Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetic Resonance Imaging for Characterization of a Chick Embryo Model of Cancer Cell Metastases

Metastasis is the most common cause of death for patients with cancer. To fully understand the steps involved in metastatic dissemination, in vivo models are required, of which murine ones are the most common. Therefore, preclinical imaging methods such as magnetic resonance imaging (MRI) have mainly been developed for small mammals and their potential to monitor cancer growth and metastasis in nonmammalian models is not fully harnessed. We have here used MRI to measure primary neuroblastoma tumor size and metastasis in a chick embryo model. We compared its sensitivity and accuracy to end-point fluorescence detection upon dissection. Human neuroblastoma cells labeled with green fluorescent protein (GFP) and micron-sized iron particles were implanted on the extraembryonic chorioallantoic membrane of the chick at E7. T2 RARE, T2-weighted fast low angle shot (FLASH) as well as time-of-flight MR angiography imaging were applied at E14. Micron-sized iron particle labeling of neuroblastoma cells allowed in ovo observation of the primary tumor and tumor volume measurement noninvasively. Moreover, T2 weighted and FLASH imaging permitted the detection of small metastatic deposits in the chick embryo, thereby reinforcing the potential of this convenient, 3R compliant, in vivo model for cancer research.
Repeated isoflurane in adult male mice leads to acute and persistent motor decrements with long‐term modifications in corpus callosum microstructural integrity

Isoflurane is a commonly used inhalational anesthetic, clinically and in animal experimental studies. Although it has been reported as safe, recent findings suggest that despite widespread use, isoflurane‐induced inhalational anesthesia can lead to various pathophysiological and cognitive alterations. Therefore, we aimed to investigate the long‐term behavioral and white matter consequences of repeated isoflurane exposure. Twenty 3‐month‐old C57BL/6J male mice received one exposure of isoflurane for 40 min or 2 exposures to isoflurane separated by 3 days. Behavioral paradigms (open field, balance beam, foot fault, rotarod, elevated zero maze, tail suspension, water maze, and social recognition tests) were administered at 1, 3, 5, 7, and 90 days post exposure. Animals exposed to repeated isoflurane showed significant motor deficits on the balance beam and increased anxiety‐like behavior. Animals exposed to single isoflurane showed impaired performance on the foot fault test. Diffusion tensor imaging showed that repeated isoflurane exposure led to long‐term disruption of water diffusivity in corpus callosum (CC) white matter. Furthermore, 2‐D structure‐tensor analysis from stained brain sections showed differences in the microstructural organization of CC white matter in mice with single versus repeated isoflurane exposures. These results suggest that behavioral deficits observed up to 90 days after repeated isoflurane exposure resulted from, at least in part, altered CC white matter microstructural integrity.
Stochastic Neighbor Embedding under f-divergences

The t-distributed Stochastic Neighbor Embedding (t-SNE) is a powerful and popular method for visualizing high-dimensional data. It minimizes the Kullback-Leibler (KL) divergence between the original and embedded data distributions. In this work, we propose extending this method to other f-divergences. We analytically and empirically evaluate the types of latent structure-manifold, cluster, and hierarchical-that are well-captured using both the original KL-divergence as well as the proposed f-divergence generalization, and find that different divergences perform better for different types of structure. A common concern with $t$-SNE criterion is that it is optimized using gradient descent, and can become stuck in poor local minima. We propose optimizing the f-divergence based loss criteria by minimizing a variational bound. This typically performs better than optimizing the primal form, and our experiments show that it can improve upon the embedding results obtained from the original $t$-SNE criterion as well.
Changes in disability and their relationship with skin thickening, in diffuse and limited cutaneous systemic sclerosis: a retrospective cohort study

