Quantitative Index Research Articles

Insight into the Predictive Power of Surrogate Diagnostic Indices for Identifying Individuals with Metabolic Syndrome.

This study aimed to assess the diagnostic capability of insulin surrogate measurements in identifying individuals with metabolic syndrome (MetS) and propose applicable indices derived from fasting values, particularly in large study populations. Data were collected from the datasets of the Surveillance of Risk Factors of NCDs in Iran Study (STEPS). MetS was defined based on the National Cholesterol Education Program (NCEP) criteria. Various insulin surrogate indices, including Homeostasis Model Assessment (HOMA), Quantitative Insulin Sensitivity Check Index (QUICKI), Fasting glucose to insulin ratio (FGIR), Reynaud, Reciprocal insulin, McAuley, Metabolic Score for Insulin Resistance (METS-IR), Triglyceride-glucose index (TyG), TG/ HDL-C, TG/ BMI, and TG/ WC ratio were assessed. Receiver Operating Characteristic (ROC) curves were used to assess pathologic conditions and determine the optimal cut-off through the highest score of the Youden index. Also, Area Under the Curve (AUC) values were established for each index totally and according to sex, age, and BMI differences. The study population consisted of 373 individuals (49.9% women; 75.1% middle age, 39.1% obese, and 27.3% overweight), of whom 117 (31.4%) had MetS. The METS-IR (AUC: 0.856; 95% CI: 0.817-0.895), TG/ HDL-C (AUC: 0.820; 95% CI: 0.775-0.886), TyG (AUC: 0.808; 95% CI: 0.759-0.857), and McAuley (AUC: 0.804; 95% CI: 0.757-0.852) indices provided the greatest AUC respectively for detection of MetS. The values of AUC for all the indices were higher in men than women. This trend was consistent after data stratification based on BMI categories, middle age, and senile individuals. The present study indicated that indices of insulin, including METS-IR, TG/HDLC, TyG, and McAuley, have an equal or better capacity in determining the risk of MetS than HOMA-IR, are capable of identifying individuals with MetS and may provide a simple approach for identifying populations at risk of insulin resistance.

Association between weight-adjusted waist index and risk of diabetes mellitus type 2 in United States adults and the predictive value of obesity indicators

BackgroundThe weight-adjusted waist index (WWI) is a quantitative anthropometric index that can be applied to evaluate obesity. This study examined the relationship between adult United States (US) residents’ risk of diabetes mellitus type 2 (T2DM) and WWI.MethodsThe NHANES (National Health and Nutrition Examination Survey) 2001–2018 provided the data for this investigation. This study used multifactorial logistic regression analysis, smoothed curve fitting, subgroup analysis, and interaction tests to assess the association between WWI and T2DM. Additionally, threshold effects were calculated using a two-stage linear regression model. The receiver operating characteristic(ROC) curves evaluated the diagnostic ability of the WWI and commonly used obesity indicators.Results20,477 participants were enrolled in the analysis, and patients with greater levels of WWI had a higher prevalence of T2DM. WWI and T2DM have a non-linear relationship, with a positive association found on the left side of the breakpoint (WWI = 12.35) (OR = 1.82, 95%CI:1.64–2.02), whereas, on the right side, no such relationship was found (OR = 0.9, 95%CI:0.61–1.34). For every unit rise in WWI, the probability of having T2DM increased by 67% after controlling for all other variables (OR:1.67,95%CI:1.53–1.83). Based on subgroup analyses, individuals under 40 had a higher correlation between WWI and T2DM (P < 0.001).ROC analyses showed that WWI had the best discrimination and accuracy in predicting T2DM compared to other obesity indicators (WC, BMI, and Weight).ConclusionHigher WWI values had a higher prevalence of T2DM in US individuals, especially in adults under 40. WWI has the strongest ability to predict T2DM. Therefore, the importance of WWI in the early identification of T2DM in US adults should be emphasized.

