Debranching of waxy maize starches by pullulanase, and structure and digestibility of spherulites formed

Master of ScienceDepartment of Grain Science and IndustryYong Cheng ShiResistant starch (RS) is notable for having several health benefits in humans, including glucose control and intestinal well-being. Pullulanase is able to debranch amylopectin and result in higher RS content. Different levels of pullulanase have been used to debranch waxy maize starch in the literature, but the changes of structure during debranching are well documented. In this study, waxy maize starch was cooked and debranched by pullulanase with 80, 160 and 240 New Pullulanase Unit Novo (NPUN)/g starch pullulanase. One NPUN was defined as the amount of enzyme, which, under standard conditions, hydrolyzes pullulan, liberating reducing carbohydrate with reducing power equivalent to 1 µmole glucose per minute. The structure of waxy maize starch during debranching was investigated and the digestibility of the debranched products was measured. When pullulanase was increased from 80 to 240 NPUN/g, more amylopectin was debranched in the same debranching time, and the degree of crystallinity and the RS content increased. After the debranched starches were crystallized at 25°C for 24 hours, the RS contents were greater than 63%. When heated and recrystallized under highly regulated conditions, the linear material formed crystallites of a range of geometries, including spherulites of a highly organized structure. Debranched waxy maize starches were used to produce crystalline structure under four conditions: spherulites formed by adding ethanol and crystallized at 4°C (ES4); spherulites formed in water (WS4) at 4°C; particles formed at 50°C (WS50); and spherulites formed at 50°C then further precipitated at 4°C (WS50-4). Spherulites formed at 50°C (WS50) had a higher proportion of smaller molecules than existed in the parent starch (Rh<15nm). ES4 and WS4 were B-type crystalline structure; whereasWS50 and WS50-4 were A-type crystalline structure. ES4 had a larger proportion of molecules with a low degree of polymerization and the RS content was also the lowest of the four samples. With cooling from 50°C to 4°C (WS50-4), the RS content was increased from 60% to 73%. ES4 and WS50-4 contained particles with spherical symmetry and WS4 had partial radial symmetry with some distortions, whereas WS50 displayed oblate particles with a parallel crystal structure

MOPRD: A multidisciplinary open peer review dataset

Open peer review is a growing trend in academic publications. Public access to peer review data can benefit both the academic and publishing communities. It also serves as a great support to studies on review comment generation and further to the realization of automated scholarly paper review. However, most of the existing peer review datasets do not provide data that cover the whole peer review process. Apart from this, their data are not diversified enough as they are mainly collected from the field of computer science. These two drawbacks of the currently available peer review datasets need to be addressed to unlock more opportunities for related studies. In response to this problem, we construct MOPRD, a multidisciplinary open peer review dataset. This dataset consists of paper metadata, multiple version manuscripts, review comments, meta-reviews, author's rebuttal letters, and editorial decisions. Moreover, we design a modular guided review comment generation method based on MOPRD. Experiments show that our method delivers better performance indicated by both automatic metrics and human evaluation. We also explore other potential applications of MOPRD, including meta-review generation, editorial decision prediction, author rebuttal generation, and scientometric analysis. MOPRD is a strong endorsement for further studies in peer review-related research and other applications

RecipeMeta: Metapath-enhanced Recipe Recommendation on Heterogeneous Recipe Network

Recipe is a set of instructions that describes how to make food. It can help people from the preparation of ingredients, food cooking process, etc. to prepare the food, and increasingly in demand on the Web. To help users find the vast amount of recipes on the Web, we address the task of recipe recommendation. Due to multiple data types and relationships in a recipe, we can treat it as a heterogeneous network to describe its information more accurately. To effectively utilize the heterogeneous network, metapath was proposed to describe the higher-level semantic information between two entities by defining a compound path from peer entities. Therefore, we propose a metapath-enhanced recipe recommendation framework, RecipeMeta, that combines GNN (Graph Neural Network)-based representation learning and specific metapath-based information in a recipe to predict User-Recipe pairs for recommendation. Through extensive experiments, we demonstrate that the proposed model, RecipeMeta, outperforms state-of-the-art methods for recipe recommendation

Automated scholarly paper review: Technologies and challenges

Peer review is a widely accepted mechanism for research evaluation, playing a pivotal role in scholarly publishing. However, criticisms have long been leveled on this mechanism, mostly because of its inefficiency and subjectivity. Recent years have seen the application of artificial intelligence (AI) in assisting the peer review process. Nonetheless, with the involvement of humans, such limitations remain inevitable. In this review paper, we propose the concept and pipeline of automated scholarly paper review (ASPR) and review the relevant literature and technologies of achieving a full-scale computerized review process. On the basis of the review and discussion, we conclude that there is already corresponding research and implementation at each stage of ASPR. We further look into the challenges in ASPR with the existing technologies. The major difficulties lie in imperfect document parsing and representation, inadequate data, defective human-computer interaction and flawed deep logical reasoning. Moreover, we discuss the possible moral & ethical issues and point out the future directions of ASPR. In the foreseeable future, ASPR and peer review will coexist in a reinforcing manner before ASPR is able to fully undertake the reviewing workload from humans

Investigation on Combination of Airflow Disturbance and Sprinkler Irrigation for Horticultural Crop Frost Protection

Frost tends to be detrimental to the growth and development of horticultural crops, leading to yield or quality reduction with sizable economic losses. Therefore, it is very important to develop frost protection technology for horticultural crops. In this study, the development of frost protection technology is reviewed, and the research of mechanized frost protection technology in recent years is analyzed. In view of the poor frost protection effect of some single mechanized frost protection technology, the combination frost protection technology is put forward. The combination frost protection technology with airflow disturbance and sprinkler irrigation is discussed and analyzed

