Slope Of Linear Regression Research Articles

Biomechanical properties of a suture anchor system from human allogenic mineralized cortical bone matrix for rotator cuff repair

BackgroundSuture anchors (SAs) made of human allogenic mineralized cortical bone matrix are among the newest developments in orthopaedic and trauma surgery. Biomechanical properties of an allogenic mineralized suture anchor (AMSA) are not investigated until now. The primary objective was the biomechanical investigation of AMSA and comparing it to a metallic suture anchor (MSA) and a bioabsorbable suture anchor (BSA) placed at the greater tuberosity of the humeral head of cadaver humeri. Additionally, we assessed the biomechanical properties of the SAs with bone microarchitecture parameters.MethodsFirst, bone microarchitecture of 12 fresh frozen human cadaver humeri from six donors was analyzed by high-resolution peripheral quantitative computed tomography. In total, 18 AMSAs, 9 MSAs, and 9 BSAs were implanted at a 60° angle. All three SA systems were systematically implanted alternating in three positions within the greater tuberosity (position 1: anterior, position 2: central, position 3: posterior) with a distance of 15 mm to each other. Biomechanical load to failure was measured in a uniaxial direction at 135°.ResultsMean age of all specimens was 53.6 ± 9.1 years. For all bone microarchitecture measurements, linear regression slope estimates were negative which implies decreasing values with increasing age of specimens. Positioning of all three SA systems at the greater tuberosity was equally distributed (p = 0.827). Mean load to failure rates were higher for AMSA compared to MSA and BSA without reaching statistical significance between the groups (p = 0.427). Anchor displacement was comparable for all three SA systems, while there were significant differences regarding failure mode between all three SA systems (p < 0.001). Maximum load to failure was reached in all cases for AMSA, in 44.4% for MSA, and in 55.6% for BSA. Suture tear was observed in 55.6% for MSA and in 22.2% for BSA. Anchor breakage was solely seen for BSA (22.2%). No correlations were observed between bone microarchitecture parameters and load to failure rates of all three suture anchor systems.ConclusionsThe AMSA showed promising biomechanical properties for initial fixation strength for RCR. Since reduced BMD is an important issue for patients with chronic rotator cuff lesions, the AMSA is an interesting alternative to MSA and BSA. Also, the AMSA could improve healing of the enthesis.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12891-022-05371-0.

A Pilot Study to Investigate the Effect of Hypercapnia Training on Cerebrovascular Reactivity in Healthy Adults

