Maternal transfer of per- and polyfluoroalkyl substances (PFAS) in wild birds: a systematic review and meta-analysis.

Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals widely used in consumer products. PFAS can accumulate in animal tissues, resulting in biomagnification and adverse effects on wildlife, such as reproductive impairment. In bird species, PFAS are transferred from mothers to eggs along with essential nutrients and may affect embryo development. However, the extent of maternal PFAS transfer across different species and compounds remains poorly understood. Here, we conducted a systematic review and meta-analysis to quantify maternal PFAS transfer in wild birds and investigate potential sources of variation. We tested the moderating effects of compounds' physicochemical properties and biological traits of studied birds. The dataset included 505 measurements of PFAS concentration and 371 effect sizes derived from 13 studies on 16 bird species and 25 compounds. Overall, across all studies and species, we found a 41% higher concentration of PFAS in offspring than in mothers. Specifically, contaminants were concentrated in the yolk, longer and heavier compounds showed preferential transfer, larger clutch size was associated with decreased PFAS transfer and a higher transfer rate was shown in species with piscivorous and opportunistic/diverse diets. A validation assessment showed good robustness of the overall meta-analytic result. Given the crucial role of birds in maintaining ecological balance, this research article has relevant implications for modelling the impacts of PFAS on wildlife, ecosystems, and human health.

Achieving High-Rate and Stable Sodium-Ion Storage by Constructing Okra-Like NiS2/FeS2@Multichannel Carbon Nanofibers.

Transition metal sulfides (TMSs) are considered as promising anode materials for sodium-ion batteries (SIBs) due to their high theoretical capacities. However, the relatively low electrical conductivity, large volume variation, and easy aggregation/pulverization of active materials seriously hinder their practical application. Herein, okra-like NiS2/FeS2 particles encapsulated in multichannel N-doped carbon nanofibers (NiS2/FeS2@MCNFs) are fabricated by a coprecipitation, electrospinning, and carbonization/sulfurization strategy. The combined advantages arising from the hollow multichannel structure in carbon skeleton and heterogeneous NiS2/FeS2 particles with rich interfaces can provide facile ion/electron transfer paths, ensure boosted reaction kinetics, and help maintain the structural integrity, thereby resulting in a high reversible capacity (457 mA h g-1 at 1 A g-1), excellent rate performance (350 mA h g-1 at 5 A g-1), and outstanding long-term cycling stability (93.5% retention after 1100 cycles). This work provides a facile and efficient synthetic strategy to develop TMS-based heterostructured anode materials with high-rate and stable sodium storage properties.

Realism and robustness require increased sample size when studying both sexes.

A recent article claimed that researchers need not increase the overall sample size for a study that includes both sexes. This Formal Comment points out that that study assumed two sexes to have the same variance, and explains why this is a unrealistic assumption.

A systematic evidence map and bibliometric analysis of the behavioural impacts of pesticide exposure on zebrafish.

Pesticides are indispensable in agriculture and have become ubiquitous in aquatic environments. Pesticides in natural environments can cause many negative impacts on aquatic species, ranging from mortality to sub-lethal physiological and behavioural changes. The complex sub-lethal impacts of pesticides are routinely tested on model species, with zebrafish (Danio rerio) being regularly used as a behavioural model. Although behavioural ecotoxicology research using zebrafish is increasing rapidly, we lack quantitative evidence to support which pesticides have been tested and how study designs are carried out. This shortcoming not only limits the deliberate planning for future primary studies to fill the knowledge gaps but also hinders evidence synthesis. To provide quantitative evidence of what pesticides are currently studied and what study designs are used, we combined a systematic evidence map approach and bibliometric analysis. This novel method has been coined research weaving and allows us to elicit gaps and clusters in our evidence base, whilst showing connections between authors and institutions. The methodology can be summarised in five primary steps: literature searching, screening, extraction, data analysis and bibliometric analysis. We identified four areas where research on the sub-lethal effects of pesticide exposure on zebrafish is lacking. First, some widely used pesticides, such as neonicotinoids, are understudied. Second, most studies do not report important elements of the study design, namely the sex and the life-stage of the zebrafish. Third, some behaviours, such as impacts of pesticide exposure on zebrafish cognition, are underexplored. And last, we revealed through the bibliometric analysis that most of the research is conducted in developed countries and there is limited cross country co-authorships. Upon identifying these gaps, we offer solutions for each limitation, emphasizing the importance of diverse global research output and cross-country co-authorships. Our systematic evidence map and bibliometric analysis provide valuable insights for helping to guide future research, which can be used to help support evidence-based policy decisions.

