AI-enabled evaluation of genome-wide association relevance and polygenic risk score prediction in Alzheimer's disease.

GWAS focuses on significance loosing false positives; machine learning probes sub-significant features relying on predictivity. Yet, these are far from orthogonal. We sought to explore how these inform each other in sub-genome-wide significant situations to define relevance for predictive features. We introduce the SVM-based RubricOE that selects heavily cross-validated feature sets, and LDpred2 PRS as a strong contrast to SVM, to explore significance and predictivity. Our Alzheimer's test case notoriously lacks strong genetic signals except for few very strong phenotype-SNP associations, which suits the problem we are exploring. We found that the most significant SNPs among ML and PRS-selected SNPs captured most of the predictivity, while weaker associations tend also to contribute weakly to predictivity. SNPs with weak associations tend not to contribute to predictivity, but deletion of these features does not injure it. Significance provides a ranking that helps identify weakly predictive features.

Recycling of animal protein wastes in the formulation of feed for Labeo rohita and Mystus vittatus-a comparative evaluation.

Lactic acid bacteria (LAB) are key players in the fermentation of organic wastes and their recycling as feedstuff for fish. Whey, a common dairy byproduct in India, is a cheap source of LAB and can be used to ferment animal byproducts. An experimental study was designed to explore whether the whey fermented animal protein blend (WFAPB) could be used as a fishmeal replacer in the formulation of feed for both stomach-less carp fish Labeo rohita and stomach-bearing catfish Mystus vittatus. Experiments were performed with five isoproteinous, isolipidous, and isoenergetic feeds with WFAPB replacing fishmeal (FM) by 0% (T1), 25% (T2), 50% (T3), 75% (T4), and 100% (T5). Fifteen days of laboratory experiments with these experimental feeds revealed that more than 50% FM replacement level could result in excess postprandial absorption (6 h) of some essential and non-essential amino acids in the plasma of both fish. The postprandial absorption was more in M. vittatus than L. rohita. Ninety-day experiments were conducted in outdoor cement vats to measure growths and deposition of amino acids (AA) in muscle. Regression analysis was performed to find the optimal FM replacement based on four growth parameters and fifteen AA deposition in muscle. A two-phase fuzzy methodology was used to obtain Pareto-optimal replacement levels for each fish. The results demonstrated that FM replacement levels were 7.63% and 36.79% respectively for L. rohita and M. vittatus when only four growth parameters were considered. However, based on the FM replacement level that maximized deposition of 15 amino acids and growth parameters, it was found that 12.23% and 40.02% replacement of FM by the WFAPB was ideal respectively for L. rohita and M. vittatus. The results revealed that only a fraction of both essential and non-essential amino acids absorbed in plasma could be converted into protein and deposited as bound amino acids in the muscle. It is concluded that fermentation by whey is an inexpensive, easily available, and environmentally sustainable technique to recycle animal protein in the formulation of feed for fish, and the stomach-bearing carnivorous fish are more efficient in utilizing fermented animal protein blend than the stomach-less carps.

Multi-criteria decision making to validate performance of RBC-based formulae to screen [Formula: see text]-thalassemia trait in heterogeneous haemoglobinopathies.

