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Abstract This study investigated for the first time a simple bio-synthesis approach for the synthesis of copper oxide nanoparticles (CuO NPs) using Annona muricata L (A. muricata) plant extract to test their anti-cancer effects. The presence of CuONPs was confirmed by UV–visible spectroscopy, Scanning electron microscope (SEM), and Transmission electron microscope (TEM). The antiproliferative properties of the synthesized nanoparticles were evaluated against (AMJ-13), (MCF-7) breast cancer cell lines, and the human breast epithelial cell line (HBL-100) as healthy cells. This study indicates that CuONPs reduced cell proliferation for AMJ-13 and MCF-7. HBL-100 cells were not significantly inhibited for several concentration levels or test periods. The outcomes suggest that the prepared copper oxide nanoparticles acted against the growth of specific cell lines observed in breast cancer. It was observed that cancer cells had minor colony creation after 24 h sustained CuONPs exposure using (IC50) concentration for AMJ-13 was (17.04 µg mL−1). While for MCF-7 cells was (18.92 µg mL−1). It indicates the uptake of CuONPs by cancer cells, triggering apoptosis. Moreover, treatment with CuONPs enhanced Lactate dehydrogenase (LDH) production, probably caused by cell membrane damage, creating leaks comprising cellular substances like lactate dehydrogenase. Hence, research results suggested that the synthesized CuONPs precipitated anti-proliferative effects by triggering cell death through apoptosis.

Abstract Engineered Cementitious Composites (ECC) are highly regarded in construction owing to their tensile ductility and crack control capabilities, making them suitable for various structural applications. The accumulation of multi-walled carbon nanotubes (MWCNTs) further enhances their mechanical properties. However, there’s a significant knowledge gap concerning MWCNTs-ECC impact resistance. The objective of this study is to tackle the challenges associated with evaluating, optimizing, and predicting MWCNTs-ECC impact resistance to ensure its safe and widespread use in critical infrastructure by applying response surface methodology (RSM). Moreover, the 13 mixtures of ECC combined with several quantities of PVA fiber and MWCNTs as input elements were utilized to calculate the first (E1) and final (E2) impact energies. The findings demonstrated that the MWCNTs-ECC combinations’ impact resistance improved as the input ingredient concentrations increased. Besides, the optimum E1 and E2 of ECC combined with 1% of PVA fiber were noted by 1398 Joules and 12,956 Joules at 0.065% of MWCNTs on 28 days respectively. Furthermore, Response prediction models for E1 and E2 were created, and after being validated with an analysis of variance (ANOVA), it was determined that they had high R2 readings of 99.30% and 99.07%, correspondingly. The optimization process produced an ideal number of input variables for MWCNTs and PVA fiber, respectively, of 0.066% and 1%, with a desirability value of 100%. Moreover, it is recommended that the usage of 0.066% of MWCNTs in ECC combined with 1.0–1.50% PVA fiber provides optimum results for the construction industry.

Abstract In recent decades, numerous studies have examined the effects of climate change on the responses of plants. These studies have primarily examined the effects of solitary stress on plants, neglecting the simultaneous effects of mixed stress, which are anticipated to transpire frequently as a result of the extreme climatic fluctuations. Therefore, this study investigated the impact of applied chitosan on boosting the resistance responses of peanuts to alkali and mixed drought-alkali stresses. Peanuts were grown in mid-alkaline soil and irrigated with full irrigation water requirements (100%IR), represented alkali condition (100% IR × alkali soil) and stress conditions (70% IR × alkali soil—represented mixed drought-alkali conditions). Additionally, the plants were either untreated or treated with foliar chitosan. The study evaluated various plant physio-chemical characteristics, including element contents (leaves and roots), seed yield, and irrigation water use efficiency (IWUE). Plants that experienced solitary alkali stress were found to be more vulnerable. However, chitosan applications were effective for reducing (soil pH and sodium absorption), alongside promoting examined physio-chemical measurements, yield traits, and IWUE. Importantly, when chitosan was applied under alkali conditions, the accumulations of (phosphorus, calcium, iron, manganese, zinc, and copper) in leaves and roots were maximized. Under mixed drought-alkali stresses, the results revealed a reduction in yield, reaching about 5.1 and 5.8% lower than under (100% IR × alkali), in the first and second seasons, respectively. Interestingly, treated plants under mixed drought-alkali stresses with chitosan recorded highest values of relative water content, proline, yield, IWUE, and nutrient uptake of (nitrogen, potassium, and magnesium) as well as the lowest sodium content in leaves and roots. Enhances the accumulation of (N, K, and Mg) instead of (phosphorus, calcium, iron, manganese, zinc, and copper) was the primary plant response to chitosan applications, which averted severe damage caused by mixed drought-alkali conditions, over time. These findings provide a framework of the nutrient homeostasis changes induced by chitosan under mixed stresses. Based on the findings, it is recommended under mixed drought-alkali conditions to treat plants with chitosan. This approach offers a promising perspective for achieving optimal yield with reduced water usage.

