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Palm oil, an indispensable global commodity, plays a crucial role in Thailand's economy (Maluin et al 2020). This versatile product, found in everything from food to biodiesel and industrial applications (Kamil and Omar 2017, Chiarawipa et al 2020), cements Thailand's position as the third-largest producer in the world. The industry is particularly vital in the southern regions, where most plantations are located (Dallinger 2011), providing employment opportunities in rural areas and bolstering the nation's GDP and foreign exchange earnings through exports. However, oil palm productivity hinges on climatic conditions, with precipitation patterns being especially influential. Both local climate and remote forces like El Niño-Southern Oscillation (ENSO) significantly impact production. In neighboring Malaysia, research by Kamil and Omar (2017) has uncovered the consequential role of ENSO events on precipitation and, subsequently, palm oil productivity.

We investigate historical and projected precipitation in Tanzania using observational and climate model data. Precipitation in Tanzania is highly variable in both space and time due to topographical variations, coastal influences, and the presence of lakes. Annual and seasonal precipitation trend analyses from 1961 to 2016 show maximum rainfall decline in Tanzania during the long rainy season in the fall (March–May), and an increasing precipitation trend in northwestern Tanzania during the short rainy season in the spring (September–November). Empirical orthogonal function (EOF) analysis applied to Tanzania’s precipitation patterns shows a stronger correlation with warmer temperatures in the western Indian Ocean than with the eastern-central Pacific Ocean. Years with decreasing precipitation in Tanzania appear to correspond with increasing sea surface temperatures (SST) in the Indian Ocean, suggesting that the Indian Ocean Dipole (IOD) may have a greater effect on rainfall variability in Tanzania than the El Niño-Southern Oscillation (ENSO) does. Overall, the climate model ensemble projects increasing precipitation trend in Tanzania that is opposite with the historical decrease in precipitation. This observed drying trend also contradicts a slightly increasing precipitation trend from climate models for the same historical time period, reflecting challenges faced by modern climate models in representing Tanzania’s precipitation.

Therapies for type 2 diabetes primarily target hyperglycemia; however, complications are also triggered by advanced glycation end products (AGEs). We hypothesize that the anti-diabetic efficacy of insulin-like growth factor-binding protein 4 (IGFBP4) is enhanced when it assembles with a specific amino acid compound-2 (AAC2) into nanostructures. Their effects were examined in vitro and in ob/ob mice treated for 30 days with the AAC2–IGFBP4 complex or its individual components. IGFBP4-mediated glucose uptake in human and mouse preadipocytes was enhanced by complex formation with AAC2. This complex was confirmed by Fourier-transform mid-infrared spectroscopy, electrophoresis, and AFM. In ob/ob mice, the complex prolonged IGFBP4 circulation and amplified the effects of the individual components, resulting in reduced hyperphagia, body weight, and hyperinsulinemia, along with improved insulin sensitivity and glucose tolerance. Notably, HbA1c levels remained at 5.9% in the complex-treated group compared to > 7% in others, with lower plasma AGE levels than in AAC2-treated mice. Transcriptomic and pathway analyses revealed that the complex upregulated genes promoting the fenestrated phenotype of liver sinusoidal endothelial cells (LSECs), facilitating AGE and waste clearance, whereas free AAC2 inhibited this process. We propose a ‘scavenger-input’ hypothesis in which free AAC2 inhibits, while the AAC2–IGFBP4 complex activates fenestrated phenotype and waste‑clearance capacity in liver sinusoidal endothelial cells (LSECs). Based on our results, AAC2 could serve as an adaptable and inherently therapeutic nanofiber modality that enhances the functional properties of bound proteins, offering multidimensional treatment possibilities for diabetes and other degenerative disorders. Graphical Abstract

