacologic strategies utilized in a concurrent/integrated or sequential manner to enhance the overall treatment effect across a variety of spasticityassociated impairments (e.g., neural and non-neural components). Moreover, within a multimodal approach, adjunctive therapy can be used to help enhance the treatment effect of one specific modality. The objectives of this paper are to clarify the differences between adjunctive and multimodal therapies, provide a brief evidence-based review of such approaches, and highlight clinical insights on selecting multimodal and adjunctive therapies in spasticity management. [ABSTRACT FROM AUTHOR]

Liver cancer is one of the leading causes of cancer death in the world, and early diagnosis is important. However, the similarity in shape, texture, and intensity values between the liver, tumors, and other neighboring organs such as the heart, spleen, stomach, and kidneys often complicates visual differentiation. Manual identification of tumors in the liver is time‐consuming, intricate, and susceptible to errors with potential repercussions for patient care. While machine learning–based approaches have emerged for liver organ recognition and segmenting the tumor, they continue to face challenges related to recognition accuracy and the inability to distinguish tumors of varied sizes. To solve the problems, a multiattention network made up of cascaded ResUNet and U‐Net with attention mechanisms was proposed in this study. We investigated liver tumor segmentation with various configurations of U‐Net, ResUNet, U‐Net with attention mechanisms, and ResUNet with attention mechanisms on augmented and nonaugmented data. We used the 3Dircadb dataset for training and validation purposes, and the proposed method was evaluated on dice score, intersection of union (IoU), recall, and precision. The performance metrics achieved with this method on the dataset are as follows: approximately 0.89 for the dice coefficient, 0.90 for IoU, 0.93 for recall, and 0.96 for precision in the case of liver segmentation without data augmentation and 0.92, 0.90, 0.92, and 0.94, respectively, for dice score, IoU, recall, and precision with data augmentation. For tumor segmentation, the metrics include 0.70 for dice coefficient, 0.61 for IoU, 0.91 for recall, and 0.94 for precision when the data were augmented but 0.83 for dice score, 0.78 for IoU, and 0.89 and 0.90, respectively, for recall and precision. [ABSTRACT FROM AUTHOR]

Significant progress has been made recently with the contribution of technological advances in studies on brain cancer. Regarding this, identifying and correctly classifying tumors is a crucial task in the field of medical imaging. The disease-related tumor classification problem, on which deep learning technologies have also become a focus, is very important in the diagnosis and treatment of the disease. The use of deep learning models has shown promising results in recent years. However, the sparsity of ground truth data in medical imaging or inconsistent data sources poses a significant challenge for training these models. The utilization of StyleGANv2-ADA is proposed in this paper for augmenting brain MRI slices to enhance the performance of deep learning models. Specifically, augmentation is applied solely to the training data to prevent any potential leakage. The StyleGanv2-ADA model is trained with the Gazi Brains 2020, BRaTS 2021, and Br35h datasets using the researchers' default settings. The effectiveness of the proposed method is demonstrated on datasets for brain tumor classification, resulting in a notable improvement in the overall accuracy of the model for brain tumor classification on all the Gazi Brains 2020, BraTS 2021, and Br35h datasets. Importantly, the utilization of StyleGANv2-ADA on the Gazi Brains 2020 Dataset represents a novel experiment in the literature. The results show that the augmentation with StyleGAN can help overcome the challenges of working with medical data and the sparsity of ground truth data. Data augmentation employing the StyleGANv2-ADA GAN model yielded the highest overall accuracy for brain tumor classification on the BraTS 2021 and Gazi Brains 2020 datasets, together with the BR35H dataset, achieving 75.18%, 99.36%, and 98.99% on the EfficientNetV2S models, respectively. This study emphasizes the potency of GANs for augmenting medical imaging datasets, particularly in brain tumor classification, showcasing a notable increase in overall accuracy through the integration of synthetic GAN data on the used datasets. [ABSTRACT FROM AUTHOR]