ObjectiveHypercapnia, an elevated concentration of carbon dioxide (CO2) in the blood, is known to promote acute increases in cerebral blood flow (CBF). This pilot study aimed to investigate whether a novel training method involving repeated bouts of hypercapnia, would influence cerebrovascular reactivity response in healthy adults.HypothesisRepeated bouts of hypercapnia (as interval training) may promote beneficial adaptation to increase cerebrovascular reactivity.MethodsAdults over 40yrs old without known disease or cardiovascular risk factors, were recruited for a non‐randomized cross‐over design pilot study. Subjects completed a 2‐week control period followed by a 2‐week intervention. The intervention involved a progressive hypercapnia training program of six sessions, whereby subjects completed 4‐6 intervals of rebreathing oxygen until a target end‐tidal CO2 (up to 25mmHg above resting) was reached, separated by recovery intervals breathing room air. Cerebrovascular reactivity was measured during study testing visits at baseline, 2‐weeks (between control and intervention) and 4‐weeks (after intervention). During study visits subjects underwent a stepped CO2 protocol, involving 5‐min stages of inspired room air, 2% CO2, 4% CO2, and 6% CO2, while measuring middle cerebral artery velocity (CBFv) using transcranial doppler ultrasound, mean arterial pressure (MAP) by finger plethysmography, ventilation, and end‐tidal CO2. Cerebrovascular conductance (CVCi) was calculated as CBFv/MAP. The reactivity of CBFv (in cm/s/mmHg), CVCi (in cm/s/mmHg2), and MAP (in mmHg/mmHg) were calculated as the linear regression slope against end‐tidal CO2 during the stepped CO2 protocol. Chemosensitivity (in L/min/mmHg) is the linear regression slope of VE vs end‐tidal CO2.ResultsSix healthy adults (age: 56 ± 10; 2 females) completed the study. Study visit data are displayed in Table 1. Individual training data are presented in Table 2. Following the control period there was a large reduction from baseline in CBFv reactivity [2.81 ± 1.50 to 2.03 ± 1.03; effect size 0.9], CVCi reactivity [0.020 ± 0.015 to 0.015 ± 0.010; effect size 0.7], and MAP reactivity [0.95 ± 0.63 to 0.62 ± 0.42; effect size 0.6] with no change in chemosensitivity [0.99 ± 0.68 to 0.98 ± 0.51; effect size 0.0]. Following the intervention there was no change from the 2‐week study visit in CBFv reactivity [2.03 ± 1.03 to 2.01 ± 1.09; effect size 0.0], CVCi reactivity [0.015 ± 0.010 to 0.014 ± 0.010; effect size 0.1], and MAP reactivity [0.62 ± 0.42 to 0.72 ± 0.44; effect size 0.1] with a small increase in chemosensitivity [0.98 ± 0.51 to 1.04 ± 0.53; effect size 0.3].ConclusionPreliminary findings suggest a very brief progressive hypercapnia training program does not increase or desensitize cerebrovascular reactivity in healthy adults. These data also suggest an exaggerated cerebrovascular reactivity response may occur to an initial exposure of a stepped CO2 protocol (without familiarization), which appears to be driven by blood pressure response not chemosensitivity. Further research, particularly in populations with impaired cerebrovascular reactivity, is of interest for CBF regulation.

Dates of arrival of the Eurasian golden oriole (Oriolus oriolus L.) in decidious forest in relation to increase of local air temperature in NW Croatia

Numerous regions worldwide are affected by Earth climate warming. Most studies of bird phenology in relation to global and regional climate change have focused on trends in arrival dates and breeding dates. The study investigates the results of phenological spring migration research on Eurasian Golden Oriole during a period of 26 years (1991–2016) in relation to average spring air temperatures (April–May) and year in deciduous forests (northwestern Croatia). Data is available for spring arrivals detected by the first song. Arrival date advanced significantly during the research period. According to the linear regression slopes, models suggested that in Eurasian Golden Oriole arrival date has advanced 6 days in period 1991–2016. Furthermore, average spring air temperatures (April–May) increased significantly during the study period. Furthermore, date of arrival was significantly negative correlated with average spring temperatures. My results provide evidence that warms spring has impacted spring migration arrival dates of Eurasian golden oriole of the deciduous forests in northwestern Croatia.

Water Evaporation Reduction Using Sunlight Splitting Technology

The imbalance between precipitation and water evaporation has caused crop yield reduction, drought, and desertification. Furthermore, most parts of the world are short of water, including China. We proposed a low-cost polymer multilayer film to reduce water evaporation by only passing through several sunlight wavelengths necessary for photosynthesis. A series of experiments were conducted to characterize the influence of partial sunlight on the reduction of water evaporation. Evaporation containers and evaporation pans were placed in open-air (CK), under a glass shed (GS), and under a glass-shed covered with multilayer films (GMF). Our results showed a significant reduction in water evaporation under GMF. Cumulative soil surface evaporation of CK, GS and GMF over 45 days was 80.53 mm, 68.12 mm, and 56.79 mm, respectively. Under GMF, cumulative water evaporation from soil and pan surfaces decreased by 29% and 26%. The slope (β1≠0) of simple linear regression showed a significant relationship between evaporation time and cumulative water evaporation (p = 0.000 &lt; α = 0.05 shown in the ANOVA table). The correlation coefficient was more than 0.91 in all treatments, suggesting a strong positive linear relationship. This study may contribute to future drought resistance and agrivoltaic sustainability development.

