Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural Characterization and Cytotoxic Activity Evaluation of Ulvan Polysaccharides Extracted from the Green Algae Ulva papenfussii

Ulvan, a sulfated heteropolysaccharide with structural and functional properties of interest for various uses, was extracted from the green seaweed Ulva papenfussii. U. papenfussii is an unexplored Ulva species found in the South China Sea along the central coast of Vietnam. Based on dry weight, the ulvan yield was ~15% (w/w) and the ulvan had a sulfate content of 13.4 wt%. The compositional constitution encompassed L-Rhamnose (Rhap), D-Xylose (Xylp), D-Glucuronic acid (GlcAp), L-Iduronic acid (IdoAp), D-Galactose (Galp), and D-Glucose (Glcp) with a molar ratio of 1:0.19:0.35:0.52:0.05:0.11, respectively. The structure of ulvan was determined using High-Performance Liquid Chromatography (HPLC), Fourier Transform Infrared Spectroscopy (FT-IR), and Nuclear Magnetic Resonance spectroscopy (NMR) methods. The results showed that the extracted ulvan comprised a mixture of two different structural forms, namely (“A3s”) with the repeating disaccharide [→4)-β-D-GlcAp-(1→4)-α-L-Rhap 3S-(1→]n, and (“B3s”) with the repeating disaccharide [→4)-α-L-IdoAp-(1→4)-α-L-Rhap 3S(1→]n. The relative abundance of A3s, and B3s was 1:1.5, respectively. The potential anticarcinogenic attributes of ulvan were evaluated against a trilogy of human cancer cell lineages. Concomitantly, Quantitative Structure–Activity Relationship (QSAR) modeling was also conducted to predict potential adverse reactions stemming from pharmacological interactions. The ulvan showed significant antitumor growth activity against hepatocellular carcinoma (IC50 ≈ 90 µg/mL), human breast cancer cells (IC50 ≈ 85 µg/mL), and cervical cancer cells (IC50 ≈ 67 µg/mL). The QSAR models demonstrated acceptable predictive power, and seven toxicity indications confirmed the safety of ulvan, warranting its candidacy for further in vivo testing and applications as a biologically active pharmaceutical source for human disease treatment.
Molecular diagnostic criteria of myeloproliferative neoplasms

ABSTRACT Introduction Myeloproliferative neoplasms (MPN) are a heterogeneous group of clonal hematopoietic stem cell neoplasms characterized by the driver mutations JAK2, CALR, and MPL. These mutations cause constitutive activation of JAK-STAT signaling, which is central to pathogenesis of MPNs. Next-generation sequencing has further expanded the molecular landscape allowing for improved diagnostics, prognostication, and targeted therapy. Areas covered This review aims to address current understanding of the molecular diagnosis of MPN not only through improved awareness of the driver mutations but also the disease modifying mutations. In addition, other genetic factors such as clonal hematopoiesis of indeterminate potential (CHIP), order of mutation, and mutation co-occurrence are discussed and how these factors influence disease initiation and ultimately progression. How this molecular information is incorporated into risk stratification models allowing for earlier intervention and targeted therapy in the future will be addressed further. Expert opinion The genomic landscape of the MPN has evolved in the last 15 years with integration of next-generation sequencing becoming the gold standard of MPN management. Although diagnostics and prognostication have become more personalized, additional studies are required to translate these molecular findings into targeted therapy therefore improving patient outcomes.
Development and Application of Oil Spill Model for Singapore Coastal Waters

This paper presents the development and application of a three-dimensional oil spill model for predicting the movement and fate of an oil slick in the coastal waters of Singapore. In the model, the oil slick is divided into a number of small elements or grids for simulating of the oil processes of spreading, advection, turbulent diffusion, evaporation, dissolution, vertical dispersion, shoreline deposition and adsorption by sediment. This model is capable of predicting the horizontal movement of surface oil slick, the mass balance of oil spill and the oil particle concentration distribution in water body. Satellite images and field observations of oil slicks on the surface in the Singapore Straits, and measurements of the vertical concentration of oil particles in flume are used to validate the newly developed model. Compared with the observations, the numerical results of the oil spill model show good conformity.
Reproducibility and predictive value of three grading systems for intraoperative adverse events in a cohort of abdominal surgery