Objective: The burden of disability associated with systemic sclerosis (SSc) is being increasingly recognized. Our aim was to test the hypothesis that changes in functional ability over time differ between patients with limited (lcSSc) and diffuse cutaneous (dcSSc) subtypes, and that in dcSSc (but not lcSSc) these changes correlate with skin thickening. Method: This was a retrospective analysis of data collected prospectively between 2005 and 2016 at a single centre. Data recorded at annual review visits included modified Rodnan skin score (mRSS) and Health Assessment Questionnaire Disability Index (HAQ-DI). Yearly rates of mRSS and HAQ-DI change were assessed by individual linear regressions, and those gradients were compared between disease groups (lcSSc/dcSSc) for each of early/late disease (less/greater than 5 years’ duration). Results: The study included 402 patients (110 dcSSc, 292 lcSSc), with mean length of follow-up of 5.5 years (sd 3.5). Mean baseline HAQ-DI was 1.4 in dcSSc and 1.2 in lcSSc. In dcSSc, increased mRSS was associated with worsening disability (ρ = 0.36, p = 0.004) during early but not late disease (ρ = 0.12, p = 0.331). In lcSSc, changes in mRSS were not associated with changes in disability for early (ρ = −0.15, p = 0.173) or late disease (ρ = 0.10, p = 0.137). Conclusion: These findings confirm high disability in patients with SSc. A relationship between HAQ-DI and mRSS (worsening mRSS associated with increasing disability) was found only in patients with early dcSSc, suggesting that in other patient subgroups other factors play the major role.
The Effect of Propofol and Dexmedetomidine Sedation on Norepinephrine Requirements in Septic Shock Patients: A Crossover Trial

Objectives: Propofol-based sedation may increase hemodynamic instability by decreasing vascular tone and venous return. Incremental exogenous catecholamines doses may be required to counteract such effects, aggravating the deleterious effects of sympathetic overstimulation. &agr;-2 adrenergic agonists have been reported to decrease norepinephrine requirements in experimental septic shock. The aim of the present study is to test the hypothesis that switching from sedation with propofol to the &agr;-2 agonist dexmedetomidine may decrease norepinephrine doses in septic shock. Design: Prospective open-label crossover study. Settings: University hospital, ICU. Patients: Thirty-eight septic shock patients requiring norepinephrine to maintain adequate mean arterial pressure and needing deep sedation with propofol and remifentanil to maintain a Richmond Agitation-Sedation Scale score between –3 and –4. Interventions: An initial set of measurements including hemodynamics, norepinephrine doses, and depth of sedation were obtained during sedation with propofol. Propofol was then replaced by dexmedetomidine and a second set of data was obtained after 4 hours of dexmedetomidine infusion. Sedation was switched back to propofol, and a final set of measurements was obtained after 8 hours. A Richmond Agitation-Sedation Scale score between –3 and –4 was maintained during the study period. Measurements and Main Results: Norepinephrine requirements decreased from 0.69 ± 0.72 &mgr;g/kg/min before dexmedetomidine to 0.30 ± 0.25 &mgr;g/kg/min 4 hours after dexmedetomidine infusion, increasing again to 0.42 ± 0.36 &mgr;g/kg/min while on propofol 8 hours after stopping dexmedetomidine (p < 0.005). Dexmedetomidine dosage was 0.7 ± 0.2 &mgr;g/kg/hr. Before and after dexmedetomidine infusion, sedative doses remained unchanged (propofol 2.6 ± 1.2 vs 2.6 ± 1.2 mg/kg/hr; p = 0.23 and remifentanil 1.27 ± 0.17 vs 1.27 ± 0.16 &mgr;g/kg/hr; p = 0.52, respectively). Richmond Agitation-Sedation Scale was –4 (–4 to –3) before, –4 (–4 to –3) during, and –4 (–4 to –4) after dexmedetomidine (p = 0.07). Conclusions: For a comparable level of sedation, switching from propofol to dexmedetomidine resulted in a reduction of catecholamine requirements in septic shock patients.
Spectrum of illness in migrants to Canada: sentinel surveillance through CanTravNet

