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.

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.

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.

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.

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.

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.

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.

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/.

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.

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.

Insights into the biodegradation and heavy metal resistance potential of the genus Brevibacillus through comparative genome analyses.

Members of the genus Brevibacillus belonging to the familyPaenibacillaceae are Gram-positive/variable, endospore-forming, and rod-shaped bacteria that dwell in various environmental habitats. Brevibacillus spp. have a wide range of enzyme activities such as degradation of various carbohydrates, plastics, and they possess resistance against heavy metals. These characteristics make them encouraging contenders for biotechnological applications.In this work, we analyzed the reference genomes of 19Brevibacillusspecies, focusing on discovering the biodegradation and heavy metal resistance capabilities of this little studied genus from genomic data. The results indicate that several strain specific traits were identified. For example Brevibacillus halotolerans s-14, and Brevibacillus laterosporus DSM 25 have more glycoside hydrolases (GHs) compared to other carbohydrate-active enzymes, and therefore might be more suitable for biodegradation of carbohydrates. In contrast, strains such as Brevibacillus antibioticus TGS2-1, with a higher number of glycosyltransfereases (GTs) may aid in the biosynthesis of complex carbohydrates. Our results also suggest some correlation between heavy metal resistance and polyurethane degradation, thus indicating that heavy metal resistance strains (e.g. Brevibacillus reuszeri J31TS6) can be a promising source of enzymes for polyurethane degradation. These strain specific features make the members of this bacterial group potential candidates for further investigations with industrial implications. This work also represents the first exhaustive study of Brevibacillus at the genome scale.

Effect of Volume Fraction on Shear Mode Properties of Fe-Co and Fe-Ni Filled Magneto-Rheological Elastomers.

In this research, the synergistic behavior of magnetorheological elastomers containing nickel and cobalt along with iron particles as magnetically polarizable fillers is examined experimentally under dynamic shear loading. Two different types of magnetorheological elastomer were fabricated having equal proportions of iron and nickel in one kind, and iron and cobalt in the other. The concentrations of magnetic particles in each type are varied from 10% to 40% and investigated for several frequencies, displacement amplitude, and magnetic field values. A test assembly with moveable permanent magnets was used to vary magnetic field density. Force displacement hysteresis loops were studied for dynamic response of magnetorheological elastomers (MREs). It was observed that MREs showed a linear behavior at low strains while nonlinearity increased with increasing strain. The percentage filler content and frequency increased the MRE stiffness whereas it decreased with displacement amplitude. The computed maximum magnetorheological (MR) effect was 55.56 percent. While MRE with iron and cobalt gave the highest effective stiffness, MRE with iron and nickel gave the highest MR effect.

Microstructure and Corrosion Behavior of Atmospheric Plasma Sprayed NiCoCrAlFe High Entropy Alloy Coating.

High entropy alloys (HEAs) are multi-elemental alloy systems that exhibit a combination of exceptional mechanical and physical properties, and nowadays are validating their potential in the form of thermal sprayed coatings. In the present study, a novel synthesis method is presented to form high entropy alloy coatings. For this purpose, thermal sprayed coatings were deposited on Stainless Steel 316L substrates using atmospheric plasma spraying technique with subsequent annealing, at 1000 °C for 4 h, to assist alloy formation by thermal diffusion. The coatings in as-coated samples as well as in annealed forms were extensively studied by SEM for microstructure and cross-sectional analysis. Phase identification was performed by X-ray diffraction studies. The annealed coatings revealed a mixed BCC and FCC based HEA structure. Potentiodynamic corrosion behavior of SS316L sprayed as well as annealed coatings were also carried out in 3.5% NaCl solution and it was found that the HEA-based annealed coatings displayed the best corrosion resistance 0.83 (mpy), as compared to coated/non-annealed and SS 316 L that showed corrosion resistance of 7.60 (mpy) and 3.04 (mpy), respectively.

Seasonal Diversity of Microeukaryotes in the Han River, Korea Through 18S rRNA Gene Metabarcoding.

Freshwater ecosystems contain a large diversity of microeukaryotes that play important roles in maintaining their structure. Microeukaryote communities vary in composition and abundance on the basis of temporal and environmental variables and may serve as useful bioindicators of environmental changes. In the present study, 18S rRNA metabarcoding was employed to investigate the seasonal diversity of microeukaryote communities during four seasons in the Han River, Korea. In total, 882 unique operational taxonomic units (OTUs) were detected, including various diatoms, metazoans (e.g., arthropods and rotifers), chlorophytes, and fungi. Although alpha diversity revealed insignificant differences based on seasons, beta diversity exhibited a prominent variation in the community composition as per seasons. The analysis revealed that the diversity of microeukaryotes was primarily driven by seasonal changes in the prevailing conditions of environmental water temperature and dissolved oxygen. Moreover, potential indicator OTUs belonging to diatoms and chlorophytes were associated with seasonal and environmental factors. This analysis was a preliminary study that established a continuous monitoring system using metabarcoding. This approach could be an effective tool to manage the Han River along with other freshwater ecosystems in Korea.

SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information.

Sortase enzymes are cysteine transpeptidases that embellish the surface of Gram-positive bacteria with various proteins thereby allowing these microorganisms to interact with their neighboring environment. It is known that several of their substrates can cause pathological implications, so researchers have focused on the development of sortase inhibitors. Currently, six different classes of sortases (A-F) are recognized. However, with the extensive application of bacterial genome sequencing projects, the number of potential sortases in the public databases has exploded, presenting considerable challenges in annotating these sequences. It is very laborious and time-consuming to characterize these sortase classes experimentally. Therefore, this study developed the first machine-learning-based two-layer predictor called SortPred, where the first layer predicts the sortase from the given sequence and the second layer predicts their class from the predicted sortase. To develop SortPred, we constructed an original benchmarking dataset and investigated 31 feature descriptors, primarily on five feature encoding algorithms. Afterward, each of these descriptors were trained using a random forest classifier and their robustness was evaluated with an independent dataset. Finally, we selected the final model independently for both layers depending on the performance consistency between cross-validation and independent evaluation. SortPred is expected to be an effective tool for identifying bacterial sortases, which in turn may aid in designing sortase inhibitors and exploring their functions. The SortPred webserver and a standalone version are freely accessible at: https://procarb.org/sortpred.

