There is evidence that kidney involvement is frequently observed in COVID-19 patients, and can manifest as mild proteinuria to advancing acute kidney injury (AKI). One of the mechanisms is microvascular and macrovascular thrombosis caused by the hypercoagulation phase of the disease. Thromboelastography (TEG) is a valuable examination to detect significant hemostatic abnormalities, including the hypercoagulable state. This study analyzed the coagulation profiles, including TEG parameters, in COVID-19 patients who developed AKI compared to those who did not during their intensive care unit (ICU) stay, to identify predictors of AKI. Our single-center cohort retrospective study involved adult patients of COVID-19 in the ICU of Sardjito Hospital in Yogyakarta, Indonesia between May and September 2021. Patients were divided into two groups of AKI and non-AKI based on 2012 KDIGO definition and the elaboration by Indonesian Society of Nephrology. Variables showing a significant difference in the two groups were then analyzed using univariate and multivariate binary logistic regression. A total of 60 COVID-19 patients were included in the study, and 35% of them developed AKI. Compared to non-AKI patients, those with AKI exhibited a greater prevalence of diabetes mellitus (66.7% versus 35.9%, P = 0.023), higher D-Dimer levels (970 versus 685 ng/mL, P = 0.045), and higher values in TEG parameters of maximum amplitude/MA (74.6 versus 65.9 mm, P = 0.001) and coagulation index/CI (2.3 versus 1.0, P = 0.033). TEG parameter of MA emerged as the sole significant predictor for the development of AKI (OR, 6.33; 95% CI, 1.56 to 25.64). Our study validated the kidney involvement of COVID-19 infection, and showed that diabetes mellitus, high D-Dimer levels, and hypercoagulability serve as prominent risk factors in the development of AKI. Furthermore, TEG parameter of MA exceeding 70 mm is the single independent significant predictor of AKI.

To clarify the risk factors for aggravation of esophagogastric varices (EGV) after hepatitis C virus (HCV) eradication with direct-acting antiviral (DAA) therapy, we enrolled 167 consecutive patients with HCV-related compensated cirrhosis who achieved a sustained virological response (SVR) after DAA therapy. During a median of 69 months, EGV was aggravated in 42 (25%) patients despite SVR. The cumulative 1, 3, 5, and 10-year aggravated EGV rates were 7%, 23%, 25%, and 27%, respectively. Multivariate analysis identified platelet count <11.0×10⁴/μL, LSM ≥18.0 kPa, total bile acid ≥33.0 μmol/L, and diameter of left gastric vein (LGV) ≥5.0 mm at HCV eradication as independent risk factors for EGV aggravation post-SVR. In groups that meet all of these risks, the cumulative EGV aggravation rates at 1, 3, and 5 years were 27%, 87%, and 91%, respectively. However, none of the patients who had only one or none of the risk factors experienced EGV aggravation. Platelet count, LSM, total bile acid, and diameter of LGV at HCV eradication were associated with aggravated EGV post-SVR. EGV tends to worsen as two or more of these risk factors increase.

Although the water absorption feature (WAF) at 970 nm is not very well-defined, it may be used alongside other indices to estimate the canopy water content. The individual performance of a number of existing vegetation water content (VWC) indices against the WAF is assessed using linear regression model. We developed a new Combined Vegetation Water Index (CVWI) by merging indices to boost the weak absorption feature. CVWI showed a promise in assessing the vegetation water status derived from the 970 nm absorption wavelength. CVWI was able to differentiate two groups of dataset when regressed against the absorption feature. CVWI could be seen as an easy and robust method for vegetation water content studies using hyperspectral field data.

Conditional Independence (CI) testing is fundamental in statistical analysis. For example, CI testing helps validate causal graphs or longitudinal data analysis with repeated measures in causal inference. CI testing is difficult, especially when testing involves categorical variables conditioned on a mixture of continuous and categorical variables. Current parametric and non-parametric testing methods designed for continuous variables and can quickly fall short in the categorical case. This paper presents a computational approach for CI testing for categorical data types, which we call computational conditional independence (CCI) testing. The test procedure is based on permutation and combines machine learning prediction algorithms and Monte Carlo Cross-Validation. We evaluated the approach through simulation studies and assessed the performance against alternative methods; the generalized covariance measure (GCM) test, the kernel conditional independence (KCI) test, and testing with multinomial regression. We find that the computational approach to testing has utility over the alternative methods, and we can achieve better control over type-I-error rates. We hope this work can expand the toolkit for CI testing for practitioners and researchers.

This research investigates the application of Fused Filament Fabrication (FFF) in producing WC-10Co hardmetals, focusing binder formulations and understanding its influence on extrusion force and the influence of printing variables on green density of the samples. By examining the interplay between different binder compositions and backbone contents, the study aims to enhance the mechanical properties of the sintered parts while reducing defects inherent in the printing process. Evidence suggested that formulated feedstocks influenced the hardness of the sintered hardmetal, not due to microstructural changes but to macrostructural responses like macro defects introduced during printing, debinding and sintering of the samples. The results demonstrate the critical role of PP-MA content in improving part density and sintered hardness, suggesting the need for specific thermal debinding protocols tailored to each feedstock. The study provides insights into the formulation of feedstocks for hardmetal FFF, proposing a path toward refining manufacturing processes for better quality and performance of 3D printed hardmetal components.

Aiming at the problem of hypersonic morphing vehicle avoiding no-fly zones and reaching the target, an improved predictor-corrector guidance method is proposed. Firstly, the aircraft motion model and the constraint model are established. Then, the basic algorithm is given, the Q-learning method is used to design the attack angle and sweep angle scheme to ensure that the aircraft can fly over the low-altitude zones. The B-spline curve is used to design the location of flight path points and the bank angle scheme is designed according to the predictor-corrector method, so that the aircraft can fly around to avoid high-altitude zones. Next, Monte Carlo reinforcement learning(MCRL) method is used to improve predictor-corrector method and Deep Neural Network(DNN) is used to fit reward function. The improved method can generate trajectory with better performance. Simulation results verify the effectiveness of the proposed algorithm.

Tennis is an individual sport characterised by high intensity action, including sprints and changes of direction (COD). There is a gap in the knowledge of limb asymmetries in adolescent tennis players and their association with performance. This study aimed to examine the relationship between asymmetry in vertical and horizontal jump tests (CMJ and HJ) and COD with lower limb performance variables in adolescent tennis players. Forty-five adolescent tennis players (age: 13.81 ± 1.08 years; height: 167.64 ± 9.9 cm; body mass: 57.48 ± 10.94 kg; body mass index: 20.27 ± 2.18 kg/m2) performed the CMJ test (unilateral and bilateral), horizontal jumps (unilateral and bilateral), 25 m sprint and 180º COD test. The single-leg countermovement jump showed the greatest asymmetries among the different tests (6.62 ± 9.35%). Significant negative relationships were found between CMJ asymmetry and COD asymmetry with unilateral horizontal jump variables (r = -0.30 to -0.53). In addition, CMJ asymmetry showed significant relationships with CMJR (r=0.49) and COD180R (r=0.29), whereas COD asymmetry showed a significant relationship with COD180L (r=0.40). On the other hand, HJ asymmetry showed no significant relationships with any variable. The main findings of this study indicate that greater imbalances in the CMJ and COD tests are associated with a reduced capacity to perform unilateral horizontal jumps. These results suggest the need to incorporate training protocols that reduce interlimb asymmetries in growing athletes to improve their performance.

Developing particles of different anisotropic shapes are the hot topic since decades as they guarantee some special features of properties not possible through other means. Again, controlling atoms to develop certain size and shape particle is a quite challenging job. In this study, gold particles of different shapes are developed via pulse-based electronphoton-solution interface process. Gold atoms of certain transition state develop monolayer assembly at solution surface around the light glow (known in argon plasma) being generated at bottom of copper capillary (known in cathode). The rate of uplifting gold atoms to solution surface is being controlled by forcing energy (travelling photons) pursuing electrons and high energy photons (in high density) entering to solution. Gold atoms dissociated from the precursor under dissipating heat energy into the solution supplied under propagating photons characteristic current through immersed graphite rod (known in anode). Placing packets of nano shape energy of tuned pulse protocol over compact monolayer assembly comprising transition state atoms develop tiny-sized particles of formed shape. On separation of joint tiny particles into two equilateral triangular-shaped tiny particles, exerting forces of surface format elongate atoms of one-dimensional arrays converting them into structures of smooth elements. Due to immersing level of force, such tiny-shaped particles pack from different zones at centre of light glow where they assembled structures of smooth elements for developing mono-layers of different shapes of particles. Developing one-dimensional particles deal assembling of structures of smooth elements of packing tiny-shaped particles from nearly rearward zones of reflection of north-south poles, whereas, developing multi-dimensional particles deal assembling of structures of smooth elements of packing tiny-shaped particles from the east-west poles and near regions. Depending on the number of assembled structures of smooth elements at point of nucleation, packing of tiny-shaped particles from different zones develop different shapes of the anisotropic particles. At fixed precursor concentration, increasing the process time results into develop particles of low aspect ratio. Under tuned parameters, developing mechanisms of particles exhibiting unprecedented features are discussed.

Febrile infections are a common cause of presentation to the emergency department (ED) in chil-dren. While viral infections are usually self-limiting, sometimes bacterial illnesses may lead to sepsis and severe complications. Inflammatory biomarkers such as C reactive protein (CRP) and procalcitonin are usually the first blood exams performed in the ED to differentiate bacterial and viral infections; the better understanding of immunochemical pathways has nowadays led to the discovery of new and more specific biomarkers that could play a role in the emergency setting. The aim of this narrative review is to provide the most recent evidence on biomarkers and predictor models combining them for serious bacterial infections (SBI) diagnosis in febrile children. Litera-ture analysis showed that inflammatory response is a complex mechanism in which many bio-chemical and immunological factors contribute to host response in SBI. CRP and procalcitonin still represent the most used biomarkers in the pediatric ED for the diagnosis of SBI. Their sensibility and sensitivity increase when combined and for this reason it is reasonable to take them both into consideration in the evaluation of a febrile children. The potential of machine learning tools, who represent a real novelty in medical practice, in conjunction with routine clinical and biological in-formation, may improve accuracy of diagnosis and target therapeutic options in SBI. However, studies on this matter are not yet validated in younger populations, making their relevance in pe-diatric precision medicine still uncertain. More data from further researches are needed to improve clinical practice and decision making using these new technologies.

Introduction: Substance-induced psychotic disorder (SIPD) is a mental health condition linked to the use of a psychotropic substance whose pathogenesis has not been totally clarified. The present retrospective observational study has the objective to identify differences in clinical features and peripheral biomarkers in SIPD patients according to the main substance of abuse.Methods: A total sample of 218 patients was divided in 3 groups according to the type of consumed substance: alcohol, cannabis and psychostimulants. The 3 groups were compared for continuous variables by one-way analyses of variance (ANOVAs) and for qualitative variables by χ² tests. After removing alcohol-induced psychotic disorder group, the same analyses were repeated. Statistically significant variables from these latter analyses were included in a binary logistic regression model to confirm their reliability as predictors for cannabis- or psychostimulants-induced psychotic disorders. Results: Psychotic cannabis abusers were younger (p<0.01) and had an earlier age at onset (p<0.01). On the contrary, alcohol consumers resulted to have a longer duration of illness (p<0.01), more previous hospitalizations (p=0.04), more frequent medical comorbidity (p<0.01), higher mean Modified Sad Person Scale scores (p<0.01). Finally, psychostimulant abusers had more frequently a lifetime history of poly-substance use disorders (p<0.01). Binary logistic regression revealed that higher mean Brief Psychiatric Rating Scale scores (p<0.01) higher sodium (p=0.012) and haemoglobin(p=0.040) plasma levels were predictors of cannabis misuse in SIPD patients.Conclusions: Different clinical factors and biochemical parameters con be associated to SIPD according to the main substance of abuse, thus requiring a specific management by clinicians.

Cooperation is essential in social life, involving collaborative efforts for mutual benefit. Individual differences in the cooperativeness trait are pivotal in these interactions. Previous research suggested that Duchenne smiles signal cooperative intent, and human-like eyes are theorized to enhance cooperation evolutionarily. However, the relationships between cooperative behavior, cooperativeness and possible behavioral cues signaling cooperative behavior are yet to be studied. In the current study, we explored the relationships among cooperativeness, Duchenne smiling with gaze, IBS during conversation, and their impacts on cooperative behavior. We hypothesized Duchenne smiling with gaze would mediate cooperativeness’ impact on cooperative behavior. Additionally, using functional near-infrared spectroscopy (fNIRS), we evaluated inter-brain synchrony (IBS) in the left prefrontal region, expecting it to predict cooperative behavior. The results demonstrated that cooperativeness significantly predicted Duchenne smiling with gaze and cooperative behavior, however, Duchenne smiling with gaze did not mediate the relationship between them. We further conducted a path analysis to examine the role of IBS during conversation in successive cooperative behavior. The path analysis result showed that cooperativeness directly affected cooperative behavior. Cooperativeness significantly predicted Duchenne smiling with gaze, however, neither Duchenne smiling with gaze nor IBS during conversation predicted successive cooperative behavior. These results suggest dispositional factors like cooperativeness may play a more decisive role than momentary expressional cues or neural synchrony in naturalistic unstructured communication in shaping cooperative behavioral outcomes after the communication. The study highlights how personality traits like cooperativeness shape nonverbal communication and social interactions.