Quantitative evaluation method for post-earthquake functionality loss of pedestrian flow pathways within a complex building using evacuation and rescue simulation

The complex building is a single building, characterized by its large size and complicated structural systems, such as high-rise-buildings, long-span-podium, and large underground space, as well as diverse functions. The pedestrian flow pathways within a complex building play a crucial role in urgent evacuation and rescue following seismic events. This study proposed a quantitative method for evaluating the functionality loss of pedestrian flow pathways within the complex building following seismic events. The method performed the component fragility analysis and constructed the speed reduction models that considered the variations of occupants' motions under different damage states of various nonstructural components. To evaluate the functionality loss of pedestrian flow pathways, the evacuation and rescue scenarios were established by analyzing the maximum speeds and behavioral delays of occupants at different spatial locations within the post-earthquake complex building. Based on the simulation, a quantitative index was constructed. Finally, a process accounting for uncertainties such as component damage was presented by generating the engineering demand parameters. A case study was employed to demonstrate the functionality loss of pedestrian flow pathways on each floor and in different areas within the post-earthquake complex building, which has reference significance for improving the post-earthquake complex building 's evacuation capacity as soon as possible to deal with secondary risk.

Quantifying the Use of Space in the Clock Drawing Test: Validity in Hemispatial Neglect.

The Clock Drawing Test (CDT) is a widely used test for cognitive screening as its execution taps into a large number of cognitive functions. Because of the involvement of visuospatial abilities, the CDT is also commonly used to assess hemispatial neglect. In the present study, we introduce a new quantitative scoring method for the CDT that aims to measure the use of space for each half of the clock face and asymmetries of space use. Two measures are introduced: the explored space (ES) and used space (US) for each half of the clock, as well as two derived asymmetry indices. Such new measures were applied to CDTs of four groups of participants: right brain-damaged patients without visuospatial neglect, two groups of right brain-damaged patients with varying degrees of visuospatial neglect, and a group of neurologically healthy participants. Analyses showed that only neglect patients explored and used the left clock half significantly less than the right one. This result was also confirmed by the asymmetry indices, where neglect patients showed a stronger rightward bias. For neglect patients, the US asymmetry index correlated with the patients' scores on the neuropsychological tests. The analyses of receiver operating characteristic curves showed that left US and left ES scores had good accuracy in categorizing neglect patients. The present work provides new quantitative indices of CDT space usage in hemispatial neglect. Cutoffs are provided for clinical practice.

Utilizing Enterprise Economic Benefit Evaluation Methods in Edge Intelligent Neural Network Applications

The core of enterprise economic benefit evaluation lies in the development of a quantitative identification model. The Back Propagation (BP) neural network possesses robust parallel computing, adaptive learning, and error correction capabilities, which can effectively reveal the economic benefits of enterprises and their relationship with influencing factors. This study establishes an economic benefit evaluation model for express delivery enterprises based on the BP neural network. The model takes the annual profit rate of enterprises as the quantitative index of economic benefits and selects 13 factors, both external and internal, influencing the annual profit rate of express delivery enterprises as inputs for the BP neural network model. The economic benefit evaluation model based on BP neural network meets the requirement of objective mean square error in the 300th training cycle. The research results demonstrate that the BP model significantly saves computing time and enables rapid, comprehensive, and objective evaluation of the economic benefits of industrial enterprises.

Dual-stage feedback network for lightweight color image compression artifact reduction

Lossy image coding techniques usually result in various undesirable compression artifacts. Recently, deep convolutional neural networks have seen encouraging advances in compression artifact reduction. However, most of them focus on the restoration of the luma channel without considering the chroma components. Besides, most deep convolutional neural networks are hard to deploy in practical applications because of their high model complexity. In this article, we propose a dual-stage feedback network (DSFN) for lightweight color image compression artifact reduction. Specifically, we propose a novel curriculum learning strategy to drive a DSFN to reduce color image compression artifacts in a luma-to-RGB manner. In the first stage, the DSFN is dedicated to reconstructing the luma channel, whose high-level features containing rich structural information are then rerouted to the second stage by a feedback connection to guide the RGB image restoration. Furthermore, we present a novel enhanced feedback block for efficient high-level feature extraction, in which an adaptive iterative self-refinement module is carefully designed to refine the low-level features progressively, and an enhanced separable convolution is advanced to exploit multiscale image information fully. Extensive experiments show the notable advantage of our DSFN over several state-of-the-art methods in both quantitative indices and visual effects with lower model complexity.

