Miracle-dz/UniFAR-FTUs huggingface dataset:
How Climate Change Will Alter the Distribution of Suitable Dendrobium Habitats

To protect endangered species and restore their habitats, it is important to be able to predict their potential geographic distributions. The Dendrobium plant is important in traditional Chinese medicine, but urban expansion and over-exploitation have led to a decrease of Dendrobium resources. To achieve sustainable development of Dendrobium resources, the spatial and temporal distribution of two Dendrobium species were systematically analyzed based on the distribution points of D. moniliforme (104) and D. nobile (87). Maximum entropy modeling (MaxEnt) was used to predict the distribution of suitable habitats for these Dendrobium species, both currently and in the future, under different representative concentration pathways (RCPs). Three RCPs included RCP2.6, RCP4.5, and RCP8.5. The results show that D. moniliforme and D. nobile are widely distributed in the south of China (Anhui, Guangdong, Jiangxi, and Hunan provinces). Worldwide, Japan and North Korea were identified as major distribution areas for D. moniliforme and D. nobile. Based on the MaxEnt model, the mean diurnal range and the minimum temperature of the coldest month were identified as the most significant bioclimatic variables controlling the distribution of D. moniliforme and D. nobile. Future climate change will likely result in an increase of suitable habitat areas for D. moniliforme (by around 16%), and a decrease for D. nobile (by around 1–10%), but climate change is unlikely to have much impact on the distribution of suitable habitats for D. moniliforme and D. nobile in Japan and North Korea. Based on our findings, measures should be taken to protect these precious medicinal plant resources, and the sites used for the artificial cultivation of Dendrobium will need to shift as the climate changes.
Exploit Multiple Reference Graphs for Semi-supervised Relation Extraction

Manual annotation of the labeled data for relation extraction is time-consuming and labor-intensive. Semi-supervised methods can offer helping hands for this problem and have aroused great research interests. Existing work focuses on mapping the unlabeled samples to the classes to augment the labeled dataset. However, it is hard to find an overall good mapping function, especially for the samples with complicated syntactic components in one sentence. To tackle this limitation, we propose to build the connection between the unlabeled data and the labeled ones rather than directly mapping the unlabeled samples to the classes. Specifically, we first use three kinds of information to construct reference graphs, including entity reference, verb reference, and semantics reference. The goal is to semantically or lexically connect the unlabeled sample(s) to the labeled one(s). Then, we develop a Multiple Reference Graph (MRefG) model to exploit the reference information for better recognizing high-quality unlabeled samples. The effectiveness of our method is demonstrated by extensive comparison experiments with the state-of-the-art baselines on two public datasets.
Avaliação da resistência antimicrobiana de enterococcus sp. isolados de frangos de corte

1 Estudante do Programa de Pós-Graduação em Microbiologia Agrícola e do AmbienteUFRGS; E-mail: ana.cassenego@ufrgs.br; 2 Estudante de Iniciação Científica do Departamento de Microbiologia, Imunologia e ParasitologiaUFRGS; 3 Professor do Departamento de MicrobiologiaUFCSPA; 4 Professor do Instituto de Ciências e Tecnologia dos AlimentosUFRGS; 5 Professor do Departamento de MicrobiologiaUFRGS
Model updating after interventions paradoxically introduces bias

Machine learning is increasingly being used to generate prediction models for use in a number of real-world settings, from credit risk assessment to clinical decision support. Recent discussions have highlighted potential problems in the updating of a predictive score for a binary outcome when an existing predictive score forms part of the standard workflow, driving interventions. In this setting, the existing score induces an additional causative pathway which leads to miscalibration when the original score is replaced. We propose a general causal framework to describe and address this problem, and demonstrate an equivalent formulation as a partially observed Markov decision process. We use this model to demonstrate the impact of such `naive updating' when performed repeatedly. Namely, we show that successive predictive scores may converge to a point where they predict their own effect, or may eventually oscillate between two values, and we argue that neither outcome is desirable. Furthermore, we demonstrate that even if model-fitting procedures improve, actual performance may worsen. We complement these findings with a discussion of several potential routes to overcome these problems.
On the distribution of coefficients of half-integral weight modular forms and the Ramanujan-Petersson Conjecture.

