Two sufi women take part in activities related to sufism and create an attraction point in the secular side in Turkey.The most striking point is that both women do not wear headscarves although they are muslim women. This article attempts to show how sufi women Cemalnur Sargut and Hayatnur Artıran have idealized a Muslim woman's identity, the work they are doing to achieve this ideal, their attidue toward Rebublic values, and the effects of these attidues on their followers. This article was created from my doctoral thesis and based on Qualitative research which has been established on the basis of 2-year long participant observation and data obtained from in-depth and focus interviews. 31 women and 20 men were interviewed in depth and focus interviews.The main conclusions of my qualitative research are that the influence of theese sufi women is related with process of modernization by accelerating the establishment of the republic in Turkey. It seems that a perception has established concerning that “public apparency’’ of woman will disappear if the “apparency of body’’ disappears and the woman will return to its role which had relapsed into silence for centuries. Besides not using the headscarf, religious conversation have been created by theese sufi women in the balance of religion, philosophy and science and the fact that they are conducting their works under secular institutions such as foundations and associations have also been observed to have a very important effect on seculer side.

Malignant cells cultured in three-dimensional (3D) models have been found to be phenotypically and biochemically different from their counterparts cultured conventionally. Since most of these studies employed solid tumor types, how 3D culture affects multiple myeloma (MM) cells is not well understood. Here, we compared MM cells (U266 and RPMI8226) in a 3D culture model with those in conventional culture. While the conventionally cultured cells were present in single cells or small clusters, MM-3D cells grew in large spheroids. We discovered that STAT3 was the pathway that was more activated in 3D in both cell lines. The active form of STAT3 (phospho-STAT3 or pSTAT3), which was absent in MM cells cultured conventionally, became detectable after 1-2 days in 3D culture. This elevated pSTAT3 level was dependent on the 3D environment, since it disappeared after transferring to conventional culture. STAT3 inhibition using a pharmacological agent, Stattic, significantly decreased the cell viability of MM cells and sensitized them to bortezomib in 3D culture. Using an oligonucleotide array, we found that 3D culture significantly increased the expression of several known STAT3 downstream genes implicated in oncogenesis. Since most primary MM tumors are naturally STAT3-active, studies of MM in 3D culture can generate results that are more representative of the disease.

The role of natural factors mainly solar eleven-year cycle variability, and volcanic eruptions on two major modes of climate variability the North Atlantic Oscillation (NAO) and El Niño Southern Oscillation (ENSO) are studied for around last 150 years period. The NAO is the primary factor to regulate Central England Temperature (CET) during winter throughout the period, though NAO is impacted differently by other factors in various time periods. Solar variability indicates a strong positive influence on NAO during 1978-1997, though suggests opposite in earlier period. Solar NAO lag relationship is also shown sensitive to the chosen times of reference and thus points towards the previously proposed mechanism/ relationship related to the sun and NAO. The ENSO is influenced strongly by solar variability and volcanic eruptions in certain periods. This study observes a strong negative association between the sun and ENSO before the 1950s, which is even opposite during the second half of 20th century. The period 1978-1997, when two strong eruptions coincided with active years of strong solar cycles, the ENSO, and volcano suggested a stronger association, and we discussed the important role played by ENSO. That period showed warming in central tropical Pacific while cooling in the North Atlantic with reference to the later period (1999-2017) and also from chosen earlier period. Here we show that the mean atmospheric state is important for understanding the connection between solar variability, the NAO and ENSO and associated mechanism. It presents a critical analysis to improve knowledge about major modes of variability and their role in climate. We also discussed the importance of detecting the robust signal of natural variability, mainly the sun.

Polarimetric features extracted from polarimetric synthetic aperture radar (PolSAR) images contain abundant scattering information of objects. Utilizing this information for PolSAR image classification can improve accuracy and enhance object monitoring. Although significant progress has been achieved in related research, there are still issues such as insufficient utilization of polarimetric information and features. In this paper, a deep learning classification method based on polarimetric channel power features for PolSAR is proposed. The distinctive characteristic of this method is that the polarimetric features inputted into the deep learning network are the power values of polarimetric channels, where these channels are treated equivalently and contain complete polarimetric information. In terms of experimental comparative study, besides polarimetric power combinations, three different data input schemes were employed as data for the input end of the convolutional neural network (CNN), taking into account existing research. The neural networks utilizes the extracted polarimetric features to classify images, and the classification accuracy analysis is employed to compare the strengths and weaknesses of different input schemes. It is worth mentioning that the polarized characteristics of the data input scheme mentioned in this article have been derived through rigorous mathematical deduction, and each polarimetric feature has clear physical meaning. By testing different data input schemes on the Gaofen-3 (GF-3) PolSAR image, the experimental results show that the method proposed in this article outperforms existing methods and can improve the accuracy of classification to a certain extent, validating the effectiveness of this method in large-scale area classification.

High rates of phosphorus (P) currently being applied to soils for the production of vegetables in the Mekong Delta, Vietnam has led to the concern of its negative effect on the economics and the environment. This research presents a comprehensive study on the determination of P supplying capacity in this region of Vietnam to examine the possibility of reducing P fertilizer input. One hundred twenty (120) soil samples were collected to evaluate total P and Bray 1 available P in the soils. Phosphorus maximum sorption, degree of P saturation, P release, and the effect of P fertilizer on corn (Zea mays L.) yield in greenhouse and fields were also determined. Total P concentrations of 56.7% soil samples evaluated yielded high P concentrations (>560 mg P/kg), while 74.2% of the samples had high Bray 1 available P concentrations (>20 mg P/kg soil). Maximum P sorption ranged from 149 to 555 mg P/kg soil, respectively and has negative correlation to available P (r = - 0.63*), degrees of P saturation ranged from 0.63 to 5.46% correlated to available P (r = 0.98**) and maximum P release ranged from 1.2 to 61.9 mg P/kg soil, respectively correlated to available P (r = 0.96**). Corn grown in soils with available P concentrations >15 mg P/kg did not respond to P fertilizer in greenhouse or field experiments. We conclude that many farmers in this region can reduce P fertilizer input, thus increasing their profits and reducing negative environmental impacts associated with excess soil P for sustainable agriculture.

In order to quickly develop a multi ship collaborative collision avoidance plan in multi ship encounter scenarios, a multi ship collision avoidance decision model is proposed in accordance with the requirements of the International Maritime Collision Avoidance Regulations, taking into account the collision avoidance operations in multi ship encounter scenarios. Firstly, the collision risk between ships is determined by the closest encounter distance (DCPA) and closest encounter time (TCPA). Secondly, a decision objective function is established based on rules, degree of ship deviation, and ship risk. Finally, the prediction mechanism is used to calculate the minimum value of the objective function and complete the collision avoidance decision. The simulation results show that in the scenario of multiple ship encounters, the collision avoidance model can achieve safe collision avoidance with a smaller steering frequency and a shorter col-lision avoidance time. The proposed multi ship collision avoidance model can provide reference for collision avoidance decision-making in multi ship encounter scenarios, which helps to im-prove the collision avoidance efficiency in multi ship collision avoidance

Acute coronary syndrome (ACS) is a life-threatening condition that requires a prompt diagnosis and therapeutic intervention. Although serum troponin T and creatinine kinase-MB (CK-MB) are established biomarkers for ACS, it may take several hours to reach diagnostic values for ACS. In this study, we attempted to explore novel biomarkers for ACS with higher sensitivity than that of troponin T and CK-MB. The metabolomic profiles of 18 patients with ACS upon hospital arrival and those of the age-matched control (HC) group consisted of 24 healthy volunteers were analyzed using liquid chromatography-time-of-flight mass spectrometry. Volcano plots showed 24 metabolites whose concentrations differed significantly between the ACS and HC groups. Using these data, we developed a multiple logistic regression model for the ACS diagnosis, in which lysine, isocitrate, and tryptophan were selected as minimum-independent metabolites. The area under the receiver operating characteristic curve value for discriminating ACS from HC was 1.00 (95% confidence interval [CI]: 1.00-1.00). In contrast, those for troponin T and CK-MB were 0.917 (95% confidence interval [CI]: 0.812-1.00) and 0.988 (95% CI: 0.966-1.00), respectively. This study showed the potential of combining three plasma metabolites to discriminate ACS from HC with a higher sensitivity than that of troponin T and CK-MB.

Balo’s concentric sclerosis is a rare disease and a subtype of multiple sclerosis. A 38-year-old male patient presented to the hospital with the complaint of numbness in the left-sided upper and lower limb, slurring of speech and left-sided headache for 15 days. Further investigation was done and in ECG ‘Delta wave’ was identified along with tachycardia. Diagnosis of Wolff Parkinson White syndrome was made. The patient’s MRI Brain has shown a non-enhancing intra-axial altered signal intensity lesion at the callose-septal interface adjacent to both lateral ventricles, which was identified as Balo’s concentric sclerosis.

The flow field around a straight chain of multiple slip spherical particles rotating steadily in an incompressible Newtonian fluid about their line of centers is analyzed at low Reynolds numbers. The particles may vary in radius, slip coefficient, and angular velocity, and they are permitted to be unevenly spaced. Through the use of a boundary collocation method, the Stokes equation governing the fluid flow is solved semi-analytically. The interaction effects among the particles are found to be noteworthy under appropriate conditions. For the rotation of two spheres, our collocation results for their hydrodynamic torques are in good agreement with the analytical asymptotic solution in the literature obtained by using a method of twin multipole expansions. For the rotation of three spheres, the particle interaction effect indicates that the existence of the third particle can influence the torques exerted on the other two particles noticeably. The interaction effect is stronger on the smaller or less slippery particles than on the larger or more slippery ones. Torque results for the rotation of chains of many particles visibly show the shielding effect among the particles.

A text obtained by a Large Language Model (LLM) such as GPT4 usually has issues in terms of incorrectness and hallucinations. We build a fact-checking system 'Truth-O-Meter' which identifies wrong facts, comparing the generation results with the web and other sources of information, and suggests corrections. Text mining and web mining techniques are leveraged to identify correct corresponding sentences; also, the syntactic and semantic generalization procedure adopted to the content improvement task. To handle inconsistent sources while fact-checking, we rely on an argumentation analysis in the form of defeasible logic programming. We compare our fact checking engine with competitive approach based on reinforcement learning on top of LLM or token-based hallucination detection. It is observed that LLM content can be substantially improved for factual correctness and meaningfulness.

As required by the EU legislation and the national risk management duties, the pesticide residues were determined by four specialized laboratories in 9,924 samples taken from 119 crops of economic importance and imported foodstuffs during 2017-2021 in Hungary. The screening method applied covered 622 pesticide residues as defined for enforcement purposes. The limit of detection ranged between 0.002-0.008 mg/kg. The 1.0% violation rate was lower than in the European Union for all commodities. No residue was detectable in 45.9% of the samples. For detailed analyses of the results, eight commodities (apple, cherry, grape, nectarine/peach, sweet peppers, and strawberry) were selected as they were analyzed over 195 samples and contained most frequently residues. Besides testing their conformity with national MRLs, applying 0.3MRL action limits for pre-export control, we found that 73% of the sampled lots would be compliant with ≥90% probability based on second independent sampling. Multiple residues (2-23) in one sample were detected in 36-50% of the tested lots. Noting the provisions of integrated pest management and the major pests and diseases of selected crops, normally 3-4, exceptionally 7-9 active ingredients of different mode of actions should be sufficient for their effective and economic protection.

Bi-clonal gammopathy is distinguished by the presence of two different or distinct monoclonal proteins, due to a proliferation of two distinct monoclonal plasma cell clones. A single monoclonal clone can give rise to two different monoclonal proteins. In this report, we present three cases of biclonal multiple myeloma and one case of Waldenstrom's disease that exhibits two biclonal peaks. Serum protein electrophoresis showed two M bands on serum protein electrophoresis.

The paper portrays a linear model for synchronous prediction of the dissolved oxygen (DO) level in a protected urban lake by using independent variables in real-time monitoring of the water quality parameters. Multi-linear regression and machine learning techniques were applied to the Dissolved Oxygen (DO) using the basic four parameters of Water Quality Index (WQI). Three real-time industry standard sensors PHEHT, CTZN, OPTOD were used for sampling, data was then interpolated through ArcGIS kriging method. 25 correlations were checked through the ML algorithm, a correlation heat map was produced and the top five relations were taken under consideration and validated by r-score and root mean squared error (RMSE) to develop the linear regression model. The performance of the model was validated through the RMSE, mean squared error (MSE), and mean absolute error (MAE) the r2 accuracy came to 0.983 when it is checked against the sample data. The suggested model can be used as a technique for unknown or malfunctioned sensor’s null DO data prediction.

