H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA.

2'-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high-throughput detection, the chemical stability of 2OM makes it difficult to detect and map in messenger RNA. Therefore, bioinformatics tools have been developed using machine learning (ML) algorithms to identify 2OM sites. These tools have made significant progress, but their performances remain unsatisfactory and need further improvement. In this study, we introduced H2Opred, a novel hybrid deep learning (HDL) model for accurately identifying 2OM sites in human RNA. Notably, this is the first application of HDL in developing four nucleotide-specific models [adenine (A2OM), cytosine (C2OM), guanine (G2OM) and uracil (U2OM)] as well as a generic model (N2OM). H2Opred incorporated both stacked 1D convolutional neural network (1D-CNN) blocks and stacked attention-based bidirectional gated recurrent unit (Bi-GRU-Att) blocks. 1D-CNN blocks learned effective feature representations from 14 conventional descriptors, while Bi-GRU-Att blocks learned feature representations from five natural language processing-based embeddings extracted from RNA sequences. H2Opred integrated these feature representations to make the final prediction. Rigorous cross-validation analysis demonstrated that H2Opred consistently outperforms conventional ML-based single-feature models on five different datasets. Moreover, the generic model of H2Opred demonstrated a remarkable performance on both training and testing datasets, significantly outperforming the existing predictor and other four nucleotide-specific H2Opred models. To enhance accessibility and usability, we have deployed a user-friendly web server for H2Opred, accessible at https://balalab-skku.org/H2Opred/. This platform will serve as an invaluable tool for accurately predicting 2OM sites within human RNA, thereby facilitating broader applications in relevant research endeavors.

APLpred: A machine learning-based tool for accurate prediction and characterization of asparagine peptide lyases using sequence-derived optimal features.

Asparagine peptide lyase (APL) is among the seven groups of proteases, also known as proteolytic enzymes, which are classified according to their catalytic residue. APLs are synthesized as precursors or propeptides that undergo self-cleavage through autoproteolytic reaction. At present, APLs are grouped into 10 families belonging to six different clans of proteases. Recognizing their critical roles in many biological processes including virus maturation, and virulence, accurate identification and characterization of APLs is indispensable. Experimental identification and characterization of APLs is laborious and time-consuming. Here, we developed APLpred, a novel support vector machine (SVM) based predictor that can predict APLs from the primary sequences. APLpred was developed using Boruta-based optimal features derived from seven encodings and subsequently trained using five machine learning algorithms. After evaluating each model on an independent dataset, we selected APLpred (an SVM-based model) due to its consistent performance during cross-validation and independent evaluation. We anticipate APLpred will be an effective tool for identifying APLs. This could aid in designing inhibitors against these enzymes and exploring their functions. The APLpred web server is freely available at https://procarb.org/APLpred/.

Comparative transcriptome analysis of two Daphnia galeata genotypes displaying contrasting phenotypic variation induced by fish kairomones in the same environment of the Han River, Korea.

BACKGROUND: Phenotypic plasticity is a crucial adaptive mechanism that enables organisms to modify their traits in response to changes in their environment. Predator-induced defenses are an example of phenotypic plasticity observed across a wide range of organisms, from single-celled organisms to vertebrates. In addition to morphology and behavior, these responses also affect life-history traits. The crustacean Daphnia galeata is a suitable model organism for studying predator-induced defenses, as it exhibits life-history traits changes under predation risk. To get a better overview of their phenotypic plasticity under predation stress, we conducted RNA sequencing on the transcriptomes of two Korean Daphnia galeata genotypes, KE1, and KB11, collected in the same environment. RESULTS: When exposed to fish kairomones, the two genotypes exhibited phenotypic variations related to reproduction and growth, with opposite patterns in growth-related phenotypic variation. From both genotypes, a total of 135,611 unigenes were analyzed, of which 194 differentially expressed transcripts (DETs) were shared among the two genotypes under predation stress, which showed consistent, or inconsistent expression patterns in both genotypes. Prominent DETs were related to digestion and reproduction and consistently up-regulated in both genotypes, thus associated with changes in life-history traits. Among the inconsistent DETs, transcripts encode vinculin (VINC) and protein obstructor-E (OBST-E), which are associated with growth; these may explain the differences in life-history traits between the two genotypes. In addition, genotype-specific DETs could explain the variation in growth-related life-history traits between genotypes, and could be associated with the increased body length of genotype KE1. CONCLUSIONS: The current study allows for a better understanding of the adaptation mechanisms related to reproduction and growth of two Korean D. galeata genotypes induced by predation stress. However, further research is necessary to better understand the specific mechanisms by which the uncovered DETs are related with the observed phenotypic variation in each genotype. In the future, we aim to unravel the precise adaptive mechanisms underlying predator-induced responses.

Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework.

Origins of replication sites (ORIs), which refers to the initiative locations of genomic DNA replication, play essential roles in DNA replication process. Detection of ORIs' distribution in genome scale is one of key steps to in-depth understanding their regulation mechanisms. In this study, we presented a novel machine learning-based approach called Stack-ORI encompassing 10 cell-specific prediction models for identifying ORIs from four different eukaryotic species (Homo sapiens, Mus musculus, Drosophila melanogaster and Arabidopsis thaliana). For each cell-specific model, we employed 12 feature encoding schemes that cover nucleic acid composition, position-specific and physicochemical properties information. The optimal feature set was identified from each encoding individually and developed their respective baseline models using the eXtreme Gradient Boosting (XGBoost) classifier. Subsequently, the predicted scores of 12 baseline models are integrated as a novel feature vector to train XGBoost and develop the final model. Extensive experimental results show that Stack-ORI achieves significantly better performance as compared with their baseline models on both training and independent datasets. Interestingly, Stack-ORI consistently outperforms existing predictor in all cell-specific models, not only on training but also on independent test. Moreover, our novel approach provides necessary interpretations that help understanding model success by leveraging the powerful SHapley Additive exPlanation algorithm, thus underlining the most important feature encoding schemes significant for predicting cell-specific ORIs.

Future advances and challenges of nanomaterial-based technologies for electromagnetic interference-based technologies: A review.

The emerging growth of the electronic devices applications has arisen the serious problems of electromagnetic (EM) wave pollution which resulting in equipment malfunction. Therefore, polymer-based composites have been considered good candidates for better EMI shielding due to their significant characteristics including, higher flexibility, ultrathin, lightweight, superior conductivity, easy fabrication processing, environmentally friendly, corrosion resistive, better adhesion with physical, chemical and thermal stability. This review article focused on the concept of the EMI shielding mechanism and challenges with the fabrication of polymer-based composites. Subsequently, recent advancements in the polymer composites applications have been critically reviewed. In addition, the impact of polymers and polymer nanocomposites with different fillers such as organic, inorganic, 2D, 3D, mixture and hybrid nano-fillers on EMI shielding effectiveness has been explored. Lastly, future research directions have been proposed to overcome the limitations of current technologies for further advancement in EMI shielding materials for industrial applications. Based on reported literature, it has been found that the low thickness based lightweight polymer is considered as a best material for excellent material for next-generation electronic devices. Optimization of polymer composites during the fabrication is required for better EMI shielding. New nano-fillers such as functionalization and composite polymers are best to enhance the EMI shielding and conductive properties.

C10Pred: A First Machine Learning Based Tool to Predict C10 Family Cysteine Peptidases Using Sequence-Derived Features.

Streptococcus pyogenes, or group A Streptococcus (GAS), a gram-positive bacterium, is implicated in a wide range of clinical manifestations and life-threatening diseases. One of the key virulence factors of GAS is streptopain, a C10 family cysteine peptidase. Since its discovery, various homologs of streptopain have been reported from other bacterial species. With the increased affordability of sequencing, a significant increase in the number of potential C10 family-like sequences in the public databases is anticipated, posing a challenge in classifying such sequences. Sequence-similarity-based tools are the methods of choice to identify such streptopain-like sequences. However, these methods depend on some level of sequence similarity between the existing C10 family and the target sequences. Therefore, in this work, we propose a novel predictor, C10Pred, for the prediction of C10 peptidases using sequence-derived optimal features. C10Pred is a support vector machine (SVM) based model which is efficient in predicting C10 enzymes with an overall accuracy of 92.7% and Matthews' correlation coefficient (MCC) value of 0.855 when tested on an independent dataset. We anticipate that C10Pred will serve as a handy tool to classify novel streptopain-like proteins belonging to the C10 family and offer essential information.

