Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and internal validation of a multivariable prediction model for tinnitus recovery following unilateral cochlear implantation: a cross-sectional retrospective study

Objective To develop and internally validate a prediction model for tinnitus recovery following unilateral cochlear implantation. Design A cross-sectional retrospective study. Setting A questionnaire concerning tinnitus was sent to patients with bilateral severe to profound hearing loss, who underwent unilateral cochlear implantation at the University Medical Center Utrecht, the Netherlands, between 1 January 2006 and 31 December 2015. Participants Of 137 included patients, 87 patients experienced tinnitus preoperatively. Data of these 87 patients were used to develop the prediction model. Primary and secondary outcome measures The outcome of the prediction model was tinnitus recovery. Investigated predictors were: age, gender, duration of deafness, preoperative hearing performance, tinnitus duration, severity and localisation, follow-up duration, localisation of cochlear implant (CI) compared with tinnitus side, surgical approach, insertion depth of the electrode, CI brand and difference in hearing threshold following cochlear implantation. Multivariable backward logistic regression was performed. Missing data were handled using multiple imputation. The performance of the model was assessed by the calibrative and discriminative ability of the model. The prediction model was internally validated using bootstrapping techniques. Results The tinnitus recovery rate was 40%. A lower preoperative Consonant-Vowel-Consonant (CVC) score, unilateral localisation of tinnitus and larger deterioration of residual hearing at 250 Hz revealed to be relevant predictors for tinnitus recovery. The area under the receiver operating characteristics curve (AUC) of the initial model was 0.722 (IQR: 0.703–0.729). After internal validation of this prediction model, the AUC decreased to 0.696 (IQR: 0.667–0.700). Conclusion and relevance Lower preoperative CVC score, unilateral localisation of tinnitus and larger deterioration of residual hearing at 250 Hz were significant predictors for tinnitus recovery following unilateral cochlear implantation. The performance of the model developed in this retrospective study is promising. However, before clinical use of the model, the conduction of a larger prospective study is recommended.
Shape-adapted motion model based on thin-plate splines and point clustering for point set registration

This paper focuses on the problem of ill-posedness of deformable point set registration and we propose a new approach to restrict the solution space using shape information. The basic elements of the investigated kind of registration algorithm are a cost functional, an optimization strategy and a motion model. The motion model determines the kind of motions and deformations that are allowed and how they are restricted. The motion model itself is mainly determined by the kind of parameterized transformation used to express the motion/deformation. Here, we observe that matching with more degrees of freedom (the parameters of the transformation) than necessary can introduce mismatches due to a higher sensitivity to noise or by destroying local shape information. In this paper we propose a cost functional which is robust to noise and we introduce a new method to specify a shape adapted deformation model based on thin-plate splines and initial control point placing using point clustering. We show that these initial positions have a strong impact on the match and we define them as cluster centers where we cluster on one of the point sets (weighting each point of this set with its distance to the other point set). Our experiments with known ground truth show that the shape adapted model recovers constantly very accurately corresponding points. In our evaluation with more than 1200 single experiments we showed that, compared to a conventional octree based scheme, we could save more than 60% of degrees of freedom while preserving matching quality.
The Spatiotemporal Evolution of Lymph Node Spread in Early Breast Cancer

Purpose: The most significant prognostic factor in early breast cancer is lymph node involvement. This stage between localized and systemic disease is key to understanding breast cancer progression; however, our knowledge of the evolution of lymph node malignant invasion remains limited, as most currently available data are derived from primary tumors. Experimental Design: In 11 patients with treatment-naïve node-positive early breast cancer without clinical evidence of distant metastasis, we investigated lymph node evolution using spatial multiregion sequencing (n = 78 samples) of primary and lymph node deposits and genomic profiling of matched longitudinal circulating tumor DNA (ctDNA). Results: Linear evolution from primary to lymph node was rare (1/11), whereas the majority of cases displayed either early divergence between primary and nodes (4/11) or no detectable divergence (6/11), where both primary and nodal cells belonged to a single recent expansion of a metastatic clone. Divergence of metastatic subclones was driven in part by APOBEC. Longitudinal ctDNA samples from 2 of 7 subjects with evaluable plasma taken perioperatively reflected the two major evolutionary patterns and demonstrate that private mutations can be detected even from early metastatic nodal deposits. Moreover, node removal resulted in disappearance of private lymph node mutations in ctDNA. Conclusions: This study sheds new light on a crucial evolutionary step in the natural history of breast cancer, demonstrating early establishment of axillary lymph node metastasis in a substantial proportion of patients. Clin Cancer Res; 24(19); 4763–70. ©2018 AACR.
Robust genetic interaction analysis

