Multisystem inflammatory syndrome in children (MIS-C) is a severe complication of SARS-CoV-2 infections in children. This syndrome manifests about a month after the initial viral infection and is characterized by fever, multiorgan dysfunction, and systemic inflammation. This chapter will review the emergence, epidemiology, clinical characteristics, diagnosis, pathophysiology, immunomodulatory treatment, prognosis, outcomes, and prevention of MIS-C. While the pathophysiology of MIS-C remains to be defined, it is a post-infection, hyperinflammatory syndrome of childhood with elevated inflammatory cytokines.

Collagenolytic proteases are widely used in the food, medical, pharmaceutical, cosmetic, and textile industries. Mesophilic collagenases exhibit collagenolytic activity under physiological conditions, but have limitations in efficiently degrading collagen-rich wastes, such as collagen from fish scales, at high temperatures due to their poor thermostability. Bacterial collagenolytic proteases are members of various proteinase families, including the bacterial collagenolytic metalloproteinase M9 and the bacterial collagenolytic serine proteinase families S1, S8, and S53. Notably, the C-terminal domains of collagenolytic proteases, such as the pre-peptidase C-terminal domain, the polycystic kidney disease-like domain, the collagen-binding domain, the proprotein convertase domain, and the β-jelly roll domain, exhibit collagen-binding or -swelling activity. These activities can induce conformational changes in collagen or the enzyme active sites, thereby enhancing the collagen-degrading efficiency. In addition, thermostable bacterial collagenolytic proteases can function at high temperatures, which increases their degradation efficiency since heat-denatured collagen is more susceptible to proteolysis and minimizes the risk of microbial contamination. To date, only a few thermophile-derived collagenolytic proteases have been characterized. TSS, a thermostable and halotolerant subtilisin-like serine collagenolytic protease, exhibits high collagenolytic activity at 60°C. In this review, we present and summarize the current research on A) the classification and nomenclature of thermostable and mesophilic collagenolytic proteases derived from diverse microorganisms, and B) the functional roles of their C-terminal domains. Furthermore, we analyze the cleavage specificity of the thermostable collagenolytic proteases within each family and comprehensively discuss the thermostable collagenolytic protease TSS.

AbstractPharmaceutical compounds are a significant source of environmental pollution, particularly in hospital wastewater, which contains high concentrations of such compounds. Constructed wetlands have emerged as a promising approach to removing pharmaceutical compounds from wastewater. This paper aims to review the current state of knowledge on the removal of pharmaceutical compounds from hospital wastewater using constructed wetlands, including the mechanism of removal, removal efficiency, and future prospects. Pharmaceutical contaminants have been considered to be one of the most emerging pollutants in recent years. In this review article, various studies on constructed wetlands are incorporated in order to remove the pharmaceutical contaminants. The nature of constructed wetland can be explained by understanding the types of constructed wetland, characteristics of hospital wastewater, removal mechanism, and removal efficiency. The results of the review indicate that constructed wetlands are effective in removing pharmaceutical compounds from hospital wastewater. The removal mechanism of these compounds involves a combination of physical, chemical, and biological processes, including adsorption, degradation, and uptake by wetland plants. The removal efficiency of constructed wetlands varies depending on several factors, including the type and concentration of pharmaceutical compounds, the design of the wetland system, and the environmental conditions. Further research is necessary to optimize the performance of these systems, particularly in the removal of emerging contaminants, to ensure their effectiveness and long-term sustainability.

Kaposi\'s sarcoma-associated herpesvirus (KSHV) is a large, oncogenic DNA virus belonging to the gammaherpesvirus subfamily. KSHV has been extensively studied with various high-throughput RNA-sequencing approaches to map the transcription start and end sites, the splice junctions, and the translation initiation sites. Despite these efforts, the comprehensive annotation of the viral transcriptome remains incomplete. In the present study, we generated a long-read sequencing data set of the lytic and latent KSHV transcriptome using native RNA and direct cDNA-sequencing methods. This was supplemented with Cap Analysis of Gene Expression sequencing based on a short-read platform. We also utilized data sets from previous publications for our analysis. As a result of this combined approach, we have identified a number of novel viral transcripts and RNA isoforms and have either corroborated or improved the annotation of previously identified viral RNA molecules, thereby notably enhancing our comprehension of the transcriptomic architecture of the KSHV genome. We also evaluated the coding capability of transcripts previously thought to be non-coding by integrating our data on the viral transcripts with translatomic information from other publications.IMPORTANCEDeciphering the viral transcriptome of Kaposi\'s sarcoma-associated herpesvirus is of great importance because we can gain insight into the molecular mechanism of viral replication and pathogenesis, which can help develop potential targets for antiviral interventions. Specifically, the identification of substantial transcriptional overlaps by this work suggests the existence of a genome-wide interference between transcriptional machineries. This finding indicates the presence of a novel regulatory layer, potentially controlling the expression of viral genes.