Different variants of thinning for discrete random variables are studied. The thinning procedure allows to introduce an analog of scale parameter for positive integer-valued random variables. Sufficient and necessary conditions for the existence of such a scale are given.

Crash prediction of the sharp horizontal curve segment (SHCS) of a freeway is an important tool in analyzing safety of SHCSs and in building a crash prediction model (CPM). The design and crash report data of 88 SHCSs from different institutions were surveyed and three negative binomial (NB) regression models and three generalized negative binomial (GNB) regression models were built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The study demonstrates the effective use of the GNB model in analyzing the interactive influence of explanatory variables and in predicting freeway basic segments. Traffic volume, highway horizontal radius, and curve length have been formulated as explanatory variables. Subsequently, we performed statistical analysis to determine the model parameters and conducted sensitivity analysis. Among the six models, the result of model 6, which considered interactive influence, is much better than those of the other models by fitting rules. We also compared the actual results from crashes of 88 SHCSs with those predicted by models 1, 3, and 6. Results demonstrate that model 6 is much more reasonable than models 1 and 3.

The study aimed to explore the correlation among Angiopoietin-1, (Ang-1) and Angiopoietin-2, (Ang-2) concentrations and the Angiopoietin-2/Angiopoietin-1 ratio, (Ang-2/Ang-1) with clinical outcomes, potentially serving as disease severity and survival biomarkers. A study at AHEPA University Hospital involved 90 COVID-19 adult patients, with 30 hospitalized in intensive care and 30 in ward units and 30 asymptomatic non-hospitalized individuals as controls. Estimated endothelial dysfunction markers related to angiogenesis were measured, and statistical analysis was performed using IBM SPSS Statistics software, version 29. There was a statistically significant difference only between outpatient and hospitalized patients (non-ICU -ICU groups) for the Ang-1 and Ang-2 indexes. The Ang-2/Ang-1 ratio has differed significantly among all individual patient groups. A ROC analysis was conducted to find an optimal threshold for distinguishing (outpatients – non-ICU) and (non-ICU –ICU) groups. It was based on Youden's Index and was 0.1122 and 0.3825, respectively. The Ang-1, Ang-2 levels, and Ang-2/Ang-1 ratio were analysed as indicators for severity in COVID-19 patients. The Ang-2/Ang-1 ratio demonstrated more excellent prognostic and diagnostic utility than individual biomarker levels. Monitoring the Ang-2/Ang-1 ratio can identify COVID-19 patients at risk and assist clinicians in tailoring treatment strategies for improved outcomes.

Fighting against the COVID-19 pandemic caused by the SARS-CoV-2 virus is one of the most critical challenges facing the global health system today. The possibility to identify the group of persons in the cohort of people under 50 years old, who are sensitive to the COVID-disease by non-invasive methods, is a very perspective approach for estimating the epidemiological state of the human population. The study aimed to identify the features of people's faces with COVID-19 that the most correlate with disease severity could serve as one of these approaches. For this aim, 525 photos of patients' faces with different outcomes of COVID-19 disease were analyzed using the Dlib face recognition convolutional neural network pre-trained for face recognition. Face descriptor vectors were obtained using the convolutional neural network. Facial features were found that predict a person's sensitivity to the SARS-CoV-2 virus (disease severity), and the contribution of each of the features to the risk of developing a severe form of COVID in a person was found. The accuracy of the binary classification of the individual severity of the COVID-19 course using the k-nearest neighbors algorithm on the test dataset was accuracy - 84%, AUC - 0.90.

The study examines academic burnout syndrome and its relation to personal and academic variables among university students in nursing and early childhood education programs in Spain. A total of 606 university students (Primary Education: 49.7%; Nursing: 49.7%) of both sexes (71.5% female), and average age 20.68 years (SD = 1.65) participated. An ex post facto retrospective single-group design was planned. The instruments used were the Maslach Burnout Inventory-Student Survey (MBI-SS) and the NEO Five Factor Inventory (NEO-FFI). Nursing students, who showed more study hours, less sleep, and lower grades, had higher academic burnout scores. Linear regression models were proposed to analyze the relationship between academic burnout, personality, and sociodemographic variables. Nursing students scored higher in emotional exhaustion and lower in cynicism, and they scored higher in neuroticism and openness. Furthermore, 16.1% of the variance in academic burnout was explained by personality variables as degree, course and study hours. These findings suggest the importance of considering both academic and personality variables in understanding academic burnout in university students.

Land plants have undergone their great diversification and evolution in the past 400 million years. Although appearing complex and hard to grasp, the evolution of plants centers around an invariable core development process, SRC (Sexual Reproductive Cycle). Various modifications have been added sequentially to this core process, and thus creating various novel organs and distinguishing their bearers one from another. Reproductive organs of all land plants, including sporangium, megasporangium, metamegasporangium (= ovule), and various metamegasporangium complexes, are derived from terminal sporangia in the earliest land plants. Throughout the million-year-long evolution, plants sequentially recruit associating accessories to protect their vulnerable core parts (spore and gamete). The occurrence of every single of these novelties is a stair marking land plant reproduction evolution and leads to enhanced offspring development conditioning (ODC).

A random variable X that is base b Benford will not in general be base c Benford when c is not equal to b. This paper builds on two of my earlier papers and is an attempt to cast some light on the issue of base dependence. Following some introductory material, the “Benford spectrum” of a positive random variable is introduced and known analytic results about Benford spectra are summarized. Some standard machinery for a “Benford analysis” is introduced and combined with my method of “seed functions” to yield tools to analyze the base c Benford properties of a base b Benford random variable. Examples are generated by applying these general methods to several families of Benford random variables. Berger and Hill's concept of “base-invariant significant digits” is discussed. Some potential extensions are sketched.

A small but growing body of literature suggests that outdoor experiences during childhood affect environment-related behaviors in adulthood. However, research on the magnitude of the effect (effect size) of outdoor experience on learners’ behaviors remains scarce. In this study, we explore the extent to which outdoor experiences associate with environmentally responsible behaviors. Our sample consisted of 143 ninth- and tenth-grade students living on a Greek island. The data were collected using a properly adjusted environmental literacy instrument. Two different methodological pathways, i.e., a quasi-experimental approach and correlation analysis, were used to analyse the data. A tentative variable representing the frequency and intensity of students’ experiential contact with nature was found to be the strongest available predictor of their self-reported pro-environmental behaviors. The findings of this study support the significance of outdoor, experiential learning during childhood in shaping individuals’ environmental behaviors.

The primary goal of picture compression is to reduce the amount of unused image data while still storing or transmitting it in a format that is appropriate. The compression of raw binary data is quite different from the compression of a picture, and these differences may be rather substantial. In light of this, compression is often regarded as an essential technique for the purposes of both data storage an d transmission in order to mitigate the excessive amounts of data that are generated by these images. In order to transmit enormous datasets, particularly for the purposes of telemedicine and teleradiology, one needs a significant amount of storage capacity as well as an expansive network. As a result, compression is an important aspect of medical imaging. In addition to the importance of compression, the quality of the photos themselves is also an essential factor in the success of analysis. In addition to this, the amount of time necessary to compress the photographs before sending them should be reduced.

There is still a lack of a clinical test to reliably identify patients with Parkinson’s disease (PD) being at risk for aspiration. In this prospective, controlled, cross-sectional study we assessed if swallowing speed for water is a useful clinical test to predict aspiration proven by flexible endoscopic evaluation of swallowing (FEES). Due to this we measured the swallowing speed for 90 ml water in 115 consecutive and unselected PD outpatients of all clinical stages and 32 healthy controls. Average swallowing speed was lower in patients compared with controls (6.5 ± 3.9 ml/s vs. 8.5 ± 3.2 ml/s; p < 0.01). The disease-independent widely used threshold of < 10 ml/s showed insufficient sensitivity of 88% and specificity of 19% with unacceptable false-positive rates of 63% for patients and 69% for controls. Receiver operating characteristic (ROC) analysis was carried out to define a suitable cut-off value for detection of aspiration of water (area under the curve 0.72, p < 0.001) in PD patients. The optimized cut-off value was 5.5 ml/s with a sensitivity of 69% and a specificity of 64%. Overall, measuring swallowing speed is prone to methodological errors and not suitable as a screening instrument to predict aspiration in PD patients.

This article investigates whether the weather has any role in terms of spread and vulnerability of COVID-19 and how that knowledge can be used to arrest this fast spreading disease. It highlights that temperature and humidity both are important for transmitting the virus- temperature being the stronger factor. A dry, cool environment is the most favourable state for the spread of the virus. In fact, high temperature environment significantly reduces the risk from the virus. Warm places and countries are likely to be less vulnerable. Only regulating temperature level can provide drastic results to stop and arrest the outbreak. Some urgent solutions are proposed based on that knowledge. The novelty of such approach is- it can be applied overnight and implemented immediately across the globe. It is very cost effective and practically without side effects. No vast amount of funding is required to adopt these solutions. Apart from existing guidelines, these additional measures are likely to reduce the spread of the disease dramatically. Following the scientific discussion, such solutions may be thought of implementing worldwide, especially to vulnerable countries, as an emergency basis.

The study tries to find out the impact of investors demographic variables on the return from the stock market. The data has been collected from the brokerage and Marchant banks for individual investors investments, return and demographic characteristics. The research covers the period from 2009 to 2021 and have a decade long enough to identify the trend. The structural break in the data also been adjusted to avoid any kind of endogeneity. The regression results suggest that demographic variables affect the investment pattern and beta return for the investors. the demographic variables such as education, location and experience have positive links with the beta whereas age and income show negative association. The education, income and experience variables are also found to be significant in the empirical analysis.

In this paper, we introduce two discrete degenerate random variables, namely the degenerate binomial and degenerate Poisson random variables. We deduce the expectations of the degenerate binomial random variables. We compute the generating function of the moments of the degenerate Poisson random variables, which leads us to define the new type degenerate Bell polynomials, and hence obtain explicit expressions for the moments of those random variables in terms of such polynomials. We also get the variances of the degenerate Poisson random variables. Finally, we illustrate two examples of the degenerate Poisson random variables.

Body image disturbance (BID) involves negative attitudes towards shape and weight and is associated with lower levels of interoception (awareness of and attention toward internal physiological states). This association is considered a risk factor for developing eating disorders (EDs) and is linked to altered sensorimotor representations of the body (i.e., body schema). BIDs manifest across genders and are currently understudied in men. This study investigated gender-related differences in BID and its relationship to the body schema and interoception. Data were collected from 86 men and women. BID was assessed using questionnaires measuring self-objectification, state, and trait body dissatisfaction. Interoceptive sensibility was measured via the MAIA-2. The body schema was indexed via an embodied mental rotation task. Results showed that women reported higher BID than men across all scales. Gender differences in sub-components of interoceptive sensibility were found. Overall, both gender and interoceptive sensibility predicted BID. However, interoceptive sensibility exhibited its own unique association with BID beyond the influence of gender. BID, IS and gender were not significant predictors of performance in the body schema task. Therefore, while gender predicts differences in BID and interoceptive sensibility, there was no evidence of gender-related differences in body schema.

The widespread integration and growing dependence among currency, stock and commodity markets make these markets often very vulnerable to shocks and at risk of collapse at the same time. As a result, these trends threaten the sustainability of the entire financial system. In this study, we explore the spillovers and nonlinear dependences between the seven major foreign ex-change rates, crude oil and gold prices, a global stock price index, and oil and stock implied volatility indices as proxy variables for global risk factors by em-ploying directional spillover network approach. We also use multi-scale de-composition method and nonlinear causality test between these variables to capture multi-level relationships at short and long horizons. Major findings are summarized as follows. First, from the multi-scale decomposition analysis, we identify that Granger causality test results and the direction and strength of return spillovers change with the level of decomposition. Second, the results of nonlinear causality tests show variation in both the significance and direction of Granger causality relationships between the decomposed currency and other series at different timescales, especially for the decomposed oil, gold, and OVX series. Third, the measured directional spillover indices identify the EUR as the largest contributor of connectedness to the other series. The central role of the EUR is a net transmitter of connectedness to gold, oil, the GBP, JPY, and CHF.

