The prolificacy of green transformational leadership in shaping employee green behavior during times of crises in small and medium enterprises: a moderated mediation model.

Besides various other potent efforts to contain and consolidate, post-pandemic crisis management requires an adequate display of green transformational leadership. Green transformational leaders exhibit a formidable commitment to sustainability in making managerial choices and subsequently inspiring and motivating their employees to participate vigorously in the ensuing green initiatives. Such initiatives could profoundly help organizations adjust to shifting market situations, follow requirements, and preserve stakeholder trust. While making appeals to the central tenants of the transformation leadership theory, social exchange theory, and the social cognition theory, the study examines the impact of green transformational leadership on employees' green behaviors during times of crises using employees' self-esteem as a mediator and work engagement as the critical moderator. The relevant context of the study has been the small and medium enterprises of Saudi Arabia. Data from 232 SMEs employing less than 250 employees selected through convenience sampling was collected using structured questionnaires. After performing hierarchical regression modeling using SPSS 23, macro V4 model 58, and Amos 24, it has been found that green transformational leadership is positively associated with employees' green behaviors, with self-esteem and work engagement acting as significant mediating and moderating conditions, respectively. Besides, providing firsthand empirical evidence on the impact of green transformational leadership on employees' green behaviors, in unique industrial (SMEs), contextual (times of crises), and regional (Middle Eastern) settings, the study offers useful implications to the managers aspiring to enhance the sustainable performance of their firms through maturing green behaviors among their employees.

Synthesis, characterization, computational assay and anti-inflammatory activity of thiosemicarbazone derivatives: Highly potent and efficacious for COX inhibitors.

Multiple studies in the literature have demonstrated that synthetic compounds containing heterocyclic rings possess a reparative potential against acute and chronic inflammation. In the present study, two novel thiosemicarbazone derivatives based on l-ethyl-6-(thiophen-2-yl)indoline-2,3-dione with different phenyl substituted thiosemicarbazides were synthesized by condensation reaction and the structures of proposed target compounds (KP-2 and KP-5) were confirmed by UV-VIS, FTIR, 1H-NMR and 13C-NMR. In-vitro anti-inflammatory behavior of KP-2 and KP-5 was confirmed by bovine serum albumin (BSA) and ovine serum albumin (OSA) analysis. Acute and chronic anti-inflammatory potential of synthesized compounds were evaluated by using carrageenan and complete Freund's adjuvant (CFA) as inflammation-inducing agents, respectively. Inhibition of pro-inflammatory mediators and prevention of protein denaturation owing to synchronization of more electronegative flouro-groups substituted on phenyl rings along with heterocyclic indoline ring provides anti-inflammatory effects and are corroborated by radiological, histopathological analysis. Additional support was provided through density functional theory (DFT) and molecular docking. KP-5 exhibited excellent lead-likeness based on its physicochemical parameters, making it a viable drug candidate. The synthesized compounds also showed promising ADMET properties, enhancing their potential as therapeutic agents. These findings emphasize the pivotal role of new compounds for drug design and development.

Epidemiology and Risk Factors for Invasive Fungal Infections in Pancreas Transplant in the Absence of Postoperative Antifungal Prophylaxis.

Background: Invasive fungal infections (IFIs) remain a rare yet dreaded complication following pancreas transplantation. Current guidelines recommend antifungal prophylaxis in patients with 1 or more risk factors. At our center, single-dose antifungal prophylaxis is administered in the operating room but none subsequently, regardless of risk factors. Here we evaluate the 1-year incidence, outcome, and risk factors associated with IFI following pancreas transplantation. Methods: A retrospective, single-center cohort study was conducted in patients who underwent pancreas transplantation between 1 January 2009 and 31 December 2019. Records were manually reviewed, and cases were adjudicated using consensus definitions. The 1-year cumulative incidence, mortality, and risk factors were analyzed by Kaplan-Meier method and differences between populations were assessed with Fisher test and Mann-Whitney U test. Results: Three hundred sixty-nine recipients were included. Twelve IFIs were identified: candidiasis (8), aspergillosis (2), histoplasmosis (1), and cryptococcosis (1). Intra-abdominal infections were the most common presentation (5), followed by bloodstream infections (3), disseminated disease (2), pulmonary disease (1), and invasive fungal sinusitis (1). Median time to IFI was 64 days (interquartile range, 30-234 days). One-year cumulative incidence was 3.25% (95% confidence interval, 1.86%-5.65%). There were no significant differences between patients with or without IFI regarding type of transplant (P = .17), posttransplant dialysis (P = .3), rejection (P = .5), cytomegalovirus serostatus (P = .45), or reoperation (P = .19). For patients with IFI, the 1-year graft and patient survival rates were 58% versus 95% (P < .0001) and 75% versus 98.6% (P < .001), respectively. Conclusions: Our study suggests that the use of a single-dose antifungal prophylaxis administered in the operating room but none subsequently does not result in an increased incidence of IFI following pancreas transplantation.

