Functional microorganisms in Baijiu Daqu: Research progress and fortification strategy for application

Daqu is a saccharifying and fermenting starter in the production of Chinese Baijiu; its quality directly affects the quality of Baijiu. The production of Daqu is highly environment-dependent, and after long-term natural domestication, it is rich in a wide variety of microorganisms with a stable composition, which provide complex and diverse enzymes and flavor (precursor) substances and microbiota for Jiupei (Fermented grains) fermentation. However, inoculation with a relatively stable microbial community can lead to a certain upper limit or deficiencies of the physicochemical properties (e.g., saccharification capacity, esterification capacity) of the Daqu and affect the functional expression and aroma formation of the Daqu. Targeted improvement of this problem can be proposed by selecting functional microorganisms to fortify the production of Daqu. This review introduced the isolation, screening, identification and functional characteristics of culture-dependent functional microorganisms in Baijiu-brewing, the core functional microbiota community of Daqu, and the related research progress of functional microorganisms fortified Daqu, and summarized the fortifying strategies of functional microorganisms, aiming to further deepen the application of functional microorganisms fortification in Daqu fermentation and provide ideas for the flavor regulation and quality control of Baijiu

The Roles of Iron and Ferroptosis in Human Chronic Diseases

Ferroptosis, an iron-dependent novel type of cell death, has been characterized as an excessive accumulation of lipid peroxides and reactive oxygen species. A growing number of studies demonstrate that ferroptosis not only plays an important role in the pathogenesis and progression of chronic diseases, but also functions differently in different diseases. As a double-edged sword, activation of ferroptosis could potently inhibit tumor growth and increase sensitivity to chemotherapy and immunotherapy in various cancer settings. Therefore, the development of more efficacious ferroptosis agonists or inhibitors remains the mainstay of ferroptosis-targeting strategy for cancer therapeutics or cardiovascular and cerebrovascular diseases and neurodegenerative diseases therapeutics

Strategic News Releases in Equity Vesting Months

This is a pre-copyedited, author-produced version of an article accepted for publication in Review of Financial Studies following peer review. The version of record Alex Edmans, Luis Goncalves-Pinto, Moqi Groen-Xu, Yanbo Wang, Strategic News Releases in Equity Vesting Months, The Review of Financial Studies, Volume 31, Issue 11, November 2018, Pages 4099–4141, https://doi.org/10.1093/rfs/hhy070 is available online at: https://doi.org/10.1093/rfs/hhy070

Proteomics identifies neddylation as a potential therapy target in small intestinal neuroendocrine tumors.

Patients with small intestinal neuroendocrine tumors (SI-NETs) frequently develop spread disease; however, the underlying molecular mechanisms of disease progression are not known and effective preventive treatment strategies are lacking. Here, protein expression profiling was performed by HiRIEF-LC-MS in 14 primary SI-NETs from patients with and without liver metastases detected at the time of surgery and initial treatment. Among differentially expressed proteins, overexpression of the ubiquitin-like protein NEDD8 was identified in samples from patients with liver metastasis. Further, NEDD8 correlation analysis indicated co-expression with RBX1, a key component in cullin-RING ubiquitin ligases (CRLs). In vitro inhibition of neddylation with the therapeutic agent pevonedistat (MLN4924) resulted in a dramatic decrease of proliferation in SI-NET cell lines. Subsequent mass spectrometry-based proteomics analysis of pevonedistat effects and effects of the proteasome inhibitor bortezomib revealed stabilization of multiple targets of CRLs including p27, an established tumor suppressor in SI-NET. Silencing of NEDD8 and RBX1 using siRNA resulted in a stabilization of p27, suggesting that the cellular levels of NEDD8 and RBX1 affect CRL activity. Inhibition of CRL activity, by either NEDD8/RBX1 silencing or pevonedistat treatment of cells resulted in induction of apoptosis that could be partially rescued by siRNA-based silencing of p27. Differential expression of both p27 and NEDD8 was confirmed in a second cohort of SI-NET using immunohistochemistry. Collectively, these findings suggest a role for CRLs and the ubiquitin proteasome system in suppression of p27 in SI-NET, and inhibition of neddylation as a putative therapeutic strategy in SI-NET

