Time series prediction of insect pests in tea gardens.

BACKGROUND: Tea-garden pest control is crucial to ensure tea quality. In this context, the time-series prediction of insect pests in tea gardens is very important. Deep-learning-based time-series prediction techniques are advancing rapidly but research into their use in tea-garden pest prediction is limited. The current study investigates the time-series prediction of whitefly populations in the Tea Expo Garden, Jurong City, Jiangsu Province, China, employing three deep-learning algorithms, namely Informer, the Long Short-Term Memory (LSTM) network, and LSTM-Attention. RESULTS: The comparative analysis of the three deep-learning algorithms revealed optimal results for LSTM-Attention, with an average root mean square error (RMSE) of 2.84 and average mean absolute error (MAE) of 2.52 for 7 days' prediction length, respectively. For a prediction length of 3 days, LSTM achieved the best performance, with an average RMSE of 2.60 and an average MAE of 2.24. CONCLUSION: These findings suggest that different prediction lengths influence model performance in tea garden pest time series prediction. Deep learning could be applied satisfactorily to predict time series of insect pests in tea gardens based on LSTM-Attention. Thus, this study provides a theoretical basis for the research on the time series of pest and disease infestations in tea plants. © 2024 Society of Chemical Industry.

Identification and Characterization of Plasmin-Independent Thrombolytic Enzymes.

RATIONALE: Current thrombolytic agents activate plasminogen to plasmin which triggers fibrinolysis to dissolve thrombi. Since plasmin is a nonspecific proteolytic enzyme, all of the current plasmin-dependent thrombolytics lead to serious hemorrhagic complications, demanding a new class of fibrinolytic enzymes independent from plasmin activation and undesirable side effects. We speculated that the mammalian version of bacterial heat-shock proteins could selectively degrade intravascular thrombi, a typical example of a highly aggregated protein mixture. OBJECTIVE: The objective of this study is to identify enzymes that can dissolve intravascular thrombi specifically without affecting fibrinogen and fibronectin so that the wound healing processes remain uninterrupted and tissues are not damaged. In this study, HtrA (high-temperature requirement A) proteins were tested for its specific proteolytic activity on intravascular thrombi independently from plasmin activation. METHODS AND RESULTS: HtrA1 and HtrA2/Omi proteins, collectively called as HtrAs, lysed ex vivo blood thrombi by degrading fibrin polymers. The thrombolysis by HtrAs was plasmin-independent and specific to vascular thrombi without causing the systemic activation of plasminogen and preventing nonspecific proteolysis of other proteins including fibrinogen and fibronectin. As expected, HtrAs did not disturb clotting and wound healing of excised wounds from mouse skin. It was further confirmed in a tail bleeding and a rebleeding assay that HtrAs allowed normal clotting and maintenance of clot stability in wounds, unlike other thrombolytics. Most importantly, HtrAs completely dissolved blood thrombi in tail thrombosis mice, and the intravenous injection of HtrAs to mice with pulmonary embolism completely dissolved intravascular thrombi and thus rescued thromboembolism. CONCLUSIONS: Here, we identified HtrA1 and HtrA2/Omi as plasmin-independent and highly specific thrombolytics that can dissolve intravascular thrombi specifically without bleeding risk. This work is the first report of a plasmin-independent thrombolytic pathway, providing HtrA1 and HtrA2/Omi as ideal therapeutic candidates for various thrombotic diseases without hemorrhagic complications.

Recent advances of nanomaterial-based optical sensor for the detection of benzimidazole fungicides in food: a review.

The abuse of pesticides in agricultural land during pre- and post-harvest causes an increase of residue in agricultural products and pollution in the environment, which ultimately affects human health. Hence, it is crucially important to develop an effective detection method to quantify the trace amount of residue in food and water. However, with the rapid development of nanotechnology and considering the exclusive properties of nanomaterials, optical, and their integrated system have gained exclusive interest for accurately sensing of pesticides in food and agricultural samples to ensure food safety thanks to their unique benefit of high sensitivity, low detection limit, good selectivity and so on and making them a trending hotspot. This review focuses on recent progress in the past five years on nanomaterial-based optical, such as colorimetric, fluorescence, surface-enhanced Raman scattering (SERS), and their integrated system for the monitoring of benzimidazole fungicide (including, carbendazim, thiabendazole, and thiophanate-methyl) residue in food and water samples. This review firstly provides a brief introduction to mentioned techniques, detection mechanism, applied nanomaterials, label-free detection, target-specific detection, etc. then their specific application. Finally, challenges and perspectives in the respective field are discussed.

