AN INVESTIGATION OF PEDIGREES OF 110 PATIENTS WITH GRAVES' DISEASE AND THE CLINICAL SIGNIFICANCE OF DETERMINATIONS OF ANTITHYROID ANTIBODIES OF THEIR FIRST-DEGREE RELATIVES

Eight hundred and ten pedigree members of 110 patients with Graves' disease were studied. In 700 first-egree relatives, inquiry of medical history, physical examination (including eyes, thyroid, heart rate, etc), thyroid function tests (serum T3, T4 and TSH levels), determinations of thyroglobulin antibodies (TgAb) and thyroid microsomal antibodies (TmAb) were performed. For male (female) probands, the incidence of Graves' disease in male (female) first-degree relatives were investigated and their serum TgAb and TmAb were analysed. The incidence of these two kinds of autoantibodies in the male (female) first-egree relatives of familial and nonfamilial Graves' disease were analysed. Eighteen persons with positive TgAb and TmAb from 5 pedigrees had been followed up one year after initial determinations. Our results suggest that the positive rates of TgAb and TmAb in the first-egree relatives of Graves' disease were coincident with the incidence of Graves' disease, and the positive results of TgAb and TmAb in the first-egree relatives of Graves' disease may be an indicator of pre-raves' disease or pre-utoimmune thyroid diseases.

A prospective comparison cohort study between baseline serum uric acid level and coronary artery disease in first-degree relatives and non-first-degree relatives of type 2 diabetes males

Objective To investigate the relationship between baseline serum uric acid and the severity of coronary artery disease(CAD)in the first-degree relatives or nonfirst-degree relatives of men with type 2 diabetes.Methods Three hundred and eighty-one men with negative coronary angiography for the first time were divided into diabetes and non-diabetes groups and followed-up for

Neurocognitive functions in obsessive-compulsive disorder patients and their unaffected first-degree relatives

Objective To find the common neurocognitive deficits in patients with obsessive-compulsive disorder (OCD) and their unaffected first-degree relatives (UFDR) and to identify neurocognitive endophenotypes for OCD. Methods Forty patients with OCD,forty UFDR of OCD probands and forty

A study of the risk for Alzheimer's disease in first-degree relatives of patients with affective disorders

Objective To investigate the prevalence of Alzheimer’s disease (AD) in first-degree relatives of patients with affective disorders,and to evaluate the risk of AD in firstdegree relatives of the patients with affective disorders.Methods Patients with affective disorders meeting"DSM-Ⅳ-TR"criteria (affective disorders group) and their healthy spouses (conrol group) were recruited in this study (n=109 each) .The first-degree relatives in-

Understanding the Relationship Between Shrinking Cities and Land Prices:Spatial Pattern,Effectiveness,and Policy Implications

Urban shrinkage has emerged as a widespread phenomenon globally and has a significant impact on land,particularly in terms of land use and price.This study focuses on 2851 county-level cities in China in 2005–2018(excluding Hong Kong,Macao,Taiwan,and‘no data’areas in Qinhai-Tibet Plateau)as the fundamental units of analysis.By employing nighttime light(NTL)data to identify shrinking cities,the propensity score matching(PSM)model was used to quantitatively examine the impact of shrinking cities on land prices,and evaluate the magnitude of this influence.The findings demonstrate the following:1)there were 613 shrinking cities in China,with moderate shrinkage being the most prevalent and severe shrinkage being the least.2)Regional disparities are evident in the spatial distribution of shrinking cities,especially in areas with diverse terrain.3)The spatial pattern of land price exhibits a significant correlated to the economic and administrative levels.4)Shrinking cities significantly negatively impact on the overall land price(ATT=–0.1241,P<0.05).However,the extent of the effect varies significantly among different spatial regions.This study contributes novel insights into the investigation of land prices and shrinking cities,ultimately serving as a foundation for government efforts to promote the sustainable development of urban areas.