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.

Effect of substrate ratios on the simultaneous carbon, nitrogen, sulfur and phosphorous conversions in microbial fuel cells.

The columbic efficiency, removal efficiency and voltage production of seven different combinations of carbon (acetic acid, albumin and sucrose) with nutrients (C:N, C:P, C:S, C:N:S, C:P:S, C:N:P and C: N:S:P) were investigated at three different ratios (20:1, 15:1 and 10:1). The effects of various pH values were also explored for these combinations of carbon, and sulfur compounds (pH 6-8). The highest columbic efficiency (75.8%), COD removal efficiency (86%) and voltage (667 mV) were recorded when the acetic acid was used in the MFC and the lowest columbic efficiency (12.8%), removal efficiency (37.6%) and voltage (145 mV) were observed in case of albumin. A marked increase in columbic efficiency, removal efficiency and voltage production were seen with the rise in the pH value from 6 to 8. The lowest columbic efficiency, removal efficiency and voltage production were seen at pH 6 and highest at pH 8. At each investigated pH, the highest removal efficiency, columbic efficiency, and voltage were found at substrate ratio of 20:1 while lower at 10:1. At all pH values, the carbon to nutrient ratios seemed to have followed a similar trend i.e., the COD removal efficiency, columbic efficiency and voltage generation was found in the order C:N > C:N:S > C:N:S:P > C:N:P > C:S > C:P:S > C:P. In all cases, nitrogen showed a higher removal as compared to phosphorous and sulfur.

Frequency of Different Dental Irregularities Associated With Cleft Lip and Palate in a Tertiary Care Dental Hospital.

Background Oro-facial clefts (OFCs) are formed due to a combination of genetic factors and environmental factors. Treatment is usually extensive and lasts till adult age. The treatment also includes a large portion of dental rehabilitation. Objective This study aims to look at the different dental anomalies associated with OFCs. Methods A total of 100 participants with OFCs were randomly selected from Clap centre Lahore. They were categorized into cleft lip (CL), cleft palate (CP), and both. Dental anomalies were recorded clinically and family history for OFCs was also recorded. Results Out of the 100 participants, 15 had CL only, 37 had a CP, and 48 had both CL and CP. Missing teeth and hypodontia were significantly associated with all OFCs (p-value > 0.05). Supernumerary teeth were only significantly associated with CP (p-value: 0.04). Other dental anomalies were not significant for OFCs. Conclusion OFCs in all its three forms are associated with dental irregularities. They can either be missing teeth or extra teeth. There is a strong need for dentists to be a part of the treatment planning of OFCs and to treat dental anomalies alongside the clefts.

Genome-Driven Discovery of Enzymes with Industrial Implications from the Genus Aneurinibacillus.

Bacteria belonging to the genus Aneurinibacillus within the family Paenibacillaceae are Gram-positive, endospore-forming, and rod-shaped bacteria inhabiting diverse environments. Currently, there are eight validly described species of Aneurinibacillus; however, several unclassified species have also been reported. Aneurinibacillus spp. have shown the potential for producing secondary metabolites (SMs) and demonstrated diverse types of enzyme activities. These features make them promising candidates with industrial implications. At present, genomes of 9 unique species from the genus Aneurinibacillus are available, which can be utilized to decipher invaluable information on their biosynthetic potential as well as enzyme activities. In this work, we performed the comparative genome analyses of nine Aneurinibacillus species representing the first such comprehensive study of this genus at the genome level. We focused on discovering the biosynthetic, biodegradation, and heavy metal resistance potential of this under-investigated genus. The results indicate that the genomes of Aneurinibacillus contain SM-producing regions with diverse bioactivities, including antimicrobial and antiviral activities. Several carbohydrate-active enzymes (CAZymes) and genes involved in heavy metal resistance were also identified. Additionally, a broad range of enzyme classes were also identified in the Aneurinibacillus pan-genomes, making this group of bacteria potential candidates for future investigations with industrial applications.

Role of Transportome in the Gills of Chinese Mitten Crabs in Response to Salinity Change: A Meta-Analysis of RNA-Seq Datasets.

Chinese mitten crab (CMC) or Eriocheir sinensis is a strong osmoregulator that can keep rigorous cellular homeostasis. CMC can flourish in freshwater, as well as seawater, habitats and represents the most important species for freshwater aquaculture. Salt stress can have direct effects on several stages (e.g., reproduction, molting, growth, etc.) of the CMC life cycle. To get a better overview of the genes involved in the gills of CMC under different salinity conditions, we conducted an RNA-Seq meta-analysis on the transcriptomes of four publicly available datasets. The meta-analysis identified 405 differentially expressed transcripts (DETs), of which 40% were classified into various transporter classes, including accessory factors and primary active transporters as the major transport classes. A network analysis of the DETs revealed that adaptation to salinity is a highly regulated mechanism in which different functional modules play essential roles. To the best of our knowledge, this study is the first to conduct a transcriptome meta-analysis of gills from crab RNA-Seq datasets under salinity. Additionally, this study is also the first to focus on the differential expression of diverse transporters and channels (transportome) in CMC. Our meta-analysis opens new avenues for a better understanding of the osmoregulation mechanism and the selection of potential transporters associated with salinity change.