BACKGROUND Due to ongoing political instability and conflict in many parts of the world, migrants are increasingly seeking asylum and refuge in Canada. METHODS We examined demographic and travel correlates of illnesses among migrants to Canada to establish a detailed epidemiologic framework of this population for Canadian practitioners. Data on ill-returned Canadian travellers presenting to a CanTravNet site between 1 January 2015 and 31 December 2015 were analyzed. RESULTS During the study period, 2415 ill travellers and migrants presented to a CanTravNet site, and of those, 519 (21.5%) travelled for the purpose of migration. Sub-Saharan Africa (n = 160, 30.8%), southeast Asia (n = 84, 16.2%) and south central Asia (n = 75, 14.5%) were the most common source regions for migrants, while the top specific source countries, of 98 represented, were the Philippines (n = 45, 8.7%), China (n = 36, 6.9%) and Vietnam (n = 31, 6.0%). Compared with non-migrant travellers, migrants were more likely to have a pre-existing immunocompromising medical condition, such as HIV or diabetes mellitus (P < 0.0001), and to require inpatient management of their illness (P < 0.0001). Diagnoses such as tuberculosis (n = 263, 50.7%), hepatitis B and C (n = 78, 15%) and HIV (n = 11, 2.1%) were over-represented in the migrant population compared with non-migrant travellers (P < 0.0001). Most cases of tuberculosis in the migrant population (n = 263) were latent (82% [n = 216]); only 18% (n = 47) were active. CONCLUSIONS Compared with non-migrant travellers, migrants were more likely to present with a communicable infectious disease, such as tuberculosis, potentially complicated by an underlying immunosuppressing condition such as HIV. These differences highlight the divergent healthcare needs in the migrant population, and underscore the importance of surveillance programmes to understand their burden of illness. Intake programming should be adequately resourced to accommodate the medical needs of this vulnerable population of new Canadians.
Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification

Implicit discourse relation classification is one of the most difficult steps in discourse parsing. The difficulty stems from the fact that the coherence relation must be inferred based on the content of the discourse relational arguments. Therefore, an effective encoding of the relational arguments is of crucial importance. We here propose a new model for implicit discourse relation classification, which consists of a classifier, and a sequence-to-sequence model which is trained to generate a representation of the discourse relational arguments by trying to predict the relational arguments including a suitable implicit connective. Training is possible because such implicit connectives have been annotated as part of the PDTB corpus. Along with a memory network, our model could generate more refined representations for the task. And on the now standard 11-way classification, our method outperforms the previous state of the art systems on the PDTB benchmark on multiple settings including cross validation.
Prevalence and Impact of Diabetes Mellitus on the Frozen Shoulder

Objectives The objective of this study was to estimate the prevalence of undiagnosed diabetes mellitus in patients with newly diagnosed frozen shoulder (FS) and study whether diabetes mellitus increases the severity of FS disease. Methods Patients with newly diagnosed FS were consecutively included in this case-control study. Patients who were not already diagnosed as having diabetes mellitus were invited to be tested with the hemoglobin A1c blood sample test. The study population was compared with a control group, consisting of five individuals from the general population matched on age and sex. The passive range of motion, Oxford Shoulder Score, and visual analog scale (VAS) for average and maximum daily pain was recorded for all of the patients in the study group. Results A total of 235 patients were included, 34 (14%) of whom were diagnosed as having diabetes mellitus before the examination. Of the remaining 201 patients, 122 (61%) agreed to be tested for diabetes mellitus. None of the tested patients had undiagnosed diabetes mellitus. This was not significantly different from the prevalence in the matched control population (P = 0.09). There was no difference between patients with and without diabetes mellitus in average daily VAS (P = 0.46) nor maximum daily VAS (P = 0.44). The Oxford Shoulder Score was similar in the two groups (P = 0.23) as was the range of motion. Conclusions The prevalence of undiagnosed diabetes mellitus is low in patients with FS and does not differ from the general population. Diabetes mellitus does not seem to affect patients’ perceived severity of an FS.
Pif1 is essential for efficient replisome progression through lagging strand G-quadruplex DNA secondary structures

