Miracle-dz/UniFAR-FTUs huggingface dataset:
Suspicious News Detection Using Micro Blog Text

We present a new task, suspicious news detection using micro blog text. This task aims to support human experts to detect suspicious news articles to be verified, which is costly but a crucial step before verifying the truthfulness of the articles. Specifically, in this task, given a set of posts on SNS referring to a news article, the goal is to judge whether the article is to be verified or not. For this task, we create a publicly available dataset in Japanese and provide benchmark results by using several basic machine learning techniques. Experimental results show that our models can reduce the cost of manual fact-checking process.
Do Bony Orbit Dimensions Predict Diel Activity Pattern in Sciurid Rodents?

Diel activity pattern (DAP) is a key aspect of an animal's ecology, but it is difficult to infer when behavior cannot be directly observed, as in the fossil record. Various anatomical correlates have therefore been used to attempt to classify DAP. Eyeball dimensions are good predictors of DAP because they relate directly to light sensitivity of the eye. Osteological characters, such as scleral ring dimensions, are also reliable proxies, but bony orbit dimensions alone have proven less reliable because soft tissues other than the eyeball can affect orbit size and shape. However, it would be useful if bony orbit dimensions could be used to determine DAP, particularly for mammals, which have no scleral ring, and nonmammalian synapsids, which infrequently preserve scleral rings. We investigated the possibility of predicting DAP in sciurids (Mammalia: Rodentia: Sciuridae) using orbit measurements and other cranial dimensions, and a variety of quantitative methods, including phylogenetic flexible discriminant analysis, classification trees, and logistic regression. The latter two methods do not require a priori assignment of DAP and therefore reflect the situation in a fossil data set. We find that although there are some interfering phylogenetic factors, nocturnal and non‐nocturnal sciurids can be differentiated from one another with over 80% accuracy using all methods investigated here; attempts to differentiate crepuscular animals from nocturnal and diurnal species proved much less successful. Our results indicate that these analyses offer several viable options for predicting DAP in the fossil record, but such analyses should be conducted in a phylogenetic context whenever possible. Anat Rec, 301:1774–1787, 2018. © 2018 Wiley Periodicals, Inc.
Histotripsy for Non-Invasive Ablation of Hepatocellular Carcinoma (HCC) Tumor in a Subcutaneous Xenograft Murine Model

Histotripsy fractionates tissue through a mechanical, non-invasive ultrasonic ablation process that precisely controls acoustic cavitation while utilizing real-time ultrasound (US) imaging guidance. This study investigates the potential, feasibility and tumor volume reduction effects of histotripsy for liver cancer ablation in a subcutaneous in vivo murine Hepatocellular Carcinoma (HCC) model. Hep3B tumors were generated in the right flanks of 14 NSG and 7 NOD-SCID mice. The mice were grouped as follows: A (acute, NSG with n=9 treatment and n=1 control), B (chronic, NSG with n=2 treatment and n=2 control) and C (chronic NODSCID, with n=6 treatment and n=1 control). Treatment was performed when the tumor diameters reached >5 mm. 1-2 cycle histotripsy pulses at 100 Hz PRF (p- >30 MPa) were delivered using a custom built 1 MHz therapy transducer attached to a motorized positioner, which scanned the transducer focus to traverse the targeted tumor volume, guided by real-time US imaging. Tumor ablation effectiveness was assessed by obtaining T1, T2 and T2* weighted MR images. Post euthanasia, treated tumor, brain, and lung tissue samples were harvested for histology. Histology of acute group A showed fractionation of targeted region with a sharp boundary separating it from untreated tissue. Groups B and C demonstrated effective tumor volume reduction post treatment on MRI as the homogenate and edema were resorbed within 23 weeks. However, as the tumor was subcutaneous, it was not possible to set adequate treatment margin and since the mice were immune-compromised, residual viable tumor cells eventually developed into tumor regrowth at 3-9 weeks after histotripsy. Groups B and C showed no signs of metastasis in the lung and brain. Our study successfully demonstrated the potential of histotripsy for non-invasive HCC ablation in a subcutaneous murine model. Additional work is ongoing to study the response of histotripsy in immune-competent orthotopic liver tumor models.
Learning Source-Invariant Deep Hashing Convolutional Neural Networks for Cross-Source Remote Sensing Image Retrieval