This article represents a systematic computational study of Fourier coefficients of cuspidal Hecke eigenforms of level $\Gamma_0(4)$ and half-integral weights. Its main focus is on exploring the distribution of the normalised Fourier coefficients. Recall that the prime-indexed normalised Fourier coefficients of an integral weight cuspidal Hecke eigenform without CM follow the Sato-Tate distribution. The experimental observations of this article lead to the question whether the distribution in the half-integral weight case can be described by a generalisation of the Gaussian distribution. Moreover, a link is established between the assumed distribution of the normalised coefficients and a probabilistic version of the Ramanujan-Petersson Conjecture. The computations of the article also lend further support to the Bruinier-Kohnen Conjecture on the equidistribution of signs of Fourier coefficients.
Fingerprint Orientation Estimation: Challenges and Opportunities

There is an exponential increase in portable electronic devices with biometric security mechanisms, in particular fingerprint biometric. A person has a limited number of fingerprints and it remains unchanged throughout his lifetime, once leaked to the adversary, it leaks for a lifetime. So, there is a need to secure the biometric template itself. In this survey paper, we review the different security models and fingerprint template protection techniques. The research challenges in different fingerprint template protection techniques are also highlighted in respective sections of the paper. This survey provides a comprehensive study of template protection techniques for fingerprint biometric systems and highlights the challenges and future opportunities.
Self-Supervised Shadow Removal

Shadow removal is an important computer vision task aiming at the detection and successful removal of the shadow produced by an occluded light source and a photo-realistic restoration of the image contents. Decades of re-search produced a multitude of hand-crafted restoration techniques and, more recently, learned solutions from shad-owed and shadow-free training image pairs. In this work,we propose an unsupervised single image shadow removal solution via self-supervised learning by using a conditioned mask. In contrast to existing literature, we do not require paired shadowed and shadow-free images, instead we rely on self-supervision and jointly learn deep models to remove and add shadows to images. We validate our approach on the recently introduced ISTD and USR datasets. We largely improve quantitatively and qualitatively over the compared methods and set a new state-of-the-art performance in single image shadow removal.
Primary Gastrointestinal Involvement in a Case of Extranodal-Extranasal Natural Killer T Cell Lymphoma

Extra-nasal types of Extra-nodal natural killer cell lymphoma (ENKL) have been known with poorer prognoses than nasal type with the worst responses to treatment. The current work introduces a case of ENKL with GI involvement with no nasal manifestations. We report a 56-year male farmer with fever, productive cough, dyspnea, anorexia, vomiting and chill in addition to malaise and cachexia of three months duration referred to a hospital with acute abdominal pain, and was diagnosed as peritonitis due to perforated terminal ileum ulcer before experiencing surgery as a case of acute abdomen. The pathologic study of the relevant biopsy showed “ulceration and necrosis with dense fibrinoleukocytic exudation and granulation tissue formation. CT scan determined a bilateral mass like haziness which was more likely to be metastatic. The review of the previous pathologic specimens raised Natural Killer/T cell Lymphoma (NKTL), the reason for which we focused on the patient’s sinuses and nasal area as well as nasopharynx. There was no finding in examination and endoscopy of sinuses. Pathology also found malignant high grade non-Hodgkin T cell lymphoma in specimens obtained from debridement of ulcer at terminal ileum. It also showed that most of the tumor cells were positive for CD3, CD56, CD8, and LCA but negative for CD19, CD20 and AE1/AE3. Positive reactions for CD30 were shown by some cells. CD56, CD3, and CD8 were expressed by neoplastic cells and CD30 were positive in few cells. Proliferative activity (Ki67 index) was high (60–70%). This was the main base to diagnose an extra-nodal extra-nasal NK/T cell lymphoma. In conclusion, Intestinal changes at middle age, especially in men with nonspecific clinical manifestations is highly advised to be studied pathologically and genetically for T cell types like CD30 positive T cells which are usually engaged in ENKTL.
Quantifying Liver-Stomach Disharmony Pattern of Functional Dyspepsia Using Multidimensional Analysis Methods