Juice Vesicles Bioreactors Technology for Constructing Advanced Carbon-Based Energy Storage.

The construction of high-quality carbon-based energy materials through biotechnology has always been an eager goal of the scientific community. Herein, juice vesicles bioreactors (JVBs) bio-technology based on hesperidium (e.g., pomelo, waxberry, oranges) is first reported for preparation of carbon-based composites with controllable components, adjustable morphologies, and sizes. JVBs serve as miniature reaction vessels that enable sophisticated confined chemical reactions to take place, ultimately resulting in the formations of complex carbon composites. The newly developed approach is highly versatile and can be compatible with a wide range of materials including metals, alloys, and metal compounds. The growth and self-assembly mechanisms of carbon composites via JVBs are explained. For illustration, NiCo alloy nanoparticles are successfully in situ implanted into pomelo vesicles crosslinked carbon (PCC) by JVBs, and their applications as sulfur/carbon cathodes for lithium-sulfur batteries are explored. The well-designed PCC/NiCo-S electrode exhibits superior high-rate properties and enhanced long-term stability. Synergistic reinforcement mechanisms on transportation of ions/electrons of interface reactions and catalytic conversion of lithium polysulfides arising from metal alloy and carbon architecture are proposed with the aid of DFT calculations. The research provides a novel biosynthetic route to rational design and fabrication of carbon composites for advanced energy storage.

A H2O2 Oxidation Approach to Ti3C2/TiO2 for Efficient Photocatalytic Removal of Distinct Organic Pollutants in Water.

To develop versatile photocatalysts for efficient degradation of distinct organic pollutants in water is a continuous pursuit in environment remediation. Herein, we directly oxidize Ti3C2 MXene with hydrogen peroxide to produce C-doped anatase TiO2 nanowires with aggregates maintaining a layered architecture of the MXene. The Ti3C2 MXene provides a titanium source for TiO2, a carbon source for in situ C-doping, and templates for nanowire aggregates. Under UV light illumination, the optimized Ti3C2/TiO2 exhibits a reaction rate constant 1.5 times that of the benchmark P25 TiO2 nanoparticles, toward photocatalytic degradations of trace phenol in water. The mechanism study suggests that photogenerated holes play key roles on the phenol degradation, either directly oxidizing phenol molecules or in an indirect way through oxidizing first the surface hydroxyl groups. The unreacted Ti3C2 MXene, although with trace amounts, is supposed to facilitate electron transfer, which inhibits charge recombination. The unique nanostructure of layered aggregates of nanowires, abundant surface oxygen vacancies arising from the carbon doping, and probably the Ti3C2/TiO2 heterojunction guarantee the high photocatalytic efficiency toward removals of organic pollutants in water. The photocatalyst also exhibits an activity superior to, or at least comparable to, the benchmark P25 TiO2 toward photodegradations for typical persistent organic pollutants of phenol, dye molecule of rhodamine B, antibiotic of tetracycline, pharmaceutical wastewater of ofloxacin, and pesticide of N,N-dimethylformamide, when evaluated in total organic carbon removal.

Species sensitivities to artificial light at night: A phylogenetically controlled multilevel meta-analysis on melatonin suppression.

The rapid urbanization of our world has led to a surge in artificial lighting at night (ALAN), with profound effects on wildlife. Previous research on wildlife's melatonin, a crucial mechanistic indicator and mediator, has yielded inconclusive evidence due to a lack of comparative analysis. We compiled and analysed an evidence base including 127 experiments with 437 observations across 31 wild vertebrates using phylogenetically controlled multilevel meta-analytic models. The evidence comes mainly from the effects of white light on melatonin suppression in birds and mammals. We show a 36% average decrease in melatonin secretion in response to ALAN across a diverse range of species. This effect was observed for central and peripheral melatonin, diurnal and nocturnal species, and captive and free-living populations. We also reveal intensity-, wavelength-, and timing-dependent patterns of ALAN effects. Exposure to ALAN led to a 23% rise in inter-individual variability in melatonin suppression, with important implications for natural selection in wild vertebrates, as some individuals may display higher tolerance to ALAN. The cross-species evidence has strong implications for conservation of wild populations that are subject to natural selection of ALAN. We recommend measures to mitigate harmful impacts of ALAN, such as using 'smart' lighting systems to tune the spectra to less harmful compositions.