Mobile Cloud Computing (MCC) allows the e-learning platform to provide teaching materials, exchange knowledge and share learning experiences. The MCC process gives various instructions, and the usage of electrical bio-signals in Alternative and Augmented Communication plays a significant role. The physical education system faces significant challenges in training physically challenged people for Para Olympics. The difficulties are resolved by applying the Virtual Reality Assisted Convolution Neural Network (VRA-CNN) framework for effective Augmentative Communication. The VRA-CNN process uses several biosensors to read various person's body parameters according to different simulated conditions using Virtual Reality Technology (VR). Further, augmentative Communication provides an interactive environment in real-time for physically challenged people. Simulation analysis shows that the introduced VRA-CNN can improve players' confidence and sports knowledge with improved SNR, response time, and less error rate. The VRA-CNN has been validated based on the optimization parameter, and real-time performance outperforms traditional approaches. [ABSTRACT FROM AUTHOR]

Background: Various strategies have been used to reduce pedicle screw loosening following lumbar instrumented fusion, but all strategies have limitations. In this prospective multicenter cohort study, outcomes of elderly patients with reduced bone density who underwent primary or revision fusion surgery using a novel technique of pedicle screw augmentation with demineralized bone fiber (DBF) anchors were evaluated. Methods: This study included elderly patients (aged >65 years) with dual-energy x-ray absorptiometry-confirmed reduced bone density who required lumbar pedicle screw fixation and were treated with supplemental DBF allograft anchors during primary or revision surgery. The need for DBF anchors was determined by evaluating preoperative computed tomography (CT) scans (for revision surgery) and by the surgeons’ tactile feedback intraoperatively during pedicle screw insertion and removal. After determining the pedicle screw void diameter with a sizing instrument, DBF anchors and pedicle screws of the same diameter were placed into the void. CT scans were obtained on postoperative day 2 to assess pedicle breach, pedicle fracture, or anchor material extrusion and at 6 and 12 months postoperatively to assess screw loosening. Thereafter, to minimize radiation exposure, CT scans were only performed for recurrence of pain. Results: Twenty-three patients (79% women; mean age, 74 years) received 50 lumbosacral pedicle screws augmented with DBF anchors. Most surgeries (n = 18, 78%) were revisions, and most anchors were inserted into revision pedicle screw trajectories (n = 33, 66%). Day-2 CT scans revealed no pedicle breach/fracture or extrusion of anchor material. During a mean follow-up of 15 months (12–20 months), no screw loosening was detected, and no patient required pedicle screw revision surgery. There were no adverse events attributable to DBF allografts. Conclusions: DBF allograft anchors appear to be safe and effective for augmenting pedicle screws during revision surgeries in female elderly patients with reduced bone density. Clinical Relevance: Clinically, DBF reduced the rate of pedicle screw loosening in patients with reduced bone density. A significant reduction in screw loosening can decrease the need for revision surgeries, which are costly and carry additional risks. Enhanced bone integration from the DBF may promote better healing and long-term stability. [ABSTRACT FROM AUTHOR]

Introduction: Restless legs syndrome (RLS) is a sensory-motor sleep disorder that affects up to 13% of adults in the Western world and 2–4% of children. It impairs night sleep with an impact on daily performances and life quality. Thus, moderate-to-severe RLS requires pharmacological treatment. Areas covered: In the present review, which is based on PubMed searches with no time limits, the authors discuss the recommended pharmacotherapy for RLS in addition to other emerging treatment options. The authors provide coverage to the current recommendations for both adults and pediatric patients with RLS. Expert opinion: Current evidence suggests removing all causes of secondary RLS, including iron deficiency, chronic renal failure, drugs, and treating other sleep disorders that may worsen symptoms. Also, intermittent RLS should be addressed with behavioral measures and on-demand therapy. For chronic persistent RLS, α2δ calcium channel ligands are a first-line pharmacological approach, whereas dopamine agonists are associated with increased risk and should be spared. When RLS is refractory to first-line treatment, polytherapy, or opioid monotherapy should be considered. Nonetheless, some patients may not reach sustained symptom relief. Further research is needed to better understand the pathophysiology of RLS and to develop newer more effective drugs. [ABSTRACT FROM AUTHOR]