Multicomponent synthesis of pyrido[2,3-b]pyrazine derivatives: electrochemical DNA sensing, nonlinear optical properties and biological activity.

We synthesized novel pyrido[2,3-b]pyrazin based heterocyclic compounds (4-7) and their chemical structures were ascertained by spectral techniques (NMR, FT-IR). Besides experimental investigation, density functional theory (DFT) computations with B3LYP/6-31G(d,p) level of theory were executed to obtain spectroscopic and electronic properties. Nonlinear optical (NLO) properties, frontier molecular orbitals (FMOs), UV-visible, vibrational analysis, natural bond orbitals (NBOs), transition density matrix (TDM) and density of states (DOS) analyses of molecules (4-7) were accomplished at B3LYP/6-31G (d,p) level. Global reactivity parameters (GRPs) were correlated with the band gap (Egap) values; compound 7 with lower Egap (3.444 eV), exhibited smaller value of hardness (1.722 eV) with greater softness value (0.290 eV-1). The dipole moment (µ), average polarizability 〈α〉, first (ßtot) and second 〈γ〉 hyper-polarizabilities were calculated for compounds (4-7). Compound 7 showed less Egap, highest absorption wavelength and remarkable NLO response. The highest 〈α〉, ßtot and 〈γ〉 values for compound 7 were observed as 3.90 × 10-23, 15.6 × 10-30 and 6.63 × 10-35 esu, respectively. High NLO response revealed that pyrido[2,3-b]pyrazin based heterocyclic compounds had very remarkable contributions towards NLO technological applications. Further compounds (4-7) are utilized for the first time in electrochemical sensing of DNA, in vitro antioxidant and antiurease activity.

Artificial intelligence models for predicting calcium and magnesium removal by polyfunctional ketone using ensemble machine learners.

Calcium (Ca2+) and magnesium (Mg2+) are the major scaling ions of reverse osmosis concentrate in zero-liquid discharge systems, causing performance decline. In this study, we predicted the removal of Ca2+ and Mg2+ from simulated reverse osmosis concentrate by functional polyketones (FPKs). Four amines, including 1,2-diaminopropane (DAP), 1-(2-aminoethyl) piperazine (AEP), 1-(3-aminopropyl) imidazole (API), and butyl amine (BA) used to synthesize FPKs. The effects of various factors such as the amount of adsorbent, feed water concentration, and pH were investigated for process optimization. In this study, ensemble learner artificial intelligence models, decision tree (DT), extreme gradient boost (XGB), and random forest (RF) were used to predict Ca2+ and Mg2+ removal by the FPKs. Datasets were collected experimentally using FPKs to remove Ca2+ and Mg2+ from the simulated reverse osmosis concentrate. The predictions were made by XGB, DT, and RF models for the first chosen amine for Ca2+ and then for Mg2+, subsequently, this process was repeated with each amine. The developed DT, RF, and XGB models demonstrated higher coefficients of determination for predicting Mg2+ removal by AEP and DAP (R2 = 0.841-0.935) than by API and BA (R2 = 0.774-0.801) except in the RF and XGB model results (R2 = 0.801-0.846). Overall, the XGB model displayed good results for both Ca2+ and Mg2+ removal but slight changes were observed in the AEP and BA predictions by DT and RF. Therefore, artificial intelligence models may be a viable alternative for further insight in predicting Ca2+ and Mg2+ removal by FPKs from simulated reverse osmosis concentrate.

Dynamic association of stock market volatility, foreign portfolio investment and macroeconomic indicators by taking the impact of structural breaks.