Identification of Multi-Parameter Fluid in Igneous Rock Reservoir Logging—A Case Study of PL9-1 in Bohai Oilfield

Since the “13th Five-Year Plan”, the exploration of large-scale structural oil and gas reservoirs in the Bohai oilfield has become more complex, and the exploration of igneous oil and gas reservoirs has become the focus of current attention. At present, igneous rock reservoir fluid identification methods are mainly based on the evaluation method of logging single parameter construction, which is primarily a qualitative identification due to lithology, physical property, and engineering factors. Accurate acquisition of interference logging data, and multi-parameter coupling and recording coupling methods are few, lacking systematic and comprehensive evaluation and analysis of logging data. Since conventional logging data in the study area have difficulty accurately and quickly identifying reservoir fluid properties, a systematic analysis was conducted of three factors: lithology, physical properties, and engineering, as well as a variety of logging parameters (gas measurement, three-dimensional quantitative fluorescence, geochemical, FLAIR, etc.) that can reflect fluid properties were integrated. Based on parameter sensitivity analysis, the quantitative characterization index FI of multi-parameter coupling fluid identification was established using the data from testing, sampling, and laboratory testing to develop the identification standard. The sensitivity analysis and optimization of characteristic parameters were carried out by integrating the data reflecting fluid properties such as gas surveys, geochemical data, and related logging data. Combined with gas logging-derived parameters and improved engineering parameters (the value of alkanes released by rock cracking per unit volume Cadjust, C1 abnormal multiple values, three-dimensional quantitative fluorescence correlation factor N), the fluid properties were identified, evaluation factors were constructed based on factor analysis, and fluid identification interactive charts were established. By analyzing test wells in the PL9-1 well area, the results of comparison test data are more reliable. Compared with conventional methods, this method reduces the dependence of a single parameter by synthesizing multiple parameters and reduces the influence of lithology, physical properties, and engineering parameters on fluid identification. It is more reasonable and practical. It can accurately and quickly identify the fluid properties of igneous rock reservoirs in the study area. It has a guiding significance for improving the accurate evaluation of logging data and increasing exploration benefits.

Dynamic characteristic analysis and key parameter optimization of throttling orifice type air damping air spring

Abstract The unique hysteretic characteristic of rubber bellows and the nonlinear flow of internal airflow in the system results in the significant nonlinear dynamic characteristic of throttling orifice type air damping air springs (OADASs). To solve the problem of mathematical representation of dynamic characteristic and key parameters optimization of throttling OADAS, this paper comprehensively considers the hysteretic characteristic of rubber bellows under variable pressure, the nonlinear dynamic characteristic model and linear model of throttling OADAS are established based on the concepts of gas thermodynamics and fluid mechanics. The static and dynamic characteristic tests of the throttling OADAS are conducted, to verify the accuracy and effectiveness of the proposed model, and to reveal the influence laws of excitation amplitude, excitation frequency, and throttling orifice diameter on the quantitative characterization indexes. Finally, a complete throttling orifice diameter optimization method is proposed based on the eight-degree-of-freedom model of the entire vehicle. Optimization results illustrate that the RMS values of the vertical acceleration of the body and the vertical acceleration of the driver are decreased by 19.02% and 38.44%, respectively. Overall, the outcomes of this paper can provide the design idea and theoretical basis for air damping matching and active suspension control.