Human ether-a-go-go-related gene (hERG) potassium channel is well-known contributor to drug-induced cardiotoxicity and therefore an extremely important target when performing safety assessments of drug candidates. Ligand-based approaches in connection with quantitative structure active relationships (QSAR) analyses have been developed to predict hERG toxicity. Availability of the recent published cryogenic electron microscopy (cryo-EM) structure for the hERG channel opened the prospect for using structure-based simulation and docking approaches for hERG drug liability predictions. In recent time, the idea of combining structure- and ligand-based approaches for modeling hERG drug liability has gained momentum offering improvements in predictability when compared to ligand-based QSAR practices alone. The present article demonstrates uniting the structure-based SILCS (site-identification by ligand competitive saturation) approach in conjunction with physicochemical properties to develop predictive models for hERG blockade. This combination leads to improved model predictability based on Pearson’s R and percent correct (represents rank-ordering of ligands) metric for different validation sets of hERG blockers involving diverse chemical scaffold and wide range of pIC50 values. The inclusion of the SILCS structure-based approach allows determination of the hERG region to which compounds bind and the contribution of different chemical moieties in the compounds to blockade, thereby facilitating the rational ligand design to minimize hERG liability.

Obesity has become a rising global health problem affecting adults’ quality of life. The objective of this study was to describe the prevalence of obesity in Indonesian adults based on the cluster of islands. The study was also aimed to identify the risk factors of obesity in each island cluster. This study analysed secondary data of Indonesian Basic Health Research 2018. Our data for analysis comprised 688,638 adults (>=15 years) randomly selected using proportionate to population size throughout Indonesia. We included 20 variables for sociodemographic and obesity-related risk factors for analysis. Obese status was defined using Body Mass Index (BMI) >= 27.5 kg/m2. Our current study defined seven major islands cluster as the unit analysis consisting of 34 provinces in Indonesia. Descriptive analysis was conducted to determine the characteristics of the population and to calculate the prevalence of obesity within provinces in each of the island’s clusters. Multivariate logistic regression analyses to calculate odds ratios (ORs) was performed using R version 3.6.3. The study results showed that all island clusters had at least one province with an obesity prevalence of more than 20%. Six out of twenty variables, comprising four diet factors (consumption of sweet food, high-salt food, meat food, and carbonated drinks) and two other factors (mental health disorders and smoking behaviour), varied across the island clusters. In conclusion, there was a variation of obesity prevalence of the provinces within and between island clusters. Variation of risk factors raised in each cluster island suggested the government rethink and reframe the intervention to address obesity.

This paper will discuss the application of statistic modeling to interpret a health system crisis in Sri Lanka due to COVID- 19.A strong focus on the preventive approach and the contact tracing with the utilization of available resources in a rational manner describes Sri Lanka’s response towards COVID- 19 prevention and mitigation. The early contact tracing, preemptive quarantining, isolation, and treatment were implemented as a concerted effort. This approach, proven efficient during the early phase of the pandemic, was sustainable when there was a rapid increase in the COVID- 19 patients since July 2021, exceeding the health system capacity.The country’s COVID- 19 situation during the period from 01st of August 2021 to 31st of October 2021 was taken into consideration. Variables used for analysis were; total number of cases, recovered cases, comorbid and O₂ dependent patients, ICU patients, and deaths. The regression model was applied to analyze the data by using the EViews 12 (x64) software application.The correlation coefficients of all the independent variables under consideration implies that they have a strong positive relationship with the number of deaths occurred during the said period. According to the computed multiple linear regression model, the number of positive cases and O₂ dependents have a positive relationship with the dependent variable. Further, the Durbin- Watson stat value of the model and multicollinearity test reflect that it is free from serial correlation thereby the model is fit. From the perspective of epidemiological control, these findings highlight the importance of keeping the number of cases within the limits of health system capacity.

Despite extensively investigating the impact of social media on fashion products’ marketing, little evidence is available on how the platforms influence sales prediction. Focusing on Lolita fashion, this study investigates the impact of social media marketing on the sales volume prediction of fashion products. Essentially, we analyzed marketing data, including comments, likes, and shares from the Weibo social platform, to forecast future sales, examine how to enhance profit performance, and make production decisions. Using a quantitative approach, we tested three different prediction models, including multiple regression, decision tree, and XGBoost. The results revealed that increasing comments and decreasing the number of likes could significantly improve the sales volumes of Lolita products. In contrast, shares exerted a less significant impact on sales. Regarding prediction models, XGBoost was found to be the best method. In the fashion industry, social media is a useful tool for forecasting market trend. A limitation of this study is that only one social media platform was used to extract data, which might limit the generalization of the findings.

In this paper we deal with the resolution of a fuzzy multiobjective programming problem using the level sets optimization. We compare it to other optimization strategies studied until now and we propose an algorithm to identify possible Pareto efficient optimal solutions.

World Health Organization (WHO) provides the guideline for managing the Particulate Matter (PM) level because when the PM level is higher, it threats the human health. For managing PM level, the procedure for measuring PM value is needed firstly. We use Tapered Element Oscillating Microbalance (TEOM)-based PM measuring sensors because it shows higher cost-effectiveness than Beta Attenuation Monitor (BAM)-based sensor. However, TEOM-based sensor has higher probability of malfunctioning than BAM-based sensor. In this paper, we call the overall malfunction as an anomaly, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that named as Hypothesis Pruning Generative Adversarial Network (HP-GAN). We experimentally compare the several anomaly detection architectures to certify ours performing better.

The increased deployment of IoT devices in specific areas results in an interference among them and the quality of communications can be severely degraded. To deal with this interference issue, the IEEE 802.11ax standard has been established in hyper-densely wireless networking systems. The 802.11ax adopts a new candidate technology that is called multiple network allocation vector in order to mitigate the interference problem. In this paper, we point out potential problem in multiple network allocation vector which can cause delays to the communication among IoT devices in hyper-dense wireless networks. Furthermore, this paper introduces an adaptive beam alignment algorithm for interference issue resolution. In addition, we analyze potential delays of communications among IoT devices under interference conditions. Lastly, we simulate our proposed algorithm in densely deployed environment and show that the interference issue can be mitigated and the IEEE 802.11ax-based IoT devices can utilize the air interface more fairly compared to conventional IEEE 802.11 distributed coordination function.

Good data analysis is required for the optimal design of water resources projects. However, data are not regularly collected due to material or technical reasons, which results in incomplete-data problems. Available data and data length are of great importance to solve those problems. Various studies have been conducted on missing data treatment. This study used data from the flow observation stations on Yeşilırmak River in Turkey. In the first part of the study, models were generated and compared in order to complete missing data using ANFIS, multiple regression and Normal Ratio Method. In the second part of the study, the minimum number of data required for ANFIS models was determined using the optimum ANFIS model. Of all methods compared in this study, ANFIS models yielded the most accurate results. A 10-year training set was also found to be sufficient as a data set.

In multi-objective optimization problems, the optimization target is to obtain a set of non-dominated solutions. Comparing solution sets is crucial in evaluating the performances of different optimization algorithms. The use of performance indicators is common in comparing those sets and, subsequently, optimization algorithms. A good solution set must be close to the Pareto-optimal front, well-distributed, maximally extended and fully filled. Therefore, an effective performance indicator must encompass these features as a whole and must be Pareto dominance compliant. Unfortunately, some of the known indicators often fail to properly reflect the quality of a solution set or cost a lot to compute. This paper demonstrates that the Degree of Approximation (DOA) quality indicator, is a weakly Pareto compliant unary indicator that gives a good estimation of the match between the approximated front and the Pareto-optimal front. Moreover, DOA computation is easy and fast.

We present a finite element model updating technique for soil-structure system identification of the Millikan Library building using the seismic data recorded during the 2002 Yorba Linda earthquake. A detailed finite element (FE) model of the Millikan Library building is developed in OpenSees and updated using a sequential Bayesian estimation approach for joint parameter and input identification. A two-step system identification approach is devised. First, the fixed-base structural model is updated to estimate the structural model parameters (including effective elastic modulus of structural components, distributed floor mass, and Rayleigh damping parameters) and some uncertain components of the foundation-level motion. Then, the identified structural model is used for soil-structure model updating wherein the Rayleigh damping parameters, the stiffness and viscosity of the soil subsystem (modeled using a substructure approach), and the foundation input motions (FIMs) are estimated. The identified model parameters are compared with state-of-practice recommendations. While a specific application is made for the Millikan Library, the present work offers a framework for integrating large-scale FE models with measurement data for model inversion. By utilizing this framework for different civil structures and earthquake records, key structural model parameters can be estimated from the real-world recorded data, which can subsequently be used for assessing and improving, as necessary, state-of-the-art seismic analysis and structural modeling techniques. This paper presents an effort towards using real-world measurements for large-scale FE model updating in the soil and structure, uniform soil time domain for joint parameter and input estimation, and thus paves the way for future applications in system identification, health monitoring and diagnosis of civil structures.

We present the case of a 28-year-old male with a history of alcohol dependency and smoking, who presented with chest pain, shortness of breath, and altered sensorium. He exhibited severe metabolic acidosis, low platelet count, and acute kidney injury. Alcoholic ketoacidosis (AKA) was suspected due to ketonuria, metabolic acidosis, and ketonemia, compounded by electrolyte abnormalities and radiographic findings of pneumonia.Prompt intervention included electrolyte correction, thiamine supplementation, broad-spectrum antibiotics, and diuretics. Thiamine played a pivotal role in the patient's recovery, with significant improvement in consciousness observed within a day. After six days, the patient was discharged in stable condition, showing normal renal and hepatic function during follow-up.This case emphasizes the need for early recognition and comprehensive management in AKA, highlighting thiamine's crucial role in treatment success.

Machine learning (ML) systems that use sensors obtain observations from sensors and use them to recognize and interpret the current situation. These systems are susceptible to sensor-based adversarial example attacks (AEs); if some sensors are vulnerable and can be compromised by an attacker, the attacker can change the output of the system by changing the values of the sensors. The detection of compromised sensors is important to defend the system against sensor-based AEs, because we can check the sensors and replace them by detecting the sensors used by the attacker. In this paper, we propose a method to detect the sensors used in sensor-based AEs by utilizing the features of the attack that cannot be avoided. In this method, we introduced a model called the feature-removable model (FRM), which allows us to select the features used as inputs into the model. Our method detects the sensors used in sensor-based AEs by finding the inconsistencies between the outputs of the FRM obtained by changing the selected features. We evaluated our method using a human activity recognition model with sensors attached to the user’s chest, wrist, and ankle. We demonstrate that our method can accurately detect sensors used by the attacker and achieves an average Recall of Detection of 0.92, and the average Precision of Detection is 0.72.

The purpose of the study is to empirically analyze the effect of quality of governance on tax revenue in South Africa. This is done by analyzing a time series dataset covering 1996 to 2020. The study used voice and accountability, regulatory quality, government effectiveness, control of corruption, political stability and rule of law as proxies of quality of governance. Multiple regression analysis was performed to test hypotheses. Based on the regression results, all quality of governance variables in South Africa have a negative effect on tax revenue except corruption control. The findings of this study also include policy recommendations. The government of South Africa must design and implement effective ways to combat poor governance, which results in a tax revenue shortfall.

The discovery of microRNAs (miRNAs) has fundamentally transformed our understanding of gene regulation. The competing endogenous RNA (ceRNA) hypothesis postulates that not only messenger RNAs but also other RNA transcripts, such as long non-coding RNAs and pseudogenes, can act as natural miRNA sponges. These RNAs influence each other’s expression levels by competing for the same pool of miRNAs through miRNA response elements on their target transcripts, thereby modulating gene expression and protein activity. In recent years, these ceRNA regulatory networks have gained considerable attention in cancer research. Several studies have identified cancer-specific ceRNA networks. Nevertheless, prior bioinformatic analyses have focused on long non-coding RNAs-associated ceRNA networks. Here, we identify an extended-ceRNA network (including both long non-coding RNAs and pseudogenes) shared across a group of four hormone-dependent (HD) cancers, i.e., prostate, breast, colorectal, and endometrial cancers, using data from The Cancer Genome Atlas (TCGA). We performed a functional enrichment analysis for differentially expressed genes in the shared ceRNA network of HD cancers, followed by a survival analysis to determine their prognostic ability. We identified two long non-coding RNAs, nine genes, and seventy-four miRNAs in the shared ceRNA network across four HD cancers. Among them, two genes and forty-one miRNAs were associated with at least one HD cancer survival. This study is the first to investigate pseudogene associated ceRNAs across a group of related cancers and highlights the value of this approach to understanding shared molecular pathogenesis in a group of related diseases.

