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A Computer Aided Diagnosis (CAD) framework to diagnose Pulmonary Edema (PE) and covid-19 from the chest Computed Tomography (CT) slices were developed and implemented in this work. The lung tissues have been segmented using Otsu’s thresholding method. The Regions of Interest (ROI) considered in this work were edema lesions and covid-19 lesions. For each ROI, the edema lesions and covid-19 lesions were elucidated by an expert radiologist, followed by texture and shape extraction. The extracted features were stored as feature vectors. The feature vectors were split into train and test set in the ratio of 80 : 20. A wrapper based feature selection approach using Squirrel Search Algorithm (SSA) with the Support Vector Machine (SVM) classifier’s accuracy as the fitness function was used to select the optimal features. The selected features were trained using the Back Propagation Neural Network (BPNN) classifier. This framework was tested on a real-time PE and covid-19 dataset. The BPNN classifier’s accuracy with SSA yielded 88.02%, whereas, without SSA it yielded 83.80%. Statistical analysis, namely Wilcoxon’s test, Kendall’s Rank Correlation Coefficient test and Mann Whitney U test were performed, which indicates that the proposed method has a significant impact on the accuracy, sensitivity and specificity of the novel dataset considered. Comparative experimentations of the proposed system with existing benchmark ML classifiers, namely Cat Boost, Ada Boost, XGBoost, RBF SVM, Poly SVM, Sigmoid SVM and Linear SVM classifiers demonstrate that the proposed system outperforms the benchmark classifiers’ results.

Premature mortality from cardiovascular disease can be reduced with early detection of heart failure by analysing the patients’ risk factors and assuring accurate diagnosis. This work proposes a clinical decision support system for the diagnosis of congenital heart failure by utilizing a data pre-processing approach for dealing missing values and a filter-wrapper based method for selecting the most relevant features. Missing values are imputed using a missForest method in four out of eight heart disease datasets collected from the Machine Learning Repository maintained by University of California, Irvine. The Fast Correlation Based Filter is used as the filter approach, while the union of the Atom Search Optimization Algorithm and the Henry Gas Solubility Optimization represent the wrapper-based algorithms, with the fitness function as the combination of accuracy, G-mean, and Matthew’s correlation coefficient measured by the Support Vector Machine. A total of four boosted classifiers namely, XGBoost, AdaBoost, CatBoost, and LightGBM are trained using the selected features. The proposed work achieves an accuracy of 89%, 84%, 83%, 80% for Heart Failure Clinical Records, 81%, 80%, 83%, 82% for Single Proton Emission Computed Tomography, 90%, 82%, 93%, 80% for Single Proton Emission Computed Tomography F, 80%, 80%, 81%, 80% for Statlog Heart Disease, 80%, 85%, 83%, 86% for Cleveland Heart Disease, 82%, 85%, 85%, 82% for Hungarian Heart Disease, 80%, 81%, 79%, 82% for VA Long Beach, 97%, 89%, 98%, 97%, for Switzerland Heart Disease for four classifiers respectively. The suggested technique outperformed the other classifiers when evaluated against Random Forest, Classification and Regression Trees, Support Vector Machine, and K-Nearest Neighbor.

Diurnal temperature range (DTR) is an important derived variable used in detecting signature of observed climate changes. Understanding its changes in recent time is important for managing and coping with climate change induced risks. This study analysed the long term trend and abrupt changes in diurnal temperature range over Nigeria from 1960 to 2019. Descriptive statistics, Mann-Kendall trend test, Sen’s slope and Pettit’s tests were used to assess the characteristics, trend, abrupt change and significance in annual DTR time series. Pearson correlation was used to examine the spatial relationship between DTR and minimum temperature, maximum temperature, precipitation and cloud cover. Results showed that mean DTR amount varied across spatio-temporal scales with steady increase from the coastal region, coupled with a weak inter-annual variability (CV < 3%). The trend analysis showed a significant decreasing DTR in most grid points (GP) and regions of Nigeria, with the exception of Enugu, Ado-Ekiti and Warri. Also a significant negative trend was observed at the Guinea, Sudan and Sahel savanna regions. Abrupt changes occurred in the 1970s in the entire regions and most of the GPs with Ado-Ekiti, Calabar, and Enugu occurred in 1991 while Maiduguri and Jos experienced such changes in 2011. Furthermore, significant abrupt changes were observed at the following GPs and regions; Ikeja, Kaduna, Kano, Katsina, Sokoto, Yelwa, Yola, Guinea, Sudan and Sahel Savannas. This decrease in DTR over most of the GP and the entire region indicate that the climate of Nigeria is becoming warmer possibly due to environmental changes with precipitation and cloud cover being the major drivers of DTR variability in Nigeria. Thus, proper climate actions should be taken through adaptation and resilient planning for the sustainable development of socio-economic and natural systems.&nbsp