Purpose This study aims to develop and validate a quantitative model for measuring severity of a typical traditional Chinese medicine (TCM) pattern for functional dyspepsia (FD) using multidimensional analysis methods including confirmatory factor analysis (CFA) and multidimensional item response theory (MIRT). Methods A scale and theoretical models were constructed according to the definition of pathogenesis about “liver-stomach disharmony” patterns of FD. With data collected from 502 patients in a cross-section study, the theoretical model was validated with CFA, and the related validity and reliability were evaluated in Amos 21.0. By the use of the MIRT paradigm, psychometric properties of the scale were estimated and evaluated for pattern quantification. Results A scale consisting of 12 items was constructed detecting three latent traits of the pattern. The theoretical model was evaluated to be with adequate consistency with clinical data as RMSEA < 0.05, CFI = 0.94, and χ2/Df = 2.29. As the correlation between symptoms and related pattern factors evaluated to be with adequate factor loading, the instrument is of preliminary interpretation. Most precision of assessment could be achieved for patients with moderate severity of the pattern as shown in test information and standard error functions. Conclusions An instrument with an interpretable conceptual framework was developed for pattern quantification in TCM clinical practice. By constructing and evaluating both psychological and physical effects in a multidimensional model of the TCM pattern of FD, the paradigm raised in this article provided a valuable reference for interpreting complex diseases and theories such as FD and TCM patterns.
Effects of MP Polyethylene Microparticles on Microbiome and Inflammatory Response of Larval Zebrafish

Plastic polymers have quickly become one of the most abundant materials on Earth due to their low production cost and high versatility. Unfortunately, some of the discarded plastic can make its way into the environment and become fragmented into smaller microscopic particles, termed secondary microplastics (MP). In addition, primary MP, purposely manufactured microscopic plastic particles, can also make their way into our environment via various routes. Owing to their size and resilience, these MP can then be easily ingested by living organisms. The effect of MP particles on living organisms is suspected to have negative implications, especially during early development. In this study, we examined the effects of polyethylene MP ingestion for four and ten days of exposure starting at 5 days post-fertilization (dpf). In particular, we examined the effects of polyethylene MP exposure on resting metabolic rate, on gene expression of several inflammatory and oxidative stress linked genes, and on microbiome composition between treatments. Overall, we found no evidence of broad metabolic disturbances or inflammatory markers in MP-exposed fish for either period of time. However, there was a significant increase in the oxidative stress mediator L-FABP that occurred at 15 dpf. Furthermore, the microbiome was disrupted by MP exposure, with evidence of an increased abundance of Bacteroidetes in MP fish, a combination frequently found in intestinal pathologies. Thus, it appears that acute polyethylene MP exposure can increase oxidative stress and dysbiosis, which may render the animal more susceptible to diseases.
Towards Fully Bilingual Deep Language Modeling

Language models based on deep neural networks have facilitated great advances in natural language processing and understanding tasks in recent years. While models covering a large number of languages have been introduced, their multilinguality has come at a cost in terms of monolingual performance, and the best-performing models at most tasks not involving cross-lingual transfer remain monolingual. In this paper, we consider the question of whether it is possible to pre-train a bilingual model for two remotely related languages without compromising performance at either language. We collect pre-training data, create a Finnish-English bilingual BERT model and evaluate its performance on datasets used to evaluate the corresponding monolingual models. Our bilingual model performs on par with Google's original English BERT on GLUE and nearly matches the performance of monolingual Finnish BERT on a range of Finnish NLP tasks, clearly outperforming multilingual BERT. We find that when the model vocabulary size is increased, the BERT-Base architecture has sufficient capacity to learn two remotely related languages to a level where it achieves comparable performance with monolingual models, demonstrating the feasibility of training fully bilingual deep language models. The model and all tools involved in its creation are freely available at this https URL
Characteristics of information spreading across nations

