Medical and welfare facilities in the Noto region of Japan were severely affected by the 2024 Noto Peninsula earthquake and subsequent torrential rains. Staff working in these facilities were disaster survivors and frontline caregivers with limited psychological support. Nonverbal social robots may provide companionship and emotional comfort; however, their effects on the health-related quality of life (QoL) and well-being of care staff in disaster-affected settings remain unclear. This study explored whether introducing a nonverbal artificial intelligence communication robot was associated with changes in health-related QoL and well-being among care facility staff working under disaster conditions. Secondary objectives were to evaluate safety, acceptability, and intention to continue use. This pragmatic, exploratory pilot study used an ABAB design conducted between February 2025 and June 2025. After a 2-week baseline period, staff in dementia care, general care, and short-stay units underwent 2-week intervention, withdrawal, reintervention, and withdrawal phases. Questionnaires were administered at each phase end. The primary outcomes were health-related QoL (EQ-5D-5L), well-being (World Health Organization-5 Well‑Being Index), and positive mental health (Mental Health Continuum-Short Form). Friedman tests compared outcomes across the 5 phases, and effect sizes were expressed as Kendall W. Safety, acceptability, and intention to continue use were compared between the first and second intervention phases using Wilcoxon signed rank tests with Bonferroni adjustment and rank-biserial correlations as effect sizes. Of the 58 staff who completed the baseline assessment, 49 (84.5%) were included in the analytic sample (25 in dementia care, 12 in general care, and 12 in short-stay units). Among these participants, 40 (81.6%) were women, and 38 (77.6%) reported disaster-related damage to their homes or families. In the pooled analysis, no phase effect was observed for the EQ-5D-5L (P=.10; Kendall W=0.032, negligible), the World Health Organization-5 Well‑Being Index (P=.70; Kendall W=0.016, negligible), or the Mental Health Continuum-Short Form (P=.44; Kendall W=0.022, negligible). No robot-related adverse events were reported. In the dementia care unit, nominal unadjusted differences were observed for "made me feel calm" (P=.045; rank-biserial correlation r=0.571, large), "like" (P=.03; r=0.559, large), and "felt at peace" (P=.02; r=0.718, large); however, none remained statistically significant after Bonferroni correction. The short-term use of a nonverbal artificial intelligence communication robot did not measurably improve health-related QoL or well-being among staff in disaster-affected care facilities. Deployment appeared feasible and was not associated with reported adverse events, but efficacy as a mental health support intervention remains unproven. Exploratory acceptability and interaction signals may inform future adequately powered studies.
Fluopyram is a nematicide for use as an at-plant, in-furrow on cotton (among other crops) in the United States. Trials (33 total) were conducted in cotton fields with and without fluopyram applied at-plant, in-furrow in Meloidogyne incognita and Rotylenchulus reniformis infested fields. Root galling caused by M. incognita was reduced on average by 24% with fluopyram. The amount of rain for the first rain after planting did affect fluopyram activity, with 1, 2, and 3 cm rain predicted to reduce root galling by 15, 26, and 39%, respectively. On average, cotton lint yield in M. incognita tests was 7% higher with fluopyram (1,270 kg lint/ha) compared to no nematicide (1,185 kg lint/ha). Fluopyram did not significantly affect R. reniformis density or cotton lint yield in R. reniformis tests. Lint yield increased as the number of days until the first rain increased, and fluopyram-treated plots yielded more than no nematicide as days until the first rain increased. Lint yield decreased in R. reniformis fields as average air temperature increased (for the first 14 days), but the warmer the temperature, the bigger the difference between fluopyram (higher yields) than no nematicide (lower yields). There is strong evidence, even in a water-limited environment like west Texas, that significant early rains will improve fluopyram reduction of M. incognita galling and increase yield. The impact of fluopyram with R. reniformis is not as clear and trended opposite for beneficial environmental conditions compared to M. incognita.