The frame construction of an apartment complex that consists of multiple buildings encounters various uncertainties, owing to the complex relationships among units of work. Currently, the period of such a construction is calculated based on the number of floors of the highest building in the complex. This study quantitatively analyzes an apartment frame construction period using a queue model and evaluates the validity of the estimated period. In this regard, a methodology is proposed for analyzing the construction period by applying the concept of a customer and a server. A case study on the duration of an apartment frame construction is conducted with Korea Land and Housing Corporation, which has supplied the largest number of apartments in South Korea. It was found that the stable state of a queue system was observed when the rate of server utilization was applied to the basement and aboveground floors. However, a stable state was not reached on the ground floor. This study includes non-working days in its calculation and quantitatively analyzes uncertainty factors during construction. Therefore, the findings can be practically utilized to quantitatively plan the durations of work units in an apartment frame construction.

Sequence alignment and comparison through pairwise, multiple, global and local techniques are the main principles that underpin comparative genomics. However, most of the algorithms used are alignment-based which imposed some limitations on their use and application. In an attempt to provide an alignment-free alternative approaches, a methodology of comparative optinalysis and statistical mirroring was used and adopted to provide a suitable alternative for multiple genomic sequence comparison. In this article, methods comparison with MUSCLE, MUFFT, Clustal Omega, and T-Coffee was designed to assess the suitability and statistical power of statistical mirroring as an alternative method for multiple genomic sequences comparison using different sets of logically generated biological sequence datasets with different problems and computational complications. The results of the comparisons validate that statistical mirroring is a suitable alignment-free alternative approach for multiple genomic sequence comparison. The applied method (statistical mirroring) distinguishes itself over MUSCLE, MUFFT, Clustal Omega, and T-Coffee in specificity to a position-specific changes, specificity to a base-specific changes, cladogram and phylogenetic linearity, alignment independency, computational simplicity, and limit of input capacity.

Machine reading comprehension aims to teach machines to understand a text like a human, and is a new challenging direction in Artificial Intelligence. Datasets play an important role while describing or building an algorithm for machine reading comprehension. The type of answers we required from developed algorithm depends on datasets.The datasets are classified into two types, namely datasets with extractive answers and datasets with descriptive answers. This article summarize both datasets with an example of each type to get better insight of datasets in machine reading comprehension and which datasets to use depending the requirements.

Multi-object tracking (MOT) in airborne videos is a challenging problem due to the uncertain airborne vehicle motion, vibrations of the mounted camera, unreliable detections, size, appearance and motion of the moving objects as well as occlusions due to the interaction between the moving objects and with other static objects in the scene.To deal with these problems, this work proposes a four-stage Hierarchical Association framework for multiple object Tracking in Airborne video (HATA). The proposed framework combines data association-based tracking (DAT) methods and target tracking using a Compressive Tracking approach, to robustly track objects in complex airborne surveillance scenes. In each association stage, different sets of tracklets and detections are associated to efficiently handle local tracklet generation, local trajectory construction, global drifting tracklet correction and global fragmented tracklet linking. Experiments with challenging airborne video datasets show significant tracking improvement compared to existing state-of-art methods.

Parallel manipulators with multiple end-effectors bring us interesting advantages over conventional parallel manipulators such as improved manipulability, workspace and avoidance of singularities. In this work the kinematics of a five-bar planar parallel manipulator equipped with two end-effectors is approached by means of the theory of screws. As an intermediate step the displacement analysis of the robot is also investigated. The input-output equations of velocity and acceleration are systematically obtained by resorting to reciprocal-screw theory. In that regard the Klein form of the Lie algebra se(3) of the Euclidean group SE(3) plays a central role. In order to exemplify the method of kinematic analysis, a case study is included. Furthermore, the numerical results obtained by means of the theory of screws are confirmed with the aid of special software like ADAMS.^TM

Chemokine C-C receptor 6 (CCR6) and its exclusive ligand CCL20 is an extremely important chemokine receptor-ligand pair which controls cell migration and immune induction during inflammatory disease. Not many scientific studies have been undertaken to study its immune mechanisms in detail, but its unique contribution to steady state cell chemotaxis in upholding immune tolerance and regulating immune homeostasis during inflammation is evident in multiple organ systems, including lung, liver, kidney, skin, brain, eye, joints, gonads and the gut in the human body. The role of CCR6 is constitutively expressed as a series of much debilitating severe inflammatory and autoimmune diseases, HIV and cancer metastasis. CD4⁺ T cells, the central organizers of adaptive immunity, are stringently mobilized by the CCR6/CCL20 axis also induced by cytokines and a host of other factors in a carefully executed immune modulation scenario, to bring about a delicate balance between pro-inflammatory T_H17 cells and regulatory T_reg cells. Although the exact immune regulatory role is not elucidated yet, CCR6/CCL20 axis is implicated as a front runner which determines the polarization of T_H17 and T_reg cells and consequently the resolution or progression of many debilitating disorders. This review therefore aims at emphasizing the pleiotropic significance of the chemokines CCR6 and CCL20 in immunologic function in multiple organ systems thereby hoping to accentuate its value in future therapeutic modalities.

A steel damper column is an energy-dissipating member that is suitable for reinforced concrete (RC) buildings and multistory housing. To assess the seismic performance of buildings with steel damper columns, the peak displacement of the whole building and the energy dissipation demand of the dampers must be evaluated. This article proposes an energy-based prediction procedure for the peak and cumulative response of an RC frame building with steel damper columns. The proposed procedure considers two energy-related seismic intensity parameters, namely the maximum momentary input energy and the total input energy. The peak displacement is predicted considering the energy balance during a half cycle of the structural response, using the maximum momentary input energy. The energy dissipation demand of the dampers is then predicted considering the energy balance during a whole response cycle using the total input energy. The local responses (e.g., peak drift, maximum plastic rotation of beams, maximum shear strain, and energy dissipation demand of dampers) are predicted using pushover analysis. Numerical analysis results for 8- and 16-story RC buildings show that the proposed prediction method achieves satisfactory accuracy.

In computer networks, loss of data packets is inevitable, in particular, because of the buffer memory overflow of at least one of the nodes located on the path from the source to the receiver, including the latter. Such losses associated with overflows are hereinafter referred to as congestion of network nodes. There are many ways to prevent and eliminate overloads; these methods, in the majority, are based on the management of data flows. A special place is taken by the maintenance of packages, taking into account their priorities. The article considers a number of original technical solutions to improve the quality of control and reduce the required amount of buffer memory of network nodes. The ideas of these solutions are quite simple for their implementation in the development of appropriate software and hardware for telecommunication devices.

Abstract: High step-up voltage gain nonisolated DC-DC converter have attracted much attention in photovoltaic, fuel cells and other renewable energy system applications. In this paper, by combining input current ripple-free boost cell with coupled-inductor voltage-doubler cell, an input current ripple-free high voltage gain nonisolated converter is proposed. In addition, passive lossless clamp circuit is adopted to recycle the leakage inductor energy and to reduce the voltage spike across the power switch. By utilizing voltage-doubler cell consisting of diode and capacitor, the voltage stress of switch is further reduced and the resonance between the leakage inductor and the stray capacitor of the output diode is eliminated. A low switch-on-resistance low-voltage-rated MOSFET can therefore be employed to reduce the conduction loss and cost. The reverse recovery loss of output diode is reduced, and the efficiency of converter can be improved. Furthermore, the proposed converter can achieve nearly zero input current-ripple and make the design of electromagnetic interference (EMI) filter easy. Steady state analysis and operation mode of the converter is performed. Finally, experimental results are presented to verify the analysis results of the proposed converter.

This study presents a detailed computational investigation aimed at identifying and evaluating potential small organic molecular scaffolds as inhibitors targeting the Pks13 protein, a critical enzyme in tuberculosis (TB) drug discovery. Our approach integrates multiple computational techniques, including quantitative structure-activity relationship (QSAR) modelling, compound library generation, molecular docking, biological activity prediction, re-evaluation of screened compounds, molecular dynamics (MD) analysis, and binding free energy estimation using the MM-GBSA approach. We developed a robust QSAR model using multiple linear regression (MLR) techniques, specifically tailored to predict the minimum inhibitory concentration (MIC) values of potential anti-TB agents. This model demonstrated excellent predictive performance, validated through rigorous internal and external validation procedures. Through molecular docking simulations, we identified five promising inhibitors Ethyl (R,Z)-2-(2-((2-chlorobenzyl) oxy)benzylidene)-5-(4-chlorophenyl)-7-methyl-3-oxo-2,3-dihydro-5H-thiazolo [3,2-a] pyrimidine-6-carboxylate (PKD1), (7R,9aR)-7-(2-hydroxyphenyl)-4-(2-methoxyphenyl)-3-methyl-7,8,9,9a-tetrahydroisoxazolo[5,4-b]quinolin-5(6H)-one (PKD2), (7-(4-methoxy phenyl)-2-methyl-3-phenylpyrazolo[1,5-a]pyrimidin-5-yl)(4-(3-(trifluoromethyl)phenyl) piperazin-1-yl)methanone (PKD3), (8S,10S)-10-(((2R,4S,5S,6S)-4-amino-5-hydroxy-6-methyl tetrahydro-2H-pyran-2-yl)oxy)-6,8,11-trihydroxy-8-(2-hydroxyacetyl)-1-methoxy-7,8,9,10-tetra-hydrotetracene-5,12-dione (PKD4), 3-(3,4-dimethylphenyl)-5-((2-(4-fluorophenyl)-2-oxoethyl) thio)-6-phenyl-2-thioxo-2,3,5,7a-tetrahydrothiazolo[4,5-d]pyrimidin-7(6H)-one (PKD5)with high binding affinities, prioritizing them based on their binding energies. These selected compounds were further assessed for their biological activity predictions, guiding the selection of candidates for experimental validation. Subsequent re-evaluation using absolute binding free energy estimation and MD simulations provided insights into the stability and dynamics of the protein-ligand complexes over time. Our findings highlighted the potential of identified compounds as stable binders within the Pks13-TE domain binding pocket, underscoring their viability as drug candidates. Additionally, binding free energy analysis using MM-GBSA reaffirmed the strong affinity of the proposed compounds towards Pks13. Our integrated computational approach offers a systematic and detailed framework for the identification and characterization of potential Pks13 inhibitors with specific relevance to TB drug discovery.

Background/Purpose: Multimorbidity of malaria, anaemia, and malnutrition (MAMM) is a condition in which an individual cohabits with two or more of these health outcomes and is becoming an emergent public health concern in sub-Saharan African countries. The independent associations of a child’s demographic variables and socioeconomic disparities with a child’s health outcomes have been established in literature. However, the effects of the intersection of these factors on MAMM, while accounting for other covariates have not been studied. Therefore, this study aimed to determine how children’s sex, age and socioeconomic status interact to explain the variations in MAMM among children aged 6-59 months in Nigeria. Methods: Data from the 2018 Nigeria Demographic and Health Survey and the 2018 National Human Development Report (NHDR) were used. The study includes weighted samples of 10,184 children aged 6-59 months in Nigeria. A three-level multilevel mixed effect ordinal logistic regression model such that individual characteristics at level 1 are nested in communities at level 2 and nested in states at level 3 was used. Subsequently, predictive probability charts and average adjusted probability tables were used to interpret the intersectional effects. Five models were created in this scenario. Model 1 is the interaction between the child's sex and wealth status; model 2, child's sex and age; model 3 is between child's age and wealth status; model 4 has the three 2-way interactions of child's sex, age, and household wealth status, and model 5 includes model 4 and the 3-way interactions between a child's sex, age, and wealth quintiles. Results: The prevalence of children with ‘none of the three diseases’ outcomes was 17.3% (1767/10,184), while, 34.4% (3499/10,184) had ‘only one of the diseases’, and 48.3% (4,918/10,184) cohabit with ‘two or more’ of MAMM. However, in the multivariate analyses, model 3 was the best fit compared with other models, so, that the two-way interaction effects of a child's age and wealth status are significant predictors in the model. Children aged 36-47 months living in the poorest households have the probability of 0.11, 0.18, and 0.32 of contracting MAMM above the probability of children of the same age and live in the middle, more prosperous and richest households, respectively, while all other covariates are held constant. Thus, the variation in MAMM over a child's age differs depending on the household wealth quintile. In other words, as the children grow older, variations in MAMM become more evident between the rich and the poor household. Conclusion: Therefore, it is recommended that policies that are geared toward economic redistribution will help bridged the disparities observed in multiple diseases among children aged 6-59 months in Nigeria

Being able to complete the production of the product project on time is the prerequisite to win more tasks, many enterprises have begun to study intelligent scheduling algorithms to achieve dynamic control of the total project duration. However, the production operation time of products is highly volatile, and it is impossible to predict the possible operation time of future products. In the actual production environment, we often encounter extreme situations such as mixed production lines, multiple varieties, variable batches, etc. A dynamic optimization CPM model in the ERP/MES system with the attention mechanism (EMA-DCPM) will collect, extract and predict the operation time through artificial intelligence technology, and it can accurately predict the exact operation time of a batch of products that have not yet been produced. Through experiments, our algorithm can realize the production scheduling and get more than 200% better precision than former algorithms and realize dynamic optimization of complex projects.