Introduction: Intraoperative adverse events (iAEs) are increasingly recognized for their impact on patient outcomes. The Kaafarani classification and Surgical Apgar Score (SAS) were developed to assess the intraoperative course; however, both have their drawbacks. ClassIntra was validated for iAEs of any origin. This study compares the Kaafarani and SAS to ClassIntra considering predictive value and interrater reliability in a cohort of abdominal surgery to support implementation of a classification in clinical practice. Methods: The authors made use of the LAParotomy or LAParoscopy and ADhesiolysis (LAPAD) study database of elective abdominal surgery. Detailed descriptions on iAEs were collected in real-time by a researcher. For the current research aim, all iAEs were graded according ClassIntra, Kaafarani, and SAS (score ≤4). The predictive value was assessed using univariable and multivariable linear regression and the area under the receiver operating curve (AUROC). Two teams graded ClassIntra and Kaafarani to assess the interrater reliability using Cohen’s Kappa. Results: A total of 755 surgeries were included, in which 335 (44%) iAEs were graded according to ClassIntra, 228 (30%) to Kaafarani, and 130 (20%) to SAS. All classifications were significantly correlated to postoperative complications, with an AUROC of 0.67 (95% CI: 0.62–0.72), 0.64 (0.59–0.70), and 0.71 (0.56–0.76), respectively. For the secondary endpoint, the interrater reliability of ClassIntra with κ 0.87 (95% CI: 0.84–0.90) and Kaafarani 0.90 (95% CI: 0.87–0.93) was both strong. Conclusion: ClassIntra, Kaafarani, and SAS can be used for reporting of iAEs in abdominal surgery with good predictive value for postoperative complications, with strong reliability. ClassIntra, compared with Kaafarani and SAS, included the most iAEs and has the most comprehensive definition suitable for uniform reporting of iAEs in clinical practice and research.
SARS-CoV-2 subgenomic RNA: formation process and rapid molecular diagnostic methods

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused coronavirus disease-2019 (COVID-19) is spreading worldwide and posing enormous losses to human health and socio-economic. Due to the limitations of medical and health conditions, it is still a huge challenge to develop appropriate discharge standards for patients with COVID-19 and to use medical resources in a timely and effective manner. Similar to other coronaviruses, SARS-CoV-2 has a very complex discontinuous transcription process to generate subgenomic RNA (sgRNA). Some studies support that sgRNA of SARS-CoV-2 can only exist when the virus is active and is an indicator of virus replication. The results of sgRNA detection in patients can be used to evaluate the condition of hospitalized patients, which is expected to save medical resources, especially personal protective equipment. There have been numerous investigations using different methods, especially molecular methods to detect sgRNA. Here, we introduce the process of SARS-CoV-2 sgRNA formation and the commonly used molecular diagnostic methods to bring a new idea for clinical detection in the future.
Wave climate reduction for medium term process based morphodynamic simulations: With application to the Durban coast

In this thesis 6 wave climate reduction methods are described and evaluated. Of these methods, 3 are commonly used in literature and 3 are machine learning methods that are adjusted so that they can be used for the reduction of a wave climate. The evaluation is executed with the use of a Delft3D model of the coast of Durban, South Africa.
CropGS-Hub: a comprehensive database of genotype and phenotype resources for genomic prediction in major crops

