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This study aimed to investigate the effects of livestock grazing on species change, quantitative and qualitative changes in plant composition, and changes in species density of different plant groups inside and outside the exclosure of the Boralan area. Vegetation and soil sampling was performed at the level of 18 transects and 360 plots by systematic-random method inside and outside the exclosure in three exclosure areas. The position of each plot was recorded using the Global Positioning System. Slope maps and geographical directions inside and outside the exclosure were prepared. Using one square meter plots, the characteristics such as canopy cover percentage, density, litter percentage, rock and gravel and bare soil amount were measured. Plant species were sampled during a growing season and identified using available resources. Diversity indexes of Shanon, Simpson, and uniformity Simpson, Camargo, and Smith and Wilson at the species level were estimated using Ecological Methodology software version 6.2. Comparison of vegetation parameters inside and outside the exclosure was analyzed using SPSS software version 16. According to the results obtained in the studied habitats (inside and outside the exclosure), there are 11 families, 24 genera, and 24 species. The inside of exclosure with 24 species has more species richness than the outside of exclosure with 17 species. The values of richness, diversity, and uniformity indexes inside the exclosure are more than that of outside the exclosure. [ABSTRACT FROM AUTHOR]

In this paper deriving of dynamical model of the load-bearing structure in form of a Trusses of the excavator with rotor is elaborated. Finite Element Methods and real working conditions are used to derive the dynamical model and to perform the local linearization of the dynamic elastic line for the structure of the load-bearing of the excavator with rotor. As carriers with continuous masses were treated the upper bearing structure of the Dynamical model and the truss levels. Finally, in accordance with the requirements of the problem, the degrees of freedom and the reference nodes are selected. The second stage of reduction, represents the final formation of the dynamical model of the load-bearing infrastructure, which however must be more accurate during the dynamic behaviour of the excavator with the rotor. The exposed work with the formation of the dynamic model of the Truss bearing structure, enables the concrete solution of the oscillations of the excavator with rotor. In addition, the model is of a universal character, respectively, can also be used during the formation of the dynamical model of truss carriers in general.

"nBackground: Kidney transplantation had been evaluated in some researches in Iran mainly with clinical approach. In this research we evaluated graft survival in kidney recipients and factors impacting on survival rate. Artificial neural networks have a good ability in modeling complex relationships, so we used this ability to demonstrate a model for prediction of 5yr graft survival after kidney transplantation."n"n"n Methods: This retrospective study was done on 316 kidney transplants from 1984 through 2006 in Isfahan. Graft survival was calculated by Kaplan-meire method. Cox regression and artificial neural networks were used for constructing a model for prediction of graft survival."n"n"n Results: Body mass index (BMI) and type of transplantation (living/cadaver) had significant effects on graft survival in cox regression model. Effective variables in neural network model were recipient age, recipient BMI, type of transplantation and donor age. One year, 3 year and 5 year graft survival was 96%, 93% and 90% respectively. Suggested artificial neural network model had good accuracy (72%) with the area under the Receiver-Operating Characteristic (ROC) curve 0.736 and appropriate results in goodness of fit test (κ2=33.924). Sensitivity of model in identification of true positive situations was more than false negative situations (72% Vs 61%)."n"n"nConclusion: Graft survival in living donors was more than cadaver donors. Graft survival decreased when the BMI increased at transplantation time. In traditional statistical approach Cox regression analysis is used in survival analysis, this research shows that artificial neural networks also can be used in constructing models to predict graft survival in kidney transplantation.

Abstract We propose a fractional-order controller to stabilize unstable fractional-order open-loop systems with interval uncertainty whereas one does not need to change the poles of the closed-loop system in the proposed method. For this, we will use the robust stability theory of Fractional-Order Linear Time Invariant (FO-LTI) systems. To determine the control parameters, one needs only a little knowledge about the plant and therefore, the proposed controller is a suitable choice in the control of interval nonlinear systems and especially in fractional-order chaotic systems. Finally numerical simulations are presented to show the effectiveness of the proposed controller.

Background: Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes., Objective: This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes., Methods: We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains., Results: A substantial proportion of studies (>85%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited., Conclusions: This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support., (©Abolfazl Mollalo, Bashir Hamidi, Leslie A Lenert, Alexander V Alekseyenko. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 15.10.2024.)

