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To allocate capital to its most sustainable use, market participants need information on companies' sustainability plans and initiatives. This information is disclosed in sustainability reports, but the disclosure process is largely unregulated and voluntary. When do sustainability reports convey relevant information? To answer this question we estimate the heterogeneous effects of nonfinancial disclosure on analysts' estimates of earnings and firms' equity values. We have found that the information content of nonfinancial disclosure is larger when firms are subject to greater information asymmetry, and when nonfinancial information is integrated within a financial context. Moreover, positive responses have a long-lasting impact while negative shocks are corrected within a short window. Overall, our work suggests that market participants' interest in standalone nonfinancial information is limited, and integrated reporting increases the value of sustainability reports. [ABSTRACT FROM AUTHOR]

This paper introduces an improved fuzzy association memory (IFAM), an advanced FAM method based on the T-conorm probability operator. Specifically, the T-conorm probability operator fuzzifies the input data and performs fuzzy logic operations, effectively handling ambiguity and uncertainty during image restoration, which enhances the accuracy and effectiveness of the restoration results. Experimental results validate the performance of IFAM by comparing it with existing fuzzy association memory techniques. The root mean square error shows that the restoration rate of IFAM reached 80%, compared to only 40% for the traditional fuzzy association memory technique. [ABSTRACT FROM AUTHOR]

Markus (2021) argues that the causal modelling frameworks of Pearl and Rubin are not 'strongly equivalent', in the sense of saying 'the same thing in different ways'. Here I rebut Markus' arguments against strong equivalence. The differences between the frameworks are best illuminated not by appeal to their causal semantics, but rather reflect pragmatic modelling choices. [ABSTRACT FROM AUTHOR]

Children with congenital heart disease (CHD) are at increased risk for neurodevelopmental challenges across the lifespan. These are associated with neurological changes and potential acquired brain injury, which occur across a developmental trajectory and which are influenced by an array of medical, sociodemographic, environmental, and personal factors. These alterations to brain development lead to an array of adverse neurodevelopmental outcomes, which impact a characteristic set of skills over the course of development. The current paper reviews existing knowledge of aberrant brain development and brain injury alongside associated neurodevelopmental challenges across the lifespan. These provide a framework for discussion of emerging and potential interventions to improve neurodevelopmental outcomes at each developmental stage. [ABSTRACT FROM AUTHOR]

Occupancy models are a vital tool for ecologists studying the patterns and drivers of species occurrence, but their use often involves selecting among models with different sets of occupancy and detection covariates. The information‐theoretic approach, which employs information criteria such as Akaike's information criterion (AIC) is arguably the most popular approach for model selection in ecology and is often used for selecting occupancy models. However, the information‐theoretic approach risks selecting models that produce inaccurate parameter estimates due to a phenomenon called collider bias, a type of confounding that can arise when adding explanatory variables to a model. Using simulations, we investigated the consequences of collider bias (using an illustrative example called M‐bias) in the occupancy and detection processes of an occupancy model, and explored the implications for model selection using AIC and a common alternative, the Schwarz criterion (or Bayesian information criterion, BIC). We found that when M‐bias was present in the occupancy process, AIC and BIC selected models that inaccurately estimated the effect of the focal occupancy covariate, while simultaneously producing more accurate predictions of the site‐level occupancy probability than other models in the candidate set. In contrast, M‐bias in the detection process did not impact the focal estimate; all models made accurate inferences, while the site‐level predictions of the AIC/BIC‐best model were slightly more accurate. Our results show that information criteria can be used to select occupancy covariates if the sole purpose of the model is prediction, but must be treated with more caution if the purpose is to understand how environmental variables affect occupancy. By contrast, detection covariates can usually be selected using information criteria regardless of the model's purpose. These findings illustrate the importance of distinguishing between the tasks of parameter inference and prediction in ecological modeling. Furthermore, our results underline concerns about the use of information criteria to compare different biological hypotheses in observational studies. [ABSTRACT FROM AUTHOR]