Genome-based analysis for the bioactive potential of Streptomyces yeochonensis CN732, an acidophilic filamentous soil actinobacterium.

BACKGROUND: Acidophilic members of the genus Streptomyces can be a good source for novel secondary metabolites and degradative enzymes of biopolymers. In this study, a genome-based approach on Streptomyces yeochonensis CN732, a representative neutrotolerant acidophilic streptomycete, was employed to examine the biosynthetic as well as enzymatic potential, and also presence of any genetic tools for adaptation in acidic environment. RESULTS: A high quality draft genome (7.8 Mb) of S. yeochonensis CN732 was obtained with a G + C content of 73.53% and 6549 protein coding genes. The in silico analysis predicted presence of multiple biosynthetic gene clusters (BGCs), which showed similarity with those for antimicrobial, anticancer or antiparasitic compounds. However, the low levels of similarity with known BGCs for most cases suggested novelty of the metabolites from those predicted gene clusters. The production of various novel metabolites was also confirmed from the combined high performance liquid chromatography-mass spectrometry analysis. Through comparative genome analysis with related Streptomyces species, genes specific to strain CN732 and also those specific to neutrotolerant acidophilic species could be identified, which showed that genes for metabolism in diverse environment were enriched among acidophilic species. In addition, the presence of strain specific genes for carbohydrate active enzymes (CAZyme) along with many other singletons indicated uniqueness of the genetic makeup of strain CN732. The presence of cysteine transpeptidases (sortases) among the BGCs was also observed from this study, which implies their putative roles in the biosynthesis of secondary metabolites. CONCLUSIONS: This study highlights the bioactive potential of strain CN732, an acidophilic streptomycete with regard to secondary metabolite production and biodegradation potential using genomics based approach. The comparative genome analysis revealed genes specific to CN732 and also those among acidophilic species, which could give some insights into the adaptation of microbial life in acidic environment.

Nocardioides euryhalodurans sp. nov., Nocardioides seonyuensis sp. nov. and Nocardioides eburneiflavus sp. nov., isolated from soil.

Three aerobic, rod-shaped actinobacterial strains, designated MMS17-SY117T, MMS17-SY207-3T and MMS17-SY213T, were isolated from soil and their taxonomic positions were analysed using a polyphasic approach. The isolates showed best growth at 30 °C, pH 7 and 0-1 % (w/v) NaCl. On the basis of 16S rRNA gene sequence similarity, the isolates were affiliated to the genus Nocardioides, and the closest species to MMS17-SY117T, MMS17-SY207-3T and MMS17-SY213T were Nocardioides aestuarii JC2056T (97.76%), Nocardioides currus IB-3T (97.41%) and Nocardioides exalbidus RC825T (98.71%), respectively. Each isolate formed a distinct cluster within the Nocardioides clade in the phylogenetic tree. The orthologous average nucleotide identity and digital DNA-DNA hybridization values were in the range of 74.4-85.7 % and 16.6-39.2 %, respectively, with the type strains of related species. The major polar lipids in all three strains were phosphatidylinositol, phosphatidylglycerol and diphosphatidylglycerol. The predominant fatty acids were iso-C16 : 0 and C17 : 1 ω8c. MK-8(H4) was the major isoprenoid quinone and ll-DAP was the major diamino acid. Galactose, glucose and rhamnose were present in the whole-cell hydrolysate, and MMS17-SY213T also contained mannose and ribose. The DNA G+C contents of MMS17-SY117T, MMS17-SY207-3T and MMS17-SY213T were 72.2, 70.4 and 71.5 mol%, respectively. The phylogenetic, phenotypic and chemotaxonomic data supported the classification of each strain as representing a new species of Nocardioides, for which the names Nocardioides euryhalodurans sp. nov. (MMS17-SY117T=KCTC 49175T=JCM 32831T), Nocardioides seonyuensis sp. nov. (MMS17-SY207-3T=KCTC 49176T=JCM 32832T) and Nocardioides eburneiflavus sp. nov. (MMS17-SY213T=KCTC 49177T=JCM 32833T) are proposed accordingly.

