Miracle-dz/UniFAR-FTUs huggingface dataset:
MRI tumor segmentation with densely connected 3D CNN

Glioma is one of the most common and aggressive types of primary brain tumors. The accurate segmentation of subcortical brain structures is crucial to the study of gliomas in that it helps the monitoring of the progression of gliomas and aids the evaluation of treatment outcomes. However, the large amount of required human labor makes it difficult to obtain the manually segmented Magnetic Resonance Imaging (MRI) data, limiting the use of precise quantitative measurements in the clinical practice. In this work, we try to address this problem by developing a 3D Convolutional Neural Network (3D CNN) based model to automatically segment gliomas. The major difficulty of our segmentation model comes with the fact that the location, structure, and shape of gliomas vary significantly among different patients. In order to accurately classify each voxel, our model captures multiscale contextual information by extracting features from two scales of receptive fields. To fully exploit the tumor structure, we propose a novel architecture that hierarchically segments different lesion regions of the necrotic and non-enhancing tumor (NCR/NET), peritumoral edema (ED) and GD-enhancing tumor (ET). Additionally, we utilize densely connected convolutional blocks to further boost the performance. We train our model with a patch-wise training schema to mitigate the class imbalance problem. The proposed method is validated on the BraTS 2017 dataset1 and it achieves Dice scores of 0.72, 0.83 and 0.81 for the complete tumor, tumor core and enhancing tumor, respectively. These results are comparable to the reported state-of-the-art results, and our method is better than existing 3D-based methods in terms of compactness, time and space efficiency.
Competitive caching with machine learned advice

Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution, as compared to an offline optimum. On the other hand, machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future, and usually come with strong guarantees on the expected generalization error. In this work, we develop a framework for augmenting online algorithms with a machine learned predictor to achieve competitive ratios that provably improve upon unconditional worst-case lower bounds when the predictor has low error. Our approach treats the predictor as a complete black box and is not dependent on its inner workings or the exact distribution of its errors. We apply this framework to the traditional caching problem—creating an eviction strategy for a cache of size k . We demonstrate that naively following the oracle’s recommendations may lead to very poor performance, even when the average error is quite low. Instead, we show how to modify the Marker algorithm to take into account the predictions and prove that this combined approach achieves a competitive ratio that both (i) decreases as the predictor’s error decreases and (ii) is always capped by O (log k ), which can be achieved without any assistance from the predictor. We complement our results with an empirical evaluation of our algorithm on real-world datasets and show that it performs well empirically even when using simple off-the-shelf predictions.
Blood donors in Iceland: a nationwide population‐based study from 2005 to 2013

Demographic information of blood donors is important to formulate strategies for recruitment and maintenance of the donor group. Factors like aging population, increasingly advanced medical treatments, and growing safety initiatives to protect donors and recipients of blood components are likely to affect the size of the donor group in the future. The purpose of this study was to determine the size of the donor group in Iceland and describe the demographic and donation characteristics.
Enumeration and identification of bacteria in chicken semen.

Three experiments were conducted to determine the bacteriological quality of chicken semen. Semen was collected from donor males, diluted, and surface inoculated onto seven different bacteriological media, from which randomly selected colonies were identified. Bacterial counts in semen averaged 5.14 log10 cfu/mL. Tryptic soy agar (TSA) was the best medium for the isolation of Gram-positive bacteria, whereas TSA + . 3% bile salts (TSABS) and violet red bile agar + 1% glucose (VRBAG) were the best media for the isolation of Gram-negative and enteric bacteria. The genera of bacteria that were isolated depended on the medium that was used for isolation. The most frequently isolated genera included Escherichia, Staphylococcus, Micrococcus, Enterococcus, and Salmonella. Most of the bacteria that were isolated were endemic to poultry and were common environmental bacteria. This indicates that the environment and feed are important sources of bacterial contamination in broilers.
HIGH-RISK GASTRIC PATHOLOGY AND PREVALENT AUTOIMMUNE DISEASES IN PATIENTS WITH PERNICIOUS ANEMIA.

