Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost–consequence analysis of a hemostatic matrix alone or in combination for spine surgery patients

Abstract Background: A five-year retrospective database analysis comparing the use of Floseal1 flowable topical hemostat alone (F) and in combination with gelatin/thrombin (F + G/T) to achieve hemostasis and control surgical bleeding showed higher resource utilization for F + G/T cases relative to F matched pairs during spinal surgery. Lower resource use in the F group was characterized by shorter hospital length of stay and surgical time as well as fewer blood transfusions and less hemostat agent used per surgery. Objective: To evaluate the cost–consequence of using F compared to F + G/T in minor, major and severe spinal surgery from the US hospital perspective. Methods: A cost–consequence model was developed using the US hospital perspective. Model inputs include clinical inputs from the literature, cost inputs (hemostatic matrices, blood product transfusion, hospital stay and operating room time) from the literature, and an analysis of annual spine surgery volume (minor, major and severe) using the 2012 National Inpatient Sample (NIS) database. Costs are reported in 2017 US dollars. One-way and probabilistic sensitivity analyses address sources of variability in the results. Results: A medium-volume hospital (130 spine surgeries per year) using F versus F + G/T for spine surgeries is expected to require 85 less hours of surgical time, 58 fewer hospital days and 7 fewer blood transfusions in addition to hemostat volume savings (F: 1 mL, thrombin: 1994 mL). The cost savings associated with the hospital resources for a medium-volume hospital are expected to be $317,959 (surgical hours = $154,746, hospital days = $125,237, blood transfusions = $19,023, hemostatic agents = $18,953) or $2445 per spine surgery. Conclusions: The use of F versus F + G/T could lead to annual cost savings for US hospitals performing a low to high volume of spinal surgeries per year.
From Text to Lexicon: Bridging the Gap between Word Embeddings and Lexical Resources

Distributional word representations (often referred to as word embeddings) are omnipresent in modern NLP. Early work has focused on building representations for word types, and recent studies show that lemmatization and part of speech (POS) disambiguation of targets in isolation improve the performance of word embeddings on a range of downstream tasks. However, the reasons behind these improvements, the qualitative effects of these operations and the combined performance of lemmatized and POS disambiguated targets are less studied. This work aims to close this gap and puts previous findings into a general perspective. We examine the effect of lemmatization and POS typing on word embedding performance in a novel resource-based evaluation scenario, as well as on standard similarity benchmarks. We show that these two operations have complimentary qualitative and vocabulary-level effects and are best used in combination. We find that the improvement is more pronounced for verbs and show how lemmatization and POS typing implicitly target some of the verb-specific issues. We claim that the observed improvement is a result of better conceptual alignment between word embeddings and lexical resources, stressing the need for conceptually plausible modeling of word embedding targets.
Oral Sensorimotor Intervention Enhances Breastfeeding Establishment in Preterm Infants.

OBJECTIVE To assess the efficacy of an oral sensorimotor intervention on breastfeeding establishment and maintenance in preterm infants. STUDY DESIGN Thirty-one preterm infants born ≤34 weeks gestation were randomized into an experimental or control group. The experimental group received a 15-minute program consisting of stroking the peri-oral structures for the first 5 minutes, tongue exercises for the next 5 minutes, followed by non-nutritive sucking for the final 5 minutes. The control group received a sham intervention for the same duration. The interventions were administered once daily for 10 days. The outcomes included: time to attainment of full oral feeding, breastfeeding acquisition (i.e., ≥50% of direct breastfeeding at hospital discharge), breastfeeding skill assessment using the Preterm Infant Breastfeeding Behavior Scale (PIBBS), length of hospitalization, and breastfeeding maintenance at 3 and 6 months posthospitalization. RESULTS Full oral feeding was attained earlier in the experimental group compared with the control (10.7 ± 2.1 vs. 19.3 ± 3.6 days, p < 0.01). This was associated with a greater number of infants in the intervention group acquiring breastfeeding at hospital discharge compared with the controls (n = 11 vs. 5, p = 0.049). There was no statistical difference in PIBBS score, length of hospitalization, and breastfeeding rates at 3 and 6 months posthospitalization between the two groups (all tests, p > 0.32). CONCLUSIONS An oral sensorimotor intervention accelerated the achievement of full oral feeding and enhanced direct breastfeeding rates at hospital discharge only. Provision of an oral sensorimotor intervention is a safe and low-cost intervention that may increase breastfeeding rates in a highly vulnerable population.
A hybrid volumetric dose verification method for single‐isocenter multiple‐target cranial SRS

