Calculated Pearson Correlation Research Articles

Religious orientations on attitude toward a secular state: Case of Evangelical Lutheran Churches in north regions of Kazakhstan

The main objective of this study was to explore the connections between orientations (Quest, Intrinsic, and Extrinsic) and peoples' attitudes towards a secular state among members of the Evangelical Lutheran Churches in the northern regions of Kazakhstan. To achieve this, established scales were used to assess participants' perspectives on secularism and their religious orientations. The article analyses the opinion on the separation of church and state using measures of tendency. There were also calculated Pearson correlation coefficients to understand how religious beliefs interacted with views on governance. The findings indicated that there was an inclination towards secularism among the participants. While some respondents valued the guidance provided by the Church in decisions, others preferred a separation between religious institutions and legislative affairs. However, when examining orientations, none showed a significant influence on attitudes towards a secular state. This highlights the relationship between religious beliefs and societal views on governance. Overall, this study emphasises that while religious orientation provides insights into beliefs, it does not solely determine attitudes towards secularism. It underscores that belief systems are multifaceted and interact intricately with governance perspectives. Future research should focus on uncovering factors that influence these perspectives within and beyond the Evangelical Lutheran Church.

Validation Study of the Spanish Version of the Measure of Happiness (MH) Questionnaire.

This study aims to validate the Spanish version of the Measure of Happiness (MH), a questionnaire that identifies the specific areas of an individual's life that are related to experienced happiness. The sample consisted of 458 Spanish native speakers (65.7% women, 34.3% men; mean age = 24.14, SD = 8.45) and was divided into two groups: Sample 1 (N = 226) underwent Exploratory Factor Analysis (EFA), and Sample 2 (N = 232) underwent Confirmatory Factor Analysis (CFA). The convergent and discriminant validity of the Spanish version of the MH and its subscales was assessed by calculating Pearson correlations between the MH factors and the Subjective Happiness Scale, the State-Trait Anxiety Inventory, the Beck Depression Inventory, and the WHOQOL-BREF. The EFA and CFA confirmed the original five-factor structure of the MH questionnaire. The only difference in the Spanish version of the MH is that Item 4, which was originally assigned to Factor 2 "Financial Status", was reallocated to Factor 1 "Psychophysical Status". The reliability of the Spanish version of the MH questionnaire was also confirmed, and the factors correlated in the expected direction with the constructs of quality of life, happiness, anxiety, and depression. The MH questionnaire demonstrated excellent psychometric properties among Spanish respondents.

Influence on Sample Determination for Deep Learning Electromagnetic Tomography.

Deep learning (DL) has been frequently applied in the image reconstruction of electromagnetic tomography (EMT) in recent years. It offers the potential to achieve higher-quality image reconstruction. Among these, research on samples is relatively scarce. Samples are the cornerstone for both large and small models, which is easy to ignore. In this paper, a deep learning electromagnetic tomography (DL-EMT) model with nine elements is established. Complete simulation and experimental samples are obtained based on this model. On the sample sets, the reconstruction quality is observed by adjusting the size and configuration of the training set. The Mann-Whitney U test shows that beyond a certain point, the addition of more samples to the training data fed into the deep learning network does not result in an obvious improvement statistically in the quality of the reconstructed images. This paper proposes a CC-building method for optimizing a sample set. This method is based on the Pearson correlation coefficient calculation, aiming to establish a more effective sample base for DL-EMT image reconstruction. The statistical analysis shows that the CC-building method can significantly improve the image reconstruction effect in a small and moderate sample size. This method is also validated by experiments.

A study demonstrating users' preference for the adapted-REQUITE patient-reported outcome questionnaire over PRO-CTCAE® in patients with lung cancer.

