The present study sought to evaluate the reproducibility of prominent findings stated by Fehr et al. (2008) in their developmental resource allocation experiment. The experiment involved children making decisions about distributing sweets between themselves and either an in-group or an out-group recipient. Fehr et al. found that (1) inequity aversion develops with age; (2) 3- to 4-year-old children are inclined toward self-advantageous allocations, whereas 7- to 8-year-olds distribute sweets more evenly in divisions, and (3) the influence of group status increases as children age. In our attempts to reproduce Fehr et al.’s original analyses and reanalyse the raw data set, we found that one of the key variables was miscoded. After rectifying the miscoded variable, the reproduction results revealed only one ambiguously irreproducible result regarding a group status main effect in the sharing mini-game—with three other tests exhibiting either strong reproducibility or ambiguous reproducibility following the classifications suggested by Artner et al. (2021). Reanalysis results indicated that Fehr et al.’s conclusions are robust when tested with alternative analytical tests.

Enzymatic catalysis is one of the fundamental processes that drives the dynamic landscape of post-translational modifications (PTMs), expanding the structural and functional diversity of proteins. Here, we assessed enzyme specificity using a top-down ion mobility spectrometry (IMS) and tandem mass spectrometry (MS/MS) workflow. We successfully applied trapped IMS (TIMS) to investigate site-specific N-ε-acetylation of lysine residues of full-length histone H4 catalyzed by histone lysine acetyltransferase KAT8. We demonstrate that KAT8 exhibits a preference for N-ε-actylation of residue K16, while also installing N-ε-acetyl groups on residues K5 and K8 as the first degree of acetylation. Achieving TIMS resolving power values of up to 300, we fully separated mono-acetylated regioisomers (H4K5ac, H4K8ac, and H4K16ac). Each of these regioisomers produce unique MS/MS fragment ions, enabling estimation of their individual mobility distributions and the exact localization of the N-ε-acetylation sites. This study highlights the potential of top-down TIMS-MS/MS for conducting enzymatic assays at the intact protein level and, more generally, for separation and identification of isomeric proteoforms and precise PTM localization.

Children with home invasive mechanical ventilation face numerous difficulties, including endocrine problems that can arise as a consequence of their condition. Endocrine problems seen in children treated with HIMV may develop due to the underlying disease, drugs used, or prolonged mechanical ventilation. This manuscript will discuss the most common endocrine problems encountered in children with home invasive mechanical ventilation, including problems in glucose metabolism, thyroid dysfunction, bone metabolism, adrenal dysfunctions, growth and puberty. Close monitoring, multidisciplinary care, and regular assessments are essential to optimize the endocrin system functions of children requiring HMV. By understanding these complications, it can develop effective management strategies to optimize the health and well-being of these vulnerable individuals.

Background and Aims: The extravascular implantable cardioverter defibrillator (EV ICD) has an extended projected battery longevity compared to the subcutaneous implantable cardioverter defibrillator (S-ICD). This study used modeling to characterize the need for generator changes, long-term complications, and overall costs for both the EV ICD and S-ICD in healthcare systems of various countries . Methods: Battery longevity data were modelled using a Markov model from averages reported in device labelling for the S-ICD and with engineering estimates based on real life usage from EV ICD Pivotal Study patient data to introduce variability. Clinical demographic data of recipients were derived from published literature. The primary outcomes were defined as the number of generator replacement surgeries, complications, and total healthcare system costs due to battery depletion over the expected lifetime of patients receiving EV ICD or S-ICD therapy. A one-way sensitivity analysis of the model was performed for the US healthcare system. Results: Average modelled battery longevity was determined to be 7.3 years for the S-ICD compared to 11.8 years for the EV ICD. The probability of a complication after a replacement procedure was 1.4%, with an operative mortality rate of 0.02%. The use of an EV ICD was associated with 1.4-1.6 fewer replacements on average over an expected patient lifetime as compared to an S-ICD and a 24.3-26.0% reduction in cost. The US sensitivity analysis found use of an EV ICD resulted in a reduction in replacement surgeries of greater than 1 (1.1-1.6) along with 5-figure cost savings in all scenarios ($18,602-$40,948). Conclusion: The longer projected battery life of the EV ICD compared to the S-ICD has the potential to meaningfully reduce long-term morbidity and healthcare resources related to generator changes from the perspective of multiple diverse healthcare systems.