Although Shannon mutual information has been widely used, its effective calculation is often difficult for many practical problems, including those in neural population coding. Asymptotic formulas based on Fisher information sometimes provide accurate approximations to the mutual information but this approach is restricted to continuous variables because the calculation of Fisher information requires derivatives with respect to the encoded variables. In this paper, we consider information-theoretic bounds and approximations of the mutual information based on Kullback--Leibler divergence and Rényi divergence. We propose several information metrics to approximate Shannon mutual information in the context of neural population coding. While our asymptotic formulas all work for discrete variables, one of them has consistent performance and high accuracy regardless of whether the encoded variables are discrete or continuous. We performed numerical simulations and confirmed that our approximation formulas were highly accurate for approximating the mutual information between the stimuli and the responses of a large neural population. These approximation formulas may potentially bring convenience to the applications of information theory to many practical and theoretical problems.

Internal and mesoscopic variables differ from each other fundamentally: both are state space variables, but mesoscopic variables are additional equipped with a distribution function introducing a statistical item into consideration which is missing in connection with internal variables. Thus, the alignment tensor of liquid crystal theory can be introduced as an internal variable or as one generated by a mesoscopic background using the microscopic director as mesoscopic variable. Because the mesoscopic variable is part of the state space, the corresponding balance equations change into mesoscopic balances, and additionally an evolution equation of the mesoscopic distribution function appears. The flexibility of the mesoscopic concept is not only demonstrated for liquid crystals, but is also discussed for dipolar media and flexible fibers.

Background: Despite the abundant scientific literature on entrepreneurship, there is still only limited information on young students’ entrepreneurial intentions. The reasons may be generally found in the different conceptual approaches to entrepreneurial intention and particularly in the variables that regulate and act as antecedents to such intentions. This bias has generated different lines of investigation into the factors relating to entrepreneurial intention among students: one is centered on the variables that influence entrepreneurial intention, in particular, relational, educational, and psychological variables; and the other is centered on the antecedents of entrepreneurial intention, among which is entrepreneurial interest. Methods: In the present paper, we seek to analyze the relationship between the entrepreneurial interest of Spanish youth and a set of socio-educational, psychological, and health-related variables using principal component analysis. A previously validated ad hoc questionnaire was administered to 1764 students (15–18 years old). Results: Notably, few Spanish youth expressed significantly high entrepreneurial interest; those who did were mostly men with a family tradition of entrepreneurial parents, who held high perceptions of their health and quality of life and considered it important in business to detect opportunities beforehand and to create employment. Conclusions: Their principal motives were to improve their professional development, to put their ideas into practice, and to achieve economic independence. This paper proposes the early detection of entrepreneurial interests in young people in order to reinforce these interests as potential long-term initiatives.

With the widespread use of electric machines there is a growing need to extract information from the machines to optimize their control systems and maintenance management. The present work shows the development of an embedded system to perform the monitoring of an squirrel cage induction motor rotor physical variables. The system comprises: circuit to acquire desirable rotor variable(s) value(s) and send it to the computer; a rectifier and power storage circuit that besides converting an alternating current in continuous also store energy for a certain amount of time to wait the motor engine shutdown; and magnetic generator that harvest energy from rotating field to power the circuits mentioned above. The embedded system is set on the rotor, making it difficult to power the system because it is rotating, problem solved with the construction of the magnetic generator eliminating the need of using batteries or collector rings and send data to the computer using a wireless NRF24L01 module. For the proposed system validation a temperature sensor (DS18b20) was used, variable known as the most important when identifying the need for maintenance and control systems. Tests were made getting satisfactory results proving the viability of using sensors on the rotor.

The GPA is a universally recognised and utilised metric of academic performance that is considered to also measure a student’s potential for academic performance in the future. In this short communication we examine to what extent the GPA of the first grade of high school predicts performance in the later grades of high school, either generally (as classified in excellent student, strong student, weak student or very weak student) or more accurately (as indicated by the exact GPA in the next grade). We also put to the test the widely held notion that it might be best if core courses such as language and mathematics contributed more to the calculation of the GPA compared to secondary courses such as physical education or music. Our findings confirm the predictive properties of the GPA but strongly rebut the notion that a weighted GPA might achieve a better reflection of students’ potential. The study is based on the academic records of every student in Greece that progressed from first to third grade of high school in the 2016-2019 period. This data set contains records of more than 85,000 students, making it one of the most extensive studies ever conducted on the topic of the properties of the GPA.

The purpose of this work is to define optimal growth conditions for batch culture of the cyanobacterium Arthrospira maxima and the microalgae Chlorella vulgaris, Isochrysis galbana and Nannochloropsis gaditana. Thus, we study the effect of three variables on algae growth: i.e., inoculum:culture medium ratio, light:darkness photoperiod and type of culture medium, including both synthetic media and wastewaters. The results showed that the initial inoculum volume did not affect the amount of biomass at the end of the growth (14 days), whereas an excess (18 h) or defect (6 h) in the number of hours of light is determinant for its development. The contribution of nutrients from different culture media modified the growth of the different species. A. maxima was favoured in seawater enriched with Guillard's F/2 as well as C. vulgaris and N. gaditana but in fresh water medium. I. galbana had the greatest growth in the marine environment enriched with Walne’s media. Nitrate was the limiting growth reagent at the end of the exponential phase of growth for C. vulgaris and N. gaditana, while iron was for A. maxima and I. galbana. All species demonstrated their capability to grow in effluents from a wastewater treatment plant and they efficiently consume nitrogen, especially the three microalgae species.

Gradients of smooth functions with non-independent variables are relevant for exploring complex models and for the optimization of functions subjected to constraints. In this paper, we investigate new and simple approximations and computations of such gradients by making use of independent, central and symmetric variables. Such approximations are well-suited for applications in which the computations of the gradients are too expansive or impossible. The derived upper-bounds of the biases of our approximations do not suffer from the curse of dimensionality for any $2$-smooth function, and theoretically improve the known results. Also, our estimators of such gradients reach the optimal (mean squared error) rate of convergence (i.e., $\mathcal{O}(N^{-1})$) for the same class of functions. Numerical comparisons based on a test case and a high-dimensional PDE model show the efficiency of our approach.

The choice of holiday destinations is highly depended on climate considerations. Nowadays, since the effects of climate crisis are being increasingly felt, the need of accurate weather and climate services for hotels is crucial. Such a service could be beneficial for both the future planning of tourists’ activities and destinations and for hotel managers as it could help in decision making about the planning and expansion of the touristic season, due to a prediction of higher temperatures for a longer time span, thus causing increased revenue for companies in the local touristic sector. The aim of this work is to calculate predictions on climatic variables using statistical techniques as well as Artificial Intelligence (AI) for a specific area of interest utilising data from in situ meteorological station, and produce valuable and reliable localised predictions with the most cost-effective method possible. This investigation will answer the question of the most suitable prediction method for time series data from a single meteorological station that is deployed in a specific location. As a result, an accurate representation of the microclimate in a specific are is achieved. To achieve this high accuracy in situ measurements and prediction techniques are used. As prediction techniques, Seasonal Auto Regressive Integrated Moving Average (SARIMA), AI techniques like the Long-Short-Term-Memory (LSTM) Neural Network and hybrid combinations of the two are used. Variables of interest are divided in the easier to predict temperature and humidity that are more periodic and less chaotic, and the wind speed as an example of a more stochastic variable with no known seasonality and patterns. Our results show that the examined Hybrid methodology performs the best at temperature and wind speed forecasts, closely followed by the SARIMA whereas LSTM perform better overall at the humidity forecast, even after the correction of the Hybrid to the SARIMA model.

Background: External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of the planning procedure, machine learning predictions of the dose distributions have been introduced to speed up the planning procedure and to serve as quality assurance. The most recent dose prediction models are based on deep learning U-Nets that give good approximations of the dose in 3D almost instantly. It is our purpose to train a 3D dose prediction for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. Methods: From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a concatenated 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes according to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases.Results: The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy.Conclusions: We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model's robustness, by targeted updating the training set, making it a useful technique for dose awareness in the contouring evaluation and quality assurance.

With covid infection having a periodic occurrence and each wave affecting millions of people, there is an urgent need for healthcare providers to develop readily available biological markers to predict the severity and mortality of COVID-19 infection at an early stage of the disease to predict the most optimal management. The Neutrophil – lymphocyte Ratio (NLR) and Platelet-lymphocyte ratio (PLR), are novel inflammatory markers suggested to predict the severity of illness in COVID -19 patients. We evaluated a retrospective cohort of 224 patients with covid 19 illnesses from August 2020 to October 2020. We categorized the patients into 'mild' and 'moderate to severe as per the severity of their illnesses. The optimal cut-off values of the continuous NLR, PLR, and Age were calculated by applying the receiver operating characteristic (ROC) curve analysis. Sensitivity, specificity, and likelihood ratios were calculated from ROC analysis. The results showed that Age, Hb, and NLR are significant predictors of the severity of illness. So we quantified the predictive contribution of those variables by its Z- score and showed that NLR is the strongest predictor followed by Age.ROC curve analysis revealed NLR to be an excellent predictor of disease severity (sensitivity 88.30% and specificity 82.60%) at a cut-off of > 4.24. A cut-off of 165.63 was obtained for PLR, which showed good predictive value for the severity of illness. A NLR > 6 was used as a predictor for mortality due to covid illness with good sensitivity and specificity. Conclusion: NLR and PLR can be used as prognostic markers to identify the severity of covid illness and the probability of mortality in the future in low-resource settings. Given its low cost, easily available, accessible, and reproducible data, it can be used as a useful marker to assist the physician in early referral and treatment of covid 19 infected patients.

We present a new penalized method for estimation in sparse logistic regression models with group structure. Group sparsity suggests us to consider the Group Lasso penalty. Being different from penalized log-likelihood estimation, our method can be viewed as a penalized weighted score function method. Under some mild conditions, we provide non-asymptotic oracle inequalities promoting group sparsity of predictors. A modified block coordinate descent algorithm based on a weighted score function is also employed. The net advantage of our algorithm over the existing Group Lasso-type procedures is that the tuning parameter can be pre-specified. The simulations show that this algorithm is considerably faster and more stable than competing methods. Finally, we illustrate our methodology with two real data sets.

The technique of superposition of motions in the space of Lagrange variables is described, which allows us to obtain the equations of combined motion by replacing the Lagrange variables of superimposed (external) motion with Euler variables of nested (internal) motion. The components of velocity and acceleration in the combined motion obtained as a result of differentiating the equations of motion in time coincide with the results of vector addition of the velocities and accelerations of the particles involved in the superimposed motions at each moment of time. Examples of motion and superposition descriptions for absolutely solid and deformable bodies with equations for the main kinematic characteristics of motion, including for robot manipulators with three independent drives, pressing with torsion, bending with tension, and cross– helical rolling, are given. For example, given the fragment of calculation of forces in the kinematic pairs shown the advantages of the description of motion in Lagrangian form for the dynamic analysis of lever mechanisms, allows to determine the required external exposure when performing the energy conservation law at any time in any part of the mechanism.

The study examined key socio-economic characteristics of smallholder farmers identified for their contribution to market participation. These variables include gender, age, marital status, level of education, household size, additional income, membership of cooperative, herd size and use of ICTs. Using a structured questionnaire, primary data was collected from a total of 129 respondents which was analysed with the Statistical Package for the Social Sciences, and presented using cross-tabulations, percentages and count data. The result indicates that age, additional income, cooperative membership and use of ICTs were important variables which contributed to market participation among respondents. The study also made applicable recommendations as the findings may have relevance for future research, policy and practice for commercializing smallholder farmers.

We studied generalization of a method for extending configurational studies to variables that are not exclusively geographical, in order to allow investigation of generic relationships in the built environment. We observed a number of limitations of the classical approach of configurational studies and we considered how a complex analytical method could be implemented in the study of non-topological variables, such as land use, noise pollution and financial or property rents. In order to do this we established a system of relationships based on a labeled primary graph. Categorization of the labels of the links was performed on a generic variable, specifically, the predominant use of roads. The graph was then contracted using an optimization algorithm, which we describe. The algorithm allows the network to be reduced to a more streamlined system that can better analyze the relationships between the different categories. Application of the method showed that it is faster to identify weaknesses in urban networks, and then take measures to resolve them. The case study concerns the pedestrianization of the Colosseum area in Rome.