The use of patient-reported outcomes (PROs) has been shown to enhance the accuracy of symptom collection and improve overall survival and quality of life. This is the first study comparing concordance and patient preference for two PRO tools: Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE®) and the adapted-REQUITE Lung Questionnaire. Patients with lung cancer were recruited to the study while attending outpatient clinics at a tertiary cancer centre. Clinician-reported outcomes were generated through initial patient assessment with CTCAE v4.03. Participants then completed the PRO-CTCAE® and adapted-REQUITE questionnaires. Concordance between the 2 questionnaires was assessed by calculating Pearson correlation coefficient. PRO-CTCAE® and CTCAE concordance was demonstrated by calculating Pearson correlation coefficient from the linear predictors of an ordinal logistic regression. P-values were also calculated. Out of 74 patients approached, 65 provided written informed consent to participate in the study. 63 (96.9%) patients completed both PRO-CTCAE® and adapted-REQUITE questionnaires. Pearson correlation coefficient between PRO tools was 0.8-0.83 (p <.001). Correlation between CTCAE and PRO-CTCAE® ranged between 0.66-0.82 (p <.001). Adapted-REQUITE and CTCAE correlation was higher for all symptoms ranging between 0.79-0.91 (p <.001). Acceptable discrepancies within one grade were present in 96.8%-100% of symptom domains for REQUITE and in 92.1%-96.8% for all domains in the PRO-CTCAE®. 54% of the total participant cohort favored the adapted-REQUITE questionnaire due to reduced subjectivity in the questions and ease of use. The adapted-REQUITE questionnaire has shown a superior correlation to clinician-reported outcomes and higher patient preference than the PRO-CTCAE®. The results of this study suggest the use of the REQUITE questionnaire for patients with lung cancer in routine clinical practice.

DNA based phenotype optimization of oryza sativa using machine learning and MolCNN

The prediction of phenotype from the genotype of oryza sativa (rice) is very crucial for optimizing the crop management. utilizing molecular convolutional neural networks (MolCNNs) and machine learning for crop management in oryza sativa provides a data-driven method for phenotype prediction based on DNA data, improving farming techniques. Data gathering, preparation, and integration of phenotypic and DNA data are all part of this process. Meaningful DNA features are extracted by MolCNN, and phenotypic qualities are predicted by a machine learning algorithm. Making educated decisions is ensured by assessing the model’s effectiveness, applying it to crop management, and updating it frequently. Choosing crop varieties, planting schedules, and management techniques are guided by molecular insights, which support sustainable agriculture and increase yields and quality. In the proposed research we are calculating pearson correlation coefficients between anticipated and actual trait values and the model’s performance on a test set. Additionally, it determines the (PCC) for every characteristic in the model and we have received a binary accuracy of 0.9998 in 139 seconds.

Measuring perceived utility of genomic sequencing: Development and validation of the GENEtic Utility (GENE-U) scale for pediatric diagnostic testing

IntroductionMeasuring effects of genomic sequencing (GS) on patients and families is critical for translational research. We aimed to develop and validate an instrument to assess parents’ perceived utility of pediatric diagnostic GS. MethodsInformed by a five-domain conceptual model, the study comprised five steps: (1) item writing, (2) cognitive testing, (3) pilot testing and item reduction, (4) psychometric testing, and (5) evaluation of construct validity. Parents of pediatric patients who had received results of clinically indicated GS participated in structured cognitive interviews and two rounds of surveys. After eliminating items based on theory and quantitative performance, we conducted an exploratory factor analysis and calculated Pearson correlations with related instruments. ResultsWe derived the 21-item Pediatric Diagnostic version of the GENEtic Utility (GENE-U) scale, which has a two-factor structure that includes an Informational Utility subscale (16 items, α = 0.91) and an Emotional Utility subscale (5 items, α = 0.71). Scores can be summed to calculate a Total scale score (α = 0.87). The Informational Utility subscale was strongly associated with empowerment and personal utility of GS, and the Emotional Utility subscale was moderately associated with psychosocial impact and depression and anxiety. DiscussionThe Pediatric Diagnostic GENE-U scale demonstrated good psychometric performance in this initial evaluation and could be a useful tool for translational genomics researchers, warranting additional validation.

