Search

Background: Hyponatraemia is a prevalent electrolyte disturbance observed in critically ill patients. The rapid correction of low plasma sodium levels by continuous renal replacement therapy (CRRT) carries the risk of developing osmotic demyelination syndrome (ODS), which can be prevented by implementing an individualized CRRT method. Aim: This study aims to introduce a CRRT protocol for the safe and gradual correction of severe hyponatraemia. Study Design: This retrospective case series study was conducted in an intensive care unit (ICU). All four patients with severe hyponatraemia (<125 mmol/L) and renal failure between October 1, 2022, and September 30, 2023, were treated by CRRT with sterile water and regional citrate anticoagulation (RCA). Data on patient demographics, laboratory biochemical parameters, urine outputs and CRRT‐related adverse events were collected. Laboratory parameters and urine outputs were compared by paired t‐tests before and after CRRT. Results: After CRRT, sodium levels were significantly increased (112.7 ± 6.7 vs. 141.9 ± 2.8 mmol/L, p =.005). Abnormal urine outputs, potassium, creatinine and bicarbonate were corrected (p for all <.05). Safe and gradual correction of hyponatraemia and internal environmental dysregulation was achieved in all patients without any complications related to CRRT, particularly ODS. Conclusion: It is a novel and simple strategy to correct severe hyponatraemia effectively while ensuring the safety of patients that can be easily implemented by experienced nurse staff. Relevance to Clinical Practice: The sterile water‐based protocol for postfilter dilution is safe to correct severe hyponatraemia with RCA and can be easily performed by experienced critical care nurses according to the precalculated formula. CRRT‐trained, experienced ICU nurses are competent to initiate and adjust sterile water infusion discretely to prevent overcorrection of hyponatraemia. [ABSTRACT FROM AUTHOR]

Warming water temperatures as a result of climate change pose a major threat to coldwater organisms. However, the rate of warming is not spatially uniform due to surface‐ground‐water interactions and stream and watershed characteristics. Coldwater habitats that are most resistant to warming serve as thermal refugia and identifying their locations is critical to regional aquatic conservation planning. We quantified the thermal sensitivity of 203 streams providing current and potential habitat for brook trout (Salvelinus fontinalis) across nearly 1000 linear km of their native range in the southern and central Appalachian Mountains region, USA, and characterized their spatial variability with landscape variables available in the National Hydrography Dataset. Using the Bayesian framework, we calculated the maximum slope of the logistic function relating paired weekly mean air temperature and stream temperature as an index of stream thermal sensitivity. Streams differed greatly in thermal sensitivity and those with more resistant water temperature regimes (i.e., thermal refugia) were consistently characterized by southerly latitudes and groundwater input. Landscape variables derived from a principal component analysis explained 16% of the variation in thermal sensitivity, indicating that the existing landscape variables were modestly successful in explaining spatial thermal heterogeneity. Using our model and spatial interpolation, we predicted thermal sensitivity at 8695 stream segments potentially suitable for brook trout in the study region. Thermal refugia were more common southward presumably due to higher elevations, but elsewhere they were also clustered at finer spatial scales. Our analysis informs prioritizing habitat conservation and restoration of this native salmonid and other aquatic organisms that depend on coldwater habitats in a warming world. [ABSTRACT FROM AUTHOR]