Background: During the pandemic, Emergency departments were overcrowded with critically ill patients and confronted ethical dilemmas to assign respiratory support to them due to scarce resources. Quick tools to evaluate patients at admission were needed; many scores were used but inaccurate to predict outcomes. The Rox Index is an easy and fast score that reflects the respiratory status in acute respiratory failure patients, this score could predict the outcome of covid 19 patients. Hypothesis: The 24-hour difference in the Rox Index discriminate accurately the mortality and needs for mechanical ventilation of patients with covid-19. Methods: Study design: Prospective analytic study. Population: 204 Patients admitted to the emergency department from May to August 2020. Data were collected from the clinical records. The Rox Index was calculated at admission and 24h later, the difference was used to establish the outcome, a logistic regression model adjusting for age, sex, presence of comorbidities and disease severity to build and perform ROC analysis. Results: Difference in respiratory ROX Index between admission and 24h is a good predictor for death AUC 0.92 and for mechanic ventilation AUC: 0.75. Each decrease in one unit of the difference at 24h had an Odds Ratio for death risk: 1.48 (95%CI: 1.31-1.67) and for mechanic ventilation: 1.16 (95%IC: 1.1-1.23). Conclusion: The 24-hour variation of Rox Index has good predictive value and allows healthcare professionals to identify which patients will benefit from invasive treatment, especially in low resource settings where emergency physicians deal with survival.

Background: Autonomic neuropathy commonly arises as a long-term complication in end stage kidney disease (ESKD) and can be diagnosed from heart rate variability (HRV), calculated from electrocardiogram (ECG) recordings. There is limited data about HRV using real-time ECG to predict HD efficiency in patients with ESKD who are routinely doing HD in real-world practice. Methods: Total of 50 patients (62.1 ± 10.7 years) with ESKD underwent continuous real-time ECG monitor (237.4 ± 15.3 min) during HD for HRV using remote monitoring system and checked electrolyte levels before/after HD. And we compared HRV according to electrolyte levels. Results: During the monitor, we checked total 2374 times of ECG and electrolyte level simultaneously of all patients. Both time and frequency domain HRV were higher when the patients had lower K+ level (&lt;0.5 mEq/L) and P+ level change (&lt;2 mEq/L) before/after HD as compared with those with higher K+ level (≥0.5 mEq/L) and P+ level (≥2 mEq/L) change respectively. In addition, there were higher incidence of arrhythmic events including atrial/ventricular premature complexes, even though no difference of mean heart rate in patients with lower K+ and P+ level change group (p &lt; 0.001). Conclusions: Higher HRV was independently associated with poor controlled K+ and P+ level during hemodialysis in patients with ESKD. This is further substantiated by independent continuous associations between real-time measures of higher HRV and K+ and P+ level. These data strongly suggest that cardiac autonomic dysfunction can be caused by lower change of electrolytes before/after hemodialysis alone.

This study assessed the hydrological performance and runoff water quality of 12 green roof (GR) modular systems located at the Universidad de los Andes campus (Bogotá, Colombia). Based on 223 rainfall events spanning a 3-year period, average rainfall retention was 85% (SD = 25%). T-tests, Welch Test, multiple linear regressions and correlation analysis were performed in order to assess the potential effect of air temperature, substrate type, vegetation cover, relative humidity, antecedent dry weather period (ADWP), rainfall duration and rainfall maximum intensity. In some cases, GR design variables (i.e. growing media and type of vegetation) were found to be significant for describing rainfall retention efficiencies and, depending on the GR type, some hydrological variables were also correlated with the rainfall retention. Rainfall and GR runoff were monitored for Total Kjeldahl Nitrogen (TKN), Nitrates, Nitrites, Ammonia, Total Phosphorus (TP), Phosphates, pH, Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Color, Turbidity, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Coliforms, metals and Poliaromatic Hydrocarbons (PAHs). The results obtained confirmed that GR systems have the ability to neutralize pH, but are source of the rest of the aforementioned parameters, excluding PAHs (with concentrations below detection limits), Ammonia, TSS, Se and Li, where differences with reference values (rainfall and plastic panel runoff) were not statistically significant. Substrate type, event size and rainfall regime are relevant variables for explaining runoff water quality.

COVID-19 emerged in Wuhan and is later declared as a pandemic by World Health Organization. Different-aged people have varying gender-wise immunity control properties that necessitates understanding COVID-19 impact on age and gender which does not exist, currently. In this paper, COVID-19 surveillance variables are extensively studied along with hospitalization, tests-performed, and recovery data. Dataset is curated from three sources; however, age and gender data belong to Belgium, particularly. Visualizations, frequencies, Pearson’s and polyserial correlation, student’s t-test, and Cramer’s V are used for enhanced analysis. Results show higher mortality rate in males and need of more ventilators to combat severe symptoms.

The present study investigates the relationship between the transmission of COVID-19 infections and climate indicators in Dhaka City, Bangladesh, using coronavirus infections data available from the Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh. The Spearman-ranked correlation test was carried out to study the association of seven climate indicators, including humidity, air quality, minimum temperature, precipitation, maximum temperature, mean temperature and wind speed with the COVID-19 outbreak in Dhaka City, Bangladesh. The study found that, among the seven indicators, only three indicators (air quality, minimum temperature and average temperature) have a significant relationship with new COVID-19 cases. The results of this paper will give health regulators and policymakers valuable information to lessen the COVID-19 infection in Dhaka and other countries around the world.

One third of a person’s life is spent on sleep, therefore the quality and habit of sleep affects health. A single case study indicated that perspiration could serve as a prognostic marker. Diagnosing nocturnal perspiration is common clinical practice, since this serves as a major symptom in many pathologies. Till this day no specific evidence-based approach for diagnosing nocturnal perspiration exists. By introducing the Q-strip, a device which quantitatively measures nocturnal perspiration, this could be acquired. The Q-strip could serve a purpose in diagnosing nocturnal perspiration more efficient without being intrusive. In addition to its health sensing potentials, the Q-strip makes it possible to visualise perspiration patterns. This introduces the possibility to examine the quality of sleep. Future research is recommended to investigate this.

The use of risk measures such as the Value at Risk (VaR) or Tail Conditional Expectation (TCE) is required by the Basel Committee on Banking Supervision in determining a bank’s risk profile. However, both measures can be shown to have shortcomings in the information that they provide to regulators and investors. In this paper we present an introduction to risk measure calculations before demonstrating the weaknesses of these measures. Through the exploration of specific cases we show how familiar yet differing risk profiles have identical values for combinations of these measures. From this evidence we recommend that a sequence of several risk measures should be used to give a more accurate representation of the risk contained on banking balance sheet.

Ethnobotany has been, for too long, a descriptive discipline. However, ethnobotanists are increasingly calling for a paradigm shift towards the formulation of unifying theories and hypothesis-driven research in ethnobotany. Here, we formulated a theory, termed time-since-introduction theory, to explain the integration of alien plants into local pharmacopoeias in their recipient environment. This theory suggests that the factor time is paramount in determining which alien plants are more likely to be included in the medicinal flora of the areas they are introduced in. The theory relies on three hypotheses, the availability and versatility hypotheses alongside the residence time hypothesis newly proposed in the present study. We tested this theory by fitting a structural equation model to ethnobotanical data collected on South Africa’s alien woody flora. Although residence time is a direct predictor of medicinal status of alien plants, it is a better predictor when mediated through plant versatility. These findings are in support of the theory, and we consequently proposed a framework with which to understand different paths linking all three hypotheses. Collectively, our study shows the value of time in the development of ethnobotanical knowledge and fully responds to the pressing call for paradigm shift in ethnobotany.

In this paper, we aim to assess the electric current parameters and report the analysis of the associated degree of ventricular recovery during left ventricular assist device (LVAD) support. An assumption was made there is a linear relationship between ventricular recovery degree and the pump electric current pulsatility index (PI). The experimental study was carried out using the ViVitro Pulse Duplicator System with Sputnik 1 LVAD connected. Cardiac output (CO) and cardiac power output (CPO) were used as a measure of ventricular recovery degree. Different heart rates (HR) (59, 73, 86 bpm) and pump speeds (7600–8400 rpm in 200 rpm steps) were investigated. Ventricular stroke volumes in the range of 30–80 ml for each heart rate at certain pump speed were used. The obtained relationships of CO and CPO vs. PI was linear as the coefficients of determination for each regression curve were more than 0.8. CO vs. PI: R2=0.9218; 0.9271; 0.9172 and CPO vs. PI: R2=0.8517; 0.841; 0.8244 for HR=59 bpm; 73 bpm; 86 bpm, respectively. Study findings suggest that adequate interpretation of parameters could potentially serve as a valuable clinical tool to assess ventricular recovery based on LVAD infrastructure without requiring any special hemodynamic assessment.

The importance of various markers such as sclerostin, Dickkopf-1 (DKK-1), Irisin, receptor activator of NF-kB ligand (RANKL), and Vitamin D have been well studied in bone metabolism. Additionally, inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) and Interleukine-6 (IL-6) have been identified to hinder muscle protein synthesis, leading to the loss of muscle and strength. However, a research gap exists in the understanding their role in muscle function and physical activity. Therefore, this study aims to explore the serum levels of sclerostin, DKK-1, irisin, IL-6, RANKL, Vitamin D, and TNF-α and assess their relationships with upper and lower body strength in young adults. In this study, 38 college-aged students 18-23 years participated and completed the protocols. The participants’ lower and upper body strength were assessed by the vertical jump test (Just Jump, Probotic, AL) with a Tendo FitroDyne (Tendo Sports Machines, Trencin, Slovak Republic) and handgrip (HG) dynamometry (Takei Scientific Instruments, Yashiroda, Japan), respectively. Fasting morning blood samples were analyzed for serum levels of the biomarkers by ELISA. The results indicate significant sex differences in sclerostin, DKK-1, irisin, and vitamin D levels (p<0.05). Furthermore, a positive association was observed between sclerostin, DKK-1, and vitamin D, with lower body muscle performance variables (p<0.05). Conversely, a significant negative correlation was observed between TNF-α and upper-body muscle performance variables (p<0.05). The results suggest that these markers may have a distinct effect on muscle performance, underscoring the need for further investigation to elucidate the concept of muscle-bone crosstalk.

Introduction: The viral respiratory disorder known as severe acute respiratory syndrome (SARS) is caused by the SARS-associated coronavirus. End of May 2021, there have been over 170 million reported cases and more than 3.53 million deaths worldwide. In the field of cardiovascular performance, the shock index (SI) is a simple and widely used formula that measures the physiological response prior to systemic hypotension. It is obtained by dividing the heart rate by the systolic blood pressure. Another parameter, the modified shock index (MSI), is defined as the ratio of heart rate to mean arterial pressure (MAP). Methods: In this retrospective study, we examined the data of COVID-19 patients who were admitted to the ICU between June 1st, 2020, and January 31st, 2021. The patient details were collected from the Medical Records Department (MRD) in a tertiary care center. A case-recorded proforma was used to record the patient information, which was then analyzed using SPSS software. Results: The mean age of the patients was 67 years, consisting of 64 males (68.9%) and 29 females (31.1%). The standard deviation was calculated as 13.557. Among the patients, the minimum age recorded was 28, while the maximum age was 97. Patients with known CAD (Coronary Artery Disease) had a higher mortality rate among comorbidities. The p-value for Shock Index and Mortality Status was 0.205 (with an alpha value greater than 0.05), indicating no significant association. However, the modified shock index (MSI) showed a significant association with mortality status. The Pearson chi-square value for MSI and Mortality Status was 4.19, with a p-value of 0.041 (with an alpha value less than 0.05). These results suggest that there is a higher risk of mortality for patients with a Modified Shock Index of less than 0.7 or greater than 1.3. The MSI Odds ratio was found to be 4.500, indicating that such patients have a 4.5 times higher risk of mortality compared to others. Conclusion: In conclusion, our study found that there is no significant association between the Shock Index (SI) and mortality, as the p-value was greater than 0.05. However, the Modified Shock Index (MSI) showed a significant association with mortality status, with a p-value less than 0.05. This suggests that patients with a Modified Shock Index less than 0.7 or greater than 1.3 are at a higher risk of mortality. It is important to consider these findings when assessing the prognosis and potential interventions for COVID-19 patients.

Osteoporotic spinal fractures represent an increasingly widespread health problem in our aging society. We aimed to investigate the treatment options for low-energy spinal fractures in the el-derly and their impact on mortality, focusing on surgical stabilization using minimally invasive and open techniques. We evaluated 1356 patients over 50 years of age with compression frac-tures sustained due to low energy trauma treated at a single trauma center. We compared the mortality of patients following stable and operated/non-operated unstable compression frac-tures and the eligibility to surgery using the open and minimally invasive approach. The pa-tients received surgical or conservative care according to severity and eligibility to surgery. The mortality hazard ratio of patients who underwent screw fixation was 1.02 (P > .91), of patients who refused surgery was 1.68 (P < .001) and that of patients with anesthesiological contraindica-tion was 1.57 (P = .08), compared to conservatively treated patients with stable fractures. The dif-ference in mortality between open and minimally invasive surgeries was not significant, but the number of patients eligible for surgery with the minimally invasive procedure was significantly higher (OR: 0.25; P < .001). Spinal stability after fractures may be an important predictor of sur-vival in patients with low-impact fractures.