Turbidity can be a result of suspended natural particles, such as sediment, or anthropogenic particles such as microplastics. This study assessed whether Daphnia magna, a pelagic filter feeder known to ingest suspended particles, have an altered response to equally turbid environments caused by the presence of either suspended bentonite or suspended polyethylene microplastics. Compared to controls, daphnids exposed to suspended bentonite maintained their feeding efficiency and increased their digestive activity, as measured by mandibular movement, peristalsis, and expulsion, to pass bentonite through the digestive tract. The same effects were not seen in microplastic-exposed individuals, in which feeding efficiency was decreased and only peristaltic movement was increased but without a coordinated increase in expulsion, suggesting that microplastics do not have the same ability as bentonite to pass through the digestive tract. This study highlights the need to discern the identities of particulates contributing to turbid environments as different particles, even of the same size, can have different effects on filter feeders, which inherently ingest suspended particles.

In the pursuit of enhancing the wine production process through the utilization of new technologies in viticulture, this study presents a novel approach for the rapid assessment of wine grape maturity levels using non-destructive, in situ infrared spectroscopy and artificial intelligence techniques. Building upon our previous work focused on estimating sugar content (∘Brix) from the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, this research expands its scope to encompass pH and titratable acidity, critical parameters determining the grape maturity degree, and in turn, wine quality, offering a more representative estimation pathway. Data were collected from four grape varieties-Chardonnay, Malagouzia, Sauvignon Blanc, and Syrah-during the 2023 harvest and pre-harvest phenological stages in the vineyards of Ktima Gerovassiliou, northern Greece. A comprehensive spectral library was developed, covering the VNIR-SWIR spectrum (350-2500 nm), with measurements performed in situ. Ground truth data for pH, titratable acidity, and sugar content were obtained using conventional laboratory methods: total soluble solids (TSS) (∘Brix) by refractometry, titratable acidity by titration (expressed as mg tartaric acid per liter of must) and pH by a pH meter, analyzed at different maturation stages in the must samples. The maturity indicators were predicted from the point hyperspectral data by employing machine learning algorithms, including Partial Least Squares regression (PLS), Random Forest regression (RF), Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), in conjunction with various pre-processing techniques. Multi-output models were also considered to simultaneously predict all three indicators to exploit their intercorrelations. A novel multi-input-multi-output CNN model was also proposed, incorporating a multi-head attention mechanism and enabling the identification of the spectral regions it focuses on, and thus having a higher interpretability degree. Our results indicate high accuracy in the estimation of sugar content, pH, and titratable acidity, with the best models yielding mean R2 values of 0.84, 0.76, and 0.79, respectively, across all properties. The multi-output models did not improve the prediction results compared to the best single-output models, and the proposed CNN model was on par with the next best model. The interpretability analysis highlighted that the CNN model focused on spectral regions associated with the presence of sugars (i.e., glucose and fructose) and of the carboxylic acid group. This study underscores the potential of portable spectrometry for real-time, non-destructive assessments of wine grape maturity, thereby providing valuable tools for informed decision making in the wine production industry. By integrating pH and titratable acidity into the analysis, our approach offers a holistic view of grape quality, facilitating more comprehensive and efficient viticultural practices.

Kaposi\'s sarcoma-associated herpesvirus (KSHV) is a large, oncogenic DNA virus belonging to the gammaherpesvirus subfamily. KSHV has been extensively studied with various high-throughput RNA-sequencing approaches to map the transcription start and end sites, the splice junctions, and the translation initiation sites. Despite these efforts, the comprehensive annotation of the viral transcriptome remains incomplete. In the present study, we generated a long-read sequencing dataset of the lytic and latent KSHV transcriptome using native RNA and direct cDNA sequencing methods. This was supplemented with CAGE sequencing based on a short-read platform. We also utilized datasets from previous publications for our analysis. As a result of this combined approach, we have identified a number of novel viral transcripts and RNA isoforms and have either corroborated or improved the annotation of previously identified viral RNA molecules, thereby notably enhancing our comprehension of the transcriptomic architecture of the KSHV genome. We also evaluated the coding capability of transcripts previously thought to be non-coding, by integrating our data on the viral transcripts with translatomic information from other publications.

