Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Entity Linking through Semantic Reinforced Entity Embeddings

Entity embeddings, which represent different aspects of each entity with a single vector like word embeddings, are a key component of neural entity linking models. Existing entity embeddings are learned from canonical Wikipedia articles and local contexts surrounding target entities. Such entity embeddings are effective, but too distinctive for linking models to learn contextual commonality. We propose a simple yet effective method, FGS2EE, to inject fine-grained semantic information into entity embeddings to reduce the distinctiveness and facilitate the learning of contextual commonality. FGS2EE first uses the embeddings of semantic type words to generate semantic embeddings, and then combines them with existing entity embeddings through linear aggregation. Extensive experiments show the effectiveness of such embeddings. Based on our entity embeddings, we achieved new sate-of-the-art performance on entity linking.
Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings

Knowledge Graphs (KG) are multi-relational graphs consisting of entities as nodes and relations among them as typed edges. Goal of the Question Answering over KG (KGQA) task is to answer natural language queries posed over the KG. Multi-hop KGQA requires reasoning over multiple edges of the KG to arrive at the right answer. KGs are often incomplete with many missing links, posing additional challenges for KGQA, especially for multi-hop KGQA. Recent research on multi-hop KGQA has attempted to handle KG sparsity using relevant external text, which isn’t always readily available. In a separate line of research, KG embedding methods have been proposed to reduce KG sparsity by performing missing link prediction. Such KG embedding methods, even though highly relevant, have not been explored for multi-hop KGQA so far. We fill this gap in this paper and propose EmbedKGQA. EmbedKGQA is particularly effective in performing multi-hop KGQA over sparse KGs. EmbedKGQA also relaxes the requirement of answer selection from a pre-specified neighborhood, a sub-optimal constraint enforced by previous multi-hop KGQA methods. Through extensive experiments on multiple benchmark datasets, we demonstrate EmbedKGQA’s effectiveness over other state-of-the-art baselines.
Türkiye Yaprakbitleri (Homoptera.'Aphididae) III. Aphidinae: Macropsophini (III, Kısım)

Türkiye yaprakbitleri faunistik listesinin bu üçüncü kısmında Macropsophini tri— busundan 13 cinse ait 38 tür yer almaktadır. Bu makalede yer alan cinslerden NeoamphorophoraMsson ve Rhopafos/phon/nusBsker ve Myzus (Nectarosiphon)iigusfry(Mosley), Neoamphorophora sp. , Rhopalosiphoninus iatysiphon {Ds>j\dsor\), Uroleucon /nui/coia {H\\\e Ris Lambers) ve U. (Belochilum) inulae(Ferrari) türleri yurdumuz için yeni kayıttır. Herbir türün Dünya'da ve yurdumuzdayayıl ışı , konukçu bitkileri yanında ekonomik zarar durumları da belirtilmiştir.
Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization

State-of-the-art argument mining studies have advanced the techniques for predicting argument structures. However, the technology for capturing non-tree-structured arguments is still in its infancy. In this paper, we focus on non-tree argument mining with a neural model. We jointly predict proposition types and edges between propositions. Our proposed model incorporates (i) task-specific parameterization (TSP) that effectively encodes a sequence of propositions and (ii) a proposition-level biaffine attention (PLBA) that can predict a non-tree argument consisting of edges. Experimental results show that both TSP and PLBA boost edge prediction performance compared to baselines.
Potential Application of Protamine for Antimicrobial Biomaterials in Bone Tissue Engineering