This study develops a predictor-corrector algorithm for the numerical simulation of IVPs involving singular generalized fractional derivatives with Mittag-Leffler kernels. The proposed algorithm converts the considered IVP into a Volterra-type integral equation and then uses Trapezoidal rule to obtain approximate solutions. Numerical approximate solutions of some singular generalized fractional derivative with Mittag-Leffler kernels models have been presented to demonstrate the efficiency and accuracy of the proposed algorithm. The algorithm describes the influence of the fractional derivative parameters on the dynamics of the studied models. The suggested method is expected to be effectively employed in the field of simulating generalized fractional derivative models

Using a theorem of Ulam and Hyers, we will prove the Hyers-Ulam stability of two-dimensional Lagrange's mean value points $(\eta, \xi)$ which satisfy the equation, $f(u, v) - f(p, q) = (u-p) f_x(\eta, \xi) + (v-q) f_y(\eta, \xi)$, where $(p, q)$ and $(u, v)$ are distinct points in the plane. Moreover, we introduce an efficient algorithm for applying our main result in practical use.

Conifer bark beetles (Coleoptera: Curculionidae: Scolytinae) are major agents of natural disturbance in temperate forests of the northern hemisphere, capable of killing billions of trees in native forests. The dynamics of these insect populations are influenced by climatic variability. To understand such a relationship, it is necessary to monitor the population fluctuation of the species present over time. In this regard, the objective of the present study was to determine the species community of the genus Dendroctonus, the population fluctuation throughout the months of the year, and the effects of relative humidity, temperature, and precipitation on the fluctuation, at three sites of mixed forests in the Municipality of Bocoyna, Chihuahua, in northern Mexico. For insect monitoring, we used Lindgren funnel traps (with 8 funnels each) baited with semiochemicals. Every two weeks, and during 2 years, we collected, identified, and counted the insects. The identification of the species was based on the external morphology and characteristics of the male seminal rod. Temperature and relative humidity were measured using climate data loggers (HOBOs). We found six Dendroctonus species. We also determined the population fluctuation patterns of D. mexicanus Hopkins, D. frontalis Zimmermann, and D. brevicomis LeConte, species with the highest number of captures at each site. Additionally, the relationship between species abundance and climatic variables was determined. The variable that significantly influenced (P&lt;0.05) abundance was temperature. Relative humidity and precipitation variables did not show a significant relationship (P&gt;0.05).

With the intensification of population aging in rural areas, it becomes increasingly important to analyze the post-retirement migration intention of rural residents and the variables influencing these intentions. In this study, we focus on rural residents aged 45 to 60 and investigate the main variables that influence the post-retirement migration intention of rural residents, using survey data collected from 164 households in three different rural areas and the logistic regression model. From the result of this study, we found that gender, part-time employment, savings level, children's residence and occupation stability, and interest in urban living positively affect migration intention. In contrast, the number of rural companions, relationships with others in rural areas, and evaluation of rural living have a negative effect. In addition, we employ age and the proportion of mobile income as control variables to examine the variables that influence the post-retirement migration intention in different age groups and mobile income groups. The analysis reveals that, the variables influencing post-retirement migration intention varied across age groups and mobile income groups, and this variation can be attributed to the differences between groups' characteristics.

Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machine and Random Forest, are compared with Multivariate Linear Regression, TVX and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using four different statistics on a daily basis allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest with residual kriging produces the best results (R$^2$=0.612 $\pm$ 0.019, NSE=0.578 $\pm$ 0.025, RMSE=1.068 $\pm$ 0.027, PBIAS=-0.172 $\pm$ 0.046), whereas TVX produces the least accurate results. The environmental conditions in the study area are not really suited to TVX, moreover this method only takes into account satellite data. On the other hand, regression methods (Support Vector Machine, Random Forest and Multivariate Linear Regression) use several parameters that are easily calculated from a Digital Elevation Model, adding very little difficulty to the use of satellite data alone. The most important variables in the Random Forest Model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.

Associating the most influential parameters with the product distribution is of uttermost importance in complex catalytic processes such as fluid catalytic cracking (FCC). These correlations can lead to the information-driven catalyst screening, kinetic modeling and reactor design. In this work, a dataset of 104 uncorrelated experiments, with 64 variables, has been obtained in an FCC simulator using 6 types of feedstock (vacuum gasoil, polyethylene pyrolysis waxes, scrap tire pyrolysis oil, dissolved polyethylene and blends of the previous), 36 possible sets of conditions (varying contact time, temperature and catalyst/oil ratio) and 3 industrial catalysts. Principal component analysis (PCA) has been applied over the dataset, showing that the main components are associated with feed composition (27.41% variance); operational conditions (19.09%) and catalyst properties (12.72%). The variables of each component have been correlated with the indexes and yields of the products: conversion, octane number, aromatics, olefins (propylene) or coke, among others.

The critical changes in physical appearance during adolescence can considerably influence the self-appraisal of body image. The purpose of this study is to analyze body self-image gender differences in Mediterranean adolescents, and his relationships to the anthropometric characteristics of this population in different phases of the adolescence. Participants were 809 Mediterranean teenagers (396 females) aged 11 to 17. A relative low prevalence of dissatisfaction with body image was observed among healthy urban Mediterranean adolescents (boys 17.3%; girls 22.7%). Girls showed statistically significantly higher mean BSQ scores than boys (M = 61.7, SD = 26.6 versus M = 56.3, SD = 27.1; p < 0.001). Girls in the late adolescence were more often classified as being dissatisfied (31%) in comparison to those in the early adolescent group (19.1%; p < 0.05). There was a good correlation of BSQ scores with all the anthropometric variables in males but not in females.

he Grassmann variables are used to formally transform a system with constraints into an unconstraint system. As a result, the Schr\"{o}dinger equation arises instead of the Wheeler-DeWitt one. The Schrodinger equation describes a system's evolution, but a definition of the scalar product is needed to calculate the mean values of the operators. We suggest an explicit formula for the scalar product related to the Klein-Gordon scalar product. The calculation of the mean values is compared with an etalon method, in which a redundant degree of freedom is excluded. Nevertheless, we could note that a complete correspondence with the etalon picture is not found. Apparently, the picture with the Grassmann variables requires a further understanding of the underlying Hilbert space.

Noisy data is always encountered in real applications, such as bioinformatics, neuroimage and remote sensing. Existing methods mainly consider linear or generalized linear errors-in-variables regression, while relatively little attention is paid for the multivariate response case, and how to evaluate the estimation performance under perturbed covariates is still an open question. In this paper, we consider the information-theoretic limitations of estimating a low-rank matrix in the multi-response errors-in-variables regression model. By application of the information theory and statistical techniques on concentration inequalities, the minimax lower bound is provided in terms of the squared Frobenius loss, which recaptures the rate provided under the clean covariate assumption in previous literatures. Hence our result further indicates that though under the more realistic errors-in-variables situation, no more samples are required so as to achieve a rate-optimal estimation.

In this study, we investigate the performance of the saddlepoint approximation of the probability mass function and the cumulative distribution function for the weighted sum of independent Poisson random variables. The goal is to approximate the hazard rate function for this complicated model. The better performance of this method is shown by numerical simulations and comparison with a performance of other approximation methods.

The work is devoted to the study of a family of linear initial value problems of partial differential equations in the complex domain, dealing with two complex time variables. The use of a truncated Laplace-like transformation in the construction of the analytic solution allows to overcome a small divisor phenomenon arising from the geometry of the problem and represents an alternative approach to the one proposed in a recent work by the first two authors. The result leans on the application of a fixed point argument and the classical Ramis-Sibuya theorem.

Soil conditions is a major aspect of interest for farmers due to the knowing of the physicochemical properties of the same can help with any necessary restoration of soil that guarantees the quality and the production of their crop. However, technology and analysis of the soil become of difficult access mainly in developing countries, by which the present paper shows the development of a system thought to estimate physicochemical variables of soils growing sugar cane through studies of spectroscopy. Its characteristic is that it is a portable system, with low cost, easy to use and can estimate physicochemical variables in-situ with the objective of knowing the degree of degradation present in the soil and through this help the farmers define possible strategies to restore it. The device uses the frequency response of the soil determining values of magnitude and phase, which are used by algorithms of artificial intelligence capable of getting an estimation of the physicochemical properties. The obtained results show errors below 8% in the estimation of the variables compared to the analysis results of the soil at laboratories.

Sample size using prevalence has been one of the most important medical research methodologies and has been used in original articles more often than any other statistical tool available. The simple formula consists of variables interdependent and it is as important to understand how they influence each other as it is to learn to use the formula to calculate the sample size. With the choice of different amounts of precision and levels of confidence available to a medical researcher, it should be of paramount importance to understand what a slight change in choosing the parameter would mean at the end of the research. Understanding of different Z values, Levels of precision, and ranging Prevalence is required to understand the effect on the sample size and subsequently on the research. Topological representation using parameter ranges can be used to demonstrate this relationship.

contaminated sites located in each school district. Previous studies showed that various environmental factors, such as exposure to toxic substances negatively impacts human health depending on their concentration and occurrence. Therefore, the study aims to use Geographical Information System (GIS) technology and secondary data on IEP numbers of each school district, chemical occurrence in contaminated sites, and demographic data to conduct a correlational analysis. The secondary data obtained from school districts and contaminated sites from Iowa Department of Natural Resources Facility Explorer were populated in ArcMap 10.5 (a GIS software) for generating maps and data to conduct statistical analysis. The contaminants were categorized into metals, polyaromatic hydrocarbons (PAH), and solvents with weighing factors as 1, 0.5, and 0.25. A total of 1 Superfund site and 39 CERCLA sites were identified as contaminated sites for this study. The majority of contaminants were heavy metals such as lead, arsenic, mercury, and cadmium. The mean toxic score of all contaminated sites combined was 13.4 (sd 14.4). The correlational analysis between the IEP numbers from each school districts and toxic scores from the contaminated sites was positive indicting that increased toxic score increased the number of students enrolled in the IEP program (F=23.7, p<0.0001). The correlational analysis between toxic score and demographics indicated some interesting results. Children under the age of 10 were living in areas with high toxic score and the showed mild correlation (p<0.00052). Similarly, higher number of black population resided in areas with high toxic score (p=0.0032) than the white population (p<0.0001). The result also indicated that children enrolled in IEP were predominantly among the black population (p<0.0001). The correlational analysis between household income and poverty percentage of people residing near the contaminated area indicated that people had low average household income (p=0.0002) and high poverty percentage (p=0.0203) residing in areas with higher toxic score. Regarding the educational status, less number of people with post graduate degree (p<0.0001) resided in areas with high toxic score and more number of people with no degree (p<0.0001) resided in areas with high toxic score. Finally, the study showed the increasing trend of eligible children enrolling in free and reduced lunch programs with increasing toxic scores (p=0.0012) and IEP levels (p=0.0416). This study emphasize on developing multiple exposure to correlate environmental factors contributing to the negative health outcome on people.

Social and academic integration variables have shown to be relevant for the understanding of university dropout. However, there is less evidence regarding the influence of these variables on dropout intention, as well as predictive models that explain their relationships. Improvements in this topic become relevant considering that dropout intention stands as a useful measure to anticipate and intervene on this phenomenon. The objective of the present study was to evaluate a predictive model for the university dropout intention that considers the relationships between social and academic variables, during the first university semester of 2020. The research was carried out using a cross-sectional associative-predictive design, with a convenience sampling (n=711) due the restrictions of pandemic period. The results showed a good fit of the proposed hypothetical model that explains 38.7% of dropout intention. Both social support and perceived social isolation predicted the sense of belonging, and through it, engagement. Previous academic performance predicted early academic performance, and through it, engagement. The set of variables predicted the intention to quit, through engagement. These results are a contribution both to the understanding of the phenomenon and to guide potential interventions in the early stages of the university experience.

Background: Heart failure with preserved ejection fraction (HFpEF) is a major health concern. There is a growing recognition of the causal interplay between arterial stiffness and HFpEF. We recently reported that phosphate retention is a trigger for arterial stiffness. This study focuses on whether arterial stiffness due to phosphate retention could be a predictor for HFpEF. Methods: The study subjects were 158 patients (68 males and 90 females, mean age 74.8±11.2). HFpEF was defined according to the guidelines of the ESC 2019. Pulse wave velocity (PWV) and central systolic blood pressure (CSBP) were used as markers for arterial stiffness and afterload, respectively. We measured serum levels of fibroblast growth factor 23 (FGF23) as a marker of phosphate retention. Results: The serum levels of FGF23 had significant relationship with PWV. PWV had significant relationships with LV mass index, plasma BNP levels, and relative wall thickness, e’ and E/e’ (p<0.001, respectively). Multivariate logistic regression analysis revealed that PWV higher values and hypertension were significant predictors for the dependent factor (HFpEF). Conclusions: Arterial stiffness amplified LV afterload, leading to LV concentric hypertrophy and LV diastolic dysfunction. This study presents that arterial stiffness due to phosphate retention, and hypertension are important predictors of HFpEF.