Air pollution remains a pressing concern in urban India, affecting human wellbeing and ecosystem sustainability. This investigation explores the spatial and temporal variations in 10 μm particulate matter (PM10), nitric oxides (NOX), and sulfur dioxide (SO2) air pollution in Navi Mumbai, India, from 2014-2023. Data from 23 monitoring points were analyzed using geographic information systems-based methods, including inverse distance weighting and weighted overlay analysis, to generate a comprehensive pollution index. Findings indicate high seasonal variation, with elevated PM10 and NOX levels during premonsoon and winter due to traffic, industrial activity, and unfavorable meteorological conditions. Monsoon rains significantly reduced pollutant levels. Industrial hotspots, particularly in Taloja and Kalamboli, and traffic-heavy corridors, such as Vashi and Nerul, remained persistent pollution sources. A noticeable drop in pollutant concentrations in 2020 coincided with the COVID-19 lockdown, although levels surged in subsequent years. The weighted overlay analysis proved effective in identifying pollution hotspots and offering a comprehensive understanding of air quality risks. Global comparisons highlight the specific challenges of coastal satellite cities, where industrial and harbor emissions contribute to seasonal smog. This study emphasizes the need for targeted emission controls and urban planning interventions to improve air quality and sustainability in rapidly growing regions.
Climate change increasingly threatens public health in West Africa, with pregnant women and young children particularly vulnerable. Despite Nigeria's high exposure to climate risks, epidemiological evidence linking temperature and rainfall to maternal and child health remains limited. This study addresses this gap using nationally representative data. We analysed data from the 2024 Nigeria Demographic and Health Survey, including 27,783 mother-child pairs. Climate exposures, i.e., daytime land surface temperature (°C) and annual rainfall (mm), were derived from the 2020 Nigeria Geospatial Covariates dataset and linked to 2024 DHS cluster geolocations. Child health outcomes included stunting, wasting, underweight, and fever. Maternal outcomes included anaemia, postpartum distress, and a composite healthcare access index. Multilevel mixed-effects regression models with survey weights were applied. Higher temperatures were associated with increased odds of stunting (aOR 1.12, 95% CI 1.04-1.22), wasting (aOR 1.15, 95% CI 1.02-1.29), underweight (aOR 1.12, 95% CI 1.04-1.21), and fever (aOR 1.08, 95% CI 1.01-1.16) in children. Among mothers, higher temperatures were linked to greater postpartum distress (β = 0.03, p < 0.05) and reduced healthcare access (β = -0.07, p < 0.01), but not anaemia. A negative interaction between temperature and rainfall suggested attenuation of heat effects on underweight and healthcare access in wetter areas. Poverty and low maternal education amplified risks. Higher temperatures are associated with poorer maternal and child health outcomes in Nigeria, particularly among socioeconomically disadvantaged groups. Integrating climate adaptation into maternal and child health programmes is essential, especially in high-risk regions.
Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor arising from the gastrointestinal tract. Accurate pathological diagnosis and appropriate treatment for this malignancy require a multidisciplinary approach. In consideration of the differences in clinical practice between Asian and Western countries, the Asian Consensus Guidelines for the Diagnosis and Management of GISTs were published in 2016 by multidisciplinary experts in Asian countries (Japan, Korea, China, and Taiwan). Given the accumulation of new evidence since the previous publication, a multidisciplinary expert panel consisting of pathologists, surgical oncologists, and medical oncologists revised the Asian consensus guidelines. This narrative review provides updated consensus recommendations reflecting available evidence, expert opinion, and current clinical practice for the diagnosis and management of GIST in Asian countries.
High-quality UAV-based aerial imagery is essential for reliable autonomous power line inspection. However, adverse weather conditions, such as haze, rain and snow, often degrade image clarity and hinder downstream analysis. This paper presents a Multi-scale Fourier Network (MFNET), an efficient all-in-one restoration framework designed to handle diverse weather degradations. The architecture integrates two primary modules: the Dual-branch Fourier Transform Block (DFTB) and the Multi-scale Structure (MSS). Specifically, the DFTB facilitates global feature extraction by independently calibrating amplitude and phase components in the frequency domain, optimizing the balance between representation capacity and computational overhead. Simultaneously, the MSS employs a cross-attention mechanism to fuse multi-scale features, enabling the model to capture hierarchical information across varying degradation levels. This integration effectively decouples entangled features inherent in unified restoration tasks. Quantitative and qualitative evaluations on multiple benchmarks demonstrate that MFNET achieves state-of-the-art performance and significantly enhances the robustness of power line instance segmentation.
