Temperature dependence of magnetic moment of rare earth ions in ErFeO3 and NdFeO3 single crystals.

The interaction between rare earth and iron spins in rare earth orthoferrites leads to remarkable phenomena, such as the spin-flip process. This is despite the rare earth spins not being magnetically ordered. Instead, they are polarized by the ordered iron spins. The interaction between the two spin families is not well understood. This study reports the temperature dependence of the net magnetic moment for rare earth spins, by measuring the overall magnetization for ErFeO3 and NdFeO3 single crystals. The obtained temperature dependence can be described well using a model based on the mean field theory, giving tanh(const./T) temperature dependence. This functional dependence is not disrupted by the spin-flip transition as the crystals are cooled.

Bayesian spatial modelling of intimate partner violence and associated factors among adult women and men: evidence from 2019/2020 Rwanda Demographic and Health Survey.

BACKGROUND: Intimate partner violence (IPV) remains a global public health concern for both men and women. Spatial mapping and clustering analysis can reveal subtle patterns in IPV occurrences but are yet to be explored in Rwanda, especially at a lower small-area scale. This study seeks to examine the spatial distribution, patterns, and associated factors of IPV among men and women in Rwanda. METHODS: This was a secondary data analysis of the 2019/2020 Rwanda Demographic and Health Survey (RDHS) individual-level data set for 1947 women aged 15-49 years and 1371 men aged 15-59 years. A spatially structured additive logistic regression model was used to assess risk factors for IPV while adjusting for spatial effects. The district-level spatial model was adjusted for fixed covariate effects and was implemented using a fully Bayesian inference within the generalized additive mixed effects framework. RESULTS: IPV prevalence amongst women was 45.9% (95% Confidence interval (CI): 43.4-48.5%) while that for men was 18.4% (95% CI: 16.2-20.9%). Using a bivariate choropleth, IPV perpetrated against women was higher in the North-Western districts of Rwanda whereas for men it was shown to be more prevalent in the Southern districts. A few districts presented high IPV for both men and women. The spatial structured additive logistic model revealed higher odds for IPV against women mainly in the North-western districts and the spatial effects were dominated by spatially structured effects contributing 64%. Higher odds of IPV were observed for men in the Southern districts of Rwanda and spatial effects were dominated by district heterogeneity accounting for 62%. There were no statistically significant district clusters for IPV in both men or women. Women with partners who consume alcohol, and with controlling partners were at significantly higher odds of IPV while those in rich households and making financial decisions together with partners were at lower odds of experiencing IPV. CONCLUSION: Campaigns against IPV should be strengthened, especially in the North-Western and Southern parts of Rwanda. In addition, the promotion of girl-child education and empowerment of women can potentially reduce IPV against women and girls. Furthermore, couples should be trained on making financial decisions together. In conclusion, the implementation of policies and interventions that discourage alcohol consumption and control behaviour, especially among men, should be rolled out.

Prevalence and factors associated with self-reported HIV testing among adolescent girls and young women in Rwanda: evidence from 2019/20 Rwanda Demographic and Health Survey.