Cancer risk in relatives of BRCA1/2 pathogenic variant carriers in a large series of unselected patients with breast cancer

Objective:The spectrum and risk of cancer in relatives of BRCA1/2 pathogenic variant carriers in the Chinese population have not been established.Methods:A family history of cancer in 9903 unselected breast cancer patients was retrospectively analyzed.BRCA1/2 status was determined for all patients and relative risks(RRs)were calculated to evaluate cancer risk in relatives of the patients.Results:The incidences of breast cancer in female relatives of BRCA1 carriers,BRCA2 carriers,and non-carriers were 33.0%,32.2%,and 7.7%,respectively.The corresponding incidences of ovarian cancer were 11.5%,2.4%,and 0.5%,respectively.The incidences of pancreatic cancer in male relatives of BRCA1 carriers,BRCA2 carriers,and non-carriers were 1.4%,2.7%,and 0.6%,respectively.The corresponding incidences of prostate cancer were 1.0%,2.1%,and 0.4%,respectively.The risks of breast and ovarian cancers in female relatives of BRCA1 and BRCA2 carriers were significantly higher than female relatives of non-carriers(BRCA1:RR=4.29,P<0.001 and RR=21.95,P<0.001;BRCA2:RR=4.19,P<0.001 and RR=4.65,P<0.001,respectively).Additionally,higher risks of pancreatic and prostate cancers were noted in male relatives of BRCA2 carriers than non-carriers(RR=4.34,P=0.001 and RR=4.86,P=0.001,respectively).Conclusions:Female relatives of BRCA1 and BRCA2 carriers are at increased risk for breast and ovarian cancers,and male relatives of BRCA2 carriers are at increased risk for pancreatic and prostate cancers.

A Joint Entity Relation Extraction Model Based on Relation Semantic Template Automatically Constructed

The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities,and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation.However,this method has some problems,such as relying on expert experience and poor portability.Inspired by the rule-based entity relation extraction method,this paper proposes a joint entity relation extraction model based on a relation semantic template automatically constructed,which is abbreviated as RSTAC.This model refines the extraction rules of relation semantic templates from relation corpus through dependency parsing and realizes the automatic construction of relation semantic templates.Based on the relation semantic template,the process of relation classification and triplet extraction is constrained,and finally,the entity relation triplet is obtained.The experimental results on the three major Chinese datasets of DuIE,SanWen,and FinRE showthat the RSTAC model successfully obtains rich deep semantics of relation,improves the extraction effect of entity relation triples,and the F1 scores are increased by an average of 0.96% compared with classical joint extraction models such as CasRel,TPLinker,and RFBFN.

Generating Sets of the Complete Semigroup of Binary Relations Defined by Semilattices of the Class Σ8(X, 5)

In this article, we study generating sets of the complete semigroups of binary relations defined by X-semilattices of unions of the class Σ8(X, 5). Found uniquely irreducible generating set for the given semigroups and when X is finite set formulas for calculating the number of elements in generating sets are derived.

Intensive care environment: Perspective of relatives of critically ill patient sustained by health technology

The intensive care unit(ICU)is a complex setting by nature,and some have described it as bizarre due to its numerous sirens that sound when anything is dangerous,constant activity,equipment,bright lights,and high fatality rate.The demands placed on nurses to care for critically ill patients in this environment frequently prevent nurses and other health‑care professionals from acknowledging the feelings of patient’s relatives or family caregivers,resulting in a hostile environment from the patient’s relative’s perspective.When a patient’s family enters the ICU,they feel that hospital administrators do little to nothing to alleviate their discomfort and fear.Despite research demonstrating the importance of providing a homely environment for patients’families,In Nigeria ICU is still far behind how a conventional ICU environment should be structured to accommodate patient’s relations in the unit.The goal of this study was to look at the patient’s relative’s perspective on providing care for a critically ill patient in an ICU,with a focus on the unit’s complexity and overall experience.Based on the findings of this study,we recommend that hospital administrators ensure that the environment of the upcoming ICU is designed to meet the needs of patient’s relatives by addressing identified environmental concerns,like caring neglect,by providing a friendly and stress‑free environment.

Dispersion Relations in Diffraction in Time

In agreement with Titchmarsh’s theorem, we prove that dispersion relations are just the Fourier-transform of the identity, g(x′)=±Sgn(x′)g(x′), which defines the property of being a truncated functions at the origin. On the other hand, we prove that the wave-function of a generalized diffraction in time problem is just the Fourier-transform of a truncated function. Consequently, the existence of dispersion relations for the diffraction in time wave-function follows. We derive these explicit dispersion relations.

Tunable dispersion relations manipulated by strain in skyrmion-based magnonic crystals

We theoretically investigate the propagation characteristics of spin waves in skyrmion-based magnonic crystals. It is found that the dispersion relation can be manipulated by strains through magneto-elastic coupling. Especially, the allowed bands and forbidden bands in dispersion relations shift to higher frequency with strain changing from compressive to tensile,while shifting to lower frequency with strain changing from tensile to compressive. We also confirm that the spin wave with specific frequency can pass the magnonic crystal or be blocked by tuning the strains. The result provides an advanced platform for studying the tunable skyrmion-based spin wave devices.