Fully Automatic Quantitative Measurement of 18F-FDG PET/CT in Thymic Epithelial Tumors Using a Convolutional Neural Network.

The aim of this study was to develop a deep learning (DL)-based segmentation algorithm for automatic measurement of metabolic parameters of 18F-FDG PET/CT in thymic epithelial tumors (TETs), comparable performance to manual volumes of interest. A total of 186 consecutive patients with resectable TETs and preoperative 18F-FDG PET/CT were retrospectively enrolled (145 thymomas, 41 thymic carcinomas). A quasi-3D U-net architecture was trained to resemble ground-truth volumes of interest. Segmentation performance was assessed using the Dice similarity coefficient. Agreements between manual and DL-based automated extraction of SUVmax, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and 63 radiomics features were evaluated via concordance correlation coefficients (CCCs) and linear regression slopes. Diagnostic and prognostic values were compared in terms of area under the receiver operating characteristics curve (AUC) for thymic carcinoma and hazards ratios (HRs) for freedom from recurrence. The mean Dice similarity coefficient was 0.83 ± 0.34. Automatically measured SUVmax (slope, 0.97; CCC, 0.92), MTV (slope, 0.94; CCC, 0.96), and TLG (slope, 0.96; CCC, 0.96) were in good agreement with manual measurements. The mean CCC and slopes were 0.88 ± 0.06 and 0.89 ± 0.05, respectively, for the radiomics parameters. Automatically measured SUVmax, MTV, and TLG showed good diagnostic accuracy for thymic carcinoma (AUCs: SUVmax, 0.95; MTV, 0.85; TLG, 0.87) and significant prognostic value (HRs: SUVmax, 1.31 [95% confidence interval, 1.16-1.48]; MTV, 2.11 [1.09-4.06]; TLG, 1.90 [1.12-3.23]). No significant differences in the AUCs or HRs were found between automatic and manual measurements for any of the metabolic parameters. Our DL-based model provides comparable segmentation performance and metabolic parameter values to manual measurements in TETs.

Impact of multidetector computed tomography scan parameters, novel reconstruction settings, and lung nodule characteristics on nodule diameter measurements: A phantom study.

Novel CT reconstruction techniques strive to maintain image quality and processing efficiency. The purpose of this study is to investigate the impact of a newer hybrid iterative reconstruction technique, Adaptive Statistical Iterative Reconstruction-V (ASIR-V), in combination with various CT scan parameters on the semi-automated quantification using various lung nodules. A chest phantom embedded with eight spherical objects was scanned using varying CT parameters such as tube current and ASIR-V levels. We calculated absolute percentage error (APE) and mean APE (MAPE) using differences between the semi-automated measured diameters and known dimensions. Predictive variables were assessed using a multivariable general linear model. The linear regression slope coefficients (β) were reported to demonstrate effect size and directionality. The APE of the semi-automated measured diameters was higher in ground-glass than solid nodules (β=9.000, p<0.001). APE had an inverse relationship with nodule diameter (mm; β=-3.499, p<0.001) and tube current (mA; β=-0.006, p<0.001). MAPE did not vary based on the ASIR-V level (range: 5.7%-13.1%). Error is dominated by nodule characteristics with a small effect of tube current. Regardless of phantom size, nodule size accuracy is not affected by tube voltage or ASIR-V level, maintaining accuracy while maximizing radiation dose reduction.

