AbstractWater levels in the Laurentian Great Lakes have fluctuated dramatically over recent decades. Since 2015, each of the lakes has reached a record high, often following a recent record or near-record low. These exceptional swings have motivated examinations of changes in lake level variability, particularly given the known climate change-driven intensification of the hydrologic cycle. Recent studies have presented evidence of rising lake level variability and changing water balance components (i.e., overlake precipitation, overlake evaporation, and basin runoff), however a full characterization of trends in variability is needed. Here, we build on previous analyses by quantitatively answering the question: are trends in hydrologic interannual variability over the Great Lakes over recent decades – both lake levels and individual hydrologic components – statistically robust, or simply the result of random chance? Using two non-parametric trend tests, we find that interannual variability of lake levels is significantly increasing in Lakes Superior, Michigan-Huron, and Erie, while decreasing in well-regulated Lake Ontario. We also find robust increasing variability in overlake precipitation, overlake evaporation, and basin runoff for the vast majority of lakes. These results suggest that critical work must follow to both attribute causes of detected trends and to determine if trends will continue increasing in the future with continued anthropogenic climate change. 1. IntroductionRecent extraordinary shifts in Great Lakes water levels have prompted questions about potential changes in year-over-year lake level variability. Changes in the variability of Great Lake levels, namely, how quickly lake levels fluctuate between higher and lower water levels, can have dramatic environmental and societal impacts. Examples include shifts in shoreline erosion patterns (Gronewold and Stow, 2014; Davidson-Arnott, 2016), shipping costs (Millerd, 2010; Lindeberg and Albercook, 2000; Wang et al., 2012), tourism and recreation (Wall, 1998; Hartmann, 1990), and risks to critical infrastructure like water resource management (de Loe and Kreutzwiser, 2000), hydropower (Meyer et al., 2017), and toxic waste facilities (Environmental Law and Policy Center, 2022). Researchers and the public alike have thus been captivated by the rapid transition of Great Lake levels between record low and high lake levels and the resultant impacts (e.g., Gronewold et al., 2021; Egan, 2021). This interest is further motivated by the observed and projected intensification of the hydrologic cycle due to anthropogenic climate change (IPCC, 2021; Seager, 2014). Within this context, Gronewold et al. (2021) presented evidence of rising lake level variability and described the situation caused by this hydrologic cycle intensification as a “continental-scale hydrological tug-of-war” between changing water balance components.Lake levels of large lakes are dominated by three net basin supply (NBS) components: overlake precipitation, overlake evaporation, and basin runoff, where the collective balance of these three components largely determine Great Lakes levels (Δlake storage = poverlake + rbasin - eoverlake) (Gronewold et al., 2021). Note that we define runoff here as the amount of water entering the lake from all incoming river systems in a respective Great Lakes basin, excepting flow from any upstream lakes. These components are all expected to change with the amplification of anthropogenic climate change and trends in these components have already been well observed. For instance, Javed et al. (2019) find increasing evaporation, spatially mixed results on precipitation, and no change in runoff, over Lakes Michigan and Huron. Harp and Horton (2022) characterize an increase in wet day precipitation intensity of ~5% over the U.S.-portion of the Great Lakes basin from 1951 to the present. Looking forward, Mailhot et al. (2019) found increases in net basin supply components with an intensifying annual cycle, but claimed “no long-term changes can be confidently estimated for lake levels.” Kayastha et al. (2022) used a regionally downscaled model to project future Great Lake levels and found a rise in both water levels and net basin supply components, particularly overlake precipitation and runoff. The climate change-driven increase in Great Lake levels was similarly projected by Van De Weghe et al. (2022). These findings differ with earlier work by Hayhoe et al. (2010) which projected falling Great Lakes levels based on increasing evaporation rates with increasing regional temperatures. Examining individual hydrologic components, Wang et al. (2018) project a 16% increase in lake evaporation by the end of the 21st century in a high greenhouse gas emissions scenario (RCP8.5). However, despite examination of trends of lake levels, little attention has been given to statistically characterizing observed trends in variability for either Great Lakes levels or their net basin supply components. Here, we address this knowledge gap by providing a statistically rigorous assessment of changes in interannual variability over the past five decades.