Hippocampal subfields and thalamic nuclei associations with clinical outcomes in multiple sclerosis: An ultrahigh field MRI study

BackgroundPrevious studies have shown that thalamic and hippocampal neurodegeneration is associated with clinical decline in Multiple Sclerosis (MS). However, contributions of the specific thalamic nuclei and hippocampal subfields require further examination. ObjectiveUsing 7 Tesla (7T) magnetic resonance imaging (MRI), we investigated the cross-sectional associations between functionally grouped thalamic nuclei and hippocampal subfields volumes and T1 relaxation times (T1-RT) and subsequent clinical outcomes in MS. MethodsHigh-resolution T1-weighted and T2-weighted images were acquired at 7T (n=31), preprocessed, and segmented using the Thalamus Optimized Multi Atlas Segmentation (THOMAS, for thalamic nuclei) and the Automatic Segmentation of Hippocampal Subfields (ASHS, for hippocampal subfields) packages. We calculated Pearson correlations between hippocampal subfields and thalamic nuclei volumes and T1-RT and subsequent multi-modal rater-determined and patient-reported clinical outcomes (∼2.5 years after imaging acquisition), correcting for confounders and multiple tests. ResultsSmaller volume bilaterally in the anterior thalamus region correlated with worse performance in gait function, as measured by the Patient Determined Disease Steps (PDDS). Additionally, larger volume in most functional groups of thalamic nuclei correlated with better visual information processing and cognitive function, as measured by the Symbol Digit Modalities Test (SDMT). In bilateral medial and left posterior thalamic regions, there was an inverse association between volumes and T1-RT, potentially indicating higher tissue degeneration in these regions. We also observed marginal associations between the right hippocampal subfields (both volumes and T1-RT) and subsequent clinical outcomes, though they did not survive correction for multiple testing. ConclusionUltrahigh field MRI identified markers of structural damage in the thalamic nuclei associated with subsequently worse clinical outcomes in individuals with MS. Longitudinal studies will enable better understanding of the role of microstructural integrity in these brain regions in influencing MS outcomes.

Radiomics analysis of contrast-enhanced computerized tomography for differentiation of gastric schwannomas from gastric gastrointestinal stromal tumors

PurposeTo assess the performance of radiomics-based analysis of contrast-enhanced computerized tomography (CE-CT) images for distinguishing GS from gastric GIST.MethodsForty-nine patients with GS and two hundred fifty-three with GIST were enrolled in this retrospective study. CT features were evaluated by two associate chief radiologists. Radiomics features were extracted from portal venous phase images using Pyradiomics software. A non-radiomics dataset (combination of clinical characteristics and radiologist-determined CT features) and a radiomics dataset were used to build stepwise logistic regression and least absolute shrinkage and selection operator (LASSO) logistic regression models, respectively. Model performance was evaluated according to sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curve, and Delong’s test was applied to compare the area under the curve (AUC) between different models.ResultsA total of 1223 radiomics features were extracted from portal venous phase images. After reducing dimensions by calculating Pearson correlation coefficients (PCCs), 20 radiomics features, 20 clinical characteristics + CT features were used to build the models, respectively. The AUC values for the models using radiomics features and those using clinical features were more than 0.900 for both the training and validation groups. There were no significant differences in predictive performance between the radiomic and clinical data models according to Delong’s test.ConclusionA radiomics-based model applied to CE-CT images showed comparable predictive performance to senior physicians in the differentiation of GS from GIST.

Abstract 55: Cerebral Autoregulatory Curves in Postpartum Individuals With and Without Preeclampsia

