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Background: The reduction in severe and moderate acute malnutrition (SAM and MAM) rates in Pakistan has been sub-optimal compared to other low-and middle-income countries (LMICs). Specially-formulated products have been designed globally to manage SAM and MAM, such as ready-to-use therapeutic food (RUTF) and ready-to-use supplementary food (RUSF), with variable efficacies. RUTF is primarily produced and patented in industrialized countries, raising supply challenges in resource-constrained regions with a high burden of acute malnutrition. RUSF minimizes costs by using locally-available ingredients while providing similar nutritional value. In this study, we compared the efficacy, side effects, and compliance of two months of supplementation with either RUTF or RUSF. Methods: Children aged nine months in the rural district of Matiari, Pakistan, with a weight-for-height z-score (WHZ) <-2 received either RUTF (500 kcal sachet) for two months in 2015 or RUSF (520 kcal sachet) for two months in 2018. Results: The RUSF group had a higher height gain and mid-upper arm circumferences (MUAC) score. Higher compliance was noted with lower side effects in the RUSF group. A higher compliance rate did correlate with the growth parameters in respective groups. Conclusion: Our study found that both RUTF and RUSF partially improve the anthropometric status of acutely malnourished children, with neither being superior to the other. [ABSTRACT FROM AUTHOR]

This work reports the development of highly sensitive Piezoresistive flexible strain sensor for human motion detection and speech recognition. Initially, a conductive polymer composite (CPC) solution comprising of thermoplastic polyurethane and Carbon Nanoparticles was prepared using Dimethylformamide as solvent and Chloroform as dispersant with the composition of 50% v/v. The solution was heated to a temperature of 60°C for evaporation of the solvent until it contained 13.5% w/v solvent for steady electrospinning. In this way, the CPC solution was used to develop Electrospun Nanofibrous Yarns (ENFYs) by applying a potential difference of 40 KV between the electrospinning needle and Aluminum collector. A cotton fabric was wrapped on the Aluminum collector to allow twisting of the deposited electrospun nanofibers. This novel collector configuration resulted in the formation of nanofibrous yarns due to the whirling action of the advancing jet of CPC solution. The cotton fabric on the collector facilitated twisting of fibers by allowing them to roll over the fabric. The fabricated ENFY sensors showed remarkable stretchability up to 102% strain while achieving a gauge factor of 70 at 100% strain. Long-term usage necessitates repeatability, which was demonstrated by cyclic loading at a crosshead speed of 40 mm/min for up to 1000 cycles using a custom-developed linear actuator, with no signs of fracture. ENFY strain sensor was attached to different parts of human body such as finger, fist, elbow, knee and ankle and was found capable of measuring and observing angle, position and frequency of motion. Owing to its ultrasensitive behavior, the developed sensor was able to measure heart rate as well. When the developed sensor was attached to Adam's apple for speech recognition it showed remarkable response towards different utterances and breathing and gulping actions with clearly distinguishable signals. These results demonstrate that the developed novel ENFY flexible strain sensor can be employed for proprioceptive sensing and speech recognition for human-machine interaction, soft robotics and wearable devices etc. [ABSTRACT FROM AUTHOR]

• Vaccine coverage and timeliness were low among children in Matiari, Pakistan. • 48.4 % of the children were fully vaccinated with all EPI antigens. • Majority of the doses were delayed, only 21.2 % received all doses on time. • Parents' education and study year were protective against missed vaccination. • Geographical road distance was predictive of untimely vaccination. Pakistan has a well-established Expanded Program on Immunization (EPI) however vaccine-preventable diseases still account for high infant and child mortality rates. This study describes the differential vaccine coverage and determinants of vaccine uptake in rural Pakistan. From October 2014 to September 2018, we enrolled children younger than 2 years of age from the Matiari Demographic Surveillance System in Sindh, Pakistan. Socio-demographic and vaccination history were collected from all participants. Vaccine coverage rates and timeliness were reported. Socio-demographic variables for missed and untimely vaccination were studied in multivariable logistic regression. Of the 3140 enrolled children, 48.4 % received all EPI recommended vaccines. Only 21.2 % of these were age appropriate. Around 45.4 % of the children were partially vaccinated, and 6.2 % were unvaccinated. Highest coverage was seen for the first dose of pentavalent (72.8 %), 10-valent Pneumococcal Conjugate Vaccine (PCV10) (70.4 %) and Oral Polio Vaccine (OPV) (69.2 %) and the lowest coverage was for measles (29.3 %) and rotavirus (1.8 %) vaccines. Primary caretakers and wage earners with a higher level of education were protective against missed and untimely vaccination. Enrollment in the 2nd, 3rd and 4th study year was negatively associated with being unvaccinated whereas distance from a major road was positively associated with non-adherence to schedule. Vaccine coverage was low among children in Matiari, Pakistan, and majority received delayed doses. Parents' education status and year of study enrollment was protective against vaccine dropout and delayed vaccination whereas geographical distance from a major road was a predictor. Vaccine promotion and outreach efforts may have had a beneficial impact on vaccine coverage and timeliness. [ABSTRACT FROM AUTHOR]

The underperformance of oral vaccines in children of low- and middle-income countries is partly attributable to underlying environmental enteric dysfunction (EED). We conducted a longitudinal, community-based study to evaluate the association of oral rotavirus vaccine (Rotarix®) seroconversion with growth anthropometrics, EED biomarkers and intestinal enteropathogens in Pakistani infants. Children were enrolled between three to six months of their age based on their nutritional status. We measured serum anti-rotavirus immunoglobulin A (IgA) at enrollment and nine months of age with EED biomarkers and intestinal enteropathogens. A total of 391 infants received two doses of rotavirus (RV) vaccine. 331/391 provided paired blood samples. Of these 331 children, 45% seroconverted at 9 months of age, 35% did not seroconvert and 20% were seropositive at baseline. Non-seroconverted children were more likely to be stunted, wasted and underweight at enrollment. In univariate analysis, insulin-like growth factor (IGF) concentration at 6 months were higher in seroconverters, median (25th, 75th percentile): 26.3 (16.5, 43.5) ng/ml vs. 22.5 (13.6, 36.3) ng/ml for non-seroconverters, p-value = 0.024. At nine months, fecal myeloperoxidase (MPO) concentrations were significantly lower in seroconverters, 3050(1250, 7587) ng/ml vs. 4623.3 (2189, 11650) ng/ml in non-seroconverted children, p-value = 0.017. In multivariable logistic regression analysis, alpha-1 acid glycoprotein (AGP) and IGF-1 concentrations were positively associated with seroconversion at six months. The presence of sapovirus and rotavirus in fecal samples at the time of rotavirus administration, was associated with non-seroconversion and seroconversion, respectively. We detected high baseline RV seropositivity and impaired RV vaccine immunogenicity in this high-risk group of children. Healthy growth, serum IGF-1 and AGP, and fecal shedding of rotavirus were positively associated with RV IgA seroconversion following immunization, whereas the presence of sapovirus was more common in non-seroconverters. Trial registration : Clinical Trials ID: NCT03588013. [ABSTRACT FROM AUTHOR]

