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Purpose: To evaluate the efficacy and safety of holmium laser enucleation of the prostate (HoLEP) in a large prospective cohort of patients with benign prostatic hyperplasia (BPH) through systematic follow-up at a single institution. Materials and Methods: Clinical outcomes were analyzed between August 2008 and June 2022. Patients were followed-up at 2 weeks, 3 months and 6 months postoperatively. Results: A total of 3,000 patients (mean age, 69.6±7.7 years) underwent HoLEP. Baseline total International Prostate Symptom Score (IPSS) was 19.3±7.7 and maximum flow rate (Qmax) was 9.4±4.8 mL/s. Mean total prostate volume was 67.7±3.4 mL. Total operation time was 60.7±31.5 minutes, and catheterization time was 1.0 days (range, 1.0–1.0 days). At 6 months postoperatively, the total IPSS decreased to 6.6±5.8 and Qmax increased to 22.2±11.3 mL/s. Complications at 6 months postoperatively included stress urinary incontinence (SUI) in 36 patients (1.9%), urgency urinary incontinence (UUI) in 25 (1.3%), bladder neck contracture (BNC) requiring transurethral incision (TUI) in 16 (0.5%), and urethral stricture in 29 (1.0%). Eleven patients (0.4%) with prostatic fossa stones required stone removal. Sixty-one patients (2.0%) required secondary surgery (transurethral coagulation, 16 [0.5%]; TUI for BNC, 16 [0.5%]; stone removal for prostatic fossa stones, 11 [0.4%]; and endoscopic internal urethrotomy for urethral stricture, 18 [0.6%]). Conclusions: Mid-term follow-up results after HoLEP in BPH patients showed excellent efficacy and low complication rates. Unlike previous reports, the incidence of SUI and UUI after HoLEP was low, but the occurrence of de novo stone formation in prostatic fossa was notable.

Abstract Passive antibody therapies, typically administered via parenteral routes, have played a crucial role in the initial response to the COVID-19 pandemic. However, the ongoing evolution of SARS-CoV-2 has revealed significant limitations of this approach, primarily due to mutational escape and the inadequate delivery of antibodies to the upper respiratory tract. To overcome these challenges, we propose a novel prophylactic strategy involving the intranasal delivery of an antibody in combination with an Fc-binding nanodisc. This nanodisc, engineered to specifically bind to the Fc regions of IgG antibodies, served two key functions: extending the antibody's half-life in the larynx and trachea, and enhancing its neutralization efficacy. Notably, Sotrovimab, an FDA-approved monoclonal antibody that has experienced a significant decline in neutralizing potency due to viral evolution, exhibited robust antiviral activity when complexed with the nanodisc against all tested Omicron variants. Furthermore, the Fc-binding nanodisc significantly boosted the antiviral efficacy of the soluble angiotensin-converting enzyme 2 (sACE2) Fc fusion protein, which possesses broad but modest antiviral activity. In ACE2 transgenic mice, the Fc-binding nanodisc protected better than sACE2-Fc alone with two more log reduction in lung viral titer. Therefore, the intranasal Fc-binding nanodisc offers a promising and powerful approach to counteract the diminished antiviral activity of neutralizing antibodies caused by mutational escape, effectively restoring antiviral efficacy against various evolving SARS-CoV-2 variants.

Abstract This paper proposes training improvement methods of artificial neural networks (ANN) trajectory predictors. First, a dynamic power system time‐series trajectory is split into several different segments to simplify the original ANN training problem. Moreover, the time‐derivative of the trajectory is included to obtain an augmented loss function. Compared to previous studies which mainly focused on increasing the prediction accuracy, the aim of these novel techniques is to reduce the computational burden where the ANN output performance is still acceptable. The effectiveness of the developed methods is validated based on the WSCC three‐machine nine‐bus and IEEE 39‐bus system models. The mean absolute error (MAE) and trajectory prediction results are analysed, in which the numbers of neurons, hidden layers, and training epochs are constrained during the ANN training process. Rotor‐angle difference between generators and the system frequency are investigated as the dynamic trajectories of the power system models. The approaches are revealed to be effective when the ANN architecture and epochs are constrained. The MAE results can be reduced by up to 65% in the power system models depending on the ANN hyperparameters and training epochs. The ANN training results can better reflect the original trajectory as well.

The transition to low-carbon electricity is crucial for meeting global climate goals. However, given the uneven spatial distribution and temporal variability of renewable resources, balancing the supply and demand of electricity will be challenging when relying on close to 100% shares of renewable energy. Here, we use an electricity planning model with hourly supply-demand projections and high-resolution renewable resource maps, to examine whether transcontinental power pools reliably meet the growing global demand for renewable electricity and reduce the system cost. If all suitable sites for renewable energy are available for development, transcontinental trade in electricity reduces the annual system cost of electricity in 2050 by 5-52% across six transcontinental power pools compared to no electricity trade. Under land constraints, if only the global top 10% of suitable renewable energy sites are available, then without international trade, renewables are unable to meet 12% of global demand in 2050. Introducing transcontinental power pools with the same land constraints, however, enables renewables to meet 100% of future electricity demand, while also reducing costs by up to 23% across power pools. Our results highlight the benefits of expanding regional transmission networks in highly decarbonized but land-constrained future electricity systems.