Understanding patterns of species abundance is essential for planning landscape‐level conservation. The complex hierarchies of dendritic ecosystems result in different levels of heterogeneity at distinct geographic scales. Species responses to dynamic environmental drivers may also vary spatially depending on their interactions with landscape features. Monitoring abundance by explicitly quantifying their spatial and temporal variation is important for strategic management.We analysed brook trout (Salvelinus fontinalis) count data collected from 173 sites in western North Carolina between 1989 and 2015. We developed a Bayesian hierarchical model that used single‐ and multi‐pass electro‐fishing data and characterized their respective capture probabilities. We quantified spatial variation using a multi‐scale process model representative of the nested stream habitats, and we investigated differences in population temporal trends and responses to seasonal weather patterns by space and life stage.Trout abundance was lower on the Atlantic slope of the Eastern Continental Divide than in the interior, on average, and the Atlantic slope juveniles were more adversely affected by high winter flows. However, Atlantic slope populations of both lifestages demonstrated positive temporal trends, whereas Interior juveniles demonstrated a negative trend. We found higher spatial variation than temporal variation in abundance when conditioned on the covariates, where the primary source of spatial heterogeneity was revealed at the segment level, compared to watershed or network levels.Our multi‐scale spatial model outperformed simpler models in abundance estimation and out‐of‐sample prediction. The inferred per‐pass capture probabilities indicated that single‐pass surveys were as efficient as multi‐pass surveys.Synthesis and applications. Our study suggested conservation priority should involve multiple criteria, including present‐day abundance, temporal trend and sensitivity to environmental drivers. Based on the inferred scale‐specific variations in trout abundance, we recommend that future surveys strategically combine single‐pass surveys with multi‐pass surveys to optimize abundance estimation. Our approach is widely applicable to other species and ecosystems occupying dendritic habitats. [ABSTRACT FROM AUTHOR]

Summary: In this article, for a class of stochastic nonlinear systems with non‐strict feedback, a neural adaptive inverse optimal output feedback control design scheme is presented. First, according to the existing inverse optimal criterion, an auxiliary system is established. On this basis, a novel observer is built to evaluate the unpredictable states. Second, in the control process, neural networks (NNs) are applied to estimate the unknown functions. Based on NNs and the backstepping technology, an adaptive neural inverse optimal output feedback controller is established. It is indicated that the proposed scheme could ensure the semi‐globally uniformly ultimately bounded of the closed‐loop system and also achieve the objective of inverse optimality. Eventually, an example is applied to testify the feasibility of this scheme. [ABSTRACT FROM AUTHOR]

Climate change affects populations over broad geographic ranges due to spatially autocorrelated abiotic conditions known as the Moran effect. However, populations do not always respond to broad‐scale environmental changes synchronously across a landscape. We combined multiple datasets for a retrospective analysis of time‐series count data (5–28 annual samples per segment) at 144 stream segments dispersed over nearly 1,000 linear kilometers of range to characterize the population structure and scale of spatial synchrony across the southern native range of a coldwater stream fish (brook trout, Salvelinus fontinalis), which is sensitive to stream temperature and flow variations. Spatial synchrony differed by life stage and geographic region: it was stronger in the juvenile life stage than in the adult life stage and in the northern sub‐region than in the southern sub‐region. Spatial synchrony of trout populations extended to 100–200 km but was much weaker than that of climate variables such as temperature, precipitation, and stream flow. Early life stage abundance changed over time due to annual variation in summer temperature and winter and spring stream flow conditions. Climate effects on abundance differed between sub‐regions and among local populations within sub‐regions, indicating multiple cross‐scale interactions where climate interacted with local habitat to generate only a modest pattern of population synchrony over space. Overall, our analysis showed higher degrees of response heterogeneity of local populations to climate variation and consequently population asynchrony than previously shown based on analysis of individual, geographically restricted datasets. This response heterogeneity indicates that certain local segments characterized by population asynchrony and resistance to climate variation could represent unique populations of this iconic native coldwater fish that warrant targeted conservation. Advancing the conservation of this species can include actions that identify such priority populations and incorporate them into landscape‐level conservation planning. Our approach is applicable to other widespread aquatic species sensitive to climate change. [ABSTRACT FROM AUTHOR]

With the development of the medical and health industry, new health monitoring devices have attracted a lot of attention. By placing sensors in different organs, intelligent detection of human behavior and diagnosis of physical health can be achieved. Traditional electronic sensor devices have huge deficiencies in capturing tiny stimulus signals and comfort. Wearable sensors based on flexible conductive hydrogels are the most promising candidates for conventional sensors. However, the development of conductive hydrogels with good self‐healing and adhesion properties is still a challenge. Herein, a flexible polyvinyl alcohol hydrogel is prepared with good self‐healing properties, self‐adhesive and conductive properties based on polyvinyl alcohol, sodium phytate solution, and borax using a simple method. The abundant hydrogen bonding interactions, dynamically reversible borate ester bonds in the hydrogel network endowed the hydrogel with good self‐healing properties, self‐adhesion, and electrical conductivity. The fractured hydrogels showed almost disappearance of cracks after 30 min of self‐healing, and the efficiency of electrochemical repair within 0.1 s is close to 100%. The hydrogels can achieve good adhesion to different substrates and can be used as flexible writable circuits and smart wearable sensors, which have promising applications in the fields of flexible electronics, smart sensing, and health monitoring. [ABSTRACT FROM AUTHOR]