For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that genetic interactions (including gene-gene and gene-environment interactions) play important roles beyond the main genetic and environmental effects. In practical genetic interaction analyses, model mis-specification and outliers/contaminations in response variables and covariates are not uncommon, and demand robust analysis methods. Compared with their nonrobust counterparts, robust genetic interaction analysis methods are significantly less popular but are gaining attention fast. In this article, we provide a comprehensive review of robust genetic interaction analysis methods, on their methodologies and applications, for both marginal and joint analysis, and for addressing model mis-specification as well as outliers/contaminations in response variables and covariates.
Cloud simulators in the implementation and evaluation of virtual machine placement algorithms

In recent years, many algorithms have been proposed for the optimized allocation of virtual machines in cloud data centers. Such algorithms are usually implemented and evaluated in a cloud simulator. This paper investigates the impact of the choice of cloud simulator on the implementation of the algorithms and on the evaluation results. In particular, we report our experiences with porting an algorithm and its evaluation framework from one simulator (CloudSim) to another (DISSECT‐CF). Our findings include limitations in the design of the simulators and in existing algorithm implementations. Based on this experience, we propose architectural guidelines for the integration of virtual machine allocation algorithms into cloud simulators.
Disrupted Cerebrocerebellar Intrinsic Functional Connectivity in Young Adults with High-Functioning Autism Spectrum Disorder: A Data-Driven, Whole-Brain, High-Temporal Resolution Functional Magnetic Resonance Imaging Study

This study examines the resting-state functional-connectivity (RsFc) in young adults with high-functioning autism spectrum disorder (HF-ASD) using state-of-the-art fMRI data acquisition and analysis techniques. High temporal resolution fMRI using simultaneous multi-slice acquisition aided unbiased whole-brain connectome-wide multivariate pattern analysis (MVPA) techniques for assessing RsFc. MVPA revealed two clusters (Crus I/II and lobule IX) of abnormal connectivity in the cerebellum that are consistent with the notion of a triple representation of nonmotor processing in the cerebellum. Whole-brain seed-based RsFc analyses informed by these clusters showed significant under connectivity between the cerebellar and social, emotional, and language brain regions in the HF-ASD group compared to healthy controls. The results we report are coherent with existing structural, functional, and RsFc literature in autism, extend previous literature reporting cerebellar abnormalities in the neuropathology of autism, and highlight the cerebellum as a potential target for therapeutic, diagnostic, predictive, and prognostic developments in HF-ASD. The description of functional connectivity abnormalities reported in this study using whole-brain, data-driven analyses has the potential to crucially advance the development of ASD biomarkers, targets for therapeutic interventions, and neural predictors for measuring treatment response.
Wnt/β-catenin signaling regulates ependymal cell development and adult homeostasis