In an asymmetric information environment, investors diversify their investments to minimize risk and maximize their wealth. Such diversification ranges from one market to another market and from one country to another country. Investors prefer foreign portfolio investment over foreign direct investment because of the economy's turmoil, changes in macroeconomic indicators, and market liberation. This study analyzes the dynamic relationship among stock market volatility, foreign portfolio investment, and macroeconomic indicators (foreign exchange rate, interest rate, and Gross Domestic Product) using the dynamic long-run Auto-regressive Distributed Lag (ARDL) model concerning the Pakistan environment. This study also considers the impact of multiple structural breaks, representing variables' endogenous and exogenous shocks. The secondary data is used from Oct. 01, 2009, to Sept. 30, 2019, with monthly frequency. The results indicate a co-integration between SMV, FPI, FXR, IR, and GDP. In short-run analyses, the error correction term is statistically significant, while in the long run, the SMV, FPI, and FXR are not impacted. As no evidence of volatility has been found between SMV and FPI, unidirectional or bi-directional policies can be devised to further attract the new FPI for strengthening the foreign reserves, the balance of payments, and other macroeconomic variables. Additionally, investors should update their knowledge based on considering the endogenous and exogenous shocks on the SMV.

Semantic segmentation of retinal exudates using a residual encoder-decoder architecture in diabetic retinopathy.

Exudates are a common sign of diabetic retinopathy, which is a disease that affects the blood vessels in the retina. Early detection of exudates is critical to avoiding vision problems through continuous screening and treatment. In traditional clinical practice, the involved lesions are manually detected using photographs of the fundus. However, this task is cumbersome and time-consuming and requires intense effort due to the small size of the lesion and the low contrast of the images. Thus, computer-assisted diagnosis of retinal disease based on the detection of red lesions has been actively explored recently. In this paper, we present a comparison of deep convolutional neural network (CNN) architectures and propose a residual CNN with residual skip connections to reduce the parameter for the semantic segmentation of exudates in retinal images. A suitable image augmentation technique is used to improve the performance of network architecture. The proposed network can robustly segment exudates with high accuracy, which makes it suitable for diabetic retinopathy screening. A comparative performance analysis of three benchmark databases: E-ophtha, DIARETDB1, and Hamilton Ophthalmology Institute's Macular Edema, is presented. The proposed method achieves a precision of 0.95, 0.92, 0.97, accuracy of 0.98, 0.98, 0.98, sensitivity of 0.97, 0.95, 0.95, specificity of 0.99, 0.99, 0.99, and area under the curve of 0.97, 0.94, and 0.96, respectively. RESEARCH HIGHLIGHTS: The research focuses on the detection and segmentation of exudates in diabetic retinopathy, a disease affecting the retina. Early detection of exudates is important to avoid vision problems and requires continuous screening and treatment. Currently, manual detection is time-consuming and requires intense effort. The authors compare qualitative results of the state-of-the-art convolutional neural network (CNN) architectures and propose a computer-assisted diagnosis approach based on deep learning, using a residual CNN with residual skip connections to reduce parameters. The proposed method is evaluated on three benchmark databases and demonstrates high accuracy and suitability for diabetic retinopathy screening.

Design, Synthesis, and Density Functional Theory Studies of Indole Hydrazones as Colorimetric "Naked Eye" Sensors for F Ions.

A new series of sensors SM-1 to SM-3 was designed and synthesized using indole carboxaldehydes (2a-2c) and 2,4-dinitrophenyl hydrazine. Accompanied by the synthesis, density functional theory investigation was also accomplished at the M06-2X/6-311G+(d,p) functional. A reduction in band gap (ΔE = 4.702-4.230 eV) along with a bathochromic shift (λmax = 433.223-471.584 nm) was seen in deprotonated chromophores than their neutral sensors. Further, significant charge transference from indole toward dinitrophenyl hydrazine was also examined. Global reactivity parameters also expressed the greater stability of sensors than that of their deprotonated form. SM-3 displayed high selectivity toward F ions as compared to SM-1 and SM-2, which respond to both F- and CN- ions. The electronic absorption spectrum was recorded in CH3CN. The sensor SM-3 showed high selectivity toward F- ions with a low detection limit (8.69 × 10-8), and the binding constant for SM-3 was determined as 7.7 × 105. The sensor displayed naked eye views as the color of solution changed from mustard to purple with a red shift of 96 nm. The mechanism suggests deprotonation from the NH group, which was confirmed by 1H NMR. The sensor is found to be useful for detection of F- ions in the real sample and for analytical application (test strip).