The reintroduction and recovery of predators can be ecologically beneficial as well as socially and economically controversial. However, the growth and expansion of predator populations, and thus their ecological, social, and economic impacts, are not static but rather they vary in space and time. We propose a spatiotemporal statistical modeling framework based on ecological diffusion to better inform the ecology and management of recovering predators and their prey. We demonstrate its utility by applying it to a recovering sea otter (Enhydra lutris) population in Southeast Alaska, where sea otters were reintroduced in the late 1960s and have exhibited unprecedented population growth. Estimated parameters yield inferences about movement and population ecology, and our approach provides useful derived quantities, such as local abundance and carrying capacity as well as a quantity we term the equilibrium differential. We used our model to examine how density dependence and carrying capacity of sea otters vary spatially across a region. The diffusion modeling approach we present can be generalized for use in other instances of (re)colonization across taxa to inform management and conservation efforts. [ABSTRACT FROM AUTHOR]

Identifying environmental drivers of demographic variation is key to predicting community‐level impacts in response to global change. Climate conditions can synchronize population trends and can occur both spatially for populations of the same species, and across multiple species within the same local community. The aim of this study was to investigate patterns of temporal variation in survival for freshwater fish communities in two geographically close but isolated sites and to understand the amount of variation accounted for by abiotic covariates including metrics of water temperature and stream flow. Using mark‐recapture data, we estimated bi‐monthly apparent survival in a Bayesian Cormack‐Jolly‐Seber framework. The model included random effects to quantify temporal variance to understand species synchrony with the rest of the fish community and between sites. Study species included bluehead chub (Nocomis leptocephalus), creek chub (Semotilus atromaculatus), and striped jumprock (Moxostoma rupiscartes) in the southeastern USA. Results showed that survival varied over time and periods of low survival were associated with higher mean water temperature. However, temporal patterns of survival differed among species and between sites, where survival was synchronous among species within a site but asynchronous between sites for the same species despite their spatial proximity. Study streams differed in summer thermal regimes, which resulted in contrasting summer survival patterns, suggesting sensitivity of these fishes to warming. We found that interspecific synchrony was greater than spatial synchrony, where regional drivers such as temperature may interact with local habitat leading to differences in survival patterns at fine spatial scales. Finally, these findings show that changes in the timing and magnitude of environmental conditions can be critical in limiting vital rates and that some populations may be more resilient to climate variation than others. [ABSTRACT FROM AUTHOR]

Flexible sensors show great application potential in wearable electronics, human‐computer interaction, medical health, bionic electronic skin and other fields. Compared with rigid sensors, hydrogel‐based devices are more flexible and biocompatible and can easily fit the skin or be implanted into the body, making them more advantageous in the field of flexible electronics. In all designs, polyvinyl alcohol (PVA) series hydrogels exhibit high mechanical strength, excellent sensitivity and fatigue resistance, which make them promising candidates for flexible electronic sensing devices. This paper has reviewed the latest progress of PVA/plant‐based biomass hydrogels in the construction of flexible sensor applications. We first briefly introduced representative plant biomass materials, including sodium alginate, phytic acid, starch, cellulose and lignin, and summarized their unique physical and chemical properties. After that, the design principles and performance indicators of hydrogel sensors are highlighted, and representative examples of PVA/plant‐based biomass hydrogel applications in wearable electronics are illustrated. Finally, the future research is briefly prospected. We hope it can promote the research of novel green flexible sensors based on PVA/biomass hydrogel. [ABSTRACT FROM AUTHOR]