Due to the urgent demand for remote sensing big data analysis, large-scale remote sensing image retrieval (LSRSIR) attracts increasing attention from researchers. Generally, LSRSIR can be divided into two categories as follows: uni-source LSRSIR (US-LSRSIR) and cross-source LSRSIR (CS-LSRSIR). More specifically, US-LSRSIR means the inquiry remote sensing image and images in the searching data set come from the same remote sensing data source, whereas CS-LSRSIR is designed to retrieve remote sensing images with a similar content to the inquiry remote sensing image that are from a different remote sensing data source. In the literature, US-LSRSIR has been widely exploited, but CS-LSRSIR is rarely discussed. In practical situations, remote sensing images from different kinds of remote sensing data sources are continually increasing, so there is a great motivation to exploit CS-LSRSIR. Therefore, this paper focuses on CS-LSRSIR. To cope with CS-LSRSIR, this paper proposes source-invariant deep hashing convolutional neural networks (SIDHCNNs), which can be optimized in an end-to-end manner using a series of well-designed optimization constraints. To quantitatively evaluate the proposed SIDHCNNs, we construct a dual-source remote sensing image data set that contains eight typical land-cover categories and 10 000 dual samples in each category. Extensive experiments show that the proposed SIDHCNNs can yield substantial improvements over several baselines involving the most recent techniques.
Living with permanent infertility: A German study on attitudes toward motherhood in individuals with Complete Androgen Insensitivity Syndrome (CAIS) and Mayer–Rokitansky–Küster–Hauser Syndrome (MRKHS)

Abstract In this study the authors examined the issue of permanent infertility in two diagnoses of the diverse sex developments (DSD) spectrum: Complete Androgen Insensitivity Syndrome (CAIS) and Mayer-Rokitansky-KÏster-Hauser Syndrome (MRKHS). The participants with CAIS (n = 12) was older, showed a lower wish for a child and was less distressed about their infertility compared to participants with MRKHS (n = 49). Our data indicated an “indifferent” attitude toward motherhood in CAIS and an “ambivalent” attitude in MRKHS. Depression was frequent in both. Infertility is a source of distress. However, the two groups seem to cope in different ways. Comprehensive medical information and psychological support should be provided.
CORDIC-Based Architecture for Computing Nth Root and Its Implementation

This paper presents a COordinate Rotation Digital Computer (CORDIC)-based architecture for the computation of Nth root and proves its feasibility by hardware implementation. The proposed architecture performs the task of Nth root simply by shift-add operations and enables easy tradeoff between the speed (or precision) and the area. Technically, we divide the Nth root computation into three different subtasks, and map them onto three different classes of the CORDIC accordingly. To overcome the drawback of narrow convergence range of the CORDIC algorithm, we adopt several innovative methods to yield a much improved convergence range. Subsequently, in terms of convergence range and precision, a flexible architecture is developed. The architecture is validated using MATLAB with extensive vector matching. Finally, using a pipelined structure with fixed-point input data, we implement the example circuits of the proposed architecture with radicand ranging from zero to one million, and achieve an average mean of approximately 10<sup>−7</sup> for the relative error. The design is modeled using Verilog HDL and synthesized under the TSMC 40-nm CMOS technology. The report shows a maximum frequency of 2.083 GHz with <inline-formula> <tex-math notation="LaTeX">$197421.00~{\mu }\text{m}^{2}$ </tex-math></inline-formula> area. The area decreases to <inline-formula> <tex-math notation="LaTeX">$169689.98~{\mu }\text{m}^{2}$ </tex-math></inline-formula> when the frequency lowers to 1.00 GHz.
Respiratory syncytial virus-associated seizures in Korean children, 2011–2016