Purpose: Granulosa cell tumors (GCTs) of the ovary are rare but clinically significant malignancies. Despite advances in treatment, recurrence has remained a substantial challenge. This study aimed to identify clinical outcomes and potential prognostic risk factors for recurrence in patients diagnosed with GCTs., Methods: In a retrospective cohort study, the ovarian cancer database of the gynecological tertiary referral cancer center, Mashhad University of Medical Sciences, Mashhad, Iran, was searched from August 2012 to August 2023 to find GCT cases. Demographic, clinical, pathological, intervention-related factors, follow-up, and survival findings were meticulously collected. Data were analyzed using SPSS v 23., Results: Ninety-two patients with GCTs, including 86 AGCT and 6 JGCT subjects, were identified. Based on further analysis of AGCT patients, most patients were ages under 50 (58.1%), clinically presented pain (32.6%), and abnormal uterine bleeding (27.9%) as the most frequent symptoms. Stages IA (64.0%) and IC (20.9%) were common. Five-year overall and progression-free survival were 98.2% and 90.8%, respectively. With a median follow-up time of 72 (0.0-180) months, disease recurrence was observed in 19 patients (23.9%), and five patients (5.4%) died of the disease. Stage IV was a hazard factor of recurrence (HR = 7.62, 95%CI (1.89-30.63); p = 0.004)., Conclusions: The present study provides valuable insights into the outcomes and potential risk factors for recurrence in ovarian AGCTs. It duplicates the importance of stage in the prognosis of AGCT patients and highlights the safety of fertility-sparing surgery in stage I and the lack of need to administer chemotherapy in stage IC., (© 2024 The Author(s). Cancer Reports published by Wiley Periodicals LLC.)

Periodontal disease (PerioD) is a chronic inflammatory disease of dysbiotic etiology. Animal models and few human data showed a relationship between oral bacteria and gut dysbiosis. However, the effect of periodontal inflammation and subgingival dysbiosis on the gut is unknown. We hypothesized that periodontal inflammation and its associated subgingival dysbiosis contribute to gut dysbiosis even in subjects free of known gut disorders. We evaluated and compared elderly subjects with Low and High periodontal inflammation (assessed by Periodontal Inflamed Surface Area (PISA)) for stool and subgingival derived bacteria (assayed by 16S rRNA sequencing). The associations between PISA/subgingival dysbiosis and gut dysbiosis and bacteria known to produce short-chain fatty acid (SCFA) were assessed. LEfSe analysis showed that, in Low PISA, species belonging to Lactobacillus , Roseburia, and Ruminococcus taxa and Lactobacillus zeae were enriched, while species belonging to Coprococcus , Clostridiales , and Atopobium were enriched in High PISA. Regression analyses showed that PISA associated with indicators of dysbiosis in the gut mainly reduced abundance of SCFA producing bacteria (Radj = -0.38, p = 0.03). Subgingival bacterial dysbiosis also associated with reduced levels of gut SCFA producing bacteria (Radj = -0.58, p = 0.002). These results suggest that periodontal inflammation and subgingival microbiota contribute to gut bacterial changes.