Introduction: Risk of maternal stroke is higher in patients with preeclampsia (PEC), with the highest risk seen in those with chronic hypertension with superimposed preeclampsia (siPEC). Most strokes occur postpartum. Changes in cerebral autoregulation (CA) could contribute to elevated postpartum stroke risk. We describe CA curves in postpartum patients with no hypertension, PEC and siPEC. Methods: We prospectively recruited postpartum patients with and without PEC and measured 10-minute continuous mean arterial pressure (MAP) and cerebral blood flow velocity (CBFV) with finger plethysmography and transcranial Doppler within 1 week of delivery. We generated polynomial S-curves to derive limits of CA, CA plateaus (defined as slope &lt; 0.15), and mean and minimum slopes between CA limits. We calculated Pearson correlation coefficients (Mx) between normalized MAP and CBVF; Mx &gt; 0.3 indicates high MAP-CBFV correlation, suggesting impaired CA. Results: CA curves were produced from 71 patients (28% normotensive, 42% PEC, 30% siPEC). Group means for MAP were higher in those with PEC and siPEC (normotensive 72 mmHg, PEC 88, siPEC 87 [p=0.001]). CA was preserved in all groups, with Mx nadirs below 0.3 (Figure). The normotensive group demonstrated a wide, flat plateau in the S-curve (minimum slope 0.02). In the PEC group, we observed a narrower and slightly steeper autoregulatory plateau (minimum slope 0.05). The siPEC group displayed the narrowest and steepest plateau (minimum slope 0.15). Conclusion: In conclusion, in our sample of postpartum patients with normotension, PEC and siPEC, we observed a gradient in CA plateau range and slope. Compared with the normotensive group, the PEC and siPEC groups had progressively narrower and steeper CA plateaus, suggesting a more limited CA range and diminished CA efficacy. Future studies should investigate whether sustained MAP outside CA limits contributes to adverse maternal outcomes, particularly in those with siPEC.

Indicators of Validity and Reliability of the Jordanian Version of Kember Reflective Thinking Scale among Upper Basic Stage Students in Amman

The study aimed to adapt Kember scale of reflective thinking to Jordanian context, and to extract the indicators of validity and reliability of the scale, the study sample consisted of (1065) male and female students, namely, (548) male and (517) female students from sixth, eighth and tenth grades from Amman, they were reached by social media after the coordination with school managers and teachers, where the scale was distributed on the social media sites of a number of schools in Amman city. To achieve the aim of this study, the Kember, et al. (2000) scale of reflective thinking has been translated, the scale contains four dimensions: Habitual behavior, understanding, reflection, and critical reflection. The indications of validity were extracted through two ways, face validity, and construct validity by calculating Pearson correlation coefficients between the items and the total degree on each dimension, whereas, indicators of reliability were also extracted by means of test re-test, and Cronbach’s Alpha methods. Further, the results showed differences in reflective thinking performance due to students’ academic achievement, whereas, they showed no differences due to gender differences nor grade differences.

Upregulation of vesicle-associated membrane protein 7 in breast cancer tissues.

Vesicle-associated membrane protein 7 (VAMP7) plays oncogenic roles in cancers. However, its clinical significance in breast cancer (BC) tissues remains unknown. To elucidate the clinical implications of VAMP7, as well as its involvement in the tumor microenvironment and molecular pathways of breast cancer. BC (n=100) and non-cancerous breast tissues (n= 100) were collected for an immunohistochemical experiment (1:200). The protein expression level of VAMP7 was determined by using a semi-quantitative scoring method. High-throughput RNA-sequencing data of BC tissues were analyzed to confirm the mRNA expression trend of VAMP7. Additionally, the largest BC prognosis cohort data were collected to mine the potential impact VAMP7 has on BC progression. The association between VAMP7 and the microenvironment of BC was evaluated by using a CIBERSORT algorithm. Moreover, we explored the co-expressed molecular mechanisms of VAMP7 in BC by calculating Pearson correlation coefficients and overexpressed genes. Finally, the biological mechanism underlying the relationship between VAMP7 and the key pathways was also explored using gene set enrichment analysis (GSEA). Potential therapeutic strategies were predicted targeting VAMP7. VAMP7 protein was significantly over-expressed in BC tissue than that in controls (p< 0.001). Compared with 459 normal breast tissues and 113 non-cancerous breast tissues, the expression level of VAMP7 mRNA was significantly increased in 1111 BC tissues. CD4+T cells, macrophages, and naïve B cells had a higher infiltration rate in BC tissues with high VAMP7 expression, while regulatory T cells and CD8+T cells had a lower infiltration rate. Over-expressed VAMP7 was associated with macrophages activation and transition from M1 to M2 polarization. Upregulated VAMP7 could predicted poorer OS, DMFS, PPS, and RFS outcomes. Upregulated VAMP7 co-expressed genes were significantly enriched in the cell cycle checkpoints. GSEA confirmed that over-expressed VAMP7 are markedly associated with functional enrichment in cell cycle related categories, including mitotic spindle, G2M checkpoint, and E2F targets. KU-55933 was predicted as a putative therapeutic drug for BC targeting VAMP7. VAMP7 was upregulated in BC tissue and correlated with poor prognosis of BC patients. VAMP7 may promote BC progression by targeting the cell cycle pathway.