Affect recognition in humans based on physiological signals guided by supervised techniques relies on appropriate affect representations. In the current state of the art, the affect representation is motivated by either the dimensional (2D and 3D) or the discrete emotion model. The question is whether these models are capable enough for ground truth establishment for different emotion categories and is it valid across all individuals. We use EEG signals as a test case supported by an unsupervised technique. The DEAP dataset is used for the experimentations. Experiments are conducted to compare the emotion identified by different subjects concerning emotion models and their corresponding EEG responses. Results are prepared based on time, frequency, and time-frequency domain analysis that provide insights into the effectiveness of emotion models. We conclude the work by suggesting the consideration of individual perspectives while choosing the emotion categories for affect recognition.

Due to its importance in the individual and national economies, stock market prediction is an important study subject. A stock market's success is based on the rise or fall of industrial, financial, medical, local, and global stock prices in a certain region and globally. Electronic news and public opinion impact stock markets. Since the Covid-19 epidemic, global stock prices have fluctuated because to economic uncertainty. Social media has been used to spread pandemic news, comments, and forecasts. Such news has affected global stock marketplaces, which are prone to polarity, by affecting investors' decision-making and changing the perspective of stock-interested people. This paper proposes a methodology to examine the influence of Covid-19 internet news data on stock market performance. News data comes from news.pk and internet news sites, while stock data is from Yahoo Finance. Text characteristics are retrieved from news information using TF-IDF, and stock-related features are produced for stock value prediction. A hybrid approach that combines evolutionary algorithms such as Genetic Algorithm, Harris hawks Optimization, and Particle Swarm Optimization with various Machine Learning and Deep Learning-based models. The empirical analysis-based results reveal the proposed hybrid model outperforms traditional ML models using standard performance evaluation measures.

The rise of AI is viewed as the next important technology in human history that would serve as a driver for sustainable development. Accordingly, several organizations have incorporated AI into their operations – including healthcare, hence, attracting extant literature to AI discussions. However, AI literature in healthcare has focused on medical diagnosis, operations, and prescriptions – to the neglect of supply chain (SC). This study bridges this knowledge gap by exploring the drivers and success factors of AI-enabled medical drones’ adoption in public healthcare SC. Drawing on data from the world’s largest medical drone programme in Ghana, we find that the need to make the public healthcare SC efficient with the aim of improving the socio-economic life of the citizens is the main driver of the policy adoption. Several success factors are identified and categorized into three phases – policy, project, and operation. Long-term policy and operating sustainability are delineated.

The timing of life history events is being altered by climate change and other anthropogenic environmental changes, with potential functional consequences. Such changes may be particularly important in mutualisms, such as plant-pollinator interactions, where timing mismatches can impact fitness in both plants and pollinators. Numerous studies have examined how changes in flowering phenology could impact the seed set of individual species, yet it is the changes to relative fitness among species that shape coexistence and community composition. To understand how phenological change can affect plant community seed set, we conducted a large-scale snowmelt acceleration experiment to advance flowering phenology in eight montane meadow communities across two valleys in the Rocky Mountains of Colorado, USA. Each snowmelt acceleration plot (10 m x 14 m) was paired with an adjacent control plot without snowmelt manipulation (16 plots total). In six plots and their adjacent controls, we measured how phenological change altered the fecundity of eight co-occurring plant species, evaluating pollen limitation in five species using hand pollination treatments. Although accelerating flowering phenology resulted in lower overall fecundity and greater pollen limitation in the examined assemblage of focal species, these changes occurred in different directions depending on the species. Species-dependent responses were also reflected in a significant change in the relative proportions of seeds produced by different plants. Our results suggest potential impacts on future plant community composition resulting from early snowmelt, and underscore the importance of studying the impacts of phenological change at a community level.