Time‐series data offer wide‐ranging opportunities to test hypotheses about the physical and biological factors that influence species abundances. Although sophisticated models have been developed and applied to analyze abundance time series, they require information about species detectability that is often unavailable. We propose that in many cases, simpler models are adequate for testing hypotheses. We consider three relatively simple regression models for time series, using simulated and empirical (fish and mammal) datasets. Model A is a conventional generalized linear model of abundance, model B adds a temporal autoregressive term, and model C uses an estimate of population growth rate as a response variable, with the option of including a term for density dependence. All models can be fit using Bayesian and non‐Bayesian methods. Simulation results demonstrated that model C tended to have greater support for long‐lived, lower‐fecundity organisms (K life‐history strategists), while model A, the simplest, tended to be supported for shorter‐lived, high‐fecundity organisms (r life‐history strategists). Analysis of real‐world fish and mammal datasets found that models A, B, and C each enjoyed support for at least some species, but sometimes yielded different insights. In particular, model C indicated effects of predictor variables that were not evident in analyses with models A and B. Bayesian and frequentist models yielded similar parameter estimates and performance. We conclude that relatively simple models are useful for testing hypotheses about the factors that influence abundance in time‐series data, and can be appropriate choices for datasets that lack the information needed to fit more complicated models. When feasible, we advise fitting datasets with multiple models because they can provide complementary information. [ABSTRACT FROM AUTHOR]

Summary: Binary regression models are ubiquitous in virtually every scientific field. Frequently, traditional generalised linear models fail to capture the variability in the probability surface that gives rise to the binary observations, and remedial methods are required. This has generated a substantial literature composed of binary regression models motivated by various applications. We describe an organisation of generalisations to traditional binary regression methods based on the familiar three‐part structure of generalised linear models (random component, systematic component and link function). This perspective facilitates both the comparison of existing approaches and the development of flexible models with interpretable parameters that capture application‐specific data‐generating mechanisms. We use our proposed organisational structure to discuss concerns with certain existing models for binary data based on quantile regression. We then use the framework to develop and compare several binary regression models tailored to occupancy data for European red squirrels (Sciurus vulgaris). [ABSTRACT FROM AUTHOR]

Landscape morphology is a significant area of landscape architecture research. One of the scientific and technological issues in recent landscape morphology research is the use of quantitative analysis technology driven by morphology indexes and computational models to describe, compare, and analyze form features. This article focuses on the form features of the polder landscape, based on existing theoretical and practical achievements in landscape morphology. First, we choose five landscape morphology indexes based on the morphological constituent units of the landscape (elongation, rectangular compactness, concavity, ellipse compactness, and fractal dimension). Then, using the self-organizing map (SOM), we create an identification model for clustering the types of constituent units. The experimental results show that the identification model can classify polder morphology and analyze the distribution of units using typical polders in the Yangtze River's south bank as study cases. This article presents a technical approach to polder landscape morphology classification as well as a reference and developable quantitative analysis method for landscape morphology research. [ABSTRACT FROM AUTHOR]

With the application of engineering management in smart city construction under Industry 4.0, the intelligent design of urban street landscape has attracted extensive attention. Affected by the low intelligent level of traditional landscape design, the existing urban landscape composite system has difficulty in meeting the needs of smart city construction. Therefore, this paper proposes the construction of street landscape big data-driven intelligent decision support system based on Industry 4.0. Based on the complex network theory, this paper analyzes the structure, links, nodes, driving forces, and functional requirements of urban street landscape and then puts forward the construction content and implementation method of urban street landscape intelligent decision support system. The system consists of four aspects: intelligent infrastructure, service, protection and maintenance, and management and evaluation system. Its implementation not only reflects the cooperation and effective application of intelligent technology in each stage of street landscape construction, but also provides reference for the application of engineering management in other fields under Industry 4.0. [ABSTRACT FROM AUTHOR]

Pluripotency and self-renewal of embryonic stem cells (ESCs) are marked by core transcription regulators such as Oct4, Sox2, and Nanog. Another important marker of pluripotency is the long noncoding RNA (lncRNA). Here, we ind that a novel long noncoding RNA (lncRNA) Lx8-SINE B2 is a marker of pluripotency. LncRNA Lx8-SINE B2 is enriched in ESCs and downregulated during ESC differentiation. By rapid amplification of cDNA ends, we identified the full-length sequence of lncRNA Lx8-SINE B2. We further showed that transposable elements at upstream of lncRNA Lx8-SINE B2 could drive the expression of lncRNA Lx8-SINE B2. Furthermore, ESC-specific expression of lncRNA Lx8-SINE B2 was driven by Oct4 and Sox2. In summary, we identified a novel marker lncRNA of ESCs, which is driven by core pluripotency regulators. [ABSTRACT FROM AUTHOR]