Finding the right power balance: Better study design and collaboration can reduce dependence on statistical power.

Power analysis currently dominates sample size determination for experiments, particularly in grant and ethics applications. Yet, this focus could paradoxically result in suboptimal study design because publication biases towards studies with the largest effects can lead to the overestimation of effect sizes. In this Essay, we propose a paradigm shift towards better study designs that focus less on statistical power. We also advocate for (pre)registration and obligatory reporting of all results (regardless of statistical significance), better facilitation of team science and multi-institutional collaboration that incorporates heterogenization, and the use of prospective and living meta-analyses to generate generalizable results. Such changes could make science more effective and, potentially, more equitable, helping to cultivate better collaborations.

Meta-analysis of variation in sport and exercise science: Examples of application within resistance training research.

Meta-analysis has become commonplace within sport and exercise science for synthesising and summarising empirical studies. However, most research in the field focuses upon mean effects, particularly the effects of interventions to improve outcomes such as fitness or performance. It is thought that individual responses to interventions vary considerably. Hence, interest has increased in exploring precision or personalised exercise approaches. Not only is the mean often affected by interventions, but variation may also be impacted. Exploration of variation in studies such as randomised controlled trials (RCTs) can yield insight into interindividual heterogeneity in response to interventions and help determine generalisability of effects. Yet, larger samples sizes than those used for typical mean effects are required when probing variation. Thus, in a field with small samples such as sport and exercise science, exploration of variation through a meta-analytic framework is appealing. Despite the value of embracing and exploring variation alongside mean effects in sport and exercise science, it is rarely applied to research synthesis through meta-analysis. We introduce and evaluate different effect size calculations along with models for meta-analysis of variation using relatable examples from resistance training RCTs.

In situ generation of highly localized chlorine by laser-induced graphene electrodes during electrochemical disinfection.

Laser-induced graphene (LIG) has gained popularity for electrochemical water disinfection due to its efficient antimicrobial activity when activated with low voltages. However, the antimicrobial mechanism of LIG electrodes is not yet fully understood. This study demonstrated an array of mechanisms working synergistically to inactivate bacteria during electrochemical treatment using LIG electrodes, including the generation of oxidants, changes in pH-specifically high alkalinity associated with the cathode, and electro-adsorption on the electrodes. All these mechanisms may contribute to the disinfection process when bacteria are close to the surface of the electrodes where inactivation was independent of the reactive chlorine species (RCS); however, RCS was likely responsible for the predominant cause of antibacterial effects in the bulk solution (i.e., ≥100 mL in our study). Furthermore, the concentration and diffusion kinetics of RCS in solution was voltage-dependent. At 6 V, RCS achieved a high concentration in water, while at 3 V, RCS was highly localized on the LIG surface but not measurable in water. Despite this, the LIG electrodes activated by 3 V achieved a 5.5-log reduction in Escherichia coli (E.coli) after 120-min electrolysis without detectable chlorine, chlorate, or perchlorate in the water, suggesting a promising system for efficient, energy-saving, and safe electro-disinfection.

Spatially Confined Fe7S8 Nanoparticles Anchored on a Porous Nitrogen-Doped Carbon Nanosheet Skeleton for High-Rate and Durable Sodium Storage.

Iron sulfides are widely explored as anodes of sodium-ion batteries (SIBs) owing to high theoretical capacities and low cost, but their practical application is still impeded by poor rate capability and fast capacity decay. Herein, for the first time, we construct highly dispersed Fe7S8 nanoparticles anchored on a porous N-doped carbon nanosheet (CN) skeleton (denoted as Fe7S8/NC) with high conductivity and numerous active sites via facile ion adsorption and thermal evaporation combined procedures coupled with a gas sulfurization treatment. Nanoscale design coupled with a conductive carbon skeleton can simultaneously mitigate the above obstacles to obtain enhanced structural stability and faster electrode reaction kinetics. With the aid of density functional theory (DFT) calculations, the synergistic interaction between CNs and Fe7S8 can not only ensure enhanced Na+ adsorption ability but also promote the charge transfer kinetics of the Fe7S8/NC electrode. Accordingly, the designed Fe7S8/NC electrode exhibits remarkable electrochemical performance with superior high-rate capability (451.4 mAh g-1 at 6 A g-1) and excellent long-term cycling stability (508.5 mAh g-1 over 1000 cycles at 4 A g-1) due to effectively alleviated volumetric variation, accelerated charge transfer kinetics, and strengthened structural integrity. Our work provides a feasible and effective design strategy toward the low-cost and scalable production of high-performance metal sulfide anode materials for SIBs.