Abstract Pif1 DNA helicase is a potent unwinder of G-quadruplex (G4) structures in vitro and functions to maintain genome stability at G4 sequences in Saccharomyces cerevisiae. Here, we developed and utilized a live-cell imaging approach to quantitatively measure the progression rates of single replication forks through different G4 containing sequences in individual yeast cells. We show that in the absence of Pif1, replication rates through specific lagging strand G4 sequences in vivo is significantly decreased. In contrast, we found that in the absence of Pif1, replication rates through the same G4s on the leading strand are not decreased relative to the respective WT strains, showing that Pif1 is essential only for efficient replication through lagging strand G4s. Additionally, we show that a canonical PIP sequence in Pif1 interacts with PCNA and that replication through G4 structures is significantly slower in the absence of this interaction in vitro and in vivo. Thus, Pif1–PCNA interaction is essential for optimal replisome progression through G4 sequences, highlighting the importance of coupling between Pif1 activity and replisome progression during yeast genome replication.
EyeSense: Towards Information Extraction on Corneal Images

Humans sense most of the environment through their eyes. Detecting people's activities and context from their visual behavior using mobile eye trackers is well studied. Typically, these systems require some kind of calibration prior to usage, unsuitable for the end user. Cameras that actively record the user's field of view became a valid alternative for lifelogging applications. But such approaches do not include gaze information and may cause privacy challenges. In this work we propose an in-depth analysis of the first long-term corneal imaging dataset (i.e., the reflection of the environment on the human eye). All data was manually labeled with information about the attended objects. This was compared to an automatic approach using a state-of-the art neural network.
Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene?

Due to the variability in characteristics of audio scenes, some scenes can naturally be recognized earlier than others. In this work, rather than using equal-length snippets for all scene categories, as is common in the literature, we study to which temporal extent an audio scene can be reliably recognized given state-of-the-art models. Moreover, as model fusion with deep network ensemble is prevalent in audio scene classification, we further study whether, and if so, when model fusion is necessary for this task. To achieve these goals, we employ two single-network systems relying on a convolutional neural network and a recurrent neural network for classification as well as early fusion and late fusion of these networks. Experimental results on the LITIS-Rouen dataset show that some scenes can be reliably recognized with a few seconds while other scenes require significantly longer durations. In addition, model fusion is shown to be the most beneficial when the signal length is short.
Diverse Expression of IL-32 in Diffuse and Intestinal Types of Gastric Cancer

Introduction Gastric cancer (GC) represents one of the most common cancers worldwide, frequently diagnosed at advanced stages with poor prognosis, indicating on need for new diagnostic and prognostic markers. The aim of the study was to determine the expression of IL-32, proinflammatory and angiogenic mediators, in patients with diffuse and intestinal gastric cancer and the relationship with clinicopathological aspects. Material and Methods The tissue samples of diffuse and intestinal types of tumor of 70 patients with gastric cancer were analyzed. Expression of IL-32, VEGF, IL-17, and CD31 was measured by immunohistochemistry. Results IL-32 expression was significantly lower in tissue samples from patients with diffuse type of gastric cancer that is also a severe and more progressive form (TNM stages III and IV, poor histological differentiation, and higher nuclear grade III). Expression of IL-17 was also decreased in patients with diffuse type of gastric cancer. Microvascular density was diminished in diffuse type of gastric cancer. Conclusions Downregulated expression of IL-32 in tumor tissue of patients with diffuse type of gastric cancer may implicate on its role in limiting ongoing proinflammatory and proangiogenic processes. This emphasizes on unrecognized role of IL-32 in biology of diffuse type of gastric cancer.
Medical code prediction with multi-view convolution and description-regularized label-dependent attention