The application of heteroatom doped cobalt (Ni@Co/C) prepared from a porous metal–organic framework as the cathode material for the rechargeable zinc–air batteries has been demonstrated. The Ni@Co/C‐modified electrode showed an excellent catalytic activity toward oxidation–reduction reaction and oxygen evolution reactions. The Co/C catalysts, which were Co particle impregnated on graphitic carbon powders, were synthesized after the calcination of Co3 (SDA)3(DMF)2 precursor, and then Ni deposition (Ni@Co/C) was followed on it by chemical reduction. Linear sweep voltammetry measurement indicates that Ni@Co/C‐H800, which had Ni particles on Co/C derived from MOF at 800 °C under N2, exhibited excellent performance. The stable charging/discharging profiles at 20 mA cm−2 of current were obtained for the 350 h in alkaline medium, suggesting Ni@Co/C‐H800 as a potential candidate for oxidation–reduction and evolution reactions. [ABSTRACT FROM AUTHOR]

Co and carbon nanotube (CNT) were prepared as anode materials to be used in lithium rechargeable batteries from the zeolitic‐imidazolate framework‐67 (ZIF‐67) using the solvothermal method. By annealing ZIF‐67 at different temperatures between 400 and 700 °C, the carbon framework was changed to CNTs, and Co or Co3O4 nanoparticles were embedded in the CNTs (Co or Co3O4/CNT). The Co/CNT was converted to Co‐Co3O4/CNT by further treatment at 160 °C in air. The Co3O4/CNT showed high initial capacities (1582 mAh/g) but poor capacity retention during cycling. However, the air treatment of the Co/CNT at 160 °C enhanced its discharge capacity (935 mAh/g) and capacity retention during cycling. [ABSTRACT FROM AUTHOR]

Thermocatalytic conversion of CO 2 into liquid fuels and chemicals is a promising approach to mitigate global warming. Although, the CO 2 conversion and long-chain hydrocarbon selectivity are highly dependent on the choice of metal-oxide promoter, but role of the promoter remains unclear. Herein, the role of MgO as a promoter for an Fe-based catalyst was investigated. Bimetallic Na-FeMgO x catalyst exhibited a high C 5+ yield of 25.1% with a CO 2 conversion of 49.1% at the early stage of the reaction. The presence of MgO facilitates the reduction of Fe oxides and formation of oxygen vacancies by transferring electrons to Fe-based phases. In addition, at the early stage of reaction, the decoration of the Mg oxide surface with nanosized χ-Fe 5 C 2 enhances the C 5+ yield. However, the progressive transformation of MgO to MgCO 3 during CO 2 conversion deactivates the Na-FeMgO x catalyst. A detailed deactivation mechanism is also discussed. [Display omitted] • MgO-promoted Fe 3 O 4 produced a C 5+ yield of 25.1% at a CO 2 conversion of 49.1%. • MgO performed its role as an electronic promoter on Fe 3 O 4 and Fe 5 C 2. • The formation of MgCO 3 suppressed the promotional effect of MgO. • Re-oxidation of Fe 5 C 2 to Fe 3 O 4 during CO 2 conversion was main deactivation mechanism. [ABSTRACT FROM AUTHOR]

Direct CO2 hydrogenation into long-chain hydrocarbons over iron catalysts is considered the most promising approach to mitigate global warming issues. However, iron catalysts typically produce olefin-rich hydrocarbons, which make it difficult to directly use them as liquid transportation fuels. Here, we present a bimetallic Na–Fe0.9Mg0.1Ox catalyst that produced paraffin-rich long-chain hydrocarbons with a high C5+ yield of 17.9% at a high CO2 conversion of 42.1%. The formation of oxygen vacant sites at the catalyst surface and electron transfer from MgO to Fe phase increased the reducibility of Fe3O4 to α–Fe. In addition, the presence of MgO increased the H2 and CO2 adsorption and facilitated the C–C coupling reaction to produce long-chain paraffins. The high CO2 conversion, high C5+ yield, and paraffin-rich products make Fe0.9Mg0.1Ox a highly promising catalyst to produce liquid transportation fuels under industry-relevant conditions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Highly porous carbon network was derived from zeolitic-imidazole frameworks. • Co-deposition on porous carbon enhanced the catalytic activity. • Co on carbon was electrochemically stable as cathode material during 520 h cycling. Porous carbon wrapped on cobalt catalyst is one of the promising candidates to replace noble metal-based catalysts for ORR. Herein, a porous carbon network (PCN) was obtained by synthesis of zeolitic-imidazole frameworks (ZIF-8) followed by pyrolysis at 1050 ℃ under a nitrogenous atmosphere and the cobalt (Co) was deposited on PCN to obtain Co-PCN by sequential reduction method. The PCN showed a relatively high specific surface area of 806.43 m2/g and decreases to 329.4 m2/g with the introduction of Co contents in PCN. The Co-PCN reveals excellent performance having a current density of 350 mA cm−2 for the ORR at 0.5 V after 20th cycles as a cathode material in rechargeable zinc-air batteries. [ABSTRACT FROM AUTHOR]

[Display omitted] • Fabrication of nickel-cobalt nanocage for oxygen evolution reactions. • Excellent oxygen evolution reaction performance with overpotential of 350 mV. • Synergic effect of cobalt-nickel catalyst improves the conductivity and charge transfer. The development of efficient, eco-friendly, and inexpensive electrocatalysts has become crucial and significant in water-splitting applications. Herein, we developed a nickel-cobalt nanocage (Ni/Co-NC) electrocatalyst via the solvothermal method which can boost the performances towards oxygen evolution reactions (OER) and hydrogen evolution reactions (HER). The Ni/Co-NC reveals significant performance at an onset potential of 350 mV for the OER at a delivered current density of 10 mA cm−2. The significant amount of oxygen vacancies generated by the addition of nickel in cobalt has been verified by XPS and the nanocage structure revealed by TEM and SEM images helps in effective performance for OER/HER. Furthermore, the synergistic effect between various Ni3+/Ni2+ and Co2+ could optimize the performance resulting in a smaller Tafel slope of 70 mV dec−1 for Ni/Co-NC. [ABSTRACT FROM AUTHOR]