Bangladesh's coastal socio-ecological systems are increasingly threatened by climate hazards and human pressures, yet system-specific risk assessments remain limited. This study applies an adapted Global Delta Risk Index to evaluate risks across five systems, including irrigated agriculture, rain-fed agriculture, freshwater prawn farming, saltwater shrimp farming, and mangrove-dependent livelihoods. Using 48 quantitative and qualitative indicators, we employ a mixed-methods approach combining spatial datasets, socioeconomic statistics, interviews, and focus group discussions to construct composite indices of exposure, susceptibility, adaptive capacity, and ecosystem condition. Results show that mangrove-dependent systems exhibit the highest risk due to extreme hazard exposure, weak adaptive capacity, and ecosystem degradation, while shrimp-based systems show comparatively lower risk. All systems are affected by freshwater scarcity, salinity, and pollution. Findings highlight cross-system trade-offs and governance challenges. Co-developed stewardship strategies with stakeholders emphasize community empowerment, institutional strengthening, resilient agroecosystems, hybrid infrastructure, and integrated resource management.
Rivers are dynamic geomorphological systems that frequently alter their courses due to erosion, sediment deposition, channel migration, and flooding. Although such changes are normal, a sudden and major change like the diversion of the Kosi River in Bihar in 2008 can cause disastrous flooding, displacement and heavy land loss. The conventional methods are a poor fit because manual interpretation of satellites and hydrological modelling is time-consuming and has limited spatial-temporal resolution and lacks predictability. This study utilizes multi-source databases to present an AI-based Geo-Informatics framework for river course change prediction and disaster risk mitigation. Other than satellite imagery the data also includes hydrology, rain and soil data. The suggested hybrid architecture aims to jointly model the spatial river morphology and the evolution of the spatial pattern over time through the use of machine learning models (e.g. Random Forest, Gradient Boosting) and deep learning components (CNN, U-Net, LSTM/ConvLSTM). The framework has the ability to create predictive geospatial risk maps, forecasts of river migration over time, and interactive visualization products that support disaster preparedness and sustainable usage of water resources. Overall, the results demonstrate the potential of AI-driven Geo-Informatics to transform river monitoring from reactive assessment to proactive prediction, contributing to resilience building in accordance with the UN Sendai Framework (2015-2030) and the Sustainable Development Goals on climate action and water management.
The taiga tick Ixodes persulcatus (Schulze, 1930) (Acari, Ixodidae), widespread in the northern Palearctic, is the vector of dangerous human pathogens including tick-borne encephalitis virus. Knowledge of its ecology is important for minimising tick bite exposure risk. We studied the diel activity of male and female I. persulcatus in the north of the range (Karelia, Russian Federation) and tested its relationship with environmental factors including temperature, relative humidity and light intensity. The field experiment was conducted in May-June 2024. Cages with unfed adult ticks (males and females) were placed in three different biotopes, and their activity was assessed every two hours. Tick activity could be described by a bimodal curve, with a peak in the morning and in the evening. Diel activity rhythms were somewhat different in male and female ticks, with females questing longer than males. Relative humidity was the key determinant of questing duration. Weather changes accompanied by decreasing atmospheric pressure, as well as rain, reduced tick activity. The greatest tick bite exposure risk in Karelia is observed from mid-May to early June, when the temperature ranges from 10 °C to 20 °C and relative humidity exceeds 70%.
To analyze the clinical characteristics and combination medication patterns of Compound Jinqiancao Granules for prostatitis in a real-world setting, and to inform the rational and safe clinical use of this preparation. This study comprehensively analyzed electronic medical record (EMR) data from 1,922 patients who received Compound Jinqiancao Granules. The data were extracted from the Hospital Information System (HIS) databases of 20 tertiary hospitals nationwide. Using analytical methods such as association rule mining and complex network analysis, along with algorithms like Apriori and Louvain, we systematically investigated the clinical medication characteristics and combination medication patterns. Most patients were aged 46-65 years. Admissions were most frequent in the Urology Department, with the highest admission volume occurring during the Rain Water solar term. Hospital stays typically ranged from 8 to 14 days. Prostatitis was the most common primary biomedical diagnosis. Frequently combined biomedical agents included finasteride, diuretics, hemostatics, antibiotics, and glucocorticoids. Concomitant Chinese proprietary preparations followed syndrome differentiation principles: patients with Dampness-Heat Toxin Accumulation often received Compound Kushen Injection, whereas those with Qi Stagnation and Blood Stasis commonly received Yuanhu Zhitong Dropping Pills. This study describes the real-world clinical use and medication patterns of Compound Jinqiancao Granules for prostatitis. Dosage, duration, and administration generally aligned with the drug labeling, and co-medication reflected routine clinical practice for prostatitis. Given the absence of a control group and adjustment for confounding factors, no causal inferences regarding efficacy or therapeutic effectiveness can be drawn. Keywords.