Recent advancement in nano-optical strategies for detection of pathogenic bacteria and their metabolites in food safety.

Pathogenic bacteria and their metabolites are the leading risk factor in food safety and are one of the major threats to human health because of the capability of triggering diseases with high morbidity and mortality. Nano-optical sensors for bacteria sensing have been greatly explored with the emergence of nanotechnology and artificial intelligence. In addition, with the rapid development of cross fusion technology, other technologies integrated nano-optical sensors show great potential in bacterial and their metabolites sensing. This review focus on nano-optical strategies for bacteria and their metabolites sensing in the field of food safety; based on surface-enhanced Raman scattering (SERS), fluorescence, and colorimetric biosensors, and their integration with the microfluidic platform, electrochemical platform, and nucleic acid amplification platform in the recent three years. Compared with the traditional techniques, nano optical-based sensors have greatly improved the sensitivity with reduced detection time and cost. However, challenges remain for the simple fabrication of biosensors and their practical application in complex matrices. Thus, bringing out improvements or novelty in the pretreatment methods will be a trend in the upcoming future.

Comparison of Si-GA-PLS and Si-CARS-PLS build algorithms for quantitation of total polyphenols in black tea using the spectral analytical system.

BACKGROUND: The objective of the current study was to compare two machine learning approaches for the quantification of total polyphenols by choosing the optimal spectral intervals utilizing the synergy interval partial least squares (Si-PLS) model. To increase the resilience of built models, the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were applied to a subset of variables. RESULTS: The collected spectral data were divided into 19 sub-interval selections totaling 246 variables, yielding the lowest root mean square error of cross-validation (RMSECV). The performance of the model was evaluated using the correlation coefficient for calibration (RC ), prediction (RP ), RMSECV, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value. The Si-GA-PLS model produced the following results: PCs = 9; RC = 0.915; RMSECV = 1.39; RP = 0.8878; RMSEP = 1.62; and RPD = 2.32. The performance of the Si-CARS-PLS model was noted to be best at PCs = 10, while RC = 0.9723, RMSECV = 0.81, RP = 0.9114, RMSEP = 1.45 and RPD = 2.59. CONCLUSION: The build model's prediction ability was amended in the order PLS < Si-PLS < CARS-PLS when full spectroscopic data were used and Si-PLS < Si-GA-PLS < Si-CARS-PLS when interval selection was performed with the Si-PLS model. Finally, the developed method was successfully used to quantify total polyphenols in tea. © 2023 Society of Chemical Industry.

Recent advances in rare earth ion-doped upconversion nanomaterials: From design to their applications in food safety analysis.

The misuse of chemicals in agricultural systems and food production leads to an increase in contaminants in food, which ultimately has adverse effects on human health. This situation has prompted a demand for sophisticated detection technologies with rapid and sensitive features, as concerns over food safety and quality have grown around the globe. The rare earth ion-doped upconversion nanoparticle (UCNP)-based sensor has emerged as an innovative and promising approach for detecting and analyzing food contaminants due to its superior photophysical properties, including low autofluorescence background, deep penetration of light, low toxicity, and minimal photodamage to the biological samples. The aim of this review was to discuss an outline of the applications of UCNPs to detect contaminants in food matrices, with particular attention on the determination of heavy metals, pesticides, pathogenic bacteria, mycotoxins, and antibiotics. The review briefly discusses the mechanism of upconversion (UC) luminescence, the synthesis, modification, functionality of UCNPs, as well as the detection principles for the design of UC biosensors. Furthermore, because current UCNP research on food safety detection is still at an early stage, this review identifies several bottlenecks that must be overcome in UCNPs and discusses the future prospects for its application in the field of food analysis.

The impact of the COVID-19 pandemic on the mental health of the adult population in Bangladesh: a nationwide cross-sectional study.