Fibromodulin and regulation of the intricate balance between myoblast differentiation to myocytes or adipocyte-like cells.

Interactions between myoblasts and the surrounding microenvironment led us to explore the role of fibromodulin (FMOD), an extracellular matrix protein, in the maintenance of myoblast stemness and function. Microarray analysis of FMODkd myoblasts and in silico studies were used to identify the top most differentially expressed genes in FMODkd, and helped establish that FMOD-based regulations of integral membrane protein 2a and clusterin are essential components of the myogenic program. Studies in knockout, obese, and diabetic mouse models helped characterize the operation of a novel FMOD-based regulatory circuit that controls myoblast switching from a myogenic to a lipid accumulation fate. FMOD regulation of myoblasts is an essential part of the myogenic program, and it offers opportunities for the development of therapeutics for the treatment of different muscle diseases.-Lee, E. J., Jan, A. T., Baig, M. H., Ahmad, K., Malik, A., Rabbani, G., Kim, T., Lee, I.-K., Lee, Y. H., Park, S.-Y., Choi, I. Fibromodulin and regulation of the intricate balance between myoblast differentiation to myocytes or adipocyte-like cells.

mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.

Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset.

Characterization of Esophageal Motility Disorders in Children Presenting With Dysphagia Using High-Resolution Manometry.

PURPOSE OF REVIEW: The Chicago classification was based on metrics derived from studies in asymptomatic adult subjects. Our objectives were to characterize esophageal motility disorders in children and to determine whether the spectrum of manometric findings is similar between the pediatric and adult populations. RECENT FINDINGS: Studies have suggested that the metrics utilized in manometric diagnosis depend on age, size, and manometric assembly. This would imply that a different set of metrics should be used for the pediatric population. There are no standardized and generally accepted metrics for use in the pediatric population, though there have been attempts to establish metrics specific to this population. Overall, we found that the distribution of esophageal motility disorders in children was like that described in adults using the Chicago classification. This analysis will serve as a prequel to follow-up studies exploring the individual metrics for variability among patients, with the objective of establishing novel metrics for the pediatric population.

Toll-like receptor 4 deficiency impairs microglial phagocytosis of degenerating axons.

Microglia are rapidly activated in the central nervous system (CNS) in response to a variety of injuries, including inflammation, trauma, and stroke. In addition to modulation of the innate immune response, a key function of microglia is the phagocytosis of dying cells and cellular debris, which can facilitate recovery. Despite emerging evidence that axonal debris can pose a barrier to regeneration of new axons in the CNS, little is known of the cellular and molecular mechanisms that underlie clearance of degenerating CNS axons. We utilize a custom micropatterned microfluidic system that enables robust microglial-axon co-culture to explore the role of Toll-like receptors (TLRs) in microglial phagocytosis of degenerating axons. We find that pharmacologic and genetic disruption of TLR4 blocks induction of the Type-1 interferon response and inhibits phagocytosis of axon debris in vitro. Moreover, TLR4-dependent microglial clearance of unmyelinated axon debris facilitates axon outgrowth. In vivo, microglial phagocytosis of CNS axons undergoing Wallerian degeneration in a dorsal root axotomy model is impaired in adult mice in which TLR4 has been deleted. Since purinergic receptors can influence TLR4-mediated signaling, we also explored a role for the microglia P2 receptors and found that the P2X7R contributes to microglial clearance of degenerating axons. Overall, we identify TLR4 as a key player in axonal debris clearance by microglia, thus creating a more permissive environment for axonal outgrowth. Our findings have significant implications for the development of protective and regenerative strategies for the many inflammatory, traumatic, and neurodegenerative conditions characterized by CNS axon degeneration.

The ryanodine receptor is expressed in human pancreatic acinar cells and contributes to acinar cell injury.

