Miracle-dz/UniFAR-FTUs huggingface dataset:
Depressive symptoms among elderly diabetic patients in Vietnam

Background and aims Depression and diabetes are becoming increasingly prevalent within the Vietnamese elderly population. However, the linkage between these health conditions in the Vietnamese elderly has not yet been fully investigated. This study aimed to assess the level of depressive symptoms and associated factors among elderly diabetic patients. Methods A cross-sectional study was conducted at National Geriatric Hospital in the elder patients aged ≥60 years with type 2 diabetes mellitus (T2DM). Depressive symptoms were assessed using the Geriatric Depression Scale, with three categories: normal (0–4 points), mild (5–9 points), and moderate/severe depressive symptoms (≥10 points). We obtained information on the patient’s sociodemographic, medical history, glycemic control (fasting plasma glucose and HbA1c), daily activities (activities of daily living [ADLs] and instruments activities of daily living [IADLs] scale), and fall risks (Time Up and Go test). Logistic regression was used to analyze the factors associated with the presence of depressive symptoms. Results Among 412 patients, 236 (57.3%) had HbA1c level at 7.0% or higher. There were 327 (79.4%) patients having depressive symptoms. The level of HbA1c was significantly different between the depressive symptom group and the non-depressive symptom group (7.74% and 6.61%, P<0.05). The increased likelihood of having depressive symptoms was associated with having risk of falls (OR: 5.50; 95% CI: 1.88–16.11), suffering from 5–10 years of diabetes (OR: 2.74, 95% CI: 1.28–5.85), uncontrolled fasting plasma glucose (OR: 4.06, 95% CI: 1.81–9.12), and an impairment of IADLs (OR: 5.74, 95% CI: 2.24–14.7). Conclusion This study highlights a high prevalence of depressive symptoms among elderly T2DM patients in Vietnam, suggesting an urgent need for screening depressive symptoms and providing mental health care services to this population promptly, particularly to those suffering from diabetes for a long period of time or co-functional impairments.
Justified indulgence: self-licensing effects on caloric consumption

Abstract Objective: Research on self-licensing, i.e. employing justifications to give into temptation, largely consists of studies examining dichotomous food choices (healthy vs. unhealthy), while evidence for its effects on how much (unhealthy) food is consumed remains scarce. The present studies aimed to demonstrate self-licensing effects on caloric consumption in both lab (Study 1 & 2) and field setting (Study 3). Design: In all studies, female student samples were recruited. They either received a justification cue (license condition) or not (control condition), after which they could eat freely from unhealthy snacks (Study 1, N = 85 and Study 2, N = 95) or choose a snack for direct consumption at a take-out lunch place (Study 3, N = 110). Main outcome measures: Caloric value of consumed snacks (Study 1 and 2) and chosen snack (Study 3). Results: In all studies, caloric consumption was higher in the license condition compared to the control condition: Participants ate more of the provided unhealthy snacks (Study 1 and 2) and chose a snack of higher caloric value (Study 3). Conclusions: The present research corroborates self-licensing as an important factor in the consumption of unhealthy foods by employing more ecologically valid outcomes.
Expanding a Standard Theory of Action Selection to Produce a More Complete Model of Cognition

A standard model of how brains produce natural cognition would provide a framework for organizing cognitive neuro-science research. A recent effort (Laird et al., in press) to build on consensus views of cognitive operations and produce a standard model of natural cognition started with common aspects of well-established cognitive architectures ACT-R, Sigma, and SOAR. The model captures scientific consensus on “how” the brain works, but it does not offer a coherent story for “why” the component modules (i.e., working memory, long-term memory, visual and motor areas) exist and interact in the ways described. This manuscript starts with background information on a well-cited theory of action selection, and extends that theory to a fuller explanation of decision-making, action and perception that includes a framework for the elements of cognition.
Diurnal and seasonal variation of particle and dissolved organic matter release by the coral Acropora tenuis

Release rates of particulate organic carbon and nitrogen (POC and PON) and dissolved organic carbon (DOC) from the scleractinian coral Acropora tenuis were measured during the day and night in summer and winter seasons. Physiological parameters including calcification, photosynthesis and respiration rates were also measured simultaneously. The release rate of both POC and DOC was significantly higher in summer compared to winter and higher during the day compared to the night. The daily release rate of total organic carbon (POC + DOC) was 1,094 and 219 μmol C cm−2 d−1 for summer and winter, respectively, being 4.9 times higher in summer. The POC:PON ratios of the particulate organic matter released during daytime in both seasons (summer: 12.8 ± 5.7, winter: 12.0 ± 4.1) were significantly higher than those during nighttime (summer: 6.1 ± 2.5, winter: 2.2 ± 1.8). The DOC:POC ratio was 0.5 ± 0.03 during summer and 0.32 ± 0.98 during winter, suggesting higher mucus release in particulate form. Daily net production was estimated to be 199 and 158 μg C cm−2d−1 for summer and winter, respectively, with the amount of carbon released as mucus accounting for 6.5% and 1.6% of the net carbon fixation, respectively. The study reveals diurnal and seasonal changes in the quantity and quality of mucus released from this coral species. Since coral mucus is used as a food source by reef macro-organisms, and can also serve as an energy source for micro-organisms, the observed changes in mucus release rates are expected to influence the seasonal dynamics of organic carbon and nitrogen cycling over coral reefs.
Importance of clinical observations