In recent years, drones have been used in a wide range of fields such as agriculture, transportation of goods, and security. Drones equipped with communication facilities are expected to play an active role as base stations in areas where ground base stations are unavailable, such as disaster areas. In addition, asynchronous operation is being considered for local 5G in order to support all kinds of use cases. In asynchronous operation, cross-link interference between base stations is an issue. This paper attempts to reduce the interference caused by the drone network by introducing circularly polarized antennas. Numerical analyses are conducted to validate the effectiveness of the proposed system, where SIRs (Signal-to-Interference Ratio) are shown to be improved significantly as the numerical evaluation results.

The aim of this study was to investigate the potential of converting potato peel waste into bio ethanol using an in-house produced multi-enzyme preparation. To achieve this, a variety of pre-treatment strategies, including thermal, chemical, and thermo-chemical methods, were applied to the potato peels. These included boiling for 30 and 60 min, steam at varying pressures and times, and different concentrations of chemicals such as H2SO4, HNO3, CH3COOH, HCl, NaOH, Ca(OH)2, KOH, NH3, and H2O2, either alone or in combination with steam. The pre-treated potato peels were then enzymatically hydrolyzed using a crude multi-enzyme cocktail derived from solid-state fermentation of wheat bran by a natural variety of Aspergillus niger P-19, containing cellulases, hemicellulases, pectinase, and amylases. The most effective pre-treatment combination was found to be 3% H2SO4 followed by steam under pressure and enzymatic hydrolysis using the crude multi-enzyme preparation. This combination resulted in the highest yield of reducing sugars (141.04 ±12.31 g/l) with a 98.49% carbohydrate conversion at a 20% substrate loading, giving an ethanol yield of 43.2 g/l. Furthermore, supplementing the medium with peptone, (NH4)(H2PO4), and ZnSO4 at 0.1%w/v each with 22% and 24% solid, respectively, resulted in appreciable yields of 51.67 and 54.75 g/l, further enhancing the ethanol yield.

The gelation time tg necessary for a solution of functional (associating) molecules to reach its gel point after a temperature jump, or a sudden concentration change, is theoretically calculated as a function of the concentration and temperature for the molecules carrying the number f of functional groups. Quite generally, tg is given by the product of the relaxation time tR and a thermodynamic factor Q. For pairwise cross-linking, and cross-linking with a fixed multiplicity k, precise values of tg are derived. For cross-linking with variable multiplicity, kinetic equation of stepwise association is derived for the reactivity pk of the functional groups, on the basis of which tg and tR are estimated. The thermodynamic factor Q is shown to generate a singularity of logarithmic divergence as the temperature (concentration) approaches the equilibrium gel point, while the relaxation time tR changes continuously across it. The retardation effect on the gelation time due to the reversibility of the cross-linking is explicitly calculated for some specific models of cross-linking. For a micellar cross-linking covering a wide range of the multiplicity, as seen in hydrophobically-modified water-soluble polymers, tR is shown to obey a formula similar to the Aniansson-Wall law.

Multiple sclerosis (MS) is a demyelinating autoimmune disorder of the central nervous system (CNS). Experimental autoimmune encephalomyelitis (EAE) has been widely used to determine the pathogenesis of the disease and evaluate new treatment strategies for MS. Therefore, we investigated the efficacy of oral administration of a Myelin Oligodendrocyte Glycoprotein (MOG) in the treatment of EAE. Female C57BL/6 mice were utilized in three groups (Control group: received PBS orally; prevention group: oral administration of MOG35-55 two weeks before EAE induction; treatment group: oral administration of MOG35-55 after EAE induction. MOG administration, both as prevention and treatment, significantly controlled clinical score, weight loss, CNS inflammation, and demyelination, mainly through the modulation of T cell proliferation, reduction of pro-inflammatory cytokines and transcription factors, including TNF-α, IFN-γ, IL-17, T-bet, and ROR-γt. MOG administration, both as prevention and treatment, also induced anti-inflammatory cytokines and transcription factors, including IL-4, TGF-β, GATA-3, and Foxp3. The results showed that oral administration of MOG, both as prevention and treatment, could efficiently control EAE development. Immunomodulatory mechanisms include the induction of Th2 and Treg cells and the suppression of pro-inflammatory Th1 and Th17 cells.

Multiple sclerosis (MS) is a demyelinating autoimmune disorder of the central nervous system (CNS). Experimental autoimmune encephalomyelitis (EAE) has been widely used to determine the pathogenesis of the disease and evaluate new treatment strategies for MS. Therefore, we investigated the efficacy of oral administration of a Myelin Oligodendrocyte Glycoprotein (MOG) in the treatment of EAE. Female C57BL/6 mice were utilized in three groups (Control group: received PBS orally; prevention group: oral administration of MOG35-55 two weeks before EAE induction; treatment group: oral administration of MOG35-55 after EAE induction. MOG administration, both as prevention and treatment, significantly controlled clinical score, weight loss, CNS inflammation, and demyelination, mainly through the modulation of T cell proliferation, reduction of pro-inflammatory cytokines and transcription factors, including TNF-α, IFN-γ, IL-17, T-bet, and ROR-γt. MOG administration, both as prevention and treatment, also induced anti-inflammatory cytokines and transcription factors, including IL-4, TGF-β, GATA-3, and Foxp3. The results showed that oral administration of MOG, both as prevention and treatment, could efficiently control EAE development. Immunomodulatory mechanisms include the induction of Th2 and Treg cells and the suppression of pro-inflammatory Th1 and Th17 cells.

Multiple Myeloma (MM) is one of the incurable types of cancer in plasma cells. While immense progress has been made in the treatment of this malignancy, a large percentage of patients were unable to adapt to such therapy. Additionally, these therapies might be associated with significant diseases and are not always tolerated well in all patients. Since cancer in plasma cells has no cure, patients develop resistance to treatments, resulting in R/R MM. BCMA is primarily produced on mature B cells. Its up-regulation and activation are associated with multiple myeloma in both murine and human models, indicating that this might be an effective therapeutic target for this type of malignancy. Additionally, BCMA's predictive value, association with effective clinical trials, and capacity to be utilized in previously difficult to observe patient populations, imply that it might be used as a biomarker for multiple myeloma. Numerous kinds of BCMA-targeting medicines have demonstrated antimyeloma efficacy in individuals with refractory/relapsed MM, including CAR T-cell treatments, ADCs, bispecific antibody constructs. Among these medications, CART cell-mediated BCMA therapy has shown significant outcomes in multiple myeloma clinical trials. This review article outlines CAR T cell mediated BCMA medicines have the efficiency to change the therapeutic pattern for multiple myeloma significantly.

To assess cognitive impairment and affective symptoms and their association with damage to normal-appearing white matter (NAWM) in patients with clinically isolated syndrome (CIS), we compared neuropsychological test scores between patients with CIS and healthy controls, and examined correlations between these and proton magnetic resonance spectroscopy (1H-MRS) outcomes in patients with CIS. Forty patients with CIS and 40 healthy participants were tested with the set of neuropsychological tests, the Beck Depression Inventory (BDI) and the Hospital Anxiety and Depression Scale (HADS). Single-voxel 1H-MRS was performed on frontal and parietal NAWM of patients with CIS to assess ratios of N-acetyl-aspartate (NAA) to creatine (Cr), myo-inositol (mI), and choline (Cho), as well as mI/Cr and Cho/Cr ratios. Patients with CIS had lower cognitive performance, higher scores for the BDI and anxiety subscale of HADS than healthy controls. There were significant correlations between the following neuropsychological tests and metabolic ratios in the frontal NAWM: Stroop Color-Word Test and Cho/Cr, Symbol Digit Modalities Test and mI/Cr as well as NAA/mI, Go/no-go reaction time and NAA/Cho as well as NAA/mI, Californian Verbal Learning Test and NAA/Cr. BDI scores were related to frontal NAA/mI and parietal NAA/Cr and Cho/Cr ratios, whereas HADS-depression scores were associated with frontal NAA/Cr and NAA/mI, and parietal NAA/Cr and Cho/Cr ratios. HADS-anxiety correlated with parietal NAA/Cr ratio. This study suggests that neurochemical changes in the NAWM assessed with single-voxel 1H-MRS are associated with cognitive performance and affective symptoms in patients with CIS.

A lightweight image encryption algorithm based on chaos induction via a 5-dimensional hyperjerk oscillator (5DHO) is presented. First, the dynamics of our 5DHO network is investigated and shown to exhibit up to five coexisting hidden attractors in the state space that depend exclusively on the system’s initial values. Further, a simple implementation of the circuit was used to validate its ability to exhibit chaotic dynamical properties. Second, an Arduino UNO platform is used to confirm the usability of our oscillator in digital implementation of the system. Finally, an efficient image encryption application is executed using the proposed chaotic networks based on the use of permutation-substitution sequences. The superior qualities of the proposed strategy are traced to the dynamic set of keys used in the substitution process which heralds the generation of the final ciphered image. Based on the results obtained from the entropy analysis (7.9976), NPCR values (99.62), UACI tests (33.69) and encryption execution time for 512x512 images (0.1179 sec), the proposed algorithm is adjudged to be fast and robust to differential and statistical attacks relative to similar approaches.

The ASCE-EWRI reference evapotranspiration (ETo) equation is recommended as a standardized method for reference crop ETo estimation. However, various climate data as input variables to the standardized ETo method are considered limiting factors in most cases and restrict the ETo estimation. This paper assessed the potential of different machine learning (ML) models for ETo estimation using limited meteorological data. The ML models used to estimate daily ETo included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF). Three input combinations of daily maximum and minimum temperature (Tmax and Tmin), wind speed (W) with Tmax and Tmin, and solar radiation (Rs) with Tmax and Tmin were considered using meteorological data during 2003–2016 from six weather stations in the Red River Valley. To understand the performance of the applied models with the various combinations, station, and yearly based tests were assessed with local and spatial approaches. Considering the local and spatial approaches analysis, the LR and RF models illustrated the lowest rate of improvement compared to GEP and SVM. The spatial RF and SVM approaches showed the lowest and highest values of the scatter index as 0.333 and 0.457, respectively. As a result, the radiation-based combination and the RF model showed the best performance with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among models and approaches.

With the great development of intelligent transportation systems (ITS), travel time prediction has attracted the attentions of many researchers and a large number of prediction methods have been developed. However, as an unavoidable topic, the predictability of travel time series is the basic premise for travel time prediction has received less attention than the methodology. Based on the analysis of the complexity of travel time series, this paper defines travel time predictability to express the probability of correct travel time prediction and proposes an entropy-based method to measure the upper bound of travel time predictability. Multiscale entropy is employed to quantify the complexity of travel time series, and the relationships between entropy and the upper bound of travel time predictability are presented. Empirical studies are made with vehicle trajectory data in an express road section. The effectiveness of time scales, tolerance, and series length to entropy and travel time predictability are analysis, and some valuable suggestions about the accuracy of travel time predictability are discussed. Finally, the comparisons between travel time predictability and actual prediction results from two prediction models, ARIMA and BPNN, are conducted. Experimental results demonstrate the validity and reliability of the proposed travel time predictability.

The nature of the interaction between Th17 cells and the blood-brain barrier (BBB) is critical for the development of autoimmune inflammation in the central nervous system (CNS). TNF-a or IL-17 stimulation is known to enhance the adherence of Th17 cells to the brain endothelium. The brain endothelial cells (bEnd.3) express VCAM-1, the receptor responsible for inflammatory cell adhesion, which binds VLA-4 on migrating effector lymphocytes at the early stage of brain inflammation. The present study examines the effect of the pro-inflammatory cytokines TNF-a and IL-17 on the adherence of Th17 cells to bEnd.3 The bEnd.3 cells were found to increase production of CCL2 and CXCL1 after stimulation by pro-inflammatory cytokines, while CCL2, CCL5, CCL20 and IL17 induced Th17 cell migration through a bEnd.3 monolayer. This interaction between Th17 cells and the brain endothelium appears to be mediated by VCAM-1 and some chemotactic cytokines. This observation may suggest potential therapeutic targets for the prevention of autoimmune neuroinflammation development in the CNS.