Abstract Cryptosporidium is one of the most important protozoan parasitic pathogens, and it is a common cause of diarrhoea in humans, domestic animals, and wild vertebrates and has serious public health threats. A cross-sectional study was designed to determine the prevalence of Cryptosporidium oocysts in raw-eaten vegetables in Maiduguri Metropolitan Council (MMC) and Jere Local Government Areas (LGAs). A total of 400 samples were collected from four (4) different locations, namely Tashan Bama, Gomboru, and Monday Markets (n = 100), while fifty (n = 50) each from 202-Vegetable-Vendors and Unimaid Commercials. A total of 16 visits were conducted in all the sampling areas (twenty-five samples per visit). The Cryptosporidium oocysts were detected using the Modified Ziehl–Neelsen Staining Technique. The locations, sources, and types of raw-eaten vegetables were also assessed. The oocysts were confirmed (100×) as bright pink spherules. Data generated were analyzed using IBM-SPSS V23.0, and p ≤ 0.05 was considered significant. Out of the total samples (n = 400) analyzed, cabbage appeared to have the highest number of 10 (12.5%) of Cryptosporidium oocysts detected, while Tomato and garden egg had 9 (11.3%) and 1 (1.2%), respectively. There was a statistically significant association (χ2 = 12.5, P = 0.014) between the presence of Cryptosporidium oocysts in raw-eaten vegetables and vegetable types. Among the sources of the vegetables sampled, Alau had the highest number of Cryptosporidium oocysts, 15 (12.5%), followed by Kilari-Abdullahi and Zabarmari sources with 4 (10.0%) and 4 (5.0%), respectively. However, Jetete appeared to have the least number 2 (2.5%) of oocysts, and there was a statistically significant association (χ2= 10.4, P = 0.034) between the presence of Cryptosporidium oocysts and the sources of vegetables and fruits. The study concludes that the raw-eaten vegetables sampled from Maiduguri Metropolis were contaminated with Cryptosporidium oocysts. The study recommends that all raw-eaten vegetables should be from cleaned sources and washed before consumption. Consumers should be enlightened on the hygienic measures in the food chain in line with the Hazard Analysis and Critical Control Points (HACCP) principles.

Abstract Fibromyalgia (FM) is a chronic disorder characterized by widespread musculoskeletal pain, fatigue, and cognitive impairment. Despite the availability of various treatment options, FM remains a challenging condition to manage. In the present study, we investigated the efficacy of formulated nanodispersions of lutein and beta-carotene in treating FM-related symptoms induced by reserpine in female Wistar rats. Several techniques have been implemented to assess this efficacy at various levels, including biochemical, bioelectrical, and behavioral. Namely, oxidative stress markers, monoamine levels, electrocorticography, pain threshold test, and open field test were conducted on control, FM-induced, and FM-treated groups of animals. Our results provided compelling evidence for the efficacy of carotenoid nanodispersions in treating FM-related symptoms. Specifically, we found that the dual action of the nanodispersion, as both antioxidant and antidepressant, accounted for their beneficial effects in treating FM. With further investigation, nano-carotenoids and particularly nano-lutein could potentially become an effective alternative treatment for patients with FM who do not respond to current treatment options.

Smallholder farmers in sub-Saharan Africa (SSA) encounter multiple livelihood challenges. Embracing circular bioeconomy principles, particularly considering agricultural and food processing residues, could enable inclusive, locally led, sustainable development pathways within rural communities. Biochar products are one such example of a bio-based material that can be generated using circular principles and deployed for sustainable community development, including among smallholder farmers. This research leverages empirical evidence from four SSA regions to explore the potential of inclusive and sustainable biochar business models, namely: (i) Northern Region, Ghana, (ii) Yamoussoukro, Côte d'Ivoire, (iii) Casamance, Senegal, and (iv) Western Region, Uganda. Co-creation workshops using the Triple-Layered Business Model Canvas framework were carried out in each region with local stakeholders to evaluate the social, ecological, and economic implications of four locally relevant biochar applications: water filtration, biogas purification, soil amendment, and cooking fuel briquettes. Data was analysed at an aggregate level for all regions and applications. The study describes this consolidated biochar business model and examines the implications for SSA communities. The resulting sustainable bio-based business model can guide value chain actors and policymakers in SSA communities towards rural sustainable development with a better understanding of the needs, opportunities, challenges, and impacts of biochar-based value chain development., (© 2024. The Author(s).)

Individual-level assessment of health and well-being permits analysis of community well-being and health risk evaluations across several dimensions of health. It also enables comparison and rankings of reported health and well-being for large geographical areas such as states, metropolitan areas, and counties. However, there is large variation in reported well-being within such large spatial units underscoring the importance of analyzing well-being at more granular levels, such as ZIP codes. In this paper, we address this problem by modeling well-being data to generate ZIP code tabulation area (ZCTA)-level rankings through spatially informed statistical modeling. We build regression models for individual-level overall well-being index and scores from five subscales (Physical, Financial, Social, Community, Purpose) using individual-level demographic characteristics as predictors while including a ZCTA-level spatial effect. The ZCTA neighborhood information is incorporated by using a graph Laplacian matrix; this enables estimation of the effect of a ZCTA on well-being using individual-level data from that ZCTA as well as by borrowing information from neighboring ZCTAs. We deploy our model on well-being data for the U.S. states of Massachusetts and Georgia. We find that our model can capture the effects of demographic features while also offering spatial effect estimates for all ZCTAs, including ones with no observations, under certain conditions. These spatial effect estimates provide community health and well-being rankings of ZCTAs, and our method can be deployed more generally to model other outcomes that are spatially dependent as well as data from other states or groups of states., (© 2024. The Author(s).)