Abstract The explosive amount of multi-omics data has brought a paradigm shift both in academic research and further application in life science. However, managing and reusing the growing resources of genomic and phenotype data points presents considerable challenges for the research community. There is an urgent need for an integrated database that combines genome-wide association studies (GWAS) with genomic selection (GS). Here, we present CropGS-Hub, a comprehensive database comprising genotype, phenotype, and GWAS signals, as well as a one-stop platform with built-in algorithms for genomic prediction and crossing design. This database encompasses a comprehensive collection of over 224 billion genotype data and 434 thousand phenotype data generated from >30 000 individuals in 14 representative populations belonging to 7 major crop species. Moreover, the platform implemented three complete functional genomic selection related modules including phenotype prediction, user model training and crossing design, as well as a fast SNP genotyper plugin-in called SNPGT specifically built for CropGS-Hub, aiming to assist crop scientists and breeders without necessitating coding skills. CropGS-Hub can be accessed at https://iagr.genomics. cn/CropGS/.
(De)constructing Binarism in Journalism: Automatic Antonym Detection in Dutch Newspaper Articles

Binary oppositions, since their introduction by Claude Levi-Strauss and other structuralists in the seven-ties, are under pressure, especially because they legitimatize societal power structures. Deconstruction of binary oppositions such as man/woman, black/white, le昀琀/right, and rich/poor is therefore increasingly encouraged. The question arises of what kind of e昀昀ect the debate about binary oppositions has had on its linguistic use. We have therefore detected antonyms in a corpus of Dutch newspaper articles from the period 1990-2020, to study the development of binarism in journalism. Our method consists of two parts: the use of a good-old lexicon, and the 昀椀netuning of a BERT model for antonym detection. In this paper, we not only present our results regarding the (de)construction of binary oppositions in Dutch journalism, but we also re昀氀ect on the two methodological stages and discuss their gain.
The Past is a Foreign Place: Improving Toponym Linking for Historical Newspapers

In this paper, we examine the application of toponym linking to digitised historical newspapers. These collections constitute the largest trove of historical text data available to researchers in the humanities. They contain varied, 昀椀ne-grained information about the past, anchored in a speci昀椀c place and time. Place names (or toponyms) are common entry points for starting exploring these collections. In this paper, we introduce a new tool for toponym linking and resolution, T-Res , a modular, 昀氀exible, and open-source pipeline, which is built on top of robust state-of-the-art approaches. We present a comprehensive step-by-step examination of this task in English, and conclude with a case study in which we show how toponym linking enables historical research in the digitised press.
"The library is open!": Open data and an open API for the HathiTrust Digital Library

This paper describes the history, policy, semantics, and uses of the HathiTrust Research Center Extracted Features dataset, an open-access representation of the 17+ million volume HathiTrust Digital Library, including a major current effort to extend computational access in a variety of more flexible and easily implemented ways, including a modern API supporting customizable visualizations and analyses
Putting Dutchcoref to the Test: Character Detection and Gender Dynamics in Contemporary Dutch Novels

Although coreference resolution is a necessary step for a wide range of automated narratological analyses, most of the systems performing this task leave much to be desired in terms of either accuracy or their practical application in literary studies. While there are coreference resolution systems that demonstrate good performance on annotated fragments of novels, evaluations typically do not consider performance on the full texts of novels. In order to optimize its output for concrete use in Dutch literary studies, we are in the process of evaluating and 昀椀netuning Dutchcoref. Dutchcoref is an implementation of the Stanford Multi-Pass Sieve Coreference System for Dutch. Using a “silver standard” of annotated data on 2,137 characters in 170 contemporary Dutch novels, we assess the extent to which Dutchcoref is able to identify the most prominent characters and their gender. Furthermore, we explore the usability of the system by exploring a speci昀椀c narratological question about the gender distribution of the characters. We 昀椀nd that Dutchcoref is highly accurate in detecting noun phrases, proper names, and pronouns referring to characters, and that it is accurate in establishing their gender. However, the ability to cluster co-references together in a character pro昀椀le, which we compare to BookNLP’s performance in this respect, is still sub-optimal and deteriorates with text length. We show that, notwithstanding current state of development, Dutchcoref can be applied for meaningful literary analysis, and we outline future prospects.
Operationalizing and Measuring Conflict in German Novels