Coral reef health is tightly connected to the coral microbiome. Coral bleaching and disease outbreaks have caused an unprecedented loss in coral cover worldwide, particularly correlated to a warming ocean. Coping mechanisms of the coral holobiont under high temperatures are not completely described, but the associated microbial community is a potential source of acquired heat-tolerance. The relationship between stress and stability in the microbiome is key to understanding the role that the coral microbiome plays in thermal tolerance. According to the Anna Karenina Principle (AKP), stress or disease will increase instability and stochasticity among animal microbiomes. Here we investigate whether heat stress results in microbiomes that follow the AKP. We used shotgun metagenomics in an experimental setting to understand the dynamics of microbial taxa and genes in the surface mucous layer (SML) microbiome of the coral Pseudodiploria strigosa under heat treatment. The metagenomes of corals exposed to heat stress showed high similarity, indicating a deterministic and stable response of the coral microbiome to disturbance, in opposition to the AKP. We hypothesize that this stability is the result of a selective pressure towards a coral microbiome that is assisting the holobiont to withstand heat stress. The coral SML microbiome responded to heat stress with an increase in the relative abundance of taxa with probiotic potential, and functional genes for nitrogen and sulfur acquisition. These consistent and specific microbial taxa and gene functions that significantly increased in proportional abundance in corals exposed to heat are potentially beneficial to coral health and thermal resistance.

Ephemeral gully erosion is a primary mode of soil erosion that is highly visible, affecting soil productivity and restricting land use. Watershed is the basic unit of soil erosion control; existing research has focused on several typical ephemeral gullies or slopes, which do not fully display changes in ephemeral gullies at a watershed scale. This study analyzed the spatial-temporal evolution and development rate of ephemeral gully erosion at the watershed scale on the Loess Plateau from 2009 to 2021 using remote sensing images (0.5 m resolution), unmanned aerial vehicles (UAV), and field investigations. The results revealed that: (1) most ephemeral gullies occurred in southwestern parts of the watershed, with many hills and large slope gradients; (2) average growth rates of each ephemeral gully frequency, length, density, dissection degree, and width were 2.87 km 2 y –1, 1.66 m y –1, 0.12 km km –2 y –1, 0.0125% y –1, and 0.04 m y –1 , respectively; (3) ephemeral gully erosion volume ( V) and length ( L) had a good power function relationship: V = 0 . 0842 L 1 . 1932   ( R 2 = 0 . 80 ) . The root mean square error (RMSE) and coefficient of determination (R 2) between the measured and predicted ephemeral gully volumes suggest that the V–L relationship has a good predictive ability for ephemeral gully volume. Thus, the V–L model was used to evaluate the development rate of ephemeral gully erosion volume in small watersheds from 2009 to 2021, revealing an average value of 743.20 m 3 y –1. This study proposed a feasible model for assessing ephemeral gully volume and volume changes at a watershed scale using high-resolution remote sensing images, providing a reference for understanding the development of ephemeral gully erosion in small watersheds over time.

Background School-aged children and school reopening dates have important roles in community influenza transmission. Although many studies evaluated the impact of reactive closures during seasonal and pandemic influenza outbreaks on medically attended influenza in surrounding communities, few assess the impact of planned breaks (i.e., school holidays) which coincide with influenza seasons, while accounting for differences in seasonal peak timing. Here, we analyze the effects of winter and spring breaks on influenza risk in school-aged children, measured by student absenteeism due to influenza-like illness (a-ILI). Methods We compared a-ILI counts in the two-week periods before and after each winter and spring break over five consecutive years in a single school district. We introduced a “pseudo-break” of 9 days’ duration between winter and spring break each year when school was still in session to serve as a control. The same analysis was applied to each pseudo-break to support any findings of true impact. Results We found strong associations between winter and spring breaks and a reduction in influenza risk, with a nearly 50% reduction in a-ILI counts post-break compared to the period before break, and the greatest impact when break coincided with increased local influenza activity. Conclusions These findings suggest that brief breaks of in-person schooling, such as planned breaks lasting 9-16 calendar days, can effectively reduce influenza in schools and community spread. Additional analyses investigating the impact of well-timed shorter breaks on a-ILI may determine an optimal duration for brief school closures to effectively suppress community transmission of influenza.

