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Background: The fatty infiltration and atrophy in the muscle after a rotator cuff (RC) tear are important in surgical decision-making and are linked to poor clinical outcomes after rotator cuff repair. An accurate and reliable quantitative method should be developed to assess the entire RC muscles. Purpose: To develop a fully automated approach based on a deep neural network to segment RC muscles from clinical magnetic resonance imaging (MRI) scans. Material and Methods: In total, 94 shoulder MRI scans (mean age = 62.3 years) were utilized for the training and internal validation datasets, while an additional 20 MRI scans (mean age = 62.6 years) were collected from another institution for external validation. An orthopedic surgeon and a radiologist manually segmented muscles and bones as reference masks. Segmentation performance was evaluated using the Dice score, sensitivities, precision, and percent difference in muscle volume (%). In addition, the segmentation performance was assessed based on sex, age, and the presence of a RC tendon tear. Results: The average Dice score, sensitivities, precision, and percentage difference in muscle volume of the developed algorithm were 0.920, 0.933, 0.912, and 4.58%, respectively, in external validation. There was no difference in the prediction of shoulder muscles, with the exception of teres minor, where significant prediction errors were observed (0.831, 0.854, 0.835, and 10.88%, respectively). The segmentation performance of the algorithm was generally unaffected by age, sex, and the presence of RC tears. Conclusion: We developed a fully automated deep neural network for RC muscle and bone segmentation with excellent performance from clinical MRI scans. [ABSTRACT FROM AUTHOR]

Background: The management of irreparable rotator cuff tears presents a surgical dilemma. However, supraspinatus muscle advancement (MA) could be used to convert irreparable to reparable tears without requiring a graft. Purpose: To compare the outcomes of patients with an irreparable tear who underwent rotator cuff repair with MA with those with a reparable large to massive cuff tear who underwent rotator cuff repair. Study Design: Cohort study; Level of evidence, 3. Methods: We enrolled 62 patients who underwent rotator cuff repair for a large to massive tear between January 2020 and May 2022. Among them, 29 patients underwent an MA procedure due to an inability to repair despite releases (MA group), whereas the other 33 patients did not require the procedure (NMA group). At 1 year postoperatively, follow-up assessments including magnetic resonance imaging were performed to evaluate group outcomes. Results: Despite there being more revision surgery cases and a poorer potential for cuff healing in the MA group, the retear rates in the MA and NMA groups were similar (31.0% vs 21.2%, respectively; P =.401), as were clinical outcomes, including the visual analog scale for pain (2.9 vs 1.9; P =.076), University of California, Los Angeles (27.0 vs 29.1; P =.185), Constant (70.1 vs 74.9; P =.063), and American Shoulder and Elbow Surgeons (74.5 vs 81.8; P =.168) scores. Postoperative muscle power during forward elevation was weaker in the MA group than in the NMA group (35.1 vs 45.8 N; P =.052), but external rotation power was comparable (49.3 vs 59.0 N; P =.121). Progress in fatty degeneration of the supraspinatus was not significantly different in the 2 groups (P =.43), although the MA group showed a nonsignificant decrease in supraspinatus atrophy (P =.092) due to the lateral shift produced by the procedure. Conclusion: The MA procedure for irreparable tears produced outcomes comparable with reparable tears, offering a valuable perspective on the efficiency of the procedure. The comparable but relatively high retear rates emphasize the need for further studies to compare with other treatment options for irreparable tears. [ABSTRACT FROM AUTHOR]

In the agricultural sector, utilizing robots for tasks such as fruit harvesting poses significant challenges, particularly in achieving accurate 6D pose estimation of the target objects, which is essential for precise and efficient harvesting. Particularly, fruit harvesting relies heavily on manual labor, leading to issues with an unstable labor supply and rising costs. To solve these problems, agricultural harvesting robots are gaining attention. However, effective harvesting necessitates accurate 6D pose estimation of the target object. This study proposes a method to enhance the performance of fruit-harvesting robots, including the development of a dataset named HWANGMOD, which was created using both virtual and real environments with tools such as Blender and BlenderProc. Additionally, we present methods for training an EfficientPose-based model for 6D pose estimation and ripeness classification, and an algorithm for determining the optimal harvest sequence among multiple fruits. Finally, we propose a multi-object tracking method using coordinates estimated by deep learning models to improve the robot's performance in dynamic environments. The proposed methods were evaluated using metrics such as A D D and A D D S , showing that the deep learning model for agricultural harvesting robots excelled in accuracy, robustness, and real-time processing. These advancements contribute to the potential for commercialization of agricultural harvesting robots and the broader field of agricultural automation technology. [ABSTRACT FROM AUTHOR]

Biomaterials derived via programmable supramolecular protein assembly provide a viable means of constructing precisely defined structures. Here, we present programmed superstructures of AuPt nanoparticles (NPs) on carbon nanotubes (CNTs) that exhibit distinct electrocatalytic activities with respect to the nanoparticle positions via rationally modulated peptide-mediated assembly. De novo designed peptides assemble into six-helix bundles along the CNT axis to form a suprahelical structure. Surface cysteine residues of the peptides create AuPt-specific nucleation site, which allow for precise positioning of NPs onto helical geometries, as confirmed by 3-D reconstruction using electron tomography. The electrocatalytic model system, i.e., AuPt for oxygen reduction, yields electrochemical response signals that reflect the controlled arrangement of NPs in the intended assemblies. Our design approach can be expanded to versatile fields to build sophisticated functional assemblies.

The selection of oxide materials for catalyzing the oxygen evolution reaction in acid-based electrolyzers must be guided by the proper balance between activity, stability and conductivity-a challenging mission of great importance for delivering affordable and environmentally friendly hydrogen. Here we report that the highly conductive nanoporous architecture of an iridium oxide shell on a metallic iridium core, formed through the fast dealloying of osmium from an Ir25Os75 alloy, exhibits an exceptional balance between oxygen evolution activity and stability as quantified by the activity-stability factor. On the basis of this metric, the nanoporous Ir/IrO2 morphology of dealloyed Ir25Os75 shows a factor of ~30 improvement in activity-stability factor relative to conventional iridium-based oxide materials, and an ~8 times improvement over dealloyed Ir25Os75 nanoparticles due to optimized stability and conductivity, respectively. We propose that the activity-stability factor is a key "metric" for determining the technological relevance of oxide-based anodic water electrolyzer catalysts.