.Cellular senescence has emerged as an important driver of aging and age-related disease in the kidney. The activity of β-galactosidase at pH 6 (SA-β-Gal) is a classic maker of senescence in cellular biology. However the predictive role of kidney tissue SA-β-Gal on eGFR loss in chronic kidney disease (CKD) is still not understood. We retrospectively studied the expression of SA-β-Gal in kidney biopsies obtained in a cohort [n=29] of incident patients who were followed up for 3 years as for standard of care. SA-β-Gal staining was approximately 4-fold higher in the tubular compartment of patients with CKD vs. controls [26.0 ± 9 vs. 7.4 ± 6% positive tubuli in patients vs. controls; p < 0.025]. Tubular expressions of SA-β-Gal, but not proteinuria at the time of biopsy correlated with eGFR loss at follow-up; moreover, SA-β-Gal expression in more than 30% of kidney tubules was associated with fast progressive kidney disease. In summary, our study shows that SA-β-Gal is upregulated in the kidney tubular compartment of adult patients affected by CKD, and suggests that tubular SA-β-Gal is associated with accelerated loss of renal function.

(1) Background: Evaluating if necrosis volumetric assessment of pre-/post-RFA in HCC can be an accurate predictor of LTP (local tumor progression) and survival.; (2) Methods: Retrospective, descriptive and analytical study conducted between 01.01.2018 - 30.09.2021 in the Radiology and Medical Imaging Department of Fundeni Clinical Institute. 35 patients, each with one LI-RADS 4 or 5 treated lesion were eligible to be included. Volumetry was performed using OsiriX MD. Initial lesion volume, ideal necrosis volume (0,5-1 cm safety margin) and actual necrosis volume at 1 and 6 months post-procedure were performed; (3) Results: Results and discussions: Actual necrosis volume is influenced by several factors: lesion position, proximity to great vessels or other critical structures. The overall recurrence-free survival rate was similar to data in other studies (77% in the first year post-RFA). This study showed that having the actual necrosis volume at 1 month smaller than ideal necrosis volume is a good predictor for local tumor progression (p &lt; 0,05); (4) Conclusions: Volumetric assessment of necrosis is easily attainable and can help improve prediction of local tumour progression.

In many previous studies, the conclusions for the effects of disturbance and environmental gradients on the diversity was remain controversial due to exclusions of trait-based diversity in their methodology. This study hypothesized trait-based indices are more likely to show monotonic and predictable relations than traditional based species diversity to guide effective restoration of tree species diversity against environmental change and anthropogenic factors. This study therefore, was initiated to access trait based functional diversity of Yayu Coffee Forest Biosphere Reserve (YCFBR), Southwest Ethiopia. Data was collected from 90 proportionally distributed plots along disturbance levels from core, buffer and transitional zones. For functional trait diversity estimation, traits relevant to plant-life strategy, competition and response to disturbances and climate change were selected. Multiple regression model was used to test the response of species diversity and functional diversity to disturbance and environmental gradients. The result revealed that, anthropogenic disturbance and elevational gradients are the most important factors influencing functional diversity in the YCFBR. Overall this study demonstrates that, trait based functional diversity is useful to predict adaptation potentials of species in heterogeneous environments. Species with low specific leaf area &amp; selective seed mass is recommended for the restoration of disturbed ecosystem.

Multiple myeloma (MM) is an incurable hematological cancer. It is preceded by monoclonal gammopathy of uncertain significance (MGUS), an asymptomatic phase. It has been demonstrated that early detection increases the 5-year survival rate. However, blood-based biomarkers that enable early disease detection are lacking. Metabolomic and lipoprotein subfraction variable profiling is gaining traction to expand our understanding of disease states and, more specifically, for identifying diagnostic markers in patients with hematological cancers. This study aims to enhance our understanding of multiple myeloma (MM) and identify candidate metabolites, allowing for more effective preventative treatment. Serum was collected from 25 healthy controls, 20 patients with MGUS, and 30 patients with MM. 1H-NMR (nuclear magnetic resonance) spectroscopy was utilized to evaluate serum samples. The metabolite concentrations were examined using multivariate, univariate, and pathway analysis. Metabolic profiles of the MGUS patients revealed lower levels of alanine (F.C. = 0.8, p = 0.002), lysine (FC = 0.8, p &lt; 0.001), leucine (FC=0.7, p &lt; 0.001) but higher levels of formic acid (FC=1.6, p ≤ 0.001) when compared to controls. However, metabolic profiling of MM patients compared to controls exhibited decreased levels of total Apolipoprotein-A1 (FC =0.6, p&lt;0.001), HDL-4 Apolipoprotein-A1 (FC = 0.5, p ≤ 0.001), HDL-4 Apolipoprotein-A2 (FC = 0.6, p &lt; 0.001), HDL Free Cholesterol (FC = 0.7, p &lt; 0.001), HDL-3 Cholesterol (FC = 0.5, p ≤ 0.001) and HDL-4 Cholesterol (FC = 0.5, p ≤ 0.001). Lastly, metabolic comparison between MGUS to MM patients primarily indicated alterations in lipoproteins levels: Total Cholesterol (FC = 0.6, p ≤ 0.001), HDL Cholesterol (FC = 0.7, p ≤ 0.001), HDL Free Cholesterol (FC = 0.4, p ≤ 0.001), Total Apolipoprotein-A1 (FC = 0.7, p ≤ 0.001), HDL Apolipoprotein-A1 (FC = 0.7, p ≤ 0.001), HDL-4 Apolipoprotein-A1 (FC = 0.6, p ≤ 0.001) and HDL-4 Phospholipids (FC = 0.6, p ≤ 0.001). This study provides novel insights into the serum metabolic and lipoprotein subfraction changes in patients as they progress from a healthy state to MGUS to MM, which may allow for earlier clinical detection and treatment.

This study explores the relationship between job satisfaction, employee loyalty and two types of flexible employment arrangements; teleworking and flexi-time. The analysis relies on data derived by the Workplace Employee Relations Survey (WERS) in 2004 and 2011. A propensity score matching and least squares regressions are applied. Furthermore, Bayesian Networks (BN) and Directed Acyclic Graphs (DAGs) are employed in order to confirm the causality between employment types explored and the outcomes of interest. Finally, an instrumental variables (IV) approach based on the BN framework is proposed and applied in this study. The results support that there is a positive causal effect from these employment arrangements on job satisfaction and employee loyalty.

The paper's main goal is to introduce a novel type of interpolative convex contraction and build up some fresh findings for it using the interpolative convex contractions' progressive approach. We have established certain conclusions using orbitally S-complete and Suzuki type contractions in F-metric spaces. My research aims to examine the fixed point method's efficacy in solving fractional differential equation with boundary conditions.

This paper examines how housing prices are determined by macroeconomic factors in Saudi Arabia, namely, Gross Domestic Product Per capita (GDPP), Consumer Prices Index (CPI), and Unemployment Rate (UNEMP). Quarterly data for a period (2014q1 – 2019q4) were collected from publications of Saudi Arabian Monetary Authority (SAMA). Vector Autoregression Analysis (VAR) is employed to capture the dynamic effect of the variables on housing prices. Granger Causality, Variance Decomposition and Impulse response function are also used. The results show that housing prices are insignificantly and positively related to GDPP, whereas it is negatively related to both (CPI & UNEMP). Only CPI has a significant relationship. The three variables, jointly, have Granger causality on HPI. Variance decompositions show that CPI is the variable with the highest explanatory power over the variation of housing prices, followed by GDPP and the UNEMP respectively indicating that CPI is the most influential determinants for housing prices.

Soil hydraulic properties are often derived indirectly, i.e. computed from easily available soil properties with pedotransfer functions (PTFs), when those are needed for catchment, regional or continental scale applications. When predicted soil hydraulic parameters are used for the modelling of the state and flux of water in soils, uncertainty of the computed values can provide more detailed information when drawing conclusions. The aim of this study was to update the previously published European PTFs (Tóth et al., 2015, euptf v1.4.0) by providing prediction uncertainty calculation built into the transfer functions. The new set of algorithms was derived for point predictions of soil water content at saturation (0 cm matric potential head), field capacity (both -100 and -330 cm matric potential head), wilting point (-15.000 cm matric potential head), plant available water, and saturated hydraulic conductivity, as well as the Mualem-van Genuchten model parameters of the moisture retention and hydraulic conductivity curve. The minimum set of input properties for the prediction is soil depth and sand, silt and clay content. The effect of including additional information like soil organic carbon content, bulk density, calcium carbonate content, pH and cation exchange capacity were extensively analysed. The PTFs were derived adopting the random forest method. The advantage of the new PTFs is that they i) provide information about prediction uncertainty, ii) are significantly more accurate than the euptfv1, iii) can be applied for more predictor variable combinations than the euptfv1, 32 instead of 5, and iv) are now also derived for the prediction of water content at -100 cm matric potential head and plant available water content.

Drug delivery through Liposomes has shown tremendous potential in terms of the therapeutic application of nanoparticles. There are several drug-loaded liposomal formulations approved for clinical use that help mitigate harmful effects of life-threatening diseases. Developments in the field of liposomal formulations and drug delivery have made it possible for clinicians and researchers to find therapeutic solutions for complicated medical conditions. A key aspect in the development of drug-loaded liposomes is a careful review of optimization techniques to improve the overall formulation stability and efficacy. Optimization studies help in improving/modulating the various properties of drug-loaded liposomes and are vital for the development of this class of delivery systems. A comprehensive overview of the various process variables and factors involved in the optimization of drug-loaded liposomes is presented in this review. The influence of different independent variables on drug release and loading properties with the application of a statistical experimental design is also explained in this article.

Human disturbances can have severe environmental impacts on freshwater ecosystems. The main aim of this study was to detect the influences of physiochemical variables, land-cover characteristics, and river connectivity on fish assemblages in Lake Chaohu Basin, China. A cluster analysis of the river connectivity variables identified four groups of sites that characterized by significantly different connectivity gradients in local scale. These four groups of sites showed increasing connectivity from the upper reaches to the lower reaches. At the same time, among the four groups, the values of the environmental variables generally increased from the upper reaches with less human activity towards the lower reaches with more human activities. For instance, some main physiochemical variables (e.g., river width, water depth, nitrate, phosphate) significantly increased among the four groups. In contrast, fish taxa richness and diversity indices were not significantly different among the four connectivity groups. However, fish assemblages showed significant variation among the connectivity groups (p=0.026). In addition, the study determined that upper riparian land uses (e.g., woodland and grassland), flow velocity, and elevation were environmental variables regulating the variance in the fish communities, and for the connectivity variables, only river order and number of branches along a path to the left of the main stem affected the variance in the fish communities. Therefore, new practices aimed at maintaining and even increasing the riparian canopy coverage and the flow velocity of rivers should be integrated into local conservation planning for freshwater ecosystems, especially in the upper reaches of the basin.

Abstract: Background: Of the many side effects suffered by cancer patients, those related to cognitive performance have become increasingly prominent in clinical practice. We know that chemotherapy generates a series of side effects, such as nausea and vomiting, alopecia, and so on, which can be counteracted by complementary medication. However, in the case of post-chemotherapy cognitive impairment, or chemo brain, these cannot be controlled with drug therapies. However, before any intervention can be considered, it is necessary to know exactly what cognitive impairment is being triggered. For this reason, we decided to study the cognitive status of breast cancer patients. Methods: analytical, prospective, three-measure longitudinal, intrasubject unifactorial, non-probabilistic and accidental assignment study. The sample came from the Medical Oncology Department at Hospital de Salamanca, in Spain. Cognitive function (Trail Making Test and Stroop Test) was established as the primary variable; the presence of sleep disorders (Insomnia Severity Index, ISI) and anaemia (haemoglobin levels in blood) were analysed as secondary variables, in addition to sociodemographic variables (age, stage, type of carcinoma, radiotherapy, menopause, social support network, marital status, years of schooling and employment status). Results: We recruited 151 individuals according to the selection criteria. We can confirm that factors including anaemia, menopause, patient support network and marital status, years of schooling, and employment status did affect the cognitive performance of the patients in active chemotherapy treatment in the study. In contrast, sleep disorders, age, radiotherapy treatment, stage of disease, and type of carcinoma did not affect the cognitive performance of the cancer patients. Conclusions: Chemotherapy does impact the cognitive performance of breast cancer patients.