BACKGROUND: India has the most significant number of children with thalassemia major worldwide, and about 10,000-15,000 children with the disease are born yearly. Scaling up e-health initiatives in rural areas using a cost-effective digital tool to provide healthcare access for all sections of people remains a challenge for government or semi-governmental institutions and agencies. METHODS: We compared the performance of a recently developed formula SCS[Formula: see text] and its web application SUSOKA with 42 discrimination formulae presently available in the literature. 6,388 samples were collected from the Postgraduate Institute of Medical Education and Research, Chandigarh, in North-Western India. Performances of the formulae were evaluated by eight different measures: sensitivity, specificity, Youden's Index, AUC-ROC, accuracy, positive predictive value, negative predictive value, and false omission rate. Three multi-criteria decision-making (MCDM) methods, TOPSIS, COPRAS, and SECA, were implemented to rank formulae by ensuring a trade-off among the eight measures. RESULTS: MCDM methods revealed that the Shine & Lal and SCS[Formula: see text] were the best-performing formulae. Further, a modification of the SCS[Formula: see text] formula was proposed, and validation was conducted with a data set containing 939 samples collected from Nil Ratan Sircar (NRS) Medical College and Hospital, Kolkata, in Eastern India. Our two-step approach emphasized the necessity of a molecular diagnosis for a lower number of the population. SCS[Formula: see text] along with the condition MCV[Formula: see text] 80 fl was recommended for a higher heterogeneous population set. It was found that SCS[Formula: see text] can classify all BTT samples with 100% sensitivity when MCV[Formula: see text] 80 fl. CONCLUSIONS: We addressed the issue of how to integrate the higher-ranked formulae in mass screening to ensure higher performance through the MCDM approach. In real-life practice, it is sufficient for a screening algorithm to flag a particular sample as requiring or not requiring further specific confirmatory testing. Implementing discriminate functions in routine screening programs allows early identification; consequently, the cost will decrease, and the turnaround time in everyday workflows will also increase. Our proposed two-step procedure expedites such a process. It is concluded that for mass screening of BTT in a heterogeneous set of data, SCS[Formula: see text] and its web application SUSOKA can provide 100% sensitivity when MCV[Formula: see text] 80 fl.

Transfer of chromium from environment to fish in East Kolkata wetlands - evaluation by structural equation modeling.

Chromium (Cr) is a significant pollutant in the effluents from leather industries and domestic city sewage. Cr was determined in water, sediment, and different tissues (gill, muscle, intestine, liver, and kidney) of Nile tilapia, Oreochromis niloticus harvested from wastewater-fed aquaculture (WFA) situated at Bamonghata, Bantala, Chowbaga and Chingrighata of East Kolkata Wetlands (EKW), a Ramsar site in West Bengal, India. The results showed that Cr concentration in surface water ranged between 0.05 to 0.15 mg/L, while Cr was detected at high concentration (100-300 mg/kg) in the sediment soil of the first three WFAs and in moderate concentration (50-110 mg/kg) in Chingrighata WFA. Average Cr concentrations in the tissues were ranked in the following sequence: kidney>liver>intestine>gill>muscle. However, the extent of accumulation of Cr in different tissues varied between the WFAs. We used Structural Equation Modeling (SEM) to determine the route of Cr transfer. The fitness of the model was evaluated by the performance measures. Cr accumulation pathways varied between the sites depending upon the level of Cr in water or sediment. Except for Bamonghata WFA, sediment was found as the principal source of accumulation of Cr in different tissues of O. niloticus. Cr refluxed from sediment into overlying water and accumulated in fish either through the food chain or through direct accumulation from water. In Bamonghata WFA, the role of sediment in the transfer of Cr could not be established due to the high water depth or biological non-availability of Cr in the sediment. It is concluded from this study that fish reared in the WFAs of EKW are still not hazardous in respect to Cr but require proper management to avoid the influx of Cr-containing effluents into the WFAs.

Prevalence and attributes of traumatic dental injuries to anterior teeth among school going children of Kolkata, India.

Background: Traumatic Dental Injuries (TDI) have emerged as a very significant public health and social problem, especially among children and adolescents. The aim of this study was to investigate the prevalence and associated risk factors of traumatic dental injuries to permanent anterior teeth in school going children of Kolkata aged 7-14 years. Method: A cross-sectional study was conducted among 3762 school going children attending various private and public schools of Kolkata aged 7-14 years. A multistage random clustering sampling technique was adopted to select the children.Type of trauma using Ellis and Davey classification of fractures along with Andresen's Epidemiological Classification of Traumatic Injuries to Anterior Teeth, including WHO codes, was used. All values were considered statistically significant at P < 0.05. Results: Prevalence of TDI to anterior teeth was found to be 9.89%. The mean age of children who presented with TDI was 11.06 ± 1.99.years. The most common place of occurrence of TDI was home. Falls were the most common causes of trauma. Children belonging to higher socioeconomic status were observed to have an increased prevalence of TDIs.The highest potential risk factor for the occurrence of trauma was a past history of trauma. Conclusion: Present study found a prevalence of 9.89%, and a very low percentage of children had received treatment.

Anthelmintic efficacy of Holarrhena pubescens against Raillietina spp. of domestic fowl through ultrastructural, histochemical, biochemical and GLCM analysis.

