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The lowest damage values were among the predator insects (Cleridae, Trogossitidae, Cantharidae) and those feeding on fungi colonized on the wood (Mordellidae, Cerylonidae, Nitidulidae). Some other predator insects of the Tenebrionidae family (Uloma cypraea, Uloma culinaris, Menephilus cylindricus) and Elateridae family (Lacon punctatus, Ampedus sp.) exhibited relatively higher damage severity values since they had built tunnels and made holes in the stored wood material. When the environmental factors were considered, the Buprestidae family exhibited a very strong positive relationship (p  less then  0.005) with insect frequency distribution (r = 0.922), number of species (r = 0.879) and insect density (r = 0.942). Both families showed the highest number and frequency during July and August, highlighting the importance of insect control and management during these months.Human papillomavirus (HPV)-independent vulvar squamous cell carcinoma (VSCC) is an aggressive clinical entity. Current diagnostic guidelines for premalignant lesions are ambiguous, and their molecular profile and progression events are still unclear. We selected 75 samples, from 40 patients, including 33 VSCC, 8 verrucous carcinomas (VC), 13 differentiated-type vulvar intraepithelial neoplasia (dVIN), 11 suspicious for dVIN (?dVIN), 6 differentiated exophytic vulvar intraepithelial lesions (DE-VIL), 2 vulvar acanthosis with altered differentiation (VAAD), and 2 usual-type vulvar intraepithelial neoplasia (uVIN/HSIL). Invasive and precursor lesions were matched in 29 cases. Clinical information, p16 immunohistochemistry, and mutation analysis were performed on all lesions. All dVIN, ?dVIN, DE-VIL, and VAAD were p16 negative, all uVIN/HSIL were p16 positive. In the HPV-independent group, mutations were identified in 6 genes TP53 (n = 40), PIK3CA (n = 20), HRAS (n = 12), MET (n = 5), PTEN (n = 4), and BRAF (n = The novel coronavirus SARS-CoV-2 (coronavirus disease 19, or COVID-19) primarily causes pulmonary injury, but has been implicated to cause hepatic injury, both by serum markers and histologic evaluation. The histologic pattern of injury has not been completely described. Studies quantifying viral load in the liver are lacking. Here we report the clinical and histologic findings related to the liver in 40 patients who died of complications of COVID-19. A subset of liver tissue blocks were subjected to polymerase chain reaction (PCR) for viral ribonucleic acid (RNA). Peak levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were elevated; median ALT peak 68 U/l (normal up to 46 U/l) and median AST peak 102 U/l (normal up to 37 U/l). Macrovesicular steatosis was the most common finding, involving 30 patients (75%). Mild lobular necroinflammation and portal inflammation were present in 20 cases each (50%). Vascular pathology, including sinusoidal microthrombi, was infrequent, seen in six cases (15%). PCR of liver tissue was positive in 11 of 20 patients tested (55%). In conclusion, we found patients dying of COVID-19 had biochemical evidence of hepatitis (of variable severity) and demonstrated histologic findings of macrovesicular steatosis and mild acute hepatitis (lobular necroinflammation) and mild portal inflammation. We also identified viral RNA in a sizeable subset of liver tissue samples.Diagnostic testing of pancreatic cyst fluid obtained by endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) has traditionally utilized elevated carcinoembryonic antigen (CEA) (≥192 ng/ml) and cytomorphologic examination to differentiate premalignant mucinous from benign pancreatic cystic lesions (PCLs). Molecular testing for KRAS/GNAS mutations has been shown to improve accuracy of detecting mucinous PCLs. Using a targeted next-generation sequencing (NGS) panel, we assess the status of PCL-associated mutations to improve understanding of the key diagnostic variables. Molecular analysis of cyst fluid was performed on 108 PCLs that had concurrent CEA and/or cytological analysis. A 48-gene NGS assay was utilized, which included genes commonly mutated in mucinous PCLs such as GNAS, KRAS, CDKN2A, and TP53. KRAS and/or GNAS mutations were seen in 59 of 68 (86.8%) cases with multimodality diagnosis of a mucinous PCL. Among 31 patients where surgical histopathology was available, the sensitivity, specificity, and diagnostic accuracy of NGS for the diagnosis of mucinous PCL was 88.5%, 100%, and 90.3%, respectively. Cytology with mucinous/atypical findings were found in only 29 of 62 cases (46.8%), with fluid CEA elevated in 33 of 58 cases (56.9%). Multiple KRAS mutations at different variant allele frequencies were seen in seven cases favoring multiclonal patterns, with six of them showing at least two separate PCLs by imaging. Among the 6 of 10 cases with GNAS + /KRAS- results, uncommon, non-V600E exon 11/15 hotspot BRAF mutations were identified. The expected high degree of accuracy of NGS detection of KRAS and/or GNAS mutations for mucinous-PCLs, as compared with CEA and cytological examination, was demonstrated. Multiple KRAS mutations correlated with multifocal cysts demonstrated by radiology. In IPMNs that lacked KRAS mutations, the concurring BRAF mutations with GNAS mutations supports an alternate mechanism of activation in the Ras pathway.Light element identification is necessary in materials research to obtain detailed insight into various material properties. However, reported techniques, such as scanning transmission electron microscopy (STEM)-energy dispersive X-ray spectroscopy (EDS) have inadequate detection limits, which impairs identification. NST-628 In this study, we achieved light element identification with nanoscale spatial resolution in a multi-component metal alloy through unsupervised machine learning algorithms of singular value decomposition (SVD) and independent component analysis (ICA). Improvement of the signal-to-noise ratio (SNR) in the STEM-EDS spectrum images was achieved by combining SVD and ICA, leading to the identification of a nanoscale N-depleted region that was not observed in as-measured STEM-EDS. Additionally, the formation of the nanoscale N-depleted region was validated using STEM-electron energy loss spectroscopy and multicomponent diffusional transformation simulation. The enhancement of SNR in STEM-EDS spectrum images by machine learning algorithms can provide an efficient, economical chemical analysis method to identify light elements at the nanoscale.