Data augmentation is widely recognized as an effective means of bolstering model robustness. However, when applied to monocular 3D object detection, non-geometric image augmentation neglects the critical link between the image and physical space, resulting in the semantic collapse of the extended scene. To address this issue, we propose two geometric-level data augmentation operators named Geometric-Copy-Paste (Geo-CP) and Geometric-Crop-Shrink (Geo-CS). Both operators introduce geometric consistency based on the principle of perspective projection, complementing the options available for data augmentation in monocular 3D. Specifically, Geo-CP replicates local patches by reordering object depths to mitigate perspective occlusion conflicts, and Geo-CS re-crops local patches for simultaneous scaling of distance and scale to unify appearance and annotation. These operations ameliorate the problem of class imbalance in the monocular paradigm by increasing the quantity and distribution of geometrically consistent samples. Experiments demonstrate that our geometric-level augmentation operators effectively improve robustness and performance in the KITTI and Waymo monocular 3D detection benchmarks. [ABSTRACT FROM AUTHOR]

Background: Despite high complication rates, patients persistently present for single-stage augmentation mastopexy. In empty, deflated breasts, we perform one-stage augmentation mastopexy; however, in heavy ptotic breasts, our preference is to stage the procedure with mastopexy and fat graft first. With volume from fat grafting focussing on the upper pole and cleavage areas, many of our patients avoid implants altogether. This reduces subsequent risks of waterfall deformity, implant displacement, rupture and a lifetime of implant exchanges. Objectives: We aim to describe our findings and technique for reducing progression to the second stage of a two-stage augmentation mastopexy with the appropriate use of moderate to high volume of fat grafting at the primary operation. Methods: This is a retrospective review of all patients who presented to the senior author (KT) requesting breast implants and requiring mastopexy, from January 2018 to December 2022. Results: Over the five-year period, 137 patients were identified. Seventy-one (51.8%) underwent single-stage augmentation mastopexy, 55 (40.1%) underwent mastopexy with fat grafting and 11 (8.0%) underwent mastopexy with no fat grafting. Our key finding in this study is that 52 of 66 (78.8%) of planned staged patients, who underwent mastopexy with or without fat grafting, were happy with the volume attained and no longer wished to undergo further implant augmentation. Conclusion: In selected patients, appropriate volume and position of fat grafting at the time of primary mastopexy can significantly obviate the need for a second stage implant (alloplastic) augmentation. Level of Evidence V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266. [ABSTRACT FROM AUTHOR]

Copyright of Arthroskopie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Generative Adversarial Network (GAN) is an exciting innovation in machine learning within the neural network field. These models are able to generate a realistic image, video or even voice output. One of the useful applications is its possibility to enrich data sets for better learning of neural network models. In the presented work, we focus on image augmentation with the use of several variations of GAN to improve the classification of convolutional neural network. Accordingly, to prove the advantage of GAN-based image augmentation in comparison with methods of classical augmentation, we used specifically three different degrees of image rotation and compared classification results of convolutional neural network that use images from these augmentation methods. Mentioned methods of image augmentation are applied to five datasets belonging to three different domains, specifically medical, astronomical and geological domain. The architecture and settings of the convolutional neural network are the same for all datasets. To evaluate classification results, we used confusion matrix, accuracy, precision, recall and F1-score.

Background: The ulnar collateral ligament (UCL) is frequently injured in overhead throwing athletes, leading to elbow instability, pain and decreased performance, and requiring surgical intervention. Augmenting with suture tape provides a novel approach to UCL repair, offering stability while preserving native anatomy, proprioception, and minimizing bone loss, with the added benefit of an expected faster rehabilitation and return to sport (RTS) compared to traditional UCL reconstruction. The purpose of this study is to assess the variability of the current publicly available rehabilitation protocols for elbow UCL repair with suture tape augmentation. Methods: A systematic review of Google and PubMed was performed to find rehabilitation protocols for UCL repair with suture tape augmentation. Two hundred nine orthopedic surgery residency programs were identified using the Fellowship and Residency Electronic Interactive Database Access. The programs were searched on Google, a broad nonspecific Google search, and a systematic literature search of journal databases for UCL repair with suture tape augmentation rehabilitation protocols was conducted. Results: Thirteen rehabilitation protocols met the inclusion criteria for analysis. Of these, 12 protocols outlined the use of an articulating brace at varying range of motion settings for the first 4-6 weeks following surgery. Return to throwing was included in all 13 protocols and began between 10 and 12 weeks following surgery. RTS was included in 11 protocols and expected athletes competing as early as 16 weeks with a mean recommended RTS at 21.5 weeks. Conclusion: Rehabilitation protocols for UCL repair with suture tape augmentation were often structured around a 5-phase program with RTS approaching 20 weeks. They utilized immobilization and range of motion restriction as well as strengthening and gradual RTS procedures. Overall, the included protocols had mild variability with initiation of throwing and RTS 12 to 24 weeks faster than traditional UCL reconstruction.