Diabetes mellitus (DM) is a significant risk factor for peripheral arterial disease (PAD), and PAD is an independent predictor of cardiovascular disorders (CVDs). Growing evidence suggests that long non-coding RNAs (lncRNAs) significantly contribute to disease development and underlying complications, particularly affecting smooth muscle cells (SMCs). So far, no study has focused on transcriptome analysis of lncRNAs in PAD patients with and without DM. Tissue samples were obtained from our Vascular Biobank. Due to the sample's heterogeneity, expression analysis of lncRNAs in whole tissue detected only ACTA2-AS1 with a 4.9-fold increase in PAD patients with DM. In contrast, transcriptomics of SMCs revealed 28 lncRNAs significantly differentially expressed between PAD with and without DM (FDR < 0.1). Sixteen lncRNAs were of unknown function, six were described in cancer, one connected with macrophages polarisation, and four were associated with CVDs, mainly with SMC function and phenotypic switch (NEAT1, MIR100HG, HIF1A-AS3, and MRI29B2CHG). The enrichment analysis detected additional lncRNAs H19, CARMN, FTX, and MEG3 linked with DM. Our study revealed several lncRNAs in diabetic PAD patients associated with the physiological function of SMCs. These lncRNAs might serve as potential therapeutic targets to improve the function of SMCs within the diseased tissue and, thus, the clinical outcome., (© 2024. The Author(s).)

Cryptosporidium is one of the most important protozoan parasitic pathogens, and it is a common cause of diarrhoea in humans, domestic animals, and wild vertebrates and has serious public health threats. A cross-sectional study was designed to determine the prevalence of Cryptosporidium oocysts in raw-eaten vegetables in Maiduguri Metropolitan Council (MMC) and Jere Local Government Areas (LGAs). A total of 400 samples were collected from four (4) different locations, namely Tashan Bama, Gomboru, and Monday Markets (n = 100), while fifty (n = 50) each from 202-Vegetable-Vendors and Unimaid Commercials. A total of 16 visits were conducted in all the sampling areas (twenty-five samples per visit). The Cryptosporidium oocysts were detected using the Modified Ziehl-Neelsen Staining Technique. The locations, sources, and types of raw-eaten vegetables were also assessed. The oocysts were confirmed (100×) as bright pink spherules. Data generated were analyzed using IBM-SPSS V23.0, and p ≤ 0.05 was considered significant. Out of the total samples (n = 400) analyzed, cabbage appeared to have the highest number of 10 (12.5%) of Cryptosporidium oocysts detected, while Tomato and garden egg had 9 (11.3%) and 1 (1.2%), respectively. There was a statistically significant association (χ 2  = 12.5, P = 0.014) between the presence of Cryptosporidium oocysts in raw-eaten vegetables and vegetable types. Among the sources of the vegetables sampled, Alau had the highest number of Cryptosporidium oocysts, 15 (12.5%), followed by Kilari-Abdullahi and Zabarmari sources with 4 (10.0%) and 4 (5.0%), respectively. However, Jetete appeared to have the least number 2 (2.5%) of oocysts, and there was a statistically significant association (χ 2 = 10.4, P = 0.034) between the presence of Cryptosporidium oocysts and the sources of vegetables and fruits. The study concludes that the raw-eaten vegetables sampled from Maiduguri Metropolis were contaminated with Cryptosporidium oocysts. The study recommends that all raw-eaten vegetables should be from cleaned sources and washed before consumption. Consumers should be enlightened on the hygienic measures in the food chain in line with the Hazard Analysis and Critical Control Points (HACCP) principles., (© 2023. The Author(s).)

Industrial chemical pollutants such as methylene blue (MB) dye are released into the water body and potentially cause harm to the human and aquatic biosphere. Therefore, this study aims to synthesize eco-friendly nanocatalysts, i.e., reduced graphene oxide (rGO), zinc oxide (ZnO), and reduced graphene oxide-zinc oxide (rGO@ZnO) nanocomposites, for efficient photocatalytic degradation of MB dye. A graphite rod was obtained from waste dry cell batteries for the electrochemical exfoliation synthesis of graphene oxide (GO) and rGO. For the eco-friendly synthesis of ZnO and rGO@ZnO nanocatalysts, Croton macrostachyus leaf extract was used as a reducing and capping agent. The synthesized nanocatalysts were characterized using a UV-Vis spectrophotometer, Fourier transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy with energy-dispersive X-ray. The eco-friendly synthesized rGO, ZnO, and rGO@ZnO nanocatalysts were applied for the photocatalytic degradation of MB dye using direct sunlight irradiation. At optimum parameters, photocatalytic degradation of MB dye efficiency reached up to 66%, 96.5%, and 99.0%, respectively. Furthermore, kinetics of the photodegradation reaction based on rGO, ZnO, and rGO@ZnO nanocatalysts follow pseudo-first-order with a rate constant of 2.16 × 10 -3  min -1 , 4.97 × 10 -3  min -1 , and 5.03 × 10 -3  min -1 , respectively. Lastly, this study promotes a low catalyst load (20 mg) for the efficient photodegradation of MB dye., (© 2023. The Author(s).)