The contamination of forest ecosystems by radiocesium (137Cs) released from the Fukushima Dai-ichi Nuclear Power Plant accident has seriously damaged forestry activities in Fukushima. Previous field-based descriptive studies have reported that a considerable proportion of 137Cs remains in the soil O horizon, where the presence of various types of organic matter at different stages of decomposition contributes to both strong retention of 137Cs by decomposing litter and a heterogeneous spatial distribution. To investigate the dynamics and mobility of 137Cs during litter decomposition and to interpret our observations, we conducted litterbag experiments over approximately two years using fallen leaves of konara oak. Litterbags were deployed either suspended above the ground or placed on the forest floor. Leaching experiments were also performed on retrieved litterbag samples. In the suspended litterbags, we observed a decline of 137Cs, mainly in the water-soluble form, likely due to washout associated with rain/snowfall and subsequent spring snowmelt. In contrast, the incorporation of soil particles by forest-floor litterbags resulted in an increase of 137Cs relative to initial conditions, mainly in forms that were scarcely extractable, even by ammonium acetate. The large variability of residual 137Cs revealed by comparisons with previous litterbag and equivalent experimental studies highlighted the difficulty of predicting 137Cs activity concentrations and distributions on the forest floor. Furthermore, comparison with potassium (K), a competing alkali metal, demonstrated that 137Cs is less mobile than K, regardless of soil particle incorporation. This low mobility likely contributes to the prolonged, strong retention of 137Cs in the O horizon.
Direct air capture (DAC) is promoted as an essential climate solution, yet thermodynamic and energy constraints make deployment at climate-relevant scales deeply problematic. Current DAC systems require 1,500-3,000 kWh per tonne of CO2 captured and stored-one to two orders of magnitude higher than point-source capture and far beyond what global clean-energy availability can support. Meeting even the lower bound of the IPCC's mid-century carbon-removal targets via DAC alone would demand more than half of today's global electricity, diverting clean energy away from direct decarbonization. Overreliance on DAC thus risks institutionalizing energy inefficiency and delaying essential emissions cuts. Historical precedents, from acid rain mitigation to ozone recovery, demonstrate that pollution is best addressed at its source. We propose a strategic realignment that prioritizes emissions prevention, strengthens natural carbon uptake through photosynthesis, and deploys proven, energy-positive, negative-emission pathways, such as biochar, that leverage rather than oppose fundamental thermodynamic constraints.
Frontline systemic treatment selection for advanced leiomyosarcoma (LMS) remains variable in routine practice. Gemcitabine-based regimens are frequently used despite randomized evidence of non-superiority compared with doxorubicin-based regimens and greater complexity in treatment delivery. This multi-centre retrospective cohort study included advanced/metastatic LMS patients treated with frontline doxorubicin- or gemcitabine-based chemotherapy at four Canadian sarcoma centres (2010-2022). The primary endpoint was overall survival (OS). Secondary endpoints were time to second-line treatment (T2T) and number of subsequent systemic treatment lines. Subgroup analyses were conducted by primary tumor site (uterine vs. non-uterine LMS). Among 217 patients, median OS was 17.1 months (95% CI 14.8-21.1). OS did not differ between frontline doxorubicin- and gemcitabine-based regimens (17.8 vs. 16.1 months; p = 0.16), nor did T2T (5.8 vs. 6.3 months; p = 0.77). Non-uterine primary site and surgery following frontline systemic treatment were independently associated with improved OS. Median OS was longer for non-uterine versus uterine LMS (23 vs. 12.8 months; p < 0.001). In this large real-world LMS cohort, outcomes were not influenced by frontline chemotherapy choice. These findings support prioritizing toxicity, quality of life, cost, and treatment delivery burden when selecting frontline therapy, particularly where doxorubicin/trabectedin use is limited.