Concerns about a crisis of mass irreplicability across scientific fields ("the replication crisis") have stimulated a movement for open science, encouraging or even requiring researchers to publish their raw data and analysis code. Recently, a rule at the US Environmental Protection Agency (US EPA) would have imposed a strong open data requirement. The rule prompted significant public discussion about whether open science practices are appropriate for fields of environmental public health. The aims of this paper are to assess (1) whether the replication crisis extends to fields of environmental public health; and (2) in general whether open science requirements can address the replication crisis. There is little empirical evidence for or against mass irreplicability in environmental public health specifically. Without such evidence, strong claims about whether the replication crisis extends to environmental public health – or not – seem premature. By distinguishing three concepts – reproducibility, replicability, and robustness – it is clear that open data initiatives can promote reproducibility and robustness but do little to promote replicability. I conclude by reviewing some of the other benefits of open science, and offer some suggestions for funding streams to mitigate the costs of adoption of open science practices in environmental public health. [ABSTRACT FROM AUTHOR]

The idea of causality has lasted for over thousands of years. Unlike the idea of statistical correlation and regression, performing causal modeling and prediction is an even more challenging job. Under the intervention framework of causality, causal modeling is gaining popularity given the advances of big data and computational ability in recent years. In different scientific research areas, there exist three powerful causal modeling methodologies, namely, the potential outcomes method in statistics, the instrumental variables method in economics and Judea Pearl's causal diagram method (do-calculus) in computer science and artificial intelligence. In this paper, by linear causal modeling assumption, we prove that the above three causal methodologies are equivalent. That is, given a causal problem, all of the three modeling methods will generate the same causal relationship conclusion, despite that they own different causal inference processes. During the past one-and-half years, the global economy suffers severe impacts from the COVID-19 pandemic. To fight the deadly pandemic, various social distancing measures and actions, taken by the countries, are effective in curbing the impact of the pandemic over the population. However, such social distancing policy has an adverse effect over the global economy growth; if more stringent measures were taken, then there would be suffering in the forms of much slower economic growth and higher unemployment. In this paper, we study the causal relationships between social distancing, fatality rate and economy growth. This work provides a useful tool for the governments to keep balance between controlling the pandemic and maintaining economic growth. [ABSTRACT FROM AUTHOR]

Using features based on correlation or noncausal dependence metrics can lead to false conclusions. However, recent research has shown that applying causal inference theory in conjunction with Bayesian networks to large-sample-size data can accurately attribute synoptic anomalies. Focusing on the East Asian summer monsoon (EASM), this study adopts a causal inference approach with model averaging to investigate causation of interannual climate variability. We attribute the EASM variability to five winter climate phenomena; our result shows that the eastern Pacific El Niño–Southern Oscillation has the largest causal effect. We also show that the causal precursors of the EASM variability are interpretable in terms of physics. Using linear regression, these precursors can predict the EASM one season ahead, outperforming correlation-based empirical models and three climate models. This study shows that even without large-sample-size data and substantial human intervention, even laymen can implement the causal inference approach to investigate the causes of climatic anomalies and construct reliable empirical models for prediction. Significance Statement: We use causal inference theory to redesign the attribution procedure fundamentally and adjust a causal inference approach to commonly used climate research data. Our study shows that the causal inference approach can exhaustively reveal the causes of climatic anomalies with little human intervention, which is impossible for correlation-based studies. According to this attribution, one can construct models with a better predictive performance than the climate and correlation-based empirical models. Therefore, our causal inference approach will tremendously help both meteorologists and laymen (e.g., stakeholders and policymakers) accurately predict climate phenomena and reveal their interpretable causes. We recommend that it become a standard practice in attribution studies and operational prediction. [ABSTRACT FROM AUTHOR]

Scope: Tomato consumption is associated with many health benefits including lowered risk for developing certain cancers. It is hypothesized that tomato phytochemicals are transported to the liver and other tissues where they alter gene expression in ways that lead to favorable health outcomes. However, the effects of tomato consumption on mammalian liver gene expression and chemical profile are not well defined., Methods and Results: The study hypothesizes that tomato consumption would alter mouse liver transcriptomes and metabolomes compared to a control diet. C57BL/6J mice (n = 11-12/group) are fed a macronutrient matched diet containing either 10% red tomato, 10% tangerine tomato, or no tomato powder for 6 weeks after weaning. RNA-Seq followed by gene set enrichment analyses indicates that tomato type and consumption, in general, altered expression of phase I and II xenobiotic metabolism genes. Untargeted metabolomics experiments reveal distinct clustering between control and tomato fed animals. Nineteen molecular formulas (representing 75 chemical features) are identified or tentatively identified as steroidal alkaloids and isomers of their phase I and II metabolites; many of which are reported for the first time in mammals., Conclusion: These data together suggest tomato consumption may impart benefits partly through enhancing detoxification potential., (© 2024 The Authors. Molecular Nutrition & Food Research published by Wiley-VCH GmbH.)