The recent COVID-19 pandemic has imposed threats on both physical and mental health since its outbreak. This study aimed to explore the impact of the COVID-19 pandemic on mental health among a representative sample of home-quarantined Bangladeshi adults. A cross-sectional design was used with an online survey completed by a convenience sample recruited via social media. A total of 1,427 respondents were recruited, and their mental health was assessed by the DASS-21 measure. The prevalence of anxiety symptoms and depressive symptoms was 33.7% and 57.9%, respectively, and 59.7% reported mild to extremely severe levels of stress. Perceptions that the pandemic disrupted life events, affected mental health, jobs, the economy and education, predictions of a worsening situation, and uncertainty of the health care system capacities were significantly associated with poor mental health outcomes. Multivariate logistic regressions showed that sociodemographic factors and perceptions of COVID-19 significantly predict mental health outcomes. These findings warrant the consideration of easily accessible low-intensity mental health interventions during and beyond this pandemic.

Is there any demand for improved cooking stoves? Evidence from Bangladesh.

Despite the multifarious benefits of improved cooking stoves (ICSs) over traditional biomass stoves, the ICSs adoption rate in rural Bangladesh remains nominal. This paper provides evidence that there is a growing demand for this environmentally friendly and less-hazardous stove. Using a discrete choice experiment (DCE) technique, we surveyed 259 sample households in the south-western region of Bangladesh. The results from the mixed logit model suggest that households are willing to pay (WTP) about $7 on average for a 'realistic' (i.e., one unit or 25 %) reduction in fuel consumption and smoke emission. Moreover, we found that a one-unit (33 %) reduction of cooking time and maintenance frequency increases households' WTP by about $3 and $5 respectively. Finally, this study underscores that extensive promotion, lower installation costs and higher social awareness about health risks and environmental degradation are likely to promote ICSs adoption.

Lanthanide ion (Ln3+ )-based upconversion sensor for quantification of food contaminants: A review.

The food safety issue has gradually become the focus of attention in modern society. The presence of food contaminants poses a threat to human health and there are a number of interesting researches on the detection of food contaminants. Upconversion nanoparticles (UCNPs) are superior to other fluorescence materials, considering the benefits of large anti-Stokes shifts, high chemical stability, non-autofluorescence, good light penetration ability, and low toxicity. These properties render UCNPs promising candidates as luminescent labels in biodetection, which provides opportunities as a sensitive, accurate, and rapid detection method. This paper intended to review the research progress of food contaminants detection by UCNPs-based sensors. We have proposed the key criteria for UCNPs in the detection of food contaminants. Additionally, it highlighted the construction process of the UCNPs-based sensors, which includes the synthesis and modification of UCNPs, selection of the recognition elements, and consideration of the detection principle. Moreover, six kinds of food contaminants detected by UCNPs technology in the past 5 years have been summarized and discussed fairly. Last but not least, it is outlined that UCNPs have great potential to be applied in food safety detection and threw new insight into the challenges ahead.

mTOR- and SGK-Mediated Connexin 43 Expression Participates in Lipopolysaccharide-Stimulated Macrophage Migration through the iNOS/Src/FAK Axis.

Connexin 43 (Cx43) deficiency was found to increase mortality in a mouse model of bacterial peritonitis, and Cx43 is upregulated in macrophages by LPS treatment. In this study, we characterized a novel signaling pathway for LPS-induced Cx43 expression in RAW264.7 cells and thioglycolate-elicited peritoneal macrophages (TGEMs). LPS alone or LPS-containing conditioned medium (CM) upregulated Cx43. Overexpression or silencing of Cx43 led to the enhancement or inhibition, respectively, of CM-induced TGEM migration. This response involved the inducible NO synthase (iNOS)/focal adhesion kinase (FAK)/Src pathways. Moreover, CM-induced migration was compromised in TGEMs from Cx43+/- mice compared with TGEMs from Cx43+/+ littermates. Cx43 was upregulated by a serum/glucocorticoid-regulated kinase 1 (SGK) activator and downregulated, along with inhibition of CM-induced TGEM migration, by knockdown of the SGK gene or blockade of the SGK pathway. LPS-induced SGK activation was abrogated by Torin2, whereas LPS-induced Cx43 was downregulated by both Torin2 and rapamycin. Analysis of the effects of FK506 and methylprednisolone, common immunosuppressive agents following organ transplantation, suggested a link between these immunosuppressive drugs and impaired macrophage migration via the Cx43/iNOS/Src/FAK pathway. In a model of Escherichia coli infectious peritonitis, GSK650349-, an SGK inhibitor, or Torin2-treated mice showed less accumulation of F4/80+CD11b+ macrophages in the peritoneal cavity, with a delay in the elimination of bacteria. Furthermore, following pretreatment with Gap19, a selective Cx43 hemichannel blocker, the survival of model mice was significantly reduced. Taken together, our study suggested that Cx43 in macrophages was associated with macrophage migration, an important immune process in host defense to infection.