BACKGROUND: HIV/AIDS remains a major public health problem globally. The majority of people living with HIV are from Sub-Saharan Africa, particularly adolescent girls and young women (AGYW) aged 15-24 years. HIV testing is crucial as it is the gateway to HIV prevention, treatment, and care; therefore this study determined the prevalence and factors associated with self-reported HIV testing among AGYW in Rwanda. METHODS: We conducted secondary data analysis on the AGYW using data extracted from the nationally representative population-based 2019/2020 cross-sectional Rwanda Demographic and Health Survey (DHS). We described the characteristics of study participants and determined the prevalence of HIV testing and associated factors using the multivariable logistic regression model. We adjusted all our analyses for unequal sampling probabilities using survey weights. RESULTS: There were a total of 5,732 AGYW, with the majority (57%) aged 15-19 years, 83% were not living with a man, 80% were from rural areas, 29% were from the East region, and 20% had a history of pregnancy. Self-reported HIV testing prevalence was 55.4% (95%CI: 53.7 to 57.0%). The odds of ever having an HIV test were significantly higher for those aged 20-24 years (aOR 2.87, 95%CI: 2.44 to 3.37); with higher education (aOR 2.41, 95%CI:1.48 to 3.93); who were rich (aOR 2.06, 95%CI:1.57 to 2.70); with access to at least one media (aOR 1.64, 95%CI: 1.14 to 2.37); who had ever been pregnant (aOR 16.12, 95%CI: 9.60 to 27.07); who ever had sex (aOR 2.40, 95%CI: 1.96 to 2.95); and those who had comprehensive HIV knowledge (aOR 1.34, 95%CI: 1.17 to 1.54). CONCLUSIONS: We report an unmet need for HIV testing among AGYW in Rwanda. We recommend a combination of strategies to optimize access to HIV testing services, especially among the 15-19 years adolescent girls, including facility-based testing, school and community outreach, awareness campaigns on HIV testing, and home-based testing through HIV self-testing.

Magnetization reversal on different time-scales for ErFeO3 and NdFeO3 single crystals.

The dynamics of the magnetic moment reversal is studied for ErFeO3 and NdFeO3 single crystals. The reversal occurs at 41 and 5.1 K for ErFeO3 and NdFeO3, respectively, at a field of 300 Oe. The dynamics of the magnetization reversal process depends on the temperature at which the reversal occurs. The reversal is abrupt if the thermal energy is far higher than the energy of Zeeman splitting of the rare earth ion levels by internal fields, as observed for ErFeO3. A gradual magnetization reversal occurs for NdFeO3 over 64 s, when the thermal energy at the temperature of the reversal is well below the Zeeman splitting energy of Nd3+ spins. A mechanism for this gradual magnetization reversal is proposed in terms of the thermal re-population of Zeeman doublets of Nd3+ ions, the splitting energy of which continuously changes during the magnetization reversal.

Effect of air abrasive polishing on nickel release, stainless steel corrosion, and nickel-titanium archwires.

Background: Orthodontic treatment is becoming more and more popular. However, using fixed orthodontic devices for treatment affects oral hygiene and raises the risk of corrosion, plaque-related illnesses, and dental discoloration-related issues. Air abrasive polishing has a superior effect over the conventional method in removing dental deposits. Using fixed orthodontic appliances affects oral hygiene and raises the risk of diseases caused by plaque, tooth discoloration, and corrosion, as well as corrosion by ions. This study evaluated the impact of air polishing on nickel ion release and corrosion from stainless steel, nickel-titanium, coated stainless steel, and coated nickel-titanium. Methods: A total of 288 (stainless steel, coated stainless-steel, nickel-titanium, and coated nickel-titanium rectangular archwires) of one brand were subjected to varying air abrasion polishing times (5, 10, and 20 seconds). Then, they were submerged in artificial saliva with a pH of 6.75 and incubated for 28 days at 37 °C. The release of nickel ions (Ni2+) was measured using an atomic absorption spectrophotometer at 7, 14, and 28 days to estimate the cumulative effect. The corrosion of the test-selected samples and surface alterations was evaluated using scanning electron microscopy (SEM). Results: Prolonged polishing significantly increased Ni2+ release and corrosion. Archwires made of coated stainless steel exhibited the least amount of Ni2+ release. Conclusion: The air polishing process increased the Ni2+ release at a subtoxic level and could be used on adult patients with long gaps between visits with a polishing period of 5 seconds.

A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data.

Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment options, provide a more suitable quality of life, and ensure increased survival rates. Breast cancer detection using gene expression involves many complexities, such as the issue of dimensionality and the complicatedness of the gene expression data. This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data downloaded from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. In the proposed model, Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process to avoid biases in the expression measures. Filtration is used for gene reduction using a threshold value of 0.25. Thereafter the pre-processed gene expression dataset was converted into images which were later converted to grayscale to meet the requirements of the model. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA), to enhance the detection of breast cancer. The traditional CNN and five hybrid algorithms were compared with the classification result of the proposed model. The competing hybrid algorithms include the Whale Optimization Algorithm (WOA-CNN), the Genetic Algorithm (GA-CNN), the Satin Bowerbird Optimization (SBO-CNN), the Life Choice-Based Optimization (LCBO-CNN), and the Multi-Verse Optimizer (MVO-CNN). The results show that the proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, an f1-score of 99%, a kappa of 90.3%, a specificity of 92.8%, and a sensitivity of 98.9% for the cancerous class. The results suggest that the proposed method has the potential to be a reliable and precise approach to breast cancer detection, which is crucial for early diagnosis and personalized therapy.

A novel feature selection algorithm for identifying hub genes in lung cancer.

Lung cancer, a life-threatening disease primarily affecting lung tissue, remains a significant contributor to mortality in both developed and developing nations. Accurate biomarker identification is imperative for effective cancer diagnosis and therapeutic strategies. This study introduces the Voting-Based Enhanced Binary Ebola Optimization Search Algorithm (VBEOSA), an innovative ensemble-based approach combining binary optimization and the Ebola optimization search algorithm. VBEOSA harnesses the collective power of the state-of-the-art classification models through soft voting. Moreover, our research applies VBEOSA to an extensive lung cancer gene expression dataset obtained from TCGA, following essential preprocessing steps including outlier detection and removal, data normalization, and filtration. VBEOSA aids in feature selection, leading to the discovery of key hub genes closely associated with lung cancer, validated through comprehensive protein-protein interaction analysis. Notably, our investigation reveals ten significant hub genes-ADRB2, ACTB, ARRB2, GNGT2, ADRB1, ACTG1, ACACA, ATP5A1, ADCY9, and ADRA1B-each demonstrating substantial involvement in the domain of lung cancer. Furthermore, our pathway analysis sheds light on the prominence of strategic pathways such as salivary secretion and the calcium signaling pathway, providing invaluable insights into the intricate molecular mechanisms underpinning lung cancer. We also utilize the weighted gene co-expression network analysis (WGCNA) method to identify gene modules exhibiting strong correlations with clinical attributes associated with lung cancer. Our findings underscore the efficacy of VBEOSA in feature selection and offer profound insights into the multifaceted molecular landscape of lung cancer. Finally, we are confident that this research has the potential to improve diagnostic capabilities and further enrich our understanding of the disease, thus setting the stage for future advancements in the clinical management of lung cancer. The VBEOSA source codes is publicly available at https://github.com/TEHNAN/VBEOSA-A-Novel-Feature-Selection-Algorithm-for-Identifying-hub-Genes-in-Lung-Cancer .

Mapping Evidence on the Burden of Breast, Cervical, and Prostate Cancers in Sub-Saharan Africa: A Scoping Review.

Background: Cancer remains a major public health problem, especially in Sub-Saharan Africa (SSA) where the provision of health care is poor. This scoping review mapped evidence in the literature regarding the burden of cervical, breast and prostate cancers in SSA. Methods: We conducted this scoping review using the Arksey and O'Malley framework, with five steps: identifying the research question; searching for relevant studies; selecting studies; charting the data; and collating, summarizing, and reporting the data. We performed all the steps independently and resolved disagreements through discussion. We used Endnote software to manage references and the Rayyan software to screen studies. Results: We found 138 studies that met our inclusion criteria from 2,751 studies identified through the electronic databases. The majority were retrospective studies of mostly registries and patient files (n = 77, 55.8%), followed by cross-sectional studies (n = 51, 36.9%). We included studies published from 1990 to 2021, with a sharp increase from 2010 to 2021. The quality of studies was overall satisfactory. Most studies were done in South Africa (n = 20) and Nigeria (n = 17). The majority were on cervical cancer (n = 93, 67.4%), followed by breast cancer (67, 48.6%) and the least were on prostate cancer (48, 34.8%). Concerning the burden of cancer, most reported prevalence and incidence. We also found a few studies investigating mortality, disability-adjusted life years (DALYs), and years of life lost (YLL). Conclusions: We found many retrospective record review cross-sectional studies, mainly in South Africa and Nigeria, reporting the prevalence and incidence of cervical, breast and prostate cancer in SSA. There were a few systematic and scoping reviews. There is a scarcity of cervical, breast and prostate cancer burden studies in several SSA countries. The findings in this study can inform policy on improving the public health systems and therefore reduce cancer incidence and mortality in SSA.