[Display omitted] • Polyvinyl alcohol (PVA) based nanofibers were prepared via centrifugal spinning. • Scanning electron microscopy images reveled the diameter of nanofibers. • PVA based nanofibers were antibacterial against S. aureus and E. coli. In this study, three types of nanofibers, i.e. polyvinyl alcohol (PVA), alginate di-aldehyde (ADA)/PVA, and ADA-gelatin (GEL)/PVA, were fabricated via centrifugal spinning. The spinability of ADA was improved by mixing with PVA. Furthermore, GEL and sesame oil were added in the ADA solution to control the degradation kinetics and to improve the viscosity of the solution, respectively. Later, the solution was fed to a rotating spinneret (3000 rpm at 250 °C) which led to the fabrication of nanofibers. The developed nanofibers were loaded with copper-silver (Cu-Ag) doped mesoporous bioactive glass nanoparticles (MBGNs) to provide therapeutic effects (antibacterial effect). Scanning electron microscopy images showed that the nanofiber diameters were 139 ± 62 nm, 258 ± 42 nm, and 281 ± 34 nm, for PVA, ADA/PVA, and ADA-GEL/PVA respectively, with web-like morphology. Fourier transform infrared spectroscopy confirmed the presence of chemical bonds attributed to the ADA, GEL, and PVA components. The results of the inhibition halo test confirmed that the nanofibers (ADA-GEL/PVA & ADA/PVA) exhibit potent antibacterial effect against Staphylococcus aureus and Escherichia coli. [ABSTRACT FROM AUTHOR]

This study aims at developing the screen-printed sensors as a viable means of depositing sensing tracks on composites for their on-line structural health monitoring. Conventional silk screen was employed in order to deposit a nano-composite solution comprising of a conductive nano-filler (carbon nano-particles) dispersed in a thermoplastic matrix (high density polystyrene) on laminated composite specimens. The solution was deposited using a squeegee and was allowed to dry. Commercially available metal foil strain gauges were also bonded alongside screen-printed sensor in order to compare the response of the screen-printed sensors with the commercially available strain gauges. The sensing ability of these screen-printed sensors was tested on a universal testing machine (MTS 810) in four-point bending configuration using a load cell of 100 kN. The sensor deposited using screen-printing technique underwent tensile loading at the lower side of the laminate. A data linearization and amplification module comprising of commercially available instrumentation amplifier (INA 118) was used in conjunction with data acquisition module (Keithley KUSB 3100). The results obtained show that the screen-printed sensors have higher gauge factors in tensile loading scenario with reasonably linear response as compared to traditional metal foil strain gauges. The ease of the deposition of a nano-composite solution via screen printing also makes the technique a viable alternative to the traditional resin bonded metal foil strain gauges which have to be bonded on the surface. Moreover, screen printing offers unlimited options for the development of smart composites in various configurations for a multitude of structural applications. [ABSTRACT FROM AUTHOR]

Background: Exclusive breastfeeding (EBF) of children until six months of age is considered one of the most critical interventions in tackling childhood undernutrition. EBF rates are suboptimal in Pakistan, particularly in rural areas where child undernutrition is most prevalent. This study aimed to explore barriers to EBF in a rural context of Pakistan. Methods: The study was conducted in the rural district Matiari of Sindh, Pakistan, during Jan-March 2020. We used a qualitative exploratory study design and conducted 36 focus group discussions (FGDs). Participants were purposively selected mothers who had not practiced EBF during their previous childbirth, their spouses and mothers-in-law, and lady health workers (LHWs) serving in the study catchment. FGDs were audio-recorded, transcribed, and translated into English from the local language and analysed using thematic content analysis. Results: Barriers to EBF included low awareness and cultural practices of prelacteal feeds, insufficient breast milk production, undernutrition of mothers, mothers' occupation as fieldworkers, less birth spacing, low awareness about the correct technique of breastfeeding, maternal and child ailments, abnormal breasts, and influence of in-laws to start top-up feeds. Several facilitators were identified: family support, appropriate maternal diet, maternal awareness, and support in the neighborhood. Conclusion: Barriers to EBF are multifaceted in rural areas, and interventions aiming to improve adherence to EBFshould be multipronged. Awareness-raising alone might not be sufficient, and other interventions should be designed to address the barriers of maternal malnutrition, insufficient milk production, and socio-cultural practices. In addition, safe alternatives to breast milk may be necessary if breastfeeding is truly not feasible. Lack of breast milk substitutes is particularly challenging for poor rural women who cannot afford infant formula milk. [ABSTRACT FROM AUTHOR]

Deep neural networks are one of the most successful classifiers across different domains. However, their use is limited in safety-critical areas due to their limitations concerning interpretability. The research field of explainable artificial intelligence addresses this problem. However, most interpretability methods align to the imaging modality by design. The paper introduces TimeREISE, a model agnostic attribution method that shows success in the context of time series classification. The method applies perturbations to the input and considers different attribution map characteristics such as the granularity and density of an attribution map. The approach demonstrates superior performance compared to existing methods concerning different well-established measurements. TimeREISE shows impressive results in the deletion and insertion test, Infidelity, and Sensitivity. Concerning the continuity of an explanation, it showed superior performance while preserving the correctness of the attribution map. Additional sanity checks prove the correctness of the approach and its dependency on the model parameters. TimeREISE scales well with an increasing number of channels and timesteps. TimeREISE applies to any time series classification network and does not rely on prior data knowledge. TimeREISE is suited for any usecase independent of dataset characteristics such as sequence length, channel number, and number of classes. [ABSTRACT FROM AUTHOR]