Physiological calcium (Ca(2+)) signals within the pancreatic acinar cell regulate enzyme secretion, whereas aberrant Ca(2+) signals are associated with acinar cell injury. We have previously identified the ryanodine receptor (RyR), a Ca(2+) release channel on the endoplasmic reticulum, as a modulator of these pathological signals. In the present study, we establish that the RyR is expressed in human acinar cells and mediates acinar cell injury. We obtained pancreatic tissue from cadaveric donors and identified isoforms of RyR1 and RyR2 by qPCR. Immunofluorescence staining of the pancreas showed that the RyR is localized to the basal region of the acinar cell. Furthermore, the presence of RyR was confirmed from isolated human acinar cells by tritiated ryanodine binding. To determine whether the RyR is functionally active, mouse or human acinar cells were loaded with the high-affinity Ca(2+) dye (Fluo-4 AM) and stimulated with taurolithocholic acid 3-sulfate (TLCS) (500 µM) or carbachol (1 mM). Ryanodine (100 µM) pretreatment reduced the magnitude of the Ca(2+) signal and the area under the curve. To determine the effect of RyR blockade on injury, human acinar cells were stimulated with pathological stimuli, the bile acid TLCS (500 µM) or the muscarinic agonist carbachol (1 mM) in the presence or absence of the RyR inhibitor ryanodine. Ryanodine (100 µM) caused an 81% and 47% reduction in acinar cell injury, respectively, as measured by lactate dehydrogenase leakage (P < 0.05). Taken together, these data establish that the RyR is expressed in human acinar cells and that it modulates acinar Ca(2+) signals and cell injury.

Development of a two-layer machine learning model for the forensic application of legal and illegal poppy classification based on sequence data.

Poppies are beneficial plants with a variety of applications, including medicinal, edible, ornamental, and industrial purposes. Some Papaver species are forensically significant plants because they contain opium, a narcotic substance. Internationally trafficked species of illegal poppies are being identified by DNA barcoding employing multiple markers in response to their forensic value. However, effective markers for precise species identification of legal and illegal poppies are still under discussion, with research on illegal poppies focusing on Papaver somniferum L., and species identification studies of Papaver bracteatum and Papaver setigerum DC. still lacking. As a result, in order to evaluate the performance of genetic markers and classify their DNA sequences in the genus Papaver, this study developed the first machine learning-based two-layer model, in which the first layer classifies legal and illegal poppies from the given sequence and the second layer identifies species of illegal poppies using their sequences. We constructed the dataset and investigated biological features from four markers, internal transcribed spacer 1 (ITS1), internal transcribed spacer 2 (ITS2), transfer RNA Leucine (trnL), transfer RNA Leucine - transfer RNA Phenylalanine intergenic spacer (trnL-trnF intergenic spacer) and their combination, using four machine learning algorithms, K-nearest neighbor (KNN), Naïve Bayes (NB), extreme gradient boost (XGBoost) and Random Forest (RF). According to our findings, for Layer 1 to classify legal and illegal poppies, KNN-based models using combined ITS region achieved the greatest performance of accuracy 0.846 and 0.889 using training and test sets, respectively. Additionally, for Layer 2 to identify illegal poppy species, KNN-based models using combined ITS region achieved the best performance of 0.833 and 1.000 for using training and test sets, respectively. To validate the model, the combined ITS region, which includes ITS 1 and 2 sequences, from blind poppy samples were used as a case study, with the Layer 1 correctly classifying legal and illegal poppies with over 0.830 accuracy. Layer 2 correctly identified P. setigerum DC., however, only one of the three P. somniferum L. species was accurately identified. Nevertheless, our research shows that machine learning can be used to classify and identify legal and illegal poppy species using DNA barcodes which can then be used as an efficient and effective forensic tool for improved law enforcement and a safer society.

GPApred: The first computational predictor for identifying proteins with LPXTG-like motif using sequence-based optimal features.