Considering the spatial omnipresence of the threat to biodiversity and limited resources and time for conservation and restoration, it is crucial to prioritize conservation and restoration activities to maximize benefits. By transporting marine-derived nutrients to freshwater and surrounding ecosystems, anadromous salmonids contribute greatly to biodiversity maintenance; however, their abundance has been decreased by human activities in many regions. Salmon populations are mainly governed by their productivity in the freshwater life stage; therefore, freshwater productivity, namely, the number of juveniles migrating to the ocean per reproducing parent, should be investigated to maintain healthy populations. Given that productivity decreases dramatically in response to flooding, the flood disturbance intensity controlled by hydrogeomorphology at a watershed scale may strongly influence the freshwater productivity of salmonids. In this study, we evaluated the effect of watershed hydrogeomorphology on the productivity of pink salmon (Oncorhynchus gorbuscha). We surveyed the escapement and number of fry migrants of pink salmon and measured environmental factors, including the average watershed slope and stream power index, as parameters of hydrogeomorphology. The freshwater productivity of pink salmon differed among the streams investigated and was negatively affected by average watershed slope, stream power, and average watershed maximum daily precipitation. These results indicated that flood disturbance reduces the freshwater productivity of pink salmon and that salmon productivity in an individual stream can be predicted by watershed hydrogeomorphology. Our approach can be applied to other anadromous salmonids that have spawning behaviour similar to that of pink salmon, which bury eggs in gravel. Predicting highly productive habitats based on the present study can contribute to planning and prioritizing habitat conservation and restoration for anadromous salmonids.

Spatial behavior, including home-ranging behaviors, habitat selection, and movement, can be extremely informative in estimating how animals respond to landscape heterogeneity. Responses in these spatial behaviors to factors such as human modification and resources on the landscape can highlight a species' spatial strategy to maximize fitness and minimize risk. These strategies can vary on spatial, temporal, and individual scales, and the combination of behaviors on these scales can lead to very different strategies among species. Harnessing the variation present at these scales, we developed a framework for predicting how species may respond to changes in their environments on a gradient ranging from generic, where a species exhibits broad-stroke spatial responses to their environment, to nuanced, in which a species uses a combination of temporal and spatial strategies paired with functional responses in selection behaviors. Using 46 GPS-tracked bobcats and coyotes inhabiting a landscape encompassing a range of human modification, we evaluated where each species falls along the generic-to-nuanced gradient. Bobcats and coyotes studied occupied opposite ends of this gradient, using different strategies in response to human modification in their home ranges, with bobcats broadly expanding their home range with increases in human modification and clearly selecting for or avoiding features on the landscape with temporal consistency. Meanwhile, coyotes did not expand their home ranges with human modification, but instead displayed temporal and spatial adjustments in their functional responses to human modification. These differences in response to habitat, resources, and risk between the two species highlighted the variation in spatial behaviors animals can use to exist in anthropogenic environments influenced by interspecific variation in behavioral plasticity. Categorizing animal spatial behavior based on the generic-to-nuanced gradient can help in predicting how a species will respond to future change based on their current spatial behavior.

Spatial isolation is a key driver of population-level variability in traits and genotypes worldwide. Geographical distance between populations typically increases isolation, but organisms face additional environmental barriers when dispersing between suitable habitat patches. Despite the predicted universal nature of the causes of isolation, global comparisons of isolation effects across taxa and geographic systems are few. We assessed the strength of isolation due to geographic and macroclimatic distance for paired marine island and paired mainland populations within the same species. Our meta-analysis included published measurements of phenotypic traits and neutral genetic diversity from 1832 populations of 112 plant and animal species at a global scale. As expected, phenotypic differentiation was higher between marine islands than between populations on the mainland, but spatial patterns of neutral genetic diversity did not vary between the two systems. Geographic distance had comparatively weak effects on the spatial patterns of phenotypes and neutral genetic diversity, but only phenotypic trait variability showed signal of system-dependence. These results suggest that spatial patterns of phenotypic variation are determined by system-dependent eco-evolutionary pressures, while the spatial variability of neutral genetic diversity might be universal. Our approach demonstrates that global biodiversity models that include island biology studies may progress our understanding of the interacting effects of spatial habitat structure, geographic- and environmental distances on biological processes underlying spatial population variability. We formulate future research directions for empirical tests and global syntheses in the field.

