Effect of Linkage Bond between Backbone and Side Chain in Comb-Like Copolymer Dispersants on Early Properties of Concentrated Cement Suspensions

AbstractComb-like copolymer, consists of backbone with ionizable group and charge neutral poly(ethylene oxide)-based “teeth” side chain grafted onto the backbone at frequent intervals, has been widely used as superplasticizer in concrete industry due to its unique properties compared to traditional polyelectrolyte dispersants, such as lower dosages, outstanding water reduction, excellent workability retention and lower shrinkage. It is easy to modify and adapt this molecular structure in order to maximize the targeted properties and also meet specific properties for different applications. However, modification attempts usually focused on the side-chain length and charge density. In fact, the linkage bond between backbone and side chain has greatly influence on the early behavior of cement suspensions due to copolymers with different linkage bond have different structure stability under the strong basic environment. In this study, two comb-like copolymer dispersants with different linkage group (ester and ether) were produced. The effects of the type of linkage bond in comb-like copolymers on the adsorption characteristics and hydration behavior of concentrated cement suspensions, were investigated by means of Total organic carbon analyzer(TOC),DSC-TG and calorimetric device and Environmental Scanning Electron Microscope(ESEM). Test results showed that the comb-like copolymer with ester linkage bond, has lower initial adsorbed amount, and thus not only accelerate the hydration rate, but also change its crystal shape of hydrated cement. This will help in the understanding of the structure-property relationship of comb-like copolymer and in the designing the molecular structure of new functional superplasticizer

Three new xanthone derivatives from an algicolous isolate of Aspergillus wentii

Three new xanthone derivatives, yicathin A (1), yicathin B (2), and yicathin C (3), and three known anthraquinone derivatives, alatinone (4), 1,5-dihydroxy-3-methoxy-7-methylanthraquinone (5), and 5-hydroxy-1,3-dimethoxy-7-methylanthraquinone (6), were isolated from the cultures of Aspergillus wentii pt-1, an endophytic fungus isolated from the marine red alga Gymnogongrus flabelliformis. Their structures were unambiguously elucidated by NMR and mass spectroscopic methods as well as quantum chemical calculations. Compound 2 was active against Escherichia coli, and 3 could inhibit E. coli, Staphylococcus aureus, and Colletotrichum lagenarium.Three new xanthone derivatives, yicathin A (1), yicathin B (2), and yicathin C (3), and three known anthraquinone derivatives, alatinone (4), 1,5-dihydroxy-3-methoxy-7-methylanthraquinone (5), and 5-hydroxy-1,3-dimethoxy-7-methylanthraquinone (6), were isolated from the cultures of Aspergillus wentii pt-1, an endophytic fungus isolated from the marine red alga Gymnogongrus flabelliformis. Their structures were unambiguously elucidated by NMR and mass spectroscopic methods as well as quantum chemical calculations. Compound 2 was active against Escherichia coli, and 3 could inhibit E. coli, Staphylococcus aureus, and Colletotrichum lagenarium. Copyright (C) 2012 John Wiley & Sons, Ltd

Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning

In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the linguistic gap by training on pseudo-labeled target-language data. However, due to sub-optimal performance on target languages, the pseudo labels are often noisy and limit the overall performance. In this work, we aim to improve self-training for cross-lingual NER by combining representation learning and pseudo label refinement in one coherent framework. Our proposed method, namely ContProto mainly comprises two components: (1) contrastive self-training and (2) prototype-based pseudo-labeling. Our contrastive self-training facilitates span classification by separating clusters of different classes, and enhances cross-lingual transferability by producing closely-aligned representations between the source and target language. Meanwhile, prototype-based pseudo-labeling effectively improves the accuracy of pseudo labels during training. We evaluate ContProto on multiple transfer pairs, and experimental results show our method brings in substantial improvements over current state-of-the-art methods.Comment: Accepted by ACL202

Insights and approaches using deep learning to classify wildlife.

The implementation of intelligent software to identify and classify objects and individuals in visual fields is a technology of growing importance to operatives in many fields, including wildlife conservation and management. To non-experts, the methods can be abstruse and the results mystifying. Here, in the context of applying cutting edge methods to classify wildlife species from camera-trap data, we shed light on the methods themselves and types of features these methods extract to make efficient identifications and reliable classifications. The current state of the art is to employ convolutional neural networks (CNN) encoded within deep-learning algorithms. We outline these methods and present results obtained in training a CNN to classify 20 African wildlife species with an overall accuracy of 87.5% from a dataset containing 111,467 images. We demonstrate the application of a gradient-weighted class-activation-mapping (Grad-CAM) procedure to extract the most salient pixels in the final convolution layer. We show that these pixels highlight features in particular images that in some cases are similar to those used to train humans to identify these species. Further, we used mutual information methods to identify the neurons in the final convolution layer that consistently respond most strongly across a set of images of one particular species. We then interpret the features in the image where the strongest responses occur, and present dataset biases that were revealed by these extracted features. We also used hierarchical clustering of feature vectors (i.e., the state of the final fully-connected layer in the CNN) associated with each image to produce a visual similarity dendrogram of identified species. Finally, we evaluated the relative unfamiliarity of images that were not part of the training set when these images were one of the 20 species "known" to our CNN in contrast to images of the species that were "unknown" to our CNN