Publication bias impacts on effect size, statistical power, and magnitude (Type M) and sign (Type S) errors in ecology and evolutionary biology.

Collaborative efforts to directly replicate empirical studies in the medical and social sciences have revealed alarmingly low rates of replicability, a phenomenon dubbed the 'replication crisis'. Poor replicability has spurred cultural changes targeted at improving reliability in these disciplines. Given the absence of equivalent replication projects in ecology and evolutionary biology, two inter-related indicators offer the opportunity to retrospectively assess replicability: publication bias and statistical power. This registered report assesses the prevalence and severity of small-study (i.e., smaller studies reporting larger effect sizes) and decline effects (i.e., effect sizes decreasing over time) across ecology and evolutionary biology using 87 meta-analyses comprising 4,250 primary studies and 17,638 effect sizes. Further, we estimate how publication bias might distort the estimation of effect sizes, statistical power, and errors in magnitude (Type M or exaggeration ratio) and sign (Type S). We show strong evidence for the pervasiveness of both small-study and decline effects in ecology and evolution. There was widespread prevalence of publication bias that resulted in meta-analytic means being over-estimated by (at least) 0.12 standard deviations. The prevalence of publication bias distorted confidence in meta-analytic results, with 66% of initially statistically significant meta-analytic means becoming non-significant after correcting for publication bias. Ecological and evolutionary studies consistently had low statistical power (15%) with a 4-fold exaggeration of effects on average (Type M error rates = 4.4). Notably, publication bias reduced power from 23% to 15% and increased type M error rates from 2.7 to 4.4 because it creates a non-random sample of effect size evidence. The sign errors of effect sizes (Type S error) increased from 5% to 8% because of publication bias. Our research provides clear evidence that many published ecological and evolutionary findings are inflated. Our results highlight the importance of designing high-power empirical studies (e.g., via collaborative team science), promoting and encouraging replication studies, testing and correcting for publication bias in meta-analyses, and adopting open and transparent research practices, such as (pre)registration, data- and code-sharing, and transparent reporting.

Prussian Blue Analogue-Derived Fe-Doped CoS2 Nanoparticles Confined in Bayberry-like N-Doped Carbon Spheres as Anodes for Sodium-Ion Batteries.

Obvious volume change and the dissolution of polysulfide as well as sluggish kinetics are serious issues for the development of high performance metal sulfide anodes for sodium-ion batteries (SIBs), which usually result in fast capacity fading during continuous sodiation and desodiation processes. In this work, by utilizing a Prussian blue analogue as functional precursors, small Fe-doped CoS2 nanoparticles spatially confined in N-doped carbon spheres with rich porosity were synthesized through facile successive precipitation, carbonization, and sulfurization processes, leading to the formation of bayberry-like Fe-doped CoS2/N-doped carbon spheres (Fe-CoS2/NC). By introducing a suitable amount of FeCl3 in the starting materials, the optimal Fe-CoS2/NC hybrid spheres with the designed composition and pore structure exhibited superior cycling stability (621 mA h g-1 after 400 cycles at 1 A g-1) and improved the rate capability (493 mA h g-1 at 5 A g-1). This work provides a new avenue for the rational design and synthesis of high performance metal sulfide-based anode materials toward SIBs.

In vivo prediction of abdominal fat and breast muscle in broiler chicken using live body measurements based on machine learning.

The purpose of this study was to predict the carcass characteristics of broilers using support vector regression (SVR) and artificial neural network (ANN) model methods. Data were obtained from 176 yellow feather broilers aged 100-day-old (90 males and 86 females). The input variables were live body measurements, including external measurements and B-ultrasound measurements. The predictors of the model were the weight of abdominal fat and breast muscle in male and female broilers, respectively. After descriptive statistics and correlation analysis, the datasets were randomly divided into train set and test set according to the ratio of 7:3 to establish the model. The results of this study demonstrated that it is feasible to use machine learning methods to predict carcass characteristics of broilers based on live body measurements. Compared with the ANN method, the SVR method achieved better prediction results, for predicting breast muscle (male: R2 = 0.950; female: R2 = 0.955) and abdominal fat (male: R2 = 0.802; female: R2 = 0.944) in the test set. Consequently, the SVR method can be considered to predict breast muscle and abdominal fat of broiler chickens, except for abdominal fat in male broilers. However, further revaluation of the SVR method is suggested.