Terrestrial pollution discharge induces spatiotemporal heterogeneity in riverine nitrogen dynamics. Accurately identifying sensitive temporal phases, spatial hotspots, and primary pollution sources is essential for effective riverine environmental management. This study developed an identification framework to delineate spatiotemporal pollution hotspots and source contributions in the Dai River basin by coupling intensive monitoring with the export coefficient method. The results indicated that the flood season was a critical period for increased total nitrogen (TN) pollution. Approximately 60% of the annual rainfall occurred in July and August, resulting in a 2.5-fold increase in TN concentrations in August compared to June. Moreover, rainfall events exceeding 100 mm/day led to 2- to 3.5-fold increases in TN concentrations relative to pre-event levels, with maximum increases surpassing 14-fold. Automatic monitoring data revealed a decreasing trend in TN concentrations from upstream to downstream. Intensive monitoring identified the Yu, Xidai, and Sha rivers at the headwaters as pollution hotspot zones, characterized by higher nitrogen concentrations and larger basin areas. The export coefficient model results showed that urban domestic wastewater and livestock farming were the dominant sources, accounting for 35.87% and 35.55% of total discharges, respectively. Yuguan town exported 33.34 t of nitrogen, contributing 47% of the total discharges. Based on hotspot identification, we proposed differentiated and integrated management measures for each town.
No large, randomized trials have compared three or more tyrosine kinase inhibitors (TKIs) in a single chronic myeloid leukemia (CML) cohort. Most studies are two-arm comparisons versus imatinib, or rely on indirect methods. A retrospective cohort study was conducted of 349 patients with chronic- and accelerated-phase CML treated between 2007 and 2023 at Cleveland Clinic centers in Northeast Ohio. Overall survival (OS), event-free survival (EFS), 12-month therapeutic milestone achievement, and BCR-ABL1 transcript decline velocity across first-, second-, and third-line TKIs were compared. Baseline demographics, comorbidities, cytogenetics, and hematologic parameters were collected. Multivariable regression and time-dependent Cox models were adjusted for confounders and varying therapy initiation times. First-line TKIs included imatinib (53%), dasatinib (30%), nilotinib (15%), and bosutinib (2%). Median age ranged from 52 to 60 years, most patients were White, and high-risk cytogenetics were rare. Five-year OS ranged from 78% for dasatinib to 90% for nilotinib, with nilotinib associated with improved OS (hazard ratio [HR], 0.43) and EFS (HR, 0.48) and the fastest BCR-ABL1 decline (β = -8.3%) versus imatinib. In second- (n = 181) and third-line therapy (n = 91), no significant differences in outcomes were observed. Across all lines, time-dependent modeling showed improved EFS only for nilotinib (HR, 0.61). Unadjusted OS was higher in patients starting treatment in 2007-2010 versus later periods but differences disappeared after adjustment. Real-world data indicate that imatinib achieves comparable response rates to newer TKIs, with durable survival. Nilotinib consistently shows faster BCR-ABL1 decline and improved EFS overall, consistent with prior network meta-analyses. Lack of improvement in OS over time suggests the need to investigate factors influencing long-term outcomes in CML.