OBJECTIVE Pernicious anemia (PA) develops from atrophic gastritis due to autoimmune destruction of parietal cells and results in achlorhydria, vitamin B12 and iron deficiencies, anemia, neurologic deficits, and premalignant and malignant stomach lesions. We report the presentation, diagnosis and gastric complications of PA in patients from an endocrinology practice. METHODS Thirty-four patients (31 female, 3 male) with PA who underwent esophagogastroduodenoscopy (EGD) or gastrectomy were identified. Pertinent clinical, laboratory, and pathology findings were reviewed and summarized. RESULTS The mean age of patients was 58.6 ± 14.2 years; the onset of PA was age 50.2 ± 15.3 years. Anemia reflected vitamin B12 and/or iron deficiencies. Parietal cell antibodies (PCA) were detected in 97% of patients, and intrinsic factor blocking antibody (IFBA) was found in 52%. Fasting gastrin and chromogranin A levels were elevated (1,518.0 ± 1,588.3 pg/mL, and 504.9.1 ± 1,524.9 ng/mL respectively). Autoimmune or immunologic diseases (AIDs) were present in 32/34 patients. Stomach pathology showed premalignant or malignant lesions in 26 patients, including gastric neuroendocrine tumors (GNETs) in 6 and adenocarcinoma in 1. One patient presented with neurologic symptoms and subacute combined degeneration of the posterior column of the spinal cord. CONCLUSION PA should be suspected in patients with unexplained anemia or neurologic symptoms. The diagnosis of PA relies on fasting gastrin and gastric auto-antibody testing, in addition to hematologic evaluation. EGD with measurement of gastric pH and biopsies of the fundus and antrum identifies patients with achlorhydria, atrophic gastritis, and premalignant and malignant stomach lesions. EGD surveillance of patients with high-risk stomach lesions is recommended. ABBREVIATIONS AID = autoimmune or immunologic disease; EGD = esophagogastroduodenoscopy; GNET = gastric neuroendocrine tumor; IFBA = intrinsic factor blocking antibody; PA = pernicious anemia; PCA = parietal cell antibody; T1D = type 1 diabetes.
What Matters in Semantic Feature Analysis: Practice-Related Predictors of Treatment Response in Aphasia.

Purpose This study investigated the predictive value of practice-related variables-number of treatment trials delivered, total treatment time, average number of trials per hour, and average number of participant-generated features per trial-in response to semantic feature analysis (SFA) treatment. Method SFA was administered to 17 participants with chronic aphasia daily for 4 weeks. Individualized treatment and semantically related probe lists were generated from items that participants were unable to name consistently during baseline testing. Treatment was administered to each list sequentially in a multiple-baseline design. Naming accuracy for treated and untreated items was obtained at study entry, exit, and 1-month follow-up. Results Item-level naming accuracy was analyzed using logistic mixed-effect regression models. The average number of features generated per trial positively predicted naming accuracy for both treated and untreated items, at exit and follow-up. In contrast, total treatment time and average trials per hour did not significantly predict treatment response. The predictive effect of number of treatment trials on naming accuracy trended toward significance at exit, although this relationship held for treated items only. Conclusions These results suggest that the number of patient-generated features may be more strongly associated with SFA-related naming outcomes, particularly generalization and maintenance, than other practice-related variables. Supplemental Materials https://doi.org/10.23641/asha.5734113.
Self-Learning Power Control in Wireless Sensor Networks

Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay.
Cost-Effectiveness of Supplemental Donor Milk Versus Formula for Very Low Birth Weight Infants