Purpose Respiratory syncytial virus (RSV) infection can cause various neurological complications. This study aimed to investigate the RSV-associated neurologic manifestations that present with seizures. Methods We retrospectively reviewed the medical records of patients aged less than 15 years with laboratory-confirmed RSV infections and seizures between January 2011 and December 2016 in a regional hospital in South Korea. Results During this period, 1,193 patients with laboratory-confirmed RSV infection were identified. Of these, 35 (35 of 1,193, 2.93%; boys, 19; girls, 16; mean age: 20.8±16.6 months) presented with seizure. Febrile seizure was the most common diagnosis (27 of 35, 77.1%); simple febrile seizures in 13 patients (13 of 27, 48.1%) and complex febrile seizures in 14 (14 of 27, 51.9%). Afebrile seizures without meningitis or encephalopathy were observed in 5 patients (5 of 35, 14.3%), seizures with meningitis in 2 (2 of 35, 5.7%), and seizure with encephalopathy in 1 (1 of 35, 2.9%) patient. Lower respiratory symptoms were not observed in 8 patients. In a patient with encephalopathy, brain diffusion-weighted magnetic resonance imaging revealed transient changes in white matter, suggesting cytotoxic edema as the mechanism underlying encephalopathy. Most patients recovered with general management, and progression to epilepsy was noted in only 1 patient. Conclusion Although febrile seizures are the most common type of seizure associated with RSV infection, the proportion of patients with complex febrile seizures was higher than that of those with general febrile seizures. Transient cytotoxic edema may be a pathogenic mechanism in RSV-related encephalopathy with seizures.
Mutations in Bcl9 and Pygo genes cause congenital heart defects by tissue-specific perturbation of Wnt/β-catenin signaling

Genetic alterations in human BCL9 genes have repeatedly been found in congenital heart disease (CHD) with as-of-yet unclear causality. BCL9 proteins and their Pygopus (Pygo) co-factors can participate in canonical Wnt signaling via binding to β-catenin. Nonetheless, their contributions to vertebrate heart development remain uncharted. Here, combining zebrafish and mouse genetics, we document tissue-specific functions in canonical Wnt signaling for BCL9 and Pygo proteins during heart development. In a CRISPR-Cas9-based genotype-phenotype association screen, we uncovered that zebrafish mutants for bcl9 and pygo genes largely retain β-catenin activity, yet develop cardiac malformations. In mouse, both systemic and lineage-specific loss of the Pygo-BCL9-β-catenin complex caused heart defects with outflow tract malformations, aberrant cardiac septation and valve formation, and compact myocardium hypoplasia. Mechanistically, these phenotypes coincide with transcriptional deregulation during heart patterning, and Pygo2 associates with β-catenin at cis-regulatory regions of cardiac genes. Taken together, our results establish BCL9 and Pygo as tissue-specific β-catenin co-factors during vertebrate heart development. Our results further implicate alterations in BCL9 and BCL9L in human CHDs as possibly causative.
Clock drawing and neuroanatomical correlates: A systematic review

The popular clock drawing test (CDT) is easy to administer, acceptable to patients, and has excellent psychometric properties. Although it has been used primarily as a cognitive screening test, many studies have attempted to establish the CDT's ability to localize specific brain lesions or pathology. This systematic review aimed to summarize the evidence on the neuroanatomical correlates of the CDT.
CardioXNet: Automated Detection for Cardiomegaly Based on Deep Learning

In this paper, we present an automated procedure to determine the presence of cardiomegaly on chest X-ray image based on deep learning. The proposed algorithm CardioXNet uses deep learning methods U-NET and cardiothoracic ratio for diagnosis of cardiomegaly from chest X-rays. U-NET learns the segmentation task from the ground truth data. OpenCV is used to denoise and maintain the precision of region of interest once minor errors occur. Therefore, Cardiothoracic ratio (CTR) is calculated as a criterion to determine cardiomegaly from U-net segmentations. End-to-end Dense-Net neural network is used as baseline. This study has shown that the feasibility of combing deep learning segmentation and medical criterion to automatically recognize heart disease in medical images with high accuracy and agreement with the clinical results.
Fast Adaptive RNN Encoder–Decoder for Anomaly Detection in SMD Assembly Machine