Bidirectional Associations Between Rheumatoid Arthritis and Depression: a Nationwide Longitudinal Study

Rheumatoid arthritis (RA) and depression may be associated with each other pathophysiologically, but few studies have been conducted on the interplay between these two diseases using longitudinal measurement. Therefore, we used the National Health Insurance Research Database of Taiwan to investigate the bidirectional associations between RA and depression. One cohort was included to analyze RA predicting the onset of depression and a second cohort for analysis of depression predicting RA. A sex- and age-matched control group was included for both. The incidence of depression in RA subjects was higher than in non-RA subjects [15.69 vs. 8.95 per 1,000 person-years (PYs)], with an adjusted hazard ratios (HRs) of 1.69 [95% confidence interval (CI), 1.51–1.87]. The incidence of RA was higher in depressed than non-depressed individuals (2.07 vs. 1.21 per 1,000 PYs), with an adjusted HRs of 1.65 (95%CI, 1.41–1.77). This population-based cohort study suggested strong bidirectional relationships between RA and depression. Healthcare providers are recommended to facilitate the implementation of more effective therapeutic interventions to achieve favorable prognosis, especially for those with new-onset or younger cases

Certain doping concentrations caused half-metallic graphene

This work is supported by National Natural Science Foundation of China (Grant No. 21173096).The singly B and N doped graphene systems are carefully studied. The highly concentrated dopants cause a spin polarization effect in the systems. The spin polarization limits are affirmed in the singly B and N doped graphene systems through periodic hybrid density functional theory studies. The spin polarization effects must be considered indeed in the B and N doped graphene systems if the dopant concentration is above 3.1% and 1.4%, respectively. The system symmetry cooperating with the presence of the spin polarization brings half-metallic properties into the doping systems. The semiconducting channels in the half-metallic systems are in two different spin directions due to the different electron configurations of the B and N dopants in graphene.National Natural Science Foundation of China 21173096; Institute of Solid State Physics, University of Latvia as the Center of Excellence has received funding from the European Union’s Horizon 2020 Framework Programme H2020-WIDESPREAD-01-2016-2017-TeamingPhase2 under grant agreement No. 739508, project CAMART

Increased risk of depression in patients with rheumatoid arthritis: a seven-year population-based cohort study

OBJECTIVE: Rheumatoid arthritis (RA) is a costly and crippling autoimmune disease that can lead to the development of depression, contributing to suboptimal clinical outcomes. However, no longitudinal studies have identified an association between rheumatoid arthritis and subsequent depression. This study aimed to investigate the incidence and risk factors of depression among RA patients in Taiwan. METHODS: Using Taiwan's National Health Insurance Research Database, we identified 3,698 newly diagnosed RA patients aged 18 years or older, together with 7,396 subjects without RA matched by sex, age and index date, between 2000 and 2004. The incidence of depression and the risk factors among RA cases were evaluated using Cox proportional-hazard regression. RESULTS: The incidence of depression was 1.74-fold greater in the RA cohort than in the non-RA cohort (11.80 versus 6.89 per 1,000 person-years;

Non-specific physiological background effects of acupuncture revealed by proteomic analysis in normal rats

BACKGROUND: The total effects of adequate real acupuncture treatment consist of pathologic-specific and non-specific physiological effects. The latter may be the fundamental component of the therapeutic effects of acupuncture. This study investigated the physiological background effects of acupuncture in normal rats treated with acupuncture. METHODS: Manual acupuncture was performed on normal rats at experienced acupoints, GV14 (Dazhui), BL12 (Fengmen) and BL13 (Feishu), once every other day for two weeks. The proteomic profile of rat lung tissue was examined using 2-DE/MS-based proteomic techniques. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were analyzed for differentially expressed proteins using the WebGestalt toolkit. RESULTS: In total, 25 differentially expressed protein spots were detected in the 2-DE gels. Among these spots, 24 corresponded to 20 unique proteins that were successfully identified using mass spectrometry. Subsequent GO and KEGG pathway analyses demonstrated that these altered proteins were mainly involved in biological processes, such as ‘protein stabilization’, ‘glycolysis/gluconeogenesis’ and ‘response to stimulus’. CONCLUSIONS: Our study indicated the non-specific background effects of acupuncture at acupoints GV14, BL12 and BL13 likely maintained internal homeostasis via regulation of the local stimulus response, energy metabolism, and biomolecule function balance, which may be important contributors to the therapeutic effects of acupuncture