Background: Periodontal disease (PerioD) is a chronic, complex inflammatory condition resulting from the interaction between subgingival dysbiotic bacteria and the host immune response leading to local inflammation. Since periodontal inflammation is characterized by multiple cytokines effects we investigated whether Periodontal Inflamed Surface Area (PISA), a continuous measure of clinical periodontal inflammation is a predictor of composite indexes of salivary cytokines., Methods and Findings: In a cross-sectional study of 67 healthy, well-educated individuals, we evaluated PISA and several cytokines expressed in whole stimulated saliva. Two salivary cytokine indexes were constructed using weighted and unweighted approaches based on a Principal Component Analysis [named Cytokine Component Index (CCI)] or averaging the (standardized) level of all cytokines [named Composite Inflammatory Index (CII)]. In regression analysis we found that PISA scores were significantly associated with both salivary cytokine constructs, (CCI: part R = 0.51, p<0.001; CII: part R = 0.40, p = 0.001) independent of age, gender and BMI showing that single scores summarizing salivary cytokines correlated with severity of clinical periodontal inflammation., Conclusions: Clinical periodontal inflammation may be reflected by a single score encompassing several salivary cytokines. These results are consistent with the complexity of interactions characterizing periodontal disease. In addition, Type I error is likely to be avoided., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Tang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Electronic (e)-phenotype specification by noninformaticist investigators remains a challenge. Although validation of each patient returned by e-phenotype could ensure accuracy of cohort representation, this approach is not practical. Understanding the factors leading to successful e-phenotype specification may reveal generalizable strategies leading to better results., Materials and Methods: Noninformaticist experts (n = 21) were recruited to produce expert-mediated e-phenotypes using i2b2 assisted by a honest data-broker and a project coordinator. Patient- and visit-sets were reidentified and a random sample of 20 charts matching each e-phenotype was returned to experts for chart-validation. Attributes of the queries and expert characteristics were captured and related to chart-validation rates using generalized linear regression models., Results: E-phenotype validation rates varied according to experts' domains and query characteristics (mean = 61%, range 20-100%). Clinical domains that performed better included infectious, rheumatic, neonatal, and cancers, whereas other domains performed worse (psychiatric, GI, skin, and pulmonary). Match-rate was negatively impacted when specification of temporal constraints was required. In general, the increase in e-phenotype specificity contributed positively to match-rate., Discussions and Conclusions: Clinical experts and informaticists experience a variety of challenges when building e-phenotypes, including the inability to differentiate clinical events from patient characteristics or appropriately configure temporal constraints; a lack of access to available and quality data; and difficulty in specifying routes of medication administration. Biomedical query mediation by informaticists and honest data-brokers in designing e-phenotypes cannot be overstated. Although tools such as i2b2 may be widely available to noninformaticists, successful utilization depends not on users' confidence, but rather on creating highly specific e-phenotypes., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

The inability to place a sufficient number of implants posteriorly may limit tooth and soft tissue replacement because of the cantilever effects that would result from the unsupported prosthesis. Fabrication of a fixed removable overdenture that is implant retained and supported by both implants and soft tissue allows adequate replacement of teeth and soft tissue for optimal mastication and esthetics. This case report describes a design for implant overdentures that conforms to fundamental removable prosthetic principles by using a rigid bar with bilateral locking passive attachments. The rigid implant overdenture provides extra retention via the locking attachments while maintaining stability with proper extensions and full soft tissue support posteriorly. This removable implant overdenture design is a suitable treatment option for patients who desire the sensation of a fixed prosthesis but are unable to receive fully implant-supported prostheses., Competing Interests: The authors report no conflicts of interest pertaining to any of the products or companies discussed in this article.