Mitochondria-Targeted Nanomedicine for Enhanced Efficacy of Cancer Therapy

Nanomedicines have been designed and developed to deliver anticancer drugs or exert anticancer therapy more selectively to tumor sites. Recent investigations have gone beyond delivering drugs to tumor tissues or cells, but to intracellular compartments for amplifying therapy efficacy. Mitochondria are attractive targets for cancer treatment due to their important functions for cells and close relationships to tumor occurrence and metastasis. Accordingly, multifunctional nanoplatforms have been constructed for cancer therapy with the modification of a variety of mitochondriotropic ligands, to trigger the mitochondria-mediated apoptosis of tumor cells. On this basis, various cancer therapeutic modalities based on mitochondria-targeted nanomedicines are developed by strategies of damaging mitochondria DNA (mtDNA), increasing reactive oxygen species (ROS), disturbing respiratory chain and redox balance. Herein, in this review, we highlight mitochondria-targeted cancer therapies enabled by nanoplatforms including chemotherapy, photothermal therapy (PTT), photodynamic therapy (PDT), chemodynamic therapy (CDT), sonodynamic therapy (SDT), radiodynamic therapy (RDT) and combined immunotherapy, and discussed the ongoing challenges.Peer reviewe

Recent insight into the role of macrophage in alcohol-associated liver disease: a mini-review

Alcohol-associated liver disease (ALD) is a condition that develops due to prolonged and excessive alcohol consumption. It encompasses various stages of liver damage, including fatty liver, alcoholic hepatitis, and cirrhosis. Immune cells, particularly macrophages, of various types play a significant role in the onset and progression of the disease. Macrophages observed in the liver exhibit diverse differentiation forms, and perform a range of functions. Beyond M1 and M2 macrophages, human macrophages can polarize into distinct phenotypes in response to various stimuli. Recent advancements have improved our understanding of macrophage diversity and their role in the progression of ALD. This mini-review provides a concise overview of the latest findings on the role and differentiation of macrophages in ALD. Additionally, it discusses potential therapeutic targets associated with macrophages and explores potential therapeutic strategies

DDI-CPI, a server that predicts drug–drug interactions through implementing the chemical–protein interactome

Drug–drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug–human protein interactions, it is reasonable to analyze the chemical–protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user's molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model. It can suggest potential DDIs between the user's molecule and our library of 2515 drug molecules. In cross-validation and independent validation, the server achieved an AUC greater than 0.85. Additionally, by investigating the CPI profiles of predicted DDI, users can explore the PK/PD proteins that might be involved in a particular DDI. A 3D visualization of the drug-protein interaction will be provided as well. The DDI-CPI is freely accessible at http://cpi.bio-x.cn/ddi/

Inferring Tabular Analysis Metadata by Infusing Distribution and Knowledge Information

Many data analysis tasks heavily rely on a deep understanding of tables (multi-dimensional data). Across the tasks, there exist comonly used metadata attributes of table fields / columns. In this paper, we identify four such analysis metadata: Measure/dimension dichotomy, common field roles, semantic field type, and default aggregation function. While those metadata face challenges of insufficient supervision signals, utilizing existing knowledge and understanding distribution. To inference these metadata for a raw table, we propose our multi-tasking Metadata model which fuses field distribution and knowledge graph information into pre-trained tabular models. For model training and evaluation, we collect a large corpus (~582k tables from private spreadsheet and public tabular datasets) of analysis metadata by using diverse smart supervisions from downstream tasks. Our best model has accuracy = 98%, hit rate at top-1 > 67%, accuracy > 80%, and accuracy = 88% for the four analysis metadata inference tasks, respectively. It outperforms a series of baselines that are based on rules, traditional machine learning methods, and pre-trained tabular models. Analysis metadata models are deployed in a popular data analysis product, helping downstream intelligent features such as insights mining, chart / pivot table recommendation, and natural language QA...Comment: 13pages, 7 figures, 9 table

Acute Colonic Pseudo-Obstruction with Feeding Intolerance in Critically Ill Patients: A Study according to Gut Wall Analysis

Objective. To compare the differences between acute colonic pseudo-obstruction (ACPO) with and without acute gut wall thickening. Methods. ACPO patients with feeding tolerance were divided into ACPO with no obvious gut wall thickening (ACPO-NT) group and ACPO with obvious acute gut wall thickening (ACPO-T) group according to computed tomography and abdominal radiographs. Patients’ condition, responses to supportive measures, pharmacologic therapy, endoscopic decompression, and surgeries and outcomes were compared. Results. Patients in ACPO-T group had a significantly higher APACHE II (11.82 versus 8.25, p=0.008) and SOFA scores (6.47 versus 3.54, p<0.001) and a significantly higher 28-day mortality (17.78% versus 4.16%, p=0.032) and longer intensive care unit stage (4 versus 16 d, p<0.001). Patients in ACPO-NT group were more likely to be responsive to supportive treatment (62.50% versus 24.44%, p<0.001), neostigmine (77.78% versus 17.64%, p<0.001), and colonoscopic decompression (75% versus 42.86%, p=0.318) than those in ACPO-T group. Of the patients who underwent ileostomy, 81.25% gained benefits. Conclusions. ACPO patients with gut wall thickening are more severe and are less likely to be responsive to nonsurgical treatment. Ileostomy may be a good option for ACPO patients with gut wall thickening who are irresponsive to nonsurgical treatment