Increasing interest in wave propagation in phononic systems and metamaterials motivates the development of experimental designs, measurement techniques, and fabrication methods for use in basic research and classroom demonstrations. The simplest phononic system, the monatomic chain, exhibits rich physics such as dispersion and frequency-domain filtering. However, a limited number of experimental studies showcase monatomic chains for macroscale observation of phonons. Herein, we discuss the design, fabrication, and testing of monatomic lattices as enabled by three-dimensional (3D) printing. Using this widely available technology, we provide design guidelines for realization of a monatomic chain composed of 3D printed serpentine springs and press-fitted cylindrical masses. We also present measurement techniques that record propagating waves and algorithms for the experimental determination of dispersion behavior.

Coffee husk (CH) is a sustainable and abundantly available cellulosic waste material. Its fiber consists of cellulose as the major structural part which leads to potential utilization for the manufacturing of microcrystalline cellulose (MCC) products that can be utilized for different industrial applications. In the present study, chemical composition of outer skin-isolated coffee husk was determined and sequential treatments of various untreated (UT) sample, ethanol—toluene treated sample through dewaxed (DW) treatment, sodium hydroxide (NaOH)—treated sample through alkali (AT) treatment, and sulfuric acid (H2SO4)—treated sample through bleaching (BL) treatment have been carried out. The Micro Crystalline Cellulose (MCC) has been extracted through hydrogen peroxide (H2O2) after BL treatment. The BL treatment for MCC extraction process was conducted without chlorine and additional harsh acid treatment, respectively. The characterization of chemically treated samples was carried out to investigate their morphological, physico-chemistry, and thermal behavior through a scanning electron microscope (SEM), Fourier transform infrared—ray (FTIR), X-ray diffraction (XRD), thermo gravimetric analysis (TGA), and differential temperature analyzer (DTA). From the chemical composition analysis; the cellulose, hemicellulose, lignin, and extractive content were determined and its values were (52.9%), (12.5%), (24.3%), and (9.4%), respectively. In the morphological examination, the great untreated (UT) fiber sample was greatly reduced into a micro-sized BL sample, revealing that (from FTIR analysis) the lignin and hemicellulose contents were greatly removed during chemical treatments and the presence of a micro crystalline cellulose region with 54.7% yield. Also, the sample AT and BL showed the lowest amorphous region in X-RD due to the removal of hemicellulose and lignin. The highest crystallinity index has been determined for the BL sample, i.e., 89.9%. Additionally, the thermal analysis shows that the AT and BL sample has great thermal stability than other (UT and DW) samples at high temperature. Therefore, the outer skin separated coffee husk was prepared from agricultural waste was subjected to eco-friendly chemical treatments to yield MCC. Thus, the extracted MCC is expected to be reliable for replacing other plant materials for the production of crystalline nanomaterial and reinforcing constituent for the fabrication of bio composite.

Software developers find it difficult to select the specific detection rules for different smell types. A set of metrics, thresholds and labels constitutes a code smells detection rule. The generated rules must be optimized efficiently to ensure successful rule selection. The objective is to identify how rules are generated from the labeled data set and selected using bio-inspired algorithms. The goals are met by employing the C4.5 and RIPPER algorithms to generate rules then, optimized using two bio-inspired algorithms, the Salp Swarm Algorithm (SSA) and Cockroach Swarm Optimization (CSO). The optimized sets of rules are evaluated using the similarity metrics which are computed with the help of expected and the detected code smells. The common rule subsets from SSA and CSO are merged to produce the optimal rule subset which can be used for code smell detection. The proposed work has been experimented on Xerces-J, Log4J, Gantt Project and JFreeChart dataset. The work detected code smells with an accuracy of 91.7% for Xerces-J, 96.7% for JFreeChart, 88.6% for Gantt Project and 98% for Log4J. The findings will be useful for both theory and research since the proposed framework allows focusing on rule selection.

A new classification framework for a Clinical Decision Support System, utilizing a Genetic algorithm and an Artificial Flora Optimized Neural Network is presented in this paper. GAFON is an artificial neural network whose topology is optimized with Genetic Algorithm and the learnable parameters are optimized with Artificial Flora Optimization algorithm. Drop out technique is used in the topology optimization phase and weight regularization is used in the parameter optimization phase. The proposed method minimizes the co-adaptation problem, reduces over-fitting of training data and improves the generalization of a feed forward neural network. The classification framework developed has been tested for classifying both multi class and binary class clinical datasets. The proposed method attained accuracy values of 86.82% for Hepatitis C Virus (HCV) for Egyptian patients, 84.91% for Vertebral Column 95.65% for Statlog Heart Disease (SHD), SHD and 93.79% for Early Stage Diabetes Risk Prediction (ESDRP), all datasets obtained from UCI repository

