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Abstract Maple syrup urine disease (MSUD) is a disorder of branched‐chain amino acid metabolism caused by a defect in the branched‐chain α‐ketoacid dehydrogenase (BCKD) complex (OMIM #248600). The hallmark presentation is encephalopathic crisis in neonates, but can also present with metabolic decompensation, developmental delays, and feeding difficulties. Biochemical evidence for MSUD includes elevated branched‐chain amino acids (BCAA) and the pathognomonic presence of alloisoleucine. The BCKD complex contains several subunits associated with autosomal recessive MSUD, while its regulatory proteins have less well‐defined disease associations. We report on two families with the same BCKDK variant (c.1115C>G (p.Thr372Arg)). Probands were detected on newborn screening and demonstrated biochemical evidence of MSUD. The variant was identified in reportedly asymptomatic parents and additional family members who had elevated BCAA and alloisoleucine, following an autosomal dominant pattern of inheritance. To better define the functional effect of the variant on the kinase, we completed molecular modeling using sequence‐based (2D), structural‐based (3D), and dynamic‐based (4D) analyses. The BCKDK variant modeling indicated a gain‐of‐function which leads to impaired BCAA catabolism consistent with the biochemical evidence in this cohort. Combining the evidence gained from molecular modeling with the absence of metabolic decompensation in our patients and several adult family members, despite encountering stressors typically problematic in classic MSUD, we suggest that heterozygous gain‐of‐function variants in BCKDK may represent a novel biochemical phenotype of MSUD with a benign clinical course.

Biodiversity conservation can entail socially controversial management interventions such as herbicide applications to eradicate aquatic invasive plants from highly productive native ecosystems. This study used a case example of the successful eradication of Alaska’s first aquatic invasive plant, Elodea spp. from two lakes surrounded by waterfront homes with the objective to measure perceived changes in the use of the lakes by local residents. A household survey was conducted with homeowners before and after the application of herbicides to measure public attitudes and human behavior changes related to the use of herbicide. We found fears and subjective expectations of survey respondents did not match the eventually collected objective scientific data on water quality. Also, we measured a significant negative change in lake-related activities over the duration of the management intervention that is smaller compared to the reduced activity level lake residents anticipated before the application of herbicide. Swimming and the consumption of fish caught in the lakes were the most negatively affected ecosystem services. The study showed the indirect and often hidden effects of management interventions on human risk perceptions that can translate to unintended human behavior changes and a reduction in related ecosystem service use. To account for complex social dimensions of controversial resource management interventions, resource managers require information on broad public value trade-offs and perceived risk beyond that which public meetings can provide. In this context, household surveys before and after resource management interventions can offer insights few other approaches can. The potential for more advanced outreach methods is discussed.

Permafrost-agroecosystems include all cultivation and pastoral activities in areas underlain by permafrost. These systems support local livelihoods and food production and are rarely considered in global agricultural studies but may become more relevant as climate change is increasing opportunities for food production in high latitude and mountainous areas. The exact locations and amount of agricultural production in areas containing permafrost are currently unknown, therefore we provide an overview of countries where both permafrost and agricultural activities are present. We highlight the socioecological diversity and complexities of permafrost-agroecosystems through seven case studies: (1) crop cultivation in Alaska, USA; (2) Indigenous food systems and crop cultivation in the Northwest Territories, Canada; (3) horse and cattle husbandry and Indigenous hay production in the Sakha Republic, Russia; (4) mobile pastoralism and husbandry in Mongolia; (5) yak pastoralism in the Central Himalaya, Nepal; (6) berry picking and reindeer herding in northern Fennoscandia; and (7) reindeer herding in northwest Russia. We discuss regional knowledge gaps associated with permafrost and make recommendations to policy makers and land users for adapting to changing permafrost environments. A better understanding of permafrost-agroecosystems is needed to help sustainably manage and develop these systems considering rapidly changing climate, environments, economies, and industries.

IntroductionVentricular unloading during prolonged bed rest, mechanical circulatory support or microgravity has repeatedly been linked to potentially life-threatening arrhythmias. It is unresolved, whether this arrhythmic phenotype is caused by the reduction in cardiac workload or rather by underlying diseases or external stimuli. We hypothesized that the reduction in cardiac workload alone is sufficient to impair ventricular repolarization and to induce arrhythmias in hearts.MethodsRat hearts were unloaded using the heterotopic heart transplantation. The ECG of unloaded and of control hearts were telemetrically recorded over 56 days resulting in >5 × 106 cardiac cycles in each heart. Long-term electrical remodeling was analyzed using a novel semi-automatic arrhythmia detection algorithm.Results56 days of unloading reduced left ventricular weight by approximately 50%. While unloading did not affect average HRs, it markedly prolonged the QT interval by approximately 66% and induced a median tenfold increase in the incidence of ventricular arrhythmias in comparison to control hearts.ConclusionThe current study provides direct evidence that the previously reported hypertrophic phenotype of repolarization during cardiac unloading translates into an impaired ventricular repolarization and ventricular arrhythmias in vivo. This supports the concept that the reduction in cardiac workload is a causal driver of the development of arrhythmias during ventricular unloading.