Bacterial infection of biomaterials is a serious problem in the field of medical devices. It is urgently necessary to develop new biomaterials with bactericidal activity. Antimicrobial peptides and proteins (AMPs), alternative antibacterial agents, are expected to overcome the bacterial resistance. The aim of this study was to develop a new intelligent material in bone tissue engineering based on protamine-loaded hydroxyapatite (protamine/HAp) that uses AMPs rather than antibiotics. It was found that the adsorption of protamine to HAp followed the Langmuir adsorption model and was due to electrostatic and/or hydrophobic interactions. In vitro bacterial adhesion and growth on protamine/HAp was inhibited in a protamine dose-dependent manner. Adherent bacteria exhibited an aberrant morphology for high dosages of protamine/HAp, resulting in the formation of large aggregates and disintegration of the membrane. The released protamine from protamine/HAp also prevented the growth of planktonic bacteria in vitro. However, a high dosage of protamine from powders at loading concentrations over 1000 μg·mL−1 induced a cytotoxic effect in vitro, although those exhibited no apparent cytotoxicity in vivo. These data revealed that protamine/HAp (less than 1000 μg·mL−1) had both antimicrobial activity and biocompatibility and can be applied for bone substitutes in orthopedic fields.
Congestion Control and Traffic Differentiation for Heterogeneous 6TiSCH Networks in IIoT

The Routing Protocol for Low power and lossy networks (RPL) has been introduced as the de-facto routing protocol for the Industrial Internet of Things (IIoT). In heavy load scenarios, particular parent nodes are likely prone to congestion, which in turn degrades the network performance, in terms of packet delivery and delay. Moreover, there is no explicit strategy in RPL to prioritize the transmission of different traffic types in heterogeneous 6TiSCH networks, each according to its criticality. In this paper, we address the aforementioned issues by introducing a congestion control and service differentiation strategies to support heterogeneous 6TiSCH networks in IIoT applications. First, we introduce a congestion control mechanism to achieve load balancing under heavy traffic scenarios. The congestion is detected through monitoring and sharing the status of the queue backlog among neighbor nodes. We define a new routing metric that considers the queue occupancy when selecting the new parent node in congestion situations. In addition, we design a multi-queue model to provide prioritized data transmission for critical data over the non-critical ones. Each traffic type is placed in a separate queue and scheduled for transmission based on the assigned queue priority, where critical data are always transmitted first. The performance of the proposed work is evaluated through extensive simulations and compared with existing work to demonstrate its effectiveness. The results show that our proposal achieves improved packet delivery and low queue losses under heavy load scenarios, as well as improved delay performance of critical traffic.
Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness

Generative dialogue systems tend to produce generic responses, which often leads to boring conversations. For alleviating this issue, Recent studies proposed to retrieve and introduce knowledge facts from knowledge graphs. While this paradigm works to a certain extent, it usually retrieves knowledge facts only based on the entity word itself, without considering the specific dialogue context. Thus, the introduction of the context-irrelevant knowledge facts can impact the quality of generations. To this end, this paper proposes a novel commonsense knowledge-aware dialogue generation model, ConKADI. We design a Felicitous Fact mechanism to help the model focus on the knowledge facts that are highly relevant to the context; furthermore, two techniques, Context-Knowledge Fusion and Flexible Mode Fusion are proposed to facilitate the integration of the knowledge in the ConKADI. We collect and build a large-scale Chinese dataset aligned with the commonsense knowledge for dialogue generation. Extensive evaluations over both an open-released English dataset and our Chinese dataset demonstrate that our approach ConKADI outperforms the state-of-the-art approach CCM, in most experiments.
Phylogeography of the Russell's viper (Daboia russelii) complex in relation to variation in the colour pattern and symptoms of envenoming

The Russell’s viper complex has a patchy (relict) distribution over large areas of Asia from Pakistan to Taiwan and the Lesser Sunda islands. In many areas it is the primary cause of snakebite mortality, and hence a serious medical problem. A multigene mitochondrial gene tree, supported by multivariate morphometry and basic colour pattern, suggests a primary split in the organismal phylogeny giving distinct, diagnosable, eastern and western forms that we recognize as full species: Daboia russelii (west of the Bay of Bengal) and Daboia siamensis (east of the Bay of Bengal). The clinical symptoms of human envenoming show marked geographic variations that are broadly unrelated to the phylogeny. The molecular phylogeny, together with current distribution and fossil record, suggests cycles of extreme expansion and contraction for this complex. Further studies on venom variation, diet and local population phylogeny are required, but the local and regional variation in symptoms may be the result of fixation of venom genes during cyclical bottlenecks which could explain the haphazard, non-phylogenetic pattern of symptoms of envenomation in this species complex.
Integrin-Targeting Dye-Doped PEG-Shell/Silica-Core Nanoparticles Mimicking the Proapoptotic Smac/DIABLO Protein