Globally, traditional knowledge systems are a powerhouse of information which can revolutionise the world, if decoded accurately and logically. Plant-based ethno-traditional and folklore curatives/medicines has a firm basis in the psyche of the common masses of West Bengal and Holarrhena pubescens is a representative example of it. This article communication on depicting the anthelmintic efficacy of ethanolic extract and Ethyl acetate fraction of the stem bark of Holarrhena pubescens against the cestode Raillietina spp. through efficacy studies, ultra-structural observations, histochemical and biochemical analysis on some tegumental enzymes i.e., Acid Phosphatase (AcPase), Alkaline Phosphatase (AlkPase), Adenosine Triphosphatase (ATPase) and 5'-Nucleotidase (5'-Nu) along with Gray Level Co-occurrence Matrix (GLCM) analysis of histochemical study. Praziquantel was used as the reference drug. Investigations revealed 10mg/ml dosage of crude extract was the most efficacious dose and amongst the fractions the ethyl acetate fraction showed the most anthelmintic property. Ultrastructural studies through Scanning Electron Microscope (SEM) and Transmission Electron Microscope (TEM) clearly depicted the damage in head, sucker, proglottids, proximal and distal cytoplasm (DC), microtriches (MT), basal lamina (BL), nuclear membrane (NM), and, nucleolus (NL) in the treated worms. Histochemical studies revealed decrease in staining intensity for all the tegumental enzymes in the treated worms compared to control. The GLCM analysis strongly supported the result of histochemical studies. Biochemical studies revealed marked reduction in enzyme activity in the treated worms with maximum reduction in the activity of 5'- Nu (77.8%) followed by ATPase (63.17%).

Impact of dietary piperine on growth performance, immune response, antioxidant status, and immune-related gene expression in pathogen-aggravated Cyprinus carpio.

This study investigated the effects of dietary piperine (PIP) on growth performance, digestive enzymes, serum biochemical parameters, antioxidant and immune responses, and gene expression in Cyprinus carpio challenged with Aeromonas hydrophila. Six diets were prepared with PIP doses of 0, 0.5, 1.0, 2.0, 3.0, and 4.0 g/kg, corresponding with the control, PR50, PR100, PR200, PR300, and PR400, respectively. Fish were challenged with Aeromonas hydrophila after 8 weeks of feeding with the respective diets. Weight gain (PWG) and specific growth rate (SGR) were significantly enhanced, whereas feed conversion ratio (FCR) was lowered in PR200. The cumulative post-challenge survival was improved to 68.43% in the PR200 group compared with 28.08% in the control. Serum total protein and albumin levels were significantly enhanced in the PR200 group compared to the control. However, dietary PIP up to 3 g/kg had no significant effect on serum glucose, cortisol, aspartate aminotransferase, or alkaline phosphatase activities; however, the alanine aminotransferase level was lower (P < 0.05) in the PR200 group than in the control. Intestinal amylase, lipase, and protease activities increased in PR300, and intestinal amylase and lipase increased in the PR100 group (P < 0.05). The serum immunological indices (lysozyme, alternative complement pathway, phagocytic activity, and respiratory burst activity) were higher (P < 0.05) in the PR200 group than in the control group. Serum superoxide dismutase (SOD), glutathione peroxidase (GPx), and catalase (CAT) activities were significantly intensified in PR200-PR300 than in the control group, with the highest activity observed in the PR200 group. Malondialdehyde was significantly lower in the PR200 group than in the control group. Furthermore, SOD, CAT, and Nrf2 expression was strongly upregulated in the liver tissue of the PR200 and PR300 groups compared to that in the control. The transcript levels of pro-inflammatory cytokines viz. IL-1ß and TNF-α were significantly upregulated in the kidneys of the PR100 and PR200 post-challenged. In contrast, the anti-inflammatory cytokine IL-10 was significantly downregulated in the kidneys of PR200. The expression of HSP70 was upregulated only in the PR400. Quadratic regression analysis showed that the optimal dietary PIP level was estimated as 2.07-2.13 g/kg to maximize growth performance. Overall, these results indicate that dietary PIP at an appropriate level can improve immunity, cytokine gene expression, and disease resistance in C. carpio.