Post-translational modifications (PTMs) either enhance a protein's activity in various sub-cellular processes, or degrade their activity which leads toward failure of intracellular processes. Tyrosine nitration (NT) modification degrades protein's activity that initiates and propagates various diseases including neurodegenerative, cardiovascular, autoimmune diseases and carcinogenesis. Identification of NT modification supports development of novel therapies and drug discoveries for associated diseases. Identification of NT modification in biochemical labs is expensive, time consuming and error-prone. To supplement this process, several computational approaches have been proposed. However these approaches fail to precisely identify NT modification, due to the extraction of irrelevant, redundant and less discriminative features from protein sequences. This paper presents the NTpred framework that is competent in extracting comprehensive features from raw protein sequences using four different sequence encoders. To reap the benefits of different encoders, it generates four additional feature spaces by fusing different combinations of individual encodings. Furthermore, it eradicates irrelevant and redundant features from eight different feature spaces through a Recursive Feature Elimination process. Selected features of four individual encodings and four feature fusion vectors are used to train eight different Gradient Boosted Tree classifiers. The probability scores from the trained classifiers are utilized to generate a new probabilistic feature space, which is used to train a Logistic Regression classifier. On the BD1 benchmark dataset, the proposed framework outperforms the existing best-performing predictor in 5-fold cross validation and independent test evaluation with combined improvement of 13.7% in MCC and 20.1% in AUC. Similarly, on the BD2 benchmark dataset, the proposed framework outperforms the existing best-performing predictor with combined improvement of 5.3% in MCC and 1.0% in AUC. NTpred is publicly available for further experimentation and predictive use at: https://sds_genetic_analysis.opendfki.de/PredNTS/. [ABSTRACT FROM AUTHOR]

A substantial part of the valuable water is wasted in form of conveyance losses in earthen watercourses. To assess the effect of watercourses improvement on water availability and productivity, ten watercourses were selected randomly. Out of which, five improved and other five were found unimproved. Water losses in both watercourses were measured to find water saving from improved watercourses. Conveyance losses were measured in improved and unimproved watercourses by current meter and cutthroat flume, respectively. Farmers were interviewed to find out the effect of watercourse improvement on crop productivity and water management practices using questionnaire proforma. The losses in five improved watercourses (lined sections) were 9, 28, 5, 11 and 20% per 1000 meter, respectively while that of unlined sections of the same watercourses were 23, 30, 50 and 21 % per 1000 m, respectively. Losses in unimproved watercourses were 27, 62, 55, 55 and 40 % per 1000 m, respectively. The conveyance efficiencies of five improved watercourses (lined sections) were 91, 72, 95, 89 and 80 % per 1000 m, respectively while that of unlined sections were 77, 70, 50 and 79 % 1000 m, respectively. The conveyance efficiencies of unimproved watercourses were 73, 37, 45, 45 and 60 % 1000 m, respectively. Based on the results, it can be concluded that watercourse improvement caused significant effect on irrigation water and it is therefore justified from economic perspectives. [ABSTRACT FROM AUTHOR]

A substantial part of the valuable water is wasted in form of conveyance losses in earthen watercourses. To assess the effect of watercourses improvement on water availability and productivity, ten watercourses were selected randomly. Out of which, five improved and other five were found unimproved. Water losses in both watercourses were measured to find water saving from improved watercourses. Conveyance losses were measured in improved and unimproved watercourses by current meter and cutthroat flume, respectively. Farmers were interviewed to find out the effect of watercourse improvement on crop productivity and water management practices using questionnaire proforma. The losses in five improved watercourses (lined sections) were 9, 28, 5, 11 and 20% per 1000 meter, respectively while that of unlined sections of the same watercourses were 23, 30, 50 and 21 % per 1000 m, respectively. Losses in unimproved watercourses were 27, 62, 55, 55 and 40 % per 1000 m, respectively. The conveyance efficiencies of five improved watercourses (lined sections) were 91, 72, 95, 89 and 80 % per 1000 m, respectively while that of unlined sections were 77, 70, 50 and 79 % 1000 m, respectively. The conveyance efficiencies of unimproved watercourses were 73, 37, 45, 45 and 60 % 1000 m, respectively. Based on the results, it can be concluded that watercourse improvement caused significant effect on irrigation water and it is therefore justified from economic perspectives. [ABSTRACT FROM AUTHOR]

[Display omitted] • Thermocatalytic decomposition of ammonia is presented. • Ammonia as a hydrogen carrier for green hydrogen production is discussed. • Ruthenium and non-ruthenium-based catalysts for ammonia conversion are reviewed. • Techno-economic analysis of ammonia and hydrogen economy is discussed. • Hydrogen source, production pathways, and energy efficiency are presented. Sustainable societal growth relies on the efficient storage, transportation, and use of renewable energies. Considerable progress has been made in sustainable hydrogen production via water electrolysis and biomass conversion, but hydrogen storage and transportation remain major challenges for commercial-scale applications. Owing to its high hydrogen content and energy density, ammonia is a promising zero-carbon energy carrier for large-scale energy storage. Therefore, the transformation of renewable hydrogen into ammonia is a promising strategy for effective hydrogen transportation and storage. Unlike the direct combustion of ammonia, which can produce NO x , catalytic cracking into nitrogen and hydrogen provides an environmentally friendly method for hydrogen regeneration. Herein, recent advances in effective ammonia decomposition via various processes, including electrochemical, photochemical, and, particularly, thermochemical routes, are summarized. In addition, hydrogen separation techniques and techno-economic analyses of the ammonia and hydrogen economy are discussed. The main objective of this review is to provide a conceptual framework for effective ammonia cracking to yield pure hydrogen as a clean fuel for developing futuristic and sustainable energy solutions. [ABSTRACT FROM AUTHOR]

Accurate prediction of deoxyribonucleic acid (DNA) modifications is essential to explore and discern the process of cell differentiation, gene expression and epigenetic regulation. Several computational approaches have been proposed for particular type-specific DNA modification prediction. Two recent generalized computational predictors are capable of detecting three different types of DNA modifications; however, type-specific and generalized modifications predictors produce limited performance across multiple species mainly due to the use of ineffective sequence encoding methods. The paper in hand presents a generalized computational approach "DNA-MP" that is competent to more precisely predict three different DNA modifications across multiple species. Proposed DNA-MP approach makes use of a powerful encoding method "position specific nucleotides occurrence based 117 on modification and non-modification class densities normalized difference" (POCD-ND) to generate the statistical representations of DNA sequences and a deep forest classifier for modifications prediction. POCD-ND encoder generates statistical representations by extracting position specific distributional information of nucleotides in the DNA sequences. We perform a comprehensive intrinsic and extrinsic evaluation of the proposed encoder and compare its performance with 32 most widely used encoding methods on |$17$| benchmark DNA modifications prediction datasets of |$12$| different species using |$10$| different machine learning classifiers. Overall, with all classifiers, the proposed POCD-ND encoder outperforms existing |$32$| different encoders. Furthermore, combinedly over 5-fold cross validation benchmark datasets and independent test sets, proposed DNA-MP predictor outperforms state-of-the-art type-specific and generalized modifications predictors by an average accuracy of 7% across 4mc datasets, 1.35% across 5hmc datasets and 10% for 6ma datasets. To facilitate the scientific community, the DNA-MP web application is available at https://sds_genetic_analysis.opendfki.de/DNA_Modifications/. [ABSTRACT FROM AUTHOR]