Significance Little is known about the cellular origin and the molecular signals that regulate spinal cord ependymal cells. In this report, we characterize Wnt-responsive progenitor cells throughout spinal cord development, showing that they are restricted to the dorsal midline and give rise to dorsal ependymal cells in a spatially restricted pattern. In the postnatal and adult spinal cord, ependymal cells continue to exhibit Wnt/β-catenin signaling activity, which promotes ependymal cell proliferation. This is demonstrated by the genetic elimination of β-catenin and inhibition of Wnt secretion in Wnt-activated ependymal cells in vivo, which result in impaired proliferation. Our results thus reveal the molecular signals underlying the formation and regulation of spinal cord ependymal cells. In the adult mouse spinal cord, the ependymal cell population that surrounds the central canal is thought to be a promising source of quiescent stem cells to treat spinal cord injury. Relatively little is known about the cellular origin of ependymal cells during spinal cord development, or the molecular mechanisms that regulate ependymal cells during adult homeostasis. Using genetic lineage tracing based on the Wnt target gene Axin2, we have characterized Wnt-responsive cells during spinal cord development. Our results revealed that Wnt-responsive progenitor cells are restricted to the dorsal midline throughout spinal cord development, which gives rise to dorsal ependymal cells in a spatially restricted pattern. This is contrary to previous reports that suggested an exclusively ventral origin of ependymal cells, suggesting that ependymal cells may retain positional identities in relation to their neural progenitors. Our results further demonstrated that in the postnatal and adult spinal cord, all ependymal cells express the Wnt/β-catenin signaling target gene Axin2, as well as Wnt ligands. Genetic elimination of β-catenin or inhibition of Wnt secretion in Axin2-expressing ependymal cells in vivo both resulted in impaired proliferation, indicating that Wnt/β-catenin signaling promotes ependymal cell proliferation. These results demonstrate the continued importance of Wnt/β-catenin signaling for both ependymal cell formation and regulation. By uncovering the molecular signals underlying the formation and regulation of spinal cord ependymal cells, our findings thus enable further targeting and manipulation of this promising source of quiescent stem cells for therapeutic interventions.
Normalizing hepcidin predicts TMPRSS6 mutation status in patients with chronic iron deficiency.

TO THE EDITOR: Iron-refractory iron deficiency anemia (IRIDA) is characterized by congenital iron deficiency (ID) that is poorly responsive to oral iron treatment. Biallelic mutations in TMPRSS6 are found in most patients with IRIDA. [1][1] TMPRSS6 negatively regulates synthesis of the iron
Unsupervised salient object segmentation from color images

This paper proposes an efficient approach for unsupervised segmentation of salient objects from color images. A set of Gaussian models are first estimated based on a pre-segmentation result of the input image, and then for each pixel, a set of normalized color likelihood measures to each Gaussian model are calculated. The color saliency and spatial saliency of Gaussian models are exploited to generate the pixel-wise saliency map. By thresholding the saliency map, the pixels are classified into object seed pixels, background seed pixels and uncertain pixels to obtain the trimap. For each pixel, the probability belonging to salient object/background is evaluated using kernel density estimation, and the geodesic distances to salient object and background are calculated based on the object likelihood map. By comparing the two geodesic distances, uncertain pixels are finally classified into salient object or background. Experimental results demonstrate the better segmentation performance of the proposed approach.
Sparse Stochastic Zeroth-Order Optimization with an Application to Bandit Structured Prediction

Stochastic zeroth-order (SZO), or gradient-free, optimization allows to optimize arbitrary functions by relying only on function evaluations under parameter perturbations, however, the iteration complexity of SZO methods suffers a factor proportional to the dimensionality of the perturbed function. We show that in scenarios with natural sparsity patterns as in structured prediction applications, this factor can be reduced to the expected number of active features over input-output pairs. We give a general proof that applies sparse SZO optimization to Lipschitz-continuous, nonconvex, stochastic objectives, and present an experimental evaluation on linear bandit structured prediction tasks with sparse word-based feature representations that confirm our theoretical results.
Guidance or No Guidance? A Decision Tree Can Help

Guidance methods have the potential of bringing considerable beneﬁts to Visual Analytics (VA), alleviating the burden on the user and allowing a positive analysis outcome. However, the boundary between conventional VA approaches and guidance is not sharply deﬁned. As a consequence, framing existing guidance methods is complicated and the development of new approaches is also compromised. In this paper, we try to bring these concepts in order, deﬁning clearer boundaries between guidance and no-guidance. We summarize our ﬁndings in form of a decision tree that allows scientists and designers to easily frame their solutions. Finally, we demonstrate the usefulness of our ﬁndings by applying our guideline to a set of published approaches.
Normalized Difference Vegetation Vigour Index: A New Remote Sensing Approach to Biodiversity Monitoring in Oil Polluted Regions