In this RCT, cost-effectiveness of supplemental DHM versus PTF for VLBW infants was evaluated from birth to 18 months’ CA from a societal perspective. OBJECTIVES: To determine the cost-effectiveness of supplemental donor human milk (DHM) versus preterm formula (PTF) for very low birth weight (VLBW, <1500 g) infants from a societal perspective to 18 months’ corrected age. METHODS: This prospective cost-effectiveness analysis of 363 VLBW infants was conducted for a randomized control trial. Infants recruited from October 2010 to December 2012 were fed DHM or PTF whenever mother’s milk was unavailable. Formal health care costs for initial hospitalization and readmissions were obtained from standardized cost-accounting systems and physician fees. Informal and nonhealth care sector costs (eg, caregiver transportation, labor market earnings) were calculated from parent reports. RESULTS: Mean infant birth weight was 996 (SD, 272) grams. Incidence of necrotizing enterocolitis (NEC) differed between groups (all stages 3.9% DHM, 11.0% PTF; P = .01). Costs to 18 months did not differ with a mean (95% confidence interval) of 217 624 (197 697–237 551) and 217 245 (196 494–237 995) 2015 Canadian dollars in the DHM and PTF groups. Postdischarge costs were lower in the DHM (46 440 [40 648–52 233]) than PTF group (55 102 [48 269–61 934]) (P = .04), driven by parent lost wages. DHM cost an additional $5328 per case of averted NEC. CONCLUSIONS: In a high mother’s milk use setting, total costs from a societal perspective to 18 months of providing supplemental DHM versus PTF to VLBW infants did not differ, although postdischarge costs were lower in the DHM group. Although supplemental DHM was not cost-saving, it reduced NEC supporting its use over PTF.
Repertoire Analysis of Antibody CDR-H3 Loops Suggests Affinity Maturation Does Not Typically Result in Rigidification

Antibodies can rapidly evolve in specific response to antigens. Affinity maturation drives this evolution through cycles of mutation and selection leading to enhanced antibody specificity and affinity. Elucidating the biophysical mechanisms that underlie affinity maturation is fundamental to understanding B-cell immunity. An emergent hypothesis is that affinity maturation reduces the conformational flexibility of the antibody’s antigen-binding paratope to minimize entropic losses incurred upon binding. In recent years, computational and experimental approaches have tested this hypothesis on a small number of antibodies, often observing a decrease in the flexibility of the Complementarity Determining Region (CDR) loops that typically comprise the paratope and in particular the CDR-H3 loop, which contributes a plurality of antigen contacts. However, there were a few exceptions, and previous studies were limited to a small handful of cases. Here, we determined the structural flexibility of the CDR-H3 loop for thousands of recently-determined homology models of the human peripheral blood cell antibody repertoire using rigidity theory. We found no clear delineation in the flexibility of naïve and antigen-experienced antibodies. To account for possible sources of error, we additionally analyzed hundreds of human and mouse antibodies in the Protein Data Bank through both rigidity theory and B-factor analysis. By both metrics, we observed only a slight decrease in the CDR-H3 loop flexibility when comparing affinity-matured antibodies to naïve antibodies, and the decrease was not as drastic as previously reported. Further analysis, incorporating molecular dynamics (MD) simulations, revealed a spectrum of changes in flexibility. Our results suggest that rigidification may be just one of many biophysical mechanisms for increasing affinity.
Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review

