Low birth weight is one of the primary causes of child mortality and several diseases of future life in developing countries, especially in Southern Asia. The main objective of this study is to ...determine the risk factors of low birth weight and predict low birth weight babies based on machine learning algorithms.
Low birth weight data has been taken from the Bangladesh Demographic and Health Survey, 2017-18, which had 2351 respondents. The risk factors associated with low birth weight were investigated using binary logistic regression. Two machine learning-based classifiers (logistic regression and decision tree) were adopted to characterize and predict low birth weight. The model performances were evaluated by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve.
The average percentage of low birth weight in Bangladesh was 16.2%. The respondent's region, education, wealth index, height, twin child, and alive child were statistically significant risk factors for low birth weight babies. The logistic regression-based classifier performed 87.6% accuracy and 0.59 area under the curve for holdout (90:10) cross-validation, whereas the decision tree performed 85.4% accuracy and 0.55 area under the curve.
Logistic regression-based classifier provided the most accurate classification of low birth weight babies and has the highest accuracy. This study's findings indicate the necessity for an efficient, cost-effective, and integrated complementary approach to reduce and correctly predict low birth weight babies in Bangladesh.
While the functionality and healthy food value of red rice have increased its popularity, such that market demand for it is expected to rise, most strains suffer from low grain yield. To perform ...diversity and population structure analyses of red rice germplasm, therefore, becomes essential for improving yields for commercial production. In this study, fifty red rice germplasm from the Bangladesh Rice Research Institute (BRRI) genebank were characterized both morphologically and genetically using fifty simple sequence repeat (SSR) markers. Overall, 162 alleles were detected by the markers with the detected allele number varying from two to seven. Additionally, 22 unique alleles were identified for use as a germplasm diagnostic tool. The highest and lowest polymorphic information content (PIC) indices were 0.75 and 0.04 found in markers RM282 and RM304, respectively, and genetic diversity was moderate, varying from 0.05 to 0.78 (average: 0.35). While phylogenetic cluster analysis of the fifteen distance-based agro-morphological traits divided the germplasm into five clusters (I, II, III, IV and V), a similar SSR analysis yielded only three major groups (I, II, and III), and a model-based population structure analysis yielded four (A, B, C and D). Both principal component and neighbors joining tree analysis from the population structure method showed the tested germplasm as highly diverse in structure. Moreover, an analysis of molecular variance (AMOVA), as well as a pairwise FST analysis, both indicated significant differentiation (ranging from 0.108 to 0.207) among all pairs of populations, suggesting that all four population structure groups differed significantly. Populations A and D were the most differentiated from each other by FST. Findings from this study suggest that the diverse germplasm and polymorphic trait-linked SSR markers of red rice are suitable for the detection of economically desirable trait loci/genes for use in future molecular breeding programs.
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a novel evolutionary divergent RNA virus, is responsible for the present devastating COVID-19 pandemic. To explore the genomic ...signatures, we comprehensively analyzed 2,492 complete and/or near-complete genome sequences of SARS-CoV-2 strains reported from across the globe to the GISAID database up to 30 March 2020. Genome-wide annotations revealed 1,516 nucleotide-level variations at different positions throughout the entire genome of SARS-CoV-2. Moreover, nucleotide (nt) deletion analysis found twelve deletion sites throughout the genome other than previously reported deletions at coding sequence of the ORF8 (open reading frame), spike, and ORF7a proteins, specifically in polyprotein ORF1ab (n = 9), ORF10 (n = 1), and 3´-UTR (n = 2). Evidence from the systematic gene-level mutational and protein profile analyses revealed a large number of amino acid (aa) substitutions (n = 744), demonstrating the viral proteins heterogeneous. Notably, residues of receptor-binding domain (RBD) showing crucial interactions with angiotensin-converting enzyme 2 (ACE2) and cross-reacting neutralizing antibody were found to be conserved among the analyzed virus strains, except for replacement of lysine with arginine at 378th position of the cryptic epitope of a Shanghai isolate, hCoV-19/Shanghai/SH0007/2020 (EPI_ISL_416320). Furthermore, our results of the preliminary epidemiological data on SARS-CoV-2 infections revealed that frequency of aa mutations were relatively higher in the SARS-CoV-2 genome sequences of Europe (43.07%) followed by Asia (38.09%), and North America (29.64%) while case fatality rates remained higher in the European temperate countries, such as Italy, Spain, Netherlands, France, England and Belgium. Thus, the present method of genome annotation employed at this early pandemic stage could be a promising tool for monitoring and tracking the continuously evolving pandemic situation, the associated genetic variants, and their implications for the development of effective control and prophylaxis strategies.
Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on ...clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.
The emerged novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has created a global health crisis that warrants an accurate and detailed characterization of the rapidly ...evolving viral genome for understanding its epidemiology, pathogenesis, and containment. Here, we explored 61,485 sequences of the nucleocapsid (N) protein, a potent diagnostic and prophylactic target, for identifying the mutations to review their roles in real‐time polymerase chain reaction based diagnosis and observe consequent impacts. Compared to the Wuhan reference strain, a total of 1034 unique nucleotide mutations were identified in the mutant strains (49.15%, n = 30,221) globally. Of these mutations, 367 occupy primer binding sites including the 3′‐end mismatch to the primer‐pair of 11 well‐characterized primer sets. Noteworthily, CDC (USA) recommended the N2 primer set contained a lower mismatch than the other primer sets. Moreover, 684 amino acid (aa) substitutions were located across 317 (75.66% of total aa) unique positions including 82, 21, and 83 of those in the RNA binding N‐terminal domain (NTD), SR‐rich region, and C‐terminal dimerization domain, respectively. Moreover, 11 in‐frame deletions, mostly (n = 10) within the highly flexible linker region, were revealed, and the rest was within the NTD region. Furthermore, we predicted the possible consequence of high‐frequency mutations (≥20) and deletions on the tertiary structure of the N protein. Remarkably, we observed that a high frequency (67.94% of mutated sequences) co‐occuring mutations (R203K and G204R) destabilized and decreased overall structural flexibility. The N protein of SARS‐CoV‐2 comprises an average of 1.2 mutations per strain compared to 4.4 and 0.4 in Middle East respiratory syndrome‐related coronavirus and SARS‐CoV, respectively. Despite being proposed as the alternative target to spike protein for vaccine and therapeutics, the ongoing evolution of the N protein may challenge these endeavors, thus needing further immunoinformatics analyses. Therefore, continuous monitoring is required for tracing the ongoing evolution of the SARS‐CoV‐2 N protein in prophylactic and diagnostic interventions.
Highlights
Analyzing 61,485 sequences of the nucleocapsid (N) protein, 1034 unique nucleotide mutations were identified in the mutant strains (49.15%, n=30,221) globally.
Of these mutations, 367 occupy primer binding sites including the 3'‐end mismatch to the primer‐pair of 11 well‐characterized primer sets.
In total, 684 amino acid (aa) substitutions in 317 unique positions including 82, 21, and 83 in RNA binding N‐terminal domain (NTD), SR‐rich region, and C‐terminal dimerization domain (CTD), respectively were detected.
Eleven in‐frame deletions found, mostly (n = 10) within the highly flexible linker region of the SARS‐CoV‐2 N protein.
The N protein of SARS‐CoV‐2 comprises an average of 1.2 mutations per strain compared to 4.4 and 0.4 in MERS‐CoV and SARS‐CoV, respectively.
Software-Defined Networking (SDN) offers an abstract view of the network and assists network operators to control the network traffic and the associated network resources more effectively. For the ...past few years, SDN has shown a lot of merits in diverse fields of applications, an important one being the Wireless Body Area Network (WBAN) for healthcare services. With the amalgamation of SDN with WBAN (SDWBAN), the patient monitoring and management system has gained much more flexibility and scalability compared to the conventional WBAN. However, the performance of the SDWBAN framework largely depends on the controller which is a core element of the control plane. The reason is that an optimal number of controllers assures the satisfactory level of performance and control of the network traffic originating from the underlying data plane devices. This paper proposes a mathematical model to determine the optimal number of controllers for the SDWBAN framework in healthcare applications. To achieve this goal, the proposed mathematical model adopts the convex optimization method and incorporates three critical SDWBAN factors in the design process: number of controllers, latency and number of SDN-enabled switches (SDESW). The proposed analytical model is validated by means of simulations in Castalia 3.2 and the outcomes indicate that the network achieves high level of Packet Delivery Ratio (PDR) and low latency for optimal number of controllers as derived in the mathematical model.