In this contribution we explore ways of detecting con昀氀ict representation in literary texts. First, we operationalize Glasl’s concept of social con昀氀ict for manual annotation and second, we adapt a word embedding-based sentiment analysis ( SentiArt ) for the attribution of con昀氀ict values based on two scalar con昀氀ict operationalizations. By translating the values of the latter approaches into binary labels, we compare the embedding approaches with the manual annotation. Though correlation between the approaches is low, the paper demonstrates possible approaches to con昀氀ict analysis in literary texts and outlines directions for future research.
Title : Bayesian texture classification and retrieval based on multiscale feature vector Year of publication : 2011

This paper proposes a supervised multiscale Bayesian texture classifier. The classifier exploits the dual-tree complex wavelet transform (DT-CWT) to obtain complex-valued multiscale representations of training texture samples for each texture class. The high-pass subbands of DT-CWT decomposition of a texture image are used to form a multiscale feature vector representing magnitude and phase features. For computational efficiency, the dimensionality of feature vectors is reduced using principal component analysis (PCA). The class conditional probability density function of low-dimensional feature vectors for each texture class is then estimated by using Parzen-window estimate with identical Gaussian kernels and is used to represent the texture class. A query texture image is classified as the corresponding texture class with the highest a posteriori probability according to a Bayesian inferencing. The superior performance and robustness of the proposed classifier is demonstrated for classifying texture images from image databases. The proposed multiscale texture feature vector extracted from both magnitude and phase of DT-CWT subbands of a query image is also shown to be effective for texture retrieval.
Reduced-dimension multiuser detection

We present a new framework for reduced-dimension multiuser detection (RD-MUD) that trades off complexity for bit-error-rate (BER) performance. This approach can significantly reduce the number of matched filter branches required by classic multiuser detection designs. We show that the RD-MUD can perform similarly to the linear MUD detector when M is sufficiently large relative to N and K, where N and K are the number of total and active users, respectively. We also study the inherent RD-MUD tradeoff between complexity (the number of correlating signals) and BER performance. This leads to a new notion of approximate sufficient statistics, whereby sufficient statistics are approximated to reduce complexity at the expense of some BER performance loss.1
Enhancing HTR of Historical Texts through Scholarly Editions: A Case Study from an Ancient Collation of the Hebrew Bible

Printed critical editions of literary texts are a largely neglected source of knowledge in computational humanities. However, under certain conditions, they hold signi昀椀cant potential for multifaceted exploration: First, through Optical Character Recognition (OCR) of the text and its apparatus, coupled with intelligent parsing of the variant readings, it becomes possible to reconstruct comprehensive manuscript collations, which can prove invaluable for a variety of investigations, including phylogenetic analyses, redaction history studies, linguistic inquiries, and more. Second, by aligning the printed edition with manuscript images, a substantial amount of Handwritten Text Recognition (HTR) ground truth can be generated. This serves as valuable material for paleography, layout analysis, as well as for assessing the quality of the collation criteria adopted by the editor. The present paper focuses on the challenges mastered in the processes of the OCR, the apparatus parsing, the text reconstruction, and the alignment with the manuscript images, taking as a case study the edition of the Hebrew Bible published by Kennicott in the late eighteenth century.
GAMIFICATION AND COPY TRADING IN FINANCE: AN EXPERIMENT 1

In recent years, gamification techniques have increasingly been adopted by online trading platforms to engage and motivate individual investors and attract new customers. While gamification can be an effective educational tool for inexperienced and financially underinformed investors, it has the potential to shift their focus away from rational financial decision-making towards emotional and social factors. This paper presents a laboratory experiment that aims to achieve two objectives: first, to examine the impact of gamification on financial decision-making, and second, to investigate the individual determinants of copy trading. The experimental task involves a standard portfolio allocation task, which is adapted from the multiplicative investment game proposed by Langer and Weber (2008) and based on the original framework by Gneezy and Potters (1997). We find that gamification stimuli increase the risk taking by subjects only when these stimuli are achievement badges on risk taking, no effect being observed for hedonic stimuli such as confetti. On the contrary, risk-taking decreases when gamification is achieved through achievement badges on the safe asset allocations. These findings highlight the nudging power of achievement badges. We also find that the copy trading setting creates an environment leading to more risk-taking behavior. Besides, being a female has a strong and positive effect on the intensity to copy.
Exploring University Students’ Justiﬁcations for Making Metacognitive Judgments of Learning