Pyrazole derivatives of pyridine and naphthyridine as proapoptotic agents in cervical and breast cancer cells.

Cancer is one of the leading causes of death worldwide. The increasing prevalence and resistance to chemotherapy is responsible for driving the search of novel molecules to combat this disease. In search of novel compounds with pro-apoptotic potential, pyrazolo-pyridine and pyrazolo-naphthyridine derivatives were investigated against cervical cancer (HeLa) and breast cancer (MCF-7) cells. The anti-proliferative activity was determined through the MTT assay. Potent compounds were then analyzed for their cytotoxic and apoptotic activity through a lactate dehydrogenase assay and fluorescence microscopy after propidium iodide and DAPI staining. Flow cytometry was used to determine cell cycle arrest in treated cells and pro-apoptotic effect was verified through measurement of mitochondrial membrane potential and activation of caspases. Compounds 5j and 5k were found to be most active against HeLa and MCF-7 cells, respectively. G0/G1 cell cycle arrest was observed in treated cancer cells. Morphological features of apoptosis were also confirmed, and an increased oxidative stress indicated the involvement of reactive oxygen species in apoptosis. The compound-DNA interaction studies demonstrated an intercalative mode of binding and the comet assay confirmed the DNA damaging effects. Finally, potent compounds demonstrated a decrease in mitochondrial membrane potential and increased levels of activated caspase-9 and -3/7 confirmed the induction of apoptosis in treated HeLa and MCF-7 cells. The present work concludes that the active compounds 5j and 5k may be used as lead candidates for the development of lead drug molecules against cervical and breast cancer.

Environmental-, social-, and governance-related factors for business investment and sustainability: a scientometric review of global trends.

Consideration of environmental, social, and governance (ESG) factors can contribute to the environmental and economic performance of organizations in terms of investment and sustainability. This article thoroughly reviews the following factors influencing decisions regarding ESG policy by businesses: economic performance, environmental sustainability, pollution and waste, corporate social responsibility, gender, and governance structure. Moreover, we review the impact of these factors considering ESG disclosure, the global pandemic, religion, governing board and size, national interest, and technological advancements. The literature reports that ESG disclosures of environmental, economic, and social sustainability performance can strengthen business sustainability and performance. Religion-based businesses demonstrated better socio-environmental performance but not governance. An independent governing board has a positive impact; however, dual-gender boards negatively impact ESG disclosure. Significant diversification potential in ESG investments was observed during the COVID-19 pandemic. Adopting an ESG policy enhances the innovation capacity, innovative activities, value creation, and financial performance of businesses. Overall, the social and environmental performance demonstrated a significantly positive relationship with business sustainability, indicating that business economy and creating value for society are mutually dependent. The literature summary presented in this review will help future research on ESG factors that influence business investments and sustainability.

Synthesis of heated aluminum oxide particles impregnated with Prussian blue for cesium and natural organic matter adsorption: Experimental and machine learning modeling.

Heated aluminum oxide particles impregnated with Prussian blue (HAOPs-PB) are synthesized for the first time using different molar ratios of aluminum sulfate and PB to improve the adsorption of cesium (133Cs+) and natural organic matter (NOM) from an aqueous solution. The Cs+ adsorption from various aqueous solutions, including surface, tap and deionized water by synthesized HAOPs-PB, is investigated. The influencing factors such as HAOPs-PB mixing ratio, pH and dosage are studied. In addition, pseudo 1st and 2nd order is tested for adsorption kinetics study. A machine learning model is developed using gene expression programming (GEP) to evaluate and optimize the adsorption process for Cs+ and NOM removal. Synthesized adsorbent showed maximum adsorption at a 1:1 M ratio of aluminum sulfate and PB in DI, tap, and surface water. The pseudo 2nd order kinetics model described the Cs + adsorption by HAOPs-PB more accurately that indicating physiochemical adsorption. Adsorption of Cs+ showed an increasing trend with higher HAOPs-PB concentration, while high pH also favored the adsorption. Maximum NOM adsorption is found at a higher HAOPs-PB dosage and a neutral pH value. Furthermore, the proposed GEP model shows outstanding performance for Cs+ adsorption modeling, whereas a modified-GEP model presents promising results for NOM adsorption prediction for testing dataset by learning the relationship between inputs and output with R2 values of 0.9348 and 0.889, respectively.