The facilitates chromatin transcription (FACT) complex is a histone H2A/H2B chaperone, which represses endogenous retroviruses (ERVs) and transcription of ERV-chimeric transcripts. It binds to both transcription start site and gene body region. Here, we investigated the downstream targets of FACT complex to identify the potential regulators of MERVL, which is a key 2-cell marker gene. H3K36me2 profile was positively correlated with that of FACT component Ssrp1. Among H3K36me2 deposition enzymes, Nsd2 was downregulated after the loss of Ssrp1. Furthermore, we demonstrated that Nsd2 repressed the expression of ERVs without affecting the expression of pluripotency genes. The expression of MERVL and 2-cell genes was partially rescued by Nsd2 overexpression. The enrichment of H3K36me2 decreased on MERVL-chimeric gene in ESCs without Ssrp1. Our study discovers that Nsd2 is a repressor of MERVL, and FACT partially represses MERVL expression by regulating the expression of Nsd2 and its downstream H3K36me2. [ABSTRACT FROM AUTHOR]

Partial differential equations (PDEs) are a useful tool for modeling spatiotemporal dynamics of ecological processes. However, as an ecological process evolves, we need statistical models that can adapt to changing dynamics as new data are collected. We developed a model that combines an ecological diffusion equation and logistic growth to characterize colonization processes of a population that establishes long‐term equilibrium over a heterogeneous environment. We also developed a homogenization strategy to statistically upscale the PDE for faster computation and adopted a hierarchical framework to accommodate multiple data sources collected at different spatial scales. We highlighted the advantages of using a logistic reaction component instead of a Malthusian component when population growth demonstrates asymptotic behavior. As a case study, we demonstrated that our model improves spatiotemporal abundance forecasts of sea otters in Glacier Bay, Alaska. Furthermore, we predicted spatially varying local equilibrium abundances as a result of environmentally driven diffusion and density‐regulated growth. Integrating equilibrium abundances over the study area in our application enabled us to infer the overall carrying capacity of sea otters in Glacier Bay, Alaska. [ABSTRACT FROM AUTHOR]

CD19+CD24hiCD38hi B cells are immature transitional B cells that, in normal individuals, exert suppressive effects by IL-10 production but are quantitatively altered and/or functionally impaired in individuals with various autoimmune diseases. Primary biliary cholangitis (PBC), an autoimmune disease, clinically presents as chronic cholestasis and nonsuppurative destructive cholangitis. A role for CD19+CD24hiCD38hi B cells in PBC is unknown. This study investigated the frequency and functional variation of circulating CD19+CD24hiCD38hi B cells in PBC patients. Flow cytometry was employed to quantify the percentage of CD19+CD24hiCD38hi B cells in peripheral blood samples. Correlations between CD19+CD24hiCD38hi B cells and routine laboratory parameters were assessed. Levels of IL-10, TNF-α, IL-6 and IL-12, and Tim-1 in CD19+CD24hiCD38hi B cells from PBC patients were analyzed. The effect of CD19+CD24hiCD38hi B cells on CD4+T cell differentiation was evaluated. The percentage of CD19+CD24hiCD38hi B cells in PBC patients was significantly higher than in healthy controls and was positively correlated with liver cholestasis. After activation by anti-B cell receptor and CpG, the production of IL-10 was decreased and the production of IL-6 and IL-12 was increased in CD19+CD24hiCD38hi B cells from PBC patients. Moreover, Tim-1 levels were significantly downregulated in CD19+CD24hiCD38hi B cells from PBC patients. Coculture showed that PBC-derived CD19+CD24hiCD38hi B cells were less capable of CD4+T cell inhibition, but promoted Th1 cell differentiation. In conclusion, PBC patients have expanded percentages, but impaired CD19+CD24hiCD38hi B cells, which correlate with disease damage. In PBC patients, this B cell subset has a skewed proinflammatory cytokine profile and a decreased capacity to suppress immune function, which may contribute to the pathogenesis of PBC. [ABSTRACT FROM AUTHOR]