Art organizations are increasingly focusing on enhancing the visitor experience to attract a wider audience. The aim objective of this study was to explore the key factors that influence the visitor experience of offline art exhibitions and to use a mixed methods to data collection and analysis. The research process was divided into three stages. In the first stage, we conducted semi-structured interviews with 12 respondents by dividing the exhibition experience into three time periods: pre, during, and post-visit. We extracted seven factors affecting visitor experience through qualitative content analysis including marketing, motivation, physical environment, exhibit quality, participation, technology and service facilities; in the second stage, we developed an assessment scale and pretested it; in the third stage, we collected 725 questionnaires to validate the factors of visitor experience in art exhibitions. The results of linear regression analyses showed that all seven key factors had a significant impact on visitor experience. This study constructed a scale to assess the factors of art exhibition experience, which provides theoretical sup-port for in-depth study of art exhibition experience and useful guidance for planning and design-ing art exhibitions to improve the quality of visitors' experience of art exhibitions.

The present study employed a modifed QuEChERS method to systematically analyze the presence of fifteen Quinolones and seven Tetracyclines antibiotic residues in local animal food. Additionally, a multi-level-four-factor Box-Behnken design (BBD) within the framework of Response surface methodology (RSM) was utilized to evaluate the various factors affecting the detection efficiency of the sample pretreament procedure. The optimization was performed by Design Expert®, the factors including volume of the acetonitrle, the addition of formic acid, the duration of extract and the addition of EDTA were combined to experiment design until reach an optimal solution. Finally, the sample test was conducted by ultra-high performance liquid chromatography-quadrupole-linear ion trap mass spectrometry in both multiple reaction monitoring (MRM) and enhanced product ion (EPI) scan modes on a QTRAP® 5500 instrument. The overall average recoveries from actual samples fortified with 22 antibiotics at three levels ranged from 74.7 to 122.6% based on the use of matrix-fortified calibration with the variation ranging from 2.1 to 18.2% (n=6). The limits of detection and quantification were 0.3 μg kg-1 and 1.0 μg kg-1, respectively.

Guessing effects frequently occur in testing data in educational or psychological applications. Different item response models have been proposed to handle guessing effects in dichotomous test items. However, it has been pointed out in the literature that the oftenemployed three-parameter logistic model poses implausible assumptions regarding the guessing process. The four-parameter guessing model has been proposed as an alternative to circumvent these conceptual issues. In this article, the four-parameter guessing model is compared with alternative item response models for handling guessing effects through a simulation study and an empirical example. It turned out that model selection for item response models should be rather based on the AIC than the BIC. However, the RMSD item fit statistic used with typical cutoff values was found to be ineffective in detecting misspecified item response models. Furthermore, sufficiently large sample sizes are required for sufficiently precise item parameter estimation. Moreover, it is argued that the criterion of statistical model fit should not be the sole criterion of model choice. The item response model used in operational practice should be valid with respect to the meaning of the ability variable and the underlying model assumptions. In this sense, the four-parameter guessing model could be the model of choice in educational large-scale assessment studies.

The importance of statistics in biological research is inevitable. The appliance of statistics is the most powerful tool to support the scientific hypothesis and to give credibility to biological research methodology, to interpret convoluted explanations and conclusions based on the research findings. The evidences from literatures point the persistent statistical errors, selection of non-proper statistical test with the consequence of misinterpretation of the scientific results which are published in international journals. One of the most extensively used statistical tests in the field of biology (preclinical and clinical) is analysis of variance (ANOVA). ANOVA test is in place of multiple T-tests to compare the means of more than two groups at a time; and there are some important points which biologists should be aware of them to avoid possible misinterpretation of results with the consequence of wrong conclusion. Accordingly, the aim of the current review is to help biologists to understand the basic concepts of ANOVA test and reach to a more valid interpretation of achieved results.

Predation is a driving force of organismal ecology and evolution. Predator-prey interactions, constrained by other environmental factors and historical contingency, shape physical and behavioral phenotypes of organisms on both sides of the interaction arrow. Yet despite the universality of trophic interactions in biology, study of the ecology and evolution of anti-predator defense is challenging because these interactions tend to be brief and unpredictable, and thus gathering direct evidence requires patience and serendipity. The evolution of prey defenses can be studied by DNA sequencing followed by phylogenetic analysis, and the ecology can be studied by field observation and bringing tractable systems into the lab. However, animal models generally do not allow genetic manipulation, strict control of environmental variables, or detailed observation of evolution in real time, all of which are important for demonstrating evolutionary causes and consequences. Developing complementary microbial predator-prey systems can help overcome this hurdle. There is an extensive body of work on microbial experimental evolution, and many microbes come with well-characterized genomes and established methods for genetic manipulation. As understanding of microbial ecology and the mechanisms of their trophic interactions grows apace, these systems are poised to make valuable contributions to our understanding of how predator behaviors evolve, why certain anti-predator defenses evolve and not others, and how multiple defenses function together in a single defense portfolio. These systems will enable us to both test hypotheses formulated from the study of larger organisms and to propose new ones that can be tested in larger organisms with existing methods.

With the increased availability of sequence data and even of fully sequenced and assembled genomes, phylogeny estimation of very large trees (even of hundreds of thousands of sequences) is now a goal for some biologists. Yet, the construction of these phylogenies is a complex pipeline presenting analytical and computational challenges, especially when the number of sequences is very large. In the last few years, new methods have been developed that aim to enable highly accurate phylogeny estimations on these large datasets, including divide-and-conquer techniques for multiple sequence alignment and/or tree estimation, methods that can estimate species trees from multi-locus datasets while addressing heterogeneity due to biological processes (e.g., incomplete lineage sorting and gene duplication and loss), and methods to add sequences into large gene trees or species trees. Here we present some of these recent advances and discuss opportunities for future improvements.

This study explored father involvement as a social determinant of child health within the context of macro-environmental changes in Mongolia. Using data for children aged 3-4 from UNICEF’s Multiple Indicator Cluster Surveys, this cross-sectional analysis examined the association between father presence and engagement with child health and educational outcomes. Multivariate regression modeling was employed to identify associations between father presence, engagement, and child outcomes including fever, respiratory illness, diarrhea and preschool attendance. In unadjusted analyses, father engagement was associated with higher odds of pre-school attendance (OR=1.12; 95% CI 1.04-1.20) but not with child illness (OR=1.04; 95% CI 0.95-1.14). Father engagement was no longer associated with pre-school attendance after controlling for potentially confounding variables (ORadj = 0.95; 95% CI 0.88-1.03). Unadjusted and adjusted analyses showed that father presence was not associated with acute illness or preschool attendance. Results also suggest that a larger proportion of children were engaged in activities by their mother compared to their father or other adults. Data indicate that father presence and engagement were not associated with child illness or pre-school attendance. Factors such as maternal education, household wealth, and region of residence are stronger predictors of preschool attendance and should continue to be considered for promoting child health and development in Mongolia.

Tremor is a prevalent symptom associated with multiple conditions, including Essential Tremor (ET), Parkinson’s disease (PD), multiple sclerosis (MS), stroke, and trauma. The surgical management of tremor evolved from stereotactic lesions to deep-brain stimulation (DBS), which allowed safe and reversible interference with specific neural networks. This paper reviews the current literature on DBS for tremor, starting with a detailed discussion of current tremor targets (Vim, Raprl, caudal Zi, Vo and STN) and continuing with a discussion of results obtained when performing DBS in the various aforementioned tremor syndromes. Future directions for DBS research are then briefly discussed.

The prevalence of food allergy has increased over the last decades and consequently the food labeling policies have improved over the time in different countries to regulate allergen presence in foods. In particular, Reg 1169 in EU mandates the labelling of 14 allergens whenever intentionally added to foods, but the inadvertent contamination by allergens still remains uncovered topic. In order to warn consumers on the risk of cross-contamination occurring in certain categories of foods, a precautionary allergen labelling system has been put in place by food industries on voluntary basis. In order to reduce the overuse of PAL, reference doses and action limits have been proposed by the VITAL project representing a guide in this jeopardize scenario. Development of sensitive and reliable mass spectrometry methods are therefore of paramount importance in this regard to check the contamination levels in foods. In this paper we describe the development of a managed time MRM method based on a triple quadrupole platform for milk and egg quantification in processed food. The method was in house validated and allowed to achieve levels of proteins lower than 0.2mg of total milk and egg proteins respectively in cookies, challenging the doses recommended by VITAL. The method was finally applied to cookies labeled as milk and egg-free. This method could represent in perspective a promising tool to be implemented along the food chain to detect even tiny amounts of allergens contaminating food commodities.

Cannabidiol is a well-known non-psychotropic phytocannabinoid from Cannabis sativa, which exerts a broad range of neuropharmacological activities in the central nervous systems. Over the past years, compelling evidence from preclinical and clinical studies support therapeutic potentials of cannabidiol in various neurological disorders, including neurodegenerative diseases. Neurodegenerative diseases are characterized by the accumulation of misfolded or aggregated protein due to the defective protein homeostasis or proteostasis network, termed as proteinopathies. Because of its role in the protein homeostasis network, cannabidiol could be a potent molecule to revert not only age-associated neurodegeneration but also other protein misfolding disorders. In this review, we discuss the potentiality of cannabidiol as a pharmacological modulator of the proteostasis network, highlighting its neuroprotective and aggregates clearing system inducing potentials in the neurodegenerative diseases.

Motivated by the theoretical model of health insurance choice with Flexible Spending Accounts (FSAs) presented in Cardon 2012, this study investigates the determinants of optional coverage (SSP) and flexible spending accounts (FSA) enrollment, among the privately insured in post-affordable-care-act (ACA) USA. To this end, we rely on semi-parametric bi-variate probit methods, along with a pooled cross-section of the 2015-2016 National Health Interview Surveys. As predicted by the theoretical model, we find that SSP and FSA are complement health solutions with a positive correlation. Our results emphasize that the most important trigger factors influencing the joint probability of SSP and FSA adoption include not only insurance premium cost, but also age, education, marital status, number of work hours, region of residency, citizenship status, and annual health expenditure level. We find that controlling for these latter factors, health status is not significant especially for FSA adoption. In addition, despite the fact that the relative frequency of individuals with FSA rises with increasing levels of medical expenditure, ACA restrictions on FSA tax exclusion to an annual adjusted maximum of $2600 (in 2017 $s) seems to adversely burden individuals with greater medical expenditure, thereby reducing their likelihood of FSA enrollment in post-ACA USA. Understanding these factors is very crucial to US health care market's stakeholders, including insurance companies, firms looking to design their health insurance offerings, but also policy-makers interested in providing new tailored health solutions for reducing health risks.

Personalized medicine is an aspect of the P4 medicine (predictive, preventive, personalized and participatory) based precisely on the customization of all medical characters of each subject. In personalized medicine, the development of medical treatments and drugs is tailored to the individual characteristics and needs of each subject, according to the study of diseases at different scales from genotype to phenotype scale. To make concrete the goal of personalized medicine, it is necessary to employ high-throughput methodologies such as Next Generation Sequencing (NGS), Genome-Wide Association Studies (GWAS), Mass Spectrometry or Microarrays, that are able to investigate a single disease from a broader perspective. For example, by using genotyping microarrays (e.g. collections of Single Nucleotide Polymorphism - SNP) it is possible to uncover the reasons (i.e. mutation in genes) because a treatment works properly in some patients (for example absence of mutated genes), but it does not work (presence of mutated genes) in others. A side effect of high-throughput methodologies is the massive amount of data produced for each single experiment, that poses several challenges (e.g. high execution time and required memory) to bioinformatic software. Thus a main requirement of modern bioinformatic software is the use of good software engineering methods and efficient programming techniques, able to face those challenges, that include the use of parallel programming and efficient and compact data structures. Thus, to exploit all the potential of this massive amount of data in the short possible time (before that data becomes obsolete), the necessity to develop parallel software tools for efficient data collection and analysis arise. Moreover, due to the heterogeneity of the data produced by the different kinds of experimental platforms, it is necessary to automatize in a comprehensive software pipeline, the various steps that compose a bioinformatic analysis, such as: the preprocessing of raw data to remove noise or corrupted data; the annotation of data with external knowledge (e.g. Gene Ontology), and the integration of molecular data with clinical data. It should be noted that such steps are necessary to make statistical or data mining analysis more effective. This paper presents the design and the experimentation of a comprehensive software pipeline, named microPipe, for the preprocessing, annotation and analysis of microarray-based SNP genotyping data. A case study in pharmacogenomics is presented. The main advantages of using microPipe are: the reduction of errors that may happen when trying to make data compatible among different tools; the possibility to analyze in parallel huge datasets; the easy annotation and integration of data.