The development of advanced technologies in variety of domains such as health care, sensor measurements, intrusion detection, motion capture, environment monitoring have directed to the emergence of large scale time stamped data that varies over time. These data are influenced by complexities such as missing values, multivariate attributes, time-stamped features. The objective of the paper is to construct temporal classification framework using stacked Gated Recurrent Unit (S-GRU) for predicting ozone level. Ozone level prediction plays a vital role for accomplishing healthy living environment. Temporal missing value imputation and temporal classification are two functions performed by the proposed system. In temporal missing value imputation, the temporal correlated k-nearest neighbors (TCO-KNN) approach is presented to address missing values. Using attribute dependency based KNN, the nearest significant set is identified for each missing value. The missing values are imputed using the mean values from the determined closest significant set. In temporal classification, the classification model is build using stacked gated recurrent unit (S-GRU). The performance of the proposed framework investigated using ozone multivariate temporal data sets shows improvement in classification accuracy compared to other state of art methods.

Oxidative stress has been implicated in the pathogenesis of many pathological cases. it is implicated in both organ and systemic dysfunction. Gestational diabetes mellitus (GDM) is a pathological condition with first onset observed during pregnancy. It causes pregnancy complications and affects 2-7% of pregnant women. For that purpose, this studywas performed with 94 pregnant women with informed consent,51 were diagnosed with GDMand 43 apparently healthy pregnant women. The following parameters were assessed: Malondialdehyde (MDA), Total Antioxidant Capacity (TAC), Total Plasma Peroxide (TPP) and Oxidative Stress Index (OSI). MDA, TPP, and TAC were determined colorimetrically and OSI calculated. Anthropometric indices (weight, height, BMI) and socio-demographic data were determined using standard methods. Data generated were analysed using ANOVA, Student t-test, LSD post hoc and Pearson correlation at P

Corruption is a monster that defies any attempt to slaying it in many respects. Further, corruption is hard to define and isolate because it mutates into many forms in very short times. Many societies have suffered because corruption becomes rampant and almost into an everyday phenomenon. Where that is the case, corruption morphs into a type of violence which robs society of its access to what is commonly known as ‘Common Good’. Common Good is the ideal which every member of a society looks forward to enjoying without undue restriction, it is the un-inalienable right that every member of society expects by virtue of being human. In parts of the African Continent, corruption prevails as though the abnormal is literary normalized. In this article, I make an assertion that corruption is a form of violence which compromises access to human dignity and modest self-esteem. In itself, violence is bad enough as it makes life and enjoyment of life impossible. The conceptualization that is presented herein including the attempt that is made to prove the observations using various evidence weaves the statements into a legitimate concept that cannot be overstated nor gainsaid. The article perceives the denial of the common good at two levels, namely at the individual and at the national levels. Taking the evidence provided and discussed in the article leaves no room for doubt in respect to the assertion that corruption is indeed a brutal form of ‘violence against the common good’.

Basal-like breast cancers are an aggressive breast cancer subtype, which often lack estrogen receptor, progesterone receptor, and Her2 expression, and are resistant to antihormonal and targeted therapy, resulting in few treatment options. Understanding the underlying mechanisms that regulate progression of basal-like breast cancers would lead to new therapeutic targets and improved treatment strategies. Breast cancer progression is characterized by inflammatory responses, regulated in part by chemokines. The CCL2/CCR2 chemokine pathway is best known for regulating breast cancer progression through macrophage-dependent mechanisms. Here, we demonstrated important biological roles for CCL2/CCR2 signaling in breast cancer cells. Using the MCF10CA1d xenograft model of basal-like breast cancer, primary tumor growth was significantly increased with cotransplantation of patient-derived fibroblasts expressing high levels of CCL2, and was inhibited with CRISP/R gene ablation of stromal CCL2. CRISP/R gene ablation of CCR2 in MCF10CA1d breast cancer cells inhibited breast tumor growth and M2 macrophage recruitment and validated through CCR2 shRNA knockdown in the 4T1 model. Reverse phase protein array analysis revealed that cell-cycle protein expression was associated with CCR2 expression in basal-like breast cancer cells. CCL2 treatment of basal-like breast cancer cell lines increased proliferation and cell-cycle progression associated with SRC and PKC activation. Through pharmacologic approaches, we demonstrated that SRC and PKC negatively regulated expression of the cell-cycle inhibitor protein p27KIP1, and are necessary for CCL2-induced breast cancer cell proliferation.Implications:This report sheds novel light on CCL2/CCR2 chemokine signaling as a mitogenic pathway and cell-cycle regulator in breast cancer cells.