Active Sites in Heterogeneous Catalytic Reaction on Metal and Metal Oxide: Theory and Practice

Active sites play an essential role in heterogeneous catalysis and largely determine the reaction properties. Yet identification and study of the active sites remain challenging owing to their dynamic behaviors during catalysis process and issues with current characterization techniques. This article provides a short review of research progresses in active sites of metal and metal oxide catalysts, which covers the past achievements, current research status, and perspectives in this research field. In particular, the concepts and theories of active sites are introduced. Major experimental and computational approaches that are used in active site study are summarized, with their applications and limitations being discussed. An outlook of future research direction in both experimental and computational catalysis research is provided

Car ownership and commuting mode of the “original” residents in a high-density city center: A case study in Shanghai

As a result of rapid urbanization and motorization in China, numerous mega-cities have emerged, and large numbers of people live and work in the city centers. Consequently, developing a public transport-oriented urban structure and promoting sustainable development are major planning strategies for the country. To understand the impact of rail transit on motorization in a high-density city center, we conduct a household travel survey in three neighborhoods around metro stations in the central area of Shanghai. We examine the car buying and commuting behavior of those Shanghai “original” residents who lived there when the city began growing, engulfing them in the center. Studies have shown that 40 percent of commuters in the city center commute outward, following a virtually reversed commute pattern, and the factors significantly affecting their car purchasing choice include their attitude toward cars and transit, household incomes, ownership of the apartments they live in, and the distance between family members’ workplaces and nearest metro stations. Despite easy access to the metro from their home in the city center, those who purchase their apartment units also likely own a car, while those who rent their apartment units are less likely to own a car; however, these odds are still higher than for those who live in an apartment unit inherited from their relatives or provided by their company. In the city center, if a family owns a car, then that car would almost certainly be used for daily commuting. A multinomial logistic model is applied to examine the factors influencing the tendency for using cars. The results show that people’s choices of commuting by alternative modes rather than cars are also shaped by their attitude toward public transportation, but other factors can also subtly change people’s commuting behavior under certain conditions. The commuting distance discourages people from walking and taking buses (but not metro). As the egress distance to the workplace increases, the metro becomes less appealing than cars. Mixed land use encourages people to walk or take buses instead of driving. Older people prefer riding buses and walking to driving, and female respondents tend to prefer walking, cycling, and riding the metro to driving compared to male respondents. These findings contribute to understanding the behavior of people who are familiar with public transportation and how to encourage them to switch from driving cars to alternative transport modes

Identifying the Acoustic Source via MFF-ResNet with Low Sample Complexity

Acoustic signal classification plays a central role in acoustic source identification. In practical applications, however, varieties of training data are typically inadequate, which leads to a low sample complexity. Applying classical deep learning methods to identify acoustic signals involves a large number of parameters in the classification model, which calls for great sample complexity. Therefore, low sample complexity modeling is one of the most important issues related to the performance of the acoustic signal classification. In this study, the authors propose a novel data fusion model named MFF-ResNet, in which manual design features and deep representation of log-Mel spectrogram features are fused with bi-level attention. The proposed approach involves an amount of prior human knowledge as implicit regularization, thus leading to an interpretable and low sample complexity model of the acoustic signal classification. The experimental results suggested that MFF-ResNet is capable of accurate acoustic signal classification with fewer training samples

Identifying the Acoustic Source via MFF-ResNet with Low Sample Complexity

Acoustic signal classification plays a central role in acoustic source identification. In practical applications, however, varieties of training data are typically inadequate, which leads to a low sample complexity. Applying classical deep learning methods to identify acoustic signals involves a large number of parameters in the classification model, which calls for great sample complexity. Therefore, low sample complexity modeling is one of the most important issues related to the performance of the acoustic signal classification. In this study, the authors propose a novel data fusion model named MFF-ResNet, in which manual design features and deep representation of log-Mel spectrogram features are fused with bi-level attention. The proposed approach involves an amount of prior human knowledge as implicit regularization, thus leading to an interpretable and low sample complexity model of the acoustic signal classification. The experimental results suggested that MFF-ResNet is capable of accurate acoustic signal classification with fewer training samples