This study employs the reflection symmetry decomposition (RSD) method to extract polarization scattering features from ground object images, aiming to determine the optimal data input scheme for deep learning networks in polarimetric synthetic aperture radar classification. Eight distinct polarizing feature combinations are designed, and the classification accuracy of various approaches is evaluated using the classic convolutional neural networks(CNNs) AlexNet and VGG16. The findings reveal that the commonly employed 6-parameter input scheme, favored by many researchers, lacks the comprehensive utilization of polarization information and warrants attention. Intriguingly, leveraging the complete 9-parameter input scheme based on the polarization coherence matrix results in improved classification accuracy. Furthermore, the input scheme incorporating all 21 parameters from the RSD and polarization coherence matrix notably enhances overall accuracy and the Kappa coefficient compared to the other 7 schemes. This comprehensive approach maximizes the utilization of polarization scattering information from ground objects, emerging as the most effective CNN input data scheme in this study. Additionally, the classification performance using the second and third component total power values (P2 and P3) from the RSD surpasses the approach utilizing surface scattering power value (PS) and secondary scattering power value (PD) from the same decomposition.

The steel damper column is an energy-dissipating member that is suitable for reinforced concrete (RC) buildings, and those used for multistory housing in particular. However, the effectiveness of steel damper columns may be affected by the behavior of surrounding members, and this effect can be severe in the case of seismic sequences. This article investigates the nonlinear response of building models having an RC moment-resisting frame (MRF) with and without steel damper columns under seismic sequences. The applicability of the concept of the momentary energy input to the prediction of the peak response of RC MRFs with damper columns under seismic sequences is also investigated. The main findings of the study are summarized as follows. (1) The peak response of RC MRFs with damper columns subjected to sequential accelerations is similar to the peak response obtained considering only the mainshock, whereas the cumulative strain energy of RC MRFs accumulates more for sequential accelerations. (2) The steel damper column is effective in reducing the peak and cumulative responses of RC MRFs in the case of sequential seismic input. (3) The relation of the hysteretic dissipated energy during a half cycle of the structural response and the peak displacement of the first modal response can be properly evaluated using the simple model proposed in this study.

Some of the barriers preventing Virtual Reality (VR) from being widely adopted are the cost and unfamiliarity of VR systems. Here we propose that in many cases the specialized controllers shipped with most VR head-mounted displays can be replaced by a regular smartphone, cutting the cost of the system, and allowing users to interact in VR using a device they are already familiar with. To achieve this, we developed SmartVR Pointer, an approach that uses smartphones as a replacement for the specialized controllers for two essential operations in VR: selection and navigation by teleporting. In SmartVR Pointer a camera mounted on the head-mounted display (HMD) is tilted downwards so that it points to where the user will naturally be holding their phone in front of them. SmartVR Pointer supports three selection modalities: tracker-based,gaze-based, and combined/hybrid. In the tracker-based SmartVR pointer selection we use image-based tracking to track a QR code displayed on the phone screen and them map the phone’s position to a pointer shown within the field of view of the camera in the Virtual Environment. In the gaze-based selection modality the user places the pointer using their gaze and taps on the phone for selection. The combined technique is a hybrid between head-gaze-based interaction in VR and smartphone-based Augmented Reality. It allows the user to control a VR pointer that behaves like a mouse pointer by moving their smartphone to select objects within the virtual environment, and to interact with the selected objects using the smartphone’s touch screen. The touchscreen is used for selection and dragging. SmartVR Pointer is simple and requires no calibration and no complex hardware assembly or disassembly. We demonstrate successful interactive applications of SmartVR Pointer in a VR environment with a demo where the user navigates in the virtual environment using teleportation points on the floor and then solves a Tetris-style key-and-lock challenge.

The United Nations’ Agenda 2030 has established a series of Sustainable Development Goals to address global challenges, including poverty, food insecurity, access to education, and social inequality. In this context, charitable organizations such as Caritas play a crucial role in mitigating the negative effects of these challenges and promoting fair and sustainable development. This study aims to analyze prevalent needs among individuals seeking assistance from Caritas in Campania and examine how the organization contributes to achieving the Agenda 2030 Goals in the region. The statistical investigation techniques considered include tandem analysis [], which considers a dimension-reduction technique, such as multiple factor analysis, and, then cluster analysis to identify similar groups of individuals. These exploratory data analysis methods have allowed for the identification of common needs, including food assistance, support for education, employment, and housing assistance. Subsequently, Caritas programs and initiatives aimed at meeting these needs and promoting sustainable development are explored. The results indicate that Caritas plays a significant role in addressing the urgent needs of the vulnerable population in Campania and contributes to the goals of Agenda 2030, particularly those related to poverty alleviation, immigration, health promotion, education, employment, and reduction of social inequalities. This study provides an important perspective on the relevance and effectiveness of Caritas’ work in the context of Agenda 2030.

E-waste management (EWM) refers to the operation-management of discarded or unproductive electronic devices and components, a challenge exacerbated due to overindulgent urbanization. This article presents a multidimensional cost-function-based analysis of the EWM framework structured on three modules - environmental, economic, and social uncertainties - which contemplate the 3 pillars of sustainability in an e-waste recycling plant, including the production-delivery-utilization process. The framework incorporates material recovery from a single e-waste facility provisioning for chemical and mechanical recycling. Each module is ranked using independent Machine Learning (ML) protocols: a) Analytical Hierarchical Process (AHP) and b) combined AHP and Principal Component Analysis (PCA). From a long list of possible contributors, the model identifies and ranks two key sustainability contributors to the EWM supply chain: overall energy consumption and volume of carbon dioxide generated. Another key finding is a precise time window for policy resurrection, which for the data considered, happens to be 400-600 days from the start of operation. Another interesting outcome is the quality of prediction using a combination of AHP and PCA, which consistently produced better results than any of these ML methods individually implemented. Model outcomes have been verified using a case study to outline a future E-waste sustained roadmap.

Oligodendrocyte progenitor cells (OPCs) represent a subtype of glia giving rise to oligodendrocytes, the myelin forming cells in the central nervous system (CNS). While OPCs are highly proliferating during development, they become relatively quiescent during adulthood, when their fate is strictly influenced by the extracellular context. In traumatic injuries and chronic neurodegenerative conditions including those of autoimmune origin, oligodendrocytes undergo apoptosis, and demyelination starts. Adult OPCs become immediately activated, they migrate at the lesion site and proliferate to replenish the damaged area, but their efficiency is hampered by the presence of a glial scar, a barrier mainly formed by reactive astrocytes, microglia, and the deposition of inhibitory extracellular matrix components. If, on one hand, glial scar limits the lesion spreading, it also blocks tissue regeneration. Therapeutic strategies aimed at reducing astrocyte or microglia activation and shifting them toward a neuroprotective phenotype have been proposed, whereas the role of OPCs has been largely overlooked. In this review, we have considered glial scar from the perspective of OPCs, analysing their behaviour when lesions originate and exploring the potential therapies aimed at sustaining OPCs to efficiently differentiate and promote remyelination.

Understanding the optimal strategy for a real estate investment and how performance changes based on characteristics is crucial for optimising the achievable return. This is prominent in tourist areas such as Paphos where there is no clear distinction as to whether short or long-term approaches are optimal. The study aimed to develop a model for predicting the optimal rental strategy whilst assessing which model performed best and which property attributes impacted its return the greatest. Short-term data was collected from AirDNA and long-term data was manually collected from real estate agents websites. Furthermore, Random Forest, K-Nearest Neighbour and Multiple Linear Regression models were created to predict the highest and best use for each property. Model accuracy varied between data sets with the best performing model for short-term properties being Random Forest (R-Squared: 0.843), and Distance-Based Multiple Linear Regression for long-term approach (R-Squared: 0.843). The study demonstrated that accurate models could be created to predict the optimal rental strategy with the number of bedrooms being the main driver for rental income, followed by luxury finishes and the presence of a pool. It found that locational characteristics didn’t impact the returns significantly assuming that the property was located within a tourist area.

The goal was to model the irrigated (IBY) and rainfed (RBY) bean yields, as a function of essential climatic variables (ECVs), in the center (Culiacán) and south (Rosario) from Sinaloa. In Sinaloa, and for the period 1982–2013 (October–March), the following were calculated: a) temperatures. b) average degree days for the bean, c) cumulative potential evapotranspiration and d) cumulative effective precipitation. For ECVs, from the European Space Agency, e) daily soil moisture. f) IBY and RBY, from the Agrifood and Fisheries Information Service. Multiple linear regressions were applied, which modeled IBY–RBY (dependent variables), as a function of ECVs (independent variables). Then, to establish each Pearson correlation (PC) as significantly different from zero, a hypothesis test was applied: PC vs Pearson's critical correlation (CPC). The four models obtained were significantly predictive: IBY–Culiacán (PC = 0.590 > CPC = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|) and RBY–Rosario (PC = 0.532 > CPC = |0.349|). This study is the first in Sinaloa to predict IBY and RBY based on ECVs, contributing to the production of sustainable food.

Dielectric elastomer actuators (DEAs) offer robust, high energy density solutions for soft robotics. The proposed end effector consists of three spring roll configuration DEAs, each acting as a robotic finger, using a 3M VHB-F9473PC adhesive membrane. The lateral force, bending angle and response time of the actuator are measured experimentally and compared with the predictions of an analytical model. The cylindrical actuator measures 70 mm in length and 15 mm in diameter and achieved a lateral force of 30 mN, a bending angle of 6.8 ° and a response time of ≈ 1 s. Spring roll configuration DEAs are shown to reduce the effects of viscoelasticity seen in the membrane, making the actuator more controllable at higher voltages. The dielectric constant of the membrane is shown to be a limiting factor of actuation, with a decrease in dielectric constant resulting in larger actuation. The end effector successfully gripped numerous light objects for extended periods, showing the applicability of spring roll DEAs for soft end effectors.

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.

The wind observations for multiple levels (40–200 m) have been conducted for a long time (2016–2020) on Jeju Island of South Korea. This study aims at understanding the vertical and temporal characteristics of lower atmosphere. Jeju Island is a region located at mid-latitude and is affected by seasonal monsoon wind. The maximum wind speed appears in the lower layer during day time and is delayed in the upper layer during latter time in diurnal cycle. In summer season, the surface layer increases up to 160 m during day time via dominant solar radiation effect, which is higher than those for other seasons. However, the maximum wind speed in winter season appears irregularly among altitudes, and the surface layer is ~100 m, which is lower than that in summer season. It can be attributed to the increase in the mean wind speed in diurnal cycle caused by the strong northwestern wind for winter season. These results imply that the relationship between near-surface and higher altitudes is primarily affected by solar radiation and seasonal monsoon winds. These results are expected to contribute to site selection criteria for wind farms and to the assessment concerning planetary boundary layer modeling.

Microgrid is one of the important ways to accommodate distributed PV generation. However, the optimal planning scheme of microgrids is closely related to solar resources, load characteristics, and the key components of microgrid systems. This paper uses HOMER software to conduct collaborative planning for grid-connected PV-storage microgrids with electric vehicles in multiple scenarios. Specifically, we construct a multi-scenario capacity optimization model for PV generation, energy storage, and converters, considering both the cleanliness and economic performance of microgrids. The performance is quantified using indices such as net present cost, levelized cost of electricity, and carbon dioxide emission under multiple scenarios. Finally, we conduct extensive case studies on a business park in Wuhan, China, to compare and discuss the planning performance under multiple scenarios, as well as sensitivity analysis with specific cases.

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.

The object detection task is usually affected by complex backgrounds. In this paper, a new image object detection method is proposed, which can perform multi-feature selection on multi-scale feature maps. By this method, a bidirectional multi-scale feature fusion network is designed to fuse semantic features and shallow features to improve the detection effect of small objects in complex backgrounds. When the shallow features are transferred to the top layer, a bottom-up path is added to reduce the number of network layers experienced by the feature fusion network, reducing the loss of shallow features. In addition, a multi-feature selection module based on the attention mechanism is used to minimize the interference of useless information on subsequent classification and regression, allowing the network to adaptively focus on appropriate information for classification or regression to improve detection accuracy. Because the traditional five-parameter regression method has severe boundary problems when predicting objects with large aspect ratios, the proposed network treats angle prediction as a classification task. The experimental results on the DOTA dataset, the self-made DOTA-GF dataset and the HRSC 2016 dataset show that, compared with several popular object detection algorithms, the proposed method has certain advantages in detection accuracy.