Supplementary Figure 1. Molecular characterization of fibroblasts. Supplemental Figure 2. Fibroblasts do not significantly affect metastasis of MCF10CA1d breast tumor xenografts. Supplemental Figure 3. CCR2 knockdown in 4T1 mammary carcinoma cells inhibits fibroblast mediated tumor growth. Supplementary Figure 4. CCL2 not affect AKT, SMAD3 or ERK1/2 phosphorylation in MCF10CA1d breast cancer cells. Supplemental Figure 5. Heatmap analysis of cell growth and cell cycle related proteins in CCR2 deficient breast cancer cells.

Supplemental Figures S1-S7

Background Thermophilic composting is a promising method of sanitizing pathogens in manure and a source of agriculturally important thermostable enzymes and microorganisms from organic wastes. Despite the extensive studies on compost prokaryotes, shifts in microbial profiles under the influence of various green materials and composting days are still not well understood, considering the complexity of the green material sources. Here, the effect of regimens of green composting material on the diversity, abundance, and metabolic capacity of prokaryotic communities in a thermophilic compost environment was examined. Methods Total community 16S rRNA was recovered from triplicate compost samples of Lantana-based, Tithonia-based, Grass-based, and mixed (Lantana + Tithonia + Grass)- based at 21, 42, 63, and 84 days of composting. The 16S rRNA was sequenced using the Illumina Miseq platform. Bioinformatics analysis was done using Divisive Amplicon Denoising Algorithm version 2 (DADA2) R version 4.1 and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States version 2 (PICRUSt2) pipelines for community structure and metabolic profiles, respectively. In DADA2, prokaryotic classification was done using the Refseq-ribosomal database project (RDP) and SILVA version 138 databases. Results Our results showed apparent differences in prokaryotic community structure for total diversity and abundance within the four compost regimens and composting days. The study showed that the most prevalent phyla during composting included Acidobacteriota, Actinobacteriota, Bacteroidota, Chloroflexi, and Proteobacteria. Additionally, there were differences in the overall diversity of metabolic pathways but no significant differences among the various compost treatments on major metabolic pathways like carbohydrate biosynthesis, carbohydrate degradation, and nitrogen biosynthesis. Conclusion Various sources of green material affect the succession of compost nutrients and prokaryotic communities. The similarity of amounts of nutrients, such as total Nitrogen, at the end of the composting process, despite differences in feedstock material, indicates a significant influence of composting days on the stability of nutrients during composting.

The family is an institution on which a nation is built. Where there are strong family structures, there are strong nations. This paper deals with a social sift whereby more and more adults are choosing not to marry and keep families but instead are opting to remain single. The discussion in this paper alleges that the needs of the single man or woman in the church are not taken care of. Consider for instance, that church programmes cater for the needs of children very well. The needs of the youth are meticulously catered for. Married people are served in elaborate family-life programmes and in a similar version, widows are provided for by religious and economic empowerment associations. But when it comes to the needs of the single adult, no provision is made to address exceptional needs such as management of sexual needs within that category. Bible writers specifically address other categories in the church, not so the single adult, man or woman. The church teaches the single adult to keep one’s sexual drive under wraps until marriage. The teaching regulates engagement in sex before marriage and out of marriage assuming that the person will eventually marry. This paper emphasizes the impact singlehood is posing to the people in the church including the life of the church today.

Three new pentacyclic triterpenes, henninglupal (3), 19-ethylene henningsyl (4), and 19-ethylene henningsal (6), along with the previously reported crystal heninngsal (2) and alpha-amyrin acetate (5) were isolated from a methanolic extract of the bark of Strychnos henningsii Gilg. The new pentacyclic triterpenes were elucidated through comprehensive spectroscopic analysis, including 1D, 2D NMR, and High-resolution LCMS. The in vitro MTT cytotoxicity of compound 2 – 6 was evaluated on Caco-2 cell lines, where all the isolated compounds exhibited low cytotoxic effects up to concentrations of 125 µM.

To describe the clinical characteristics of varicella patients seeking medical consultation and the use of antimicrobials for their management in Thailand in the absence of universal varicella vaccination (UVV). A multicenter, retrospective chart review of 260 children and adults with a primary diagnosis of varicella was conducted at one private and three public hospitals in Bangkok, Thailand. Charts of varicella patients (inpatient or outpatient) were randomly selected over a 5-year period. Key outcomes included clinical complications and the use of antibiotics, antivirals, and other medications. Charts of 200 children (mean age 5.7 years, range 0.3–16 years) and 60 adults (mean age 27.9 years, range 18–50 years) were reviewed. Fourteen patients (including 8 children) were hospitalized. Five percent of the children and none of the adults were immunocompromised. At least 1 varicella-related complication was reported by 7.3% (7% of children, 8.3% of adults, p = .778) of all patients, including 57.1% (62.5% of children, 50% of adults) of inpatients (p < .001, compared with outpatients). Skin/soft tissue infection (47.7%) and dehydration (47.4%) were the most common complications. Antivirals (mainly oral acyclovir) were prescribed to 46.5% of patients (31.5% of children, 96.7% of adults, p < .001). Antibiotics were prescribed to 20.8% of patients (19% of children, 26.7% of adults, p = .199). Topical, oral, and intravenous antibiotics were prescribed to 12.3%, 8.5%, and 1.2% of patients, respectively. Antimicrobial prescriptions were higher among adults (p < .001) and immunocompromised patients (p = .025). Apart from antimicrobials, acetaminophen (62.3%) and oral antihistamines (51.5%) were the most prescribed. A considerable number of varicella patients, both children and adults, seeking medical consultation in Thai hospitals are prescribed antibiotics and antivirals, with one-fifth of patients being prescribed an antibiotic and almost half prescribed an antiviral. The study may be of interest to policymakers in Thailand and other Asia-Pacific countries considering UVV implementation.