A comparison between the characteristics of a biochar-NPK granule and a commercial NPK granule for application in the soil.

Continual application of nitrogen (N), phosphorous (P) and potassium (K) fertilizer may not return a profit to farmers due to the costs of application and the loss of NPK from soil in various ways. Thus, a combination of NPK granule with a porous biochar (termed here as BNPK) appears to offer multiple benefits resulting from the excellent properties of biochar. Given the lack of information on the properties of NPK and BNPK fertilizers, it is necessary to investigate the characteristics of both to achieve a good understanding of why BNPK granule is superior to NPK granule. Therefore, this study aims to investigate the characteristics of a maize straw biochar mixed with NPK granule, before and after application to soil, and compare them to those for a commercial NPK granule. The BNPK granule, with a greater surface area and porosity, showed a higher capacity to store and donate electrons than the NPK granule. Relatively lower concentrations of Ca, P, K, Si and Mg were dissolved from the BNPK, indicating the ability of the BNPK granule to maintain these mineral elements and reduce dissolution rate. To study the nutrient storage mechanism of the BNPK granule in the soil, short- and long-term leaching experiments were conducted. During the experiments, organo-mineral clusters, comprising C, P, K, Si, Al and Fe, were formed on the surface and inside the biochar pores. However, BNPK was not effective in reducing N leaching, in the absence of plants, in a red chromosol soil.

Predictors of colorectal cancer survival using cox regression and random survival forests models based on gene expression data.

Understanding and identifying the markers and clinical information that are associated with colorectal cancer (CRC) patient survival is needed for early detection and diagnosis. In this work, we aimed to build a simple model using Cox proportional hazards (PH) and random survival forest (RSF) and find a robust signature for predicting CRC overall survival. We used stepwise regression to develop Cox PH model to analyse 54 common differentially expressed genes from three mutations. RSF is applied using log-rank and log-rank-score based on 5000 survival trees, and therefore, variables important obtained to find the genes that are most influential for CRC survival. We compared the predictive performance of the Cox PH model and RSF for early CRC detection and diagnosis. The results indicate that SLC9A8, IER5, ARSJ, ANKRD27, and PIPOX genes were significantly associated with the CRC overall survival. In addition, age, sex, and stages are also affecting the CRC overall survival. The RSF model using log-rank is better than log-rank-score, while log-rank-score needed more trees to stabilize. Overall, the imputation of missing values enhanced the model's predictive performance. In addition, Cox PH predictive performance was better than RSF.

A stacking ensemble deep learning approach to cancer type classification based on TCGA data.

Cancer tumor classification based on morphological characteristics alone has been shown to have serious limitations. Breast, lung, colorectal, thyroid, and ovarian are the most commonly diagnosed cancers among women. Precise classification of cancers into their types is considered a vital problem for cancer diagnosis and therapy. In this paper, we proposed a stacking ensemble deep learning model based on one-dimensional convolutional neural network (1D-CNN) to perform a multi-class classification on the five common cancers among women based on RNASeq data. The RNASeq gene expression data was downloaded from Pan-Cancer Atlas using GDCquery function of the TCGAbiolinks package in the R software. We used least absolute shrinkage and selection operator (LASSO) as feature selection method. We compared the results of the new proposed model with and without LASSO with the results of the single 1D-CNN and machine learning methods which include support vector machines with radial basis function, linear, and polynomial kernels; artificial neural networks; k-nearest neighbors; bagging trees. The results show that the proposed model with and without LASSO has a better performance compared to other classifiers. Also, the results show that the machine learning methods (SVM-R, SVM-L, SVM-P, ANN, KNN, and bagging trees) with under-sampling have better performance than with over-sampling techniques. This is supported by the statistical significance test of accuracy where the p-values for differences between the SVM-R and SVM-P, SVM-R and ANN, SVM-R and KNN are found to be p = 0.003, p = < 0.001, and p = < 0.001, respectively. Also, SVM-L had a significant difference compared to ANN p = 0.009. Moreover, SVM-P and ANN, SVM-P and KNN are found to be significantly different with p-values p = < 0.001 and p = < 0.001, respectively. In addition, ANN and bagging trees, ANN and KNN were found to be significantly different with p-values p = < 0.001 and p = 0.004, respectively. Thus, the proposed model can help in the early detection and diagnosis of cancer in women, and hence aid in designing early treatment strategies to improve survival.

