Effect of microwave treatment on the physicochemical properties of potato starch granules

BACKGROUND: The degree of polymerization of amylose starch in potato was so large that the gel was hardness after gelatinization. Therefore, it is one of the most important ways that the microwave treatment was used to change the physicochemical properties of starch gel to make it suitable for the preparation of instant food. RESULTS: The effect of microwave treatment on the physicochemical properties including morphology, crystalline structure, molecular weight distribution and rheological properties of potato starch granules was evaluated by treating time of varying duration (0, 5, 10, 15, 20 s) at 2450 MHz and 750 W. Scanning electron micrographs (SEM) of potato starch granules showed flaws or fractures on the surface after 5 to 10s of microwaving and collapse after 15 to 20 s. Polarized light microscopy (PLM) indicated that microwave treating damaged the crystalline structure of potato starch, such that the birefringence of starch granules gradually decreased after 5 to 10s and even disappeared after microwaving from 15 to 20 s. The molecular weight (Mw) values of potato starch and the proportion of large M(W) fraction were considerably reduced with increasing the microwave treating time from 0 to 20s. The molecular weight slowly decreased over 5 ~ 15 s microwave treating but decreased abruptly at the time of 20s microwave treating. The apparent viscosity decreased as shear rate increased and presented shear-thinning behavior. The magnitudes of the storage modulus (G’) and loss modulus (G”) obtained at each shear rate increased with duration of microwave treating from 0 to 15 s but decreased from 15 to 20 s. CONCLUSIONS: These results demonstrated that the morphology and crystalline structure was damaged by microwave treatment. The high molecular weight of potato starch above 2 × 10(8) Da was so sensitive to the vibrational motion of the polar molecules due to the application microwave energy and broke easily for longer dextran chains. The fracture of starch granules, molecular chains leached from the starch granules and degradation of dextran chains contributing to the development of rheological properties

Karar Ağacı Algoritması Kullanılarak Çin Topraklarındaki Orta Dereceli Okul Öğrencilerine İlişkin Jeo-Uzamsal Düşünme Yeteneğinin Tahmin Edilmesi

Predicting secondary school students' geospatial thinking ability can provide targeted guidance for teachers. To date, few scholars have focused on predicting students’ geospatial thinking ability. In this paper, we address this gap by constructing a prediction model based on the decision tree algorithm, to predict the geospatial thinking ability of secondary school students. A total of 1029 secondary school students were surveyed using the Spatial Thinking Ability Test, the Students' Geography Learning Status Questionnaire, and the Middle Students Motivation Test. Our model indicates that geospatial thinking ability can be predicted by nine factors, in order of importance: academic achievement in geography, geography learning strategy, geography classroom environment, gender, learning initiative, learning goals, extra-curricular time spent learning geography, ego-enhancement drive, and interest in learning geography. The model accuracy is 81.25%. Specifically, our study is the first to predict geospatial thinking ability. It provides a tool for teachers that can help them identify and predict students' geospatial thinking ability, which is conducive to designing better teaching plans and making adjustments to the curriculum.Orta dereceli okul öğrencilerinin jeo-uzamsal düşünme yeteneklerinin tahmin edilmesi öğretmenler için hedefe yönelik rehberlik sağlayabilir. Şimdiye kadar az sayıda bilim insanı, öğrencilerin jeo-uzamsal düşünme yeteneklerinin tahmin edilmesine odaklanmıştır. Bu makalede, orta dereceli okul öğrencilerinin jeo-uzamsal düşünme yeteneklerinin tahmin edilmesi amacıyla karar ağacı algoritmasına dayanan bir tahmin modeli oluşturarak bu boşluğu doldurmayı amaçlıyoruz. Uzamsal Düşünme Yeteneği Testi, Öğrencilerin Coğrafya Öğrenimi Durumu Anketi ve Orta Dereceli Okul Öğrencileri Motivasyon Testi kullanılarak toplam 1029 orta dereceli okul öğrencisine anket uygulanmıştır. Modelimiz, jeo-uzamsal düşünme yeteneğinin dokuz etmenle tahmin edilebileceğine işaret etmektedir. Önem sırasına göre bu etmenler; coğrafya dersindeki akademik başarı, coğrafya öğrenimi stratejisi, coğrafya sınıf ortamı, cinsiyet, öğrenme inisiyatifi, öğrenme hedefleri, coğrafya öğreniminde harcanan müfredat harici zaman, benlik geliştirme dürtüsü ve coğrafya öğrenimine ilgi şeklindedir. Model doğruluk oranı %81,25’tir. Özellikle, çalışmamız jeo-uzamsal düşünme yeteneğinin tahmin edilmesine yönelik ilk çalışmadır. Öğretmenlere öğrencilerin jeo-uzamsal düşünme yeteneklerini saptamalarına ve tahmin etmelerine yardımcı olabilecek bir araç sunan çalışmamız böylelikle daha iyi eğitim planları hazırlanmasında ve müfredatta düzenlemeler yapılmasında fayda sağlayacaktır