Prevalence and Association of Insulin Resistance in Non-Diabetic Hemodialysis Patients: A Descriptive-Analytic Cross-Sectional Study in Vietnam

Background Patients with chronic kidney disease (CKD) experience high mortality rates from cardiovascular disease (CVD). Insulin resistance (IR) is a frequent complication of CKD and is associated with poorer cardiovascular outcomes. This study investigates the prevalence and associations of IR in hemodialysis (HD) patients. Materials and Methods A descriptive-analytic cross-sectional study was conducted on 103 HD patients. We used the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and the Quantitative Insulin Sensitivity Check Index (QUICKI) to measure IR. We examined potential associations between IR and the following factors: age, gender, etiology of kidney failure, BMI, waist circumference, blood lipids, hemoglobin concentration, uric acid, and duration of HD. Results The prevalence of IR, as measured by HOMA-IR, was 61.2%, and by QUICKI, it was 48.5%. Age, gender, etiology of kidney failure and increased waist circumference did not significantly influence IR. A significant associations were observed between IR and higher BMI, anemia, dyslipidemia, and longer duration of HD therapy. Interestingly, the HOMA-IR and QUICKI indices correlated for most factors except total cholesterol, LDL-C, and uric acid. Conclusion This study found a high prevalence of IR in HD patients, with 61.2% identified by HOMA-IR and 48.5% by QUICKI. We confirmed significant associations between IR and BMI, anemia, dyslipidemia, and duration of HD therapy in this population.

A method for evaluating the effects of gentle remediation options (GRO) on soil health: Demonstration at a DDX-contaminated tree nursery in Sweden

Healthy soils provide valuable ecosystem services (ES), but soil contamination can inhibit essential soil functions (SF) and pose risks to human health and the environment. A key advantage of using gentle remediation options (GRO) is the potential for multifunctionality: to both manage risks and improve soil functionality. In this study, an accessible, scientific method for soil health assessment directed towards practitioners and decision-makers in contaminated land management was developed and demonstrated for a field experiment at a DDX-contaminated tree nursery site in Sweden to evaluate the relative effects of GRO on soil health (i.e., the ‘current capacity’ to provide ES). For the set of relevant soil quality indicators (SQI) selected using a simplified logical sieve, GRO treatment was observed to have highly significant effects on many SQI according to statistical analysis due to the strong influence of biochar amendment on the sandy soil and positive effects of nitrogen-fixing leguminous plants. The SQI were grouped within five SF and the relative effects on soil health were evaluated compared to a reference state (experimental control) by calculating quantitative treated-SF indices. Multiple GRO treatments are shown to have statistically significant positive effects on many SF, including pollutant attenuation and degradation, water cycling and storage, nutrient cycling and provisioning, and soil structure and maintenance. The SF were in turn linked to soil-based ES to calculate treated-ES indices and an overall soil health index (SHI), which can provide simplified yet valuable information to decision-makers regarding the effectiveness of GRO. The experimental GRO treatment of the legume mix with biochar amendment and grass mix with biochar amendment are shown to result in statistically significant improvements to soil health, with overall SHI values of 141 % and 128 %, respectively, compared to the reference state of the grass mix without biochar (set to 100 %).

A Quantitative Index for Evaluating Maize Leaf Wilting and Its Sustainable Application in Drought Resistance Screening

A Quantitative Index for Evaluating Maize Leaf Wilting and Its Sustainable Application in Drought Resistance Screening

Optimal Implant Positioning Following Total Knee Arthroplasty Using Predictive Dynamic Simulation.

In this paper, a novel method is proposed for the determination of the optimal subject-specific placement of knee implants based on predictive dynamic simulations of human movement following total knee arthroplasty (TKA). Two knee implant models are introduced. The first model is a comprehensive 12-degree-of-freedom (DoF) representation that incorporates volumetric contact between femoral and tibial implants, as well as patellofemoral contact. The second model employs a single-degree-of-freedom equivalent kinematic (SEK) approach for the knee joint. A cosimulation framework is proposed to leverage both knee models in our simulations. The knee model is calibrated and validated using patient-specific data, including knee kinematics and ground reaction forces. Additionally, quantitative indices are introduced to evaluate the optimality of implant positioning based on three criteria: balancing medial and lateral load distributions, ligament balancing, and varus/valgus alignment. The knee implant placement is optimized by minimizing the deviation of the indices from their user-defined desired values during predicted sit-to-stand motion. The method presented in this paper has the potential to enhance the results of knee arthroplasty and serve as a valuable instrument for surgeons when planning and performing this procedure.