Traditional silicon binary circuits continue to face challenges such as high leakage power dissipation and large area of interconnections. Multiple-Valued Logic (MVL) and nano-devices are two feasible solutions to overcome these problems. In this paper, we present a novel method to design ternary logic circuits based on Carbon Nanotube Field Effect Transistors (CNFETs). The proposed designs use the unique properties of CNFETs, e.g., adjusting the Carbon Nanotube (CNT) diameters to have the desired threshold voltage and have the same mobility of P-FET and N-FET transistors. Each of our designed logic circuits implements a logic function and its complementary via a control signal. Also, these circuits have a high impedance state which saves power while the circuits are not in use. We show a more detailed application of our approach by designing a two-digit adder-subtractor circuit. We simulate the proposed ternary circuits using HSPICE via standard 32nm CNFET technology. The simulation results indicate the correct operation of the designs under different process, voltage and temperature (PVT) variations. Moreover, we designed a two-digit adder/subtractor and a power efficient ternary logic ALU based on the proposed gates. Simulation results show that the two-digit adder/subtractor using our proposed gates has 12X and 5X lower power consumption and PDP (power delay product) respectively, compared to previous designs.

The language we use to describe the past can have a strong influence on the audience’s interpretation of our story. In our experiment, we explore, using 3 different conditions, how the framing and language of an accident report can affect the audience’s proposed solutions to manage the problems found. We find that the approach used to create an accident report can have a powerful influence on the audience’s decision making. Whether we are describing an accident in a similar manner to a crime, using a systems approach or we are accepting of multiple stories which are not linear or coherent, the methods we use to capture and communicate the story have a profound impact on the actions decided upon by the reader.

Optimizing the NACA0015 airfoil which is widely applied in small-scale vertical axis wind turbine to make it has a better aerodynamic performance. In the optimization process, using CST parameterization method to perturb the airfoil geometry, the thickness and camber of the airfoil are selected as the constraint, and the value of the maximum tangential force coefficient is chosen as the objective function, the genetic algorithm based on non-dominated sorting (NSGA-II)is selected as an optimization method, calculates the aerodynamic performance of the airfoil by applying the approach of combining XFOIL program and Viterna-Corrigan post-stall mode ,and establishes the optimizing process by the optimization software modefrontier for NACA0015 airfoil’s muti-point optimization, validate the airfoil’s performance with CFD finally. The result illustrates that, by comparing with the NACA0015 airfoil, the optimized airfoil’s lift to drag ratio is improved over a wide range of attack angles, the stall performance is more gentle. The maximum lift coefficient, the maximum lift-drag ratio and the maximum tangential force coefficient are increased by 7.5%,9 and 8.87%, respectively. The optimized airfoil has a wide variable condition performance, more suitable for the operating conditions of a vertical axis wind turbine. Finally, predict the rotor efficiency with optimized airfoil and NACA0015 airfoil for different tip speed ratios and different solidities with multiple streamtube model, the result shows the rotor with optimized airfoil has a higher efficiency.

Footprints are powerful indicators for evaluating the impact of the bioeconomy of a country on environmental goods, domestically and abroad. In this study, we apply a hybrid approach combining a Multi-Regional Input-Output model and land use modelling to compute the agricultural land footprint (aLF). Furthermore, we added information on land-use change to the analysis and allocated land conversion to specific commodities. The German case study shows that the aLF abroad is larger by a factor of 2.5 to 3 than the aLF in Germany. In 2005 and 2010, conversion of natural and semi-natural land-cover types abroad allocated to Germany due to import increases was 2.5 times higher than the global average. Import increases to Germany slowed down in 2015 and 2020, reducing land conversion attributed to the German bioeconomy to the global average. The case study shows that the applied land footprint provides clear and meaningful information for policymakers and other stakeholders. The presented methodological approach can be applied to other countries and regions covered in the underlying database EXIOBASE. It can be adapted, also for an assessment of other ecosystem functions, such as water or soil fertility.

Film cooling technology is of great significance to enhance the performance of aero-engines and extend service life. With the increasing requirements for film cooling efficiency, researchers and engineers have carried out a lot of work on the precision and digital measurement of cooling holes. Based on the above, this paper outlines the importance and principles of film cooling technology, reviews the evolution of cooling holes. Also, this paper details the traditional measurement methods of the cooling hole used in current engineering scenarios with their limitations and categorizes digital measurement methods into five main types, including probing measurement technology, optical measurement technology, infrared imaging technology, computer tomography(CT) scanning technology, and composite measurement technology. The five types of methods and integrated automated measurement platforms are also analyzed. Finally, through a generalize and analysis of cooling hole measurement methods, this paper points out technical challenges and future trends, providing a reference and guidance for forward researches.

Tularemia is a zoonotic disease caused by {\it Francisella tularensis} bacteria, a gram-negative coccobacillus-shaped bacterium. There are multiple transmission routes of the infection to humans such as consumption of contaminated food or water, handling of infected animals or bites from {\it haematophagous} arthropods (such as ticks, deer flies, or mosquitoes). In this study we focus on transmission via the bites of ticks and developed a deterministic model of ordinary and impulsive differential equations to gain insight in the differential effect of prescribed fire on {\it Demacenta variablis} and {\it Amblyomma americanum} ticks and the prevalence of tularemina. We found that prescribed fire differentially reduce the number of the two ticks with {\it A. americanum} reduced the least compare to {\it D. variablis} subsequently leading to differential increase of tularemia new infected cases in humans and rodents. Our result further indicates that the spatial arrangement of burn against unburn areas may not matter as either arrangement led to fewer ticks and reduction in tularemia transmission when prescribed fire was implemented. The results of this study provide important new understandings of the intricate effect of prescribed fire on tick species, and the dynamics of the tick-borne disease, tularemia disease in this study.

In order to reduce the risk of grid transient voltage instability brought about by the new energy and system dynamic loads and the increasing proportion of new energy access, this paper proposes a differentiated dynamic reactive power compensation configuration method for suppressing transient voltage dip instability. The method first establishes a dynamic reactive power device configuration index system based on a multi-binary table, and then establishes a differentiated dynamic reactive power compensation device configuration model based on the node instability risk characteristics by combining with the regional division of system transient voltage instability risk. The model takes the investment cost and suppression effect as the optimization objectives, and realizes differentiated dynamic reactive power compensation by flexibly adjusting the optimization weights of economic benefits and reactive power compensation effects in the configuration process; Compared with the traditional compensation methods, differentiated compensation can significantly improve the economic benefits of reactive power compensation configuration. Finally, taking the IEEE39 node test system as an example, the improved TLBO algorithm is used to solve the model, which verifies the accuracy and effectiveness of the method proposed in this paper.

To investigate the dynamics of land surface temperature (LST) in Afghanistan in the period 2000-2021 and to assess the impact of such factors as soil moisture, precipitation, and vegetation coverage on it, remotely sensed soil moisture data from Land Data Assimilation System (FLDAS), precipitation data from Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), and NDVI and LST from Moderate Resolution Imaging Spectroradiometer (MODIS) were downloaded and correlations between them were analyzed using the regression method. The result shows that the LST in Afghanistan has a slightly decreasing, but insignificant trend during the study period (R=0.2, p-value=0.25), while vegetation coverage, precipitation, and soil moisture had an increasing trend. It was revealed that soil moisture has the highest impact on LST (R=0.7, p-value=0.0007), and the soil moisture, precipitation, and vegetation coverage explain almost 80% of spring (R2=0.73) and summer (R2=0.76) LST variability in Afghanistan. The LST variability analysis done separately for Afghanistan’s rivers subbasins shows that the LST of the Amu Darya subbasin had an upward trend in the study period, while for the Kabul subbasin the trend was downward.

NF-κB is a central mediator of inflammation, response to DNA damage and oxidative stress. As a result of its central role in so many important cellular processes, NF-κB dysregulation has been implicated in the pathology of important human diseases. NF-κB activation causes inappropriate inflammatory responses in diseases including rheumatoid arthritis (RA) and multiple sclerosis (MS). Thus, modulation of NF-κB signaling is being widely investigated as an approach to treat chronic inflammatory diseases, autoimmunity and cancer. The emergence of COVID-19 in late 2019, the subsequent pandemic and the huge clinical burden of patients with life-threatening SARS-CoV-2 pneumonia led to a massive scramble to repurpose existing medicines to treat lung inflammation in a wide range of healthcare systems. These efforts continue and these efforts continue to be con-troversial. Drug repurposing strategies are a promising alternative to de-novo drug development, as they minimize drug development timelines and reduce the risk of failure due to unexpected side effects. Different experimental approaches have been applied to identify existing medicines which inhibit NF-κB that could be repurposed as anti-inflammatory drugs.

Human endogenous retroviruses (HERVs) are ancient retroviral DNA sequences established into germline. They contain regulatory elements and encoded proteins few of which may provide benefits to hosts when co-opted as cellular genes. Their tight regulation is mainly achieved by epigenetic mechanisms which can be altered by environmental factors, e.g. viral infections, leading to HERV activation. Aberrant expression of HERVs associates with neurological disease, such as multiple sclerosis (MS) or amyotrophic lateral sclerosis (ALS), inflammatory processes and neurodegeneration. This review summarizes recent advances on the epigenetic mechanisms controlling HERV expression and the pathogenic effects triggered by HERV derepression. The article ends describing new promising therapies targeting HERV elements, one of which, temelimab, has completed phase II trials with encouraging results in treating MS. The information gathered here may turn helpful in the design of new strategies to unveil epigenetic failures behind HERV-triggered disease, opening new possibilities for druggable targets and/or for extending the use of temelimab to treat other associated diseases.

We are concerned with the use of some classical spectral collocation methods as well as with the new software system Chebfun in order to compute high order (index) eigenpairs of singular as well as regular Schrodinger eigenproblems. We want to highlight both the qualities as well as the shortcomings of these methods and evaluate them vis-a-vis the usual ones. In order to resolve a boundary singularity we use Chebfun with the simple domain truncation technique. Although this method is equally easy to apply with spectral collocation, things are more nuanced in the case of these methods. A special technique to introduce boundary conditions as well as a coordinate transform which maps an unbounded domain to a nite one are the ingredients. A challenging set of "hard" benchmark problems, for which usual numerical methods (f. d., f. e. m., shooting etc.) fail, are analysed. In order to separate "good"and "bad"eigenvalues we estimate the drift of the set of eigenvalues of interest with respect to the order of approximation and/or scaling of domain parameter. It automatically provides us with a measure of the error within which the eigenvalues are computed and a hint on numerical stability. We pay a particular attention to problems with almost multiple eigenvalues as well as for problems with a mixed (continuous) spectrum. In the latter case we try to numerically highlight its existence. Special attention will be paid to the higher eigenpairs (the pair of eigenvalue and the corresponding eigenfunction approximated by an eigenvector spanning its nodal values).

Soil quality assessment based on crop yields and identification of key indicators of it can be used for better management of agricultural production. In the current research, the weighted additive soil quality index (SQIw), factor analysis (FA) and multiple linear regression (MLR) method are used to assess the soil quality of rainfed winter wheat fields with two soil orders on 53.20 km2 of agricultural land in western Iran. A total of 18 soil quality indicators were determined for 100 soil samples (0-20 cm depth) from two soil orders (Inceptisols and Entisols). The soil properties measured were: pH, soil texture, organic carbon (OC), cation exchange capacity (CEC), electrical conductivity (EC), soil microbial respiration (SMR), carbonate calcium equivalent (CCE), soil porosity (SP), bulk density (BD), exchangeable sodium percentage (ESP), mean weight diameter (MWD), available potassium (AK), total nitrogen (TN), available phosphorus (AP), available Fe (AFe), available Zn (AZn), available Mn (AMn), and available Cu (ACu). Mean wheat grain yield for the two years for all of the 100 sampling sites was also gathered. The SQIw was calculated using two weighting methods (FA and MLR) and maps were created using a digital soil mapping framework. The soil indicators taken in the minimum data set (MDS) were AK, clay, CEC, AP, SMR, and sand. The correlation between the MLR weighting technique (SQI-M) and the rainfed wheat yield (r=0.62) was slightly larger than that the correlation of yield with the FA weighted technique (SQI-F) (r=0.58). Results showed that the means of both SQI-M and SQI-F and rainfed wheat yield for Inceptisols were higher than for Entisols although these differences were not statistically significant. Both SQI-M and SQI-F showed that areas with Entisols had lower proportions of good soil quality grades (Grade I and II), and higher proportions of poor soil quality grades (Grade IV and V) compared to Inceptisols. Based on these results, soil type must be considered for soil quality assessment in future studies to maintain and enhance soil quality and sustainable production. The overall soil quality of the study region was of poor and moderate grades. To improve soil quality, it is therefore recommended that effective practices such as the implementation of scientific integrated nutrient management involving the combined use of organic and inorganic fertilizers in rainfed wheat fields be promoted.