Motivation Post-genome-wide association studies (pGWAS) analysis is designed to decipher the functional consequences of significant single-nucleotide polymorphisms (SNPs) in the era of GWAS. This can be translated into research insights and clinical benefits such as the effectiveness of strategies for disease screening, treatment and prevention. However, the setup of pGWAS (pGWAS) tools can be quite complicated, and it mostly requires big data. The challenge however is, scientists are required to have sufficient experience with several of these technically complex and complicated tools in order to complete the pGWAS analysis. Results We present SysBiolPGWAS, a pGWAS web application that provides a comprehensive functionality for biologists and non-bioinformaticians to conduct several pGWAS analyses to overcome the above challenges. It provides unique functionalities for analysis involving multi-omics datasets and visualization using various bioinformatics tools. SysBiolPGWAS provides access to individual pGWAS tools and a novel custom pGWAS pipeline that integrates several individual pGWAS tools and data. The SysBiolPGWAS app was developed to be a one-stop shop for pGWAS analysis. It targets researchers in the area of the human genome and performs its analysis mainly in the autosomal chromosomes. Availability and implementation SysBiolPGWAS web app was developed using JavaScript/TypeScript web frameworks and is available at: https://spgwas.waslitbre.org/. All codes are available in this GitHub repository https://github.com/covenant-university-bioinformatics.

Diabetes mellitus is the third leading cause of death after heart disease and cancer. It is a growing global problem and places a heavy financial burden on healthcare services due to the cost of orthodox medicine and associated toxicities. Thus, the demand for cheaper and safer alternatives is on the increase. Carissa edulis is widely used traditionally to cure diabetes and it was found to be relatively safe. This research is aimed to evaluate the effect of methanolic leaf extract of Carissa edulis on the glucose level and the body weight of alloxan-induced diabetic rats. Carissa edulis leaves were harvested fresh in a garden at Chikun Local Government Area of Kaduna State. Cold maceration was used for the extraction using methanol as solvent. Diabetes mellitus was induced by the administration of alloxan hydrate (150 mg/kg body weight). The extract was administered orally at 75 mg/kg, 150 mg/kg, and 300 mg/kg body weight while a standard drug, metformin was administered at 100 mg/kg body weight. All treatments lasted for 14 days. There was a significant decrease in blood glucose concentration in the groups treated with 150 mg/kg and 300 mg/kg methanolic leaf extract of Carissa edulis compared to the negative control group. The group treated with 300 mg/kg b.w had lower glucose levels compared to the normal control group. A significant increase in body weight was observed in the extract control group compared to the remaining groups. It can be inferred that the methanol leaf extract of Carissa edulis has antihyperglycemic effect and it reduces the level of blood glucose in a dose dependent manner and can potentially be explored in the search for new anti-diabetic agents.

Three new proaporphine alkaloids, cissamaline (

UK guidelines recommend an HbA1c 0.05). There was no difference in 30-day re-admission rates between groups 2.8%, 2.9% and 3.3% (p = 0.983). At 6 months and 1 year, there was sustained and equal reduction in HbA1c in all groups (p

Water is a sustaining element which is vital for all living being ,the discharge is a major concept in hydrography that presents information on the availability of water for human use and resource management. The aim of the research is to analyze the volume of discharge of River Benue at Jimeta with a view to provide early warning changes for flood. The primary data used were sounding, planimetric points and the velocity while the secondary data includes the discharge of previous years of 1999, 2002, and 2012 and the topographic map of the study area. The statistical analysis carried out using ANOVA to ascertain the assessment of the discharge; the result shows that there was decrease in the volume of discharge in the preceding years. The average velocity of water obtained was 1.03 m/s; it is as a result of the channel platform with 1.42% slope within the study area .In conclusion, the volume of discharge is decreasing with increase in sedimentation within the study area. Based on the findings of the research, it is recommended that there should be restoration measures such as dredging and buffer zone so as to create more space for adequate flow of water and to reduce the risk of flooding.