To evaluate treatment and disease burden among patients with polycythemia vera (PV) receiving the current standard of care (SoC) in the US. This retrospective study utilized MarketScan® Commercial/Medicare Databases to identify patients with PV diagnosis and treatment claims between 1/1/2011-12/31/2022 (index date=earliest PV treatment date), continuous enrollment (6 months pre-index and ≥12 months post-index), and no pre-index disease progression (myelofibrosis, acute myeloid leukemia, or myelodysplastic syndrome). Patients were categorized by thrombosis risk (high/low-risk) and 12-month post-index treatments: phlebotomy (PHL) only, hydroxyurea (HU) only, PHL+HU, or ruxolitinib/interferons (RUX/IFN). Outcomes included incident TE, iron deficiency anemia, disease progression, and PV-related symptoms. Hematocrit (HCT) control was evaluated in patients with ≥2 HCTs post-index (HCT analysis). All-cause costs during the 12 months post-TE were reported in patients with an incident TE and ≥12 months post-TE follow-up (TE analysis). Among 11,311 patients (51.8% high-risk; 48.2% low-risk; median follow-up ~2.8 years), 12-month post-index treatments included PHL only (75.0%), HU only (12.2%), PHL+HU (10.9%), and RUX/IFN (1.9%). Frequent PHL (≥3 PHL in 6 months or ≥5 PHL in 12 months post-index; 46.2% of PHL users) and high-dose HU (≥1000mg/day; 41.7% of HU users) were common. Within 12 months post-index, 50.9% of patients experienced burdensome treatment (frequent PHL, high-dose HU) and/or an incident TE. During the full follow-up, 15.3% experienced incident TE, 9.6% experienced incident iron deficiency anemia, 4.4% experienced disease progression, and 83.2% experienced symptoms. For the HCT analysis (N=1,268), 85.3% had uncontrolled HCTs≥45%, with 55.4% having HCTs≥50%. For the TE analysis (N=1,159), mean 12-month all-cause costs post-TE were $71,195, with 29.2% attributable to the index TE. Patients with PV have high treatment and disease burden with the current SoC. Current PV therapies (PHL, cytoreductive agents) do not consistently maintain HCT<45%, leaving patients at increased risk for life-threatening and costly TEs. Polycythemia vera (PV) is a chronic blood cancer characterized by the overproduction of red blood cells. Disease management aims to control red blood cell levels (ie, achieve and maintain hematocrit [HCT] <45%) to reduce the risk of thromboembolic events (TEs). However, real-world evidence suggests that disease control remains sub-optimal with the current standard of care (SoC). This study evaluated treatment profiles, clinical outcomes, HCT control, and TE–related healthcare costs among patients with PV receiving SoC in the United States. Using insurance claims data, 11,311 treated PV patients were identified between 2011–2022 and followed for an average of 3.6 years (median 2.8 years). Most patients (75%) were treated with phlebotomy (PHL) alone, and many required frequent PHL procedures (40%). Within the first 12 months, 51% experienced burdensome treatment (based on the receipt of frequent PHLs, high-dose HU, or both) and/or an incident TE. Over the full follow-up, about 15% of all patients had a TE, nearly 10% developed iron deficiency anemia, 4% progressed to more severe cancers, and 83% had at least one documented PV-related symptom. Among 1,268 patients with available HCT measurements, the majority (85%) had uncontrolled HCT with HCTs≥45%, with 55.4% having HCTs≥50%. Among 1,159 patients who experienced a TE, average healthcare costs during the year following the TE were $71,195, with approximately one-third of costs attributable to the initial TE. Overall, these findings indicate that current SoC for PV often do not adequately control HCT levels, leaving patients at increased risk of serious and costly cardiovascular complications.
Climate change has intensified the frequency and magnitude of extreme precipitation events on the Qinghai-Tibetan Plateau (QTP), profoundly altering phosphorus (P) cycling in alpine meadow ecosystems. As P is a key limiting nutrient in these systems, understanding how precipitation shifts govern plant P acquisition remains critical. However, previous studies have mainly focused on individual P-acquisition pathways, leaving the coordination among multiple P-acquisition strategies, soil P fractions, and plant P status under long-term altered precipitation poorly understood, particularly in P-limited QTP alpine meadows. Here, we conducted an 8-year field manipulation experiment involving five precipitation treatments: 90%, 50%, and 30% decrement (DP90, DP50, DP30), control (CK, ambient precipitation), and 50% increment (IP50). We quantified plant leaf and root P, soil P fractions, and key P-acquisition drivers (including acid phosphatase, carboxylates, arbuscular mycorrhizal fungi (AMF), and P-related bacteria) to elucidate the mechanisms underlying plant P acquisition under contrasting precipitation regimes. We found that both increased precipitation and extreme drought reduced soil available P and plant leaf P content, thereby exacerbating plant P limitation. Under increased precipitation, AMF primarily facilitated plant P acquisition, likely through the direct uptake of available soil P. Conversely, under decreased precipitation, AMF and acid phosphatase jointly mediated plant P acquisition, suggesting their potential role in mobilizing soil occluded P. Overall, this study highlights the importance of mycorrhizal fungi and phosphatase in regulating plant P acquisition under varying precipitation regimes on the QTP. Our findings provide new insights into the intricate feedback between plant P demand and soil P supply in alpine meadow ecosystems, clarifying the potential mechanisms of plant adaptation to global climate change.