Wildfires are increasingly threatening Nepal, particularly during the dry pre-monsoon months (March-May), leading to severe ecological impacts and disruptions to local communities. To improve wildfire prediction and preparedness, this study evaluated four advanced machine learning algorithms—Random Forest, Radial Basis Function Neural Network, Artificial Neural Network, and Support Vector Machine—using comprehensive dataset (2001–2023) of meteorological, topographical, anthropogenic, locational, and vegetation variables. The Random Forest (RF) model outperformed others, achieving the highest accuracy (88.6%) and predictive reliability (AUC: 0.96). Notably, vapor pressure deficit emerged as the strongest predictor, contrasting previous studies where precipitation was often considered dominant. Utilizing the robust RF model, a high resolution (1-km) wildfire risk map identified 11.1% of Nepal, encompassing 12 districts and 48 municipalities primarily in the southwestern region, as very high-risk areas. By integrating daily meteorological data into wildfire predictions, this research provides an innovative framework that enhances risk management strategies, offering actionable insights for decision-makers and supporting resilience-building efforts in fire prone regions.

This study describes a downslope wind storm event observed over the Medicine Bow range (Wyoming, USA) on 11 January 2013. The University of Wyoming King Air (UWKA) made four along-wind passes over a five-hour period over the mountain of interest. These passes were recognized as among the most turbulent ones encountered in many years by crew members. The MacCready turbulence meter aboard the UWKA measured moderate to severe turbulence conditions on each pass in the lee of the mountain range, with eddy dissipation rate values over 0.5 m2/3 s-1. Three rawinsondes were released from an upstream location at different times. This event is simulated using the non-hydrostatic Weather Research and Forecast (WRF) model at an inner- domain resolution of 1 km. The model produces a downslope wind storm, notwithstanding some discrepancies between model and rawinsonde data in terms of upstream atmospheric conditions. Airborne Wyoming Cloud Radar (WCR) vertical-plane Doppler velocity data from two beams, one pointing to the nadir and one pointing slant forward, are synthesized to obtain a two-dimensional velocity field in the vertical plane below flight level. This synthesis reveals the fine-scale details of an orographic wave breaking event, including strong, persistent downslope acceleration, a strong leeside updraft (up to 10 m*s-1) flanked by counter-rotating vortices, and deep turbulence, extending well above flight level. The analysis of WCR-derived cross-mountain flow in 19 winter storms over the same mountain reveals that cross-mountain flow acceleration and downslope wind formation are difficult to predict from upstream wind and stability profiles.

The rising incidence of dermatophytosis, marked by atypical presentations and increasing resistance to treatment, has posed significant challenges to effective clinical management, particularly in regions like Nepal, where localized guidance is limited. In response, a panel of dermatology experts in Nepal conducted a structured literature review and employed a modified Delphi process to develop updated consensus recommendations. These guidelines aim to assist clinicians in making informed decisions regarding diagnosis and treatment, with an emphasis on improving patient outcomes. The consensus highlights key treatment principles, including the potential role of combination therapy and considerations for both localized and more complex presentations of the disease.

Climate change has appeared as a major issue in recent years, and its impacts are seen multi-dimensionally. The local people are the key eyewitnesses of climate change, although the discourse is disciplinary, geographic, and gender biased. In this context, this paper documents the perceptions and narratives of the Tamang, an Indigenous people, who live on the outskirts of the Kathmandu Valley. This is an ethnographic study and applied quantitative and qualitative data. The data of the study were gathered using triangulation methods, i.e., household questionnaire survey (HQS), key informant interview (KII), and focus group discussion (FGD). A total of 94 HQS, nine KII, and three FGD were carried out in 2018 in three sample sites in the outskirts of the valley. The station-based observed climatic data from 1969 to 2022 were collected from the Department of Hydrology and Meteorology. The observed data shows increasing annual rainfall and temperature in Kathmandu; however, the rate of temperature increase is much larger. Seasonal precipitation shows decreasing rainfall in post-monsoon, which enhances the winter drought. The Tamang are the key eyewitness of the changes in climate and this knowledge is inbuilt with their memories which are closely bound to the place. Hence, the life history of elderly people can be an appropriate way of understanding the micro-climatic changes in the local context, which largely failed or ignored to document in scientific or macro-level assessments.