Optimal early childhood development (ECD) is crucial in shaping future academic and economic accomplishments. Recognising its profound influence, the United Nations has included a specific target and indicator related to ECD in the Sustainable Development Goals to ensure universal access to high-quality ECD for all preschoolers by 2030. This study investigated whether caregiver-child engagements, early stimulation, and learning opportunities are associated with ECD among children aged 36-59 months. Data on 6752 children were pooled from the 2011 and 2017 Ghana Multiple Indicator Cluster Surveys. The ECD outcomes of interest were literacy-numeracy, social-emotional, learning-cognitive, and physical development, measured with UNICEF's Early Childhood Development Index (ECDI). Binary logistic regression assessed the effects of various caregiver-child engagements and early learning opportunities on the ECD outcomes. We found that most children were developmentally on track in the physical (95.0%) and learning-cognitive (86.4%) domains, but fewer were on track in the literacy-numeracy (36.7%) and social-emotional (68.6%) domains. Reading to or with a child (aOR 1.72; 95% CI 1.35-2.19), telling them stories (aOR 1.61; 95% CI 1.26-2.04), counting or drawing with them (aOR 1.63; 95% CI 1.30-2.04) and a child's attendance at preschool (aOR 4.62; 95% CI 3.34-6.38) were associated with a higher odds for on track literacy-numeracy development. Playing with a child was associated with higher odds of on-track social-emotional development (aOR 1.29; 95% CI 1.04-1.59), physical development (aOR 1.61; 95% CI 1.01-2.55), and learning-cognitive development (aOR 1.51; 95% CI 1.14-2.00). However, singing songs to or with a child (aOR 0.78; 95% CI 0.62-0.89) and taking a child outside the home (aOR 0.78; 95% CI 0.64-0.95) were associated with lower odds for on track social-emotional development. Access to children's books at home was associated with higher odds for on track literacy-numeracy and social-emotional development. In subgroup analysis, some observed associations varied depending on a child's residence (urban or rural). Our findings confirmed that caregiver-child  interaction and early learning opportunities could increase a child's likelihood of achieving early developmental milestones., (© 2023. Springer Nature Limited.)

The need for evidence-based data, to inform policy decisions on malaria vector control interventions in Nigeria, necessitated the establishment of mosquito surveillance sites in a few States in Nigeria. In order to make evidence-based-decisions, predictive studies using available data becomes imperative. We therefore predict the distribution of the major members of the Anopheles gambiae s.l. in Nigeria. Immature stages of Anopheles were collected from 72 study locations which span throughout the year 2020 resulted in the identification of over 60,000 Anopheline mosquitoes. Of these, 716 breeding sites were identified with the presence of one or more vector species from the An. gambiae complex and were subsequently used for modelling the potential geographical distribution of these important malaria vectors. Maximum Entropy (MaxEnt) distribution modeling was used to predict their potentially suitable vector habitats across Nigeria. A total of 23 environmental variables (19 bioclimatic and four topographic) were used in the model resulting in maps of the potential geographical distribution of three dominant vector species under current climatic conditions. Members of the An. gambiae complex dominated the collections (98%) with Anopheles stephensi, Anopheles coustani, Anopheles funestus, Anopheles moucheti, Anopheles nilli also present. An almost equal distribution of the two efficient vectors of malaria, An. gambiae and Anopheles coluzzii, were observed across the 12 states included in the survey. Anopheles gambiae and Anopheles coluzzii had almost equal, well distributed habitat suitability patterns with the latter having a slight range expansion. However, the central part of Nigeria (Abuja) and some highly elevated areas (Jos) in the savannah appear not suitable for the proliferation of these species. The most suitable habitat for Anopheles arabiensis was mainly in the South-west and North-east. The results of this study provide a baseline allowing decision makers to monitor the distribution of these species and establish a management plan for future national mosquito surveillance and control programs in Nigeria., (© 2023. Springer Nature Limited.)

The benefits of exclusive breastfeeding (EBF) for infant health and survival are well documented. However, its impact on educational outcomes has been contested and poorly researched in Africa. It has been hypothesised that positive associations reported in high-income countries can be attributed to residual confounding by socioeconomic status (SES). Our study investigated whether EBF duration in infancy is associated with educational attainment and age-for-grade attainment trajectories at school-age in rural Malawi. Longitudinal data on 1021 children at the Karonga demographic surveillance site in Malawi were analysed. Breastfeeding data were collected 3 months after birth and again at age one. The school grade of each child was recorded each year from age 6 until age 13. We calculated age-for-grade based on whether a child was at, over, or under the official expected age for a grade. Generalised estimating equations estimated the average effect of breastfeeding on age-for-grade. Latent class growth analysis identified age-for-grade trajectories, and multinomial logistic regression examined their associations with EBF. Maternal-child characteristics, SES, and HIV status were controlled. Overall, 35.9% of the children were exclusively breastfed for 6 months. Over-age for grade steadily increased from 9.6% at age 8 to 41.9% at age 13. There was some evidence that EBF for 6 months was associated with lower odds of being over-age for grade than EBF for less than 3 months (aOR = 0.82, 95%CI = 0.64-1.06). In subgroup analyses, children exclusively breastfed for 6 months in infancy were less likely to be over-age for grades between ages 6-9 (aOR = 0.64, 95%CI = 0.43-0.94). Latent class growth analysis also provided some evidence that EBF reduced the odds of falling behind in the early school grades (aOR = 0.66, 95%CI = 0.41-1.08) but not later. Our study adds to the growing evidence that EBF for 6 months has benefits beyond infant health and survival, supporting the WHO's recommendation on EBF., (© 2023. The Author(s).)