Background Bipolar disorder (BD) is a severe mental illness characterised by recurrent manic, hypomanic and depressive episodes alternating with euthymic periods. The burden of BD is vast, and many patients have unmet needs in their treatment. To better support patients in their personal recovery and well-being, positive psychology interventions (PPIs) have shown to be a promising tool. Recently, a mobile application has been developed to offer PPIs: the WELLBE BD-app. Aim The current study was designed to study the acceptability of the WELLBE BD-app and evaluate the feasibility of the design for use in a larger controlled trial (CT). We also studied the potential effects on mental health. Method This pilot-study used a mixed-methods quantitative and qualitative approach in which participants were randomly assigned to an intervention- or a treatment-as-usual control group, each with 20 participants with BD. The study sample consisted of a seven weeks during intervention. To assess acceptability, we held semi-structured interviews in the intervention group and collected log data and questionnaire data on the actual use of the app and perceived value of the accompanying exercises. Feasibility was determined by the number of completers of the intervention in both the intervention and control groups. Potential effects on mental health outcomes were measured using an extensive set of pre and post-intervention questionnaires. Results The intervention was fully completed by 52.7% (n = 11) of the participants and partly completed (1 to 4 modules) by 37.8% (n = 8). The post-test response rate was 73% in both groups. On average, the exercises were rated with a value of 7.5 on a scale of 1 to 10 ( SD = 1.2). Users found the application easy to use, useful for people with BD, and to have an attractive design. Problems with installation, technical problems, and lack of support were barriers to using the app. Guidance by an expert by experience (in videos before the exercises) was preferred by 80% of the participants instead of guidance by a professional. Effects on mental health outcomes were small and statistically non-significant, both between- and within groups. Although we found no significant results in the quantitative part of our study, the qualitative results show that people with BD appreciated the content and design of the intervention. The minimal effects on mental health may be partly explained by the small sample size and the relatively high levels of mental health of the participants at baseline. Conclusions and Implications for Practice Based on this study a larger trial on the effects of the WELLBE-app appears feasible and warranted. Next to minor modifications based on this pilot study, to create optimal impact including patients with lower levels of well-being is recommended and the guidance by experts or peers needs to be considered.

Introduction: Psychopathological disorders such as anxiety, depression and body image distress are common in women with PCOS and negatively impact their mental health. It is important to identify mental health latent subgroups of PCOS females and provide tailored measures to reduce psychopathological distress and improve their subjective well-being. Methods: LPA was conducted in Mplus version 8.3 to identify mental health latent profiles in women with PCOS based on the dual-factor approach. Differences in demographic and anthropometric variables, cognitive reappraisal, expressive suppression, and social support across mental health profiles were examined through multinomial logistic regression. Results: The current study identified three distinct mental health profiles within women with PCOS: Symptomatic but Content Profile Complete Mental Health Profile and Troubled Profile, with group proportions of 52.3%, 35.7%, and 11.1 %, respectively. The results of the multinomial regression analysis revealed that cognitive reappraisal and social support as predictors of positive mental health adjustment and expression suppression is an indicator of negative barriers in women with PCOS. Conclusion: This study identified three distinct mental health profiles in women with PCOS, which provides evidence for more precisely targeted interventions to address PCOS women’s diverse needs of psychopathological symptoms and subjective well-being.

Landlocking is a process whereby a population of normally diadromous fish becomes limited to freshwater, potentially leading to behavioural, morphological, and genetic changes, and occasionally speciation. The study of recently landlocked populations can shed light on how populations adapt to environmental change, and how such life-history shifts affect population-genetic structure. Kōaro (Galaxias brevipinnis) is a facultatively diadromous Southern Hemisphere galaxiid fish that frequently becomes landlocked in inland lakes. This study compares seven landlocked kōaro populations to diadromous populations from main and offshore islands of New Zealand. Genotyping-by-sequencing was used to obtain genotypes at 18,813 single nucleotide polymorphism sites for each population. Analyses of population structure revealed that most landlocked populations were genetically highly distinct from one another, as well as from diadromous populations. A few particularly isolated island and lake populations were particularly strongly genetically differentiated. Landscape characteristics were measured to test whether lake elevation, size, or distance from the sea predicted genetic diversity or differentiation from diadromous kōaro. While there were no significant relationships indicating isolation-by-distance or isolation-by-environment, we detected a trend toward lower genetic diversity in lakes at higher elevations. Our findings illustrate the critical role that landlocking can play in the structure of intraspecific genetic diversity within and between populations.