Advanced characterization of biomineralization at plaque layer and inside rice roots amended with iron- and silica-enhanced biochar.

Application of iron (Fe)- and silica (Si)-enhanced biochar compound fertilisers (BCF) stimulates rice yield by increasing plant uptake of mineral nutrients. With alterations of the nutrient status in roots, element homeostasis (e.g., Fe) in the biochar-treated rice root was related to the formation of biominerals on the plaque layer and in the cortex of roots. However, the in situ characteristics of formed biominerals at the micron and sub-micron scale remain unknown. In this study, rice seedlings (Oryza sativa L.) were grown in paddy soil treated with BCF and conventional fertilizer, respectively, for 30 days. The biochar-induced changes in nutrient accumulation in roots, and the elemental composition, distribution and speciation of the biomineral composites formed in the biochar-treated roots at the micron and sub-micron scale, were investigated by a range of techniques. Results of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) showed that biochar treatment significantly increased concentrations of nutrients (e.g., Fe, Si, and P) inside the root. Raman mapping and vibrating sample magnetometry identified biochar particles and magnetic Fe nanoparticles associated with the roots. With Fe plaque formation, higher concentrations of FeOx- and FeOxH- anions on the root surface than the interior were detected by time-of-flight secondary ionization mass spectrometry (ToF-SIMS). Analysis of data from scanning electron microscopy energy-dispersive spectroscopy (SEM-EDS), and from scanning transmission electron microscopy (STEM) coupled with EDS or energy electron loss spectroscopy (EELS), determined that Fe(III) oxide nanoparticles were accumulated in the crystalline fraction of the plaque and were co-localized with Si and P on the root surface. Iron-rich nanoparticles (Fe-Si nanocomposites with mixed oxidation states of Fe and ferritin) in the root cortex were identified by using aberration-corrected STEM and in situ EELS analysis, confirming the biomineralization and storage of Fe in the rice root. The findings from this study highlight that the deposition of Fe-rich nanocomposites occurs with contrasting chemical speciation in the Fe plaque and cortex of the rice root. This provides an improved understanding of the element homeostasis in rice with biochar-mineral fertilization.

Prediction of perinatal death using machine learning models: a birth registry-based cohort study in northern Tanzania.