The cell surface proteins of gram-positive bacteria are involved in many important biological functions, including the infection of host cells. Owing to their virulent nature, these proteins are also considered strong candidates for potential drug or vaccine targets. Among the various cell surface proteins of gram-positive bacteria, LPXTG-like proteins form a major class. These proteins have a highly conserved C-terminal cell wall sorting signal, which consists of an LPXTG sequence motif, a hydrophobic domain, and a positively charged tail. These surface proteins are targeted to the cell envelope by a sortase enzyme via transpeptidation. A variety of LPXTG-like proteins have been experimentally characterized; however, their number in public databases has increased owing to extensive bacterial genome sequencing without proper annotation. In the absence of experimental characterization, identifying and annotating these sequences is extremely challenging. Therefore, in this study, we developed the first machine learning-based predictor called GPApred, which can identify LPXTG-like proteins from their primary sequences. Using a newly constructed benchmark dataset, we explored different classifiers and five feature encodings and their hybrids. Optimal features were derived using the recursive feature elimination method, and these features were then trained using a support vector machine algorithm. The performance of different models was evaluated using independent datasets, and a final model (GPApred) was selected based on consistency during cross-validation and independent assessment. GPApred can be an effective tool for predicting LPXTG-like sequences and can be further employed for functional characterization or drug targeting. Availability: https://procarb.org/gpapred/.

Presence of mediastinal lymphadenopathy in hospitalized Covid-19 patients in a tertiary care hospital in Pakistan-A cross-sectional study.

BACKGROUND: The aim of this study was to investigate the presence of mediastinal lymphadenopathy in hospitalized Covid-19 patients in a tertiary care hospital in the metropolitan city of Lahore, Pakistan from September 2020 till July 2021. METHODS: We retrospectively collected data of Covid-19 patients hospitalized from September 2020 till July 2021. Only those patients who tested PCR positive through a nasopharyngeal swab, were enrolled in the study. Patients' whose data were missing were excluded from this study. Our exclusion criteria included patients who tested negative on Covid-19 PCR, patients with comorbidities that may cause enlarged mediastinal lymphadenopathies such as haemophagocytic lymphohistiocytosis, neoplasia, tuberculosis, sarcoidosis or a systemic disease. The extent of lung involvement in Covid-19 patients was quantified by using a 25-point visual quantitative assessment called the Chest Computed Tomography Score. This score was then correlated with the presence of mediastinal lymphadenopathy. FINDINGS: Of the 210 hospitalized patients included in the study, 131 (62.4%) had mediastinal lymphadenopathy. The mean and median Severity Score of Covid-19 patients with mediastinal lymphadenopathy (mean: 17.1, SD:5.7; median: 17, IQR: 13-23) were higher as compared to those without mediastinal lymphadenopathy (mean: 12.3, SD:5.4; median: 12, IQR:9-16). INTERPRETATION: Our study documents a high prevalence of mediastinal lymphadenopathy in hospitalized patients with Covid-19 with the severity score being higher in its presence representing a more severe course of disease.

PRR-HyPred: A two-layer hybrid framework to predict pattern recognition receptors and their families by employing sequence encoded optimal features.

Pattern recognition receptors (PRRs) recognize distinct features on the surface of pathogens or damaged cells and play key roles in the innate immune system. PRRs are divided into various families, including Toll-like receptors, retinoic acid-inducible gene-I-like receptors, nucleotide oligomerization domain-like receptors, and C-type lectin receptors. As these are implicated in host health and several diseases, their accurate identification is indispensable for their functional characterization and targeted therapeutic approaches. Here, we construct PRR-HyPred, a novel two-layer hybrid framework in which the first layer predicts whether a given sequence is PRR or non-PRR using a support vector machine, and in the second, the predicted PRR sequence is assigned to a specific family using a random forest-based classifier. Based on a 10-fold cross-validation test, PRR-HyPred achieved 83.4 % accuracy in the first layer and 95 % in the second, with Matthew's correlation coefficient values of 0.639 and 0.816, respectively. This is the first study that can simultaneously predict and classify PRRs into specific families. PRR-HyPred is available as a web portal at https://procarb.org/PRRHyPred/. We hope that it could be a valuable tool for the large-scale prediction and classification of PRRs and subsequently facilitate future studies.

A Low-Cost Silica Fiber/Epoxy Composite with Excellent Dielectric Properties, and Good Mechanical and Thermal Stability.