In this article, by means of fixed point theorem on mixed monotone operator, we establish the uniqueness of positive solution for some nonlocal singular higher-order fractional differential equations involving arbitrary derivatives. We also give iterative schemes for approximating this unique positive solution. [ABSTRACT FROM AUTHOR]

Most of the human genome can be transcribed into RNAs, but only a minority of these regions produce protein-coding mRNAs whereas the remaining regions are transcribed into noncoding RNAs. Long noncoding RNAs (lncRNAs) were known for their influential regulatory roles in multiple biological processes such as imprinting, dosage compensation, transcriptional regulation, and splicing. The physiological functions of protein-coding genes have been extensively characterized through genome editing in pluripotent stem cells (PSCs) in the past 30 years; however, the study of lncRNAs with genome editing technologies only came into attentions in recent years. Here, we summarize recent advancements in dissecting the roles of lncRNAs with genome editing technologies in PSCs and highlight potential genome editing tools useful for examining the functions of lncRNAs in PSCs. [ABSTRACT FROM AUTHOR]

Interferon-induced transmembrane protein 1 ( IFITM1), a member of the IFITM protein family, is a component of a multimeric complex involved in the transduction of antiproliferation and cell adhesion signals. IFITM1 is thought to play a role in antiproliferation and immune surveillance, and has been shown to restrict infection by numerous viruses. It is highly expressed in human embryonic stem cells ( hESCs) but its role in hESCs remains to be elucidated. In this study, knockout of IFITM1 mediated by CRISPR/Cas9 in hESCs did not affect self-renewal, pluripotency, telomerase activity or telomeres. However expression of human endogenous retroviruses ( HERVs) was higher than in wild-type hESCs, and there was also a reduced level of trimethylation of histone H3 on lysine 9 at HERV loci. These data show that IFITM1 suppresses HERVs in hESCs by regulating epigenetic modifications. [ABSTRACT FROM AUTHOR]

ABSTRACT Understanding the complex biological process of aging is of great value, especially as it can help develop therapeutics to prolong healthy life. Predicting biological age from gene expression data has shown to be an effective means to quantify aging of a subject, and to identify molecular and cellular biomarkers of aging. A typical approach for estimating biological age, adopted by almost all existing aging clocks, is to train machine learning models only on healthy subjects, but to infer on both healthy and unhealthy subjects. However, the inherent bias in this approach results in inaccurate biological age as shown in this study. Moreover, almost all existing transcriptome‐based aging clocks were built around an inefficient procedure of gene selection followed by conventional machine learning models such as elastic nets, linear discriminant analysis etc. To address these limitations, we proposed DeepQA, a unified aging clock based on mixture of experts. Unlike existing methods, DeepQA is equipped with a specially designed Hinge‐Mean‐Absolute‐Error (Hinge‐MAE) loss so that it can train on both healthy and unhealthy subjects of multiple cohorts to reduce the bias of inferring biological age of unhealthy subjects. Our experiments showed that DeepQA significantly outperformed existing methods for biological age estimation on both healthy and unhealthy subjects. In addition, our method avoids the inefficient exhaustive search of genes, and provides a novel means to identify genes activated in aging prediction, alternative to such as differential gene expression analysis. [ABSTRACT FROM AUTHOR]

Rigorous and rapid assessment of ambient ozone exposure is important for informing the public about ozone levels that may lead to adverse health effects. In this paper, we use hierarchical modeling to enable real‐time forecasting of 8‐hr average ozone exposure. This contrasts with customary retrospective analysis of exposure data. Specifically, our contribution is to show how incorporating temperature data in addition to the observed ozone can significantly improve forecast accuracy, as measured by predictive performance and empirical coverage. We adopt two‐stage autoregressive models, also introducing periodicity and heterogeneity while still maintaining our objective of forecasting in real time. The entire effort is illustrated through modeling data collected at the Village Green monitoring station in Durham, North Carolina. [ABSTRACT FROM AUTHOR]