SERS-based rapid detection of 2,4-dichlorophenoxyacetic acid in food matrices using molecularly imprinted magnetic polymers.

In order to remove the limitations of natural antibodies or enzymes, a nano-magnetic biomimetic platform based on a surface-enhanced Raman scattering (SERS) sensor has been developed for highly sensitive capture and detection of 2,4-dichlorophenoxyacetic acid (2,4-D) in food and water samples. Magnetic-based molecular imprinted polymer nanoparticles (Mag@MIP NPs) were constructed to capture the target 2,4-D molecule via biomimetic recognition, and gold nanoparticles (Au NPs) served as SERS-based probes, which are bound to the Mag@MIP NPs by electrostatic adsorption. The as-prepared SERS-MIP sensor for sensing of 2,4-D achieved a good linear relationship with a low detection limit (LOD) of 0.00147 ng/mL within 2 h and exhibited high sensitivity. The sensor was successfully applied to detect 2,4-D in milk and tap water and achieved good recoveries ranging from 93.5 to 102.2%. Moreover, the designed sensor system exhibited satisfactory results (p > 0.05) compared to HPLC by validation analysis. Hence, the findings demonstrated that the proposed method has significant potential for practical application in food safety and environmental protection. Graphical abstract .

Enhanced Antibacterial Property of Sulfate-Doped Ag3PO4 Nanoparticles Supported on PAN Electrospun Nanofibers.

Heterojunction nanofibers of PAN decorated with sulfate doped Ag3PO4 nanoparticles (SO42--Ag3PO4/PAN electrospun nanofibers) were successfully fabricated by combining simple and versatile electrospinning technique with ion exchange reaction. The novel material possessing good flexibility could exhibit superior antibacterial property over sulfate undoped species (Ag3PO4/PAN electrospun nanofibers). FESEM, XRD, FTIR, XPS and DRS were applied to characterize the morphology, phase structure, bonding configuration, elemental composition, and optical properties of the as fabricated samples. FESEM characterization confirmed the successful incorporation of SO42--Ag3PO4 nanoparticles on PAN electrospun nanofibers. The doping of SO42- ions into Ag3PO4 crystal lattice by replacing PO43- ions can provide sufficient electron-hole separation capability to the SO42--Ag3PO4/PAN heterojunction to generate reactive oxygen species (ROS) under visible light irradiation and enhances its antibacterial performance. Finally, we hope this work may offer a new paradigm to design and fabricate other types of flexible self-supporting negative-ions-doped heterojunction nanofibers using electrospinning technique for bactericidal applications.

Development of a novel wavelength selection method for the trace determination of chlorpyrifos on Au@Ag NPs substrate coupled surface-enhanced Raman spectroscopy.

A novel wavelength selection method, namely interval combination population analysis-minimal redundancy maximal relevance (ICPA-mRMR), was employed for the trace level detection of chlorpyrifos (CPS) coupled surface-enhanced Raman spectroscopy (SERS). Herein, a highly sensitive SERS enhancement substrate, Au@Ag nanoparticles (NPs), was synthesized possessing strong enhancement of Raman signals for CPS quantification (enhancement factor: 2.5 × 106). Compared with other established methods such as partial least squares (PLS), synergy interval partial least squares-genetic algorithm (siPLS-GA) and competitive adaptive reweighted sampling-partial least squares (CARS-PLS), ICPA-mRMR yielded the best results with higher correlation coefficients (Rc = 0.9917, RP = 0.9895), ratios of performance to deviation (RPD = 6.8797), and lower root mean square errors (RMSEC = 0.1998, RMSEP = 0.2271). The proposed method was employed for the determination of trace level CPS in tea samples, and the recovery percentages were in the range 90%-108%. Meanwhile, this method was validated using a standard GC-MS method indicating no significant difference (P > 0.05). The proposed methodology offers a rapid, sensitive and powerful analytical platform for the detection of pesticide residues in food.

Quantification of antibiotics in food by octahedral gold-silver nanocages-based SERS sensor coupling multivariate calibration.