In many electronic applications, the dielectric and structural properties of reinforced composites are vital. In this research work, the influence of fiber proportion on the properties of a silica fiber/epoxy (SFE) composite was investigated. The structure, morphology, dielectric constant and loss factor, mechanical properties, and thermal stability were determined. The increase of wt.% of silica fiber (SiO2 (f)) x = 30 to 90, reduced the dielectric constant (εr) and dielectric loss (δ) of the SFE composite from their original values to 18.9% and 48.5%, lowering local charge displacement towards the applied electric field. The SFE composite showed higher mechanical properties with the increase in SiO2 (f), x = 30 to 80, the tensile strength (UTS) was raised from 91.6 MPa to 155.7 MPa, the compression strength (UCS) was increased from 261.1 MPa to 409.6 MPa and the flexural strength was enhanced from 192.3 MPa to 311.9 MPa. Upon further addition of SiO2 (f) to the composite, i.e., x = 90, the mechanical properties were reduced a little, but the dielectric properties were not changed. Increasing SiO2 (f) improved the thermal stability as weight loss was found to be 69% (x = 30) and 24% (x = 90), and average moisture absorption was found to be 1.1 to 1.8%. A silica fiber/epoxy composite, for microelectronics, can be made from a low-cost fiber, and its dielectric properties as well as its mechanical and thermal stability can be tuned or improved by varying fiber fractions.

Silica-Fiber-Reinforced Composites for Microelectronic Applications: Effects of Curing Routes.

For curing of fiber-reinforced epoxy composites, an alternative to thermal heating is the use of microwave energy, which cures quickly and consumes less energy. Employing thermal curing (TC) and microwave (MC) curing methods, we present a comparative study on the functional characteristics of fiber-reinforced composite for microelectronics. The composite prepregs, prepared from commercial silica fiber fabric/epoxy resin, were separately cured via thermal and microwave energy under curing conditions (temperature/time). The dielectric, structural, morphological, thermal, and mechanical properties of composite materials were investigated. Microwave cured composite showed a 1% lower dielectric constant, 21.5% lower dielectric loss factor, and 2.6% lower weight loss, than thermally cured one. Furthermore, the dynamic mechanical analysis (DMA) revealed a 20% increase in the storage and loss modulus along with a 15.5% increase in the glass transition temperature (Tg) of microwave-cured compared to thermally cured composite. The fourier transformation infrared spectroscopy (FTIR) showed similar spectra of both the composites; however, the microwave-cured composite exhibited higher tensile (15.4%), and compression strength (4.3%) than the thermally cured composite. These results illustrate that microwave-cured silica-fiber-reinforced composite exhibit superior electrical performance, thermal stability, and mechanical properties compared to thermally cured silica fiber/epoxy composite in a shorter time and the expense of less energy.

In Situ Synthesis of Crystalline MoS2@ZIF-67 Nanocomposite for the Efficient Removal of Methyl Orange Dye from Aqueous Media.

The zeolitic imidazolate framework-67 (ZIF-67) adsorbent and its composites are known to effectively remove organic dyes from aqueous environments. Here, we report a unique crystalline MoS2@ZIF-67 nanocomposite adsorbent for the efficient removal of methyl orange (MO) dye from an aqueous medium. In situ synthetic techniques were used to fabricate a well-crystalline MoS2@ZIF-67 nanocomposite, which was then discovered to be a superior adsorbent to its constituents. The successful synthesis of the nanocomposite was confirmed using XRD, EDX, FTIR, and SEM. The MoS2@ZIF-67 nanocomposite exhibited faster adsorption kinetics and higher dye removal efficiency compared with its constituents. The adsorption kinetic data matched well with the pseudo-second-order model, which signifies that the MO adsorption on the nanocomposite is a chemically driven process. The Langmuir model successfully illustrated the MO dye adsorption on the nanocomposite through comparing the real data with adsorption isotherm models. However, it appears that the Freundlich adsorption isotherm model was also in competition with the Langmuir model. According to the acquired thermodynamics parameters, the adsorption of MO on the MoS2@ZIF-67 nanocomposite surface was determined to be spontaneous and exothermic. The findings of this research open an avenue for using the MoS2@ZIF-67 nanocomposite to efficiently remove organic dyes from wastewater efflux.