Pathology of the long head of the biceps tendon is commonly associated with massive rotator cuff tears (MRCTs), which account for roughly one third of all rotator cuff tears. Treatment options for this condition include tenotomy, tenodesis, augmentation, and the use of the tendon as a graft for partial superior capsule reconstruction. Augmentation and superior capsular reconstruction are evolving techniques in the management of MRCTs. However, similar to the lack of consensus on the treatment of MRCTs, there are no clear guidelines for the management of concurrent biceps tendon pathology.

Abstract Lumpy skin disease virus (LSDV) is an extremely infectious, viral, and chronic skin disease that is caused by the Capripox virus. This viral disease is predominantly found in cows. Mosquitoes and ticks are the primary transmitters for the spread of this virus. Recently, LSDV has been rapidly spreading all over the world, especially in several areas of Pakistan, India, and Iran. Thousands of cows have died due to this infectious virus in Pakistan and early detection of LSDV is needed to avoid further loss. The prediction and classification of LSDV are hindered by the lack of publicly available datasets. Despite a few studies using LSDV datasets, such datasets are often small, which may lead to model overfitting. In this regard, we collect the dataset from several online sources, as well as, collecting images from veterinary farms in different areas of Pakistan. Deep learning has been widely used in the medical field for disease detection and classification. Therefore, this study leverages DenseNet deep learning models for LSDV detection and classification. Experiments are performed using VGG-16, ResNet-50, MobileNet-V2, custom-designed convolutional neural network, and Inception-V3. The DenseNet architecture presents a Convolutional Block Attention Module (CBAM) and Spatial Attention (SA) for the prediction and classification of LSD. Results demonstrate that a 99.11% accuracy can be obtained on the augmented dataset while a 94.23% accuracy can be achieved with the original dataset for chicken pox, monkey pox, and LSDV. Comparison with state-of-the-art studies corroborates the superior performance of the proposed model.

Abstract Insomnia was diagnosed by analyzing sleep stages obtained during polysomnography (PSG) recording. The state-of-the-art insomnia detection models that used physiological signals in PSG were successful in classification. However, the sleep stages of unbalanced data in small-time intervals were fed for classification in previous studies. This can be avoided by analyzing the insomnia detection structure in different frequency bands with artificially generated data from the existing one at the preprocessing and post-processing stages. Hence, the paper proposes a double-layered augmentation model using Modified Conventional Signal Augmentation (MCSA) and a Conditional Tabular Generative Adversarial Network (CTGAN) to generate synthetic signals from raw EEG and synthetic data from extracted features, respectively, in creating training data. The presented work is independent of sleep stage scoring and provides double-layered data protection with the utility of augmentation methods. It is ideally suited for real-time detection using a single-channel EEG provides better mobility and comfort while recording. The work analyzes each augmentation layer’s performance individually, and better accuracy was observed when merging both. It also evaluates the augmentation performance in various frequency bands, which are decomposed using discrete wavelet transform, and observed that the alpha band contributes more to detection. The classification is performed using Decision Tree (DT), Ensembled Bagged Decision Tree (EBDT), Gradient Boosting (GB), Random Forest (RF), and Stacking classifier (SC), attaining the highest classification accuracy of 94% using RF with a greater Area Under Curve (AUC) value of 0.97 compared to the existing works and is best suited for small datasets.

