Pyoderma Gangrenosum of the Genitalia, Anus, and Perineum: Two Case Reports and a Review of Published Cases.

ABSTRACT: Pyoderma gangrenosum is an inflammatory skin disease that presents with rapidly progressive ulcers with violaceous, undermined borders. Despite most commonly affecting the lower extremities, pyoderma gangrenosum can rarely present in the genital, anal, and perineal regions. We describe 2 cases and report a review of published cases.

Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks.

BACKGROUND: Timely diagnosis plays a critical role in determining melanoma prognosis, prompting the development of deep learning models to aid clinicians. Questions persist regarding the efficacy of clinical images alone or in conjunction with dermoscopy images for model training. This study aims to compare the classification performance for melanoma of three types of CNN models: those trained on clinical images, dermoscopy images, and a combination of paired clinical and dermoscopy images from the same lesion. MATERIALS AND METHODS: We divided 914 image pairs into training, validation, and test sets. Models were built using pre-trained Inception-ResNetV2 convolutional layers for feature extraction, followed by binary classification. Training comprised 20 models per CNN type using sets of random hyperparameters. Best models were chosen based on validation AUC-ROC. RESULTS: Significant AUC-ROC differences were found between clinical versus dermoscopy models (0.661 vs. 0.869, p < 0.001) and clinical versus clinical + dermoscopy models (0.661 vs. 0.822, p = 0.001). Significant sensitivity differences were found between clinical and dermoscopy models (0.513 vs. 0.799, p = 0.01), dermoscopy versus clinical + dermoscopy models (0.799 vs. 1.000, p = 0.02), and clinical versus clinical + dermoscopy models (0.513 vs. 1.000, p < 0.001). Significant specificity differences were found between dermoscopy versus clinical + dermoscopy models (0.800 vs. 0.288, p < 0.001) and clinical versus clinical + dermoscopy models (0.650 vs. 0.288, p < 0.001). CONCLUSION: CNN models trained on dermoscopy images outperformed those relying solely on clinical images under our study conditions. The potential advantages of incorporating paired clinical and dermoscopy images for CNN-based melanoma classification appear less clear based on our findings.

SIRT6 stabilization and cytoplasmic localization in macrophages regulates acute and chronic inflammation in mice.

Acute and chronic inflammations are key homeostatic events in health and disease. Sirtuins (SIRTs), a family of NAD-dependent protein deacylases, play a pivotal role in the regulation of these inflammatory responses. Indeed, SIRTs have anti-inflammatory effects through a myriad of signaling cascades, including histone deacetylation and gene silencing, p65/RelA deacetylation and inactivation, and nucleotide­binding oligomerization domain, leucine rich repeat, and pyrin domain­containing protein 3 inflammasome inhibition. Nevertheless, recent findings show that SIRTs, specifically SIRT6, are also necessary for mounting an active inflammatory response in macrophages. SIRT6 has been shown to positively regulate tumor necrosis factor alpha (TNFα) secretion by demyristoylating pro-TNFα in the cytoplasm. However, how SIRT6, a nuclear chromatin-binding protein, fulfills this function in the cytoplasm is currently unknown. Herein, we show by Western blot and immunofluorescence that in macrophages and fibroblasts there is a subpopulation of SIRT6 that is highly unstable and quickly degraded via the proteasome. Upon lipopolysaccharide stimulation in Raw 264.7, bone marrow, and peritoneal macrophages, this population of SIRT6 is rapidly stabilized and localizes in the cytoplasm, specifically in the vicinity of the endoplasmic reticulum, promoting TNFα secretion. Furthermore, we also found that acute SIRT6 inhibition dampens TNFα secretion both in vitro and in vivo, decreasing lipopolysaccharide-induced septic shock. Finally, we tested SIRT6 relevance in systemic inflammation using an obesity-induced chronic inflammatory in vivo model, where TNFα plays a key role, and we show that short-term genetic deletion of SIRT6 in macrophages of obese mice ameliorated systemic inflammation and hyperglycemia, suggesting that SIRT6 plays an active role in inflammation-mediated glucose intolerance during obesity.

High throughput genotyping of structural variations in a complex plant genome using an original Affymetrix® axiom® array.