Noncoding polymorphism frequently associates with phenotypic variation, but causation and mechanism are rarely established. Noncoding single-nucleotide polymorphisms (SNPs) characterize the major haplotypes of the Arabidopsis thaliana floral repressor gene FLOWERING LOCUS C (FLC). This noncoding polymorphism generates a range of FLC expression levels, determining the requirement for and the response to winter cold. The major adaptive determinant of these FLC haplotypes was shown to be the autumnal levels of FLC expression. Here, we investigate how noncoding SNPs influence FLC transcriptional output. We identify an upstream transcription start site (uTSS) cluster at FLC, whose usage is increased by an A variant at the promoter SNP-230. This variant is present in relatively few Arabidopsis accessions, with the majority containing G at this site. We demonstrate a causal role for the A variant at -230 in reduced FLC transcriptional output. The G variant upregulates FLC expression redundantly with the major transcriptional activator FRIGIDA (FRI). We demonstrate an additive interaction of SNP-230 with an intronic SNP+259, which also differentially influences uTSS usage. Combinatorial interactions between noncoding SNPs and transcriptional activators thus generate quantitative variation in FLC transcription that has facilitated the adaptation of Arabidopsis accessions to distinct climates.

Guava (Psidium guajava L.) is an important fruit crop of the Indian sub-continent, with potential for improvements in quality and yield. The goal of the present study was to construct a genetic linkage map in an intraspecific cross between the elite cultivar \'Allahabad Safeda\' and the Purple Guava landrace to identify the genomic regions responsible for important fruit quality traits, viz., total soluble solids, titratable acidity, vitamin C, and sugars. This population was phenotyped in field trials (as a winter crop) for three consecutive years, and showed moderate-to-high values of heterogeneity coefficients along with higher heritability (60.0%-97.0%) and genetic-advance-over-mean values (13.23%-31.17%), suggesting minimal environmental influence on the expression of fruit-quality traits and indicating that these traits can be improved by phenotypic selection methods. Significant correlations and strong associations were also detected among fruit physico-chemical traits in segregating progeny. The constructed linkage map consisted of 195 markers distributed across 11 chromosomes, spanning a length of 1,604.47 cM (average inter-loci distance of 8.80 markers) and with 88.00% coverage of the guava genome. Fifty-eight quantitative trait loci (QTLs) were detected in three environments with best linear unbiased prediction (BLUP) values using the composite interval mapping algorithm of the BIP (biparental populations) module. The QTLs were distributed on seven different chromosomes, explaining 10.95%-17.77% of phenotypic variance, with the highest LOD score being 5.96 for qTSS.AS.pau-6.2. Thirteen QTLs detected across multiple environments with BLUPs indicate stability and utility in a future breeding program for guava. Furthermore, seven QTL clusters with stable or common individual QTLs affecting two or more different traits were located on six linkage groups (LGs), explaining the correlation among fruit-quality traits. Thus, the multiple environmental evaluations conducted here have increased our understanding of the molecular basis of phenotypic variation, providing the basis for future high-resolution fine-mapping and paving the way for marker-assisted breeding of fruit-quality traits.

The global characterization of transcriptional regulatory networks almost exclusively uses in vivo conditions, thereby providing a snapshot on multiple regulatory interactions at the same time. To complement these approaches, we developed and applied a method for characterizing bacterial promoters genome-wide by in vitro transcription coupled to transcriptome sequencing specific for native 5\'-ends of transcripts. This method, called ROSE (run-off transcription/RNA-sequencing), only requires chromosomal DNA, ribonucleotides, RNA polymerase (RNAP) core enzyme, and a specific sigma factor, recognizing the corresponding promoters, which have to be analyzed. ROSE was performed on E. coli K-12 MG1655 genomic DNA using Escherichia coli RNAP holoenzyme (including σ70) and yielded 3226 transcription start sites, 2167 of which were also identified in in vivo studies, and 598 were new. Many new promoters not yet identified by in vivo experiments might be repressed under the tested conditions. Complementary in vivo experiments with E. coli K-12 strain BW25113 and isogenic transcription factor gene knockout mutants of fis, fur, and hns were used to test this hypothesis. Comparative transcriptome analysis demonstrated that ROSE could identify bona fide promoters that were apparently repressed in vivo. In this sense, ROSE is well-suited as a bottom-up approach for characterizing transcriptional networks in bacteria and ideally complementary to top-down in vivo transcriptome studies.