Airborne vertical-plane dual-Doppler cloud radar data, collected on wind-parallel flight legs over a mountain in Wyoming during 16 winter storms, are used to analyze the growth, transport, and sedimentation of snow. In all storms the wind is rather strong, such that the flow is unblocked. The sampled clouds are mixed phase, shallow, and generally produce snowfall over the mountain only. The 2D scatterers’ mean motion in the vertical along-track plane below flight level is synthesized using one radar antenna pointing to nadir, and one 30° forward of nadir. This yields instantaneous cross-mountain hydrometeor streamlines. The dynamics of the orographic flow dominate the precipitation patterns across the mountain. Three patterns are distinguished: the first two contain small convective cells, either boundary layer (BL) convection or elevated convection, the latter likely due to the release of potential instability in orographically lifted air. In these patterns the cross-mountain flow is relatively undisturbed. Precipitation from BL convection falls mostly on the windward side but precipitation from elevated convection may fall mostly in the lee. The third pattern is marked by more stratified flow, often with vertically propagating mountain waves, and with strong, plunging flow in the lee, resulting in rapid clearing of the storm across the crest and occasionally a hydraulic jump. In this case, most snow tends to fall upwind of the crest, although a shallow, sublimating snow “foot” is often seen over the leeward slopes.

Measured 94-GHz reflectivity in midlevel, stratiform ice clouds was compared with reflectivity calculated from size distributions determined with a particle imaging probe. The radar and the particle probe were carried on the same aircraft, the Wyoming King Air, ensuring close spatial correspondence between the two measurements. Good overall agreement was found within the range from −18 to +16 dBZ, but there is an important degree of scatter in the results. Two different assumptions about particle density led to calculated values that bracket the observations. The agreement found for reflectivity supports the use of the data for establishing relationships between the measured reflectivity and ice water content and between precipitation rate and reflectivity. The resulting equation for ice water content (IWC vsZ) agrees with the results of Liu and Illingworth within a factor of 2 over the range of overlap between the two datasets. The equation here reported for precipitation rate (PR vsZ) has a shallower slope in the power-law relationship than that reported by Matrosov as a consequence of sampling particles of greater densities. Because the radar and the particle probe were collocated on the same platform, errors arising from differences in sampling locations and volumes were minimized. Therefore it is concluded that the roughly factor-of-10 spread in IWC and in PR for givenZis, primarily, a result of variations in ice crystal shape and density. Retrievals of IWC and PR from cloud radar data can be expected to have that level of uncertainty.

Natural small-scale microphysical and dynamical mechanisms are identified in a winter orographic snowstorm over the Sierra Madre Range of Wyoming during an intensive observational period (IOP) from the AgI Seeding Cloud Impact Investigation (ASCII; January–March 2012). A suite of high-resolution radars, including a ground-based scanning X-band dual-polarization Doppler on Wheels radar, vertically pointing Ka-band Micro Rain Radar (MRR), and airborne W-band Wyoming Cloud Radar (WCR), and additional in situ and remote sensing instruments are used in the analysis. The analysis focuses on a deep postfrontal period on 16 January 2012 (IOP2) when clouds extended throughout the troposphere and cloud liquid water was absent following the passage of a baroclinic front. A turbulent shear layer was observed in this postfrontal environment that was created by a midlevel cross-barrier jet riding over a decoupled Arctic air mass that extended above mountaintop. MRR and WCR observations indicate additional regions of turbulence aloft that favor rapid particle growth at upper levels of the cloud. Plunging flow in the lee of the Sierra Madre was also observed during this case, which caused sublimation of snow up to 20 km downwind. The multi-instrument analysis in this paper suggests that 1) shear-induced turbulent overturning cells do exist over cold continental mountain ranges like the Sierra Madre, 2) the presence of cross-barrier jets favors these turbulent shear zones, 3) this turbulence is a key mechanism in enhancing snow growth, and 4) snow growth enhanced by turbulence primarily occurs through deposition and aggregation in these cold (<−15°C) postfrontal continental environments.