Background: Of the many side effects suffered by cancer patients, those related to cognitive performance have become increasingly prominent in clinical practice. We know that chemotherapy generates a series of side effects, such as nausea and vomiting, alopecia, and so on, which can be counteracted by complementary medication. However, in the case of post-chemotherapy cognitive impairment, or chemo brain, these cannot be controlled with drug therapies. However, before any intervention can be considered, it is necessary to know exactly what cognitive impairment is being triggered. For this reason, we decided to study the cognitive status of breast cancer patients. Methods: analytical, prospective, three-measure longitudinal, intrasubject unifactorial, non-probabilistic and accidental assignment study. The sample came from the Medical Oncology Department at Hospital de Salamanca, in Spain. Cognitive function (Trail Making Test and Stroop Test) was established as the primary variable; the presence of sleep disorders (Insomnia Severity Index, ISI) and anaemia (haemoglobin levels in blood) were analysed as secondary variables, in addition to intervening variables (age, stage, type of carcinoma, radiotherapy, menopause, social support network, marital status, years of schooling and employment status). Results: We recruited 151 individuals according to the selection criteria. We can confirm that factors including anaemia, menopause, patient support network and marital status, years of schooling, and employment status did affect the cognitive performance of the patients in the study. In contrast, sleep disorders, age, radiotherapy treatment, stage of disease, and type of carcinoma did not affect the cognitive performance of the cancer patients. Conclusions: Chemotherapy does impact the cognitive performance of breast cancer patients.

Background: Of the many side effects suffered by cancer patients, those related to cognitive performance have become increasingly prominent in clinical practice. We know that chemotherapy generates a series of side effects, such as nausea and vomiting, alopecia, and so on, which can be counteracted by complementary medication. However, in the case of post-chemotherapy cognitive impairment, or chemo brain, these cannot be controlled with drug therapies. However, before any intervention can be considered, it is necessary to know exactly what cognitive impairment is being triggered. For this reason, we decided to study the cognitive status of breast cancer patients. Methods: analytical, prospective, three-measure longitudinal, intrasubject unifactorial, non-probabilistic and accidental assignment study. The sample came from the Medical Oncology Department at Hospital de Salamanca, in Spain. Cognitive function (Trail Making Test and Stroop Test) was established as the primary variable; the presence of sleep disorders (Insomnia Severity Index, ISI) and anaemia (haemoglobin levels in blood) were analysed as secondary variables, in addition to intervening variables (age, stage, type of carcinoma, radiotherapy, menopause, social support network, marital status, years of schooling and employment status). Results: We recruited 151 individuals according to the selection criteria. We can confirm that factors including anaemia, menopause, patient support network and marital status, years of schooling, and employment status did affect the cognitive performance of the patients in the study. In contrast, sleep disorders, age, radiotherapy treatment, stage of disease, and type of carcinoma did not affect the cognitive performance of the cancer patients. Conclusions: Chemotherapy does impact the cognitive performance of breast cancer patients.

Carotenoids are essential pigments that play a vital role in the photosynthetic process of algae and are important for human health and nutrition. Despite their potential value, algal carotenoids are being neglected by biofuel industries. Recent research is aimed at producing microalgal lipids and carotenoids simultaneously under abiotic stresses. In this study, the effect of different abiotic stress factors such as Nitrogen (N), Potassium (K), Phosphorous (P), Sulphur (S), light intensity, and photoperiod on biomass, lipid, and carotenoids synthesis in Monoraphidium sp. (MP) were explored utilizing a statistical method. In addition, 2, 7-dichlorofluorescein diacetate (DCF-DA) mediated spectrofluorimetry was employed to detect the ele-vation in ROS level. The results revealed that variations in nutrients and light con-ditions are the key factors in the up-regulation of biomolecules lipids and carote-noids in the algae. The carotenoids profiling was done by Ultra Performance Liquid Chromatography Quadrupole Time-of-Flight Mass Spectroscopy (UPLC-Q-ToF-MS). Among consisting carotenoids violaxanthin, astaxanthin, and beta-carotene were enhanced by 1.3 folds, 1.19 folds, 1.08 folds, while lutein was declined by 1.32 folds under stressed condition compared to control. The identified carotenoids are rec-ognized for their importance in the pharmaceutical and nutraceutical industries, highlighting the significant role of MP in these sectors.

Background: It has been reported that general adiposity in adulthood and early adulthood, and greater height may increase the risk of almost all types of lympho-haematopoietic cancers while a study done in MD Anderson found that obesity and adult weight gain are independent risk factors for CML however no study evaluated the role of obesity in the disease progression while more studies investigate the impact of translocation types. Method: We conducted a retrospective analysis of the files of 37 patients being treated in our center for CML in chronic phase (CMP-CP) with known BCR-ABL1 breakpoints, Results: patients’ management and response assessment was done based on ELN 2013 guidelines. Analysis is done based on two main groups, obese vs normal BMI, and then based on BCR-ABL1 transcripts: e13a2 vs e14a2. Although the number of cases is limited, in the patient-cohort studied an e14a2 BCR-ABL1 transcript type / normal body weight was associated with an inferior outcome when compared to e13a2 transcript / obesity groups Conclusion: our finding suggest the need to enlarge the series to better evaluate a potential role of altered metabolism and/or specific transcripts in the response to TKI in CML.

Recently natural gas (NG) global market attracted much attention in case it is cleaner than oil, and simultaneously in most regions is cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological development, emerging unconventional resources, energy security challenges, and shipment are some of the forces that made the NG market more dynamic and complex. From a policy-making perspective, it is vital to uncover demand-side future trends. This paper proposed an intelligent forecasting model to forecast NG global demand, however investigating a multi-dimensional purified input vector. The model starts with a data mining (DM) step to purify input features, identify the best time lags, and to pre-process selected input vector. Then a hybrid artificial neural network (ANN) which equipped with genetic optimizer is applied to set up ANN’s characteristics. Among 13 available input features, six features (e.g. Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption) selected as the most critical feature via the DM step. Then, the hybrid prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different error based evaluation statistics. Besides, as the model proposed the best input feature set, results compared to the model which used the raw input set, with no DM purification process.

Understanding how climatic variables impact the reference evapotranspiration (ET0) is essential for water resource management, especially considering potential fluctuations due to climate change. Therefore, we used the Sobol method to analyze the spatiotemporal variations of Penman-Monteith ETo sensitivity to the climatic variables: downward solar radiation, relative humidity, maximum and minimum air temperature, and wind speed. The Sobol’s indices variances were estimated by Monte Carlo integration, setting the sample limits to 2.5 and 97.5 percentiles of the daily data of 33 automatic weather stations located in the state of Mato Grosso, Brazil. The results of the Sobol analysis indicate considerable spatiotemporal variations in the sensitivity of ETo to climatic variables and their interactions. The dominant climatic variable responsible for ET0 fluctuations in Mato Grosso is incident solar radiation, which has a more significant impact in humid environments, as observed in the areas of the Amazon biome in the state. Air relative humidity and wind speed have higher sensitivity indices during the dry season in the areas of the Cerrado biome (Savanna) in Mato Grosso. Our findings show that changes in solar radiation, relative humidity, and wind speed cannot be ignored when analyzing changes in reference evapotranspiration.

Despite the wide variety of variables commonly applied to measure different aspects of rehabilitation, the assessment and subsequent definition of indicators of environmental rehabilitation status are not simple tasks. The main challenges are comparing rehabilitated sites with target ecosystems as well as integrating individual environmental and eventually collinear variables into a single tractable measure of the state of a system before effective indicators that track rehabilitation may be modeled. For that, a consensus is lacking regarding which and how many variables need to be surveyed. Our approach considered ecological processes, vegetation structure, and community diversity from nonrehabilitated, rehabilitating and reference sites. We applied this approach to a curated set of 32 environmental variables retrieved from nonrevegetated, rehabilitating and reference sites associated with iron ore mines from the Urucum Massif, Mato Grosso do Sul, Brazil. By integrating variables from a single attribute or the entire set of variables into a single estimation of rehabilitation status, the proposed multivariate approach is straightforward and able to adequately address collinearity among variables. The proposed approach allows for the identification of biases towards single variables, surveys or analyses, which is necessary to rank environmental variables regarding their importance to the assessment. Furthermore, we show that bootstrapping permitted the detection of the minimum number of environmental variables necessary to achieve reliable estimations of the rehabilitation status. Finally, we show that the proposed variable integration enables the definition of environmental indicators for more comprehensive monitoring of mineland rehabilitation. Thus, the proposed multivariate ordination represents a powerful tool to outline the benefits of rehabilitating sites for the maintenance of biodiversity and ecosystem functions and services provided that sufficient environmental variables related to ecological processes, diversity and vegetation structure are gathered from nonrehabilitated, rehabilitating and reference study sites. By identifying deviations from predicted rehabilitation trajectories and providing assessments for environmental agencies, this proposed multivariate ordination increases the effectiveness of (mineland) rehabilitation.

The purpose of the present study was to determine the relationship between healthy lifestyle behaviors, physical fitness and coronary risk factors in university students. 320 male and female (n_m:171; n_f:149) students from a university participated in this study voluntarily. For the determination of body composition and Body Mass Indexes (BMI), subjects’ height, body weight, and skinfold thickness were taken and body fat percentage (%Fat) was determined. Healthy lifestyle behaviors were determined using the healthy lifestyle behaviors questionnaire. Indicators of physical fitness included flexibility (sit-up) (F), muscle strength and endurance (isometric knee (KS), back strength (BS) and a total of shuttle (TS), sprint performance, BMI, and body fat percentage (%fat). Coronary heart disease risk factors included mean arterial blood pressure (systolic (SBP) and diastolic (DBP)), fasting blood levels of triglycerides (TG), total cholesterol (TC), hematocrit (HT), and hemoglobin (HM). Results indicated subjects have normal body mass index, body fat percentage, SBP, DBP, TG, TCF, BS, KS. The results of the Pearson Product Moment Correlation Analysis, indicated that SBP, DBP, TG, TCF, BS, KS for male and female was significantly correlated with flexibility (sit-up) (F), muscle strength and endurance (isometric knee (KS), back strength (BS) and total of shuttle (TS)), sprint performance. (p<0.01; p<0.05), In conclusion, the findings of the present study indicated that physical fitness and healthy lifestyle behaviors play a determinant role in coronary heart disease risk factors for male and female students from a university.

This study analyzed the CO2 capture potential for Parque Natural Regional Serrania de las Quinchas buffer area in Colombia. For this purpose, multitemporal analysis for land covering for years 1989, 2000, 2006, 2011, 2017 and 2021 was made using the Normalized Difference Vegetation Index (NDVI), and Corine Land Cover (CLC) methodology. In the same way, Aboveground Biomass (AGB) was measured for representative parcels by measuring tree’s diameters and heights and applying the adequate allometric models; carbon content in soils was measured too. Results show that dense forest has grown since 1989 and the carbon stock in all the vegetation covers increased by 14,3% from 1989 until 2021, using literature data for tropical humid forests. If measured AGB values obtained in this study are used, the carbon stock average is 3 times this value. Measured carbon average content in AGB was 374,3 Ton C/Ha, well above the average for the content reported for humid tropical forests in Colombia; carbon content in soils was 897,19 Ton/Ha, 2,4 times that found in the AGB. In total, an average of 1271,49 Ton C/Ha (AGB+ soil) was found and a pattern of dense forest recovery was found, but in a more dispersed way than in the past. A very interesting potential for existing forests recovery was found for this area. Strategies for this include the development of sustainable practices, land use management, everything pursuing biodiversity preservation and the participation and leading of the local communities.

The service sector, whose value has started to increase in recent years, has gained momentum with the acceleration of economic developments especially after the 2000s in Turkey. In a world where competition is increasing and social purchase perception is changing, companies started to differentiate in the service sector to create a competitive advantage. Companies that would like to grow in the industry and aspire to a greater proportion need credit to extend. There are many factors to consider in order to allocate the credit. Banks make financial and non-financial analysis to make credit decisions. This study has been prepared to assess the effect of financial and non-financial features of middle segment companies that need credit in the service sector on the credit decision to be allocated by banks. The group of variables that explain the credit decision at the highest level in these companies are non-financial variables.

Multicriteria decision aid requires a database as a decision matrix, in which two or more alternatives are evaluated according to two or more variables selected as decision criteria. Several problems of this nature use measures by Likert scales. Depending on the method, parameters from these data (e.g. means, modes or medians) are required for calculations. This parameterization of data in ordinal scales has fueled controversy for decades between authors who favor mathematical/statistical rigor and argue against the procedure, stating that ordinal scales should not be parameterized, and scientists from other areas who have shown gains from the process that compensate for this relaxation. The aim of this article is to demonstrate the advantages of the Composition of Probabilistic Preferences (CPP) method in multicriteria problems with data from Likert scales. The CPP is capable of allaying the protests raised and obtaining more accurate results than descriptive statistics or parametric models can bring. The proposed algorithm in R-code involves the use of CPP with empirical distributions and fitting histograms of data measured by Likert scales. Two case studies with simulated datasets having peculiar characteristics and a real case illustrate the advantages of the CPP.