Infill Well Location Optimization Method Based on Recoverable Potential Evaluation of Remaining Oil

Infill well location optimization poses significant challenges due to its complexity and time-consuming nature. Currently, determining the scope of infill wells relies heavily on field engineers’ experience, often using single indices such as the remaining oil saturation or abundance of remaining oil reserves to evaluate the potential of remaining oil. However, this approach lacks effectiveness in guiding the precise tapping of remaining oil in ultra-high water cut reservoirs. To address this, our study comprehensively considers the factors influencing the recoverable potential of remaining oil in such reservoirs. We characterize the differences in reservoir heterogeneity, scale of recoverable remaining oil reserves, water flooding conditions, and oil–water flow capacity to construct a quantitative evaluation index system for the recoverable potential of remaining oil. Recognizing the varying degrees of influence of different indices on the recoverable potential of remaining oil, we determine the objective weight of each evaluation index by combining an accelerated genetic algorithm with the projection pursuit model. This approach enables the construction of a recoverable potential index for remaining oil and forms a quantitative evaluation method for the recoverable potential of remaining oil in ultra-high water cut reservoirs. Subsequently, we establish a mathematical model for infill well location optimization, integrating and optimizing the infill well location coordinates, well length, well inclination angle, and azimuth angle. Using the main layer sand body of an oilfield in Bohai as a case study, we conducted evaluations of the remaining oil potential and infill well location optimization. Our results demonstrate that the assessment of the remaining oil potential comprehensively characterizes the influence of the reservoir’s physical properties and oil–water diversion capacity on the remaining oil potential across different regional positions. This evaluation can effectively guide the determination of infill well location ranges based on the evaluation results. Furthermore, infill well location optimization can effectively enhance reservoir development outcomes.

Rural Settlement Optimization for Ecologically Sensitive Area Evaluations Based on Geo-Proximity and the Soil‒Water Conservation Capacity

Rural communities in ecologically sensitive areas are confronted with environmental challenges and land waste. The hollowing out of villages because of the steady loss of young people has brought these problems to the fore. Research on the remediation of rural settlements in ecologically sensitive regions is scarce. This paper focuses on Tong Yu County, a region located in the agricultural and pastoral intertwined zones of western Jilin, which is known for its ecological sensitivity. On the basis of the results of the geographical assessment of the area via hierarchical analysis (AHP method)and soil and water conservation capacity evaluation via the revised universal soil loss equation model(RUSLE) and the net primary production quantitative index model(NPP model), we propose a rural development program that divides the study area into three different zones with different development orientations, namely, potential enhancement, agglomeration development, and expansion restrictions. Moreover, we conceived a rural settlement remediation scheme using the kernel density estimation method and provided remediation strategies for rural settlements of various density levels. The rural settlement development program and the rural settlement remediation scheme led to the optimization of rural settlements. Notably, with increasing kernel density, the area of rural settlements tends to increase and then decrease. Our research helps save 5.059 km2 of land resources in the study area and offers guidance for improving the rural settlement layout in Tong Yu County.