BackgroundStress is a significant risk factor for psychiatric disorders such as major depressive disorder (MDD) and panic disorder (PD). This highlights the need for advanced stress-monitoring technologies to improve treatment. Stress affects the autonomic nervous system, which can be evaluated via heart rate variability (HRV). While machine learning has enabled automated stress detection via HRV in healthy individuals, its application in psychiatric patients remains underexplored. This study evaluated the feasibility of using machine-learning algorithms to detect stress automatically in MDD and PD patients, as well as healthy controls (HCs), based on HRV features.MethodsThe study included 147 participants (MDD: 41, PD: 47, HC: 59) who visited the laboratory up to five times over 12 weeks. HRV data were collected during stress and relaxation tasks, with 20 HRV features extracted. Random forest and multilayer perceptron classifiers were applied to distinguish between the stress and relaxation tasks. Feature importance was analyzed using SHapley Additive exPlanations, and differences in HRV between the tasks (ΔHRV) were compared across groups. The impact of personalized longitudinal scaling on classification accuracy was also assessed.ResultsRandom forest classification accuracies were 0.67 for MDD, 0.69 for PD, and 0.73 for HCs, indicating higher accuracy in the HC group. Longitudinal scaling improved accuracies to 0.94 for MDD, 0.90 for PD, and 0.96 for HCs, suggesting its potential in monitoring patients’ conditions using HRV. The HC group demonstrated greater ΔHRV fluctuation in a larger number of and more significant features than the patient groups, potentially contributing to higher accuracy. Multilayer perceptron models provided consistent results with random forest, confirming the robustness of the findings.ConclusionThis study demonstrated that differentiating between stress and relaxation was more challenging in the PD and MDD groups than in the HC group, underscoring the potential of HRV metrics as stress biomarkers. Psychiatric patients exhibited altered autonomic responses, which may influence their stress reactivity. This indicates the need for a tailored approach to stress monitoring in these patient groups. Additionally, we emphasized the significance of longitudinal scaling in enhancing classification accuracy, which can be utilized to develop personalized monitoring technologies for psychiatric patients.

Typical applications of LCA assume that the magnitude of life-cycle impact grows proportionally to the volume of demand, while in reality the additional impact due to marginal increase in demand may differ from the average impact. In the literature, the calculation of marginal life-cycle impacts often involves the use of optimization models, where typically the total economic costs are minimized. However, modeling spatially explicit marginal responses of a system involving multiple producers and consumers has not been discussed in LCA literature. In this paper, we demonstrate a spatial optimization technique for modeling marginal responses of a multi-producer, multi-consumer system. Our model determines the optimal production-by-location mix and associated environmental stressor at minimum systems cost. We demonstrate the model using a preliminary case study on blue water consumption by potato. We collected state-by-state data on potato yield, cost of potato production, and water use for irrigation, as well as interstate transportation fuel costs. We also estimated the marginal increase in demand for potato following USDA's recommended diet. The results show that the cradle-to-gate blue water consumption of potatoes based on 2016 demand was 96 m3/ton potato, which changes non-linearly along with the growth of potato demands. In order to meet the USDA's recommended diet, the additional demand on potato (530,000 ton per year) would result in a 29% lower blue water consumption per ton of potato (68 m3/ton) as compared to the average result of the current production system. In addition, we tested the model to analyze the marginal impacts under two scenarios: (1) high fuel tax and (2) high water price. The preliminary results indicate that water pricing is more effective than a fuel tax increase in reducing the marginal blue water consumption of potato based on our scenarios of the recommended diet demand. The results demonstrate that our model can be used to understand the non-linear behavior of marginal effect over demand crease, and for testing alternative policy scenarios involving a system with multiple producers and consumers across regions.

The role of a topologically close-packed (TCP) phase (μ phase) on the plastic deformation of a Ni-based superalloy was investigated employing a combination of in situ scanning electron microscope micropillar compression and atomic-scale characterization using atom probe tomography and transmission electron microscopy. Micropillar tests revealed two distinct slip behaviors: TCP-free pillars deformed via multiple slip systems, whereas TCP-containing pillars deformed by single slip. Notably, while previous studies have reported fracture at the TCP/γ' interface, our findings revealed that in the TCP-containing pillars, deformation was rarely observed at the interface. Instead, slip predominantly occurred in regions approximately 50–100 nm away from the interface. Chemical analysis near the TCP/γ' interface via APT showed an excess Ta content near the interface increasing the antiphase boundary energy and enhancing local order strengthening. Moreover, an approximate 8% lattice misfit at the TCP/γ' interface, coupled with the elastic mismatch between the two phases, provided additional slip resistance in the vicinity of the interface. This study sheds light on the intricate interplay between TCP phase formation, microstructural evolution, and mechanical properties in Ni-based superalloys offering valuable insights into the role of the TCP phases.