BACKGROUND: Insertions/deletions (InDels) and more specifically presence/absence variations (PAVs) are pervasive in several species and have strong functional and phenotypic effect by removing or drastically modifying genes. Genotyping of such variants on large panels remains poorly addressed, while necessary for approaches such as association mapping or genomic selection. RESULTS: We have developed, as a proof of concept, a new high-throughput and affordable approach to genotype InDels. We first identified 141,000 InDels by aligning reads from the B73 line against the genome of three temperate maize inbred lines (F2, PH207, and C103) and reciprocally. Next, we designed an Affymetrix® Axiom® array to target these InDels, with a combination of probes selected at breakpoint sites (13%) or within the InDel sequence, either at polymorphic (25%) or non-polymorphic sites (63%) sites. The final array design is composed of 662,772 probes and targets 105,927 InDels, including PAVs ranging from 35 bp to 129kbp. After Affymetrix® quality control, we successfully genotyped 86,648 polymorphic InDels (82% of all InDels interrogated by the array) on 445 maize DNA samples with 422,369 probes. Genotyping InDels using this approach produced a highly reliable dataset, with low genotyping error (~ 3%), high call rate (~ 98%), and high reproducibility (> 95%). This reliability can be further increased by combining genotyping of several probes calling the same InDels (< 0.1% error rate and > 99.9% of call rate for 5 probes). This "proof of concept" tool was used to estimate the kinship matrix between 362 maize lines with 57,824 polymorphic InDels. This InDels kinship matrix was highly correlated with kinship estimated using SNPs from Illumina 50 K SNP arrays. CONCLUSIONS: We efficiently genotyped thousands of small to large InDels on a sizeable number of individuals using a new Affymetrix® Axiom® array. This powerful approach opens the way to studying the contribution of InDels to trait variation and heterosis in maize. The approach is easily extendable to other species and should contribute to decipher the biological impact of InDels at a larger scale.

Chaos analysis and explicit series solutions to the seasonally forced SIR epidemic model.

Despite numerous studies of epidemiological systems, the role of seasonality in the recurrent epidemics is not entirely understood. During certain periods of the year incidence rates of a number of endemic infectious diseases may fluctuate dramatically. This influences the dynamics of mathematical models describing the spread of infection and often leads to chaotic oscillations. In this paper, we are concerned with a generalization of a classical Susceptible-Infected-Recovered epidemic model which accounts for seasonal effects. Combining numerical and analytic techniques, we gain new insights into the complex dynamics of a recurrent disease influenced by the seasonality. Computation of the Lyapunov spectrum allows us to identify different chaotic regimes, determine the fractal dimension and estimate the predictability of the appearance of attractors in the system. Applying the homotopy analysis method, we obtain series solutions to the original nonautonomous SIR model with a high level of accuracy and use these approximations to analyze the dynamics of the system. The efficiency of the method is guaranteed by the optimal choice of an auxiliary control parameter which ensures the rapid convergence of the series to the exact solution of the forced SIR epidemic model.

Multiobjective Optimization of a Plate Heat Exchanger in a Waste Heat Recovery Organic Rankine Cycle System for Natural Gas Engines.

A multiobjective optimization of an organic Rankine cycle (ORC) evaporator, operating with toluene as the working fluid, is presented in this paper for waste heat recovery (WHR) from the exhaust gases of a 2 MW Jenbacher JMS 612 GS-N.L. gas internal combustion engine. Indirect evaporation between the exhaust gas and the organic fluid in the parallel plate heat exchanger (ITC2) implied irreversible heat transfer and high investment costs, which were considered as objective functions to be minimized. Energy and exergy balances were applied to the system components, in addition to the phenomenological equations in the ITC2, to calculate global energy indicators, such as the thermal efficiency of the configuration, the heat recovery efficiency, the overall energy conversion efficiency, the absolute increase of engine thermal efficiency, and the reduction of the break-specific fuel consumption of the system, of the system integrated with the gas engine. The results allowed calculation of the plate spacing, plate height, plate width, and chevron angle that minimized the investment cost and entropy generation of the equipment, reaching 22.04 m2 in the heat transfer area, 693.87 kW in the energy transfer by heat recovery from the exhaust gas, and 41.6% in the overall thermal efficiency of the ORC as a bottoming cycle for the engine. This type of result contributes to the inclusion of this technology in the industrial sector as a consequence of the improvement in thermal efficiency and economic viability.

Sequence analysis of European maize inbred line F2 provides new insights into molecular and chromosomal characteristics of presence/absence variants.