Eight new genera, 28 new species, four epitypes, two lectotypes, and 21 interesting new host and / or geographical records are introduced in this study. New genera include: Amesomyces (based on Amesomyces atrobrunneus), Carteromyces (based on Carteromyces arctostaphyli), Scolecofusariella (based on Fusarium peltigerae), Nothoniesslia (based on Nothoniesslia solidaginis), Paraacanthostigma (based on Paraacanthostigma eucalypti), Paraphaeophleospora (based on Paraphaeophleospora tripteridis), Parapolyscytalum (based on Parapolyscytalum minutum) and Subverticillium (based on Subverticillium juncicola). New species include: Bisifusarium duo (from human cornea, India), Capronia parasitica (on Eutypella sorbi on branches of Sorbus aucuparia, Switzerland), Castanediella acericola on dead leaf of Acer cf. pseudoplatanus, Germany), Cladophialophora calamagrostidis (on culm of Calamagrostis arenaria, The Netherlands), Cladophialophora paramycetomatis (on culms of Elegia tectorum, South Africa), Cladophialophora yuccae (on dead leaf of Yucca sp., Germany), Cordana ligni (on dead wood, Germany), Curvularia moniliformis (on leaves of unidentified Poaceae, Chile), Davidhawksworthia rubi (on Rubus stems, Germany), Exophiala ligni (on dead wood, Germany), Fusarium aloetica (on symptomatic leaves of Aloe ferox, South Africa), Harzia cupressicola (on needles of Cupressus sp., The Netherlands), Hoehneliella falsiundulosetulata (on bark of woody host, Germany), Mjuua pseudoclavispora (in association with Fusarium paeoniae and a bacterium, on dead fruit of Alnus glutinosa, Germany), Monilinia yunnanensis (on fruit of Prunus persica, China), Niesslia goniomae (on leaf of Gonioma kamassi, South Africa), Nothoniesslia solidaginis (on dead stems of Solidago sp., Germany), Paraacanthostigma eucalypti (on bark of Eucalyptus globulus, Australia), Phaeococcomyces mesembryanthemi (on Mesembryanthemum schultzii, South Africa), Phialemonium parasulfureum (on algae, Germany), Phytophthora caput-medusae (from rhizosphere soil of Citrus × aurantium, Italy), Pleurophragmium fallopiae (on dead leaf Fallopia sp., Germany), Rhinotrichella carpini (on dead branches of Carpinus betulus, Ukraine), Stagonosporopsis citri (on peel of living fruit of Citrus latifolia, quarantine interception), Subverticillium juncicola (on culms of Juncus effusus, Netherlands), Veronaea parabrunnea (on Eutypella prunastri on twigs of Prunus spinosa, France), Veronaea parasiticola (on Euonymus europaeus, Germany), Verrucocladosporium mesembryanthemi (on Mesembryanthemum schultzii, South Africa). New combinations include: Amesomyces atrobrunneus (based on Chaetomium atrobrunneum), Amesomyces cymbiformis (based on Chaetomium cymbiforme), Amesomyces dreyfussii (based on Chaetomium dreyfussii), Amesomyces gelasinosporus (based on Chaetomium gelasinosporum), Amesomyces hispanicus (based on Amesia hispanica), Amesomyces khuzestanicus (based on Amesia khuzestanica), Amesomyces nigricolor (based on Chaetomium nigricolor), Amesomyces raii (based on Chaetomium raii), Carteromyces arctostaphyli (based on Carteria arctostaphyli), Carteromyces canariensis (based on Carteria canariensis), Capronia americana (based on Cadophora americana), Didymella conyzaphthora (based on Phoma conyzaphthora), Heterotruncatella watsoniae (based on Pestalotia watsoniae), Hoehneliella undulosetulata (based on Paramenisporopsis undulosetulata), Microascus stellatus (based on Humicola stellata), Neoceratosperma marasasii (based on Mycosphaerella marasasii), Paraphaeophleospora tripteridis (based on Septoria tripteridis), Parapolyscytalum minutum (based on Infundichalara minuta), Scolecofusariella peltigerae (based on Fusarium peltigerae) and Veronaea brunnea (based on Exophiala brunnea). Zygophiala is reduced to synonymy under Schizothyrium, Paramenisporopsis and Klebahnopycnis under Hoehneliella, and the descriptions of the order Comminutisporales and family