Surface Mounted Device (SMD) assembly machine manufactures various products on a flexible manufacturing line. An anomaly detection model that can adapt to the various manufacturing environments very fast is required. In this paper, we proposed a fast adaptive anomaly detection model based on a Recurrent Neural Network (RNN) Encoder–Decoder with operating machine sounds. RNN Encoder–Decoder has a structure very similar to Auto-Encoder (AE), but the former has significantly reduced parameters compared to the latter because of its rolled structure. Thus, the RNN Encoder–Decoder only requires a short training process for fast adaptation. The anomaly detection model decides abnormality based on Euclidean distance between generated sequences and observed sequence from machine sounds. Experimental evaluation was conducted on a set of dataset from the SMD assembly machine. Results showed cutting-edge performance with fast adaptation.
Learning Text Representations for 500K Classification Tasks on Named Entity Disambiguation

Named Entity Disambiguation algorithms typically learn a single model for all target entities. In this paper we present a word expert model and train separate deep learning models for each target entity string, yielding 500K classification tasks. This gives us the opportunity to benchmark popular text representation alternatives on this massive dataset. In order to face scarce training data we propose a simple data-augmentation technique and transfer-learning. We show that bag-of-word-embeddings are better than LSTMs for tasks with scarce training data, while the situation is reversed when having larger amounts. Transferring a LSTM which is learned on all datasets is the most effective context representation option for the word experts in all frequency bands. The experiments show that our system trained on out-of-domain Wikipedia data surpass comparable NED systems which have been trained on in-domain training data.
Headache at the Time of First Symptom Manifestation of Multiple Sclerosis: A Prospective, Longitudinal Study

Aims: Headaches have not been considered a typical symptom of multiple sclerosis (MS), although since 2000, almost every study showed high prevalence. We screened 50 MS patients at the time of first occurrence of neurological symptoms and found the highest prevalence of headache in MS that was ever recorded (78%). Postmortem histological analyses of MS patients’ brains revealed lymphoid follicle-like structures in the cerebral meninges and a pathophysiological link between headache, and high inflammatory activity especially in the initial phase of MS could be suspected. The aim of this study was to get insights into the persistence of headache in the further progress of the disease. Methods: In a prospective, multicenter study, 50 MS patients at the time of first symptom manifestation were screened for the presence of headache within the last 4 weeks and again 6 months later with help of the Rostock Headache Questionnaire (Rokoko) as well as using the evaluation of case history and clinical-neurological investigation. Results: We found a decrease of headache prevalence after 6 months from 78 to 61% (p = 0.01). We could also show a decrease of headache frequency, measured by days with headache in the last 4 weeks (9.5 vs. 5.9, p = 0.001). Migraine or probable migraine was the most frequent headache. In both investigations, the most frequent headache was recurrent pain with pulsating and throbbing character that lasted between 4 and 72 h. Conclusion: Headaches should be taken seriously as an important symptom in early MS. The decrease of headache 6 months after first symptom manifestation of MS could be a result of the immunomodulatory therapy. Young patients in whom migraine-like headaches occur should obligatory undergo an MRI of the head, and in the case of abnormal findings differential diagnosis should be initiated. This could reduce latency until final diagnosis of MS and enable early treatment.
Bayesian Transfer Learning for the Prediction of Self-reported Well-being Scores

Predicting the severity and onset of depressive symptoms is of great importance. User-specific models have better performance than a general model but require significant amounts of training data from each individual, which is often impractical to obtain. Even when this is possible, there is a significant lag between the beginning of the data-collection phase and when the system is completely trained and thus able to start making useful predictions. In this study, we propose a transfer learning Bayesian modelling method based on a Markov Chain Monte Carlo (MCMC) sampler and Bayesian model averaging for dealing with the challenge of building user-specific predictive models able to make predictions of self-reported well-being scores with limited sparse training data. The evaluation of our method using real-world data collected within the NEVERMIND project showed a better predictive performance for the transfer learning model compared to conventional learning with no transfer.
Pharmacotherapy for Atrial Fibrillation in Patients With Chronic Kidney Disease: Insights From ORBIT‐AF