Predictive modeling of pharmaceutical product removal by a managed aquifer recharge system: Comparison and optimization of models using ensemble learners.

Pharmaceutical products (PPs) are emerging water pollutants with adverse environmental and health-related impacts, owing to their toxic, persistent, and undetectable microscopic nature. Globally, increasing scientific knowledge and advanced technologies have allowed researchers to study PP-associated problems and their removal for water reuse. Experimental modeling methods require laborious, lengthy, expensive, and environmentally hazardous lab-work to optimize the process. On the other hand, predictive machine learning (ML) models can trace the complex input-output relationship of a process using available datasets. In this study, ensemble ML techniques, including decision tree (DT), random forest (RF), and Xtreme gradient boost (XGB), were used to explore PP (diclofenac, iopromide, propranolol, and trimethoprim) removal by a managed aquifer recharge (MAR) system. The model input parameters included characteristics of reclaimed water and soil used in the columns, pH, dissolved organic carbon, operating time, nitrogen dioxide, sulfate, nitrate, electrical conductivity, manganese, and iron. The selected PP removal was the model output. Datasets were collected through a one-year experimental study of continuous MAR system operation to predict the removal of PPs. DT, RF, and XGB models were then developed for one of the selected compounds and tested for the others to check the reliability of the ML model results. The developed models were assessed using statistical performance matrices. The experimental results showed >80% removal of propranolol and trimethoprim; however, removal of diclofenac and iopromide was only ≈50% by the MAR system. The proposed DT and RF models presented higher coefficients of determination (R2 ≥ 0.92) for diclofenac, propranolol, and trimethoprim than for iopromide (R2 ≤ 0.63). In contrast, the XGB model showed better results for diclofenac, iopromide, propranolol, and trimethoprim, with R2 values of 0.92, 0.72, 0.96, and 0.97, respectively. Therefore, XGB could be the best predictive model to provide insight into the adaptation of ML models to predict PP removal by the MAR system, thereby minimizing experimental work.

An Early Warning System for Earthquake Prediction from Seismic Data Using Batch Normalized Graph Convolutional Neural Network with Attention Mechanism (BNGCNNATT).

Earthquakes threaten people, homes, and infrastructure. Early warning systems provide prior warning of oncoming significant shaking to decrease seismic risk by providing location, magnitude, and depth information of the event. Their usefulness depends on how soon a strong shake begins after the warning. In this article, the authors implement a deep learning model for predicting earthquakes. This model is based on a graph convolutional neural network with batch normalization and attention mechanism techniques that can successfully predict the depth and magnitude of an earthquake event at any number of seismic stations in any number of locations. After preprocessing the waveform data, CNN extracts the feature map. Attention mechanism is used to focus on important features. The batch normalization technique takes place in batches for stable and faster training of the model by adding an extra layer. GNN with extracted features and event location information predicts the event information accurately. We test the proposed model on two datasets from Japan and Alaska, which have different seismic dynamics. The proposed model achieves 2.8 and 4.0 RMSE values in Alaska and Japan for magnitude prediction, and 2.87 and 2.66 RMSE values for depth prediction. Low RMSE values show that the proposed model significantly outperforms the three baseline models on both datasets to provide an accurate estimation of the depth and magnitude of small, medium, and large-magnitude events.

Synthesis, Biological Evaluation, and In Silico Studies of Novel Coumarin-Based 4H,5H-pyrano[3,2-c]chromenes as Potent ß-Glucuronidase and Carbonic Anhydrase Inhibitors.

The search for novel heterocyclic compounds with a natural product skeleton as potent enzyme inhibitors against clinical hits is our prime concern in this study. Here, a simple and facile two-step strategy has been designed to synthesize a series of novel coumarin-based dihydropyranochromenes (12a-12m) in a basic moiety. The synthesized compounds were thus characterized through spectroscopic techniques and screened for inhibition potency against the cytosolic hCA II isoform and ß-glucuronidase. Few of these compounds were potent inhibitors of hCA II and ß-glucuronidase with varying IC50 values ranging from 4.55 ± 0.22 to 21.77 ± 3.32 µM and 440.1 ± 1.17 to 971.3 ± 0.05 µM, respectively. Among the stream of synthesized compounds, 12e and 12i were the most potent inhibitors of ß-glucuronidase, while 12h, 12i, and 12j showed greater potency against hCA II. In silico docking studies illustrated the significance of substituted groups on the pyranochromene skeleton and binding pattern of these highly potent compounds inside enzyme pockets.

