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A major limitation of screening breast ultrasound (US) is a substantial number of false-positive biopsy. This study aimed to develop a deep learning-based computer-aided diagnosis (DL-CAD)-based diagnostic model to improve the differential diagnosis of screening US-detected breast masses and reduce false-positive diagnoses. In this multicenter retrospective study, a diagnostic model was developed based on US images combined with information obtained from the DL-CAD software for patients with breast masses detected using screening US; the data were obtained from two hospitals (development set: 299 imaging studies in 2015). Quantitative morphologic features were obtained from the DL-CAD software, and the clinical findings were collected. Multivariable logistic regression analysis was performed to establish a DL-CAD-based nomogram, and the model was externally validated using data collected from 164 imaging studies conducted between 2018 and 2019 at another hospital. Among the quantitative morphologic features extracted from DL-CAD, a higher irregular shape score ( P  = .018) and lower parallel orientation score ( P  = .007) were associated with malignancy. The nomogram incorporating the DL-CAD-based quantitative features, radiologists’ Breast Imaging Reporting and Data Systems (BI-RADS) final assessment ( P  = .014), and patient age ( P  < .001) exhibited good discrimination in both the development and validation cohorts (area under the receiver operating characteristic curve, 0.89 and 0.87). Compared with the radiologists’ BI-RADS final assessment, the DL-CAD-based nomogram lowered the false-positive rate (68% vs. 31%, P  < .001 in the development cohort; 97% vs. 45% P  < .001 in the validation cohort) without affecting the sensitivity (98% vs. 93%, P  = .317 in the development cohort; each 100% in the validation cohort). In conclusion, the proposed model showed good performance for differentiating screening US-detected breast masses, thus demonstrating a potential to reduce unnecessary biopsies.

Tumor necrosis commonly exists and predicts poor prognoses in many cancers. Although it is thought to result from chronic ischemia, the underlying nature and mechanisms driving the involved cell death remain obscure. Here, we show that necrosis in glioblastoma (GBM) involves neutrophil-triggered ferroptosis. In a hyperactivated transcriptional coactivator with PDZ-binding motif-driven GBM mouse model, neutrophils coincide with necrosis temporally and spatially. Neutrophil depletion dampens necrosis. Neutrophils isolated from mouse brain tumors kill cocultured tumor cells. Mechanistically, neutrophils induce iron-dependent accumulation of lipid peroxides within tumor cells by transferring myeloperoxidase-containing granules into tumor cells. Inhibition or depletion of myeloperoxidase suppresses neutrophil-induced tumor cell cytotoxicity. Intratumoral glutathione peroxidase 4 overexpression or acyl-CoA synthetase long chain family member 4 depletion diminishes necrosis and aggressiveness of tumors. Furthermore, analyses of human GBMs support that neutrophils and ferroptosis are associated with necrosis and predict poor survival. Thus, our study identifies ferroptosis as the underlying nature of necrosis in GBMs and reveals a pro-tumorigenic role of ferroptosis. Together, we propose that certain tumor damage(s) occurring during early tumor progression (i.e. ischemia) recruits neutrophils to the site of tissue damage and thereby results in a positive feedback loop, amplifying GBM necrosis development to its fullest extent. Tumour necrosis is associated with tumour aggressiveness and poor outcomes in patients with glioblastomas, but the underlying mechanisms remain poorly understood. Here, the authors show that in a xenograft mouse model of glioblastoma, tumour-infiltrating neutrophils amplify necrosis by promoting myeloperoxidase-induced tumour cell ferroptosis.

The objectives of this study were to develop a real-time tillage depth measurement system for agricultural tractor performance analysis and then to validate these configured systems through soil non-penetration tests and field experiment during plow tillage. The real-time tillage depth measurement system was developed by using a sensor fusion method, consisting of a linear potentiometer, inclinometer, and optical distance sensor to measure the vertical penetration depth of the attached implement. In addition, a draft force measurement system was developed using six-component load cells, and an accuracy of 98.9% was verified through a static load test. As a result of the soil non-penetration tests, it was confirmed that sensor fusion type A, consisting of a linear potentiometer and inclinometer, was 6.34–11.76% more accurate than sensor fusion type B, consisting of an optical distance sensor and inclinometer. Therefore, sensor fusion type A was used during field testing as it was found to be more suitable for use in severe working environments. To verify the accuracy of the real-time tillage depth measurement system, a linear regression analysis was performed between the measured draft and the predicted values calculated using the American Society of Agricultural and Biological Engineers (ASABE) standards-based equation. Experimental data such as traveling speed and draft force showed that it was significantly affected by tillage depth, and the coefficient of determination value at M3–Low was 0.847, which is relatively higher than M3–High. In addition, the regression analysis of the integrated data showed an R-square value of 0.715, which is an improvement compared to the accuracy of the ASABE standard prediction formula. In conclusion, the effect of tillage depth on draft force of agricultural tractors during plow tillage was analyzed by the simultaneous operation of the proposed real-time tillage depth measurement system and draft force measurement system. In addition, system accuracy is higher than the predicted accuracy of ± 40% based on the ASABE standard equation, which is considered to be useful for various agricultural machinery research fields. In future studies, real-time tillage depth measurement systems can be used in tractor power train design and to ensure component reliability, in accordance with agricultural working conditions, by predicting draft force and axle loads depending on the tillage depth during tillage operations.