Advanced methods and implementations for the meta-analyses of animal models: Current practices and future recommendations.

Meta-analytic techniques have been widely used to synthesize data from animal models of human diseases and conditions, but these analyses often face two statistical challenges due to complex nature of animal data (e.g., multiple effect sizes and multiple species): statistical dependency and confounding heterogeneity. These challenges can lead to unreliable and less informative evidence, which hinders the translation of findings from animal to human studies. We present a literature survey of meta-analysis using animal models (animal meta-analysis), showing that these issues are not adequately addressed in current practice. To address these challenges, we propose a meta-analytic framework based on multilevel (linear mixed-effects) models. Through conceptualization, formulations, and worked examples, we illustrate how this framework can appropriately address these issues while allowing for testing new questions. Additionally, we introduce other advanced techniques such as multivariate models, robust variance estimation, and meta-analysis of emergent effect sizes, which can deliver robust inferences and novel biological insights. We also provide a tutorial with annotated R code to demonstrate the implementation of these techniques.

A comprehensive database of amphibian heat tolerance.

Rising temperatures represent a significant threat to the survival of ectothermic animals. As such, upper thermal limits represent an important trait to assess the vulnerability of ectotherms to changing temperatures. For instance, one may use upper thermal limits to estimate current and future thermal safety margins (i.e., the proximity of upper thermal limits to experienced temperatures), use this trait together with other physiological traits in species distribution models, or investigate the plasticity and evolvability of these limits for buffering the impacts of changing temperatures. While datasets on thermal tolerance limits have been previously compiled, they sometimes report single estimates for a given species, do not present measures of data dispersion, and are biased towards certain parts of the globe. To overcome these limitations, we systematically searched the literature in seven languages to produce the most comprehensive dataset to date on amphibian upper thermal limits, spanning 3,095 estimates across 616 species. This resource will represent a useful tool to evaluate the vulnerability of amphibians, and ectotherms more generally, to changing temperatures.

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis.

BACKGROUND: Tuberculosis (TB) had been the leading lethal infectious disease worldwide for a long time (2014-2019) until the COVID-19 global pandemic, and it is still one of the top 10 death causes worldwide. One important reason why there are so many TB patients and death cases in the world is because of the difficulties in precise diagnosis of TB using common detection methods, especially for some smear-negative pulmonary tuberculosis (SNPT) cases. The rapid development of metabolome and machine learning offers a great opportunity for precision diagnosis of TB. However, the metabolite biomarkers for the precision diagnosis of smear-positive and smear-negative pulmonary tuberculosis (SPPT/SNPT) remain to be uncovered. In this study, we combined metabolomics and clinical indicators with machine learning to screen out newly diagnostic biomarkers for the precise identification of SPPT and SNPT patients. METHODS: Untargeted plasma metabolomic profiling was performed for 27 SPPT patients, 37 SNPT patients and controls. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was then conducted to screen differential metabolites among the three groups. Metabolite enriched pathways, random forest (RF), support vector machines (SVM) and multilayer perceptron neural network (MLP) were performed using Metaboanalyst 5.0, "caret" R package, "e1071" R package and "Tensorflow" Python package, respectively. RESULTS: Metabolomic analysis revealed significant enrichment of fatty acid and amino acid metabolites in the plasma of SPPT and SNPT patients, where SPPT samples showed a more serious dysfunction in fatty acid and amino acid metabolisms. Further RF analysis revealed four optimized diagnostic biomarker combinations including ten features (two lipid/lipid-like molecules and seven organic acids/derivatives, and one clinical indicator) for the identification of SPPT, SNPT patients and controls with high accuracy (83-93%), which were further verified by SVM and MLP. Among them, MLP displayed the best classification performance on simultaneously precise identification of the three groups (94.74%), suggesting the advantage of MLP over RF/SVM to some extent. CONCLUSIONS: Our findings reveal plasma metabolomic characteristics of SPPT and SNPT patients, provide some novel promising diagnostic markers for precision diagnosis of various types of TB, and show the potential of machine learning in screening out biomarkers from big data.