Cerebral ischemia/reperfusion (I/R) injury is a complex pathophysiological process that can lead to neurological function damage and the formation of cerebral infarction. The p38 MAPK pathway has attracted considerable attention in cerebral I/R injury (IRI), but little research has been carried out on its direct role in vivo. In this study, to observe the effects of p38 MAPK endogenous inhibition on cerebral IRI, p38 heterozygous knockdown (p38KI/+) mice were used. We hypothesized that p38 signaling might be involved in I/R injury and neurological damage reduction and that neurological behavioral deficits improve when p38 MAPK is inhibited. First, we examined the neurological damage and neurological behavioral deficit effects of I/R injury in WT mice. Cerebral I/R injury was induced by the bilateral common carotid artery occlusion (BCCAO) method. The cerebral infarction area and volume were assessed and analyzed by 2,3,5-triphenyltetrazolium chloride (TTC) staining. p38 MAPK and caspase-3 were detected by western blotting. Neuronal apoptosis was measured using TUNEL staining. Neurological deficits were detected by behavioral testing. Furthermore, to assess whether these neuroprotective effects occurred when p38 MAPK was inhibited, p38 heterozygous knockdown (p38KI/+) mice were used. We found that p38 MAPK endogenous inhibition rescued hippocampal cell apoptosis, reduced ischemic penumbra, and improved neurological behavioral deficits. These findings showed that p38 MAPK endogenous inhibition had a neuroprotective effect on IRI and that p38 MAPK may be a potential therapeutic target for cerebral IRI. [ABSTRACT FROM AUTHOR]

Many biological traits covary with body size, resulting in an allometric relationship. Identifying the evolutionary drivers of these traits is complicated by possible relationships between a candidate selective agent and body size itself, motivating the widespread use of multiple regression analysis. However, the possibility that multiple regression may generate misleading estimates when predictor variables are correlated has recently received much attention. Here, we argue that a primary source of such bias is the failure to account for the complex causal structures underlying brains, bodies, and agents. When brains and bodies are expected to evolve in a correlated manner over and above the effects of specific agents of selection, neither simple nor multiple regression will identify the true causal effect of an agent on brain size. This problem results from the inclusion of a predictor variable in a regression analysis that is (in part) a consequence of the response variable. We demonstrate these biases with examples and derive estimators to identify causal relationships when traits evolve as a function of an existing allometry. Model mis‐specification relative to plausible causal structures, not collinearity, requires further consideration as an important source of bias in comparative analyses. [ABSTRACT FROM AUTHOR]

Models at various levels of resolution are commonly used for both forest management and ecological research. They all have comparative advantages and disadvantages, making desirable a better understanding of the relationships between various approaches. Accounting for crown and root morphological plasticity in the limit where equilibrium among neighbors is reached (perfect plasticity) transforms spatial models into nonspatial, distance-independent versions. The links between spatial and nonspatial models obtained through a perfect plasticity assumption are more realistic than ignoring spatial structure by a mean field approximation. This article also reviews the connection between distance-independent models and size distributions and how distributions evolve over time and relate to whole-stand descriptions. In addition, some ways in which stand-level knowledge feeds back into detailed individual-tree formulations are demonstrated. This presentation is intended to be accessible to nonspecialists. [ABSTRACT FROM AUTHOR]