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Nonstationarity due to climate change, human impacts, and invasive species presents a major challenge for ecosystem forecasting, as past behavior cannot necessarily predict future behavior. While time-varying linear models may adequately detect nonstationarity in some cases, such models are often poor at forecasting and fail to correctly identify nonstationarity in nonlinear systems. Here we propose a nonlinear generalization of existing models that improves nonstationarity quantification and forecast skill. We demonstrate the effectiveness of the method in simulated datasets and experimental and field time series known to be stationary or nonstationary. Evaluating nonstationarity over subsets of the empirical time series shows that apparent nonstationarity strongly depends on the length and starting time of the series analyzed. Thus, any evaluation of nonstationarity should be conditional on a certain time window. This method could aid both in quantifying the intensity of nonstationarity in ecosystems and producing better forecasts in a changing world.

Plants harbour a great chemodiversity, i.e., diversity of specialized metabolites (SMs), at different scales. For instance, individuals can produce a large number of SMs and populations can differ in their metabolite composition. Given the ecological and economic importance of plant chemodiversity, it is important to understand how it arises and is maintained over evolutionary time. For other dimensions of biodiversity, i.e., species diversity and genetic diversity, quantitative models play an important role in addressing such questions. Here we provide a synthesis of existing hypotheses and quantitative models, i.e. mathematical models and computer simulations, for the evolution of plant chemodiversity. We describe each model’s ingredients, i.e., the biological processes that shape chemodiversity, the scales it considers, and whether it has been formalized as a quantitative model. Although we identify several quantitative models, not all are dynamic and many influential models have remained verbal. To fill these gaps, we identify quantitative models used for genetic variation that may be adapted for chemodiversity. We end by outlining our vision for the future of chemodiversity modeling, presenting a flexible framework for the creation of individual-based models that address different scales of chemodiversity and combine different ingredients that bring this chemodiversity about.

Introduction:Acute intermittent porphyria (AIP) represents an autosomal dominant disorder stemming from a partial deficiency in the enzymatic activity of porphobilinogen deaminase, which is also referred to as hydroxymethylbilane synthase (HMBS), the third enzyme involved in the synthesis of heme. This deficiency leads to the accumulation of toxic heme metabolites, namely aminolevulinic acid (ALA) and porphobilinogen (PBG) (1) The precise mechanisms through which porphyrin precursors trigger the symptoms of AIP are not fully understood (1,2)AIP presents with a range of clinical manifestations that can be categorized into three distinct phases: the prodromal phase, the visceral symptom phase, and the neurological phase. Prominent clinical symptoms encompass the occurrence of acute neurovisceral episodes, marked by intense abdominal pain, mental disturbances, and heightened sympathetic activity (2)Acute attacks of porphyria can be precipitated by a range of factors, encompassing alcohol ingestion, stress, fasting, menstruation, surgical procedures, infection, and the administration of certain drugs (1,3). Preliminary assessment for AIP involves the detection of markedly elevated porphyrin levels in urinary, stool or serum during an acute attack, followed by subsequent genetic analysis to confirm the presence of HMBS gene mutations (4). The management of AIP encompasses a comprehensive approach that includes the management of acute attacks, prevention of future episodes, long-term monitoring, and the treatment of associated complications (2).Among the various acute porphyrias, AIP is recognized as a prevalent condition on a global scale, being both the most common and severe form among the acute hepatic porphyrias (5) AIP affects both males and females, however, in the context of AIP episodes, females tend to experience more severe impacts in terms of higher frequencies of attacks, longer durations of attacks, and an increased likelihood of requiring hospitalization (6).The HMBS gene is situated at the chromosomal position 11q23.3 and has been linked to more than 400 pathogenic mutations (5). Individuals harboring pathogenic variants of the HMBS gene, regardless of their symptomatic or latent status, are prone to experiencing acute attacks. Furthermore, chronic complications such as hepatocellular carcinoma, hypertension, and chronic renal failure can manifest in both symptomatic individuals with AIP and those who are asymptomatic but carry pathogenic HMBS variants (7).To date, limited knowledge exists regarding certain variants of pathogenic HMBS, particularly in low and middle-income countries such as Mexico. Consequently, there is uncertainty about the disease behavior in these individuals. Further research is warranted to address these gaps and enhance our understanding of AIP management in this specific patient population.