Circular ribonucleic acids (circRNAs) are novel non-coding RNAs that emanate from alternative splicing of precursor mRNA in reversed order across exons. Despite the abundant presence of circRNAs in human genes and their involvement in diverse physiological processes, the functionality of most circRNAs remains a mystery. Like other non-coding RNAs, sub-cellular localization knowledge of circRNAs has the aptitude to demystify the influence of circRNAs on protein synthesis, degradation, destination, their association with different diseases, and potential for drug development. To date, wet experimental approaches are being used to detect sub-cellular locations of circular RNAs. These approaches help to elucidate the role of circRNAs as protein scaffolds, RNA-binding protein (RBP) sponges, micro-RNA (miRNA) sponges, parental gene expression modifiers, alternative splicing regulators, and transcription regulators. To complement wet-lab experiments, considering the progress made by machine learning approaches for the determination of sub-cellular localization of other non-coding RNAs, the paper in hand develops a computational framework, Circ-LocNet, to precisely detect circRNA sub-cellular localization. Circ-LocNet performs comprehensive extrinsic evaluation of 7 residue frequency-based, residue order and frequency-based, and physio-chemical property-based sequence descriptors using the five most widely used machine learning classifiers. Further, it explores the performance impact of K-order sequence descriptor fusion where it ensembles similar as well dissimilar genres of statistical representation learning approaches to reap the combined benefits. Considering the diversity of statistical representation learning schemes, it assesses the performance of second-order, third-order, and going all the way up to seventh-order sequence descriptor fusion. A comprehensive empirical evaluation of Circ-LocNet over a newly developed benchmark dataset using different settings reveals that standalone residue frequency-based sequence descriptors and tree-based classifiers are more suitable to predict sub-cellular localization of circular RNAs. Further, K-order heterogeneous sequence descriptors fusion in combination with tree-based classifiers most accurately predict sub-cellular localization of circular RNAs. We anticipate this study will act as a rich baseline and push the development of robust computational methodologies for the accurate sub-cellular localization determination of novel circRNAs. [ABSTRACT FROM AUTHOR]

Viral-host protein protein interaction (PPI) analysis is essential to decode the molecular mechanism of viral pathogen and host immunity processes which eventually help to control viral diseases and optimize therapeutics. The state-of-the-art viral-host PPI predictor leverages unsupervised embedding learning technique (doc2vec) to generate statistical representations of viral-host protein sequences and a Random Forest classifier for interaction prediction. However, doc2vec approach generates the statistical representations of viral-host protein sequences by merely modelling the local context of residues which only partially captures residue semantics. The paper in hand proposes a novel technique for generating better statistical representations of viral and host protein sequences based on the infusion of comprehensive local and global contextual information of the residues. While local residue context aware encoding captures semantic relatedness and short range dependencies of residues. Global residue context aware encoding captures comprehensive long-range residues dependencies, positional invariance of residues, and unique residue combination distribution important for interaction prediction. Using concatenated rich statistical representations of viral and host protein sequences, a robust machine learning framework "LGCA-VHPPI" is developed which makes use of a deep forest model to effectively model complex non-linearity of viral-host PPI sequences. An in-depth performance comparison of the proposed LGCA-VHPPI framework with existing diverse sequence encoding schemes based viral-host PPI predictors reveals that LGCA-VHPPI outperforms state-of-the-art predictor by 6%, 2%, and 2% in terms of matthews correlation coefficient over 3 different benchmark viral-host PPI prediction datasets. [ABSTRACT FROM AUTHOR]

As data-driven AI systems become increasingly integrated into industry, concerns have recently arisen regarding potential privacy breaches and the inadvertent leakage of sensitive user data through the exploitation of these systems. In this paper, we explore the intersection of data privacy and AI-powered document analysis systems, presenting a comprehensive benchmark of well-known privacy-preserving methods for the task of document image classification. In particular, we investigate four different privacy methods—Differential Privacy (DP), Federated Learning (FL), Differentially Private Federated Learning (DP-FL), and Secure Multi-Party Computation (SMPC)—on two well-known document benchmark datasets, namely RVL-CDIP and Tobacco3482. Furthermore, we investigate the performance of each method under a variety of configurations for thorough benchmarking. Finally, the privacy strength of each approach is assessed by subjecting the private models to well-known membership inference attacks. Our results demonstrate that, with sufficient tuning of hyperparameters, Differential Privacy (DP) can achieve reasonable performance on the task of document image classification while also ensuring rigorous privacy constraints, both in standalone and federated learning setups. On the other hand, while FL-based approaches present less implementation complexity and incur little to no loss in performance on the task, they do not offer sufficient protection against privacy attacks. By rigorously benchmarking various privacy approaches, our study paves the way for integrating deep document classification models into industrial pipelines while meeting regulatory and ethical standards, including GDPR and the AI Act 2022. [ABSTRACT FROM AUTHOR]

Pollution from dye-containing industrial wastewater is a major health hazard in many nations, necessitating modern remediation approaches. Herein, zinc oxide (ZnO) was employed to degrade methyl orange (MO) as an organic dye pollutant under UV light irradiation. The performance was observed experimentally and theoretically under optimized conditions including the pH (11), the concentration of the nanoparticle solution (900 ppm), and time (3 h), resulting in a degradation efficiency of 89.6%. Furthermore, the influence of various parameters on MO degradation was evaluated by response surface methodology (RSM). X-ray diffraction (XRD) and transmission electron microscopy (TEM) was performed to investigate the crystallinity and morphological behavior of ZnO-NPs. In addition, the surface chemical composition was evaluated by the XPS analysis. This study evaluates the degradation efficiency of ~ 90% using single metal oxide to degrade MO, opening new opportunities for environmental applications. [ABSTRACT FROM AUTHOR]