Abstract A commercial semi‐empirical volumetric dose verification system (PerFraction [PF], Sun Nuclear Corp.) extracts multi‐leaf collimator positions from the electronic portal imaging device movies collected during a pre‐treatment run, while the rest of the delivered control point information is harvested from the accelerator log files. This combination is used to reconstruct dose on a patient CT dataset with a fast superposition/convolution algorithm. The method was validated for single‐isocenter multi‐target SRS VMAT treatments against absolute radiochromic film measurements in a cylindrical phantom. The targets ranged in size from 0.8 to 3.6 cm and in number from 3 to 10 per plan. A total of 17 films rotated at different angles around the cylinder axis were analyzed. Each of 27 total targets was intercepted by at least one film, and 2–4 different films were analyzed per plan. Film dose was always scaled to the ion chamber measurement in a high‐dose, low‐gradient area deliberately created at the isocenter. The planar dose agreement between PF and film using 3%(Global dose‐difference normalization)/1 mm gamma analysis was on average 99.2 ± 1.1%. The point dose difference in the low‐gradient area in the middle of every target was below 3%, while PF‐reconstructed and film dose centroids for individual targets showed submillimeter agreement when measured on a well aligned accelerator. Volumetrically, all voxels in all plans agreed between PF and the primary treatment planning system at the 3%/1 mm level. With proper understanding of its advantages and shortcomings, the tool can be applied to patient‐specific QA in routine radiosurgical clinical practice.
AnlamVer: Semantic Model Evaluation Dataset for Turkish - Word Similarity and Relatedness

In this paper, we present AnlamVer, which is a semantic model evaluation dataset for Turkish designed to evaluate word similarity and word relatedness tasks while discriminating those two relations from each other. Our dataset consists of 500 word-pairs annotated by 12 human subjects, and each pair has two distinct scores for similarity and relatedness. Word-pairs are selected to enable the evaluation of distributional semantic models by multiple attributes of words and word-pair relations such as frequency, morphology, concreteness and relation types (e.g., synonymy, antonymy). Our aim is to provide insights to semantic model researchers by evaluating models in multiple attributes. We balance dataset word-pairs by their frequencies to evaluate the robustness of semantic models concerning out-of-vocabulary and rare words problems, which are caused by the rich derivational and inflectional morphology of the Turkish language.
Surveillance for respiratory syncytial virus and parainfluenza virus among patients hospitalized with pneumonia in Sarawak, Malaysia

Background Respiratory syncytial virus (RSV) and parainfluenza virus (PIV) are frequent causes of pneumonia and death among children at Sibu and Kapit Hospitals in Sarawak, Malaysia. Objectives To determine the prevalence and risk factors for RSV subtypes A and B and PIV types 1–4 among patients hospitalized with pneumonia. Methods In a cross-sectional, pilot study nasopharyngeal swabs were studied with real-time reverse transcription polymerase chain reaction assays. Concurrently, we helped Sibu and Kapit Hospitals adapt their first molecular diagnostics for RSV and PIV. Results Of 129 specimens collected (June to July 2017), 39 tested positive for RSV-A (30.2%), two were positive for RSV B (1.6%), one was positive for PIV-3 (0.8%) and one was positive for PIV-4 (0.8%). No samples were positive for PIV-1 or PIV-2. Of the 39 RSV-A positive specimens, 46.2% were collected from children under one year of age and only 5.1% were from patients over the age of 18. A multivariable analysis found the odds of children <1 year of age testing positive for RSV-A were 32.7 (95% CI: 3.9, 276.2) times larger than >18 years of age, and the odds of patients hospitalized at Kapit Hospital testing positive for RSV-A were 3.2 (95% CI: 1.3, 7.8) times larger than patients hospitalized at Sibu Hospital. Conclusion This study found an unusually high prevalence of RSV-A among pneumonia patients admitted to the two hospitals. Subsequently, Sibu Hospital adapted the molecular assays with the goal of providing more directed care for such pneumonia patients.
Contextual String Embeddings for Sequence Labeling

Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters. By learning to predict the next character on the basis of previous characters, such models have been shown to automatically internalize linguistic concepts such as words, sentences, subclauses and even sentiment. In this paper, we propose to leverage the internal states of a trained character language model to produce a novel type of word embedding which we refer to as contextual string embeddings. Our proposed embeddings have the distinct properties that they (a) are trained without any explicit notion of words and thus fundamentally model words as sequences of characters, and (b) are contextualized by their surrounding text, meaning that the same word will have different embeddings depending on its contextual use. We conduct a comparative evaluation against previous embeddings and find that our embeddings are highly useful for downstream tasks: across four classic sequence labeling tasks we consistently outperform the previous state-of-the-art. In particular, we significantly outperform previous work on English and German named entity recognition (NER), allowing us to report new state-of-the-art F1-scores on the CoNLL03 shared task. We release all code and pre-trained language models in a simple-to-use framework to the research community, to enable reproduction of these experiments and application of our proposed embeddings to other tasks: https://github.com/zalandoresearch/flair
Hepatoprotective Effect of Polysaccharides Isolated from Dendrobium officinale against Acetaminophen-Induced Liver Injury in Mice via Regulation of the Nrf2-Keap1 Signaling Pathway