Distinguishing between pathologic complete response and residual cancer after neoadjuvant chemotherapy (NAC) is crucial for treatment decisions, but the current imaging methods face challenges. To address this, we developed deep-learning models using post-NAC dynamic contrast-enhanced MRI and clinical data. A total of 852 women with human epidermal growth factor receptor 2 (HER2)-positive or triple-negative breast cancer were randomly divided into a training set ( n  = 724) and a validation set ( n  = 128). A 3D convolutional neural network model was trained on the training set and validated independently. The main models were developed using cropped MRI images, but models using uncropped whole images were also explored. The delayed-phase model demonstrated superior performance compared to the early-phase model (area under the receiver operating characteristic curve [AUC] = 0.74 vs. 0.69, P  = 0.013) and the combined model integrating multiple dynamic phases and clinical data (AUC = 0.74 vs. 0.70, P  = 0.022). Deep-learning models using uncropped whole images exhibited inferior performance, with AUCs ranging from 0.45 to 0.54. Further refinement and external validation are necessary for enhanced accuracy.

This study aims to design, develop, and evaluate the traction performance of an electric all-wheel-drive (AWD) tractor based on the power transmission and electric systems. The power transmission system includes the electric motor, helical gear reducer, planetary gear reducer, and tires. The electric system consists of a battery pack and charging system. An engine-generator and charger are installed to supply electric energy in emergency situations. The load measurement system consists of analog (current) and digital (battery voltage and rotational speed of the electric motor) components using a controller area network (CAN) bus. A traction test of the electric AWD tractor was performed towing a test vehicle. The output torques of the tractor motors during the traction test were calculated using the current and torque curves provided by the motor manufacturer. The agricultural work performance is verified by comparing the torque and rpm (T–N) curve of the motor with the reduction ratio applied. The traction is calculated using torque and specifications of the wheel, and traction performance is evaluated using tractive efficiency (TE) and dynamic ratio (DR). The results suggest a direction for the improvement of the electric drive system in agricultural research by comparison with the conventional tractor through the analysis of the agricultural performance and traction performance of the electric AWD tractor.

Assessment of functional independence and residual disability is very important for measuring treatment outcome after stroke. The modified Rankin Scale (mRS) and the modified Barthel Index (MBI) are commonly used scales to measure disability or dependence in activities of daily living (ADL) of stroke survivors. Lack of consensus regarding MBI score categories has caused confusion in interpreting stroke outcomes. The purpose of this study was to identify the optimal corresponding MBI and modified Rankin scale (mRS) grades for categorization of MBI. The Korean versions of the MBI (K-MBI) and mRS were collected from 5,759 stroke patients at 3 months after onset of stroke. The sensitivity and specificity were calculated at K-MBI score cutoffs for each mRS grade to obtain optimally corresponding K-MBI scores and mRS grades. We also plotted receiver operating characteristic (ROC) curves of sensitivity and specificity and determined the area under the curve (AUC). The K-MBI cutoff points with the highest sum of sensitivity and specificity were 100 (sensitivity 0.940; specificity 0.612), 98 (sensitivity 0.904; specificity 0.838), 94 (sensitivity 0.885; specificity 0.937), 78 (sensitivity 0.946; specificity, 0.973), and 55 (sensitivity 937; specificity 0.986) for mRS grades 0, 1, 2, 3, and 4, respectively. From this result, the K-MBI cutoff score range for each mRS grade can be obtained. For mRS grade 0, the K-MBI cutoff score is 100, indicating no associated score range. For mRS grades 1, 2, 3, 4, and 5, the K-MBI score ranges is from 99 to 98, 97 to 94, 93 to 78, 77 to 55, and under 54, respectively.The AUC for the ROC curve was 0.791 for mRS grade 0, 0.919 for mRS grade 1, 0.970 for mRS grade 2, 0.0 for mRS grade 3, and 0.991 for mRS grade 4. The K-MBI cutoff score ranges for representing mRS grades were variable; mRS grades 0, 1, and 2 had narrow K-MBI score ranges, while mRS grades 3, 4, and 5 exhibited broad K-MBI score ranges. mRS grade seemed to sensitively differentiate mild residual disability of stroke survivors, whereas K-MBI provided more specific information of the functional status of stroke survivors with moderate to severe residual impairment.