Neurological deficits are a serious and common sequelae of congenital heart disease (CHD). While their underlying mechanisms have not been fully characterized, their manifestations are well-known and understood to persist through adulthood. Development of therapies to address or prevent these deficits are critical to attenuate future morbidity and improve quality of life. In this review, we aim to summarize the current status of neuroprotective therapy in CHD. Through an exploration of present research in the pre-operative, intra-operative, and post-operative phases of patient management, we will describe existing clinical and bench efforts as well as current endeavors underway within this research area. [ABSTRACT FROM AUTHOR]

Teleconnections are sources of predictability for regional weather and climate, but the relative contributions of different teleconnections to regional anomalies are usually not understood. While physical knowledge about the involved mechanisms is often available, how to quantify a particular causal pathway from data are usually unclear. Here, we argue for adopting a causal inference-based framework in the statistical analysis of teleconnections to overcome this challenge. A causal approach requires explicitly including expert knowledge in the statistical analysis, which allows one to draw quantitative conclusions. We illustrate some of the key concepts of this theory with concrete examples of well-known atmospheric teleconnections. We further discuss the particular challenges and advantages these imply for climate science and argue that a systematic causal approach to statistical inference should become standard practice in the study of teleconnections. [ABSTRACT FROM AUTHOR]

Rubin and Pearl offered approaches to causal effect estimation and Lewis and Pearl offered theories of counterfactual conditionals. Arguments offered by Pearl and his collaborators support a weak form of equivalence such that notation from the rival theory can be re-purposed to express Pearl's theory in a way that is equivalent to Pearl's theory expressed in its native notation. Nonetheless, the many fundamental differences between the theories rule out any stronger form of equivalence. A renewed emphasis on comparative research can help to guide applications, further develop each theory, and better understand their relative strengths and weaknesses. [ABSTRACT FROM AUTHOR]

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We propose a new modified Parker's method for efficient gravitational forward modeling and inversion using general polyhedral models. We have made several important modifications to the classical method, including: (a) The new method is now applicable to a general polyhedron represented by triangulated surface or tetrahedral mesh, and with arbitrarily variable 3D density distribution. (b) An optimal Fourier‐domain sampling strategy is used to improve the numerical accuracy of the new algorithm significantly. (c) A simple and effective automatic layering technique is introduced to accelerate the convergence rate of Parker's method. The method is demonstrated using both synthetic and real polyhedral models, including a sphere model approximated by a polyhedron, two asteroids, a digital elevation model in the Himalaya region, and the Yucca Flat basin model in Nevada. The numerical results show that, compared with analytical solutions of polyhedral models in the space domain, the modified Parker's method can improve the computational efficiency by several orders of magnitude while obtaining almost the same simulation results. The difference is well below existing instrumentation error level. By embedding the new forward algorithm into an iterative process, it can be used for fast inversion of density interfaces. Our new method is suitable for the efficient modeling and inversion of gravitational potential, gravitational vector, and gravitational gradient tensor caused by polyhedral models with a large number of faces, representing geological abnormal bodies, asteroids, and single or multilayer density interface models with triangulated surfaces. Plain Language Summary: The original Parker's method for gravitational forward modeling of layered models using regular grid data is modified to include general polyhedron models with irregular surface and either constant or variable density distributions. An optimal Fourier‐domain sampling strategy, together with an effective automatic layering technique, are introduced to improve the numerical accuracy and convergence rate of the new modified Parker's method. A combination of the forward algorithm with an iterative process provides an effective way for the inversion of density interfaces. We have successfully applied the new algorithm to the gravitational forward modeling of a synthetic sphere model, two asteroids, a digital elevation model in the Himalaya region, and to the inversion of basement structure of Yucca Flat basin (Nevada). Key Points: Parker's method for layered models with regular grids is modified to include polyhedrons with irregular surfaces and arbitrary densityOptimal spectral sampling and automatic layering techniques are introduced to improve the accuracy and convergence of the new methodThe new method has been successfully applied to the gravity forward and inverse modeling of synthetic and real‐world polyhedral models [ABSTRACT FROM AUTHOR]

In this paper, the author enumerates cardiac defects with a functionally single ventricle, summarizes single ventricle physiology, presents a summary of management strategies to address the single ventricle defects, goes over the steps of staged total cavo-pulmonary connection, cites the prevalence of inter-stage mortality, names the causes of inter-stage mortality, discusses strategies to address the inter-stage mortality, reviews post-Fontan issues, and introduces alternative approaches to Fontan circulation. [ABSTRACT FROM AUTHOR]