The abuse of antibiotics has caused gradually increases drug-resistant bacterial strains that pose health risks. Herein, a sensitive SERS sensor coupled multivariate calibration was proposed for quantification of antibiotics in milk. Initially, octahedral gold-silver nanocages (Au@Ag MCs) were synthesized by Cu2O template etching method as SERS substrates, which enhanced the plasmonic effect through sharp edges and hollow nanostructures. Afterwards, five chemometric algorithms, like partial least square (PLS), uninformative variable elimination-PLS (UVE-PLS), competitive adaptive reweighted sampling-PLS (CARS-PLS), random frog-PLS (RF-PLS), and convolutional neural network (CNN) were applied for TTC and CAP. RF-PLS performed optimally for TTC and CAP (Rc = 0.9686, Rp = 0.9648, RPD = 3.79 for TTC and Rc = 0.9893, Rp = 0.9878, RPD = 5.88 for CAP). Furthermore, the detection limit of 0.0001 µg/mL for both TTC and CAP was obtained. Finally, satisfactory (p > 0.05) results were obtained with the standard HPLC method. Therefore, SERS combined RF-PLS could be applied for fast, nondestructive sensing of TTC and CAP in milk.

NeuroNet19: an explainable deep neural network model for the classification of brain tumors using magnetic resonance imaging data.

Brain tumors (BTs) are one of the deadliest diseases that can significantly shorten a person's life. In recent years, deep learning has become increasingly popular for detecting and classifying BTs. In this paper, we propose a deep neural network architecture called NeuroNet19. It utilizes VGG19 as its backbone and incorporates a novel module named the Inverted Pyramid Pooling Module (iPPM). The iPPM captures multi-scale feature maps, ensuring the extraction of both local and global image contexts. This enhances the feature maps produced by the backbone, regardless of the spatial positioning or size of the tumors. To ensure the model's transparency and accountability, we employ Explainable AI. Specifically, we use Local Interpretable Model-Agnostic Explanations (LIME), which highlights the features or areas focused on while predicting individual images. NeuroNet19 is trained on four classes of BTs: glioma, meningioma, no tumor, and pituitary tumors. It is tested on a public dataset containing 7023 images. Our research demonstrates that NeuroNet19 achieves the highest accuracy at 99.3%, with precision, recall, and F1 scores at 99.2% and a Cohen Kappa coefficient (CKC) of 99%.

HGATT_LR: transforming review text classification with hypergraphs attention layer and logistic regression.

Text classification plays a major role in research such as sentiment analysis, opinion mining, and customer feedback analysis. Text classification using hypergraph algorithms is effective in capturing the intricate relationships between words and phrases in documents. The method entails text preprocessing, keyword extraction, feature selection, text classification, and performance metric evaluation. Here, we proposed a Hypergraph Attention Layer with Logistic Regression (HGATT_LR) for text classification in the Amazon review data set. The essential keywords are extracted by utilizing the Latent Dirichlet Allocation (LDA) technique. To build a hypergraph attention layer, feature selection based on node-level and edge-level attention is assessed. The resultant features are passed as an input of Logistic regression for text classification. Through a comparison analysis of different text classifiers on the Amazon data set, the performance metrics are assessed. Text classification using hypergraph Attention Network has been shown to achieve 88% accuracy which is better compared to other state-of-the-art algorithms. The proposed model is scalable and may be easily enhanced with more training data. The solution highlights the utility of hypergraph approaches for text classification as well as their applicability to real-world datasets with complicated interactions between text parts. This type of analysis will empower the business people will improve the quality of the product.

Generalized ratiometric surface-enhanced Raman scattering biosensor for okadaic acid in food based on Au-triggered signal amplification.