Design and development of a patient-specific temporomandibular joint implant: Probing the influence of bone condition on biomechanical response.

Total temporomandibular joint (TMJ) replacement is widely recognized as an effective treatment for TMJ disorders. The long-term stability of TMJ implants depends on two important factors which are design concepts for fixation to anatomical locations in the mandible and bone conditions. Other factors include stress distribution, microstrain in the peri-implant, bone attributes like bone conditions leading to the clinical complications and failures. This study addresses these limitations by examining the influence of patient-specific design concepts and bone conditions on TMJ implant performance. Clinical evidences support the importance of implant design on healing ability. Previous studies have focused on achieving precise implant fit based on geometric considerations, however those published studies did not explore the impact of such. Against this perspective, the present study reports the extensive finite element analysis (FEA) results, while analyzing the impact of a newly designed patient-specific TMJ implant to address clinical complications associated with various bone conditions, particularly osteoporotic bone. In validating the FEA results, the performance of additively manufactured patient-specific TMJ implants was compared with designs resembling two commonly used clinically approved implant designs. By addressing the limitations of previous research and emphasizing the importance of bone conditions, the study provides valuable guidelines for the development of next-generation TMJ implants. These findings contribute to enhanced clinical outcomes and long-term success in the treatment of TMJ disorders.

Customer retention and churn prediction in the telecommunication industry: a case study on a Danish university.

In this study, we explore the possible factors affecting churn in the Danish telecommunication industry and how those factors connect with retention strategies. The Danish telecommunication industry is experiencing a saturated market regarding the number of customers, but the number of service providers has increased significantly in recent years. Due to the high costs of acquiring new customers, the telecommunication industry put great emphasis on retaining customers in such an intensely competitive industry. We employ five machine learning algorithms: random forest, AdaBoost, logistic regression, extreme gradient boosting classifier, and decision tree classifier on four datasets from two geographical regions, Denmark and the USA. The first three datasets are from online repositories, and the last one contains responses from 311 students from Aalborg University collected through a survey. We identify key features extracted by the best-performing algorithms based on five performance measures. Based on that, we aggregate all the features that appear important for each dataset. The results demonstrate that customers' preferences are not aligned. Among the prominent drivers, we find that service quality, customer satisfaction, offering subscription plan upgrades, and network coverage are unique to the Danish student population. Telecommunication companies need to integrate the sociohistoric milieu of the Nordic countries to tailor their retention policies to different consumer cultures. Supplementary Information: The online version contains supplementary material available at 10.1007/s42452-023-05389-6.

Dietary Syzygium cumini leaf extract influences growth performance, immunological responses and gene expression in pathogen-challenged Cyprinus carpio.

This study evaluated the effects of jamun leaf extract (JLE) as a feed supplement on growth performance, haemato-immunological, oxidative stress-related parameters, and cytokine gene expression in Cyprinus carpio challenged with Aeromonas hydrophila.. Diets containing four different JLE concentrations, that is, 0 (basal diet), 5 (JLE5), 10 (JLE10), and 15 g kg-1 (JLE15), were fed to carp (6.17 ± 0.43 g) for eight weeks. Growth performance was significantly higher in JLE10. Haemato-immunological and antioxidant parameters were determined in fish at 48 h post-challenge with A. hydrohila. The cumulative survival was highest in JLE10 (69.69%) 14 days post-challenge. Serum protein (2.18 ± 0.06 g dL-1), lysozyme (32.38 ± 1.2 U mL-1), alternative complement pathway (70.43 ± 1.61 U mL-1), phagocytic activity (21.18 ± 0.48%), respiratory burst activity (0.289 ± 0.09 OD630nm), and immunoglobulin levels (6.67 ± 0.36 U mg mL-1) were significantly higher in JLE10 than in the control. Serum alanine aminotransferase (44.06 ± 1.62 Unit mL-1), aspartate aminotransferase (31.58 ± 1.82 Unit mL-1), and malondialdehyde (2.57 ± 0.26 nmol mL-1) levels were lower in JLE10 than in the control (p < 0.05), whereas myeloperoxidase activity was significantly higher in JLE5 and JLE10 than in the control. Superoxide dismutase levels in the serum were higher (p < 0.05) in JLE5 and JLE10 than in the other groups. Gene expression analysis revealed that the mRNA expression of pro-inflammatory cytokines TNF-α and IL-1ß was upregulated (p < 0.05) in the liver, head-kidney, and intestine of challenged carp in JLE10. The signalling molecule NF-κB p65 was upregulated in lymphoid organs in JLE10 but not in the liver. The anti-inflammatory cytokine IL-10 was significantly downregulated in challenged carp in JLE10 compared with that in the control. Quadratic regression analysis showed that optimal dietary JLE was estimated to be 9.03-10.15 g kg-1 to maximize the growth performance. Results of the present study revealed that dietary JLE at 10 g kg-1 can significantly improve the immunity and disease resistance of C. carpio. Thus, JLE is a promising food additive for carp aquaculture.