According to the hypothesis-generator account, valid extrapolations from a source to a target system are circular, since they rely on knowledge of relevant similarities and differences that can only be obtained by investigating the target, thus removing the need to extrapolate; hence, extrapolative reasoning can only be useful as a method for generating hypotheses. I reject this view in favour of an inferential account, focused on extrapolations underpinning the aggregation of experimental results, and explore two lines of argumentation supporting the conclusion that these extrapolations can be validated in a noncircular manner. The first argument relies on formal proofs of inferential validity demonstrating that it is possible to reason from prior knowledge of causal structures in order to determine whether a claim can be extrapolated. The second argument builds on the fact that the hypothesis-generator account overlooks key inferential and experimental practices resulting in progressively better-informed extrapolations. [ABSTRACT FROM AUTHOR]

In this paper, I elaborate a realist post-disciplinary methodology. Its starting point are disciplines. Some features of disciplines are well understood. But the implications of their irreducibility to mechanical procedure is rarely acknowledged. Building on this observation this paper proceeds through investigating transdisciplinary methodologies. First science as usual in which disciplines are the building blocks of interdisciplinary science, additive in producing new approaches to the investigation of increasingly complex and broader issues. And then a pragmatic methodology in which knowledge is revised at the interface between the scientific community and a multitude of social actors with their competing demands and criteria of assessment. This investigation offers contrast that allows for the distinctive features of a post-disciplinary realist methodology to be identified; a narrative arc of inference to be described; and a call for a far more expansive and coherent post-disciplinary science. [ABSTRACT FROM AUTHOR]

The primary goal of drug developers is to establish efficient and effective therapeutic protocols. Multifactorial pathologies, including dynamical diseases and complex disorders, can be difficult to treat, given the high degree of inter- and intra-patient variability and nonlinear physiological relationships. Quantitative approaches combining mechanistic disease modeling and computational strategies are increasingly leveraged to rationalize pre-clinical and clinical studies and to establish effective treatment strategies. The development of clinical trials has led to new computational methods that allow for large clinical data sets to be combined with pharmacokinetic and pharmacodynamic models of diseases. Here, we discuss recent progress using in silico clinical trials to explore treatments for a variety of complex diseases, ultimately demonstrating the immense utility of quantitative methods in drug development and medicine. [ABSTRACT FROM AUTHOR]

Illuminating dimerization: Proteins of the SMC family are chromosomal organizers involved in sister chromatid cohesion, chromosome condensation, and DNA repair. Unlike other eukaryotic family members, SMCHD1 forms homodimers, rather than heterodimers, and has a distinct domain architecture. Dysregulation of SMCHD1 function results in a form of muscular dystrophy and a developmental disorder. Chen et al. solved the x-ray crystal structure of the Smchd1 hinge domain, which is important for homodimerization and nucleic acid binding. Site-directed mutagenesis studies identified critical residues involved in SMCHD1 function in cells. Together, these data suggest how mutations in the SMCHD1 hinge domain contribute to human disease. Structural maintenance of chromosomes flexible hinge domain containing 1 (SMCHD1) is an epigenetic regulator in which polymorphisms cause the human developmental disorder, Bosma arhinia micropthalmia syndrome, and the degenerative disease, facioscapulohumeral muscular dystrophy. SMCHD1 is considered a noncanonical SMC family member because its hinge domain is C-terminal, because it homodimerizes rather than heterodimerizes, and because SMCHD1 contains a GHKL-type, rather than an ABC-type ATPase domain at its N terminus. The hinge domain has been previously implicated in chromatin association; however, the underlying mechanism involved and the basis for SMCHD1 homodimerization are unclear. Here, we used x-ray crystallography to solve the three-dimensional structure of the Smchd1 hinge domain. Together with structure-guided mutagenesis, we defined structural features of the hinge domain that participated in homodimerization and nucleic acid binding, and we identified a functional hotspot required for chromatin localization in cells. This structure provides a template for interpreting the mechanism by which patient polymorphisms within the SMCHD1 hinge domain could compromise function and lead to facioscapulohumeral muscular dystrophy. [ABSTRACT FROM AUTHOR]