This study aimed to determine and analyze the design loads required for the electrification of a 55‑kW agricultural tractor through field experiments. A measurement system was installed to record data from the engine, driving axles, power take‑off (PTO), and hydraulic pump during plow tillage, rotary tillage, and driving operation in a silt loam paddy field. The study specifically focused on power requirement analysis, load duration distribution (LDD), and rainflow counting (RFC)–based load spectrum generation for durability assessment of the electric tractor powertrain. Plow tillage imposed the highest loads, with total power peaking at 52.6 kW (95% of rated power) dominated by axle torque, while rotary tillage was PTO‑driven and driving operation showed low average loads with intermittent traction peaks. Compared with a previously studied 78‑kW tractor, the 55‑kW tractor exhibited lower overall power requirement and a more traction‑balanced distribution, while hydraulic requirements remained minimal. LDD and RFC analyses revealed that a few load cases and low‑amplitude cycles dominate the operating profile, and critical high‑load cycles occur primarily during tillage. These findings provide essential design data for electric powertrain components and establish a systematic measurement‑to‑spectrum methodology for deriving design loads for agricultural machinery, supporting the future development of utility electric tractors and durability‑driven powertrain design.

In this study, an e-powertrain for 55 kW electric tractors was designed and analyzed using agricultural workload data. The electric tractor power transmission system structure was analyzed, and three types were selected: the single-motor, the dual-motor, and the dual-motor including a planetary gear set (PGS). The single-motor specification for type I was 62.8 kW at 199.5 Nm. In type II, the power take-off (PTO) motor specification was 55.3 kW at 176.0 Nm, and the traction motor specification was 58.4 kW at 185.3 Nm. In type III, the PTO motor specification was 55.3 kW at 176.0 Nm, and the traction motor specification was 11.8 kW at 37.7 Nm. The power and torque of the single motor of type I were the highest. In type II, both the PTO and traction motor specifications were above those of the 55.3-kW engine. In type III, the PTO motor specifications were identical to those of type II. Moreover, the adoption of the traction motor specification could significantly reduce the required output by 80% compared with that of type II. A comparison of the mechanical components by e-powertrain type showed that the number of mechanical components exhibited the descending order of type II, type III, and type I. Depending on the tractor power, the powertrain structure can be appropriately applied. This study is expected to facilitate future development and optimization of the e-powertrain.

This study presents a multi-objective macro-geometry optimization of a compound planetary geartrain for an electric tractor powertrain. The proposed framework simultaneously minimizes peak-to-peak static transmission error and maximizes gear mesh efficiency under representative agricultural load conditions derived from load duration distribution data. The macro-geometry design variables include normal module, pressure angle, helix angle, and face width for two planetary gear sets integrated into a developed electric tractor prototype. A detailed geartrain model was developed using commercial analysis software, and the optimization was performed using the nondominated sorting genetic algorithm II with strength constraints based on ISO 6336 to ensure durability. The Pareto-optimal solutions were ranked using a criterion importance method based on variability and intercriteria correlation. Compared with the baseline prototype configuration, the optimized designs achieved a 14–16% reduction in transmission error across all gear pairs while maintaining or slightly improving mesh efficiency (up to + 0.16%p). The results demonstrate that macro-geometry refinement within fixed gear ratio and packaging constraints can effectively reduce excitation-related transmission error without compromising efficiency. Experimental validation of the optimized gear sets is planned in future work.

Understanding soil–metal adhesion under varying moisture and texture conditions is essential for predicting soil–tool interactions in agricultural machinery. However, despite its importance, research that directly quantifies soil adhesion and provides physically based parameters for modeling remains limited, particularly across the diverse conditions encountered in field operations. In this study, we experimentally measured soil–metal adhesion across multiple penetration speeds and integrated the results with discrete element method (DEM) simulations based on the Edinburgh Elasto-Plastic Adhesion (EEPA) model. Adhesion tests were conducted for three soil textures (sandy loam, sandy clay loam, and loam) and four water contents (10–25%) at penetration speeds of 50 and 500 mm·min⁻¹. Adhesive force increased with water content and peaked near the liquid limit (20–25%), while remaining nearly independent of penetration rate (< 5% variation). DEM calibration showed that surface energy (Δγ) is the dominant parameter governing compressive behavior, whereas the constant pull-off force (f₀) primarily controls adhesive strength. Two-way ANOVA confirmed that these mechanisms operate independently ( p  > 0.05). The calibrated model achieved R² ≥ 0.93 and RMSE ≤ 0.1 N across all textures, and validation at an intermediate speed of 250 mm·min⁻¹ demonstrated stable predictive performance (R² ≥ 0.93 for compression; R² ≥ 0.98 for adhesion). By linking multi-speed adhesion measurements with a physically based DEM contact model, this work establishes a robust and transferable Δγ–f₀ calibration framework for modeling soil adhesion in cohesive soils.