The effect of polysaccharides isolated from Dendrobium officinale (DOP) on acetaminophen- (APAP-) induced hepatotoxicity and the underlying mechanisms involved are investigated. Male Institute of Cancer Research (ICR) mice were randomly assigned to six groups: (1) control, (2) vehicle (APAP, 230 mg/kg), (3) N-acetylcysteine (100 mg/kg), (4) 50 mg/kg DOP, (5) 100 mg/kg DOP, and (6) 200 mg/kg DOP. Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels in the serum and glutathione (GSH), malondialdehyde (MDA), catalase (CAT), total antioxidant capacity (T-AOC), myeloperoxidase (MPO), and reactive oxygen species (ROS) levels in the liver were determined after the death of the mice. The histological examination of the liver was also performed. The effect of DOP on the Kelch-like ECH-associated protein 1- (Keap1-) nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway was evaluated using Western blot analysis and real-time polymerase chain reaction (PCR). The results showed that DOP treatment significantly alleviated the hepatic injury. The decrease in ALT and AST levels in the serum and ROS, MDA, and MPO contents in the liver, as well as the increases in GSH, CAT, and T-AOC in the liver, were observed after DOP treatment. DOP treatment significantly induced the dissociation of Nrf2 from the Nrf2−Keap1 complex and promoted the Nrf2 nuclear translocation. Subsequently, DOP-mediated Nrf2 activation triggered the transcription and expressions of the glutamate–cysteine ligase catalytic (GCLC) subunit, glutamate–cysteine ligase regulatory subunit (GCLM), heme oxygenase-1 (HO-1), and NAD(P)H dehydrogenase quinone 1 (NQO1) in APAP-treated mice. The present study revealed that DOP treatment exerted potentially hepatoprotective effects against APAP-induced liver injury. Further investigation about mechanisms indicated that DOP exerted the hepatoprotective effect by suppressing the oxidative stress and activating the Nrf2−Keap1 signaling pathway.
A modified paranotal theory of insect wing origin

The theory of Kukalova‐Peck ('78) is examined and rejected except for the hypothesis of the partially pleural origin of wings. Data suggest that the arthropods ancestral to insects left the water, and that movable precursors of the wings, possibly exopodites, were immobilized and fused with the tergum to form part of the complex paranota. Later, during insect adaptation for flight, parts of the complex paranota were separated secondarily and became wings.
Transfer Learning for Entity Recognition of Novel Classes

In this reproduction paper, we replicate and extend several past studies on transfer learning for entity recognition. In particular, we are interested in entity recognition problems where the class labels in the source and target domains are different. Our work is the first direct comparison of these previously published approaches in this problem setting. In addition, we perform experiments on seven new source/target corpus pairs, nearly doubling the total number of corpus pairs that have been studied in all past work combined. Our results empirically demonstrate when each of the published approaches tends to do well. In particular, simpler approaches often work best when there is very little labeled target data, while neural transfer approaches tend to do better when there is more labeled target data.
'Misinformation? What of it?' Motivations and individual differences in misinformation sharing on social media

Misinformation is common on social media. This causes misunderstanding and compromises the credibility of the medium. What motivates individuals to share inaccurate information? Are there individual differences? This study surveyed 171 university students on 16 motivations. Potential differences in motivations by gender and the Big Five personality traits were tested with multiple regression. The study found that the respondents considered truthfulness to be quite important, but over two-thirds of them had still shared misinformation. The top motivations of misinformation sharing were: to obtain others' opinions on that information, to express own opinions, and to interact with others. Gender showed no significant differences, but personality did. Extroverts, for example, were more prone to share misinformation for socializing purpose. The findings suggest that it is beneficial to include affective and motivational factors in information behavior research. Information literacy training may also draw from the attitude change literature to deliver training that combines knowledge on information evaluation and sharing with affective messages that target individuals' motivations such as their interest in self-expression.
Assessment of Gingival Biotype and Keratinized Gingival Width of Maxillary Anterior Region in Individuals with Different Types of Malocclusion.