The mechanism of information diffusion on social media platforms such as retweeting in Twitter has been largely studied. Existing studies mainly look into the social or discursive features of information that are popular in virtual space, few have related the diffusion of information to the real‐world context and studied the characteristics of information that can spread across nations. In the context of globalization, understanding information spreading across nations not only facilitates marketing and propagation but also helps to understand transnational activities such as transnational social movements. We conduct a preliminary study to analyze the sentiment and cognition components of tweets that disseminate transnationally in the MeToo movement. Tweets that spread across nations are generally popular, that is retweeted more. However, popular tweets do not always spread across nations. We find popular tweets that disseminate transnationally contain more elements of politics, religion, or social bond indicators. Our study provides insights for propagation in globalization and may help social movement organizations that aim for transnational activities.
Integrating research and teaching for data curation in iSchools

The quickly changing nature of information science and technology creates unique and remarkable challenges in terms of developing curriculum focused on building data competencies. Faculties responsible for teaching current developments in information studies have the unique burden of needing to continuously update our curricula without sacrificing our broader teaching goals. This panel features diverse perspectives on teaching data curation skills in five US‐based schools of information at the undergraduate and graduate levels. Panelists will present their unique perspectives on pedagogical approaches in courses dedicated to data curation, digital preservation, description and access standards, as well as data access and interchange. Topics introduced will range from flipped classroom techniques, finding messy datasets, common pitfalls, hands‐on labs, cloud based tools, data carpentry labs, and sequencing learning objectives to match stages of the data life cycle. This panel will give ASIST conference participants an opportunity to see a range of junior faculty, each with IMLS funded research projects related to data curation, share their experiences of teaching data competencies in the classroom.
Privacy considerations when predicting mental health using social media

In recent years the number of individuals struggling with mental illness has increased, and traditional mental health services are now considered insufficient under the current circumstances which has prompted researchers to develop new approaches for mental healthcare. Social media usage is growing, and it is been utilized to help provide additional insight on mental health by using the information shared by individuals, as well as data taken from their social media activity. While this approach may provide a unique and effective perspective for mental health services, it is critical that privacy risks and protections are considered in the process. Social media services collect, process, and stores a substantial amount of information about its users and how that information is shared as well as what type of predictions are made may pose serious privacy concerns. This study aims to understand how privacy is addressed and emphasized during the process of using social media data for mental healthcare by constructing a systematic review on previous scholarly papers related to the topic. Solove's taxonomy of privacy is used to evaluate these publications privacy considerations and to demonstrate the privacy risks that may arise when social media data is used for mental health.
Measures of Sleep in Rheumatologic Diseases: Sleep Quality Patient‐Reported Outcomes in Rheumatologic Diseases