Ecosystem organic carbon storage and their drivers across the drylands of China

Earth’s drylands store large amounts of organic carbon and thus play an important role in regulating atmospheric CO2 concentrations and mitigating climate change. However, little is known about the magnitude and the drivers of organic carbon stocks because of limited data, in particular in desert ecosystems. To fill this gap, we surveyed 763 plots and collected 5091 soil samples from 170 sites covering grassland and desert ecosystems across the drylands of China. The results show that 10.96 Pg organic carbon is stored in these ecosystems (7.07 Pg in grasslands and 3.89 Pg in deserts). The effects of environmental variables on carbon density were found to be contingent on ecosystem type and soil depth. Mean annual precipitation had a strong positive effect on the spatial distributions of vegetation carbon density in both types of ecosystems. The spatial variations of surface (0–40 cm) and subsurface (40–100 cm) soil organic carbon (SOC) density in grasslands were mainly correlated with plant productivity and mean annual temperature (MAT), respectively, while in desert ecosystems, they were mainly correlated with soil salinity (electrical conductivity). The spatial sensitivities (linear regression slopes) of the surface SOC density versus MAT increased from dry subhumid to semi-arid regions and then gradually shifted to decrease from semi-arid to hyper-arid regions, indicating that the surface SOC stock is more sensitive to temperature in semi-arid regions. This study provides a comprehensive survey of ecosystem carbon stocks in China’s drylands, fills a gap in our knowledge about carbon stocks in deserts, complements estimates of ecosystem carbon stocks throughout China, and provides insights into our understanding of the carbon cycle in drylands.

New Predictive Resting Metabolic Rate Equations for High-Level Athletes: A Cross-Validation Study.

The present study aims a) to assess the agreement between the measured resting metabolic rate (RMR) using indirect calorimetry and different predictive equations (predicted RMR), and b) to propose and cross-validate two new predictive equations for estimating the RMR in high-level athletes. The RMR of 102 athletes (44 women) was assessed using indirect calorimetry, whereas the body composition was assessed using skinfolds. Comparisons between measured and predicted RMR values were performed using one-way ANOVA. Mean difference, root mean square error (RMSE), simple linear regression, and Bland-Altman plots were used to evaluate the agreement between measured and predicted RMR. The accuracy of predictive equations was analyzed using narrower and wider accuracy limits (±5% and ±10%, respectively) of measured RMR. Multiple linear regression models were employed to develop the new predictive equations based on traditional predictors (equation 1) and the stepwise method (equation 2). The new equations 1 and 2 presented good agreement based on the mean difference (3 and -15 kcal·d -1 ), RMSE (200 and 192 kcal·d -1 ), and R2 (0.71 and 0.74), respectively, and accuracy (61% of subjects between the limit of ±10% of measured RMR). Cunningham's equation provided the best performance for males and females among the existing equations, whereas Jagim's equation showed the worst performance for males (mean difference = -335 kcal·d -1 ; RMSE = 386 kcal·d -1 ). Compared with measured RMR, most predictive equations showed heteroscedastic distribution (linear regression's intercept and slope significantly different from zero; P ≤ 0.05), mainly in males. The new proposed equations can estimate the RMR in high-level athletes accurately. Cunningham's equation is a good option from existing equations, and Jagim's equation should not be used in high-level male athletes.

Comparison between the Original- and a Standardized Version of a Physical Assessment Test for the Dorsal Chain - A Cohort-Based Cross Sectional Study.

This cohort-based cross-sectional study compares the original (OV) and a newly developed standardized version (SV) of the Bunkie Test, a physical test used to assess the dorsal chain muscles. Twenty-three participants (13 females, 10 males; median age of 26 ± 3 years) performed the test, a reverse plank, with one foot on a stool and the contralateral leg lifted. In the SV, the position of the pelvis and the foot were predefined. The test performance time (s) and surface electromyography (sEMG) signals of the dorsal chain muscles were recorded. We performed a median power frequency (MPF) analysis, using short-time Fourier transformation, and calculated the MPF/time linear regression slope. We compared the slopes of the linear regression analysis (between legs) and the performance times (between the OV and SV) with the Wilcoxon test. Performance times did not differ between SV and OV for either the dominant (p = 0.28) or non-dominant leg (p = 0.08). Linear regression analysis revealed a negative slope for the muscles of the tested leg and contralateral erector spinae, with a significant difference between the biceps femoris of the tested (-0.91 ± 1.08) and contralateral leg (0.01 ± 1.62) in the SV (p = 0.004). The sEMG showed a clearer pattern in the SV than in the OV. Hence, we recommend using the SV to assess the structures of the dorsal chain of the tested leg and contralateral back.