Cancer cells demonstrate elevated expression levels of the inhibitor of apoptosis proteins (IAPs), contributing to tumor cell survival, disease progression, chemo-resistance, and poor prognosis. Smac/DIABLO is a mitochondrial protein that promotes apoptosis by neutralizing members of the IAP family. Herein, we describe the preparation and in vitro validation of a synthetic mimic of Smac/DIABLO, based on fluorescent polyethylene glycol (PEG)-coated silica-core nanoparticles (NPs) carrying a Smac/DIABLO-derived pro-apoptotic peptide and a tumor-homing integrin peptide ligand. At low μM concentration, the NPs showed significant toxicity towards A549, U373, and HeLa cancer cells and modest toxicity towards other integrin-expressing cells, correlated with integrin-mediated cell uptake and consequent highly increased levels of apoptotic activity, without perturbing cells not expressing the α5 integrin subunit.
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality

Previous studies in multimodal sentiment analysis have used limited datasets, which only contain unified multimodal annotations. However, the unified annotations do not always reflect the independent sentiment of single modalities and limit the model to capture the difference between modalities. In this paper, we introduce a Chinese single- and multi-modal sentiment analysis dataset, CH-SIMS, which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations. It allows researchers to study the interaction between modalities or use independent unimodal annotations for unimodal sentiment analysis.Furthermore, we propose a multi-task learning framework based on late fusion as the baseline. Extensive experiments on the CH-SIMS show that our methods achieve state-of-the-art performance and learn more distinctive unimodal representations. The full dataset and codes are available for use at https://github.com/thuiar/MMSA.
Long‐term impact of new‐onset atrial fibrillation complicating acute myocardial infarction on heart failure

New‐onset atrial fibrillation (NOAF) complicating acute myocardial infarction (AMI) has been associated with poor survival, but the clinical implication of NOAF on heart failure (HF) is still not well characterized. We aimed to investigate the relationship between NOAF complicating AMI and HF hospitalization.
Chemical composition and antifungal activity of essential oil of Salvia sclarea L. from Bulgaria against clinical isolates of Candida species

The essential oil of Salvia sclarea L. , growing in Bulgaria, was analyzed by gas chromatography – mass spectrometry. A total of 52 different compounds were identified, representing 98.25% of total oil content. Linalyl acetate (56.88%) and linalool (20.75%) were determined as major essential oil constituents, followed by germacrene D (5.08%) and β-cariophyllene (3.41%). Antifungal activities of clary sage essential oil and major compounds linalyl acetate and linalool against 30 clinical isolates, belonging to species Candida albicans, Candida tropicalis, Candida krusei, Candida glabrata and Candida parapsilosis were evaluated. Essential oil characterized with stronger anticandidial activity in comparison with pure compounds.
Spaced education and student learning: Results from a medical school

It is critical for medical students to retain and apply their knowledge of basic sciences to become competent prescribing physicians. Poor long‐term retention is a pervasive concern. We investigated the impact of ‘spaced education’ teaching modules to improve knowledge retention in medical students on a topic of public health: vitamin D and its role in health and disease.
Morphological and metabolic profiling of a tropical‐adapted potato association panel subjected to water recovery treatment reveals new insights into plant vigor