Performance analysis of machine learning algorithms and screening formulae for ß-thalassemia trait screening of Indian antenatal women.

BACKGROUND: Currently, more than forty discrimination formulae based on red blood cell (RBC) parameters and some supervised machine learning algorithms (MLAs) have been recommended for ß-thalassemia trait (BTT) screening. The present study was aimed to evaluate and compare the performance of 26 such formulae and 13 MLAs on antenatal woman data with a recently developed formula SCSBTT, which is available for evaluation in over seventy countries as an Android app, called SUSOKA[16]. METHODS: A diagnostic database of 2942 antenatal females were collected from PGIMER, Chandigarh, India and was used for this analysis. The data set consists of hypochromic microcytic anemia, BTT, Hemoglobin E trait, double heterozygote for Hemoglobin S and BTT, heterozygote for Hemoglobin D Punjab and normal subjects. Performance of the formulae and the MLAs were assessed by Sensitivity, Specificity, Youden's Index, and AUC-ROC measures. A final recommendation was made from the ranking obtained through two Multiple Criteria Decision-Making (MCDM) techniques, namely, Simultaneous Evaluation of Criteria and Alternatives (SECA) and TOPSIS. RESULTS: It was observed that Extreme Learning Machine (ELM) and Gradient Boosting Classifier (GBC) showed maximum Youden's index and AUC-ROC measures compared to all discriminating formulae. Sensitivity remains maximum for SCSBTT. K-means clustering and the ranking from MCDM methods show that SCSBTT, Shine & Lal and Ravanbakhsh-F4 formula ensures higher performance among all formulae. The discriminant power of some MLAs and formulae was found considerably lower than that reported in original studies. CONCLUSION: Comparative information on MLAs can aid researchers in developing new discriminating formulae that simultaneously ensure higher sensitivity and specificity. More multi-centric verification of the formulae on heterogeneous data is indispensable. SCSBTT and Shine & Lal formula, and ELM and GBC are recommended for screening BTT based on MCDM. SCSBTT can be used with certainty as a tangible cost-saving screening tool for mass screening for antenatal women in India and other countries.

A robust and stable gene selection algorithm based on graph theory and machine learning.

BACKGROUND: Nowadays we are observing an explosion of gene expression data with phenotypes. It enables us to accurately identify genes responsible for certain medical condition as well as classify them for drug target. Like any other phenotype data in medical domain, gene expression data with phenotypes also suffer from being a very underdetermined system. In a very large set of features but a very small sample size domain (e.g. DNA microarray, RNA-seq data, GWAS data, etc.), it is often reported that several contrasting feature subsets may yield near equally optimal results. This phenomenon is known as instability. Considering these facts, we have developed a robust and stable supervised gene selection algorithm to select a set of robust and stable genes having a better prediction ability from the gene expression datasets with phenotypes. Stability and robustness is ensured by class and instance level perturbations, respectively. RESULTS: We have performed rigorous experimental evaluations using 10 real gene expression microarray datasets with phenotypes. They reveal that our algorithm outperforms the state-of-the-art algorithms with respect to stability and classification accuracy. We have also performed biological enrichment analysis based on gene ontology-biological processes (GO-BP) terms, disease ontology (DO) terms, and biological pathways. CONCLUSIONS: It is indisputable from the results of the performance evaluations that our proposed method is indeed an effective and efficient supervised gene selection algorithm.