Establishment of aerosol optical depth dataset in the Sichuan Basin by the random forest approach

The Sichuan Basin has become one of the four city clusters and heavy polluted regions in China. In this study, the random forest (RF) machine learning method and multiple datasets are used to establish aerosol optical depth (AOD) dataset in the cloudy Sichuan Basin. Multiple datasets include ground-based PM10 and PM2.5, the AOD from the Sun-sky radiometer Observation Network (SONET) and the Second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) aerosol reanalysis, and several meteorological variables. The correlation analysis, variance inflation factor method, covariance test, and important scores are used to select variables for the model. Eight independent variables, including MERRA-2 AOD, PM10, PM2.5/PM10, low cloud cover, 2 m air temperature, relative humidity, wind direction and boundary layer height, and one dependent variable SONET AOD are selected for the model in Chengdu, the capital of Sichuan, and then extended to the Sichuan Basin. The 10-fold cross validation and statistical comparison of the Multi-Angle implementation of Atmospheric Correction (MAIAC) and the MERRA-2 AOD are conducted. Results show that the values of PM10 and PM2.5, and MERRA-2 AOD are highest at the bottom of the basin, followed by that at the edge of the basin, and the lowest at the plateau areas. Comparing with the SONET AOD, the MERRA-2 and MAIAC underestimate the AOD in the Sichuan Basin, with the linear regression slope of 0.57 and 0.74, respectively. The RF AOD shows the best accuracy with the 10-fold cross-validation correlation coefficient of 0.79, the smallest RMSE of 0.17 and MAE of 0.14.

Spontaneous State Detection Using Time-Frequency and Time-Domain Features Extracted From Stereo-Electroencephalography Traces.

As a minimally invasive recording technique, stereo-electroencephalography (SEEG) measures intracranial signals directly by inserting depth electrodes shafts into the human brain, and thus can capture neural activities in both cortical layers and subcortical structures. Despite gradually increasing SEEG-based brain-computer interface (BCI) studies, the features utilized were usually confined to the amplitude of the event-related potential (ERP) or band power, and the decoding capabilities of other time-frequency and time-domain features have not been demonstrated for SEEG recordings yet. In this study, we aimed to verify the validity of time-domain and time-frequency features of SEEG, where classification performances served as evaluating indicators. To do this, using SEEG signals under intermittent auditory stimuli, we extracted features including the average amplitude, root mean square, slope of linear regression, and line-length from the ERP trace and three traces of band power activities (high-gamma, beta, and alpha). These features were used to detect the active state (including activations to two types of names) against the idle state. Results suggested that valid time-domain and time-frequency features distributed across multiple regions, including the temporal lobe, parietal lobe, and deeper structures such as the insula. Among all feature types, the average amplitude, root mean square, and line-length extracted from high-gamma (60–140 Hz) power and the line-length extracted from ERP were the most informative. Using a hidden Markov model (HMM), we could precisely detect the onset and the end of the active state with a sensitivity of 95.7 ± 1.3% and a precision of 91.7 ± 1.6%. The valid features derived from high-gamma power and ERP in this work provided new insights into the feature selection procedure for further SEEG-based BCI applications.

Examining partial-column density retrieval of lower-tropospheric CO2 from GOSAT target observations over global megacities