Nowadays, interactions with others do not only involve human peers but also automated systems. Many studies suggest that the motor predictive systems that are engaged during action execution are also involved during joint actions with peers and during other human generated action observation. Indeed, the comparator model hypothesis suggests that the comparison between a predicted state and an estimated real state enables motor control, and by a similar functioning, understanding and anticipating observed actions. Such a mechanism allows making predictions about an ongoing action, and is essential to action regulation, especially during joint actions with peers. Interestingly, the same comparison process has been shown to be involved in the construction of an individual's sense of agency, both for self-generated and observed other human generated actions. However, the implication of such predictive mechanisms during interactions with machines is not consensual, probably due to the high heterogeneousness of the automata used in the experimentations, from very simplistic devices to full humanoid robots. The discrepancies that are observed during human/machine interactions could arise from the absence of action/observation matching abilities when interacting with traditional low-level automata. Consistently, the difficulties to build a joint agency with this kind of machines could stem from the same problem. In this context, we aim to review the studies investigating predictive mechanisms during social interactions with humans and with automated artificial systems. We will start by presenting human data that show the involvement of predictions in action control and in the sense of agency during social interactions. Thereafter, we will confront this literature with data from the robotic field. Finally, we will address the upcoming issues in the field of robotics related to automated systems aimed at acting as collaborative agents.
Assessment of increased sampling pump flow rates in a disposable, inhalable aerosol sampler

ABSTRACT A newly designed, low-cost, disposable inhalable aerosol sampler was developed to assess workers personal exposure to inhalable particles. This sampler was originally designed to operate at 10 L/min to increase sample mass and, therefore, improve analytical detection limits for filter-based methods. Computational fluid dynamics modeling revealed that sampler performance (relative to aerosol inhalability criteria) would not differ substantially at sampler flows of 2 and 10 L/min. With this in mind, the newly designed inhalable aerosol sampler was tested in a wind tunnel, simultaneously, at flows of 2 and 10 L/min flow. A mannequin was equipped with 6 sampler/pump assemblies (three pumps operated at 2 L/min and three pumps at 10 L/min) inside a wind tunnel, operated at 0.2 m/s, which has been shown to be a typical indoor workplace wind speed. In separate tests, four different particle sizes were injected to determine if the sampler's performance with the new 10 L/min flow rate significantly differed to that at 2 L/min. A comparison between inhalable mass concentrations using a Wilcoxon signed rank test found no significant difference in the concentration of particles sampled at 10 and 2 L/min for all particle sizes tested. Our results suggest that this new aerosol sampler is a versatile tool that can improve exposure assessment capabilities for the practicing industrial hygienist by improving the limit of detection and allowing for shorting sampling times.
Cannabidiol (CBD) as an Adjunctive Therapy in Schizophrenia: A Multicenter Randomized Controlled Trial.

OBJECTIVE Research in both animals and humans indicates that cannabidiol (CBD) has antipsychotic properties. The authors assessed the safety and effectiveness of CBD in patients with schizophrenia. METHOD In an exploratory double-blind parallel-group trial, patients with schizophrenia were randomized in a 1:1 ratio to receive CBD (1000 mg/day; N=43) or placebo (N=45) alongside their existing antipsychotic medication. Participants were assessed before and after treatment using the Positive and Negative Syndrome Scale (PANSS), the Brief Assessment of Cognition in Schizophrenia (BACS), the Global Assessment of Functioning scale (GAF), and the improvement and severity scales of the Clinical Global Impressions Scale (CGI-I and CGI-S). RESULTS After 6 weeks of treatment, compared with the placebo group, the CBD group had lower levels of positive psychotic symptoms (PANSS: treatment difference=-1.4, 95% CI=-2.5, -0.2) and were more likely to have been rated as improved (CGI-I: treatment difference=-0.5, 95% CI=-0.8, -0.1) and as not severely unwell (CGI-S: treatment difference=-0.3, 95% CI=-0.5, 0.0) by the treating clinician. Patients who received CBD also showed greater improvements that fell short of statistical significance in cognitive performance (BACS: treatment difference=1.31, 95% CI=-0.10, 2.72) and in overall functioning (GAF: treatment difference=3.0, 95% CI=-0.4, 6.4). CBD was well tolerated, and rates of adverse events were similar between the CBD and placebo groups. CONCLUSIONS These findings suggest that CBD has beneficial effects in patients with schizophrenia. As CBD's effects do not appear to depend on dopamine receptor antagonism, this agent may represent a new class of treatment for the disorder.
Shall We Report Cardiomegaly at Routine Computed Tomography of the Chest?