Serum from one hundred and ten breast cancer patients and thirty healthy female volunteers, were prospectively collected and evaluated for serum levels of Shh and IL-6 using human Shh and IL-6 ...specific enzyme-linked immunoassays. All patients were regularly monitored for event free survival (EFS) and overall survival (OS). Overall outcome analysis was based on serum Shh and IL-6 levels. In patients with progressive metastatic BC, both serum Shh and IL-6 concentrations were elevated in 44% (29 of 65) and 63% (41 of 65) of patients, respectively, at a statistically significant level Shh (p = 0.0001) and IL-6 (p = 0.0001) compared to the low levels in healthy volunteers. Serum levels tended to increase with metastatic progression and lymph node positivity. High serum Shh and IL-6 levels were associated with poor EFS and OS opposite to the negative or lower levels in serum Shh and IL-6. The elevated levels of both serum Shh and IL-6 were mainly observed in BC patients who had a significantly higher risk of early recurrence and bone metastasis, and associated with a worse survival for patients with progressive metastatic BC. Further studies are warranted for validating these biomarkers as prognostic tools in a larger patient cohort and in a longer follow-up study.
The two-dimensional nonlinear complex coupled Maccari system is a significant model in optics, quantum mechanics, plasma physics, hydrodynamics and some other fields. In this article, we have ...investigated scores of broad-spectral soliton solutions to the stated system via the auxiliary equation technique. The obtained solutions are established as an integration of the rational function, hyperbolic function, trigonometric function and exponential function. We have portrayed the three- and two-dimensional combined structures of the obtained solutions for a better interpretation of the waves, and it is determined that
λ
is the most effective and influential parameter that significantly affects the change in wave type, as shown in the 2D figure. The effects of other parameters have also been discussed. The numerical results show that the approach is reliable, straightforward and potent to examine other nonlinear evolution equations that emerged in optics, nonlinear physics, applied mathematics, and engineering.
The photocatalytic characteristics of two-dimensional (2D) GeC-based van der Waals heterobilayers (vdW-HBL) are systematically investigated to determine the amount of hydrogen (H
) fuel generated by ...water splitting. We propose several vdW-HBL structures consisting of 2D-GeC and 2D-SiC with exceptional and tunable optoelectronic properties. The structures exhibit a negative interlayer binding energy and non-negative phonon frequencies, showing that the structures are dynamically stable. The electronic properties of the HBLs depend on the stacking configuration, where the HBLs exhibit direct bandgap values of 1.978 eV, 2.278 eV, and 2.686 eV. The measured absorption coefficients for the HBLs are over ~ 10
cm
, surpassing the prevalent conversion efficiency of optoelectronic materials. In the absence of external strain, the absorption coefficient for the HBLs reaches around 1 × 10
cm
. With applied strain, absorption peaks are increased to ~ 3.5 times greater in value than the unstrained HBLs. Furthermore, the HBLs exhibit dynamically controllable bandgaps via the application of biaxial strain. A decrease in the bandgap occurs for both the HBLs when applied biaxial strain changes from the compressive to tensile strain. For + 4% tensile strain, the structure I become unsuitable for photocatalytic water splitting. However, in the biaxial strain range of - 6% to + 6%, both structure II and structure III have a sufficiently higher kinetic potential for demonstrating photocatalytic water-splitting activity in the region of UV to the visible in the light spectrum. These promising properties obtained for the GeC/SiC vdW heterobilayers suggest an application of the structures could boost H
fuel production via water splitting.
Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for performance enhancement in next-generation cellular communications. Compared to orthogonal frequency division ...multiple access, which is a well-known high-capacity orthogonal multiple access technique, NOMA offers a set of desirable benefits, including greater spectrum efficiency. There are different types of NOMA techniques, including power-domain and code-domain. This paper primarily focuses on power-domain NOMA that utilizes superposition coding at the transmitter and successive interference cancellation at the receiver. Various researchers have demonstrated that NOMA can be used effectively to meet both network-level and user-experienced data rate requirements of fifth-generation (5G) technologies. From that perspective, this paper comprehensively surveys the recent progress of NOMA in 5G systems, reviewing the state-of-the-art capacity analysis, power allocation strategies, user fairness, and user-pairing schemes in NOMA. In addition, this paper discusses how NOMA performs when it is integrated with various proven wireless communications techniques, such as cooperative communications, multiple-input multiple-output, beamforming, space-time coding, and network coding among others. Furthermore, this paper discusses several important issues on NOMA implementation and provides some avenues for future research.
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