BACKGROUND: Reliability and robustness have been recognized as key challenges for Surface-enhanced Raman scattering (SERS) analytical techniques. Quantifying the concentration of an analyte using a single characteristic peak from SERS has been a controversial topic because the Raman signal is susceptible to highly concentrated electromagnetic hotspots, inhomogeneity of SERS substrate, or non-standardization of measurement conditions. Ratiometric SERS strategies have been demonstrated as a promising solution to effectively balance and compensate for signal fluctuations caused by matrix heterogeneity. However, it is not easy to construct ratiometric SERS sensors with monitoring the ratio of two different signal intensities for target analysis. RESULTS: An attempt has been made to develop a novel ratiometric biosensor that can be applied to detect okadaic acid (OA). Aptamer-anchored magnetic particles were first combined with gold-tagged short complementary DNA (Au-cDNA) to create heterogeneous nanostructures. When the target was present, the Au-cDNA was dissociated from nanostructures, and 4-nitrothiophenol (4-NTP) was initiated to reduce to 4-aminothiophenol (4-ATP) in the presence of hydrogen sources. The SERS ratio change of 4-NTP and 4-ATP was finally detected by AuNPs-coated film. OA was successfully quantified, and the detection limit was as low as 2.4524 ng/mL. The constructed biosensor had good stability and reproducibility with a relative standard deviation of less than 4.47%. The proposed method used gold nanoparticles as an intermediate to achieve catalytic signal amplification and subsequently increased the sensitivity of the biosensor. SIGNIFICANCE AND NOVELTY: Catalytic reaction-based ratiometric SERS biosensors combine the multiple advantages of catalytic signal amplification and signal self-calibration and provide new insights into the development of stable, reproducible, and reliable SERS detection techniques. This ratiometric SERS technique offered a universal method that is anticipated to be applicable for the detection of other targets by substituting the aptamer.

AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach.

Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and naïve Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work.

Individual and community-level determinants of quality antenatal care in six South Asian countries.

Maternal health is a global public health concern. The paucity of antenatal care (ANC) during pregnancy is directly associated with maternal mortality. This study assessed the individual and community-level determinants of quality  ANC in six South-Asian countries. Data were obtained from a Demographic health survey of six South-Asian countries. This study included a sample of 180,567 (weighted) women aged 15-49 who had given birth in the preceding three years prior to the survey. The quality of ANC was determined by assessing whether a woman had received blood pressure monitoring, urine and blood sample screening, and iron supplements at any ANC visits. Frequency, percentage distribution, and inferential analysis (multilevel mixed-effects model) were conducted. The proportion of quality antenatal care utilization in South Asia was 66.9%. The multilevel analysis showed that women aged 35-49 years (AOR = 1.16; 95% CI = 1.09-1.24), higher education (AOR = 2.84; 95% CI = 2.69-2.99), middle wealth status (AOR = 1.55; 95% CI = 1.49-1.62), richest wealth status (AOR = 3.21; 95% CI = 3.04-3.39), unwanted pregnancy (AOR = 0.92; 95% CI = 0.89-0.95) and 2-4 birth order (AOR = 0.86; 95% CI = 0.83-0.89) were among the individual-level factors that were significantly associated with quality ANC utilization. In addition, rural residence (AOR = 0.77; 95% CI = 0.74-0.8), and big problem - distance to health facility (AOR = 0.63; 95% CI: 0.53-0.76) were the among community level factors there were also significantly associated with use of quality ANC. Meanwhile, women who lived in India (AOR: 22.57; 95% CI: 20.32-25.08) and Maldives (AOR: 33.33; 95% CI: 31.06-35.76) had higher odds of quality ANC than those lived in Afghanistan. Educational status, wealth status, pregnancy wantedness, sex of household head, birth order, place of residence, and distance to health facility were associated with quality ANC. Improving educational status, improving wealth status, reducing the distance to health facilities, and providing rural area-friendly interventions are important to increase the quality of ANC in South Asia.

An augmentation aided concise CNN based architecture for COVID-19 diagnosis in real time.

Over 6.5 million people around the world have lost their lives due to the highly contagious COVID 19 virus. The virus increases the danger of fatal health effects by damaging the lungs severely. The only method to reduce mortality and contain the spread of this disease is by promptly detecting it. Recently, deep learning has become one of the most prominent approaches to CAD, helping surgeons make more informed decisions. But deep learning models are computation hungry and devices with TPUs and GPUs are needed to run these models. The current focus of machine learning research is on developing models that can be deployed on mobile and edge devices. To this end, this research aims to develop a concise convolutional neural network-based computer-aided diagnostic system for detecting the COVID 19 virus in X-ray images, which may be deployed on devices with limited processing resources, such as mobile phones and tablets. The proposed architecture aspires to use the image enhancement in first phase and data augmentation in the second phase for image pre-processing, additionally hyperparameters are also optimized to obtain the optimal parameter settings in the third phase that provide the best results. The experimental analysis has provided empirical evidence of the impact of image enhancement, data augmentation, and hyperparameter tuning on the proposed convolutional neural network model, which increased accuracy from 94 to 98%. Results from the evaluation show that the suggested method gives an accuracy of 98%, which is better than popular transfer learning models like Xception, Resnet50, and Inception.