SUMMARY Potato (Solanum tuberosum L.) is one of the world's most important crops, but it is facing major challenges due to climatic changes. To investigate the effects of intermittent drought on the natural variability of plant morphology and tuber metabolism in a novel potato association panel comprising 258 varieties we performed an augmented block design field study under normal irrigation and under water‐deficit and recovery conditions in Ica, Peru. All potato genotypes were profiled for 45 morphological traits and 42 central metabolites via nuclear magnetic resonance. Statistical tests and norm of reaction analysis revealed that the observed variations were trait specific, that is, genotypic versus environmental. Principal component analysis showed a separation of samples as a result of conditional changes. To explore the relational ties between morphological traits and metabolites, correlation‐based network analysis was employed, constructing one network for normal irrigation and one network for water‐recovery samples. Community detection and difference network analysis highlighted the differences between the two networks, revealing a significant correlational link between fumarate and plant vigor. A genome‐wide association study was performed for each metabolic trait. Eleven single nucleotide polymorphism (SNP) markers were associated with fumarate. Gene Ontology analysis of quantitative trait loci regions associated with fumarate revealed an enrichment of genes regulating metabolic processes. Three of the 11 SNPs were located within genes, coding for a protein of unknown function, a RING domain protein and a zinc finger protein ZAT2. Our findings have important implications for future potato breeding regimes, especially in countries suffering from climate change.
Drug use by men admitted to a psychiatric hospital*

Objective: to assess risk related to drug use in men admitted to a psychiatric hospital and to identify associations with sociodemographic, socioeconomic variables, and risk conditions. Method: a cross-sectional study with the application of a screening test in 209 participants hospitalized for mental and behavioral disorders due to the use of psychoactive substances. Statistical analysis was performed using descriptive statistics and adjustment of a binary logistic regression model for moderate or high risk of drug use. The odds ratio measured the strength of association. Results: high use in life was observed, with alcohol and tobacco experimentation in adolescence. A high prevalence of related risk was observed for alcohol, tobacco, smoked and inhaled cocaine, and marijuana. Moderate and elevated risks were found for tobacco (22.5% and 62.5%, respectively), alcohol (13.5% and 73%), marijuana (16% and 32.5%), smoked cocaine (3% and 41%) and inhaled cocaine (9% and 19.5%). Conclusion: the results showed high use in life, with an age of early experimentation. Tobacco and alcohol are the main drugs used by hospitalized men.
Novel synthesis of thiolated gold nanoclusters induced by lanthanides for ultrasensitive and luminescent detection of the potential anthrax spores biomarker.

In this study, we reported facile, one-pot and "green" synthesis of glutathione-protected gold nanoclusters (GSH@AuNCs) initiated by samarium (Sm3+) lanthanides for the first time. Sm3+ lanthanides can more efficiently induced the formation of GSH@AuNCs with significantly enhanced luminescence than other lanthanides or heavy metal ions (Cd2+, Pb2+) did. Using this strategy, a detection for Sm3+ was made with the linearity range of (10.0 µM-100.0 µM) and the limit of detection (LOD) of 0.5 µM. The Sm3+-based GSH@AuNCs were characterized by eco-friendly, photostability and low-cost synthesis with low biological toxicity and had a high potential in the application for biosensing and bioimaging. They were successfully employed in the detection of dipicolinic acid (DPA), a well-reported biomarker for sensing potential infection by strongly hazardous anthrax spores. A good linear response was obtained for DPA detection ranging from 1.0 µM to 120.0 µM with low LOD of 0.1 µM, which was much lower (600 times) than the infectious dosage of anthrax spores (6 × 10-5 M). The detection was due to the strong binding affinity and strong chelation capability of DPA to Sm3+ lanthanides, which caused the dissociation of the aggregates with obvious decrease or even turning-off effect of their luminescence.
Growth responses and mortality of Scots pine (Pinus sylvestris L.) after a pine sawfly outbreak

The focus of this study has been to provide estimates of growth losses and tree mortality after an outbreak by the large pine sawfly Diprion pini (L.) and preliminary estimates of the economic value of losses. The study was carried out in SW Finland. Increment cores were sampled for radial growth measurements from trees subjected to slight, moderate and heavy defoliation. Tree mortality during the recovery period was estimated for three different hierarchy positions of trees; hold-overs, dominants and intermediates.
xtgeebcv: A command for bias-corrected sandwich variance estimation for GEE analyses of cluster randomized trials