An imprecise probability approach-based determination of over-represented southern African plant genera and families used in ethnopharmacology

Ethnopharmacological relevanceThe analyses of patterns of over-representation of southern African traditional medicinal plants at the genus and family level provide information about the differences in plant use among southern African countries and disease categories. ‘Over-representation’ refers to the phenomenon whereby the proportion of plants belonging to a taxonomic group is higher in ethnobotanical pharmacopoeia than in the total flora. Aim of the studyThis study aimed to use the Imprecise Dirichlet Model (IDM) to analyse data from ten southern African countries to establish how over-represented medicinal plant families compare with over-represented genera, how over-represented medicinal taxa differ among countries in this region of Africa, and how over-represented taxa differ among six major disease categories. Materials and methodsFloral data for the total species composition of each country were obtained from online databases. Medicinal plant species lists were generated from published surveys, inventories, and books. IDM calculations were executed using the inverse of the cumulative beta probability density function in Microsoft Excel™. Python programming language source code was used to calculate Pearson correlation (r) values and Jaccard coefficients (J). ResultsNine of forty-two over-represented medicinal plant families in southern Africa (group 1) do not have over-represented genera. Seven of the forty genera with the highest margins of over-representation belong to under-represented families. Nineteen of the forty-two over-represented families have margins of over-representation smaller than the cumulative margins of their over-represented genera. Groups of countries with similar overall flora (J ≥ 0.333) are Botswana and Namibia (group 2), Malawi, Mozambique, Zambia and Zimbabwe (group 3). The families and genera with the highest margins of over-representation are Loganiaceae and Albizia in group 1, Combretaceae and Vachellia in group 2, Dioscoreaceae and Senna in group 3, and Sapotaceae and Solanum in group 4 (South Africa). The families and genera with the highest margins of over-representation across disease categories are Ebenaceae and Albizia, Canellaceae and Dicoma, Combretaceae and Pterocelastrus, Ebenaceae and Bersama, Francoaceae and Erythrina, and Aristolochiaceae and Strychnos for plants used in the treatment of STIs, febrile and mosquito-vector diseases, microbial infections, pain, skin conditions, and female sexual/reproductive problems, respectively. ConclusionsGenus-level calculations are more efficient in generating taxonomic lists that can be used for ethnopharmacological investigations due to the exclusion of under-represented genera. Limiting the size of geographical areas from which medicinal plant lists are sampled and targeting plants used to treat specific types of disease prevents the underestimation of niche over-represented taxa.

Muscle Pressure Correlates With Pain Levels in Fibromyalgia Patients.

We evaluated patients with fibromyalgia syndrome (FMS) to determine whether there is a correlation between pain scores based on a 0- to 10-point visual analog scale (VAS) and muscle pressure. One hundred forty-two patients who satisfied the American College of Rheumatology classification criteria for FMS and 38 non-FMS controls comprised the study groups. Muscle pressure was measured in mm Hg using a pressure gauge attached to a no. 22 needle inserted into the midportion of the trapezius muscle. The muscle pressure was then correlated with the VAS pain score of 0 to 10, some with an increment of 0.5. A second muscle pressure was obtained from 19 patients at a subsequent visit, which was compared with their pain scores. The mean (SD) pain score for 142 patients with FMS was 6.6 (SD, 1.84) on a 0- to 10-point VAS. The mean pain score in the non-FMS subjects was 0.7 (SD, 1.26). The mean muscle pressure in the FMS group was 32.9 (SD, 6.57) mm Hg. The mean muscle pressure in the non-FMS subjects was 10.6 (SD, 3.85) mm Hg. The calculated Pearson correlation coefficient for muscle pressure versus pain score was 0.8312 ( p < 0.0001). This indicates a highly significant association between subjects' muscle pressure and pain scores. For the repeat muscle pressures, the change in muscle pressure was correlated with the change in pain score, and the resulting Pearson correlation coefficient was 0.9255 ( p < 0.0001). These results again indicate a highly significant association between subjects' muscle pressure and pain scores. The results show that increased muscle pressure may be a significant cause of pain in FMS, and the etiology of the pain may have a large peripheral component in addition to a centralized origin of the pain.