Reliable estimates of subnational vaccination coverage are critical to track progress towards global immunisation targets and ensure equitable health outcomes for all children. However, conflict can limit the reliability of coverage estimates from traditional household-based surveys due to an inability to sample in unsafe and insecure areas and increased uncertainty in underlying population estimates. In these situations, model-based geostatistical (MBG) approaches offer alternative coverage estimates for administrative units affected by conflict. We estimated first- and third-dose diphtheria-tetanus-pertussis vaccine coverage in Borno state, Nigeria, using a spatiotemporal MBG modelling approach, then compared these to estimates from recent conflict-affected, household-based surveys. We compared sampling cluster locations from recent household-based surveys to geolocated data on conflict locations and modelled spatial coverage estimates, while also investigating the importance of reliable population estimates when assessing coverage in conflict settings. These results demonstrate that geospatially-modelled coverage estimates can be a valuable additional tool to understand coverage in locations where conflict prevents representative sampling., (© 2023. The Author(s).)

Tuberculosis (TB) is major public health concern and Isoniazid Preventive Therapy (IPT) helps to prevent TB development among patients living with human immune deficiency virus (PLWHIV). However, the evidence is limited especially in the study area. Therefore, this study aimed to determine the prevalence and factors associated with TB among IPT users and non-IPT users of PLWHIV in Dessie, Ethiopia. A comparative cross-sectional study was employed for1 month in Dessie. A total of 326 respondents were selected using systematic random sampling. Bivariable and multivariable logistic regression analyses were computed to identify factors associated with Tuberculosis. In multivariable analysis, AOR with 95% CI was used to declare statistically significant variables with TB. The prevalence of TB among non-IPT users was 48.5%, (95% CI 40.8-56.2%), and among IPT users was 8%, (95% CI 5-13%). Cotrimoxazole prophylaxis therapy (CPT) (AOR = 5.835, 95% CI 2.565-13.274), IPT (AOR = 10.359, 95% CI 4.054-26.472), ART adherence (AOR = 30.542, 95% CI 12.871-72.475), and believing that IPT use prevents TB (AOR = 0.093, 95% CI 0.018-0.484) were statistically significant factors. The prevalence of TB was higher among non-IPT users than among IPT users. Therefore, efforts should be strengthened to implement widespread use of IPT among adult PLWHIV., (© 2022. The Author(s).)

Immune checkpoint inhibitors (ICI) with anti-PD-1/PD-L1 agents have improved the survival of patients with metastatic non-small cell lung cancer (mNSCLC). Tumor PD-L1 expression is an imperfect biomarker as it does not capture the complex interactions between constituents of the tumor microenvironment (TME). Using multiplex fluorescent immunohistochemistry (mfIHC), we modeled the TME to study the influence of cellular distribution and engagement on response to ICI in mNSCLC. We performed mfIHC on pretreatment tissue from patients with mNSCLC who received ICI. We used primary antibodies against CD3, CD8, CD163, PD-L1, pancytokeratin, and FOXP3; simple and complex phenotyping as well as spatial analyses was performed. We analyzed 68 distinct samples from 52 patients with mNSCLC. Patients were 39-79 years old (median 67); 44% were male and 75% had adenocarcinoma histology. The most used ICI was atezolizumab (48%). The percentage of PD-L1 positive epithelial tumor cells (EC), degree of cytotoxic T lymphocyte (CTL) engagement with EC, and degree of CTL engagement with helper T lymphocytes (HTL) were significantly lower in non-responders versus responders (p = 0.0163, p = 0.0026 and p = 0.0006, respectively). The combination of these 3 characteristics generated the best sensitivity and specificity to predict non-response to ICI and was also associated with shortened overall survival (p = 0.0271). The combination of low CTL engagement with EC and HTL along with low expression of EC PD-L1 represents a state of impaired endogenous immune reactivity. Together, they more precisely identified non-responders to ICI compared to PD-L1 alone and illustrate the importance of cellular interactions in the TME., (© 2022. The Author(s).)

This study examined the physical properties of agricultural drought (i.e., intensity, duration, and severity) in Hungary from 1961 to 2010 based on the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The study analyzed the interaction between drought and crop yield for maize and wheat using standardized yield residual series (SYRS), and the crop-drought resilient factor (CDRF). The results of both SPI and SPEI (-3, -6) showed that the western part of Hungary has significantly more prone to agricultural drought than the eastern part of the country. Drought frequency analysis reveals that the eastern, northern, and central parts of Hungary were the most affected regions. Drought analysis also showed that drought was particularly severe in Hungary during 1970-1973, 1990-1995, 2000-2003, and 2007. The yield of maize was more adversely affected than wheat especially in the western and southern regions of Hungary (1961-2010). In general, maize and wheat yields were severely non-resilient (CDRF < 0.8) in the central and western part of the country. The results suggest that drought events are a threat to the attainment of the second Sustainable Development Goals (SDG-2). Therefore, to ensure food security in Hungary and in other parts of the world, drought resistant crop varieties need to be developed to mitigate the adverse effects of climate change on agricultural production., (© 2022. The Author(s).)