Abstract Background There is currently no robust prognostic model for sarcomatous renal cell carcinoma (sRCC), which could help physicians make better decisions. Objectives To build an accurate predictive model for patients who have sRCC by investigating the important characteristics that influence the overall survival of patients. Design and Methods The Surveillance, Epidemiology and Results (SEER) database of the U.S. National Cancer Institute was used for gathering the dataset of sRCC patients. Following data preprocessing, the data was separated into the training set and the test set in an 8:2 ratio. Mann-Whitney U test and Chi-square test were used to verify whether the data set was evenly divided. Univariate Cox proportional hazard model, Kaplan-Meier analysis and machine learning (ML) algorithm were employed to identify the risk features on overall survival (OS). 10 reliable features were selected to construct six ML models. Model performance, predictive accuracy, and clinical benefits were evaluated by the receiver operating characteristic curves (ROC), calibration plots, and decision curve analysis (DCA) respectively. Results After data preprocessing, 692 patients with sRCC from 1975 to 2019 were included in this study. Ten variables including stage group, T stage, M stage, age, surgery, N stage, tumor size, chemotherapy, histological grade, and radiotherapy were selected as reliable features for machine learning model training. All the models show good prediction performance, among which XGBoost has the best prediction accuracy and stability. The DCA showed that all models except Adaboost could be used to support clinical decision-making with the 90-day, 1-, 2-, 3- and 5-year OS model. Conclusions Six machine learning models were developed to predict 90-day, 1-, 2-, 3- and 5-year overall survival in patients with sRCC. Model evaluations showed that the XGBoost model had the best predictive accuracy and clinical net benefit. These models can help make treatment decisions for patients with sRCC.

Rationale: Impurities refer to any substances that affect the purity of pharmaceuticals. Controlling impurities has always been a significant concern during drug development. Impurities impact the drug’s purity, diminish the efficacy of pharmaceuticals, and alter their appearance, physical, and chemical properties. Additionally, impurities can compromise the stability of pharmaceuticals and elevate their toxic and side effects. Methods: Impurity source analysis is the basis of drug impurity control. To clarify the source of impurity can optimize the synthesis process, prescription process, packaging and storage conditions of the pharmaceuticals, and control the impurity within a reasonable limit to achieve the ultimate goal of impurity research. Results: Analysis method is a means to obtain impurity information, and diversified analysis methods are also possible for effective control of different types of impurities. At present, there are relatively many quantitative studies on impurities, but there are still some challenges for the structure analysis of impurities, especially trace impurities. Conclusion: The research progress of drug impurity control and evaluation from the sources of various drug impurities and impurity analysis techniques was reviewed in this article, with the aim of providing references for the related research.

Stable isotopes of the water molecule have emerged as powerful tracers of the sources and trajectories of water leading to precipitation, at different spatial and temporal scales. However, the high cost of commercially available rain samplers for isotopic analysis, have made using them for high spatial resolution networks and for studies being conducted in developing countries prohibitively expensive. We have designed a low-cost, simple, and robust rain sampler capable of sampling precipitation for isotopic analysis on the event and monthly scale, based on the existing designs provided in the literature. The event rain samplers were tested to determine the minimum amount of rainfall to minimize isotopic fractionation, both from post-sampling evaporation and equilibration. These new rain samplers will enable isotopic sampling of precipitation at high spatial resolutions. All the instructions for constructing and using these samplers are made openly accessible to the scientific community so they can easily be repeated and adapted to the needs of each project. This open access and low-cost methodology will help democratize the use of isotopes for hydrological studies in developing countries.