A Novel Pathway Network Analytics Method Based on Graph Theory.

A biological pathway is an ordered set of interactions between intracellular molecules having collective activity that impacts cellular function, for example, by controlling metabolite synthesis or by regulating the expression of sets of genes. They play a key role in advanced studies of genomics. However, existing pathway analytics methods are inadequate to extract meaningful biological structure underneath the network of pathways. They also lack automation. Given these circumstances, we have come up with a novel graph theoretic method to analyze disease-related genes through weighted network of biological pathways. The method automatically extracts biological structures, such as clusters of pathways and their relevance, significance of each pathway and gene, and so forth hidden in the complex network. We have demonstrated the effectiveness of the proposed method on a set of genes associated with coronavirus disease 2019.

Assessment of Jaw Bone Quality for Designing Patient-Specific Dental Implant Using Computed Tomography Data.

It is essential to assess bone density among mandible teeth as well as among patients and also to observe the variation in all mechanical parameters of the bone for accurate design of patient-specific dental implants. This information helps in the design of implants to create a more osseointegration-friendly environment at the bone adjacent to the implant. For this study, 40 patients were chosen irrespective of age, sex, and bone density. Hounsfield Units were calculated using cone beam computed tomography data. Seven teeth were studied: central incisor, lateral incisor, canine, first premolar, second premolar, first molar, and second molar. A total of 12 arbitrary points were chosen in both buccal and lingual sides which were further divided into external and internal. From the analysis, it was observed that the bone density of the central incisor and that of the canine is greater than that of the molars.

Impact of Clinical and Genomic Factors on COVID-19 Disease Severity.

To date, there have been 180 million confirmed cases of COVID-19, with more than 3.8 million deaths, reported to WHO worldwide. In this paper we address the problem of understanding the host genome's influence, in concert with clinical variables, on the severity of COVID-19 manifestation in the patient. Leveraging positive-unlabeled machine learning algorithms coupled with RubricOE, a state-of-the-art genomic analysis framework, on UK BioBank data we extract novel insights on the complex interplay. The algorithm is also sensitive enough to detect the changing influence of the emergent B.1.1.7 SARS-CoV-2 (alpha) variant on disease severity, and, changing treatment protocols. The genomic component also implicates biological pathways that can help in understanding the disease etiology. Our work demonstrates that it is possible to build a robust and sensitive model despite significant bias, noise and incompleteness in both clinical and genomic data by a careful interleaving of clinical and genomic methodologies.

Oral and maxillofacial injuries in children: a retrospective study.

OBJECTIVES: The purpose of this retrospective epidemiological study was to determine the etiology and pattern of maxillofacial injuries in a pediatric population. MATERIALS AND METHODS: Data for pediatric maxillofacial trauma patients aged 12 years and younger who were registered at the Department of Pediatric and Preventive Dentistry, Dr. R. Ahmed Dental College and Hospital, Kolkata, India, were reviewed and examined. Patients who were treated between October 2016 and September 2018 were analyzed according to age, sex, cause of injury, frequency and site of facial fractures, and soft tissue injuries. The chi-square tests were carried out for statistical analyses with a significance level of 5%. RESULTS: Of 232 patients with a mean age of 6.77±3.25 years, there were 134 males (57.8%) and 98 females (42.2%). The overall male to female ratio was 1.39:1. The most common causes of injuries were falls (56.5%) and motor vehicle accidents (16.8%). Incidence of falls decreased significantly with age (P<0.001). Dentoalveolar injuries (61.6%) and soft tissue injuries (57.3%) were more common than facial fractures (42.7%). Mandibular fractures (82.8%) were the most common facial fractures, and perioral or lip injuries were the most prevalent injuries in our patient population. There was a positive association between facial fractures and soft tissue injury (P<0.01) (odds ratio 0.26; confidence interval 0.15-0.46). CONCLUSION: Falls were the leading cause of maxillofacial trauma in our sample of children, and the most common site of fractures was the mandible.

A decision support scheme for beta thalassemia and HbE carrier screening.