We retrieved and examined the partial-column densities of carbon dioxide (CO2) in the lower (LT, typically 0–4 km) and upper (UT, typically 4–12 km) troposphere (XCO2LT and XCO2UT) collected over six global megacities: Beijing, New Delhi, New York City, Riyadh, Shanghai, and Tokyo. The radiance spectra were collected using the Thermal And Near-infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT). Our retrieval method uniquely utilizes reflected sunlight with two orthogonal components of polarization and thermal emissions. We defined megacity concentration enhancement due to surface CO2 emissions as XCO2LT minus XCO2UT, allowing us to overcome some of the challenges in the enhancement analysis using existing column density data. We examined the relationship between the XCO2LT enhancements from the time series of intensive target observations over megacities and the inverse of simulated wind speed, which could be potentially used to estimate surface emissions. Next, we attempted to estimate the average emission intensity for each city from the linear regression slope. We also compared our obtained emission estimates with the Open-Data Inventory for Anthropogenic CO2 (ODIAC) inventory for evaluation. Our results demonstrate the potential utility of the new partial-column density retrievals for estimating megacity CO2 emissions. More frequent and comprehensive coverage characterizing the spatial distribution of emissions is necessary to reduce random error and bias associated with the obtained estimate.

Modification of small dissolution chamber system for long-acting periodontal drug product evaluation

Conventional dissolution testing methods may not be suitable for long-acting periodontal drug products due to the small volume, slow fluid flow rate, and environment in the periodontal pocket. The objective of this study was to evaluate a 3D-printed small volume flow-through dissolution chamber system (modified from a previous study) for biorelevant and dose-discriminating testing. Three periodontal drug products with different dosage forms were tested: Atridox, Arestin, and PerioChip. Modifications were made to suit the specific characteristics of these dosage forms. No significant differences were observed between the % drug release profiles in vitro and in vivo except for Atridox. The differences observed with Atridox could be related to the exposing surface area of the drug product. Similar differences were observed from this effect in COMSOL model simulations. Overall, the drugs show reasonable in vitro-in vivo correlations (R2 ≥ 0.91) with linear regression slopes close to unity. For dose discrimination between 75% and full dosing, significant differences were observed in the drug release data at specific time points of the products (p ≤ 0.05). The present results suggest that a small volume dissolution chamber with slow flow rate could potentially provide biologically relevant and dose-discriminating evaluations for periodontal drug products.

Turbulence Parameters in the Troposphere—Lower Stratosphere Observed by Beijing MST Radar

Based on three years of data observed by Beijing MST radar between 2012 and 2014, this study gives the distribution characteristics of the turbulence energy dissipation rate ε and the vertical turbulence diffusion coefficient Kz in the troposphere–lower stratosphere over Beijing based on the spectral width method. Additionally, from the perspective of atmospheric stability expressed by the Brunt–Väisälä frequency N and the gradient Richardson number Ri, the seasonal variations of turbulence parameters at different heights are analyzed. The results show that ε (Kz) is distributed in the range of 10−4 to 10−1.5 m2 s−3 (100 to 101 m2 s−1), with median values of 10−3 to 10−2.5 m2 s−3 (100.4 to 100.75 m2 s−1). In the height range of −1 km to about +1 km from the tropopause, the profiles of ε have no noticeable seasonal difference from October to next May, and ε increases sharply with the slope of linear regression ∇log(ε)/∇h = 0.25 (m2 s−3) km−1. The seasonal variation of turbulence parameters has noticeable differences at different atmospheric layers. Furthermore, the atmospheric static/dynamic stability and turbulence intensity are the influencing factors of turbulence parameters. From the tropopause to 14 km, the values of ε and N both have a peak from October to next May, and this peak layer disappears from July to August. Within the range of 2–4 km from the ground, Kz and the frequency of Ri &lt; 0.25 have a significant positive correlation, with a Pearson correlation coefficient greater than 0.5, and both have large value layers in March to May and September to October. The turbulence intensity above 15 km is likely the main factor affecting the turbulence dissipation rate and vertical turbulence diffusion coefficient.