We report a case of a middle-aged female who presented with altered mental status, hypotension, and hypoglycemia and was diagnosed with secondary adrenal insufficiency. She was also found to have elevated troponin I on initial evaluation with diffuse T wave inversions on electrocardiogram. Transthoracic echocardiogram revealed ejection fraction of 38% with apical akinesia. Subsequent left heart catheterization revealed clean coronary arteries. She was diagnosed with typical Takotsubo cardiomyopathy secondary to adrenal insufficiency. She was managed with IV hydrocortisone with resolution of symptoms. This article adds to the select few cases in the literature of the association of Takotsubo cardiomyopathy resulting from secondary adrenal insufficiency. [ABSTRACT FROM AUTHOR]

Recent discussions of model selection and multimodel inference highlight a general challenge for researchers: how to convey the explanatory content of a hypothesized model or set of competing models clearly. The advice from statisticians for scientists employing multimodel inference is to develop a well‐thought‐out set of candidate models for comparison, though precise instructions for how to do that are typically not given. A coherent body of knowledge, which falls under the general term causal analysis, now exists for examining the explanatory scientific content of candidate models. Much of the literature on causal analysis has been recently developed, and we suspect may not be familiar to many ecologists. This body of knowledge comprises a set of graphical tools and axiomatic principles to support scientists in their endeavors to create "well‐formed hypotheses," as statisticians are asking them to do. Causal analysis is complementary to methods such as structural equation modeling, which provides the means for evaluation of proposed hypotheses against data. In this paper, we summarize and illustrate a set of principles that can guide scientists in their quest to develop explanatory hypotheses for evaluation. The principles presented in this paper have the capacity to close the communication gap between statisticians, who urge scientists to develop well‐thought‐out coherent models, and scientists, who would like some practical advice for exactly how to do that. [ABSTRACT FROM AUTHOR]

We have reported consistent associations of prenatal organophosphate pesticide (OP) exposure with poorer cognitive function and behavior problems in our Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS), a birth cohort of Mexican American youth in California's agricultural Salinas Valley. However, there is little evidence on how OPs affect neural dynamics underlying associations. We used functional near-infrared spectroscopy (fNIRS) to measure cortical activation during tasks of executive function, attention, social cognition, and language comprehension in 95 adolescent CHAMACOS participants. We estimated associations of residential proximity to OP use during pregnancy with cortical activation in frontal, temporal, and parietal regions using multiple regression models, adjusting for sociodemographic characteristics. OP exposure was associated with altered brain activation during tasks of executive function. For example, with a 10-fold increase in total OP pesticide use within 1 km of maternal residence during pregnancy, there was a bilateral decrease in brain activation in the prefrontal cortex during a cognitive flexibility task (β = -4.74; 95% CI: -8.18, -1.31 and β = -4.40; 95% CI: -7.96, -0.84 for the left and right hemispheres, respectively). We also found that prenatal OP exposure was associated with sex differences in brain activation during a language comprehension task. This first functional neuroimaging study of prenatal OP exposure suggests that pesticides may impact cortical brain activation, which could underlie previously reported OP-related associations with cognitive and behavioral function. Use of fNIRS in environmental epidemiology offers a practical alternative to neuroimaging technologies and enhances our efforts to assess the impact of chemical exposures on neurodevelopment. [ABSTRACT FROM AUTHOR]

Childhood asthma is a major public health concern and has significant adverse impacts on the lives of the children and their families, and on society. There is an emerging link between air pollution, which is ubiquitous in our environment, particularly in urban centers, and incident childhood asthma. Here, using data from 3 successive cohorts recruited from the same 9 communities in southern California over a span of 20 y (1993 to 2014), we estimated asthma incidence using G-computation under hypothetical air pollution exposure scenarios targeting nitrogen dioxide (NO2) and particulate matter <2.5 µm (PM2.5) in separate interventions. We reported comparisons of asthma incidence under each hypothetical air pollution intervention with incidence under the observed natural course of exposure; results that may be more tangible for policymakers compared with risk ratios. Model results indicated that childhood asthma incidence rates would have been statistically significantly higher had the observed reduction in ambient NO2 in southern California not occurred in the 1990s and early 2000s, and asthma incidence rates would have been significantly lower had NO2 been lower than what it was observed to be. For example, compliance with a hypothetical standard of 20 ppb NO2 was estimated to result in 20% lower childhood asthma incidence (95% CI, -27% to -11%) compared with the exposure that actually occurred. The findings for hypothetical PM2.5 interventions, although statistically significant, were smaller in magnitude compared with results for the hypothetical NO2 interventions. Our results suggest a large potential public health benefit of air pollutant reduction in reduced incidence of childhood asthma. [ABSTRACT FROM AUTHOR]