Ecological stoichiometry is an important approach to understand the nutrient cycling and balance through the leaf-litter-soil system of Mongolian pine among different stand origins in desert regions. To reveal the variations in Mongolian pine carbon (C), nitrogen (N), and phosphorus (P) stoichiometry and stoichiometric homeostasis among different stand origins, we measured C, N, and P concentrations of leaves, litter, and soil, and analyzed the nutrient resorption efficiencies of leaves in differently aged plantations and natural forests from semi-arid and dry sub-humid regions. The results showed that (a) the stand origin had a significant effect on the C-N-P stoichiometry, and also significantly affected leaf N and P reabsorption efficiencies. Leaf N/P ratios indicated that Mongolian pine was co-limited by N and P in the NF, HB and HQ, and was mainly limited by P in MU. (2) With increasing stand age, C concentrations in the leaf-litter-soil system initially increased and then decreased, the N and P concentrations and reabsorption efficiencies in the leaf-litter-soil system were gradually increased. Overall, stand age had a significant effect on N concentrations, C/N and C/P ratios in the leaf-litter-soil system. (3) The C and N elements between the leaf-litter-soil system had a strong coupling relationship, and the P element between litter-soil had a strong coupling relationship. In addition, plantations exhibited greater N/P homeostasis than natural forests, and N/P exhibited greater homeostasis than N and P alone, which may be a nutrient utilization strategy for forests to alleviate N or P limitation. (4) Environmental factors have a significant influence on C-N-P stoichiometry in the leaf-litter-soil system, the most important soil properties and meteorological factors being soil water content and precipitation, respectively. These results will be essential to provide guidance for plantation restoration and management in desert regions.

Background: IgE-mediated cow’s milk allergy (IgE-CMA) is one of the first allergies to arise in early childhood and may result from exposure to various milk allergens, of which β-lactoglobulin (BLG) and casein are the most important. Understanding the underlying mechanisms behind IgE-CMA is imperative for the discovery of novel biomarkers and the design of innovative treatment and prevention strategies. Methods: We report a longitudinal in vivo murine model, in which 2 mice strains (BALB/c and C57Bl/6) were sensitized to BLG using either cholera toxin or an oil emulsion (n=6 per group). After sensitization, mice were challenged orally, their clinical signs monitored, antibody (IgE and IgG1) and cytokine levels (IL-4 and IFN-γ) measured, and fecal samples subjected to metabolomics. The results of the murine models were further supported by fecal microbiome-metabolome data from our population of IgE-CMA (n=24) and healthy (n=23) children (Trial: NCT04249973), on which polar metabolomics, lipidomics and 16S rRNA metasequencing were performed. In vitro gastrointestinal digestions and multi-omics corroborated the microbial origin of proposed metabolic changes. Results: During sensitization, we observed multiple microbially derived metabolic alterations, most importantly bile acid, energy and tryptophan metabolites, that preceded allergic inflammation. The latter was reflected in a disturbed sphingolipid metabolism. We confirmed microbial dysbiosis, and its causal effect on metabolic alterations in our patient cohort, which was accompanied by metabolic signatures of low-grade inflammation. Conclusion: Our results indicate that gut dysbiosis precedes allergic inflammation and nurtures a chronic low-grade inflammation in children on elimination diets, opening important new opportunities for future prevention and treatment strategies.

Motivated by applications in fluid dynamics involving the harmonic Bergman projection, we aim to extend the theory of single and double layer potentials (well documented for functions with H ℓoc 1 regularity) to locally square integrable functions. Having in mind numerical simulations for which functions are usually defined on a polygonal mesh, we wish this theory to cover the cases of non-smooth domains (i.e.with Lipschitz continuous or polygonal boundaries).