ANALYTICAL STUDY OF THE IMPACT OF EDUCATION ON THE SENUFO HOUSEHOLDS’ INCOME IN SOUTHERN MALI

This paper conducts an analytical study to investigate the profound impact of education on the household incomes of the Senufo community in southern Mali. Recognizing the pivotal role education plays in socio-economic development, this study aims to examine the association between household income and educational attainment in the Senufo’s community. Using a linear regression technique to calculate Pearson correlation, the researcher explores the determined association between variables. Correlational analysis indicates that the degree of correlation between the level of households’ education and their income is moderately positive. The finding of the study contributes to the existing literature on the socio-economic dynamic of rural Senufo community, offer valuable insights for policymakers and educators seeking to enhance the status of education in the community.

Optimization of Pedicle Screw Parameters for Enhancing Implant Stability Based on Finite Element Analysis

ObjectiveTo improve the implant stability, the parameters including outer diameter, thread depth and pitch of pedicle screw were optimized by finite element analysis. MethodsThe insertion and pullout of pedicle screw were simulated by finite element analysis, and the precision of simulation was evaluated by comparing with mechanical tests. The influences of the parameters on the maximum insertion torque and maximum pullout force were analyzed by computational simulations including single-factor analysis and orthogonal experiment. ResultsThe simulation results agree with the mechanical tests. The influencing orders of the parameters on insertion torque and pullout force are both: outer diameter > pitch > thread depth. When the pilot hole diameter is 0.1 mm larger than the inner diameter of pedicle screw, the calculated Pearson correlation coefficient between the maximum insertion torque and maximum pullout force is r = 0.99. The optimized pedicle screw shows a maximum insertion torque of 485.16 N·mm and a maximum pullout force of 1726.33 N, which are 23.9% and 9.1% higher than those of standard screw, respectively. ConclusionsThe presently used models are feasible to evaluate the implant stability of pedicle screw. The maximum insertion torque and maximum pullout force of pedicle screw are highly correlated. The insertion torque and pullout force can be improved by increasing outer diameter and decreasing pitch. Beside the parameters of pedicle screw, the size of pedicle and bone mineral density are two considerable factors for a better implant stability.

COMPARISON OF CLINICAL BIOMARKER AND DNAM-DERIVED BIOLOGICAL AGE MEASURES IN AN ELECTIVE SURGERY COHORT

Abstract Older adults face greater post-operative morbidity due to decreasing biological aging (BA)-associated physical resilience. BA measures may quantify these vulnerabilities. Common BA quantification approaches include measures derived from clinical biomarkers and whole blood DNA methylation. The Duke Surgery One Thousand Patient Cohort is a prospective biospecimen and data repository of patients undergoing elective surgery. Using a subset of this cohort (n=119; 61±13 years of age; 50% female; 21% African American), we sought to determine: 1) the inter-relationship of BA measures; 2) associations of race, sex, BMI, and surgery type with BA; and 3) associations of BA with American Society of Anesthesia (ASA) Classification Score . Clinical biomarker BA measures included Klemera-Doubal method (KDM), homeostatic dysregulation (HD), and PhenoAge. DNAm measures included the DNAm PhenoAge, Hannum Age, and GrimAge. We calculated BA advancement (BAA) as BA minus Chronological Age (CA). We calculated Pearson correlation coefficients and fitted regression models, adjusting for CA, sex, race, and BMI. Overall, participants were biologically older than their CA. Male sex and African American race were associated with greater BAA by most measures . Associations of surgery type with BAA were consistent across measures . Greater BAA by most measures was associated with a higher ASA Score, reflecting higher baseline surgical risk. In conclusion, BA measures using different quantitative approaches showed anticipated associations with demographic factors, surgery type, and surgical risk. Ongoing work will identify BA measures that predict adverse surgical outcomes, and that could be used to identify patients for perioperative prehabilitation.