Background Chronic kidney disease (CKD) is a common comorbidity in patients with atrial fibrillation. The presence of CKD complicates drug selection for stroke prevention and rhythm control. Methods and Results Patients enrolled in ORBIT AF (Outcomes Registry for Better Informed Treatment of Atrial Fibrillation) with baseline renal function and follow‐up data were included (N=9019). CKD was defined as an estimated creatinine clearance <60 mL/min. Patient characteristics were compared by CKD status, and Cox proportional hazards modeling was used to examine the association between oral anticoagulant (OAC) use and outcomes and antiarrhythmic drug use and outcomes stratified by CKD stages. At enrollment, 3490 (39%) patients had an estimated creatinine clearance <60 mL/min. Patients with CKD were older and had higher CHA 2 DS 2 VASc and Anticoagulant and Risk Factors in Atrial Fibrillation (ATRIA) scores. A rhythm control strategy was selected less frequently in patients with CKD, while OAC use was lower among Stage IV and V CKD patients. After adjustment, no significant interaction was noted for OAC and CKD on all‐cause mortality (P=0.5442) or cardiovascular death (P=0.1233), although a trend for increased major bleeding (P=0.0608) and stroke, systemic embolism or transient ischemic attack (P=0.0671) was observed. No interaction was noted for antiarrhythmic drug use and CKD status on all‐cause mortality (P=0.9706), or stroke, systemic embolism or transient ischemic attack (P=0.4218). Conclusions Patients with atrial fibrillation and CKD are less likely to be treated with rhythm control. Patients with advanced CKD are less likely to receive OAC. Finally, outcomes with OAC in patients with advanced CKD may be materially different with higher rates of both bleeding and stroke.
9‐Year Trend in the Management of Acute Heart Failure in Japan: A Report From the National Consortium of Acute Heart Failure Registries

Background Acute heart failure (AHF) is a heterogeneous condition, and its characteristics and management patterns differ by region. Furthermore, limited evidence is available on AHF outside of Western countries. A project by the National Consortium of Acute Heart Failure Registries was designed to evaluate the trends over time in patient backgrounds, in‐hospital management patterns, and long‐term outcomes of patients with AHF over 9 years in Japan. Methods and Results Between 2007 and 2015, registry data for patients with AHF were collected from 3 large‐scale quality AHF registries (ATTEND/WET‐HF/REALITY‐AHF). Predefined end points were trends over time in age, sex, and clinical outcomes, including short‐ and long‐term mortality and readmission for heart failure. The final data set consisted of 9075 patients with AHF. No significant differences in patient backgrounds and laboratory findings (eg, anemia or renal function) were observed, with the exception of patient age; mean age became substantially higher over 9 years (71.6–77.0 years; P for trend, <0.001). On the contrary, length of hospital stay became shorter (mean, 26–16 days). These changes were not associated with in‐hospital mortality (4.7–7.5%) or 30‐day heart failure readmission rate (4.8–5.4%), as well as 1‐year mortality and heart failure readmission rate (20.1–23.3% and 23.6–26.2%, respectively). Conclusions Length of hospital stay in patients with AHF shortened over the 9‐year period despite the increasing age of the patients. However, short‐ and long‐term outcomes do not seem to be affected; continuous efforts to monitor clinical outcomes in patients with AHF are needed.
Cutting through the MADness: Expectations about what a target is doing impact how likely it is to be found in dynamic visual displays

When searching for things in the world, we seldom encounter the static environment so often afforded by laboratory search tasks. Dynamic events tend to capture attention; however, Kunar and Watson previously found that dynamic search displays (search for vowel targets among moving and blinking letter stimuli) resulted in strikingly high miss rates. A possible explanation for the high miss rates is that the presence of dynamic features resulted in participants becoming sensitive to the likelihood of appearance for each target category and dynamic feature combination (e.g., moving A, blinking A, moving E), even though dynamic features did not define the targets. Overall target prevalence was high in these studies, but the prevalence of each target + dynamic feature combination was low, which may have led to a low-prevalence effect, whereby infrequent targets were disproportionately missed. Searchers may have preferentially searched for (or avoided) certain dynamic features, even though the dynamic features did not differentiate targets from distractors. We investigated whether the frequency with which targets possessed particular dynamic features would affect the likelihood of search misses. We found that targets possessing features which rarely accompanied a “hit” were more often missed than those with dynamic features more strongly associated with target detection. This suggests that searchers are sensitive to the prevalence of non-defining features and use this information to direct their searches.