Over the last 20 years, begomoviruses have emerged as devastating pathogens, limiting the production of different crops worldwide. Weather conditions increase vector populations, with negative effects on crop production. In this work we evaluated the relationship between the in-cidence of begomovirus and climatic conditions before and during the crop cycle. Soybean and bean fields from the northwest (NW) of Argentina were monitored for 14 years and classified as moderate (≤50%) and severe (> 50%) according to the relative incidence. Two hundred bio-meteorological variables were constructed, summarizing meteorological data in 10-day peri-ods from June to March of each crop year. The studied variables included temperature, precipi-tation, relative humidity, wind (speed and direction), pressure, cloudiness and visibility. For bean, high maximum winter temperatures, low spring humidity and precipitation 10 days before planting correlated with severe incidence. In soybeans, high late winter and pre-planting tem-peratures, and low spring precipitations were found to be good predictors of high incidence of begomovirus presence. The results suggest that temperature and pre-sowing precipitations can be used to predict incidence status [predictive accuracy: 82% (bean) and 75% (soybean)]. Thus, these variables can be incorporated in early warning systems for crop management deci-sion-making to reduce the virus impact on bean and soybean crops.

The importance of vertical jump in sport fields and rehabilitation is widely recognized. Furthermore, Force-Time variables of vertical jump are factors affecting jumping height. Exclusive review of each of this variables, in eccentric and concentric phases, can lead to a specific focus on them during jumping exercises. So, the aims of his study were to a) reviewing the relationship between force-time curve variables of eccentric and concentric phases with jump height and b) description of this variables in Iran national youth volleyball players society. This is an observational study. 12 elite volleyball player (Male, Iran national youth volleyball players, 17±0.7 years) have participated in this study. Correlation between Force-Time variables - included peak force (PF), relative peak force (RPP), peak power (PP), average power (AP), relative peak power (RPP), and Modified Reactive Strength Index (MRSI) - in eccentric and concentric phases and ultimate jump height has been studied. Results showed that the average power (r=0.7) and relative peak force (r=0.75) of concentric phase and MRSI (r=0.83) have significant correlation with ultimate jump height (JH). Relative peak power and average power of concentric phase can massively effect Jump Height in sports like volleyball, which vertical jump is an integral part of them. Focus on both of these factors, which has been studied in this research, in training programs, can improve athlete jump performance significantly.

Previous studies have reported that clinical walk tests could not predict development of frailty. With advancements in wearable technology, it may be possible to predict the development of frailty using loading asymmetry parameters during clinical walk test. This prospective cohort study aimed to test the hypothesis that increased limb loading asymmetry predicts frailty risk in community-living older adults. Sixty-two independently ambulant community-living adults aged ≥ 65 years were recruited, and forty-seven subjects completed the one-year follow-up after baseline. Loading asymmetry index of net and regional (forefoot, midfoot, and rearfoot) plantar forces were collected using force sensing insoles (100 Hz) during a 10-m walk test with their maximum speed. Development of frailty was defined if the participant progressed from baseline at least one grading group of frailty at follow-up period using the Kihon Checklist. Fourteen subjects developed frailty at follow-up period. Increased risk of frailty was associated with each 1% increase in loading asymmetry of net impulse (Odds ratio 1.153, 95% CI 1.001 to 1.329). Loading asymmetry of net impulse significantly correlated with asymmetry of peak force in midfoot force. These results indicate the feasibility of measuring plantar forces of gait during clinical walking tests, and underscore the potential of using load asymmetry as a tool to augment frailty risk assessment in community-dwelling older adults.

Background: Ceramides are a new kind of lipid biomarkers and have already been demonstrated to be valuable risk predictors in coronary patients. Patients with peripheral artery disease (PAD) are a population with worse prognosis and higher mortality risk compared to coronary artery disease (CAD) patients. However, the value of ceramides for risk prediction in PAD patients is still vague and has been addressed in the present study. Methods: This observational study included 379 PAD patients. The primary endpoint was all-cause mortality at 10 years of follow-up. A set of ceramides was measured by LC-MS/MS and combined according to the coronary event risk test (CERT) score which categorizes patients into one of four risk groups (low risk, moderate risk, high risk, very high risk). Results: Kaplan Meier survival curves revealed that the overall survival of patients has decreased with the increasing risk predicted by the four CERT categories, advancing from low risk to very high risk. Cox regression analysis demonstrated that every 1-step increase in category resulted in a 35% rise in overall mortality risk (HR=1.35 [1.16-1.58]). Multivariable adjustment, including amongst others age, T2DM, and statin treatment before baseline, did not abrogate this significant association (HR=1.22 [1.04-1.43]). Moreover, we found that the beneficial effect of statin is significantly stronger in patients with a higher risk according to CERT. Conclusion: We conclude that the ceramide-based risk score CERT is a strong predictor of the 10-year mortality risk in patients with PAD.

Type 2 diabetes mellitus (T2DM) is an endocrine illness associate with various changes in the immune system and adaptor protein levels. Cytokine dependent hematopoietic cell linker (CLNK) is an adapter protein that regulates immune receptor signaling and acts as a regulator of the receptor signaling of T-cells and natural killer T-cell. The role of CLNK in T2DM is not studied previously. In the present study, serum CLNK level was measured and correlated with some sociodemographic and insulin resistance (IR) parameters. This is achieved by performing measurement of CLNK and insulin parameters (glucose, insulin, and HbA1c in addition to the calculation of the functions of IR (HOMA2IR), insulin sensitivity (HOMA%S), and beta-cell function (HOMA%B)) in 60 T2DM patients and 30 controls. The results indicated a significant increase (p=0.025) in serum CLNK in patients group in comparison with the controls. Multivariate generalized linear model (GLM) analysis revealed no significant effect of age, BMI, and sex on the CLNK level. The results of tests for between-subjects showed that the CLNK affects diagnosis significantly (F=7.445, p=0.008, partial η² =0.081) and its effect is approximately the same as the effect of insulin (F=8.107, p=0.006, partial η² =0.087). The correlation study showed a highly significant positive correlation between CLNK and the duration of disease (rho=0.420, p<0.001). It can be concluded that the increase CLNK in T2DM revealing the role of the adaptor proteins level in the nature of disease. Elevation of CLNK level may be used as a predictor for diabetes complications, which needs more investigations.

AML is a highly aggressive and heterogeneous form of hematological cancer. Proteomics-based stratification of patients into more refined subgroups may contribute to a more precise characterization of the patient-derived AML cells. Here, we reanalyzed liquid chromatography-tandem mass spectrometry (LC-MS/MS) generated proteomic and phosphoproteomic data from 26 chemoresistant/relapse (RELAPSE) and 15 relapse-free (REL_FREE) AML patients. We considered not only the RELAPSE and REL_FREE characteristics but also integrated the French-American-British (FAB) classification, along with considering the presence of nucleophosmin 1 (NPM1) mutation and cytogenetically normal AML. We found a significant number of differentially enriched proteins (911) and phosphoproteins (257) between the various FAB subtypes in RELAPSE patients. Patients with the myeloblastic M1/M2 subtype showed higher levels of RNA processing-related routes and lower levels of signaling related to terms like translation and degranulation, when compared to the M4/M5 subtype. Moreover, we found that high abundance of proteins associated to mitochondrial translation and oxidative phosphorylation, particularly observed in the RELAPSE M4/M5 NPM1 mutated subgroup, distinguishes relapsing from non-relapsing AML patient cells with the FAB subtype M4/M5. Thus, the discovery of subtype-specific biomarkers through proteomic profiling may complement the existing classification system for AML and potentially aid in selecting personalized treatment strategies for individual patients.

The aim of this study was to introduce a new activity-based balance index by using accelerometer data. Twenty-seven junior soccer players from the Iranian premier league were selected. Four functional tests, consisting of one leg stance, dynamic Y balance, running and dribbling tests, were conducted to assess the players’ balance, activity and skill. During these four tests, besides their relative scores, the acceleration of their body center was also recorded. Activity-based balance index (ABI) was calculated using these acceleration data. The results showed positive correlations between ABI and both static and dynamic balance scores. Additionally, negative correlations were found between ABI and dribbling scores, which demonstrate the agility required for this skill. It seems that this new index achieves the evaluation of both the balance and the skill level of soccer players. Perhaps this is a new way of talent identification and also a re-development of balance tests from traditional to modern.

(1) Background: The influence of the menstrual cycle on physical fitness in athletes is controversial in the scientific literature. There is a marked fluctuation of sex hormones at three key points of the menstrual cycle, where estrogen and progesterone vary significantly. Hormonal contraception induces hormonal levels different from the natural menstrual cycle, requiring specific study in relation to physical fitness. (2) Method: Women aged 18 to 40 years with regular natural menstrual cycles and women using hormonal contraception were recruited, creating two study groups. All participants needed to be athletes classified as level II-III according to the classification by McKay et al (2021), based on training volume/physical activity metrics, among other variables. To assess their physical fitness, Cardiorespiratory Fitness (measured by VO2max), high-speed strength, hand grip strength, and flexibility were evaluated. Blood samples were taken to determine the menstrual cycle phase through analysis of sex hormone levels. Additionally, urine tests for ovulation detection were performed for the natural menstrual cycle group. Neurosensory stimulation tests were incorporated to measure sensory threshold and pain threshold in each phase. Body composition in each phase and its relationship with the other variables were also taken into account. (3) Results: Athletes in the natural cycling group showed differences in VO2max (mL·kg-1·min-1) (phase I =41.75 vs phase II =43,85 and (p=0.004) and phase I vs phase III=43,25 mL·kg-1·min-1 (p =0.043)), as well as in body weight (phase I = 63.23 vs. phase III = 62.48 kg; p=0.006), first pain threshold (phase I = 1.34 vs. phase II = 1.69 (p=0.027) and phase III = 1.59 mA (p=0.011)), and sensitive threshold (phase I = 0.64 vs. phase II = 0.76 mA (p=0.017)). The pain threshold was found to be an important covariate in relation to VO2max, explaining 31.9% of the variance in phase I (p=0.006). These findings were not observed between the two phases of contraceptive cycling. (4) Conclusion: The natural menstrual cycle will cause significant changes in the physical fitness of athletes. The use of hormonal contraception is not innocuous. Women with natural cycles show a decrease in cardiorespiratory fitness in phases II and III, which is a factor to be considered in relation to training level and workload.

Oxalate, a compound produced by many edible plants and as a terminal metabolite in the liver of mammals, is a toxin that has a detrimental role to human health. Humans and other mam-mals do produce the enzymatic machinery to degrade oxalate. However, numerous oxa-late-degrading bacteria reside in the mammalian gut and thus provide an important function for hosts. The current review focuses on the environmental factors that influence the efficacy of pro-biotic oxalate-degrading bacteria, relative to oxalate metabolism. We describe the mechanism of oxalate catabolism and its consumption by obligate and facultative anaerobic oxalate-degrading bacteria, in both in vitro and in vivo environments. We also explore the environmental variables that impact oxalate degradation. Studies on single species degrade oxalate have not shown a strong impact on oxalate metabolism especially in high oxalate conditions such as consumption of foods high in oxalate (such as coffee and chocolate for humans or halogeton in animal feed). Considering effective variables which enhance oxalate degradation could be used in application of effective probiotic as a therapeutic tool in individuals with hyperoxaluria. This study indicates probiotics can be considered a good source of naturally occurring oxalate degrading agent in human colon

This study aimed to identify the utility of diffusion-weighted magnetic resonance (MR) imaging with an apparent diffusion coefficient (ADC) map as a predictor of the intrahepatic response of hepatocellular carcinoma (HCC) to cisplatin-based hepatic arterial infusion chemotherapy (HAIC). We retrospectively evaluated 113 consecutive patients with HCC who underwent gadoxetic acid-enhanced and diffusion-weighted MR imaging. The appropriate cutoff for the tumor-to-liver ADC ratio was determined to be 0.741. Of the 113 patients, 51 (45%) presented with a tumor-to-liver ADC ratio < 0.741. Evaluation of the intrahepatic treatment response after 2-3 cycles of HAIC in these 51 patients revealed that 20 patients (39%) experienced an objective response to HAIC. On the other hand, only 10 of the 62 patients with a tumor-to-liver ADC ratio ≥ 0.741 (16%) experienced an objective response. Thus, the objective response rate was significantly higher in patients with a tumor-to-liver ADC ratio < 0.741 than in those with a tumor-to-liver ADC ratio ≥ 0.741 (P = 0.006). Multivariate logistic regression analysis using parameters including perfusion alteration, percentage of a non-enhancing portion, and tumor-to-liver ADC ratio revealed that a tumor-to-liver ADC ratio < 0.741 (odds ratio 3.03; P = 0.015) is the sole predictor of an objective response to HAIC. Overall survival rates were significantly higher in patients with objective responses to HAIC than in those without objective responses (P = 0.001 by log-rank test). In conclusion, patients with unresectable HCC with a tumor-to-liver ADC ratio < 0.741 showed a favorable intrahepatic response to HAIC. Therefore, diffusion-weighted MR imaging can play a critical role as a predictor of response to cisplatin-based HAIC in unresectable HCC.