Morphological characterization of the Polatli sheep in terms of live weight using data mining algorithms.

The aim of this research is both to estimate the live weight (LW) of Polatli sheep (Ile de France × Akkaraman (G1)) by considering some body measurements (withers height (WH), rump height (RH), body length (BL), chest depth (CD), chest width (CD), chest girth (CG), cannon bone circumference (CBC)), age, and sex variables as independent variables using C&RT (Classification and Regression Tree), CHAID (Chi-square Automatic Interaction Detector), and MARS (Multivariate Adaptive Regression Splines) algorithms and to determine the significant independent variables in the estimation of live weight. For this purpose, a total of 210 sheep were used, including 180 females and 30 males of different ages, for the estimation of LW. The calculated Pearson correlation coefficients between LW and WH, RH, BL, CD, CW, CG, and CBC characteristics are 0.897, 0.896, 0.853, 0.948, 0.550, 0.914, and 0.798, respectively (p < 0.05). In the application of data mining algorithms as prediction models, a cross-validation of 10 was used, while for tree-based algorithms, the parent node was set to 10 and the child node to 5. While CHAID and C&RT algorithms each used 8 independent variables to explain the variation observed in LW, the MARS algorithm used 9 independent variables. In Polatli sheep, the sheep with the highest live weight was found in the node with age > 3 and CD > 36 cm cutting point in the CHAID algorithm (93.571 kg). In the C&RT algorithm, it was predicted to be (91.316 kg) when age > 0, CD > 36.5 cm, and CBC > 9.5 cm. When evaluated considering commonly used criteria, the prediction performances of CHAID, C&RT, and MARS algorithms were calculated as follows: RMSE (root mean square error) values are "5.788, 5.103, 4.005"; SDR (standard deviation ratio) values are "0.254, 0.224, 0.176"; MAPE (mean absolute percentage error) values are "7.555, 6.675, 5.682"; Adj-Rsq (adjusted R-squared) values are "0.935, 0.950, 0.969"; and AIC (Akaike information criterion) values are "741.436, 688.489, 582.792," respectively. In terms of prediction performance, among the tree algorithms (CHAID and C&RT), C&RT was found to be the best, while considering all performance measures, it was observed that the MARS algorithm exhibited the best performance. Consequently, it has been determined that C&RT and MARS algorithms can be safely employed in morphological characterization studies for the identification of indirect criteria and the formation of elite herds in terms of LW. This decision allows for the reliable use of these algorithms to facilitate the selection of indirect variables and the establishment of elite populations in breeding programs focusing on live weight.

Predicting water quality through daily concentration of dissolved oxygen using improved artificial intelligence

As an important hydrological parameter, dissolved oxygen (DO) concentration is a well-accepted indicator of water quality. This study deals with introducing and evaluating four novel integrative methods for the prediction of DO. To this end, teaching–learning-based optimization (TLBO), sine cosine algorithm, water cycle algorithm (WCA), and electromagnetic field optimization (EFO) are appointed to train a commonly-used predictive system, namely multi-layer perceptron neural network (MLPNN). The records of a USGS station called Klamath River (Klamath County, Oregon) are used. First, the networks are fed by the data between October 01, 2014, and September 30, 2018. Later, their competency is assessed using the data belonging to the subsequent year (i.e., from October 01, 2018 to September 30, 2019). The reliability of all four models, as well as the superiority of the WCA-MLPNN, was revealed by mean absolute errors (MAEs of 0.9800, 1.1113, 0.9624, and 0.9783) in the training phase. The calculated Pearson correlation coefficients (RPs of 0.8785, 0.8587, 0.8762, and 0.8815) plus root mean square errors (RMSEs of 1.2980, 1.4493, 1.3096, and 1.2903) showed that the EFO-MLPNN and TLBO-MLPNN perform slightly better than WCA-MLPNN in the testing phase. Besides, analyzing the complexity and the optimization time pointed out the EFO-MLPNN as the most efficient tool for predicting the DO. In the end, a comparison with relevant previous literature indicated that the suggested models of this study provide accuracy improvement in machine learning-based DO modeling.