The most effective way to combat ß-thalassemias is to prevent the birth of children with thalassemia major. Therefore, a cost-effective screening method is essential to identify ß-thalassemia traits (BTT) and differentiate normal individuals from carriers. We considered five hematological parameters to formulate two separate scoring mechanisms, one for BTT detection, and another for joint determination of hemoglobin E (HbE) trait and BTT by employing decision trees, Naïve Bayes classifier, and Artificial neural network frameworks on data collected from the Postgraduate Institute of Medical Education and Research, Chandigarh, India. We validated both the scores on two different data sets and found 100% sensitivity of both the scores with their respective threshold values. The results revealed the specificity of the screening scores to be 79.25% and 91.74% for BTT and 58.62% and 78.03% for the joint score of HbE and BTT, respectively. A lower Youden's index was measured for the two scores compared to some existing indices. Therefore, the proposed scores can obviate a large portion of the population from expensive high-performance liquid chromatography (HPLC) analysis during the screening of BTT, and joint determination of BTT and HbE, respectively, thereby saving significant resources and cost currently being utilized for screening purpose.

Multi-Objective Human Resource Allocation Approach for Sustainable Traffic Management.

Efficient human resource deployment is one of the key aspects of road traffic management for maintaining the lifelines of any metropolitan city. The problem becomes relevant when collaboration between human resources with different skills in day-to-day operations is necessary to maintain public and commercial transport, manage various social events and emergency situations, and hence reduce congestion, injuries, emissions, etc. This study proposes a two-phase fuzzy multi-objective binary programming model for optimal allocation of five different categories of human resources to minimize the overall operational cost, maximize the allocation to accident-prone road segments, minimize the number of volunteer personnel and maximize the direct contact to reduce emissions and road traffic violations, simultaneously. A binary programming model is formulated to provide an efficient individual manpower allocation schedule for multiple road segments at different shifts. A case study is proposed for model evaluation and to derive managerial implications. The proposed model can be used to draw insights into human resource allocation planning in traffic management to reduce road traffic congestion, injuries and vehicular emissions.

Smart Glycopolymeric Nanoparticles for Multivalent Lectin Binding and Stimuli-Controlled Guest Release.

The synthesis and self-assembly of a polymer featuring a self-complementary supramolecular binding motif guanidiniocarbonyl pyrrole carboxylate zwitterion (GCP-zwitterion) bearing lactose moieties are reported. The GCP-zwitterion acts as a cross-linker to facilitate self-assembly of the polymeric chain into nanoparticles (NPs) at neutral pH in an aqueous medium. The formation of polymeric NPs can be controlled by addition of external stimuli (acid or base), which disfavors self-assembly of the GCP-zwitterion because of protonation or deprotonation of the GCP units in the polymer chain. The small-sized (<40 nm) NPs have a hydrophobic cavity and accessible lactose units on the outer shell for multivalent lectin binding. The multivalent interaction between NPs and the lectin peanut agglutinin was confirmed by agglutination experiments. In addition, the stimuli-responsive property of NPs was exploited for the uptake and release of a hydrophobic guest Nile red. Furthermore, the selectivity toward different cell lines (HEK 296T, HeLa, and Hep2G) was tested, and a cellular uptake of cargo-loaded NPs was found for Hep2G cells bearing the lactose-specific asialogylcoprotein receptor, whereas all other cells showed no NP interaction.

Multi Criteria Decision Making for the Multi-Satellite Image Acquisition Scheduling Problem.

The multi-satellite image acquisition scheduling problem is traditionally seen as a complex optimization problem containing a generic objective function that represents the priority structure of the satellite operator. However, the majority of literature neglect the collective and contemporary effect of factors associated with the operational goal in the objective function, i.e., uncertainty in cloud cover, customer priority, image quality criteria, etc. Consequently, the focus of the article is to integrate a real-time scoring approach of imaging attempts that considers these aspects. This is accomplished in a multi-satellite planning environment, through the utilization of the multi-criteria decision making (MCDM) models, Elimination and Choice Expressing Reality (ELECTRE-III) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the formulation of a binary linear programming model. The two scoring approaches belong to different model classes of MCDM, respectively an outranking approach and a distance to ideal point approach, and they are compared with a naive approach. Numerical experiments are conducted to validate the models and illustrate the importance of criteria neglected in previous studies. The results demonstrate the customized behaviour allowed by MCDM methods, especially the ELECTRE-III approach.