Due to the COVID-19 pandemic, countries across the globe has enforced lockdown restrictions that influence the people's socio-economic lifecycle. The objective of this paper is to predict the communal emotion of people from different locations during the COVID-19 lockdown. The proposed work aims in developing a deep spatio-temporal analysis framework of geo-tagged tweets to predict the emotions of different topics based on location. An optimized Latent Dirichlet Allocation (LDA) approach is presented for finding the optimal hyper-parameters using grid search. A multi-class emotion classification model is then built via a Recurrent Neural Network (RNN) to predict emotions for each topic based on locations. The proposed work is experimented with the twitter streaming API dataset. The experimental results prove that the presented LDA model-using grid search along with the RNN model for emotion classification outperforms the other state of art methods with an improved accuracy of 94.6%. © 2021 - IOS Press. All rights reserved.

Quinine (an antimalarial) and aspirin (a nonsteroidal anti-inflammatory drug) were combined into a new drug–drug salt, quininium aspirinate, C20H25N2O2 +·C9H7O4 −, by liquid-assisted grinding using stoichiometric amounts of the reactants in a 1:1 molar ratio, and water, EtOH, toluene, or heptane as additives. A tetrahydrofuran (THF) solution of the mechanochemical product prepared using EtOH as additive led to a single crystal of the same material obtained by mechanochemistry, which was used for crystal structure determination at 100 K. Powder X-ray diffraction ruled out crystallographic phase transitions in the 100–295 K interval. Neat mechanical treatment (in a mortar and pestle, or in a ball mill at 20 or 30 Hz milling frequencies) gave rise to an amorphous phase, as shown by powder X-ray diffraction; however, FT–IR spectroscopy unambiguously indicates that a mechanochemical reaction has occurred. Neat milling the reactants at 10 and 15 Hz led to incomplete reactions. Thermogravimetry and differential scanning calorimetry indicate that the amorphous and crystalline mechanochemical products form glasses/supercooled liquids before melting, and do not recrystallize upon cooling. However, the amorphous material obtained by neat grinding crystallizes upon storage into the salt reported. The mechanochemical synthesis, crystal structure analysis, Hirshfeld surfaces, powder X-ray diffraction, thermogravimetry, differential scanning calorimetry, FT–IR spectroscopy, and aqueous solubility of quininium aspirinate are herein reported.

Background The health system in South Sudan faces extreme domestic resource constraints, low capacity, and protracted humanitarian crises. Supportive supervision is believed to improve the quality of health care and service delivery by compensating for flaws in health workforce management. This study aimed to explore the current supervision practices in South Sudan and identify areas for quality improvement. Methods The study employed qualitative approaches to collect and analyse data from six purposefully selected counties. Data were collected from 194 participants using semi-structured interviews (43 health managers) and focus group discussions (151 health workers). Thematic content analysis was used to yield an in-depth understanding of the supervision practices in the health sector. Results The study found that integrated supportive supervision and monitoring visits were the main approaches used for health services supervision in South Sudan. Supportive supervision focused more on health system administration and less on clinical matters. Although fragmented, supportive supervision was carried out quarterly, while monitoring visits were either conducted monthly or ad hoc. Prioritization for supportive supervision was mainly data driven. Paper-based checklists were the most commonly used supervision tools. Many supervisors had no formal training on supportive supervision and only learned on the job. The health workers received on-site verbal feedback and, most times, on-the-job training sessions through coaching and mentorship. Action plans developed during supervision were inadequately followed up due to insufficient funding. Insecurity, poor road networks, lack of competent health managers, poor coordination, and lack of adequate means of transport were some of the challenges experienced during supervision. The presumed outcomes of supportive supervision were improvements in human resource management, drug management, health data reporting, teamwork, and staff respect for one another. Conclusion Supportive supervision remains a daunting task in the South Sudan health sector due to a combination of external and health system factors. Our study findings suggest that strengthening the processes and providing inputs for supervision should be prioritized if quality improvement is to be attained. This necessitates stronger stewardship from the Ministry of Health, integration of different supervision practices, investment in the capacity of the health workforce, and health infrastructure development.

As the amount of data being sent has risen dramatically in recent years; protecting that data has become increasingly important. Cryptography is the process of transforming plain text messages into ones that can no longer be decoded. An information security system's algorithm for encrypting and decrypting data cannot function without them. Data transmission via a communication network can benefit greatly from the use of encryption. Short message service (SMS) is still popular despite the availability of several online messaging platforms. Despite this, these services can be easily cracked, making SMS less secure for transferring critical information. The usage of appropriate cryptographic algorithms is essential to ensuring the safety of your data. Cryptanalysis and pattern prediction are better protected. This study uses a hybridized version of the Vigenère cipher with AES. In tests, the Vigenère-AES came out on top, and it may be used to keep sensitive data safe from prying eyes.