Nowadays, Z-source inverter has received several attentions compared to the other power converters owing to its simplicity and reliability. In this paper, a refine control methodology of capacitor voltage control is presented based on third-order small signal analysis. The proposed control is applied to the improved switched inductor Z-Source inverter (ISL ZSI). Proportional, integral and derivative controller (PID) is adopted due to its capability to operate with large operation levels , reliable operation, and cheaper in implementation. In addition, the function of this control can be achieved even with incomplete system data or parameters variations. ISL-ZSI has been presented because of its advantages such as the high gain of DC voltage, low voltage stress for both the Z-network and the inverter bridge switches and ensure good soft starting. in addition, the inrush current has been removed based on its configuration. The proposed control is validated by both experimental and simulation results under input voltage change, load change, and steady state operations using DSP F28335 and MATLAB SIMULINK Real-Time Workspace (RTW).

Single-cell RNA sequencing is an emerging technique in the field of biology that departs radically from the previous assumption of gene-expression homogeneity within a tissue. The large quantity of data generated by this technology enables discoveries of cellular biology and disease mechanics that were previously not possible, and calls for accurate, scalable, and efficient processing pipelines. In this work, we propose SSCAE (spiking single-cell autoencoder), a novel SNN-based autoencoder for sc-RNA-seq dimensionality reduction. We apply this architecture to a variety of datasets, and the results show that it can match and surpass the performance of current state-of-the-art techniques. Moreover, the potential of this technique lies in its ability to be scaled up and to take advantage of neuromorphic hardware, circumventing the memory bottleneck that currently limits the size of sequencing datasets that can be processed.

In this work, ensemble methods are presented and tested as universal ways to improve the performance of Mem-ristive Deep Neural Networks (MDNNs) with non-idealities. The Generalized Ensemble Method and Weighted Voting ensemble methods improve the accuracy of classification on the MNIST dataset by 6.5% and 6.6% respectively, thus showing that they are more effective than basic Ensemble Averaging which has been investigated before, as well as other methods such as Voting. Different weighting schemes for Weighted Voting were tested, and we present Algorithm 1 and 2, which are the theoretically and experimentally optimal weighting schemes respectively. Our work serves as a guideline for choosing ensemble methods for MDNNs.

In this work, we solve nonlinear systems of ordinary differential equations coupled to noisy forcing, commonly used for models of neurons such as the Hodgkin-Huxley equation, over a memristor crossbar based computing system. We demonstrate stability and faithfulness of the distributions even under the effects of nonidealities of the memristors and the system itself. We investigate the properties of the dynamical systems under quantization faithfulness, varying the level of precision of the fixed point integer representation and concluding that 24 bits is enough for solution of the Hodgkin-Huxley equations, demonstrating that our solver can operate with both high precision and achieve speedups with low precision approximate computation.

Von Neumann architecture-based deep neural network architectures are fundamentally bottlenecked by the need to transfer data from memory to compute units. Memristor crossbar-based accelerators overcome this by leveraging Kir-choff's law to perform matrix-vector multiplication (MVM) in-memory. They still, however, are relatively inefficient in their device programming schemes, requiring individual devices to be written sequentially or row-by-row. Parallel writing schemes have recently emerged, which program entire crossbars simultaneously through the outer product of bit-line and word-line voltages and pulse widths respectively. We propose a scheme that leverages singular value decomposition and low-rank approximation to generate all word-line and bit-line vectors needed to program a convolutional neural network (CNN) onto a memristive crossbar-based accelerator. Our scheme reduces programming latency by 90% from row-by-row programming schemes, while maintaining high test accuracy on state of the art image classification models.