Abstract Significant progress has been made recently with the contribution of technological advances in studies on brain cancer. Regarding this, identifying and correctly classifying tumors is a crucial task in the field of medical imaging. The disease-related tumor classification problem, on which deep learning technologies have also become a focus, is very important in the diagnosis and treatment of the disease. The use of deep learning models has shown promising results in recent years. However, the sparsity of ground truth data in medical imaging or inconsistent data sources poses a significant challenge for training these models. The utilization of StyleGANv2-ADA is proposed in this paper for augmenting brain MRI slices to enhance the performance of deep learning models. Specifically, augmentation is applied solely to the training data to prevent any potential leakage. The StyleGanv2-ADA model is trained with the Gazi Brains 2020, BRaTS 2021, and Br35h datasets using the researchers’ default settings. The effectiveness of the proposed method is demonstrated on datasets for brain tumor classification, resulting in a notable improvement in the overall accuracy of the model for brain tumor classification on all the Gazi Brains 2020, BraTS 2021, and Br35h datasets. Importantly, the utilization of StyleGANv2-ADA on the Gazi Brains 2020 Dataset represents a novel experiment in the literature. The results show that the augmentation with StyleGAN can help overcome the challenges of working with medical data and the sparsity of ground truth data. Data augmentation employing the StyleGANv2-ADA GAN model yielded the highest overall accuracy for brain tumor classification on the BraTS 2021 and Gazi Brains 2020 datasets, together with the BR35H dataset, achieving 75.18%, 99.36%, and 98.99% on the EfficientNetV2S models, respectively. This study emphasizes the potency of GANs for augmenting medical imaging datasets, particularly in brain tumor classification, showcasing a notable increase in overall accuracy through the integration of synthetic GAN data on the used datasets.

Introduction: Ankle sprains remain one of the most commonly reported injuries in athletes. These injuries occur as a result of forced plantar flexion and inversion causing failure of the anterior talofibular ligament followed by the calcaneofibular ligament. Acute ankle sprains are generally treated conservatively. Nevertheless, 20%–40% of patients with an acute ankle sprain might develop chronic ankle instability (CAI). Surgical treatment is advocated for active young patients who develop CAI. MATERIALS AND METHODS: This study is a case series of 10 cases of CAI treated by the modified open Brostrom-Gould procedure from 2014 to 2021. The AOFAS ankle-hindfoot score and visual analog scores were used to evaluate the patient preoperatively and at regular intervals postoperatively. Results: All the patients had good-to-excellent results. There was a significant improvement in the mean AOFAS ankle-hindfoot scores from a preoperative 59.9 to a postoperative value of 91.3. The mean visual analog scores significantly dropped from a preoperative value of 5.9 to a postoperative value of 1.2. The minimum follow-up duration is around 1 year, while the maximum is around 8 years. Conclusion: The open modified Brostrom-Gould lateral ligament repair provides excellent results for chronic lateral ankle instability in active young individuals.

Global Navigation Satellite Systems (GNSS) has become an essential component in modern times for positioning, navigation, and timing. A fast-growing economic region in Malaysia required a GNSS-based augmentation positioning and navigation service. To improve navigation solution, several augmentation techniques exist, such as a differential Global Positioning System (DGPS) assisted by reference station, or augmentation service provided by commercial communication satellite. The DGPS correction is applied through a real-time communication medium and can be received at the user side by several communication methods including internet-based, and this method is favourable for land and near-coast areas. This case study aims to investigate the reliability of internet-based DGPS and SBAS along the Peninsular Malaysia. A test was conducted and data were collected over 15 hours at a rate of 1 Hz from the available GNSS satellite using a geodetic-grade receiver mounted on a moving vehicle. The obtained results showed that DGPS and SBAS perform better than the navigation solution with an accuracy of 1.536 m and 0.955 m respectively, compared to the navigation solution with an accuracy of 3.159 m. The limitations of both augmentation techniques were also analysed and discussed in this study.

Background: Machine learning models can provide quick and reliable assessments in place of medical practitioners. With over 50 million adults in the United States suffering from osteoarthritis, there is a need for models capable of interpreting musculoskeletal ultrasound images. However, machine learning requires lots of data, which poses significant challenges in medical imaging. Therefore, we explore two strategies for enriching a musculoskeletal ultrasound dataset independent of these limitations: traditional augmentation and diffusion-based image synthesis. Methods: First, we generate augmented and synthetic images to enrich our dataset. Then, we compare the images qualitatively and quantitatively, and evaluate their effectiveness in training a deep learning model for detecting thickened synovium and knee joint recess distension. Results: Our results suggest that synthetic images exhibit some anatomical fidelity, diversity, and help a model learn representations consistent with human opinion. In contrast, augmented images may impede model generalizability. Finally, a model trained on synthetically enriched data outperforms models trained on un-enriched and augmented datasets. Conclusions: We demonstrate that diffusion-based image synthesis is preferable to traditional augmentation. Our study underscores the importance of leveraging dataset enrichment strategies to address data scarcity in medical imaging and paves the way for the development of more advanced diagnostic tools.