OBJECTIVE: We aimed to determine the key predictors of perinatal deaths using machine learning models compared with the logistic regression model. DESIGN: A secondary data analysis using the Kilimanjaro Christian Medical Centre (KCMC) Medical Birth Registry cohort from 2000 to 2015. We assessed the discriminative ability of models using the area under the receiver operating characteristics curve (AUC) and the net benefit using decision curve analysis. SETTING: The KCMC is a zonal referral hospital located in Moshi Municipality, Kilimanjaro region, Northern Tanzania. The Medical Birth Registry is within the hospital grounds at the Reproductive and Child Health Centre. PARTICIPANTS: Singleton deliveries (n=42 319) with complete records from 2000 to 2015. PRIMARY OUTCOME MEASURES: Perinatal death (composite of stillbirths and early neonatal deaths). These outcomes were only captured before mothers were discharged from the hospital. RESULTS: The proportion of perinatal deaths was 3.7%. There were no statistically significant differences in the predictive performance of four machine learning models except for bagging, which had a significantly lower performance (AUC 0.76, 95% CI 0.74 to 0.79, p=0.006) compared with the logistic regression model (AUC 0.78, 95% CI 0.76 to 0.81). However, in the decision curve analysis, the machine learning models had a higher net benefit (ie, the correct classification of perinatal deaths considering a trade-off between false-negatives and false-positives)-over the logistic regression model across a range of threshold probability values. CONCLUSIONS: In this cohort, there was no significant difference in the prediction of perinatal deaths between machine learning and logistic regression models, except for bagging. The machine learning models had a higher net benefit, as its predictive ability of perinatal death was considerably superior over the logistic regression model. The machine learning models, as demonstrated by our study, can be used to improve the prediction of perinatal deaths and triage for women at risk.

Biochar-based fertilizer: Supercharging root membrane potential and biomass yield of rice.

Biochar-based compound fertilizers (BCF) and amendments have proven to enhance crop yields and modify soil properties (pH, nutrients, organic matter, structure etc.) and are now in commercial production in China. While there is a good understanding of the changes in soil properties following biochar addition, the interactions within the rhizosphere remain largely unstudied, with benefits to yield observed beyond the changes in soil properties alone. We investigated the rhizosphere interactions following the addition of an activated wheat straw BCF at an application rates of 0.25% (g·g-1 soil), which could potentially explain the increase of plant biomass (by 67%), herbage N (by 40%) and P (by 46%) uptake in the rice plants grown in the BCF-treated soil, compared to the rice plants grown in the soil with conventional fertilizer alone. Examination of the roots revealed that micron and submicron-sized biochar were embedded in the plaque layer. BCF increased soil Eh by 85 mV and increased the potential difference between the rhizosphere soil and the root membrane by 65 mV. This increased potential difference lowered the free energy required for root nutrient accumulation, potentially explaining greater plant nutrient content and biomass. We also demonstrate an increased abundance of plant-growth promoting bacteria and fungi in the rhizosphere. We suggest that the redox properties of the biochar cause major changes in electron status of rhizosphere soils that drive the observed agronomic benefits.

Pyrolysis of attapulgite clay blended with yak dung enhances pasture growth and soil health: Characterization and initial field trials.

Recent studies have shown that the pyrolysis of biomass combined with clay can result in both lower cost and increase in plant yields. One of the major sources of nutrients for pasture growth, as well as fuel and building materials in Tibet is yak dung. This paper reports on the initial field testing in a pasture setting in Tibet using yak dung, biochar, and attapulgite clay/yak dung biochars produced at ratios of 10/90 and 50/50 clay to dung. We found that the treatment with attapulgite clay/yak dung (50/50) biochar resulted in the highest pasture yields and grass nutrition quality. We also measured the properties and yields of mixtures of clay/yak dung biochar used in the field trials produced at 400°C and 500°C to help determine a possible optimum final pyrolysis temperature and dung/clay ratio. It was observed that increasing clay content increased carbon stability, overall biochar yield, pore size, carboxyl and ketone/aldehyde functional groups, hematite and ferrous/ferric sulphate/thiosulphate concentration, surface area and magnetic moment. Decreasing clay content resulted in higher pH, CEC, N content and an enhanced ability to accept and donate electrons. The resulting properties were a complex function of both processing temperature and the percentage of clay for the biochars processed at both 400°C and 500°C. It is possible that the increase in yield and nutrient uptake in the field trial is related to the higher concentration of C/O functional groups, higher surface area and pore volume and higher content of Fe/O/S nanoparticles of multiple oxidation state in the 50/50 clay/dung. These properties have been found to significantly increase the abundance of beneficial microorganisms and hence improve the nutrient cycling and availability in soil. Further field trials are required to determine the optimum pyrolysis production conditions and application rate on the abundance of beneficial microorganisms, yields and nutrient quality.