Spatial pattern modelling concepts are being increasingly used in capturing disease heterogeneity. Quantification of heterogeneity in the tumor microenvironment is extremely important in pancreatic ductal adenocarcinoma (PDAC), which has been shown to co-occur with other pancreatic diseases and neoplasms with certain attributes that make visual discrimination difficult. In this paper, we propose the GaWRDenMap framework, that utilizes the concepts of geographically weighted regression (GWR) and a density function-based classification model, and apply it to a cohort of multiplex immunofluorescence images from patients belonging to six different pancreatic diseases. We used an internal cohort of 228 patients comprised of 34 Chronic Pancreatitis (CP), 71 PDAC, 70 intraductal papillary mucinous neoplasm (IPMN), 16 mucinous cystic neoplasm (MCN), 29 pancreatic intraductal neoplasia (PanIN) and 8 IPMN-associated PDAC patients. We utilized GWR to model the relationship between epithelial cells and immune cells on a spatial grid. The GWR model estimates were used to generate density signatures which were used in subsequent pairwise classification models to distinguish between any two pairs of disease groups. Image-level, as well as subject-level analysis, were performed. When applied to this dataset, our classification model showed significant discrimination ability in multiple pairwise comparisons, in comparison to commonly used abundance-based metrics, like the Morisita-Horn index. The model was able to best discriminate between CP and PDAC at both the subject- and image-levels. It was also able to reasonably discriminate between PDAC and IPMN. These results point to a potential difference in the spatial arrangement of epithelial and immune cells between CP, PDAC and IPMN, that could be of high diagnostic significance. Further validation on a more comprehensive dataset would be warranted., (© 2022. The Author(s).)

This study aims to generate rock units based on core permeability and porosity of OW oilfield in the Bredasdorp Basin offshore South Africa. In this study, we identified and classified lithofacies based on sedimentology reports in conjunction with well logs. Lucia's petrophysical classification method is used to classify rocks into three classes. Results revealed three lithofacies as A (sandstone, coarse to medium-grained), B (fine to medium-grained sandstone), and C (carbonaceous claystone, finely laminated with siltstone). Lithofacies A is the best reservoir quality and corresponds to class 1, while lithofacies B and C correspond to class 2 and 3, which are good and poor reservoir quality rock, respectively. An integrated reservoir zonation for the rocks is based on four different zonation methods (Flow Zone indicator (FZI), Winland r35, Hydraulic conductivity (HC), and Stratigraphy modified Lorenz plot (SMLP)). Four flow zones Reservoir rock types (RRTs) were identified as RRT1, RRT3, RRT4, and RRT5, respectively. The RRT5 is the best reservoir quality composed of a megaporous rock unit, with an average FZI value between 5 and 10 µm, and HC from 40 to 120 mD/v 3 , ranked as very good. The most prolific flow units (RRT5 and RRT4 zones) form more than 75% of each well's flow capacities are supplied by two flow units (FU1 and FU3). The RRT1 is the most reduced rock quality composed of impervious and nanoporous rock. Quartz is the dominant framework grain, and siderite is the dominant cement that affects flow zones. This study has demonstrated a robust approach to delineate flow units in the OW oilfield. We have developed a useful regional petrophysical reservoir rock flow zonation model for clastic reservoir sediments. This study has produced, for the first time, insights into the petrophysical properties of the OW oilfield from the Bredasdorp Basin South Africa, based on integration of core and mineralogy data. A novel sandstone reservoir zonation classification criteria developed from this study can be applied to other datasets of sandstone reservoirs with confidence., (© 2022. The Author(s).)

To describe the clinical features, epidemiology, autoantibody status, HLA haplotypes and genetic mechanisms of type 1 diabetes mellitus (T1DM). Patients (0-18 years) with diabetes were recruited. Clinical data was collected, autoantibodies and c-peptide were measured. Whole Genome Sequencing was performed. Genomic data analysis was compared with the known genes linked with T1DM and HLA alleles were studied. 1096 patients had one or more antibody positivity. The incidence of T1DM in 2020 was 38.05 per 100,000 children and prevalence was 249.73. GADA was the most common autoantibody followed by IAA. Variants in GSTCD, SKAP2, SLC9B1, BANK1 were most prevalent. An association of HLA haplotypes DQA1*03:01:01G (OR = 2.46, p value = 0.011) and DQB1*03:02:01G (OR = 2.43, p value = 0.022) was identified. The incidence of T1DM in Qatar is the fourth highest in the world, IA2 autoantibody was the most specific with some patients only having ZnT8 or IA2 autoantibodies thus underlining the necessity of profiling all 4 autoantibodies. The genes associated with T1DM in the Arab population were different from those that are common in the Caucasian population. HLA-DQ was enriched in the Qatari patients suggesting that it can be considered a major risk factor at an early age., (© 2021. The Author(s).)