Treating reverse osmosis concentrate to address scaling and fouling problems in zero-liquid discharge systems: A scientometric review of global trends.

Currently, reverse osmosis concentrate (ROC) treatment is one of the most promising techniques for its disposal because it produces freshwater with high recovery and valuable materials such as salts and reduces waste volume and environmental pollution. Public attention to the severe consequences of water pollution and strict environmental regulations on wastewater discharge has pushed water-polluting industries toward zero-liquid discharge (ZLD). However, scaling and fouling problems increase energy consumption and limit permeate flux at high salt concentrations, mainly due to calcium, magnesium, and silica precipitation, ultimately decreasing ZLD performance. Therefore, this study discusses drivers and ROC pretreatment technologies to improve ZLD efficiency and presents a scientometric review of global trends. The advantages, disadvantages, and economic and environmental aspects of conventional and emerging pre-treatment technologies were studied. Traditional treatment of chemical processes combined with precipitation removes a large amount of scaling ions; however, high operation and maintenance costs and limited full-scale plant experience are the main drawbacks. Softening and coagulation are most commonly applied to treat large volumes at a moderate cost; however, substantial sludge production and increased conductivity are major operational issues. Moreover, emerging technologies efficiently remove scale-forming ions with high capital and operating costs. New variations in standard reverse osmosis technologies have improved ZLD efficiency; nonetheless, scaling and fouling are of concern. Therefore, this review presents the studies on ROC pre-treatment technologies for removing scaling ions to enhance ZLD efficiency, which can help in future research.

Deep Learning-Based Image Reconstruction for Different Medical Imaging Modalities.

Image reconstruction in magnetic resonance imaging (MRI) and computed tomography (CT) is a mathematical process that generates images at many different angles around the patient. Image reconstruction has a fundamental impact on image quality. In recent years, the literature has focused on deep learning and its applications in medical imaging, particularly image reconstruction. Due to the performance of deep learning models in a wide variety of vision applications, a considerable amount of work has recently been carried out using image reconstruction in medical images. MRI and CT appear as the ultimate scientifically appropriate imaging mode for identifying and diagnosing different diseases in this ascension age of technology. This study demonstrates a number of deep learning image reconstruction approaches and a comprehensive review of the most widely used different databases. We also give the challenges and promising future directions for medical image reconstruction.

Vasopressin for Post-kidney Transplant Hypotension.

Introduction: Hypotension after deceased donor kidney transplant (DDKT) is a risk factor for delayed graft function (DGF) and poor graft survival (GS). We hypothesize that vasopressin use in hypotensive DDKT recipients (DDKTRs) to increase blood pressure (BP) reduces DGF rates and is safe without increasing mortality. Methods: Group with vasopressin "study group" (n = 45) was defined as DDKTRs between 2012 and 2017 who required vasopressin for hypotension systolic BP (SBP) <120 mm Hg or diastolic BP (DBP) <60 mm Hg. DDKTRs with no-vasopressin "comparison group" (n = 90) were propensity score-matched DDKTRs between 2012 and 2017 without vasopressin use. Primary outcomes were GS, creatinine and allograft biopsy rate at 1 year, DGF rate, and death during transplant hospitalization. Results: Vasopressin group had lower mean maximum and minimum SBP and DBP in the operating room (OR). Median vasopressin start time post-DDKT was 2 hours (interquartile range [IQR] 1-6), and duration of use was 42 hours (IQR 24-63). DGF, creatinine at 1 year, and allograft biopsy rates were comparable. No deaths occurred during transplant hospitalization. Multivariable analysis did not find an effect of vasopressin use on GS. Conclusion: Treatment of hypotensive DDKTRs with vasopressin is safe and facilitated similar graft function and survival with that of nonhypotensive patients. In the absence of a randomized control trial, our study supports the safety of vasopressin therapy to prevent the adverse effects of hypotension.