Objective. To determine whether the administration of intra-arrest cyclosporine (CCY) and methylprednisolone (MP) preserves left ventricular ejection fraction (LVEF) and cardiac output (CO) after return of spontaneous circulation (ROSC). Methods. Eleven, 25-30kg female swine were randomized to receive 10mg/kg CCY + 40mg MP or placebo, anesthetized and given a transthoracic shock to induce ventricular fibrillation. After 8 minutes, standard CPR was started. After two additional minutes, the experimental agent was administered. Animals with ROSC were supported for up to 12h with norepinephrine as needed. Echocardiography was performed at baseline, and 1, 2, 6 and 12h post-ROSC. Analysis was performed using generalized estimating equations (GEE) after downsampling continuously sampled data to 5 minute epochs. Results. Eight animals (64%) achieved ROSC after a median of 7 [IQR 5-13] min of CPR, 2 [ IQR 1-3] doses of epinephrine and 2 [IQR 1-5] defibrillation shocks. Animals receiving CCY+MP had higher post ROSC MAP (GEE coefficient -10.2, P = <0.01), but reduced cardiac output (GEE coefficient 0.8, P = <0.01) compared to placebo. There was no difference in LVEF or vasopressor use between arms. Conclusions. Intra-arrest cyclosporine and methylprednisolone decreased post-arrest cardiac output and increased mean arterial pressure without affecting left ventricular ejection fraction. [ABSTRACT FROM AUTHOR]

The extreme length of chromosomal DNA requires organizing mechanisms to both promote functional genetic interactions and ensure faithful chromosome segregation when cells divide. Microscopy and genome‐wide contact frequency analyses indicate that intra‐chromosomal looping of DNA is a primary pathway of chromosomal organization during all stages of the cell cycle. DNA loop extrusion has emerged as a unifying model for how chromosome loops are formed in cis in different genomic contexts and cell cycle stages. The highly conserved family of SMC complexes have been found to be required for DNA cis‐looping and have been suggested to be the enzymatic core of loop extruding machines. Here, the current body of evidence available for the in vivo and in vitro action of SMC complexes is discussed and compared to the predictions made by the loop extrusion model. How SMC complexes may differentially act on chromatin to generate DNA loops and how they could work to generate the dynamic and functionally appropriate organization of DNA in cells is explored. Numerous studies have suggested that SMC complexes can act as the enzymatic core of chromosomal machines that drive looping of DNA. Here, the authors discuss the current body of evidence available for the in vivo and in vitro action of SMC complexes, and compare them to the predictions made by the loop extrusion model. [ABSTRACT FROM AUTHOR]

Dynamic environment near heterogeneous asteroids is worth studying since many small bodies have varied internal structures. In this paper, we investigated the effect of temporary resonance with the heterogeneous Itokawa. Zero-power curves divide the space into four regions. The test particle experiences orbital energy-enhancing in the Region I and Region III, but receding in the Region II and Region IV. Moreover, there are differences in zero-power curves between the heterogeneous and homogeneous cases. The absolute values of gravity power in the gravitational field of heterogeneous Itokawa are more significant than those of the homogeneous one. It means the particle suffers from stronger energy change near the heterogeneous body. Numerical simulations conducted in Region II and Region III verify the difference. Thus, vicinal ejecta is easier to run away from a heterogeneous Itokawa, which may lead to a more frequent exchange of materials on the surface. These results give a better understanding of dust’s motion near a heterogeneous body. Moreover, the performance of temporary resonance near a heterogeneous body might serve as a guiding principle for probe orbiting an asteroid with a varied internal structure. [ABSTRACT FROM AUTHOR]