BackgroundSierra Leone has one of the world’s highest rates of maternal mortality. Preventing unintended pregnancies reduces the burden of maternal morbidity and mortality. Unfortunately, 25% of reproductive-age women do not have access to modern contraceptive services, and the proportion of demand met for modern contraception remains low at 46% in Sierra Leone. Rural Sierra Leonean women use modern contraception less frequently than urban women. This study aimed to quantify the rural-urban disparity in modern contraceptive use among Sierra Leonean women of reproductive age and to identify factors that explain it.MethodData from 2019 Sierra Leone demographic health survey was used. Participants were sexually active women aged 15 to 49 (n=13,975). Modern contraceptive use was the outcome variable. Explanatory variables were grouped into materialistic, behavioral/cultural, and psychosocial theoretical perspectives. Descriptive statistics, intermediary analysis, and blinder Oaxaca decomposition analysis were used to summarize and identify the factors that explain inequalities in modern contraceptive use between rural and urban women. Data were analyzed using Stata version 14.0.ResultsThere was a rural-urban disparity in modern contraceptive use of 18 percentage points favoring urban women. The exposure variables explained 68% of this disparity. Education (76%), marital status (39%), hearing about family planning on the radio (16%), age of respondent (13%), problems with distance to a healthcare facility (12%), and problems getting permission to seek treatment (9%) made a significant contribution to the explanation of the modern contraceptive use disparity between urban and rural women.ConclusionsThere was a large rural-urban disparity in modern contraceptive use in Sierra Leone that favoured urban women. Material, behavior/cultural, psychosocial, and demographic explanatory factors jointly explained 68% of the disparity in modern contraceptive utilization between rural and urban women. To close the rural-urban disparity in modern contraceptive use, policy makers must address inequities in education, mass media (radio), and healthcare access. Rural women should be empowered to have the autonomy to access healthcare. Educating men about modern contraceptives and involving them in contraceptive programs can increase rural women’s ability to get permission to seek care hence increasing modern contraceptive utilization and consequently bridging the rural-urban gap.

We present a modular framework for the streamlined design, fabrication, and experimental exploration of phononic chains composed of interchangeable unit cells. Increased interest in periodic media motivates experimental set-ups for exploring re-configurable lattice designs with variable couplings. We employ additive manufacturing as a means to rapidly assemble lattices interfaced by unit cells in a reconfigurable and modular manner. Additive manufacturing produces lightweight spring-like structures while boasting batch printing capabilities. Batch printing reduces production time and increases uniformity in the lattice by reducing the number of joints. The framework reported herein allows for either linear or nonlinear unit cells to be inserted at interface locations. We discuss two example nonlinear unit cells and explore their effects on wave transmission through an otherwise uniform monatomic lattice. One nonlinear cell blocks low amplitude wave packets but transmits high amplitude signals, whereas the second nonlinear cell yields the opposite effect. These cells may inspire devices such as noise filters and limiters based on signal amplitude. The modular framework also enables serial combinations of lattices and cell types that individually integrate frequency filtering, amplitude filtering, and directional control. By combining unit cells with different filtering effects, it is possible to produce combination filters with multifunctional capability — e.g., simultaneous frequency and amplitude filtering.

A framework for Computer-Aided Diagnosis (CAD) to diagnose Cavitary TB and Miliary TB from chest Computed Tomography (CT) slices has been designed and implemented. The lung tissues from the CT slic...

A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate databases. The features selected by each bioinspired algorithm are used to train three back propagation neural networks (BPNN) independently using the conjugate gradient algorithm (CGA). Classifier testing is performed by using the testing set on each trained classifier, and the diagnostic results obtained are used to evaluate the performance of each classifier. The classification results obtained for each instance of the testing set of the three classifiers and the class label associated with each instance of the testing set will be the candidate instances for training and testing the super learner. The training set comprises of 80% of the instances, and the testing set comprises of 20% of the instances. Experimentation has been carried out using seven clinical datasets from the University of California Irvine (UCI) machine learning repository. The super learner has achieved a classification accuracy of 96.83% for Wisconsin diagnostic breast cancer dataset (WDBC), 86.36% for Statlog heart disease dataset (SHD), 94.74% for hepatocellular carcinoma dataset (HCC), 90.48% for hepatitis dataset (HD), 81.82% for vertebral column dataset (VCD), 84% for Cleveland heart disease dataset (CHD), and 70% for Indian liver patient dataset (ILP).