Clinical case reports can be powerful and informative; they can suggest novel hypotheses, identify gaps of knowledge, and provide suggestions for improvement. In this issue of Neurology® Neuroimmunology & Neuroinflammation, Hottinger et al.1 report a fascinating case with interesting implications. The authors describe a 71-year-old woman with metastatic small-cell lung cancer (SCLC) who developed anti-Hu–associated paraneoplastic encephalitis 4 days after receiving treatment with immune checkpoint inhibitors (nivolumab and ipilimumab). Considering that this disorder is mediated by cytotoxic T cells against a family of onconeuronal antigens (Hu proteins), the authors reasoned that natalizumab would block the migration of onconeuronal-specific T cells across the blood-brain barrier preventing neuronal damage, while not affecting T-cell activity against the systemic cancer. Indeed, after treatment with natalizumab, the patient showed substantial improvement of the neurologic disorder as well as a durable oncologic response. In addition to several interesting points raised by the authors, the case is remarkable for the following reasons: (1) it strongly supports the concept that anti-Hu–associated paraneoplastic symptoms are predominantly mediated by cytotoxic T-cell mechanisms. Although this syndrome develops in association with antibodies that are excellent biomarkers of the paraneoplastic disorder, the underlying (and still not well understood) mechanisms are predominantly dependent on T cells. The most powerful evidence of this comes from autopsy studies demonstrating neuronophagic nodules of T cells along with extensive neuronal loss and glial proliferation.2 Steroids, plasma exchange, or IV immunoglobulin may partially ameliorate symptoms but rarely stop progression of the disease.3 These features are in contrast to those of neurologic diseases mediated by antibodies, such as myasthenia gravis or several types of encephalitis associated with antibodies against cell surface proteins4; (2) this case and previous reports of patients who developed paraneoplastic syndromes after treatment with immune-mediated checkpoint inhibitors5 suggest the occurrence of silent or subclinical onconeuronal-specific cytotoxic T cells that can potentially cause severe neurologic dysfunction if unleashed by drugs that restore their cytotoxic potential to attack the cancer. Furthermore, one would expect this complication to be more frequent in patients with neuroendocrine tumors or tumors that express a wide variety of neuronal proteins, such as SCLC; and (3) the observation by Hottinger et al. deserves further investigation and if confirmed, prospective clinical trials assessing natalizumab as a potential treatment of anti-Hu and other similarly devastating paraneoplastic syndromes should be considered.
Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries

According to official statistics, cancer is considered as the second leading cause of human fatalities. Among different types of cancer, brain tumor is seen as one of the deadliest forms due to its aggressive nature, heterogeneous characteristics, and low relative survival rate. Determining the type of brain tumor has significant impact on the treatment choice and patient’s survival. Human-centered diagnosis is typically error-prone and unreliable resulting in a recent surge of interest to automatize this process using convolutional neural networks (CNNs). CNNs, however, fail to fully utilize spatial relations, which is particularly harmful for tumor classification, as the relation between the tumor and its surrounding tissue is a critical indicator of the tumor’s type. In our recent work, we have incorporated newly developed CapsNets to overcome this shortcoming. CapsNets are, however, highly sensitive to the miscellaneous image background. The paper addresses this gap. The main contribution is to equip CapsNet with access to the tumor surrounding tissues, without distracting it from the main target. A modified CapsNet architecture is, therefore, proposed for brain tumor classification, which takes the tumor coarse boundaries as extra inputs within its pipeline to increase the CapsNet’s focus. The proposed approach noticeably outperforms its counterparts.
Primary School Student Teachers’ Perceived and Actual Knowledge in Biology

Individuals’ perceptions of their knowledge can have an important role in shaping their cognition and influencing their behaviour. However, there has been a scarcity of studies in biology on how perceived knowledge relates to actual knowledge. The focus of this article is on quantitative results analysing and interpreting student teachers’ perceived knowledge of biological content in relation to their actual animal and species name knowledge linked to the ecosystem in which they live. K-means cluster analysis and ANOVA were used. The results show a high- and low-level perceived knowledge cluster group among the participants. They further indicate that the difference in actual animal and species name knowledge betweenthese cluster groups remained the same during the five years of the study. The student teachers with a higher level of perceived knowledge tended to have better actual animal and species name knowledge than those in the low-level group. The actual animal name knowledge in these cluster groups was similar with regard to the local Finnish ecosystems but differed concerning the exotic species by year. The year that the participants enrolled in the study programme had an impact on their actual animal and species name knowledge. Strategies for coping with work-related demands and maintaining engagement in one’s career would be important additions to the teacher education curriculum.
Simple Attention-Based Representation Learning for Ranking Short Social Media Posts