Abstract Background High rates of labour augmentation with oxytocin have been found in some low- and lower-middle-income countries, causing potential perinatal harm. It is critical to understand the reasons for this overuse. Aim was to explore factors that shape practices around using oxytocin for labour augmentation in a high-volume labour ward in Dar es Salaam, Tanzania. Methods Mixed-methods data collection was conducted from March 2021 to February 2022, including structured observations of 234 births, 220 h of unstructured labour ward observations and 13 individual in-depth interviews with birth attendants. Thematic network analysis and descriptive statistics were used to analyse data. We used a time-lens to understand practices of oxytocin for labour augmentation in time-pressured labour wards. Results Birth attendants constantly had to prioritise certain care practices over others in response to time pressure. This led to overuse of oxytocin for augmentation to ensure faster labour progression and decongestion of the, often overburdened, ward. Simultaneously, birth attendants had little time to monitor foetal and maternal condition. Surprisingly, while oxytocin was used in 146 out of 234 (62.4%) structured labour observations, only 9/234 (4.2%) women had active labour lasting more than 12 h. Correspondingly, 21/48 (43.8%) women who were augmented with oxytocin in the first stage of labour had uncomplicated labour progression at the start of augmentation. While the partograph was often not used for decision-making, timing of starting oxytocin often correlated with natural cycles of ward-rounds and shift-turnovers instead of individual women’s labour progression. This resulted in co-existence of ‘too early’ and ‘too late’ use of oxytocin. Liberal use of oxytocin for labour augmentation was facilitated by an underlying fear of prolonged labour and low alertness of oxytocin-related risks. Conclusions Time scarcity in the labour ward often made birth attendants deviate from clinical guidelines for labour augmentation with oxytocin. Efforts to navigate time pressure resulted in too many women with uncomplicated labour progression receiving oxytocin with little monitoring of labour. Fear of prolonged labour and low alertness to oxytocin-mediated risks were crucial drivers. These findings call for research into safety and benefits of oxytocin in low-resource settings and interventions to address congestion in labour wards to prevent using oxytocin as a time-management tool.

Fractures of the proximal humerus account for 4%-8% of injuries to the appendicular skeleton. Most are stable, minimally displaced osteoporotic fractures in the elderly, and are the result of low-energy falls. A large majority of these patients regain adequate shoulder function without operative intervention. Surgery is considered in approximately 20% of patients because they require improved shoulder function for their activities of daily living or because of the significant deformity of their fracture and the need to restore functional alignment, length, and rotation in active, higher demand individuals. However, fixation of these fractures can pose a challenge due to poor bone quality and displacing forces of the rotator cuff. This is especially true in 3-part and 4-part fractures. These factors lead to the high failure rates seen with early attempts at osteosynthesis. In the last 2 decades, locking plate technology has been an innovation in treating these complex fractures. Despite the improvements in torsional strength and rigidity, outcome studies on locking plate technology demonstrate equivocal results with complication rates as high as 20%-30% and a revision rate of 10%. Specifically, these complications include avascular necrosis, varus collapse, intra-articular screw penetration, and postoperative stiffness. Varus collapse occurs when the weak osteoporotic bone fails around the implant. In turn, fibular strut endosteal augmentation was introduced to provide additional support and decrease implant failure rates in displaced fractures with varus coronal malalignment and significant metaphyseal bone loss. Although clinically successful and biomechanically superior to plate-only constructs, a few concerns remain. In turn, we introduce a novel technique of creating individual cancellous femoral head allograft struts or “French fries” that provides structural support for the humeral head but does not have the potential problems of a cortical fibular strut.

Human-AI collaboration is increasingly considered an alternative to fears of automation and large-scale job loss that are typically attributed to AI. However, the current scholarly understanding of this type of collaboration falls victim to a one-sided, rather optimistic view. More specifically, human-AI collaboration is commonly associated with augmenting or enhancing work. In this scenario, I use insights from qualitative research on the use of AI in practice to develop alternative perspectives around unexpected and unintended consequences of human-AI collaboration for worker safety. As such, I argue for future research to study human-AI collaboration in practice, to carefully unpack the ripple effects of changing or “enhanced” work practices, and to consider AI systems as being embedded in wider systems of cognition including, but not limited to, the mind, the body, and technologies.