Objective The aim of the present study is to evaluate the relationship of gingival thickness and width of keratinized gingiva with different malocclusion groups and amount of crowding. Methods A total of 181 periodontally healthy subjects were enrolled in the present study. The study participants were divided into three malocclusion groups: Angle Class I, Angle Class II, and Angle Class III. Each group was divided into subgroups according to the amount of dental crowding, namely mild, moderate, and severe. The width of keratinized gingiva was calculated as the distance between mucogingival junction and free gingival margin, whereas gingival thickness was determined by a transgingival probing technique. Results Tooth numbers 13 and 23 were observed to have thin gingival biotype. The width of keratinized gingiva for tooth numbers 13 and 23 was narrower in the severe crowding group than in the moderate and mild crowding groups. The relationship of gingival thickness and width of keratinized gingiva with Angle classification was not found to be significant. Conclusion Although it is thought that there is a relationship between gingival thickness, width of keratinized gingiva, and Angle classification with regard to malaligned teeth, this cross-sectional evaluation of 181 patients failed to show a significant relationship.
Machine Learning in Agriculture: A Review

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.
Unilateral Autologous Breast Reconstruction with Bi-pedicled, Conjoined Deep Inferior Epigastric Perforator Flaps

Abstract Background The abdomen remains the most popular and reliable donor site for autologous breast reconstruction. Some patients, however, lack sufficient tissue to recreate an aesthetic breast mound using a single-pedicle, deep inferior epigastric perforator (DIEP) flap, particularly when matching a contralateral native breast. The amount of abdominal skin and/or soft tissue reliably supplied by one vascular pedicle is frequently insufficient to adequately restore the breast skin envelope and “footprint. ” This study summarizes our experience with using bipedicled DIEP flaps to improve the aesthetic results of unilateral breast reconstruction in such patients. Methods Consecutive patients undergoing unilateral breast reconstruction with bi-pedicled, conjoined DIEP flaps over a 4-year period were retrospectively reviewed. Primary and secondary flap microvascular anastomoses were performed to the antegrade internal mammary (IM) vessels and to either the retrograde IM vessels or a primary DIEP pedicle side branch, respectively. Clinical characteristics and outcomes were recorded. Results Sixty-three patients underwent immediate (n = 29) or delayed (n = 34) reconstruction, with age and body mass index of 54.1 ± 8.4 years and 26.6 ± 4.7, respectively. Mean follow-up was 14.1 months. Twenty-eight (44.4%) patients received prereconstruction radiation therapy. All patients had preoperative abdominal imaging, including 57 (90.5%) using magnetic resonance angiography. There were no flap losses with three operative interventions for flap salvage. Conclusion Unilateral breast reconstruction with bipedicled, conjoined DIEP flaps is safe and reliable. These procedures can be performed with a complication profile similar to single-pedicle DIEP flaps. The additional skin and soft tissue available with bi-pedicled flaps allows for greater flexibility in matching the shape and projection of a woman's contralateral breast, and in some cases is necessary to achieve an aesthetically acceptable reconstruction. With growing expectations among breast reconstruction patients, conjoined bi-pedicled flaps represent a tool for meeting their reconstructive needs and exceeding the status quo for aesthetic outcomes.
Embedding WordNet Knowledge for Textual Entailment

In this paper, we study how we can improve a deep learning approach to textual entailment by incorporating lexical entailment relations from WordNet. Our idea is to embed the lexical entailment knowledge contained in WordNet in specially-learned word vectors, which we call “entailment vectors. ” We present a standard neural network model and a novel set-theoretic model to learn these entailment vectors from word pairs with known lexical entailment relations derived from WordNet. We further incorporate these entailment vectors into a decomposable attention model for textual entailment and evaluate the model on the SICK and the SNLI dataset. We find that using these special entailment word vectors, we can significantly improve the performance of textual entailment compared with a baseline that uses only standard word2vec vectors. The final performance of our model is close to or above the state of the art, but our method does not rely on any manually-crafted rules or extensive syntactic features.
Protein Intake during the First Two Years of Life and Its Association with Growth and Risk of Overweight

Growth patterns early in life could exert a long-term impact on overweight and obesity development. Among all potential manipulative factors, infant diet is one of the most influential and could affect growth and subsequent health status during adolescence and adulthood. Dietary protein, as an important macronutrient in infants’ diet, has been of special interest to researchers. Compared with human milk, infant formula tends to have a higher protein content and is associated with greater weight gain and later-in-life obesity risk. However, the effect of protein from other sources on infant growth trajectories during complementary feeding is not clear. Emerging research suggests that meat protein during early complementary feeding promotes linear growth while not increasing risk of overweight compared with dairy protein; and the gut microbiota might be a mediator between protein quality and growth trajectories. This review addresses the current knowledge of protein intake from birth to 24 months and its relationship with growth and risk of overweight.