BACKGROUND: Maize is well known for its exceptional structural diversity, including copy number variants (CNVs) and presence/absence variants (PAVs), and there is growing evidence for the role of structural variation in maize adaptation. While PAVs have been described in this important crop species, they have been only scarcely characterized at the sequence level and the extent of presence/absence variation and relative chromosomal landscape of inbred-specific regions remain to be elucidated. RESULTS: De novo genome sequencing of the French F2 maize inbred line revealed 10,044 novel genomic regions larger than 1 kb, making up 88 Mb of DNA, that are present in F2 but not in B73 (PAV). This set of maize PAV sequences allowed us to annotate PAV content and to analyze sequence breakpoints. Using PAV genotyping on a collection of 25 temperate lines, we also analyzed Linkage Disequilibrium in PAVs and flanking regions, and PAV frequencies within maize genetic groups. CONCLUSIONS: We highlight the possible role of MMEJ-type double strand break repair in maize PAV formation and discover 395 new genes with transcriptional support. Pattern of linkage disequilibrium within PAVs strikingly differs from this of flanking regions and is in accordance with the intuition that PAVs may recombine less than other genomic regions. We show that most PAVs are ancient, while some are found only in European Flint material, thus pinpointing structural features that may be at the origin of adaptive traits involved in the success of this material. Characterization of such PAVs will provide useful material for further association genetic studies in European and temperate maize.

How Complex, Probable, and Predictable is Genetically Driven Red Queen Chaos?

Coevolution between two antagonistic species has been widely studied theoretically for both ecologically- and genetically-driven Red Queen dynamics. A typical outcome of these systems is an oscillatory behavior causing an endless series of one species adaptation and others counter-adaptation. More recently, a mathematical model combining a three-species food chain system with an adaptive dynamics approach revealed genetically driven chaotic Red Queen coevolution. In the present article, we analyze this mathematical model mainly focusing on the impact of species rates of evolution (mutation rates) in the dynamics. Firstly, we analytically proof the boundedness of the trajectories of the chaotic attractor. The complexity of the coupling between the dynamical variables is quantified using observability indices. By using symbolic dynamics theory, we quantify the complexity of genetically driven Red Queen chaos computing the topological entropy of existing one-dimensional iterated maps using Markov partitions. Co-dimensional two bifurcation diagrams are also built from the period ordering of the orbits of the maps. Then, we study the predictability of the Red Queen chaos, found in narrow regions of mutation rates. To extend the previous analyses, we also computed the likeliness of finding chaos in a given region of the parameter space varying other model parameters simultaneously. Such analyses allowed us to compute a mean predictability measure for the system in the explored region of the parameter space. We found that genetically driven Red Queen chaos, although being restricted to small regions of the analyzed parameter space, might be highly unpredictable.

Evolutionary history of Methyltransferase 1 genes in hexaploid wheat.

BACKGROUND: Plant and animal methyltransferases are key enzymes involved in DNA methylation at cytosine residues, required for gene expression control and genome stability. Taking advantage of the new sequence surveys of the wheat genome recently released by the International Wheat Genome Sequencing Consortium, we identified and characterized MET1 genes in the hexaploid wheat Triticum aestivum (TaMET1). RESULTS: Nine TaMET1 genes were identified and mapped on homoeologous chromosome groups 2A/2B/2D, 5A/5B/5D and 7A/7B/7D. Synteny analysis and evolution rates suggest that the genome organization of TaMET1 genes results from a whole genome duplication shared within the grass family, and a second gene duplication, which occurred specifically in the Triticeae tribe prior to the speciation of diploid wheat. Higher expression levels were observed for TaMET1 homoeologous group 2 genes compared to group 5 and 7, indicating that group 2 homoeologous genes are predominant at the transcriptional level, while group 5 evolved into pseudogenes. We show the connection between low expression levels, elevated evolution rates and unexpected enrichment in CG-dinucleotides (CG-rich isochores) at putative promoter regions of homoeologous group 5 and 7, but not of group 2 TaMET1 genes. Bisulfite sequencing reveals that these CG-rich isochores are highly methylated in a CG context, which is the expected target of TaMET1. CONCLUSIONS: We retraced the evolutionary history of MET1 genes in wheat, explaining the predominance of group 2 homoeologous genes and suggest CG-DNA methylation as one of the mechanisms involved in wheat genome dynamics.