Nowadays, nanostructured lipid carriers are used as carrier systems to control the release of drug molecules and promote their physicochemical stability. A better understanding of the dynamic behavior of lipid nano-tubules can facilitate a better drug delivery and other relevant applications. The dynamic behaviors of these lipid nano-tubules are susceptible to temperature variations. Hence, a deeper insight into these effects, as mentioned above, is necessary to ensure higher analysis efficiency. However, the models used in previous researches did not take into account these effects. The lipid nano-tubules often possess a small length-to-width ratio and as a result, the use of the Euler-Bernoulli beam theory for modeling them will significantly reduce the accuracy of calculations, in which Timoshenko beam model is used to increase the accuracy of the model. The main objective of the current study is to investigate the bending vibrations of lipid nano-tubules by considering the temperature effects based on nonlocal strain gradient theory. To this end, based on Hamilton's principle, the governing equations of a dynamical system were extracted by taking into account the temperature effects. The equations were solved using the Navier solution method. Moreover, the dimensionless natural frequency and critical temperature of the lipid nano-tubules were calculated. Our findings demonstrate that the critical temperature is a function of the nonlocal parameter and length-scale parameter variations. It is also observed that at a certain interval of the length scale parameter, increasing the value of the nonlocal parameter results in intensification of the hardening behavior of lipid nano-tubules. Based on our findings, the proposed model indicates good accuracy and prediction., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The ability to analyze human specimens is the pillar of modern-day translational research. To enhance the research availability of relevant clinical specimens, we developed the Living BioBank (LBB) solution, which allows for just-in-time capture and delivery of phenotyped surplus laboratory medicine specimens. The LBB is a system-of-systems integrating research feasibility databases in i2b2, a real-time clinical data warehouse, and an informatics system for institutional research services management (SPARC). LBB delivers deidentified clinical data and laboratory specimens. We further present an extension to our solution, the Living µBiome Bank, that allows the user to request and receive phenotyped specimen microbiome data. We discuss the details of the implementation of the LBB system and the necessary regulatory oversight for this solution. The conducted institutional focus group of translational investigators indicates an overall positive sentiment towards potential scientific results generated with the use of LBB. Reference implementation of LBB is available at https://LivingBioBank.musc.edu., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Many important exposure-response relationships, such as diet and weight, can be influenced by intermediates, such as the gut microbiome. Understanding the role of these intermediates, the mediators, is important in refining cause-effect theories and discovering additional medical interventions (e.g., probiotics, prebiotics). Mediation analysis has been at the heart of behavioral health research, rapidly gaining popularity with the biomedical sciences in the last decade. A specific analytic challenge is being able to incorporate an entire 'omics assay as a mediator. To address this challenge, we propose a hypothesis testing framework for multivariate omnibus distance mediation analysis (MODIMA). We use the power of energy statistics, such as partial distance correlation, to allow for analysis of multivariate exposure-mediator-response triples. Our simulation results demonstrate the favorable statistical properties of our approach relative to the available alternatives. Finally, we demonstrate the application of the proposed methods in two previously published microbiome datasets. Our framework adds a new tool to the toolbox of approaches to the integration of 'omics big data.

Objective: Gut microorganisms contribute to the metabolism of environmental toxicants, including methylmercury (MeHg). Our main objective was to investigate whether associations between biomarkers for prenatal MeHg exposure and maternal gut microbiota differed between early and late gestation., Methods: Maternal blood and stool samples were collected during early (8.3-17 weeks, n=28) and late (27-36 weeks, n=24) gestation. Total mercury and MeHg concentrations were quantified in biomarkers, and inorganic mercury was estimated by subtraction. The diversity and structure of the gut microbiota were investigated using 16S rRNA gene profiling (n = 52). Biomarkers were dichotomized, and diversity patterns were compared between high/low mercury concentrations. Spearman's correlation was used to assess bivariate associations between MeHg biomarkers (stool, blood, and meconium), and 23 gut microbial taxa (genus or family level, >1% average relative abundance)., Results: Within-person and between-person diversity patterns in gut microbiota differed between early/late gestation. The overall composition of the microbiome differed between high/low MeHg concentrations (in blood and stool) during early gestation, but not late gestation. Ten (of 23) taxa were significantly correlated with MeHg biomarkers (increasing or decreasing); however, associations differed, depending on whether the sample was collected during early or late gestation. A total of 43% of associations (69/161) reversed the direction of correlation between early/late gestation., Conclusions: The time point at which a maternal fecal sample is collected may yield different associations between gut microorganisms and MeHg biomarkers, which may be due in part to remodeling of maternal microbiota during pregnancy. Our results suggest the effectiveness of dietary interventions to reduce prenatal MeHg exposure may differ between early and late gestation., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Community-wide analyses provide an essential means for evaluation of the effect of interventions or design variables on the composition of the microbiome. Applications of these analyses are omnipresent in microbiome literature, yet some of their statistical properties have not been tested for robustness towards common features of microbiome data. Recently, it has been reported that PERMANOVA can yield wrong results in the presence of heteroscedasticity and unbalanced sample sizes., Findings: We develop a method for multivariate analysis of variance, [Formula: see text], based on Welch MANOVA that is robust to heteroscedasticity in the data. We do so by extending a previously reported method that does the same for two-level independent factor variables. Our approach can accommodate multi-level factors, stratification, and multiple post hoc testing scenarios. An R language implementation of the method is available at https://github.com/alekseyenko/WdStar ., Conclusion: Our method resolves potential for confounding of location and dispersion effects in multivariate analyses by explicitly accounting for the differences in multivariate dispersion in the data tested. The methods based on [Formula: see text] have general applicability in microbiome and other 'omics data analyses.