Cluster randomized trials, where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on individuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must consider that outcomes for members of the same cluster tend to be more similar than outcomes for members of other clusters. A popular individual-level analysis technique is generalized estimating equations (GEE). However, it is common to randomize a small number of clusters (for example, 30 or fewer), and in this case, the GEE standard errors obtained from the sandwich variance estimator will be biased, leading to inflated type I errors. Some bias-corrected standard errors have been proposed and studied to account for this finite-sample bias, but none has yet been implemented in Stata. In this article, we describe several popular bias corrections to the robust sandwich variance. We then introduce our newly created command, xtgeebcv, which will allow Stata users to easily apply finite-sample corrections to standard errors obtained from GEE models. We then provide examples to demonstrate the use of xtgeebcv. Finally, we discuss suggestions about which finite-sample corrections to use in which situations and consider areas of future research that may improve xtgeebcv.
An efficient and robust simulator for wear of total knee replacements

Wear on total knee replacements is an important criterion for their performance characteristics. Numerical simulations of such wear have seen increasing attention over the last years. They have the potential to be much faster and less expensive than the in vitro tests in use today. While it is unlikely that in silico tests will replace actual physical tests in the foreseeable future, a judicious combination of both approaches can help making both implant design and pre-clinical testing quicker and more cost-effective. The challenge today for the design of simulation methods is to obtain results that convey quantitative information and to do so quickly and reliably. This involves the choice of mathematical models as well as the numerical tools used to solve them. The correctness of the choice can only be validated by comparing with experimental results. In this article, we present finite element simulations of the wear in total knee replacements during the gait cycle standardized in the ISO 14243-1 document, used for compliance testing in several countries. As the ISO 14243-1 standard is precisely defined and publicly available, it can serve as an excellent benchmark for comparison of wear simulation methods. We use comparatively simple wear and material models, but we solve them using a new wear algorithm that combines extrapolation of the geometry changes with a contact algorithm based on nonsmooth multigrid ideas. The contact algorithm works without Lagrange multipliers and penalty parameters, achieving unparalleled stability and efficiency. We compare our simulation results with the experimental data from physical tests using two different actual total knee replacements. Even though the model is simple, we can predict the total mass loss due to wear after 5-million gait cycles, and we observe a good match between the wear patterns seen in experiments and our simulation results. When compared with a state-of-the-art penalty-based solver for the same model, we measure a roughly fivefold increase of execution speed.
Laboratory Trials of the Efficacy of the Crown Weevil Mortadelo horridus (Coleoptera: Curculionoidae) for the Biological Control of Slender Thistles Carduus pycnocephalus and C. tenuiflorus in Southwestern Australia

The hypothesis that larval crown weevil (Mortadelo horridus Alonso-Zarazaga and Sanchez-Ruiz) can suppress vegetative growth, seed set and viability in the winter annual slender thistles (Carduus pycnocephalus L. and C. tenuiflorus Curt.) was tested in laboratory gardens. Different densities (eight and 16) of M. horridus larvae and eggs were inoculated onto slender thistle rosettes between one and four times. Controls received no eggs or larvae. Larval feeding destroyed the apical meristems of treated plants, producing multi-crowned rosettes and significant declines in leaf area in both species, irrespective of the number of larvae. Treated thistles bolted at the same time and had a 4-fold increase in stem number compared to controls. The stems of control plants were up to one-third taller and thicker than attacked plants. Relative to controls, the leaves of treated plants had significantly fewer spines, decreased leaf area and decreased leaf perimeter, with less deeply lobed leaf margins. The total number of capitula produced by control and experimental groups was not significantly different, but consideration of fertile capitula only revealed a reduction in fertile capitula production following weevil attack. This was most marked in C. pycnocephalus. Control plants also produced significantly wider capitula. No control plants died, compared to 17 and 26% of the attacked plants of C. pycnocephalus and C. tenuiflorus, respectively. Implications for biocontrol of slender thistles are discussed.