Trend analysis of temperature data using innovative polygon trend analysis and modeling by gene expression programming.

Presenting temperature data using recently introduced innovative polygon trend analysis (IPTA) can improve our understanding of the effects of climate change. This method was applied to analyze temperature trends at six stations in Turkey: Istanbul (17,064), Ankara (17,131), Bursa (17,116), Iznik (17,661), Gemilik (17,663), and Sakarya (17,069). At station 17,064, there was an increasing trend in temperature data for seven months, while only one month showed a decreasing trend, and the remainder presented no trend. For station 17,131, there was a decreasing trend for two months, an increasing trend for five months, and no trend for the remaining months. At station 17,116, an increasing trend was present for nine months, with a decreasing trend for two months and only one month indicating no trend. An increasing trend over seven months was noted at station 17,661, while two and three months showed a decreasing and no trend, respectively. For station 17,663, there was an increasing trend for nine months, one month showed no trend, and two months presented a decreasing trend. At station 17,069, five, four, and three months showed increasing, decreasing, and no trends, respectively. The gene expression programming (GEP) model was tested to predict the short-term monthly average temperature for this dataset. The proposed GEP model presented good prediction results for all selected stations by tracing the relationship with a coefficient of determination (R-Sq) ≥ 0.90. Trend analysis by IPTA can help understand temperature trends better, aiding future decision-making, and the GEP model can effectively predict short-term values.

Nondirected Living Kidney Donation and Recipient Outcomes in the United States: A 20-Year Review.

Introduction: Nondirected donation (NDD) of the kidneys is a growing practice where donors who do not have any genetic or emotional relationship are selected to donate to a wide variety of recipients with a range of selection criteria and decisions which are left up to individual transplant centers. Methods: We review all adult living kidney donor-recipient (DR) pairs and outcomes from NDDs who were recorded in United Network for Organ Sharing (UNOS) database as code 10 (anonymous) from October 1997 to September 2017 for demographics and outcomes. Results: A total of 2174 DR pairs were identified. The number of NDDs increased from 18 in 2000 to 256 in 2016. Survival analysis showed higher death-censored-graft survival (DC-GS) when recipient was 20 years or more older than donor followed by recipient-donor within 20 years of age and lowest when donor was 20 years or more older than recipient (P = 0.0114). Conclusion: Overall, the number of NDDs has increased significantly in the 20-year review period. Transplants from NDDs have excellent long-term outcomes. Better matching of controllable DR factors, such as age and body mass index (BMI), could further improve GS. Further research is needed to incorporate these DR factors into paired kidney donation programs potentially enhancing the utility and beneficence of this invaluable donation.

Molecular Epidemiology of Na+-Taurocholate Cotransporting Polypeptide Deficiency in Guangdong Province, China: A Pilot Study by Screening for Four Prevalent Variants of the Causative Gene SLC10A1.

Na+-taurocholate cotransporting polypeptide deficiency (NTCPD) is an autosomal recessive disorder arising from biallelic SLC10A1 mutations. As a newly-described inborn error of bile acid metabolism, the epidemiology of this condition remains largely unclear in Chinese population so far. In this study, a total of 2,828 peripheral blood samples were collected from 12 cities in Guangdong, a province with the largest population in China, and the four prevalent SLC10A1 variants c.800C > T (p.Ser267Phe), c.263T > C (p.Ile88Thr), c.595A > C (p.Ser199Arg) and c.665T > C (p.Leu222Ser) were screened for by using polymerase chain reaction (PCR)- restriction fragment length polymorphism (RFLP). As a result, 663 mutated SLC10A1 alleles were detected, and the mutated allele frequency was calculated to be 11.72% (663/5,656), with a carrier frequency 20.69% (1/5) and a theoretical morbidity rate 1.37% (1/73) of NTCPD in Guangdong province. The variant c.800C > T (p.Ser267Phe) exhibited highest allele frequency among the four prevalent variants (χ2 = 1501.27, p < 0.0001) as well as higher allele frequency in the peripheral region than that within the Pearl River Delta (χ2 = 4.834, p < 0.05). The results suggested that NTCPD might be a disorder rather common in Guangdong province. The findings depicted the molecular epidemiologic features of NTCPD, providing preliminary but significant laboratory evidences for the subsequent NTCPD diagnosis and management in Guangdong population.