Disentangled Contrastive Learning for Learning Robust Textual Representations

Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs. Intuitively, the representations should be similar in the feature space with subtle input permutations, while large variations occur with different meanings. This motivates us to investigate the learning of robust textual representation in a contrastive manner. However, it is non-trivial to obtain opposing semantic instances for textual samples. In this study, we propose a disentangled contrastive learning method that separately optimizes the uniformity and alignment of representations without negative sampling. Specifically, we introduce the concept of momentum representation consistency to align features and leverage power normalization while conforming the uniformity. Our experimental results for the NLP benchmarks demonstrate that our approach can obtain better results compared with the baselines, as well as achieve promising improvements with invariance tests and adversarial attacks. The code is available in https://github.com/zjunlp/DCL.Comment: Work in progres

PromptKG: A Prompt Learning Framework for Knowledge Graph Representation Learning and Application

Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. KG representation models should consider graph structures and text semantics, but no comprehensive open-sourced framework is mainly designed for KG regarding informative text description. In this paper, we present PromptKG, a prompt learning framework for KG representation learning and application that equips the cutting-edge text-based methods, integrates a new prompt learning model and supports various tasks (e.g., knowledge graph completion, question answering, recommendation, and knowledge probing). PromptKG is publicly open-sourced at https://github.com/zjunlp/PromptKG with long-term technical support.Comment: Work in progres

From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer

Knowledge graph completion aims to address the problem of extending a KG with missing triples. In this paper, we provide an approach GenKGC, which converts knowledge graph completion to sequence-to-sequence generation task with the pre-trained language model. We further introduce relation-guided demonstration and entity-aware hierarchical decoding for better representation learning and fast inference. Experimental results on three datasets show that our approach can obtain better or comparable performance than baselines and achieve faster inference speed compared with previous methods with pre-trained language models. We also release a new large-scale Chinese knowledge graph dataset AliopenKG500 for research purpose. Code and datasets are available in https://github.com/zjunlp/PromptKG/tree/main/GenKGC.Comment: Accepted by WWW 2022 Poste

Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction

Recent neural-based relation extraction approaches, though achieving promising improvement on benchmark datasets, have reported their vulnerability towards adversarial attacks. Thus far, efforts mostly focused on generating adversarial samples or defending adversarial attacks, but little is known about the difference between normal and adversarial samples. In this work, we take the first step to leverage the salience-based method to analyze those adversarial samples. We observe that salience tokens have a direct correlation with adversarial perturbations. We further find the adversarial perturbations are either those tokens not existing in the training set or superficial cues associated with relation labels. To some extent, our approach unveils the characters against adversarial samples. We release an open-source testbed, "DiagnoseAdv" in https://github.com/zjunlp/DiagnoseAdv.Comment: IJCKG 202

Prediction and Identification of Potential Immunodominant Epitopes in Glycoproteins B, C, E, G, and I of Herpes Simplex Virus Type 2

Twenty B candidate epitopes of glycoproteins B (gB2), C (gC2), E (gE2), G (gG2), and I (gI2) of herpes simplex virus type 2 (HSV-2) were predicted using DNAstar, Biosun, and Antheprot methods combined with the polynomial method. Subsequently, the biological functions of the peptides were tested via experiments in vitro. Among the 20 epitope peptides, 17 could react with the antisera to the corresponding parent proteins in the EIA tests. In particular, five peptides, namely, gB2466–473 (EQDRKPRN), gC2216–223 (GRTDRPSA), gE2483–491 (DPPERPDSP), gG2572–579 (EPPDDDDS), and gI2286-295 (CRRRYRRPRG) had strong reaction with the antisera. All conjugates of the five peptides with the carrier protein BSA could stimulate mice into producing antibodies. The antisera to these peptides reacted strongly with the corresponding parent glycoproteins during the Western Blot tests, and the peptides reacted strongly with the antibodies against the parent glycoproteins during the EIA tests. The antisera against the five peptides could neutralize HSV-2 infection in vitro, which has not been reported until now. These results suggest that the immunodominant epitopes screened using software algorithms may be used for virus diagnosis and vaccine design against HSV-2