We estimate relative hazards and absolute risks (or cumulative incidence or crude risk) under cause-specific proportional hazards models for competing risks from double nested case-control (DNCC) data. In the DNCC design, controls are time-matched not only to cases from the cause of primary interest, but also to cases from competing risks (the phase-two sample). Complete covariate data are available in the phase-two sample, but other cohort members only have information on survival outcomes and some covariates. Design-weighted estimators use inverse sampling probabilities computed from Samuelsen-type calculations for DNCC. To take advantage of additional information available on all cohort members, we augment the estimating equations with a term that is unbiased for zero but improves the efficiency of estimates from the cause-specific proportional hazards model. We establish the asymptotic properties of the proposed estimators, including the estimator of absolute risk, and derive consistent variance estimators. We show that augmented design-weighted estimators are more efficient than design-weighted estimators. Through simulations, we show that the proposed asymptotic methods yield nominal operating characteristics in practical sample sizes. We illustrate the methods using prostate cancer mortality data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Study of the National Cancer Institute. [ABSTRACT FROM AUTHOR]

The article considers the available methods of image recognition of continuous wavelet spectra of noisy signals with linear and nonlinear frequency modulations using convolutional neural networks. A procedure is proposed for preparing spectral images for their processing in a neural network, ensuring a sufficient probability of recognizing a given type of signal out of twenty possible ones. The methodology for solving the problem finds an image preparation algorithm that provides image augmentation by changing continuous wavelets, which ensures the identification of signals under conditions of limited resonance frequency and bandwidth. The algorithm changes the frequency of the continuous spectrum by processing the phase grating signal with different continuous wavelets after adding non-stationary noise. Signals with linear and nonlinear modulation prepared in this way, as well as signal spectra of other regular forms, are used as input data of the convolutional neural network. The procedure of dividing images of wavelet spectra into classes is performed by checking the homogeneity of the class based on the Shannon entropy value. The minimum entropy value indicates the homogeneity of the subset and the absence of "impurities" from images of other classes. The developed model of a neural network with augmentation by continuous wavelet spectra under the conditions of a limited data set has an accuracy of up to 97.95%. [ABSTRACT FROM AUTHOR]

Background: Machine learning models can provide quick and reliable assessments in place of medical practitioners. With over 50 million adults in the United States suffering from osteoarthritis, there is a need for models capable of interpreting musculoskeletal ultrasound images. However, machine learning requires lots of data, which poses significant challenges in medical imaging. Therefore, we explore two strategies for enriching a musculoskeletal ultrasound dataset independent of these limitations: traditional augmentation and diffusion-based image synthesis. Methods: First, we generate augmented and synthetic images to enrich our dataset. Then, we compare the images qualitatively and quantitatively, and evaluate their effectiveness in training a deep learning model for detecting thickened synovium and knee joint recess distension. Results: Our results suggest that synthetic images exhibit some anatomical fidelity, diversity, and help a model learn representations consistent with human opinion. In contrast, augmented images may impede model generalizability. Finally, a model trained on synthetically enriched data outperforms models trained on un-enriched and augmented datasets. Conclusions: We demonstrate that diffusion-based image synthesis is preferable to traditional augmentation. Our study underscores the importance of leveraging dataset enrichment strategies to address data scarcity in medical imaging and paves the way for the development of more advanced diagnostic tools. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the impact of selected data augmentation techniques on the learning performance of neural networks for dynamic signature verification. The paper investigates selected data augmentation techniques in deep learning for verification purpose of dynamic signature. Two neural networks were used as classifiers: MLP and LSTM-FCN. Investigation of five selected augmentation methods and experiments were performed on the open source signature database SVC2004. The authors tested both classifiers without augmentation and then with data augmentation for three extensions of the learning set and three sizes of the user database. They presented the results of the experiments in tabular form for each augmentation method. The results were compared with the existing dynamic signature verification methods and given in the paper. [ABSTRACT FROM AUTHOR]