Phosphorus (P) is one of the essential nutrients needed for plant growth and development. The P has a significant ecological and economic importance; therefore, its application is considered to maximize the yield of various crops, including chickpea. The current experiment was performed to assess the impact of phosphorus application on yield of 15 chickpea genotypes during the growing season of 2017-18 at the University of Agriculture, Peshawar. A randomized complete block design was used with three replications under two treatments. One treatment was a control without phosphorous application, while the second treatment was application of phosphorous at a rate of 35.3 kg (P2O5) ha-1. Data were documented on plant height, pod number plant-1, seed number pod-1, seed number plant-1, 100-seed weight, seed yield and harvest index. Amongst genotypes, highly significant variation was observed for the studied characters. Similarly, significant results were obtained for genotype by environment (GE) interaction for all traits except plant height. Application of phosphorus enormously improved seed yield in all chickpea genotypes. Based on mean data, chickpea plants with P application produced more seed number plant-1 (60.9), pods plant-1 (57.3) and seed yield (557.2 kg ha-1). Higher seed yields were produced by genotypes NDC-4-20-1 (1004 kg ha-1) and NKC-5-S-15 (851 kg ha-1). Seed yield had significantly negative phenotypic correlation with plant height. Similarly at genotypic level, it was significantly correlated with seed number pod-1 while negatively with 100-seed weight. Among the tested genotypes, NDC-4-20-1 and NKC-5-S-15 were identified as better performing with phosphorus application while genotype NKC-5-S-12 followed by NDC-4-20-1 performed well in both environments. Therefore, the screened lines could be used in various chickpea breeding programs for higher seed yield. Considering current findings, this study suggests that application of phosphorus to chickpea fields is recommendable for higher seed yield and hence is justified from economic perspective. [ABSTRACT FROM AUTHOR]

Herbal drugs play an imperative role in healthcare programs in developing countries. Curry leaves have wide medicinal importance and are used to treat various diseases traditionally. The current study was carried out to estimate the extent of mercury toxicity and the potential effect of curry leaves against defined toxicity. The study group comprised 24 rats weighing between 130 and150 g. Group 1 was kept normal, and group 2 was exposed to mercury at 0.4 mg/kg of body weight in the form of mercuric chloride (HgCl2). The group 3 animals were treated with curry leaves with a dosage of 300 mg/kg of body weight. Group 4 was treated with curry leaves along with mercury with a dosage of 300 and 0.4 mg/kg consecutively. After 28 days, the rats were killed. Blood sample of all groups were evaluated separately to determine the results of different parameters. The results show that ALP, AST, ALT, urea, bilirubin, and creatinine increased with mercury application and decreased with curry leaf exposure. SOD, CAT, GPx, and GR of the liver as well as the kidney depleted on mercury exposure whereas they increased with curry leaf application. HDL increased with curry leaf application and decreased with mercury treatment, while LDL, triglyceride, and cholesterol decreased with curry leaves and increased with mercury exposure. Organ index in mercury along with curry leaf application got close to normal. [ABSTRACT FROM AUTHOR]

Objective: Pakistan was one of the first South-Asian countries to introduce the ten-valent pneumococcal conjugate vaccine (PCV10) at the national level, using a 3+0 schedule without catchup, in 2013. Methods: From 2014–18, fifteen children <2 years old were recruited every week in Matiari, Sindh, and nasopharyngeal swabs were collected. The samples were cultured, and pneumococcus was further serotyped through multiplex PCR at the Aga Khan University Hospital as per the method described by the Centers for Disease Control and Prevention, USA. Results: Pneumococcus was detected in 2370/3140 (75%) children. Vaccine type (VT) and non-vaccine type (NVT) serotypes were carried by 379 and 1990 children. There was a significant decline in VT carriage (by 40.3%, p-value <0.001), whereas overall NVT carriage remained the same. The prevalence of VT serotypes 6B, 9V/9A, and 19F showed a significant decline by 58.8%, 79.3%, and 56%, respectively. The prevalence of NVT serotypes 19A, 21, and 10A increased by 70%, 33.3%, and 65.6%, respectively, whereas serotypes 13 and 9N/9L decreased by 53.4% and 51.8%, respectively. Serotype-specific vaccine effectiveness estimates that reached statistical significance were for 9V/9A (VE = 65.0, 95% CI 26.0–83.5%), 19F (VE = 55.3, 95% CI 15.5–76.4%) and for the vaccine related serotype 6A (VE = 28.4, 95% CI 0.9–48.2%). Conclusion: The emergence of NVT serotypes, primarily 19A replacing VT serotypes in this rural community, necessitates continuous monitoring of serotypes in the carriage and invasive disease to evaluate the utility of existing vaccine formulations. [ABSTRACT FROM AUTHOR]

This study aim to investigate the diagnostic potential of Raman spectroscopy in comparison with rapid urease test and histopathology in diagnosis of H. pylori infection. A comparative study was conducted at Pathology Laboratory and a total of 94 samples were collected from patients based on Rome IV criteria. Sensitivity, specificity and accuracy of histopathology, rapid urease test and for Raman spectroscopy were investigated. Rapid urease test showed 23 false negative results of H. pylori as compared to Raman spectroscopy and histopathology. We concluded that Raman spectroscopy showed sensitivity (94.5%), accuracy (94.0%) and specificity of (87.5%) in the diagnosis of H. pylori infection. However rapid urease test showed specificity of 92.5% while low sensitivity 75%, and 78% accuracy as compared to Raman spectroscopy and histopathology. This study illustrates the applicability of Raman spectroscopy as a potent innovative detection tool for the molecular detection of H. pylori infection in gastritis. [ABSTRACT FROM AUTHOR]

Background: Intestinal inflammation and malabsorption in environmental enteric dysfunction (EED) are associated with early childhood growth faltering in impoverished settings worldwide.Objectives: The goal of this study was to identify candidate biomarkers associated with inflammation, EED histology, and as predictors of later growth outcomes by focusing on the liver-gut axis by investigating the bile acid metabolome.Methods: Undernourished rural Pakistani infants (n = 365) with weight-for-height Z score (WHZ) < -2 were followed up to the age of 24 mo and monitored for growth, infections, and EED. Well-nourished local children (n = 51) were controls, based on consistent WHZ > 0 and height-for-age Z score (HAZ) > -1 on 2 consecutive visits at 3 and 6 mo. Serum bile acid (sBA) profiles were measured by tandem MS at the ages of 3-6 and 9 mo and before nutritional intervention. Biopsies and duodenal aspirates were obtained following upper gastrointestinal endoscopy from a subset of children (n = 63) that responded poorly to nutritional intervention. BA composition in paired plasma and duodenal aspirates was compared based on the severity of EED histopathological scores and correlated to clinical and growth outcomes.Results: Remarkably, >70% of undernourished Pakistani infants displayed elevated sBA concentrations consistent with subclinical cholestasis. Serum glycocholic acid (GCA) correlated with linear growth faltering (HAZ, r = -0.252 and -0.295 at the age of 3-6 and 9 mo, respectively, P <0.001) and biomarkers of inflammation. The proportion of GCA positively correlated with EED severity for both plasma (rs = 0.324 P = 0.02) and duodenal aspirates (rs = 0.307 P = 0.06) in children with refractory wasting that underwent endoscopy, and the proportion of secondary BA was low in both undernourished and EED children.Conclusions: Dysregulated bile acid metabolism is associated with growth faltering and EED severity in undernourished children. Restoration of intestinal BA homeostasis may offer a novel therapeutic target for undernutrition in children with EED. This trial was registered at clinicaltrials.gov as NCT03588013. [ABSTRACT FROM AUTHOR]