Quantitative evaluation of 67Ga-citrate scintigraphy in the management of nephritis

In 67Ga-citrate scintigraphy (Ga-S), visual assessment is used by evaluating renal-uptake comparison with liver and spine and is simple and objective. We adopted the standardized uptake value (SUV) for 67Ga-citrate and proposed two quantitative indices, active nephritis volume (ANV) and total nephritis uptake (TNU). This study clarified the utility of new Ga-S-based quantitative indices in nephritis management. Before SUV measurement, the Becquerel calibration factor of 67Ga-citrate was obtained using a phantom experiment. Seventy patients who underwent SPECT/CT imaging were studied. SUV, ANV, and TNU were calculated using a quantitative analysis software for bone SPECT. SUVmean, ANV, and TNU were analyzed using the (1) threshold method (set 40%) and constant-value method for (2) vertebral SUVmax, and (3) vertebral SUVmean. ROC analysis was used to evaluate SUV, ANV, and TNU diagnostic abilities to distinguish nephritis presence and absence as well as interstitial nephritis (IN) and non-IN. The area under the curve (AUC) for nephritis presence or absence had a good value (0.80) for SUVmean (1), ANV (3), and TNU (3). The AUC for differentiation between IN and non-IN groups had a good value (0.80) for SUVmean (1). Thus, the new Ga-S-based quantitative indices were useful to evaluate nephritis and distinguish IN and non-IN.

Hydrodynamic cavitation overall intensity evaluation via noise characterization and its effect on phenol oxidative degradation

In the present research, noise signals generated by Hydrodynamic cavitation (HC) are charcterized to provide an applicable direct measuring for overall intensity estimation of the phenomenon. The signals amplitude plots as well as spectrogram graph and Fast Fourier Transform (FFT) are used for the characterization of the noise generated by the cavitation phenomenon for each experiment. In order to validate the outcomes, results are complied with the chemical activity. For such a purpose, phenol oxidative degradation kinetics is selected. Experiments are performed at various HC inlet pressure (4–7 bar), operating temperature (20–40 °C) and concentration of H2O2 (0–1000 mg L−1). It was found that the amplitude mean square of the cavitation noise signals could be introduced as a quantitative index for HC overall intensity. The results imply a direct correlation of HC overall intensity with chemical reaction at constant temperature. According to the results, it can also be also concluded that H2O2 presence can affect the intensity of HC and facilitate the super cavitation occurrence, as well as its expected beneficial effect on phenol degradation. The optimum values of the signal characterization and chemical experiments are in the same wavelength showing an 88.9 % degradation in the presence of 1000 mg L−1 H2O2 at 5 bar inlet pressure.

The biological variation of insulin resistance markers: data from the European Biological Variation Study (EuBIVAS).

An insulin resistant state is characteristic of patients with type 2 diabetes, polycystic ovary syndrome, and metabolic syndrome. Identification of insulin resistance (IR) is most readily achievable using formulae combining plasma insulin and glucose results. In this study, we have used data from the European Biological Variation Study (EuBIVAS) to examine the biological variability (BV) of IR using the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) and the Quantitative Insulin sensitivity Check Index (QUICKI). Ninety EuBIVAS non-diabetic subjects (52F, 38M) from five countries had fasting HOMA-IR and QUICKI calculated from plasma glucose and insulin samples collected concurrently on 10weekly occasions. The within-subject (CVI) and between-subject (CVG) BV estimates with 95 % CIs were obtained by CV-ANOVA after analysis of trends, variance homogeneity and outlier removal. The CVI of HOMA-IR was 26.7 % (95 % CI 25.5-28.3), driven largely by variability in plasma insulin and the CVI for QUICKI was 4.1 % (95 % CI 3.9-4.3), reflecting this formula's logarithmic transformation of glucose and insulin values. No differences in values or BV components were observed between subgroups of men or women below and above 50 years. The EuBIVAS, by utilising a rigorous experimental protocol, has produced robust BV estimates for two of the most commonly used markers of insulin resistance in non-diabetic subjects. This has shown that HOMA-IR, in particular, is highly variable in the same individual which limits the value of single measurements.

The IMPACT Score: Does Sex Matter?