Comparison of Two Methods for the Measurement of Blood Plasma and Capillary Blood Glucose in Tropical Highland Grassing Dairy Cows.

(1) Background: There is lack of published studies validating specific cow-side glucometers such as Centrivet GK (CVGK). (2) Methods: The aims were (1) to measure and compare the blood glucose concentrations in 52 tropic highland grassing cows by using CVGK and the traditional enzymatic/photometric assay (EPA) in plasma and serum (reference method) and (2) to establish if glucose concentrations obtained via these methods could be affected by several demographic and zootechnical parameters of the dairy herd evaluated. (3) Results: Glucose concentrations were significantly (p = 0.00) affected by the method used for their measurement. The intra-assay coefficient of variation (CV) for glucose concentrations in plasma EPA and for CVGK was 14% for both methods with serum EPA, whereas the inter-assay CV for plasma EPA and CVGK was 8% and 13.7%, respectively, with serum EPA. Pearson correlation coefficient calculations between the reference method in serum and plasma presented a slightly positive significant (p = <0.000) correlation (r = 0.56), whereas there was not a significant (p = 0.413) correlation between serum EPA and CVGK (r = 0.135). The Passing and Bablok regressions were out of the ideal expected values for the slope (β = 1) and the intercept (α = 0) (11), whereas the Bland-Altman plots showed a bias of 5.29 ± 11.73 (mg/dL) for serum and plasma and 11.01 ± 15.74 (mg/dL) for serum and CVGK. The ROC curve showed no sensitivity in detecting normoglycemic cows (area = 53.7 %, e.d = 12.5 %, p = 0.759) for CVGK when compared to plasma EPA (area = 36.1 %, e.d = 14.2 %, p = 0.256). Plasma EPA exhibited a better but not significant effect in detecting hyperglycemic cows (area = 63.9%, e.d = 14.2%, p = 0.256) when compared to HHD (area = 46.3 %, e.d = 12.5 %, p = 0.759). General glucose concentrations, independently of the method used, were significantly (p = <0.001) greater in young cows when compared to adult and old cows. (4) Conclusions: Glucose concentration measurement in plasma by using EPA or in capillary blood via CVGK were not reliable methods when compared with the reference method.

An IoT-Based Intelligent Selection of Multidomain Feature for Smart Healthcare Using Reinforcement Learning in Schizophrenia

Currently, the health industry by the Internet of Things (IoT) is developing rapidly, and Electroencephalogram (EEG) signal has become a bridge for human-machine communication. EEG signal feature selection is a key link in brain nerves research and has important practical application value. Therefore, we propose an IoT-based Intelligent Selection of Multi-domain Feature for multimodal transformer EEG signal using reinforcement learning in Schizophrenia. First of all, the features of the multimodal EEG signal sequence by the IoT were extracted from time domain, frequency domain, and spatial domain; Second, the average pooling layer was added to improve the design of the transformer model for deep feature extraction of EEG signal; finally, reinforcement learning was introduced to run operation with the extracted features as input and the improved transformer model as agent. At the same time, entropy and Pearson correlation coefficient calculation were introduced to select feature subsets, and feature intelligent selection of EEG signal in multiple domains was completed through interaction between the agent and the environment. Experiments were performed on DEAP, EEG Motor Movement/Imagery Data set, BCI2008 competition data set and data set of a hospital. The results show that the feature extraction and feature visualization effect of the proposed algorithm is good, and the feature selection precision is as high as about 90%, and the average time is only about 13 seconds. These findings indicate the feasibility of the proposed feature intelligent selection algorithm in the study of EEG signal in schizophrenia, which provides a good basis for further study on the integration of IoT with the healthy industry.