Mesopelagic community supported by epipelagic production in the western North Pacific Ocean based on stable isotope ratios of carbon and nitrogen

We compared stable isotope ratios of carbon (δ13C) and nitrogen (δ15N) of epi- and mesopelagic mesozooplankton communities at subarctic (K2) and subtropical (S1) sites in the North Pacific Ocean to evaluate the relationship between mesopelagic communities and epipelagic production. Although δ13C and δ15N varied among the taxonomic groups, ostracods, carnivorous copepods, and chaetognaths tended to exhibit higher δ13C and δ15N than omnivorous copepods within the same layers. Mesozooplankton δ13C and δ15N were higher in the upper (200–500 m) and lower (500–1000 m) mesopelagic layers than in the epipelagic layer (0–150 m). δ13C and δ15N of mesozooplankton were positively correlated at all three depth-ranges at both sites. Although the slopes of linear regressions of δ15N vs. δ13C exhibited no significant differences among layers or between sites, the intercept was lower for mesozooplankton at S1. We attributed the similarity of slopes to common isotopic fractionation during protein synthesis among the different taxonomic groups, and the lower intercept to primary producers supported by recycled nutrients and atmospheric nitrogen fixation under the high thermal regime at S1. Our results further confirm earlier evidence that metabolic requirements of mesopelagic communities are supported by epipelagic production through the biological pump.

Mechanical Properties of GaN Single Crystals upon C Ion Irradiation: Nanoindentation Analysis.

Mechanical properties of gallium nitride (GaN) single crystals upon carbon ion irradiation are examined using nanoindentation analysis at room temperature. Pop-in events in the load-depth curves are observed for unirradiated and irradiated GaN samples. A statistical linear relationship between the critical indentation load for the occurrence of the pop-in event and the associated displacement jump is exhibited. Both the slope of linear regression and the measured hardness increase monotonically to the ion fluence, which can be described by logistic equations. Moreover, a linear relationship between the regression slope as a micromechanical characterization and the hardness as a macroscopic mechanical property is constructed. It is also found that the maximum resolved shear stress of the irradiated samples is larger than that of the unirradiated samples, as the dislocation loops are pinned by the irradiation-induced defects. Our results indicate that the nanoindentation pop-in phenomenon combined with a statistical analysis can serve as a characterization method for the mechanical properties of ion-irradiated materials.

Statistical analysis of annual and seasonal temperature regime change in Rasina River basin, Serbia

In this research, changes in annual and seasonal trends of mean temperatures were analyzed on the territory of the Rasina River basin (central Serbia). We used data from four meteorological stations during three periods: 1961–1989, 1990–2018, and 1979–2013. The change detection analysis has been conceded using the Pettitt’s test, von Neumann ratio test, Buishand’s range test, and standard normal homogeneity (SNH) test, while the linear regression, Mann-Kendall, and Sen's slope tests have been applied for trend analysis. The results show that the change in summer temperatures occurred shortly after 1980. The analysis results showed that inhomogeneous structures are generally observed between 1976 and 1984, between 1997 and 1998, and in 2006. The trend of all the data on annual basis showed positive increasing trend. The analysis indicated that the average annual, winter, and summer temperatures show significant increasing trend both in the longer period (1961–2018) and in the second part of the period (1990–2018). In the first part of the period (1961–1989), autumn temperatures in Kruševac, summer and autumn temperatures in Blace, as well as winter and autumn temperatures in Goč showed significant decreasing trend. The significant rising trend in the summer and winter months in the last 30 years may affect water availability and water demands in the region.

Зависимость степени коартикуляции согласного и гласного от типа ударения

The paper evaluates the quantitative impact of prominence (neutral vs emphatic stress) in CV sequences. The study serves the practical needs of speech synthesis and recognition and attempts to fill an important gap as there is insufficient research in the field with regard to the Russian language. We used the locus equation method to assess the degree of CV coarticulation. The experiment was performed on CVCV nonsense words imbedded in the middle position of a carrier phrase. The first syllable of the target words was made prominent both with neutral and emphatic stress. The consonants and vowels were presented by the same consonant and vowel sounds. Russian voiced occlusive bilabial, forelingual and backlingual consonants [b], [d] and [ɡ] were combined with Russian non-palatal vowels [ɛ], [ɑ] and [u] occupying cardinal positions in the Fant triangle. Six male speakers were recorded in a sound attenuated studio using high-quality recording equipment. The total number of tokens N = 486. Praat software was used to measure formant frequencies by means of fast Fourier transformation and linear predictive coding. The experiment showed that linear regression slopes differed both in the Y-intercepts and slope coefficients for the vowels uttered with emphatic and neutral stress. We compared locus equation slopes for CV transitions of the consonants of similar place of obstruction combined with the vowels pronounced with neutral and emphatic stress, which revealed a quantitative difference in the degree of coarticulation of the bilabial and forelingual consonants both in the Y-intercept values and slope coefficients. In case of the backlingual consonant, locus equations differed only in the Y-intercept value, while the slope coefficients were practically the same. The results obtained were interpreted on the basis of existing experimental data for the Russian, English, and Arabic languages.