Background: In the United States, there is a national shortage of organs donated for transplant. Among the solid organs, most often kidneys are donated by living donors, but the lack of information and complicated processes limit the number of individuals who serve as living kidney donors. Social media can be a tool for advocacy, educating the public about the need, process, and outcomes of live kidney donors, yet little is known about social media use by kidney transplant patients., Objective: The purpose of this study was to examine the social media use of potential kidney transplant patients and their willingness to use social media and their networks to advocate and educate about living kidney donation., Methods: Using a validated survey, we modified the instrument to apply to the patient population of interest attending the Medical University of South Carolina, Charleston, SC, USA. The questions on the survey inquired about current social media use, sites visited, frequency and duration of social media use, and willingness to use social media to share the need for living kidney donors. We asked patients who had received a transplant and those awaiting a transplant to complete the survey during an office visit. Participation was voluntary., Results: A total of 199 patients completed the survey. Approximately half of all kidney transplant patients surveyed used social media (104/199, 52.3%), and approximately one-third (66/199, 33.2%) had more than 100 friends in their social media network. Facebook was the most popular site, and 51% (102/199) reported that they would be willing to post information about living kidney donation on their social networks. More than a quarter of the sample (75/199, 37.7%) had posted about their health status in the past., Conclusions: Social media holds great promise for health-related education and awareness. Our study shows the current social media use of kidney transplant patients. In turn, such information can be used to design interventions to ensure appropriate decision making about live kidney donation. Transplant programs can help increase the number of living donors by providing guidance to kidney transplant patients in how to use social media, to be advocates, and to provide information about living kidney donation to their social network., Competing Interests: Conflicts of Interest: None declared., (©Abby Swanson Kazley, Bashir Hamidi, Wendy Balliet, Prabhakar Baliga. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 20.12.2016.)

Aiming at gathering further information to evaluate the recently proposed1,2 mechanism of the salt effect in aqueous polymer solutions, experimental vapor-liquid equilibria (VLE), liquid-liquid equilibria (LLE), and volumetric-compressibility measurements were carried out for several polymer-amino acid aqueous systems. The constant water activity lines (obtained through the isopiestic method at 298.15 K) of aqueous polypropylene glycol 400 (PPG400) + alanine or glycine systems, which form aqueous biphasic systems (salting-out effect), have a concave and convex slope, respectively, in the one-phase and two-phase regions. However, all the investigated polyethylene glycols (PEG400, PEG2000, PEG6000, and PEG10000) do not form aqueous biphasic systems with alanine or glycine (salting-in effect) and their constant water activity lines have a convex slope. In the second part of this work, the apparent molar volume and isentropic compressibility of transfer of alanine and glycine from water to aqueous solutions of PEG200, PEG2000, PEG10000, and PPG400 were studied at different temperatures. The third part of this work is concerned with the determination of LLE phase diagrams for several ternary polymer-amino acid aqueous systems containing polymers PPG400 and PPG725 and amino acids alanine, glycine, serine, and proline at different temperatures. On the basis of the obtained cloud point values of aqueous solutions of PPG725 in the absence and presence of various amino acids, it was found that all the investigated amino acids have a salting-out effect on PPG725 in aqueous solutions and entropy is the driving force for biphasic formation.

In this study, the author measured the frequency of symptoms and/or alterations in respiratory functions in workers of the sour gas refining industry. All workers (n = 62) were employed in the most-exposed units of the Kangan Sour Gas Refinery. The refinery is approximately 250 km east of Bushehr Port along the Persian Gulf. This cross-sectional study involved a comprehensive health questionnaire, standardized clinical examinations by physicians blinded to subjects' symptoms and concerns, and multiple spirometric values. Although gas refinery workers experienced more respiratory symptoms than the 30 controls (i.e., 37.7% vs. 23.3%, respectively), who were matched for age and smoking status, pulmonary function data were not statistically different (p > .05) between the groups. The authors concluded, therefore, that in Kangan Sour Gas Refinery workers there were no respiratory or spirometric values associated with chronic low-dose exposure to sour gas plant emissions, including hydrogen sulfide.