This paper explores the problem of ranking short social media posts with respect to user queries using neural networks. Instead of starting with a complex architecture, we proceed from the bottom up and examine the effectiveness of a simple, word-level Siamese architecture augmented with attention-based mechanisms for capturing semantic “soft” matches between query and post tokens. Extensive experiments on datasets from the TREC Microblog Tracks show that our simple models not only achieve better effectiveness than existing approaches that are far more complex or exploit a more diverse set of relevance signals, but are also much faster.
New and emerging concepts in the use of denosumab for the treatment of osteoporosis

Denosumab is a fully human monoclonal antibody to receptor activator of nuclear factor kappa-B ligand (RANKL), a cytokine expressed by cells of the osteoblast lineage that is a key regulator of osteoclastic bone resorption. By binding and neutralizing RANKL, denosumab inhibits osteoclast differentiation, activity, and survival. Clinical trials in postmenopausal women with osteoporosis have shown that it reduces the risk of vertebral fractures, nonvertebral fractures, and hip fractures, with a generally favorable safety profile. With a dose of 60 mg subcutaneously every 6 months, it is approved for: treatment of postmenopausal women and men with osteoporosis, and for women and men with glucocorticoid-induced osteoporosis who are at high risk for fracture; treatment to increase bone mass in men at high risk for fracture receiving androgen-deprivation therapy for nonmetastatic prostate cancer; and treatment to increase bone mass in women at high risk for fracture receiving adjuvant aromatase inhibitor therapy for breast cancer. Atypical femur fractures and osteonecrosis of the jaw have been reported in patients treated with denosumab. Discontinuation of denosumab is followed by rapidly rising bone turnover markers, decreasing bone density, and vertebral fracture risk that returns to baseline, with a possible increase in the risk of multiple vertebral fractures. Further study is needed to clarify this potential risk. After stopping long-term denosumab, patients should be switched to another antiresorptive agent to maintain the benefit achieved with denosumab.
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation

Building an accurate automatic speech recognition (ASR) system requires a large dataset that contains many hours of labeled speech samples produced by a diverse set of speakers. The lack of such open free datasets is one of the main issues preventing advancements in ASR research. To address this problem, we propose to augment a natural speech dataset with synthetic speech. We train very large end-to-end neural speech recognition models using the LibriSpeech dataset augmented with synthetic speech. These new models achieve state of the art Word Error Rate (WER) for character-level based models without an external language model.
Effects of ecology and prey characteristics on the use of alternative social foraging tactics in crows, Corvus caurinus


The producer–scrounger model assumes that producers are animals that search for food, and scroungers are animals that steal food found by producers. By following foraging decisions made by beach-foraging crows, we assessed individual variability in strategy use, variables influencing the finder’s share (when defined as the probability of retaining a prey item in the presence of food theft behaviour) and the benefits and costs of producing and scrounging. Data were collected using focal and all-occurrences scan sampling. Results indicated that more individuals were likely to use both producing and scrounging tactics than to be ‘pure’ producers. No pure scroungers were documented. When producers obtained scroungeable items, scroungers attempted to steal the item 46% of the time. Forty-one per cent of attempted scrounges were successful. Thus, producers suffered a 19% loss rate of scroungeable items to scroungers, although producers had access to large numbers of small (nonscroungeable) items. Although there were significantly more scroungeable prey items available at lower tides, there was no significant relationship between prey density and scrounge attempts or actual thefts. No ecological factors (group size, temperature, time of day, season) or individual characteristics (age, sex) influenced the probability of retaining prey. Prey retention was determined only by characteristics of the prey items: larger prey items or those with longer handling times were most likely to be stolen.  2003 Published by Elsevier Ltd on behalf of The Association for the Study of Animal Behaviour.
Progress and Tradeoffs in Neural Language Models

In recent years, we have witnessed a dramatic shift towards techniques driven by neural networks for a variety of NLP tasks. Undoubtedly, neural language models (NLMs) have reduced perplexity by impressive amounts. This progress, however, comes at a substantial cost in performance, in terms of inference latency and energy consumption, which is particularly of concern in deployments on mobile devices. This paper, which examines the quality-performance tradeoff of various language modeling techniques, represents to our knowledge the first to make this observation. We compare state-of-the-art NLMs with "classic" Kneser-Ney (KN) LMs in terms of energy usage, latency, perplexity, and prediction accuracy using two standard benchmarks. On a Raspberry Pi, we find that orders of increase in latency and energy usage correspond to less change in perplexity, while the difference is much less pronounced on a desktop.
CD9 Controls Integrin α5β1-Mediated Cell Adhesion by Modulating Its Association With the Metalloproteinase ADAM17