Purpose To examine the accuracy of the cardiothoracic ratio (CTR) at routine computed tomography (CT) of the chest to diagnose cardiomegaly by using echocardiography (ECHO) as a criterion standard, and secondarily, to search for CT cardiac measurements correlating with left ventricular hypertrophy (LVH) at ECHO. Materials and Methods We searched 2 databases: cancer patients who had chest CT who also underwent routine ECHO by one experienced cardiologist, from January to March 2008. Consecutive patients were enrolled who had chest radiography (CXR) within 1 month and ECHO within 6 months of CT. Cardiothoracic ratio was defined as the transverse greatest cardiac diameter from outer to outer myocardium on axial images divided by the transverse greatest thoracic diameter from inner to inner chest wall on axial images, as measured by 2 board-certified radiologists. Left ventricular short diameter on CT was measured from inner to inner myocardium. Left ventricular hypertrophy at ECHO was defined using the standard American Society of Echocardiography criteria. Other data captured included cardiac and chemotherapy history, and secondary signs of heart failure. The Pearson correlation coefficient (r) and Wilcoxon rank sum tests and receiver operating characteristic (ROC) curves were used for statistical analysis. Results 101 patients, 52 men and 49 women, with a mean age of 58 years met the entry criteria. Cardiothoracic ratio at CXR and CT were highly correlated (r = 0.802) (P < 0.001). There was moderate ability of CT CTR to identify LVH (area under the receiver operating characteristic curve AUC = 0.70; 95% CI, 0.51–0.90). The CT left ventricular short diameter showed moderate correlation with the ECHO left ventricular internal diameter (r = 0.49) and left ventricular mass (r = 0.37). Conclusion In patients with cancer undergoing routine ECHO, the cardiothoracic ratio at routine CT scans was highly correlated with that at CXR. Preliminary estimates in this small study indicate a low likelihood of LVH when the CTR is less than 0.49.
Studies of TCP's retransmission timeout mechanism

This paper focuses on the initial value of TCP's retransmission timeout (RTO) timer. We make a three-fold contribution to the work in this area. Firstly, to motivate this work, we conduct a study of Internet traffic to determine loss rates for those packets that rely on timeouts with the initial RTO value to recover. Secondly, we experiment with TCP stacks in nine different operating systems to understand the initial RTO values used. Finally, we conduct simulations with HTTP traffic models to study the impact of different initial RTO values on the transfer delay of Web objects. The experiments show that average transfer delay can be impacted by as much 70% on bottleneck links with loss rates above 6%. Further, TCP flows with timer granularity below 100 ms or round trip times (RTT) below 250 ms can suffer similar performance degradation. The results suggest that TCP's initial RTO value can be reduced from its current recommended value of 3s to between 500 ms and 1s.
Learning Deep Disentangled Embeddings with the F-Statistic Loss

Deep-embedding methods aim to discover representations of a domain that make explicit the domain's class structure. Disentangling methods aim to make explicit compositional or factorial structure. We combine these two active but independent lines of research and propose a new paradigm for discovering disentangled representations of class structure; these representations reveal the underlying factors that jointly determine class. We propose and evaluate a novel loss function based on the $F$ statistic, which describes the separation of two or more distributions. By ensuring that distinct classes are well separated on a subset of embedding dimensions, we obtain embeddings that are useful for few-shot learning. By not requiring separation on all dimensions, we encourage the discovery of disentangled representations. Our embedding procedure matches or beats state-of-the-art procedures on deep embeddings, as evaluated by performance on recall@$k$ and few-shot learning tasks. To evaluate alternative approaches on disentangling, we formalize two key properties of a disentangled representation: modularity and explicitness. By these criteria, our procedure yields disentangled representations, whereas traditional procedures fail. The goal of our work is to obtain more interpretable, manipulable, and generalizable deep representations of concepts and categories.