Background: Vitamin D deficiency is one of the most common nutritional deficiencies. Cardiovascular disease patients are also prone to this condition. Recently, a relationship between vitamin D deficiency and cardiovascular diseases has been suggested. This study aims to compare the relationship between ventricular systolic function and vitamin D deficiency. Methods: This study investigated patients without obvious coronary artery disease between 2020 and 2021. First, vitamin D levels were measured in the patients. Then, they were divided into two groups based on a 30 ng/dl cut-off point. All patients underwent echocardiography and ventricular systolic function parameters were evaluated and compared. Results: In this study, 27 patients with normal vitamin D levels and 47 patients with vitamin D deficiency entered the study. There was no significant difference in demographic variables and underlying diseases between these two groups. There was no significant difference between left ventricular (LV) systolic function parameters including ejection fraction (EF), and LV end-systolic/diastolic volume. No significant difference was also observed between right ventricular (RV) systolic function parameters including Tricuspid Annular Plane Systolic Excursion (TAPSE), RV fractional area change (RVFAC), Right ventricular systolic velocity (RVSM) in tissue Doppler echocardiography as well as RV diastolic parameters such as A, E, E´, deceleration time (DT), right atrial volume (RAVi) as a precursor of right ventricular systolic dysfunction groups. Conclusion: Based on the results of this study, there is no relationship between vitamin D levels and ventricular systolic dysfunction.

A method using questionnaire data for estimating the level of under reporting during an outbreak is presented. It is based on rewriting the conditional probabilities for getting tested, being infected, and having symptoms. It shows very good agreement with seroprevalence studies of blood donors. On the one hand, this shows the strength of questionnaires when testing the general population during an outbreak as a means to find the true prevalence. On the other, applying it to covid-19 demonstrates that the asymptomatic cases likely make up around 50% of the infected.

Terrestrial dissolved organic carbon (tDOC) is significant for coastal carbon cycling, and absorbance and fluorescence spectroscopy of chromophoric and fluorescent dissolved organic matter (CDOM, FDOM) are widely used to study tDOC cycling. However, CDOM and FDOM are often amongst the more labile components of tDOC. Because few studies have compared CDOM and FDOM to measurements of both bulk tDOC concentration and tDOC remineralization, it remains unclear how accurately CDOM and FDOM actually trace tDOC in coastal waters when tDOC undergoes extensive remineralization. We collected a 4-year coastal timeseries in Southeast Asia, where tropical peatlands supply large tDOC inputs. A carbon stable isotope mass balance shows that on average 56% of tDOC was remineralized upstream of our site, while 77% of CDOM was bleached. Despite this extensive tDOC remineralization and preferential CDOM loss, optical properties could reliably quantify tDOC. CDOM spectral slope properties, such as S275–295, are exponentially related to tDOC; these are highly sensitive tDOC tracers at low, but not at high, tDOC concentrations. Other properties are linearly related to tDOC, and both specific ultraviolet absorbance (SUVA254) and DOC-normalized fluorescence intensity may be suitable to quantify tDOC over a wider range of concentrations. However, the optical properties did not show consistent changes with the extent of tDOC remineralization. Our data support the validity of CDOM and FDOM spectroscopy to trace tDOC across coastal gradients even after the majority of tDOC has been remineralized, but they also show that these measurements may not provide much information about tDOC biogeochemical processing.

A 31-year-old man with fever, dyspnea, abdominal pain, and jaundice has been admitted to the hospital in the southeast of Iran. Due to the presence of a pathognomonic skin lesion (Tache noire), the patient was diagnosed with Mediterranean spotted fever (MSF) and was treated with doxycycline.

A 66-year-old breast ductal carcinoma patient developed obstructive jaundice, presenting with epigastric discomfort and dark-colored urine. Contrast-enhanced computed tomography and endoscopic retrograde cholangiopancreatography revealed bile duct stenosis. Brushing cytology and tissue biopsy confirmed bile duct metastasis, and a self-expandable metallic stent was placed/replaced endoscopically, extending the patient’s life.