The formation of protein aggregates is a hallmark of neurodegenerative diseases. Observations on patient samples and model systems demonstrated links between aggregate formation and declining mitochondrial functionality, but causalities remain unclear. We used Saccharomyces cerevisiae to analyze how mitochondrial processes regulate the behavior of aggregation‐prone polyQ protein derived from human huntingtin. Expression of Q97‐GFP rapidly led to insoluble cytosolic aggregates and cell death. Although aggregation impaired mitochondrial respiration only slightly, it considerably interfered with the import of mitochondrial precursor proteins. Mutants in the import component Mia40 were hypersensitive to Q97‐GFP, whereas Mia40 overexpression strongly suppressed the formation of toxic Q97‐GFP aggregates both in yeast and in human cells. Based on these observations, we propose that the post‐translational import of mitochondrial precursor proteins into mitochondria competes with aggregation‐prone cytosolic proteins for chaperones and proteasome capacity. Mia40 regulates this competition as it has a rate‐limiting role in mitochondrial protein import. Therefore, Mia40 is a dynamic regulator in mitochondrial biogenesis that can be exploited to stabilize cytosolic proteostasis. SYNOPSIS: The accumulation of mitochondrial precursor proteins in the cytosol induces the formation of protein aggregates. Overexpression of mitochondrial import factor Mia40 improves mitochondrial protein import and increases resistance against proteotoxic insults in Saccharomyces cerevisiae. S. cerevisiae mitochondrial import mutants are hypersensitive to cytosolic polyQ proteins.Overexpression of Mia40 prevents the formation of polyQ aggregates in yeast and human cells.Mia40 levels affect the solubility of the cytosolic prion protein Rnq1.Increasing mitochondrial import augments robustness of cytosolic proteostasis. [ABSTRACT FROM AUTHOR]

Environmental enteric dysfunction (EED) is a subclinical condition of intestinal inflammation, barrier dysfunction and malabsorption associated with growth faltering in children living in poverty. This study explores association of altered duodenal permeability (lactulose, rhamnose and their ratio) with higher burden of enteropathogen in the duodenal aspirate, altered histopathological findings and higher morbidity (diarrhea) that is collectively associated with linear growth faltering in children living in EED endemic setting. In a longitudinal birth cohort, 51 controls (WHZ > 0, HAZ > −1.0) and 63 cases (WHZ< -2.0, refractory to nutritional intervention) were recruited. Anthropometry and morbidity were recorded on monthly bases up to 24 months of age. Dual sugar assay of urine collected after oral administration of lactulose and rhamnose was assessed in 96 children from both the groups. Duodenal histopathology (n = 63) and enteropathogen analysis of aspirate via Taqman array card (n = 60) was assessed in only cases. Giardia was the most frequent pathogen and was associated with raised L:R ratio (p = 0.068). Gastric microscopy was more sensitive than duodenal aspirate in H. pylori detection. Microscopically confirmed H. pylori negatively correlated with HAZ at 24 months (r = −0.313, p = 0.013). Regarding histopathological parameters, goblet cell reduction significantly correlated with decline in dual sugar excretion (p< 0.05). Between cases and controls, there were no significant differences in the median (25th, 75th percentile) of urinary concentrations (μg/ml) of lactulose [27.0 (11.50, 59.50) for cases vs. 38.0 (12.0, 61.0) for controls], rhamnose [66.0 (28.0, 178.0) vs. 86.5 (29.5, 190.5)] and L:R ratio [0.47 (0.24, 0.90) vs. 0.51 (0.31, 0.71)] respectively. In multivariable regression model, 31% of variability in HAZ at 24 months of age among cases and controls was explained by final model including dual sugars. In conclusion, enteropathogen burden is associated with altered histopathological features and intestinal permeability. In cases and controls living in settings of endemic enteropathy, intestinal permeability test may predict linear growth. However, for adoption as a screening tool for EED, further validation is required due to its complex intestinal pathophysiology. Author summary: EED is a subclinical condition of compromised gut integrity secondary to frequent and repeated exposure to enteropathogens in global settings with a high prevalence of undernutrition. In this study, we reported association of gut mucosal architecture with a dual sugar intestinal permeability assay (lactulose-rhamnose) in Pakistani children. In the presence of duodenal enteropathogens, features such as chronic inflammation, intra-epithelial lymphocytosis, enterocyte injury and Paneth cell reduction were consistently observed. When comparing undernourished cases and controls living in the same setting, we found urinary excretion of the sugars was similar among groups; however, variability in HAZ among children at 24 months was partially explained by a model that includes excretion values. [ABSTRACT FROM AUTHOR]

• Online handwriting classification dataset. • Feature set. • A comparative analysis with existing feature sets. • Feature impact evaluation and contributions. • A combination of feature set based on evaluation. This paper has a threefold contribution. First, it presents a novel online handwriting database captured using a digital/sensor pen (Apple pencil) and digital/sensor screen (iPad). The captured data are continuous streams of multi-dimensional points, analyzed and processed to classify handwritten sequences into plain text, mathematical expressions, and plots/graphs. Second, a new feature set for online handwritten sequence classification is proposed. The said feature set is used to establish a benchmark for the proposed dataset using various machine-learning classifiers. Third, an ablation study is performed to look into the performance of the proposed feature set compared to the existing feature sets. Here, the proposed feature set has outperformed all the existing feature sets in various evaluation metrics. Furthermore, the proposed dataset and the feature set have been made publicly available along with the benchmark evaluation to enable further research in the field. [ABSTRACT FROM AUTHOR]