Integrin α5β1 is a crucial adhesion molecule that mediates the adherence of many cell types to the extracellular matrix through recognition of its classic ligand fibronectin as well as to other cells through binding to an alternative counter-receptor, the metalloproteinase ADAM17/TACE. Interactions between integrin α5β1 and ADAM17 may take place both in trans (between molecules expressed on different cells) or in cis (between molecules expressed on the same cell) configurations. It has been recently reported that the cis association between α5β1 and ADAM17 keeps both molecules inactive, whereas their dissociation results in activation of their adhesive and metalloproteinase activities. Here we show that the tetraspanin CD9 negatively regulates integrin α5β1-mediated cell adhesion by enhancing the cis interaction of this integrin with ADAM17 on the cell surface. Additionally we show that, similarly to CD9, the monoclonal antibody 2A10 directed to the disintegrin domain of ADAM17 specifically inhibits integrin α5β1-mediated cell adhesion to its ligands fibronectin and ADAM17.
More Than Money: Experienced Positive Affect Reduces Risk-Taking Behavior on a Real-World Gambling Task

Previous research indicates that when people participate in multi-trial games of chance, the results of previous trials impact subsequent wager size. For example, the “house money” and “break even” effects suggest that an individual’s risk-taking propensity increases when financially winning or losing during a gambling session. Additionally, the “mood maintenance hypothesis” and affect regulation hypothesis suggest that people in positive and negative affective states are less and more likely to gamble than when in neutral affective states, respectively. In the present study, participants completed a series of trials on three computerized slot machines with varying expected values (EV; −10, 0, +10%) of return on investment, and they were paid a percentage of their final bankrolls in real money. Although results did not support the “house money” or “break even” effects, the “mood maintenance hypothesis” was robustly supported in all EV conditions. This is some of the first evidence supporting this theory using an ecologically valid, real-money gambling task.
Multiple-Attribute Text Style Transfer

The dominant approach to unsupervised "style transfer" in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "style". In this paper, we show that this condition is not necessary and is not always met in practice, even with domain adversarial training that explicitly aims at learning such disentangled representations. We thus propose a new model that controls several factors of variation in textual data where this condition on disentanglement is replaced with a simpler mechanism based on back-translation. Our method allows control over multiple attributes, like gender, sentiment, product type, etc., and a more fine-grained control on the trade-off between content preservation and change of style with a pooling operator in the latent space. Our experiments demonstrate that the fully entangled model produces better generations, even when tested on new and more challenging benchmarks comprising reviews with multiple sentences and multiple attributes.
Simulation of the Ontogeny of Social Jet Lag: A Shift in Just One of the Parameters of a Model of Sleep-Wake Regulating Process Accounts for the Delay of Sleep Phase Across Adolescence

The term “social jet lag” was introduced for defining the conflict between social and biological clocks due to the general practice of shifting weekday risetime on early morning hours. The phase delay of the sleep-wake cycle during adolescence is one of the most remarkable features of the ontogenesis of sleep that is incompatible with early school start times. It was previously proposed that the process of accumulation of sleep pressure during wakefulness is slowing down in post-pubertal teens to allow them to stay awake for a longer period of time thus causing the delay of their bedtime. In order to examine this proposition, we traced the ontogeny of social jet lag using sleep times reported for 160 samples of study participants of different ages as an input to a model of sleep-wake regulatory process. The simulations suggested that a gradual change in just one of the model’s parameters, the time constant of wakefulness phase of the sleep-wake regulatory process, might explain the association of the transition between childhood and adulthood with the prolongation of time staying awake, delay of sleep time, and reduction of sleep duration. We concluded that the implication of the sleep-wake regulating model would be of help for understanding precisely how social jet lag varies with age and what are the chronophysiological causes of this variation.
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks

We address the problem of abstractive summarization in two directions: proposing a novel dataset and a new model. First, we collect Reddit TIFU dataset, consisting of 120K posts from the online discussion forum Reddit. We use such informal crowd-generated posts as text source, in contrast with existing datasets that mostly use formal documents as source such as news articles. Thus, our dataset could less suffer from some biases that key sentences usually located at the beginning of the text and favorable summary candidates are already inside the text in similar forms. Second, we propose a novel abstractive summarization model named multi-level memory networks (MMN), equipped with multi-level memory to store the information of text from different levels of abstraction. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the Reddit TIFU dataset is highly abstractive and the MMN outperforms the state-of-the-art summarization models.