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Key Clinical Message This is the first case report in which computed tomography (CT) images of a patient with emphysematous pyelonephritis (EPN) capture the time course of emphysema from onset to resolution through conservative treatment. To re‐evaluate EPN, including refractory urinary tract infections, CT scans after 72 h might be helpful.

Maize is the staple food crop in Bhutan, which has not achieved national food self-sufficiency. On-farm assessment of yield variability would provide insights into the priorities for Bhutan's maize development program. Here, we conducted three studies in Bhutan: a household survey, on-station experiment, and on-farm monitoring. First, we interviewed 100 households and collected information on maize crop management options and farming characteristics. Second, we evaluated maize growth at two research stations in different elevation zones (640 and 1,700 ​m a.s.l.). Third, we harvested maize from 25 farm fields at low and high elevations. The gaps between potential yield (with the best management practices at the research stations) and average farm yield and between the best and average farm yields were 53% and 23%, respectively, at the low elevation, and 23% and 20%, respectively, at the high elevation. The classification and regression tree (CART) model showed that field location (distance from the farmer's home), seed source (certified vs. self-produced), and the number of household members involved in farming were the key farming characteristics that affected yield variability, and the manure application regime, urea application, sowing method, and weeding frequency were key management practices. Our results suggest that future research should clarify the most suitable sowing methods and nutrient and weed management regimes, and identify optimal cultivars for each elevation zone, with the goal of developing crop management guidelines for smallholder farmers in Bhutan.

Abstract The prognosis for patients who achieve a pathologic complete response in bladder cancer is excellent, but the association between their prognosis and the tumor microenvironment is unclear. We investigated the tumor immune microenvironment of those with pathological complete response after platinum-based neoadjuvant chemotherapy for cT2-4aN0M0 bladder cancer using multiplex fluorescence immunohistochemistry. Our retrospective study included 12 patients with pathological complete response who underwent radical cystectomy following neoadjuvant chemotherapy for cT2-4aN0M0 muscle-invasive bladder cancer. We assessed the density of several immune cell types in pretreatment and posttreatment tissues via multiplex fluorescence immunohistochemical analysis. The median age was 67 years; 10 patients were male. Nine (75%) and 3 (25%) patients were cT2 and cT3, respectively. The 5-year progression-free and overall survivals were 90% and 100%, respectively. The densities of regulatory T cells (Treg; CD3+CD4+FoxP3+ cell) were significantly decreased and almost disappeared in the tumor microenvironment of posttreatment tissue compared with pretreatment tissue. Other immune cells, such as effector T cells or M2 macrophages, were not significantly changed between posttreatment and pretreatment tissues. In pathological complete response, Tregs in the tumor immune microenvironment were significantly decreased after platinum-based chemotherapy for muscle-invasive bladder cancer. The temporary arresting of immune response in the tumor microenvironment may reflect a favorable prognosis due to the decrease of Tregs with tumor shrinkage and improve the host tumor immune response.

Weed is a major biological factor causing declines in crop yield. However, widespread herbicide application and indiscriminate weeding with soil disturbance are of great concern because of their environmental impacts. Site-specific weed management (SSWM) refers to a weed management strategy for digital agriculture that results in low energy loss. Deep learning is crucial for developing SSWM, as it distinguishes crops from weeds and identifies weed species. However, this technique requires substantial annotated data, which necessitates expertise in weed science and agronomy. In this study, we present a channel attention mechanism-driven generative adversarial network (CA-GAN) that can generate realistic synthetic weed data. The performance of the model was evaluated using two datasets: the public segmented Plant Seedling Dataset (sPSD), featuring nine common broadleaf weeds from arable land, and the Institute for Sustainable Agro-ecosystem Services (ISAS) dataset, which includes five common summer weeds in Japan. Consequently, the synthetic dataset generated by the proposed CA-GAN obtained an 82.63% recognition accuracy on the sPSD and 93.46% on the ISAS dataset. The Fréchet inception distance (FID) score test measures the similarity between a synthetic and real dataset, and it has been shown to correlate well with human judgments of the quality of synthetic samples. The synthetic dataset achieved a low FID score (20.95 on the sPSD and 24.31 on the ISAS dataset). Overall, the experimental results demonstrated that the proposed method outperformed previous state-of-the-art GAN models in terms of image quality, diversity, and discriminability, making it a promising approach for synthetic agricultural data generation.