In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it provides a publicly available benchmark dataset manually tagged against 6 classes. Second, it investigates the performance impact of traditional machine learning-based Urdu text document classification methodologies by embedding 10 filter-based feature selection algorithms which have been widely used for other languages. Third, for the very first time, it assesses the performance of various deep learning-based methodologies for Urdu text document classification. In this regard, for experimentation, we adapt 10 deep learning classification methodologies which have produced best performance figures for English text classification. Fourth, it also investigates the performance impact of transfer learning by utilizing Bidirectional Encoder Representations from Transformers approach for Urdu language. Fifth, it evaluates the integrity of a hybrid approach which combines traditional machine learning-based feature engineering and deep learning-based automated feature engineering. Experimental results show that feature selection approach named as normalized difference measure along with support vector machine outshines state-of-the-art performance on two closed source benchmark datasets CLE Urdu Digest 1000k, and CLE Urdu Digest 1Million with a significant margin of 32% and 13%, respectively. Across all three datasets, normalized difference measure outperforms other filter-based feature selection algorithms as it significantly uplifts the performance of all adopted machine learning, deep learning, and hybrid approaches. The source code and presented dataset are available at Github repository https://github.com/minixain/Urdu-Text-Classification. [ABSTRACT FROM AUTHOR]

• AI is crucial for deployment of computer-aided diagnosis systems. • Framework for multimodal justification for decisions in medical image analysis. • Concept-based xAI assures comprehensible explanation and knowledge discovery. • Dermoscopy as a prime use-case for ExAID framework. Background and objectives: One principal impediment in the successful deployment of Artificial Intelligence (AI) based Computer-Aided Diagnosis (CAD) systems in everyday clinical workflows is their lack of transparent decision-making. Although commonly used eXplainable AI (XAI) methods provide insights into these largely opaque algorithms, such explanations are usually convoluted and not readily comprehensible. The explanation of decisions regarding the malignancy of skin lesions from dermoscopic images demands particular clarity, as the underlying medical problem definition is ambiguous in itself. This work presents ExAID (Explainable AI for Dermatology), a novel XAI framework for biomedical image analysis that provides multi-modal concept-based explanations, consisting of easy-to-understand textual explanations and visual maps, to justify the predictions. Methods: Our framework relies on Concept Activation Vectors to map human-understandable concepts to those learned by an arbitrary Deep Learning (DL) based algorithm, and Concept Localisation Maps to highlight those concepts in the input space. This identification of relevant concepts is then used to construct fine-grained textual explanations supplemented by concept-wise location information to provide comprehensive and coherent multi-modal explanations. All decision-related information is presented in a diagnostic interface for use in clinical routines. Moreover, the framework includes an educational mode providing dataset-level explanation statistics as well as tools for data and model exploration to aid medical research and education processes. Results: Through rigorous quantitative and qualitative evaluation of our framework on a range of publicly available dermoscopic image datasets, we show the utility of multi-modal explanations for CAD-assisted scenarios even in case of wrong disease predictions. We demonstrate that concept detectors for the explanation of pre-trained networks reach accuracies of up to 81.46%, which is comparable to supervised networks trained end-to-end. Conclusions: We present a new end-to-end framework for the multi-modal explanation of DL-based biomedical image analysis in Melanoma classification and evaluate its utility on an array of datasets. Since perspicuous explanation is one of the cornerstones of any CAD system, we believe that ExAID will accelerate the transition from AI research to practice by providing dermatologists and researchers with an effective tool that they can both understand and trust. ExAID can also serve as the basis for similar applications in other biomedical fields. [ABSTRACT FROM AUTHOR]

With the rise in the employment of deep learning methods in safety-critical scenarios, interpretability is more essential than ever before. Although many different directions regarding interpretability have been explored for visual modalities, time series data has been neglected, with only a handful of methods tested due to their poor intelligibility. We approach the problem of interpretability in a novel way by proposing TSInsight, where we attach an auto-encoder to the classifier with a sparsity-inducing norm on its output and fine-tune it based on the gradients from the classifier and a reconstruction penalty. TSInsight learns to preserve features that are important for prediction by the classifier and suppresses those that are irrelevant, i.e., serves as a feature attribution method to boost the interpretability. In contrast to most other attribution frameworks, TSInsight is capable of generating both instance-based and model-based explanations. We evaluated TSInsight along with nine other commonly used attribution methods on eight different time series datasets to validate its efficacy. The evaluation results show that TSInsight naturally achieves output space contraction; therefore, it is an effective tool for the interpretability of deep time series models. [ABSTRACT FROM AUTHOR]

• Proving that CNNs have the potential to capture "micro deformations". • Learning the discrimination from a two-phase CNN based feature extractor. • Achieving state of the art on four benchmark datasets of different languages. For signature verification systems, micro deformations can be defined as the small differences in the same strokes of signatures or special writing habits of different signers. These micro deformations can reveal the core distinction between the genuine signatures and skilled forgeries. In this paper, we prove that Convolutional Neural Networks (CNNs) have the potential to extract those micro deformations by max-pooling. More specifically, the micro deformations can be determined by watching the location coordinates of the maximum values in pooling windows of max-pooling. Extensive analysis and experiments demonstrate that it is possible to achieve state-of-the-art performance by using this location information as a new feature for capturing micro deformations, along with convolutional features. The proposed method outperforms the state-of-the-art systems on four publicly available datasets of different languages, i.e., English (GPDSsynthetic, CEDAR), Persian (UTSig), and Hindi (BHSig260). [ABSTRACT FROM AUTHOR]

Apart from protein-coding Ribonucleic acids (RNAs), there exists a variety of non-coding RNAs (ncRNAs) which regulate complex cellular and molecular processes. High-throughput sequencing technologies and bioinformatics approaches have largely promoted the exploration of ncRNAs which revealed their crucial roles in gene regulation, miRNA binding, protein interactions, and splicing. Furthermore, ncRNAs are involved in the development of complicated diseases like cancer. Categorization of ncRNAs is essential to understand the mechanisms of diseases and to develop effective treatments. Sub-cellular localization information of ncRNAs demystifies diverse functionalities of ncRNAs. To date, several computational methodologies have been proposed to precisely identify the class as well as sub-cellular localization patterns of RNAs). This paper discusses different types of ncRNAs, reviews computational approaches proposed in the last 10 years to distinguish coding-RNA from ncRNA, to identify sub-types of ncRNAs such as piwi-associated RNA, micro RNA, long ncRNA, and circular RNA, and to determine sub-cellular localization of distinct ncRNAs and RNAs. Furthermore, it summarizes diverse ncRNA classification and sub-cellular localization determination datasets along with benchmark performance to aid the development and evaluation of novel computational methodologies. It identifies research gaps, heterogeneity, and challenges in the development of computational approaches for RNA sequence analysis. We consider that our expert analysis will assist Artificial Intelligence researchers with knowing state-of-the-art performance, model selection for various tasks on one platform, dominantly used sequence descriptors, neural architectures, and interpreting inter-species and intra-species performance deviation. [ABSTRACT FROM AUTHOR]