Sleep is a fundamental human need and can be impacted by biological, psychosocial, and environmental influences. Suboptimal sleep is associated with a decrease in quality of life and an increase in mortality (1). Common subjective sleep complaints in individuals with rheumatic diseases include difficulty falling or staying asleep, nonrestorative sleep, and increased daytime sleepiness or fatigue. Primary sleep disorders, such as obstructive sleep apnea (OSA) or restless legs syndrome, are more common in patients with rheumatic diseases, compared with the general population (2–4). Because disruption in sleep has been linked with an induction of proinflammatory cytokines (5,6), the evaluation of sleep disturbances and primary sleep disorders is particularly relevant to the comprehensive care and management of patients with rheumatic disease (7). Although objective measurements such as polysomnography are the gold standard in diagnosing sleep disorders, expense and access to these specialized facilities limit routine use. Patientreported outcome (PRO) measures are widely available validated instruments that can be used for assessment of sleep function and screening for sleep disorders. PRO measures are subjective measurements, and it is important that the clinician and/or researcher understand the psychometric properties and appropriate applications of different PRO measures. In the following sections, we discuss the practical applications (Table 1) and psychometric properties (Table 2) of several sleeprelated PRO measures and their use in rheumatology, updating and expanding on the 2011 review by Omachi (8). We begin with reviewing two legacy instruments, the Pittsburgh Sleep Quality Index (PSQI) (9) and the Epworth Sleepiness Scale (ESS) (10), which assess sleep quality and daytime somnolence, respectively. We then introduce the Patient-Reported Outcomes Measurement Information System (PROMIS) Sleep Disturbance (PROMIS-SD) and PROMIS Sleep-Related Impairment (PROMIS-SRI) instruments, which globally assess sleep function (11). Finally, we discuss PRO measures evaluating specific sleep disorders, including the Insomnia Severity Index (ISI) and the Athens Insomnia Scale (AIS) for insomnia (12,13) and the Berlin Questionnaire (BQ) for OSA (14).
Bioenergetic characterization in Aurelia aurita (Cnidaria: Scyphozoa) polyps and application to natural polyp populations

: Bioenergetic modeling is useful in understanding the benthic polyp population dynamics of metagenetic scyphozoan jellyfishes. We investigated the effects of environmental factors on the respiration and feeding rates of polyps of Aurelia aurita s.l. under controlled laboratory conditions in order to construct an empirical energy budget model for application to natural polyp populations. The carbon weight-specific respiration rate of polyps increased exponentially with increasing temperature from 8 to 28°C and was constant at salinities ranging from 15 to 33. The general functional response of polyps feeding on various zooplankton taxa was expressed by a linear increase in ingestion rate as a function of prey density, although it was affected by the size and/or swimming ability of prey organisms. Accordingly, the clearance rate was constant irrespective of prey density at a given temperature. The rate increased linearly with temperature from 8 to 26°C. Integrating these results, we constructed a carbon budget model of A. aurita polyps as a function of temperature and mesozooplankton prey density. An application of this model to polyps in Fukuyama Harbor, the Inland Sea of Japan, suggested that they consistently attain a positive growth (somata and offspring) rate, ranging from 0.0039 to 0.34 µg C µg C −1 d −1 . Our model allows estimation of the potential growth rates of polyps in temperate coastal waters where temperature and mesozooplankton biomass data are available, contributing to comprehensive understanding of polyp population dynamics in nature.
Parkinson’s Disease With Depression: The Correlations Between Neuroinflammatory Factors and Neurotransmitters in Cerebrospinal Fluid

Background: To explore the changes of neuroinflammatory factors in cerebrospinal fluid (CSF) and their correlation with monoamine neurotransmitters in Parkinson’s disease (PD) with depression (PD-D) patients. Methods: Neuroinflammatory factors and neurotransmitters in CSF were measured and compared between PD with no depression (PD-ND) and PD-D groups. The relationship between PD-D and neuroinflammatory factors was studied by binary logistic regression equation, and the related factors of PD-D were adjusted. The correlations of the levels of neuroinflammatory factors and neurotransmitters in PD-D group were analyzed. Results: The levels of tumor necrosis factor (TNF)-α in CSF from PD-D group were significantly higher and there were no significant differences in the levels of interleukin-1β, prostaglandin (PG) E2, hydrogen peroxide (H2O2), and nitric oxide (NO). The 24-item Hamilton Depression Scale (HAMD-24) score was positively correlated with the level of TNF-α in CSF. Binary logistic regression showed that the OR of CSF TNF-α level was 1.035 (95% CI 1.002–1.069). The level of dopamine (DA) in CSF of PD-D group was significantly lower than that in PD-ND group. TNF-α level was negatively correlated with DA level in CSF from PD patients (r = −0.320, P = 0.003). Conclusions: Neuroinflammatory factors, especially TNF-α, may play an important role in PD-D. It may cause damage to DA neurons and lead to the depletion of DA, which is related to the occurrence and development of PD-D.