Occurrence and temperature dependence of atmospheric gas-phase organophosphate esters in high-mountain areas (Pyrenees)

The air concentrations of organophosphate esters (OPEs) were studied in a network of six remote high-mountain areas of the Pyrenees located along an altitudinal profile between 1619 m and 2453 m above sea level on a restricted planar surface to assess their vertical distribution based on long-range atmospheric transport and temperature gradients. Polyurethane foam passive samplers were used in five periods spanning over three years (September 2017–October 2020). The sum of concentrations of five OPEs were between 5.3 and 100 pg m−3, averaging 16–53 pg m−3 across campaigns at the different locations. These concentrations were much lower than those observed in areas under anthropogenic influence but also than those found in low altitude remote continental sites. A significant progressive change in predominant compounds was observed along the altitudinal gradient, with prevalence of tris(1-chloro-2-propyl) phosphate (TCIPP) or tris(2-chloroethyl) phosphate (TCEP) below or above 2300 m above sea level, respectively. This trend was consistent with the higher volatility of TCEP, which was retained at greater extent at lower environmental temperatures (higher altitude).A significant temperature dependence of the gas phase concentrations was observed for TCEP, TCIPP and triphenyl phosphate (TPHP), which could be explained by retention in the cold periods, predominantly adsorbed in snow, and their release to the atmosphere during snowmelt. This mechanism was consistent with the good agreement found between the vaporization enthalpies measured under laboratory conditions and the experimental values obtained from the slopes of the significant linear regressions when representing the vertical gradients.

New Predictive Resting Metabolic Rate Equations for High-Level Athletes: A Cross-validation Study.

The present study aims a) to assess the agreement between the measured resting metabolic rate (RMR) using indirect calorimetry and different predictive equations (predicted RMR), and b) to propose and cross-validate two new predictive equations for estimating the RMR in high-level athletes. The RMR of 102 athletes (44 women) was assessed using indirect calorimetry, while the body composition was assessed using skinfolds. Comparisons between measured and predicted RMR values were performed using one-way ANOVA. Mean difference, Root Mean Square Error, Simple Linear Regression, and Bland-Altman plots were used to evaluate the agreement between measured and predicted RMR. The accuracy of predictive equations was analyzed using narrower and wider accuracy limits (±5% and ± 10%, respectively) of measured RMR. Multiple linear regressions models were employed to develop the new predictive equations based on traditional predictors (Equation 1) and the stepwise method (Equation 2). The new Equations 1 and 2 presented good agreement based on the mean difference (3 and -15 kcal.d-1), RMSE (200 and 192 kcal.d-1), and R2 (0.71 and 0.74), respectively, and accuracy (61% of subjects between the limit of ±10% of measured RMR). Cunningham's equation provided the best performance for males and females among the existing equations, whereas Harris & Benedict's equation showed the worst performance for males (mean difference = 406 kcal.d-1; RMSE = 473 kcal.d-1). Compared to measured RMR, most predictive equations showed heteroscedastic distribution (linear regression's intercept and slope significantly different from zero; p ≤ 0.05), mainly in males. The new proposed equations can estimate the RMR in high-level athletes accurately. Cunningham's equation is a good option from existing equations, and Harris & Benedict's equation should not be used in high-level male athletes.