Background: The Covid-19 pandemic has posed enormous challenges and has become a burden of morbidity and mortality while severely disrupting economic activities around the world. Vaccines for the disease has been discovered; however, there exists misconceptions and mistrusts among health workers which may constitute barriers to Covid-19 uptake. Aim: The study aimed to determine the knowledge and acceptability of the Covid-19 vaccine among health care provider and to determine the association between the socio-demographic variables and Covid-19 acceptability. Study Design: A snowball sampling technique was relied upon in the distribution of the questionnaires. Place and Duration: Online questionnaires sent in the form of a link through social media outlets such as Whatsapp, Facebook and emails within four weeks interval. Methodology: Health care providers in Nigeria aged 18 years and above participated in this study.The analysis was performed using the Statistical Software Package SPSS version 22.0.Four hundred and forty-five respondents filled the questionnaire from the six geopolitical zones of the country. Results: There was a good knowledge of the Covid-19 vaccination as 411(92.4%) knew about the vaccine. However, only 53.5% of respondents were willing to get vaccinated against the disease. The most identified perceived barrier to covid-19 vaccine acceptance was fear of side effects 309 (69.4%) and there was no association between socio-demographic characteristics and covid-19 vaccine acceptability. Conclusion: Healthcare providers recommendation and confidence in a vaccine plays an influential role in their patients’ vaccination behaviour. They serve as an important source of information for the general populace and their consultation can also be a key factor in patients’ decision to get vaccinated or not.

Availability of low-cost, reliable, and portable Electrocardiography (ECG) devices is still very important in the medical world today. Despite the tremendous technological advancement, Cardiovascular Diseases (CVDs) remain a serious health burden claiming millions of lives on an annual basis globally. This is more prevalent in Low and Middle-Income Countries (LMICs) where there are huge financial instability and lack of critical infrastructure and support services for the health care system. Efforts aimed at reducing the prevalence of CVDs are confounded by late diagnosis, frequently, caused by lack of access to or non-availability of basic diagnostic modalities such as the ECG. Hence effective mitigation of the effect of CVDs in LMICs depend on the development of such devices at low-cost with reliability, accuracy and energy efficiency. This paper therefore, was developed to understand the state of the art of low-cost ECG acquisition systems with respect to design features and system capabilities for different use cases. In addition, different design options and taxonomies of available low-cost ECG devices, case studies reports of efficacy tests have been provided. The paper proposes a generalised ECG framework and provides implementation challenges and open research directions that should be considered when developing such devices for proper management of CVDs.

This in-process project report describes a critical youth-led participatory heritage project that seeks to document, preserve, and make digitally accessible oral histories, archives, and artifacts of an urban, predominantly African American high school with a rich history and legacy. As a long-standing community institution, the narratives emerging from this high school are intricately connected with the larger story of the city of Philadelphia. This article uses an equity-based lens to demonstrate how youth-led participatory heritage can contribute to youth empowerment, critical consciousness development, and critical digital literacies. Implications for schools and communities experiencing gentrification, displacement, and community change are provided, including how participatory heritage with youth can utilize collaborative, asset-based efforts to foster change that allows youth and communities to have agency over their individual and collective stories, community history and legacy, and their futures.

Vaccines found to be highly effective will enable uptake than those with low effectiveness. Health care providers in Nigeria aged 18 years and above participated in this study. The study was conducted to assess the enablers and perceived risks of Covid-19 vaccination among healthcare providers in Nigeria. A snowball sampling technique was relied upon in the distribution of the online questionnaires sent in the form of a link through social media outlets such as Whatsapp, Face book and emails within four weeks interval. The analysis was performed using the Statistical Software Package SPSS version 22.0.Four Hundred and forty-five respondents filled the questionnaire from the six geopolitical zones of the country. It was observed that safety of the vaccine, proven efficacy and good knowledge of the vaccine were enablers to the Covid-19 vaccine uptake. This finding shows that a higher proportion of health care providers was in support of the covid-19 vaccine under the presumption that the vaccine was proven effective. 243(54.6%) perceived that a clinical sequalae may result later after they take the Covid-19 vaccine, 207(46.5%) perceived that the clinical trial of the Covid-19 vaccine was inadequate and 187(42.0%) perceived that a new virus strain may emerge and therefore, bring about another vaccine. In addition, 180(40.4%) respondents perceived that the Covid-19 vaccine might worsen their co-morbidities and 61(13.7%) showed concerns as they think the Covid-19 vaccine may affect their immune systems and make them more susceptible to new viral strains. Proven vaccine safety, proven efficacy and good knowledge were identified as enablers of the covid-19 vaccine while clinical sequalae and inadequate trials were noted as the perceived risks of covid-19 vaccine uptake among health care providers.

OBJECTIVE To provide a framework for effective mentorship of residents underrepresented in medicine (UIM) situated in the context of their experiences in healthcare teams. DESIGN A perspective summarizing the important elements for the effective mentorship of UIM residents. CONCLUSION Mentorship of trainees is of profound importance in medical education as it provides tangible benefits for professional and personal development. However, given their unique experiences and position in our teams as well as the larger healthcare construct, the mentorship of UIM residents requires special consideration and focus. Implementing programs that foster diversity, cross-cultural mentorship, and sponsorship are imperative.