Soil erosion (SE) and climate change are closely related to environmental challenges that influence human wellbeing. However, the potential impacts of both processes in semi-arid areas are difficult to be predicted because of atmospheric variations and non-sustainable land use management. Thus, models can be employed to estimate the potential effects of different climatic scenarios on environmental and human interactions. In this research, we present a novel study where changes in soil erosion by water in the central part of Iran under current and future climate scenarios are analyzed using the Climate Model Intercomparison Project-5 (CMIP5) under three Representative Concentration Pathway-RCP 2.6, 4.5 and 8.5 scenarios. Results showed that the estimated annual rate of SE in the study area in 2005, 2010, 2015 and 2019 averaged approximately 12.8 t ha -1  y -1 . The rangeland areas registered the highest soil erosion values, especially in RCP2.6 and RCP8.5 for 2070 with overall values of 4.25 t ha -1  y -1 and 4.1 t ha -1  y -1 , respectively. They were followed by agriculture fields with 1.31 t ha -1  y -1 and 1.33 t ha -1  y -1 . The lowest results were located in the residential areas with 0.61 t ha -1  y -1 and 0.63 t ha -1  y -1 in RCP2.6 and RCP8.5 for 2070, respectively. In contrast, RCP4.5 showed that the total soil erosion could experience a decrease in rangelands by - 0.24 t ha -1  y -1 (2050), and - 0.18 t ha -1  y -1 (2070) or a slight increase in the other land uses. We conclude that this study provides new insights for policymakers and stakeholders to develop appropriate strategies to achieve sustainable land resources planning in semi-arid areas that could be affected by future and unforeseen climate change scenarios.

Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurrence. In this study, we proposed to use a multiparametric MRI data as a sequence input for the convolutional neural network with the recurrent neural network based deep learning structure to discriminate between pseudoprogression and true tumor progression. In this study, 43 biopsy-proven patient data identified as diffuse infiltrating glioma patients whose disease progressed/recurred were used. The dataset consists of five original MRI sequences; pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, FLAIR, and ADC images as well as two engineered sequences; T1post-T1pre and T2-FLAIR. Next, we used three CNN-LSTM models with a different set of sequences as input sequences to pass through CNN-LSTM layers. We performed threefold cross-validation in the training dataset and generated the boxplot, accuracy, and ROC curve, AUC from each trained model with the test dataset to evaluate models. The mean accuracy for VGG16 models ranged from 0.44 to 0.60 and the mean AUC ranged from 0.47 to 0.59. For CNN-LSTM model, the mean accuracy ranged from 0.62 to 0.75 and the mean AUC ranged from 0.64 to 0.81. The performance of the proposed CNN-LSTM with multiparametric sequence data was found to outperform the popular convolutional CNN with a single MRI sequence. In conclusion, incorporating all available MRI sequences into a sequence input for a CNN-LSTM model improved diagnostic performance for discriminating between pseudoprogression and true tumor progression.

Diabetic retinopathy (DR) is a severe retinal disorder that can lead to vision loss, however, its underlying mechanism has not been fully understood. Previous studies have taken advantage of Optical Coherence Tomography (OCT) and shown that the thickness of individual retinal layers are affected in patients with DR. However, most studies analyzed the thickness by calculating summary statistics from retinal thickness maps of the macula region. This study aims to apply a density function-based statistical framework to the thickness data obtained through OCT, and to compare the predictive power of various retinal layers to assess the severity of DR. We used a prototype data set of 107 subjects which are comprised of 38 non-proliferative DR (NPDR), 28 without DR (NoDR), and 41 controls. Based on the thickness profiles, we constructed novel features which capture the variation in the distribution of the pixel-wise retinal layer thicknesses from OCT. We quantified the predictive power of each of the retinal layers to distinguish between all three pairwise comparisons of the severity in DR (NoDR vs NPDR, controls vs NPDR, and controls vs NoDR). When applied to this preliminary DR data set, our density-based method demonstrated better predictive results compared with simple summary statistics. Furthermore, our results indicate considerable differences in retinal layer structuring based on the severity of DR. We found that: (a) the outer plexiform layer is the most discriminative layer for classifying NoDR vs NPDR; (b) the outer plexiform, inner nuclear and ganglion cell layers are the strongest biomarkers for discriminating controls from NPDR; and (c) the inner nuclear layer distinguishes best between controls and NoDR.

The Cretaceous Afowo Formation in the Eastern Dohamey Basin is characterized by an admixture of lithofacies ranging from sandstones, claystones, shales, clays, sand/shale, and sand/clay intercalations. The sandy facies, a mix of sandstone, clay, shale, and intercalations, contain biodegraded hydrocarbons while the shales and claystones that underlie it are rich in organic matter. The hydrocarbon-bearing interval is commonly referred to as the oil sand or tar sand. In this study, Afowo clay type underlying an outcrop of the oil sand was appraised for its hydrocarbon potential with loss on ignition, thermogravimetry, and rock evaluation pyrolysis. Results obtained from loss on ignition showed that total organic matter content, a proxy to total organic carbon, for the Afowo clay type ranged from 9.410 to 38.750 wt%. The organic maturation temperature (Tmax) was determined using both thermogravimetry and rock evaluation pyrolysis (Rock-Eval). Thermogravimetric analysis produced reliable Tmax within the range of 417-424 °C for all the samples. The results from rock evaluation pyrolysis on the same samples showed that total organic carbon ranged from 0.81 to 18.46 wt% with Tmax ranging from 417 to 424 °C. It was not possible to determine Tmax for one of the samples with Rock-Eval due to a small S2 value (0.22 mg Hc/g). The variations in organic matter contents from loss on ignition agree with total organic carbon computed from rock evaluation pyrolysis; samples with high organic matter contents have corresponding high TOC values. This study demonstrates that loss on ignition and thermogravimetry could complement and augment rock evaluation pyrolysis data for petroleum source rock characterization.