TikTok, a social media platform designed for sharing short videos (“microvlogs”), provides an opportunity to learn how nonprofits adapt and implement social media strategies. Similarly, exploring nonprofits’ behavior on TikTok is useful for analyzing the impact that strategy has on the content nonprofits produce and user engagement. Using a mixed-methods design, this study analyzed data from 29 interviews and 575 microvlogs to answer three questions. First, how are nonprofits incorporating microvlogging into their social media strategy? Second, applying the hierarchy of engagement framework, do nonprofits produce social media content aligned with their expressed strategy? Finally, does alignment between strategy and output affect user engagement? The findings indicate that, unlike Facebook and Twitter/X, nonprofits on TikTok harness community-building content to facilitate information sharing and action. Strategy-output alignment significantly increases user engagement, but only for community-building content. Thus, social media strategy may be less important than authenticity on TikTok.

Abstract Background About half of AML patients achieving complete remission (CR) display measurable residual disease (MRD) and eventually relapse. FLYSYN is an Fc-optimized antibody for eradication of MRD directed to FLT3/CD135, which is abundantly expressed on AML cells. Methods This first-in-human, open-label, single-arm, multicenter trial included AML patients in CR with persisting or increasing MRD and evaluated safety/tolerability, pharmacokinetics and preliminary efficacy of FLYSYN at different dose levels administered intravenously (cohort 1–5: single dose of 0.5 mg/m2, 1.5 mg/m2, 5 mg/m2, 15 mg/m2, 45 mg/m2; cohort 6: 15 mg/m2 on day 1, 15 and 29). Three patients were treated per cohort except for cohorts 4 and 6, which were expanded to nine and ten patients, respectively. Primary objective was safety, and secondary efficacy objective was ≥ 1 log MRD reduction or negativity in bone marrow. Results Overall, 31 patients were treated, of whom seven patients (22.6%) experienced a transient decrease in neutrophil count (two grade 3, others ≤ grade 2). No infusion-related reaction or dose-limiting toxicity was observed. Adverse events (AEs) were mostly mild to moderate, with the most frequent AEs being hematologic events and laboratory abnormalities. Response per predefined criteria was documented in 35% of patients, and two patients maintained MRD negativity until end of study. Application of 45 mg/m2 FLYSYN as single or cumulative dose achieved objective responses in 46% of patients, whereas 28% responded at lower doses. Conclusions FLYSYN monotherapy is safe and well-tolerated in AML patients with MRD. Early efficacy data are promising and warrant further evaluation in an up-coming phase II trial. Trial registration This clinical is registered on clinicaltrials.gov (NCT02789254).

Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning - when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create "ClimateNet" - an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in highresolution climate model output from a simulation of a recent historical period.We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification - i.e., segmentation - of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or "the background" at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset - ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

Identifying, detecting and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection and segmentation have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of Deep Learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning; when labeled datasets are readily available. Reliable, labeled training data for extreme weather and climate events is scarce. We create ClimateNet – an open, community-sourced human expert-labeled curated dataset – that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification, i.e. segmentation, of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs or the background at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, hi-fidelity and highly precise analytics of climate data using a curated expert-labelled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns, and applied to observational and reanalysis data products via transfer learning.

Dengue is caused by four genetically distinct viral serotypes, dengue virus (DENV) 1-4. Following transmission by Aedes mosquitoes, DENV can cause a broad spectrum of clinically apparent disease ranging from febrile illness to dengue hemorrhagic fever and dengue shock syndrome. Progress in the understanding of different dengue serotypes and their impacts on specific host-virus interactions has been hampered by the scarcity of tools that adequately reflect their antigenic and genetic diversity. To bridge this gap, we created and characterized infectious clones of DENV1–4 originating from South America, Africa, and Southeast Asia. Analysis of whole viral genome sequences of five DENV isolates from each of the four serotypes confirmed their broad genetic and antigenic diversity. Using a modified circular polymerase extension reaction (CPER), we generated de novo viruses from these isolates. The resultant clones replicated robustly in human and insect cells at levels similar to those of the parental strains. To investigate in vivo properties of these genetically diverse isolates, representative viruses from each DENV serotype were administered to NOD Rag1−/−, IL2rgnull Flk2−/− (NRGF) mice, engrafted with components of a human immune system. All DENV strains tested resulted in viremia in humanized mice and induced cellular and IgM immune responses. Collectively, we describe here a workflow for rapidly generating de novo infectious clones of DENV – and conceivably other RNA viruses. The infectious clones described here are a valuable resource for reverse genetic studies and for characterizing host responses to DENV in vitro and in vivo.