: The accuracy of students’ judgments has important implications for their learning and performance in educational settings. However, little is known about how students make these judgments. This study explored university students’ justiﬁcations for making their judgments of learning in a developmental psychology course. Two independent samples were involved, including a total number of 115 senior sport students. Participants responded to a knowledge test and provided their judgments at the local (Sample 1) or at the global level (Sample 2) and then provided their justiﬁcations for making these judgments. Students’ justiﬁcations for making their judgments were classiﬁed in ten categories, including the study of the learning materials, the conﬁdence for answering (or not) correctly, the memory, the general knowledge, the knowledge of the answer and a general reference to common sense, experience, lectures, and judgment. Variations in the frequency of these justiﬁcations were found across the local and the global level, low and high accurate students, and low and high performers. These results are discussed regarding their theoretical and practical implications for undergraduate students’ learning
ABCB1 regulates myeloid-derived suppressor cells-related immune factors in breast cancer

Purpose Resistance to standard chemotherapy is a critical problem for breast cancer patients. The ATP-binding cassette (ABC) superfamily transporters actively pump out drugs and play an important role in chemoresistance. ABCB1 (ABC subfamily B, member 1, also named as multidrug resistance protein 1, MDR1) and suppressive myeloid-derived suppressor cells (MDSCs) potentially involve in chemoresistance of breast cancer. The relationship between ABCB1 and MDSC is less studied. Methods Microarray or RNA sequencing data was obtained from The Cancer Genome Atlas Breast Invasive Carcinoma in Genomic Data Commons Data Portal (GDC TCGA-BRCA) and GEO database. Expression of ABCB1 and MDSC-related genes was compared. Patient-derived xenograft (PDX) from HER2-enriced breast cancer was established to investigate the association of ABCB1 and MDSC-related genes in breast cancer. Results Expression of ABCB1 was increased in doxorubicin-selected MCF-7/ADR cells. High expression of ABCB1 mRNA was correlated with lymph node metastasis and worse overall survival of breast cancer patients. ABCB1 was positively correlated with IL6, CSF1, CSF3, or PTGS2 and negatively correlated with VEGF. PDX model from HER2-enriched stage IIA breast cancer was established. Treatment with doxorubicin or paclitaxel suppressed growth of P2 tumors and expression of ABCB1. Expression of IL6, CSF1, CSF3, PTGS2 was suppressed by paclitaxel, but not by doxorubicin. Intrasplenic MDSCs, including CD11b + Ly6G + and CD11b + Ly6C + cells, were higher than intratumor MDSCs in PDX-carrying nude mice. Clinically, the patient developed cancer recurrence after adjuvant chemotherapy with doxorubicin-based regimen and was well-controlled after paclitaxel-trastuzumab combined therapy. Conclusions
Journal of Childhood

The research will discuss child participation in the decision-making process, the leadership led by children is essential to be discussed because children often seen as a human who have not capacity to make decisions or always influenced by an adult. Through this research we will seen how children take the decisions using participation in decision-making (PDM) which are upward and downward information flow. The novelty of this research is how children are given the authority to take the decisions. This research are using qualitative research by having FGD with the child members in children's forum and secondary data from literature reviews. As a result of this research, children using upward information flow to compile children's voices and downward information flow as a part of the cognitive model in decision-making process. The participation of children will not be meaningful if an adult that accompany the children was not trained and professional adult as it was mentioned in this research on how adult able to hinder children voice because of her lack of capacity. In implementing meaningful participation in the decision-making process, it is important for children to have the right access of the right information.