Alien species can have massive impacts on native biodiversity and ecosystem functioning. Assessing which species from currently cultivated alien floras may escape into the wild and naturalize is hence essential for ecosystem management and biodiversity conservation. Climate change has promoted the naturalization of many alien plants in temperate regions, but whether outcomes are similar in (sub)tropical areas is insufficiently known. In this study, we used species distribution models to evaluate the current naturalization risk of 1,527 cultivated alien plants in 10 countries of Southern Africa and how their invasion risk might change due to climate change. We assessed changes in climatic suitability across the different biomes of Southern Africa. Moreover, we assessed whether climatic suitability for cultivated alien plants varied with their naturalization status and native origin. The results of our study indicate that a significant proportion (53.9%) of the species are projected to lack suitable climatic conditions in Southern Africa, both currently and in the future. Based on the current climate conditions, 10.0% of Southern Africa is identified as an invasion hotspot (here defined as the top 10% of grid cells that provide suitable climatic conditions to the highest numbers of species). This percentage is expected to decrease slightly to 7.1% under moderate future climate change and shrink considerably to 2.0% under the worst-case scenario. This decline in climatic suitability is observed across most native origins, particularly under the worst-case climate change scenario. Our findings indicate that climate change is likely to have an opposing effect on the naturalization of currently cultivated average plants in (sub)tropical Southern Africa compared to colder regions. Specifically, the risk of these plants’ naturalizing is expected to decrease due to the region’s increasingly hot and dry climate, which will be challenging for the persistence of both native and alien plant species.

Individuals with depression experience more negative imagery and less vivid positive imagery, and the late positive potential (LPP) is considered as a viable biomarker for negative attentional and memory biases in depression; however, the LPP response to emotional imagery in depressed individuals remains unclear. This study aims to investigate neural response to emotional imagery in depressed individuals. ERPs were recorded from 40 depressed participants and 44 healthy controls during the encoding-imagery task. Depressed participants scored significantly lower in the valence rating of sad and neutral imagery compared to healthy participants. Importantly, the LPP amplitudes to sad imagery in depressed participants were significantly larger than healthy controls, particularly in the middle (800-1,400 ms) and late time windows(1,400-2,000 ms). Furthermore, depressed individuals exhibited significantly higher LPP amplitudes for sad imagery compared to happy imagery, whereas healthy participants showed the opposite pattern. The present study provides evidence that depressed individuals display abnormal electrophysiological reactivity to sad imagery, which offers a new perspective for understanding the mechanisms underlying depression.

Clinical characteristics and literature review of chronic active Epstein-Barr virus-associated enteritisYaJie Meng1+, RenDong Li2+, JieWen Ding1, Bo Xiang1,Qin Wang1,Min Wang1 *and KeJiang Tang1**Correspondence:KeJiang Tang :[email protected] and Min Wang+Equal contributors.1Department of Gastroenterology, The People’s Hospital of Nanchuan, No. 16South Street, Nanchuan District, Chongqing 408400, China2The People’s Hospital of Nanchuan, No. 16 South Street, Nanchuan District, Chongqing 408400, China

In this paper, we provide a systematic and constructive way to build a Lyapunov-Krasovskii functional for time-delay systems whose stability can be established through the Razumikhin or the Halanay approaches. The constructed Lyapunov-Krasovskii functional turns out to be coercive, meaning sandwiched between functions of the state history norm, and to dissipate in terms of the whole history norm. We present these results in the framework of input-to-state stability (ISS) in order to further account for the influence of input disturbances. A special emphasis is also given on exponential stability and exponential ISS. We illustrate our findings though the study of a coupled ODE-PDE model of a chemical reactor, and show that, unlike most results in that area, our approach happens to ensure ISS in terms of the supremum norm of the state.