Lentinula edodes, one of the most popular, edible mushroom species with a high content of proteins and polysaccharides as well as unique aroma, is widely cultivated in many Asian countries, especially in China, Japan and Korea. As a white rot fungus with lignocellulose degradation ability, L. edodes has the potential for application in the utilization of agriculture straw resources. Here, we report its 41.8-Mb genome, encoding 14,889 predicted genes. Through a phylogenetic analysis with model species of fungi, the evolutionary divergence time of L. edodes and Gymnopus luxurians was estimated to be 39 MYA. The carbohydrate-active enzyme genes in L. edodes were compared with those of the other 25 fungal species, and 101 lignocellulolytic enzymes were identified in L. edodes, similar to other white rot fungi. Transcriptome analysis showed that the expression of genes encoding two cellulases and 16 transcription factor was up-regulated when mycelia were cultivated for 120 minutes in cellulose medium versus glucose medium. Our results will foster a better understanding of the molecular mechanism of lignocellulose degradation and provide the basis for partial replacement of wood sawdust with agricultural wastes in L. edodes cultivation.

Despite substantial efforts, it remains difficult to identify reliable neuroanatomic biomarkers of autism spectrum disorder (ASD) based on magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Studies which use standard statistical methods to approach this task have been hampered by numerous challenges, many of which are innate to the mathematical formulation and assumptions of general linear models (GLM). Although the potential of alternative approaches such as machine learning (ML) to identify robust neuroanatomic correlates of psychiatric disease has long been acknowledged, few studies have attempted to evaluate the abilities of ML to identify structural brain abnormalities associated with ASD. Here we use a sample of 110 ASD patients and 83 typically developing (TD) volunteers (95 females) to assess the suitability of support vector machines (SVMs, a robust type of ML) as an alternative to standard statistical inference for identifying structural brain features which can reliably distinguish ASD patients from TD subjects of either sex, thereby facilitating the study of the interaction between ASD diagnosis and sex. We find that SVMs can perform these tasks with high accuracy and that the neuroanatomic correlates of ASD identified using SVMs overlap substantially with those found using conventional statistical methods. Our results confirm and establish SVMs as powerful ML tools for the study of ASD-related structural brain abnormalities. Additionally, they provide novel insights into the volumetric, morphometric, and connectomic correlates of this epidemiologically significant disorder.

A concise and efficient photoinduced rearrangement of (E)-3-arylvinyl-4H-chromen-4-ones for the synthesis of benzoaryl-5-yl(2-hydroxyphenyl)methanones is described. Benzoaryl-5-yl-(2-hydroxyphenyl)methanones were obtained in 77-95% yields via the irradiation of (E)-3-arylvinyl-chromones in the 95% EtOH with a high-pressure mercury lamp at room temperature under Ar atmosphere. The reported method provides a novel procedure for the synthesis of α,α'-diaryl ketone derivatives without addition of any transition metals and oxidants or other additives. A plausible mechanism was proposed, and the rearrangement product was characterized by NMR, HRMS, and X-ray.

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Background Vascular calcification is a closely linked to cardiovascular diseases, such as atherosclerosis, chronic kidney disease, diabetes, hypertension and aging. The extent of vascular calcification is closely correlate with adverse clinical events and cardiovascular all-cause mortality. The role of autophagy in vascular calcification is complex with many mechanistic unknowns. Methods In this review, we analyze the current known mechanisms of autophagy in vascular calcification and discuss the theoretical advantages of targeting autophagy as an intervention against vascular calcification. Results Here we summarize the functional link between vascular calcification and autophagy in both animal models of and human cardiovascular disease. Firstly, autophagy can reduce calcification by inhibiting the osteogenic differentiation of VSMCs related to ANCR, ERα, β-catenin, HIF-1a/PDK4, p62, miR-30b, BECN1, mTOR, SOX9, GHSR/ERK, and AMPK signaling. Conversely, autophagy can induce osteoblast differentiation and calcification as mediated by CREB, degradation of elastin, and lncRNA H19 and DUSP5 mediated ERK signaling. Secondly, autophagy also links apoptosis and vascular calcification through AMPK/mTOR/ULK1, Wnt/β-catenin and GAS6/AXL synthesis, as apoptotic cells become the nidus for calcium-phosphate crystal deposition. The failure of mitophagy can activate Drp1, BNIP3, and NR4A1/DNA‑PKcs/p53 mediated intrinsic apoptotic pathways, which have been closely linked to the formation of vascular calcification. Additionally, autophagy also plays a role in osteogenesis by regulating vascular calcification, which in turn regulates expression of proteins related to bone development, such as osteocalcin, osteonectin, etc. and regulated by mTOR, EphrinB2 and RhoA. Furthermore, autophagy also promotes vitamin K2-induced MC3T3 E1 osteoblast differentiation and FGFR4/FGF18- and JNK/complex VPS34–beclin-1-related bone mineralization via vascular calcification. Conclusion The interaction between autophagy and vascular calcification are complicated, with their interaction affected by the disease process, anatomical location, and the surrounding microenvironment. Autophagy activation in existent cellular damage is considered protective, while defective autophagy in normal cells result in apoptotic activation. Identifying and maintaining cells at the delicate line between these two states may hold the key to reducing vascular calcification, in which autophagy associated clinical strategy could be developed.

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This paper studies the synthesis of high-dimensional datasets with differential privacy (DP). The state-of-the-art solution addresses this problem by first generating a set M of noisy low-dimensional marginals of the input data D , and then use them to approximate the data distribution in D for synthetic data generation. However, it imposes several constraints on M that considerably limits the choices of marginals. This makes it difficult to capture all important correlations among attributes, which in turn degrades the quality of the resulting synthetic data. To address the above deficiency, we propose PrivMRF, a method that (i) also utilizes a set M of low-dimensional marginals for synthesizing high-dimensional data with DP, but (ii) provides a high degree of flexibility in the choices of marginals. The key idea of PrivMRF is to select an appropriate M to construct a Markov random field (MRF) that models the correlations among the attributes in the input data, and then use the MRF for data synthesis. Experimental results on four benchmark datasets show that PrivMRF consistently outperforms the state of the art in terms of the accuracy of counting queries and classification tasks conducted on the synthetic data generated.