ObjectiveThe Index for Mortality Prediction After Cardiac Transplantation (IMPACT) score is a quantitative risk index that predicts 1-yr mortality risk, derived from United Network for Organ Sharing (UNOS) data in which women are underrepresented. The validity of the IMPACT score in 1-yr mortality risk after OHT in women is unknown. The objective of this study is to assess differences in score performance by sex. We hypothesize that the IMPACT score is a poor predictor of 1-yr mortality risk after OHT in women. DesignExternal validation study including patients 18 years and older first-time single organ OHT recipients from The International Society of Heart and Lung Transplantation (ISHLT) data from 2009-2018. Demographic and clinical characteristics were compared by sex. IMPACT score was calculated and regression models constructed for the entire sample, and stratified by sex. Model discrimination was assessed with the area under the receiver operating characteristic curve (AUROC) and calibration was assessed graphically. SettingN/A InterventionsN/A ParticipantsPatients 18 years and older first time single OHT recipients from ISHLT registry from 2009-2018. Measurements and Main ResultsFor 1-yr mortality, the AUROC (95% CI) for the full sample was 0.59 (0.57-0.60); 0.58 (0.55-0.61) for women; 0.59 (0.58-0.61) for men. 1-yr mortality was 9.4% in the overall cohort, with no difference in mortality by sex (9.0% vs 9.6% women vs men, p=0.22). ConclusionsThe IMPACT score exhibited poor discrimination and calibration in the ISHLT 2009-2019 cohort, overall and by sex. There was no difference in 1-yr mortality between women and men.

Mandibular bone mass density in a medieval population and its relationship with stable isotopes δ13C and δ15N.

The aim of this study was to compare the level of bone mass in digital orthopantomograms in two populations (medieval and current) using two radiomorphometric indexes, and to correlate the mandibular bone mass value, in the medieval mandible population, with stable isotope data δ13C and δ15N. An observational, cross-sectional, and analytical study on mandibles from two diachronic groups, 15 mandibles from the medieval settlement of La Torrecilla (Granada, Spain) and 15 mandibles from current patients at the Faculty of Dentistry of the University of Granada (Spain), matched by age and sex was conducted. The bone mass density was determined using the Mandibular Cortical Width Index (MCW) and the Mandibular Panoramic Index (PMI) in digital panoramic radiographs. In the medieval group, the values of bone mass density were correlated with those of two stable isotopes (δ13C and δ15N). The mean value of MCW in mm in the medieval group was 3.96 ± 0.60 (mean ± standard deviation) and in the current group was 4.02 ± 1.01. The PMI was 0.33 ± 0.06 and 0.35 ± 0.08 in the medieval and current groups respectively, with similar results in both groups (p = 0.820 and p = 0.575). A negative correlation was found between both morphometric indices and the δ15N isotope (rs = 0.56, p = 0.030 and rs = 0.61, p = 0.016, respectively). The bone mass density in mandibles belonging to the two compared populations, determined by two quantitative radiomorphometric indices, is similar. Within the medieval population, there is an inverse correlation between the δ15N value and bone mass density.

Reseeding inhibits grassland vegetation degradation – Global evidence

Global grassland faces different degradation degrees, and reseeding is commonly used to restore degraded grasslands. However, the factors affecting the results of reseeding restorations worldwide remain unclear. Here, we screened 141 independently published articles and performed a meta-analysis of the extracted 1386 data pairs. We selected five quantitative indices and four α diversity indices to analyze the effects of reseeding on the degraded grassland. Our results showed that reseeding significantly increased the quantitative indices and diversity index of degraded grasslands worldwide. Mixed reseeding has the best effect on improving the diversity of degraded grassland. A high mixed reseeding number was more conducive to restoring degraded grassland diversity. The more severe the grassland degradation, the better the restoration effect of reseeding. Short- and medium-term restorations significantly increased the diversity of degraded grassland. However, the restoration time increased the response proportion of aboveground biomass and cover. After reseeding, the proportion of aboveground biomass (AGB) response in humid areas was greater than in arid regions. This study explored the overall effect of reseeding restoration and the factors affecting reseeding restoration, providing a basis for the global restoration of degraded grassland and promoting the healthy development and sustainable use of grassland.