The chemical characteristics of different sodium iron ethylenediaminetetraacetate sources and their relative bioavailabilities for broilers fed with a conventional corn‑soybean meal diet

Background Our previous studies demonstrated that divalent organic iron(Fe)proteinate sources with higher complexation or chelation strengths as expressed by the greater quotient of formation(Qf)values displayed higher Fe bioavailabilities for broilers.Sodium iron ethylenediaminetetraacetate(NaFeEDTA)is a trivalent organic Fe source with the strongest chelating ligand EDTA.However,the bioavailability of Fe when administered as NaFeEDTA in broilers and other agricultural animals remains untested.Herein,the chemical characteristics of 12 NaFeEDTA products were determined.Of these,one feed grade NaFeEDTA(Qf=2.07×10^(8)),one food grade NaFeEDTA(Qf=3.31×10^(8)),and one Fe proteinate with an extremely strong chelation strength(Fe-Prot ES,Qf value=8,590)were selected.Their bioavailabilities relative to Fe sulfate(FeSO_(4)·7H_(2)O)for broilers fed with a conventional corn-soybean meal diet were evaluated during d 1 to 21 by investigating the effects of the above Fe sources and added Fe levels on the growth performance,hematological indices,Fe contents,activities and gene expressions of Fe-containing enzymes in various tissues of broilers.Results NaFeEDTA sources varied greatly in their chemical characteristics.Plasma Fe concentration(PI),transferrin saturation(TS),liver Fe content,succinate dehydrogenase(SDH)activities in liver,heart,and kidney,catalase(CAT)activity in liver,and SDH mRNA expressions in liver and kidney increased linearly(P<0.05)with increasing levels of Fe supplementation.However,differences among Fe sources were detected(P<0.05)only for PI,liver Fe content,CAT activity in liver,SDH activities in heart and kidney,and SDH mRNA expressions in liver and kidney.Based on slope ratios from multiple linear regressions of the above indices on daily dietary analyzed Fe intake,the average bioavailabilities of Fe-Prot ES,feed grade NaFeEDTA,and food grade NaFeEDTA relative to the inorganic FeSO_(4)·7H_(2)O(100%)for broilers were 139%,155%,and 166%,respectively.Conclusions The bioavailabilities of organic Fe sources relative to FeSO_(4)·7H_(2)O were closely related to their Qf values,and NaFeEDTA sources with higher Qf values showed higher Fe bioavailabilities for broilers fed with a conventional corn-soybean meal diet.

Optimal investment based on relative performance and weighted utility

This paper studies the optimal portfolio allocation of a fund manager when he bases decisions on both the absolute level of terminal relative performance and the change value of terminal relative performance comparison to a predefined reference point. We find the optimal investment strategy by maximizing a weighted average utility of a concave utility and an Sshaped utility via a concavification technique and the martingale method. Numerical results are carried out to show the impact of the extent to which the manager pays attention to the change of relative performance related to the reference point on the optimal terminal relative performance.

Case Retrieval Strategy of Turning Process Based on Grey Relational Analysis

To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform,a case-based reasoning framework is proposed.Specifically,a Hamming distance and Euclidean distance combined method is designed to measure the similarity of case features which have both numeric and category properties.In addition,AHP(Analytic Hierarchy Process)and entropy weight method are integrated to provide features weight,where both user preferences and comprehensive impact of the index have been concerned.Grey relation analysis is used to obtain the similarity of a new problem and alternative cases.Finally,a platform is also developed on Visual Studio 2015,and a case study is demonstrated to verify the practicality and efficiency of the proposed method.This method can obtain cutting parameters which is suitable without iterative calculation.Compared with the traditional PSO(Particle swarm optimization algorithm)and GA(Genetic algorithm),it can obtain faster response speed.This method can provide ideas for selecting processing parameters in industrial production.While guaranteeing the characteristic information is similar,this approach can select processing parameters which is the most appropriate for the production process and a lot of time can be saved.

Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

Relative orbit determination algorithm of space targets with passive observation

Angles-only relative orbit determination for space non-cooperative targets based on passive sensor is subject to weakly observable problem of the relative state between two spacecraft. Previously, the evidence for angles-only observability was found by using cylindrical dynamics, however, the solution of orbit determination is still not provided. This study develops a relative orbit determination algorithm with the cylindrical dynamics based on differential evolution. Firstly, the relative motion dynamics and line-of-sight measurement model for nearcircular orbit are established in cylindrical coordinate system.Secondly, the observability is qualitatively analyzed by using the dynamics and measurement model where the unobservable geometry is found. Then, the angles-only relative orbit determination problem is modeled into an optimal searching frame and an improved differential evolution algorithm is introduced to solve the problem. Finally, the proposed algorithm is verified and tested by a set of numerical simulations in the context of highEarth and low-Earth cases. The results show that initial relative orbit determination(IROD) solution with an appropriate accuracy in a relative short span is achieved, which can be used to initialize the navigation filter.

Evaluation of internal void related defects in reinforced concrete slab using electromagnetic wave properties

This study aims to develop a damage-detection algorithm based on the electromagnetic wave properties inside a reinforced concrete structure.The proposed method involves employing two algorithms based on data measured using ground-penetrating radar—a common electromagnetic wave method in civil engineering.The possible defect area was identified based on the energy dissipated by the damage in the frequency-wavenumber domain,with the damage localized using the calculated relative permittivity of the measurements.The proposed method was verified through a finite difference time-domain-based numerical analysis and a testing slab with artificial damage.As a result of verification,the proposed method quickly identified the presence of damage inside the concrete,especially for honeycomb-like defects located at the top of the rebar.This study has practical significance in scanning structures over a large area more quickly than other non-destructive testing methods,such as ultrasonic methods.

Relational Turkish Text Classification Using Distant Supervised Entities and Relations

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing applications.This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)models.Focusing on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from texts.The adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational information.Experiments conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural Network.The results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP applications.Contributions of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational database.By demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)tools.Additionally,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

The Impact of Finerenone on Changes in Pulse Wave Velocity, Arterial Pressure and Heart Related Deaths in Hemodialysis Patients—Study Perspective

The description in the abstract lacks clear logic and a comprehensive summary of this study, so please revise and improve it according to the design theme and main content of this study, and describe it in the order of (research background), purpose/aim, method, results and conclusions. The introduction of the abstract and preface is rather lengthy, but the summary of the whole study and the presentation of the research background are not perfect (mainly because the logic of the context is not clear and orderly), so it will appear a bit messy. Hope to be able to modify (this has been mentioned in the preliminary opinion). Cardiovascular events (CVE) pose a significant threat to individuals with end-stage renal disease (ESRD), yet these patients are often excluded from cardiovascular clinical trials, leaving prognostic factors associated with CVE in ESRD patients largely unexplored. Recent human studies have demonstrated elevated circulating aldosterone levels in ESRD patients, correlating with left ventricular hypertrophy. Additionally, animal models have shown improvements in uremic cardiomyopathy with spironolactone therapy, prompting interest in assessing the efficacy of spironolactone or eplerenone in reducing mortality and improving cardiovascular function in dialysis patients. Clinicians have historically been cautious about prescribing mineralocorticoid receptor antagonists (MRAs) to congestive heart failure patients with chronic kidney disease (CKD) due to hyperkalemia risk. However, the emergence of finerenone, a novel MR antagonist with a favorable safety profile and lower hyperkalemia risk, has renewed interest in MRA therapy in this population. Heart disease, including coronary artery disease, hypertension, and left ventricular failure, is alarmingly prevalent in dialysis patients, contributing significantly to elevated mortality rates compared to the general population. Arterial stiffness, as indicated by pulse wave velocity (PWV), progressively worsens with advancing CKD stages, peaking in severity among ESRD patients undergoing dialysis. High PWV serves as a crucial risk stratification tool in ESRD. Elevated NT-proBNP and BNP levels in ESRD patients are well-documented, with significant associations observed between baseline peptide concentrations and cardiovascular morbidity and mortality. By incorporating finerenone into our study, we aim to investigate its potential benefits in reducing arterial stiffness, lowering blood pressure, and ultimately mitigating heart-related mortality among hemodialysis patients. This study holds substantial implications for hypertension and cardiovascular risk management in this vulnerable patient population. Eligible participants must have been on chronic hemodialysis for at least three months, with ACE inhibitors or angiotensin receptor blockers included in their therapy at maximum tolerable doses. Serum potassium levels 5.7 mmol/L, left ventricular ejection fraction 50%, and PWV higher than age-estimated values are also prerequisites for study entry. Randomized allocation will be conducted using a permuted block design, stratified by center, with allocation communicated via signed study forms during initial examinations. All steps of this research will be conducted in accordance with the principles of the Helsinki Declaration.

Graph Convolutional Networks Embedding Textual Structure Information for Relation Extraction

Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.