Genome maintenance requires various nucleoid‐associated factors in prokaryotes. Among them, the SMC (Structural Maintenance of Chromosomes) protein has been thought to play a static role in the organization and segregation of the chromosome during cell division. However, recent studies have shown that the bacterial SMC is required to align left and right arms of the emerging chromosome and that the protein dynamically travels from origin to Ter region. A rod form of the SMC complex mediates DNA bridging and has been recognized as a machinery responsible for DNA loop extrusion, like eukaryotic condensin or cohesin complexes, which act as chromosome organizers. Attention is now turning to how the prototype of the complex is loaded on the entry site and translocated on chromosomal DNA, explaining its overall conformational changes at atomic levels. Here, we review and highlight recent findings concerning the prokaryotic SMC complex and discuss possible mechanisms from the viewpoint of protein architecture. [ABSTRACT FROM AUTHOR]

Dynamical subseasonal-to-seasonal (S2S) weather forecasting has made strides in recent years, thanks partly to better initialization and representation of physical variables in models. For instance, realistic initializations of snow and soil moisture in models yield enhanced temperature predictability on S2S time scales. Snow depth and soil moisture also mediate month-to-month persistence of near-surface air temperature. Here the role of snow depth as predictor of temperature one month ahead in the Northern Hemisphere is examined via two causal pathways. Through the first pathway, snow depth anomalies in month 1 persist into month 2 and are then linked to temperature anomalies through snow-temperature feedback mechanisms. The first pathway is active from fall to summer, and its effect peaks before the melting season: in winter in the low latitudes, in spring in the midlatitudes, and in early summer in the high latitudes. The second pathway, where snow depth anomalies in month 1 lead to soil moisture anomalies in month 2 (through melting), which are then linked to temperature anomalies in month 2 through soil moisture-temperature feedbacks, is most active in spring and summer. The effect of the second pathway peaks during the melting season, namely, later in the year than the first pathway. The latitudes of the highest mediated effect through both pathways follow a seasonal cycle, shifting northward along with the seasonal insolation cycle. In keeping with this seasonal cycle, the highest snow depth mediation occurs to the north and the highest soil moisture mediation to the south of the latitudes with the highest overall temperature predictability from snow depth. [ABSTRACT FROM AUTHOR]

In the title compound, C15H12N4O4, the dihedral angle between the heterocyclic rings is 12.60 (8)°, and that between the benzene ring and the adjacent heterocyclic ring is 85.14 (6)°. In the crystal, a combination of N--H⋯O and O--H⋯O hydrogen bonds link molecules related by a glide plane into a C(5) C(6)[R²2(9)] chain of rings, which is a distinctly different packing motif to those observed in hydrated modifications of this compound. [ABSTRACT FROM AUTHOR]

34-year-old woman, 16 weeks pregnant, with a personal history of cervical conization and previous cesarean section. Consultation for discomfort in the right groin region and signs of inflammation in the area, without a clear tumor. She refers that in the first pregnancy she presented the same symptoms. In the intergestational period she has not noticed these symptoms. On physical examination, the 16-week pregnant abdomen did not reveal clear signs of hernia, although there was slight pain on palpation. It was decided to request a pelvic ultrasound to rule out the presence of hernia or other soft tissue lesions in the groin region. [ABSTRACT FROM AUTHOR]

Heart failure (HF) remains a major cause of death and hospitalization worldwide. Despite medical advances, the prognosis of HF remains poor and new therapeutic approaches are urgently needed. The development of new therapies for HF is hindered by inappropriate or incomplete preclinical studies. In these guidelines, we present a number of recommendations to enhance similarity between HF animal models and the human condition in order to reduce the chances of failure in subsequent clinical trials. We propose different approaches to address safety as well as efficacy of new therapeutic products. We also propose that good practice rules are followed from the outset so that the chances of eventual approval by regulatory agencies increase. We hope that these guidelines will help improve the translation of results from animal models to humans and thereby contribute to more successful clinical trials and development of new therapies for HF. [ABSTRACT FROM AUTHOR]