A ubiquitous task in processing electronic medical data is the assignment of standardized codes representing diagnoses and/or procedures to free-text documents such as medical reports. This is a difficult natural language processing task that requires parsing long, heterogeneous documents and selecting a set of appropriate codes from tens of thousands of possibilities---many of which have very few positive training samples. We present a deep learning system that advances the state of the art for the MIMIC-III dataset, achieving a new best micro F1-measure of 55.85\%, significantly outperforming the previous best result (Mullenbach et al. 2018). We achieve this through a number of enhancements, including two major novel contributions: multi-view convolutional channels, which effectively learn to adjust kernel sizes throughout the input; and attention regularization, mediated by natural-language code descriptions, which helps overcome sparsity for thousands of uncommon codes. These and other modifications are selected to address difficulties inherent to both automated coding specifically and deep learning generally. Finally, we investigate our accuracy results in detail to individually measure the impact of these contributions and point the way towards future algorithmic improvements.
Does the use of interferential current prior to pilates exercises accelerate improvement of chronic nonspecific low back pain?

AIM To evaluate whether active interferential current (AIC) before pilates exercises improves pain faster than placebo interferential current (PIC) in patients with chronic nonspecific low back pain (CNLBP). METHODS A total of 142 patients with CNLBP were treated with AIC or PIC before pilates exercises. Pain intensity was measured daily before and after treatment by Pain Numerical Rating Scale. Statistical analysis was performed using survival analysis for Kaplan-Meier method. RESULTS The AIC group presented 30% reduction of pain one session, 50% reduction of pain two sessions and 100% reduction of pain three sessions faster than the PIC group and these improvements were statistically significant (p < 0.05). CONCLUSION The AIC before pilates exercises can reduce pain faster than PIC in patients with CNLBP.
Multiuser Wirelessly Powered Backscatter Communications: Nonlinearity, Waveform Design, and SINR-Energy Tradeoff

Wireless power transfer and backscatter communications have emerged as promising solutions for energizing and communicating with power limited devices. Despite some progress in wirelessly powered backscatter communications, the focus has been on backscatter and energy harvesters (EHs). Recently, significant progress has been made on the design of the transmit multisine waveform, adaptive to the channel state information at the transmitter (CSIT), in a point-to-point backscatter system. In this paper, we leverage the work and study the design of the transmit multisine waveform in a multiuser backscatter system, made of one transmitter, one reader, and multiple tags active simultaneously. We derive an efficient algorithm to optimize the transmit waveform so as to identify the tradeoff between the amount of energy harvested at the tags and the reliability of the communication, measured in terms of signal-to-interference-plus-noise ratio (SINR) at the reader. The performance with the optimized waveform based on the linear and nonlinear EH models is studied. The numerical results demonstrate the benefits of accounting for the EH nonlinearity, multiuser diversity, frequency diversity, and multisine waveform adaptive to the CSIT to enlarge the SINR-energy region.
Deep Robust Framework for Protein Function Prediction Using Variable-Length Protein Sequences

The order of amino acids in a protein sequence enables the protein to acquire a conformation suitable for performing functions, thereby motivating the need to analyze these sequences for predicting functions. Although machine learning based approaches are fast compared to methods using BLAST, FASTA, etc., they fail to perform well for long protein sequences (with more than 300 amino acids). In this paper, we introduce a novel method for construction of two separate feature sets for protein using bi-directional long short-term memory network based on the analysis of fixed 1) single-sized segments and 2) multi-sized segments. The model trained on the proposed feature set based on multi-sized segments is combined with the model trained using state-of-the-art Multi-label Linear Discriminant Analysis (MLDA) features to further improve the accuracy. Extensive evaluations using separate datasets for biological processes and molecular functions demonstrate not only improved results for long sequences, but also significantly improve the overall accuracy over state-of-the-art method. The single-sized approach produces an improvement of +3.37 percent for biological processes and +5.48 percent for molecular functions over the MLDA based classifier. The corresponding numbers for multi-sized approach are +5.38 and +8.00 percent. Combining the two models, the accuracy further improves to +7.41 and +9.21 percent, respectively.