Huanglongbing (HLB) is a serious disease of Citrus sp. worldwide. In Africa and the Mascarene Islands, a similar disease is known as African citrus greening (ACG) and is associated with the bacterium Candidatus Liberibacter africanus (Laf). In recent years, Candidatus Liberibacter asiaticus (Las) associated with the severe HLB has been reported in Ethiopia. Thus, we aimed to identify the Liberibacter species affecting citrus, the associated vectors in Eastern Africa and their ecological distribution. We assessed the presence of generic Liberibacter in symptomatic leaf samples by quantitative PCR. Subsequently, we sequenced the 50 S ribosomal protein L10 (rplJ) gene region in samples positive for Liberibacters and identified the species by comparison with public sequence data using phylogenetic reconstruction and genetic distances. We detected generic Liberibacter in 26%, 21% and 66% of plants tested from Uganda, Ethiopia and Kenya, respectively. The rplJ sequences revealed the most prevalent Liberibacters in Uganda and Ethiopia were LafCl (22%) and Las (17%), respectively. We detected Las in Kenya for the first time from three sites in the coastal region. Finally, we modelled the potential habitat suitability of Las in Eastern Africa using MaxEnt. The projection showed large areas of suitability for the pathogen in the three countries surveyed. Moreover, the potential distribution in Eastern Africa covered important citrus-producing parts of Ethiopia, Kenya, Uganda and Tanzania, and included regions where the disease has not been reported. These findings will guide in the development of an integrated pest management strategy to ACG/HLB management in Africa.

Colorectal cancer is currently the third leading cause of cancer related deaths. There is considerable interest in using dietary intervention strategies to prevent chronic diseases including cancer. Cardamonin is a spice derived nutraceutical and herein, for the first time we evaluated the therapeutic benefits of cardamonin in Azoxymethane (AOM) induced mouse model of colorectal cancer. Mice were divided into 4 groups of which three groups were given six weekly injections of AOM. One group served as untreated control and remaining groups were treated with either vehicle or Cardamonin starting from the same day or 16 weeks after the first AOM injection. Cardamonin treatment inhibited the tumor incidence, tumor multiplicity, Ki-67 and β-catenin positive cells. The activation of NF-kB signaling was also abrogated after cardamonin treatment. To elucidate the mechanism of action a global microRNA profiling of colon samples was performed. Computational analysis revealed that there is a differential expression of miRNAs between these groups. Subsequently, we extend our findings to human colorectal cancer and found that cardamonin inhibited the growth, induces cell cycle arrest and apoptosis in human colorectal cancer cell lines. Taken together, our study provides a better understanding of chemopreventive potential of cardamonin in colorectal cancer.

ATP synthases (ATPases) are enzymes that produce ATP and control the pH in the cell or cellular compartments. While highly conserved over different species, ATPases are structurally well-characterised but the existence and functional significance of many post-translational modifications (PTMs) is not well understood. We combined a range of mass spectrometric techniques to unravel the location and extent of PTMs in the chloroplast ATP synthase (cATPase) purified from spinach leaves. We identified multiple phosphorylation and acetylation sites and found that both modifications stabilise binding of ε and δ subunits. Comparing cross-linking of naturally modified cATPase with the in vitro deacetylated enzyme revealed a major conformational change in the ε subunit in accord with extended and folded forms of the subunit. Locating modified residues within the catalytic head we found that phosphorylated and acetylated residues are primarily on α/β and β/α interfaces respectively. By aligning along different interfaces the higher abundance acetylated residues are proximal to the regulatory sites while the lower abundance phosphorylation sites are more densely populated at the catalytic sites. We propose that modifications in the catalytic head, together with the conformational change in subunit ε, work in synergy to fine-tune the enzyme during adverse conditions.

Outlier detection in bioinformatics data streaming mining has received significant attention by research communities in recent years. The problems of how to distinguish noise from an exception and deciding whether to discard it or to devise an extra decision path for accommodating it are causing dilemma. In this paper, we propose a novel algorithm called ODR with incrementally Optimized Very Fast Decision Tree (ODR-ioVFDT) for taking care of outliers in the progress of continuous data learning. By using an adaptive interquartile-range based identification method, a tolerance threshold is set. It is then used to judge if a data of exceptional value should be included for training or otherwise. This is different from the traditional outlier detection/removal approaches which are two separate steps in processing through the data. The proposed algorithm is tested using datasets of five bioinformatics scenarios and comparing the performance of our model and other ones without ODR. The results show that ODR-ioVFDT has better performance in classification accuracy, kappa statistics, and time consumption. The ODR-ioVFDT applied onto bioinformatics streaming data processing for detecting and quantifying the information of life phenomena, states, characters, variables and components of the organism can help to diagnose and treat disease more effectively.