Comminutisporaceae are emended. Citation: Crous PW, Akulov A, Alfenas AC, Alfenas RF, Aloi F, Balashov S, Barreto RW, Bensch K, Cacciola SO, Cantillo T, Castillo R, Conti Taguali S, Czachura P, da Silva NF, Delgado MA, Denman S, de Silva NI, de Vries RP, Figge M, Guarnaccia V, Guterres DS, Hongsanan S, Horta Jung M, Houbraken JA, Hülsewig T, Jung T, Jurjević Ž, La Spada F, Madrid H, Mombert A, Osieck ER, Pane A, Parlascino R, Pereira CM, Piątek M, Piattino V, Riolo M, Sandoval-Denis M, Scanu B, Starink-Willemse M, Stryjak-Bogacka M, Tennakoon DS, van Ingen-Buijs VA, van Iperen AL, Verkley GJM, Lamprecht SC, Wang XW, Braun U, Wingfield MJ, Groenewald JZ (2026). New and Interesting Fungi. 8. Persoonia 56: 457-547. doi: 10.3114/persoonia.2026.56.08.
Nutrient dynamics in bay-estuary systems are shaped by the complex interplay between hydrology, biogeochemistry and hydrodynamics. Riverine nutrient loads are widely recognized as primary drivers of nutrients, yet how these loads translate into internal nutrient dynamics across varying hydrologic and salinity regimes remains poorly resolved. We addressed this gap by investigating long-term (19 years) dynamics of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) loads and their role in governing nutrient variability under varying freshwater inflow and salinity regimes in Apalachicola Bay, Florida. We applied a series of generalized additive models (GAMs) that incrementally accounted for freshwater inflow, nutrient load and estuarine salinity alongside Neural Additive Models (NAMs) with concurvity regularization to account for the correlation among these drivers. Our results showed that riverine nutrient load is the dominant driver of estuarine nutrient dynamics. Although freshwater inflow and riverine nutrient loads are often used interchangeably in past studies, we show that "inflow as a proxy for nutrient load" is an oversimplification, particularly under low-flow conditions. We further showed that estuarine DIN exhibits more predictable, seasonally coupled variability with freshwater inflow and salinity. In contrast, episodic and localized biogeochemical processes make DIP dynamics less predictable and obscure the influence of salinity on DIP at monthly timescales. However, we showed that the dependence of estuarine DIN concentrations on the riverine load is strongly structured by salinity regimes, with fresher conditions favoring this dependence. Increasing variability in sea level and freshwater supply under climate change can make these dynamics less predictable.
To address the measurement inaccuracies of weighing-type rain gauges caused by environmental disturbances such as vibration, temperature drift, and creep, this study aims to develop a robust error modeling and compensation framework adaptable to complex conditions. A nonlinear error model was constructed by analyzing multi-source disturbance factors and incorporating both linear and nonlinear temperature terms. A BP neural network was employed to compensate for complex error patterns, and several intelligent optimization algorithms (a genetic Algorithm (GA), a particle swarm algorithm (PSO), and a GOOSE algorithm (GOOSE)) were used to enhance training performance. An improved adaptive GOOSE algorithm (ADGOOSE) was further proposed to optimize the BP network by integrating dynamic control coefficients and perturbation-based restart strategies. Experiments under various rainfall intensities and temperatures demonstrated that the ADGOOSE-BP model outperformed traditional filtering and other optimization methods, achieving the lowest RMSE of 0.0494 and the highest R2 of 0.9835. The proposed method effectively models and compensates for environmentally induced errors in weighing rain gauges, demonstrating strong potential as a high-precision, adaptive compensation framework that provides a solid foundation for future field-deployable hydrological monitoring systems.