The formation of high-melting-point Cu₆Sn₅ interconnections is crucial to overcome the collapse of Sn-based micro-bumps and produce reliable intermetallic interconnections in three-dimensional (3D) package. However, because of the multiple reflows in 3D package manufacturing, Cu₆Sn₅ interconnections will experience the cyclic polymorphic transitions in the solid state. The repeated and abrupt change in the Cu₆Sn₅ lattice due to the cyclic polymorphic transitions can cause extreme strain oscillations, producing damages at the surface and in the interior of the Cu₆Sn₅ matrix. Moreover, because of the polymorphic-transition-induced grain splitting and superstructure phase formation, the reliability of Cu₆Sn₅ interconnections will thus face great challenges in 3D package. In addition, the Cu₆Sn₅ polymorphic transition is structure-dependent, and the η′↔η polymorphic transition will occur at the surface while the η′↔ηs↔η polymorphic transition will occur in the deep matrix. Our results can provide in-depth understandings of structural evolution and damage mechanism of Cu₆Sn₅ interconnections in real 3D package manufacturing.

The spatial formation of coherent random laser modes in strongly scattering disordered random media is a central feature in the understanding of the physics of random lasers. We derive a quantum field theoretical method for random lasing in disordered samples of complex amplifying Mie resonators which is able to provide self-consistently and free of any fit parameter the full set of transport characteristics at and above the laser phase transition. The coherence length and the correlation volume respectively is derived as an experimentally measurable scale of the phase transition at the laser threshold. We find that the process of stimulated emission in extended disordered arrangements of active Mie resonators is ultimately connected to time-reversal symmetric multiple scattering in the sense of photonic transport while the diffusion coefficient is finite. A power law is found for the random laser mode diameters in stationary state with increasing pump intensity.

Multiple lines of evidence indicate that CD8$^+$ T cells are important in the control of HIV-1 (HIV) replication. However, CD8$^+$ T cells induced by natural infection cannot eliminate the virus or reduce viral loads to acceptably low levels in most infected individuals. Understanding the basic quantitative features of CD8$^+$ T-cell responses induced during the course of HIV infection may therefore inform us about the limits that HIV vaccines, which aim to induce protective CD8$^+$ T-cell responses, must exceed. Using previously published experimental data from a cohort of HIV-infected individuals with sampling times from acute to chronic infection we defined the quantitative properties of CD8$^+$ T-cell responses to the whole HIV proteome. In contrast with a commonly held view, we found that the relative number of HIV-specific CD8$^+$ T-cell responses (response breadth) changed little over the course of infection (first 400 days post-infection), with moderate but statistically significant changes occurring only during the first 35 symptomatic days. This challenges the idea that a change in the T-cell response breadth over time is responsible for the slow speed of viral escape from CD8$^+$ T cells in the chronic infection. The breadth of HIV-specific CD8$^+$ T-cell responses was not correlated with the average viral load for our small cohort of patients. Metrics of relative immunodominance of HIV-specific CD8$^+$ T-cell responses such as Shannon entropy or the Evenness index were also not significantly correlated with the average viral load. Our mathematical-model-driven analysis suggested extremely slow expansion kinetics for the majority of HIV-specific CD8$^+$ T-cell responses and the presence of intra- and interclonal competition between multiple CD8$^+$ T-cell responses; such competition may limit the magnitude of CD8$^+$ T-cell responses, specific to different epitopes, and the overall number of T-cell responses induced by vaccination. Further understanding of mechanisms underlying interactions between the virus and virus-specific CD8$^+$ T-cell response will be instrumental in determining which T-cell-based vaccines will induce T-cell responses providing durable protection against HIV infection.

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named interacting multiple model fifth-degree spherical simplex-radial cubature filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and fifth-degree spherical simplex-radial cubature filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with IMMUKF, IMMCKF and IMM5thCKF.

Continuation methods are presented that are capable of treating frequency-domain flutter equations including multiple nonlinearities represented by describing functions. A small problem demonstrates how a series of continuation processes can find all limit-cycle oscillations within a specified region with a reasonable degree of confidence. Curves of the limit-cycle amplitude variation with velocity, indicating regions of stability and instability with colors give a compact view of the nonlinear behavior throughout the flight regime. A continuation technique for reducing limit-cycle amplitudes by adjusting various system parameters is presented. These processes are economical enough to be a routine part of aircraft design and certification.

Fermatean fuzzy sets (FFSs) are a versatile tool for handling uncertain problems and have shown great effectiveness in practical applications. However, many existing Fermatean fuzzy similarity measures exhibit counter-intuitive situations, making it challenging to accurately measure the similarity or difference between FFSs. To address this issue, several similarity and distance measures for FFSs are proposed, inspired by the Tanimoto similarity measure. The properties of the proposed measures are also explored, along with several comparative examples with existing measures for FFSs, which illustrate their superior performance in processing fuzzy information from FFSs. The proposed measures have effectively overcome the counter-intuitive challenges posed by many existing measures and significantly outperforms existing measures in differentiating different FFSs. Furthermore, we demonstrate the practical applicability of the proposed measures in solving pattern recognition, medical diagnosis and multiple-attribute decision-making problems.

Sphingosine 1-phosphate (S1P) is a signaling lipid molecule involved in various cellular processes. It is important to develop a quantitative method for S1P to determine endogenous levels and to investigate its functions. Especially since S1P is a tiny lipid component of most biological samples, highly sensitive analysis by LC-MS/MS is required. The main challenge in S1P analysis by chromatography is peak broadening due to the presence of a polar phosphate and zwitterion formation in S1P. In this study, we used hydrogen fluoride (HF) to efficiently remove a phosphate and then analyzed the surrogate, sphingosine, as a sharp peak by LC-ESI-MS/MS. We optimized the dephosphorylation reaction in terms of reaction time, temperature, and HF concentration. Multiple reaction monitoring (MRM) for dephosphorylated form of S1P and C17-S1P as internal standard at m/z transition 300.4&gt;282.4 (quantification ion), 300.4&gt;262.4 (qualification ion), 286.3&gt;268.2 (internal standard). This method was validated by essential parameters such as specificity, linearity, range, LOQ, LOD, accuracy, precision, and repeatability. To confirm this new method, we quantified S1P levels in various serum products (100.02 ~ 284.38 nM). To apply the method in vivo, we analyzed S1P in plasma and kidney tissues obtained from a chronic kidney disease (CKD) mouse model. S1P levels were increased only in CKD kidney tissue but not in plasma. In conclusion, by applying the dephosphorylation step with HF, we established a new sensitive LC-MS/MS quantitative method for S1P that can be applied to biological samples.

The short-term intake (ESTI) of Hungarian consumers to pesticide residues was assessed based on 2331 test results obtained during 2017-2021 monitoring program in the frequently analyzed apples, sour cherries, table grapes, peaches, nectarines, peppers and strawberries (23.5% of all samples taken from 119 crops). The age-specific consumption data were obtained from two representative national food consumption surveys (2009 and 2018-2020). The exposure was characterized with Hazard Quotient and Hazard Index considering the acute reference doses of pesticide residues detected in the samples. When ESTI was calculated with all detected “single“ residues and variability factor of 3.6, recommended for evaluation of monitoring results, the HI only exceeded 1 for children &lt;3 years old eating grape (1.50-1.81). HI was &lt;1 when any of the 6 foods were eaten together within one day. Between forty and fifty percent of samples contained 2-23 residues. Though, the individual residue concentrations were below the corresponding MRLs, multiple residues being present in one sample resulted in maximum HI values in apples (1.14); grapes (6.57); peaches and nectarines (2.57); strawberries (2.74); peppers (10.44). Residues with low ARfD values contributed most. Applying HI is simple, but provides only point estimates; therefore, it should only be used in first-tier risk assessment.

Based on the Generalized bracket, or Beris-Edwards, formalism of non-equilibrium thermodynamics, we have recently proposed [Stephanou et al. Materials, 13, 2867 (2020)] a new differential constitutive model for the rheology of entangled polymer melts and solutions. It has amended the shortcomings of a previous model that was too strict in the values of the convective constraint release parameter for the model not to violate the second law of thermodynamics and has been shown capable of predicting a transient stress undershoot (following the overshoot) at high shear rates. In this work, we wish to further examine this model’s capability of predicting the rheological response of industrial polymer systems by extending it to its multiple-mode version. The comparison against industrial rheological data (High-Density Polyethylene resins), as compared against available experimental data in (a) Small Amplitude Oscillatory shear, (b) start-up shear, and (c) start-up uniaxial elongation, is noted to be good.

Streptococcus pneumoniae is one of the associated bacteria that can cause the rare but high mortality hematological disease known as purpura fulminans (PF) in both adults and children. Patients with PF can progress quickly to sepsis and multiorgan failure, especially in immunocompromised individuals and young children. Due to the thrombotic blockage of blood arteries in PF, there is diffuse intravascular thrombosis and hemorrhagic infarction of the skin, which evolves from ecchymosis to skin necrosis, risk of limb sequelae, sepsis and fatality. We present a case of a previously healthy 1-year and 9-month female who was admitted to the Intensive Care Unit of the National Institute of Infectious Diseases "Prof. Dr. Matei Balș"- Bucharest, Romania. The patient presented with erythematous patches on left upper limb and trunk, tachycardia, tachypnea, hypotension, anuria and altered sensorium with an indication of intubation for 8 Glasgow Coma Scale (GCS). Cerebrospinal culture grew Streptococcus pneumoniae serotype 1. She required peritoneal dialysis due to acute kidney injury (AKI) and surgeries for affected areas. After multiple organ system failures, our patient evolved rapidly to irreversible tissue necrosis and death. We aim to report a rare case of PF associated with pneumococcal meningoencephalitis in immunocompetent children to better appreciate the risk of fatal evolution when managing this disease in children. To date, few studies report favourable evolution. We discuss the etiology, rapid diagnosis and treatment of immunocompetent children with PF and the importance of interprofessional teams to increase the outcomes in patients with infectious diseases.

Purpose: The purpose of this study was to evaluate the dosimetric impact of multiple channels compared to a single channel in high dose rate (HDR) vaginal cuff brachytherapy (VCBT). Methods and materials: Sample plans of HDR VCBT from both single and multiple channel cylinders at 2.5, 3.0 and 3.5 cm in diameter were created and analyzed. The doses were prescribed either at the 0.5 cm depth with 5.5 Gy for 4 fractions or at the cylinder surface with 8.8 Gy for 4 frac¬tions, in various treatment lengths ranging from 3 to 8 cm. A 0.5 cm ring target volume called PTV_Eval was contoured for each plan and served as the target volume for dosimetric evaluation. Mean doses and the doses covering 90% (D90), 10% (D10), and 5% (D5) of PTV_Eval were calculated and compared. The same treatment lengths, same prescription regimens, same target volume contouring and same plan evaluation methods were repeated for the similar size single channel and multiple channel cylinders. Results: The mean doses of both regimens with the multi-channel cases (6.3 to 7.1 Gy) were comparable to single channel ones (6.4 and 7.5 Gy). The ranges of D90, D10 and D5 were from 3.654 to 5.04 Gy, 8.44 to 10.08 Gy and 8.84 to 10.86 Gy in the multi-channel cases, but from 5.36 to 5.76 Gy, 7.40 to 9.97 Gy and 8.16 to 11.93 Gy in the single channel cases, respectively. Conclusion: The single channel and multiple-channel cylinder deliver a similar mean dose, but with marked heterogeneity in D90, D5 and D10 for the same prescription dose and target volume. Multi-channel cylinders adequately cover the target volume, but given the heterogeneity, increased treatment time and cost are best suited for asymmetric target volumes.

We report the first case of Daratumumab interference of allogeneic crossmatch tests repeatedly causing aberrant false-positive results, which inadvertently delayed transplant for a waitlisted renal patient with multiple myeloma. Daratumumab is an IgG1κ human monoclonal antibody commonly used to treat multiple myeloma, characterized by cancerous plasma cells and often leads to renal failure requiring kidney transplant, by depleting CD38-expressing plasma cells. In this case study, the patient had end-stage renal disease secondary to multiple myeloma and was continuously receiving Daratumumab infusions. The patient did not have any detectable antibodies to human leukocyte antigens but repeatedly had unexpected positive crossmatch by the flow cytometry-based method with 26 of the 27 potential deceased organ donors, implying donor-recipient immunological incompatibility. However, further review and analysis suggested that the positive crossmatches were likely false-positive as a result of interference from Daratumumab binding to donor cell surface CD38 as opposed to the presence of donor-specific antibodies. The observed intensity of the false-positive crossmatches was also highly variable, potentially due to donor- and/or cell-dependent expression of CD38. The variability of CD38 expression was, therefore, for the first time, characterized on the T and B cells isolated from various tissues and peripheral blood of 78 individuals. Overall, T cells were found to have a lower CD38 expression profile than the B cells, and no significant difference was observed between deceased and living individuals. Finally, we show that a simple cell treatment by dithiothreitol can effectively mitigate Daratumumab interference thus preserving the utility of pre-transplant crossmatch in multiple myeloma patients awaiting kidney transplant.