Biodiversity loss remains a global challenge despite international commitment to the United Nations Convention on Biodiversity. Biodiversity monitoring methods are often limited in their geographical coverage or thematic content. Furthermore, remote sensing-based integrated monitoring methods mostly attempt to determine species diversity from habitat heterogeneity somewhat reflected in the spectral diversity of the image used. Up to date, there has been no standardized method for monitoring biodiversity against the backdrop of ecosystem or environmental pressures. This study presents a new method for monitoring the impact of oil pollution an environmental pressure on biodiversity at regional scale and presents a case study in the Niger delta region of Nigeria. It integrates satellite remote sensing and field data to develop a set of spectral metrics for biodiversity monitoring. Using vascular plants of various lifeforms observed on polluted and unpolluted (control) locations, as surrogates for biodiversity, the normalized difference vegetation vigour index (NDVVI) variants were estimated from Hyperion wavelengths sensitive to petroleum hydrocarbons and evaluated for potential use in biodiversity monitoring schemes. The NDVVI ranges from 0 to 1 and stems from the presupposition that increasing chlorophyll absorption in the green vegetation can be used as a predictor to model vascular plant species diversity. The performances of NDVVI variants were compared to traditional narrowband vegetation indices (NBVIs). The results show strong links between vascular plant species diversity and primary productivity of vegetation quantified by the chlorophyll content, vegetation vigour and abundance. An NDVVI-based model gave much more accurate predictions of species diversity than traditional NBVIs (R-squared and prediction square error (PSE) respectively for Shannon’s diversity = 0.54 and 0.69 for NDVVIs and 0.14 and 0.9 for NBVIs). We conclude that NDVVI is a superior remote sensing index for monitoring biodiversity indicators in oil-polluted areas than traditional NBVIs.
Spatially dispersed synapses yield sharply‐tuned place cell responses through dendritic spike initiation

The literature offers evidence for a critical role of spatially-clustered iso-feature synapses in eliciting dendritic spikes essential for sharp feature selectivity, with apparently contradictory evidence demonstrating spatial dispersion of iso-feature synapses. Here, we reconcile this apparent contradiction by demonstrating that the generation of dendritic spikes, the emergence of an excitatory ramp in somatic voltage responses and sharp tuning of place-cell responses are all attainable even when iso-feature synapses are randomly dispersed across the dendritic arbor. We found this tuning sharpness to be critically reliant on dendritic sodium and transient potassium channels and on N-methyl-D-aspartate receptors. Importantly, we demonstrate that synaptic potentiation targeted to afferents from one specific place field is sufficient to effectuate place-field selectivity even when intrinsically disparate neurons received randomly dispersed afferents from multiple place-field locations. These conclusions proffer dispersed localization of isofeature synapses as a strong candidate for achieving sharp feature selectivity in neurons across sensory-perceptual systems. Highlights Systematic analysis of four synaptic localization profiles for sharp spatial tuning Dendritic spikes are generated even with randomly dispersed iso-feature synapses Sharp feature tuning could be achieved even in the absence of synaptic clusters Targeted, yet dispersed, synaptic plasticity sufficient for place-cell emergence
Prenatal Exposure to Antibiotics and Risk of Childhood Obesity in a Multicenter Cohort Study

Mounting evidence has linked postnatal antibiotic use with body mass index (BMI) in children, but the influence of prenatal antibiotic use on offspring obesity risk remains unclear. We aimed to assess the association between fetal exposure to antibiotics and obesity at ages 4 and 7 years among 43,332 children using a multicenter prospective cohort of the US Collaborative Perinatal Project (1959-1976). Antibiotic use was ascertained for mothers during pregnancy. Modified Poisson regression models were used to estimate risk ratios for obesity (BMI >95th percentile), and linear mixed models were applied to assess the association with BMI z score. Repeated prenatal exposure to antibiotics was associated with childhood obesity at age 7 years, and risk of obesity tended to increase with an increasing number of antibiotic exposures (for 2-3 exposures, risk ratio (RR) = 1.22, 95% confidence interval (CI): 1.04, 1.44; for ≥4 exposures, RR = 1.34, 95% CI: 1.03, 1.73). The magnitude of association was strongest for repeated exposures in the second trimester (RR = 1.40, 95% CI: 1.16, 1.71). Prenatal antibiotic use was not associated with obesity or BMI z score at age 4 years. These findings support an increased risk of mid-childhood obesity with repeated use of antibiotics during pregnancy.
CNNs on FPGAs for Track Reconstruction

With recent increases in the luminosity of High Luminosity Large Hadron Collider (HL-LHC) collisions creating more tracking data, an efficient track reconstruction solution has become necessary. As it currently stands, during the level 1 trigger, it is necessary to identify 50 million particle tracks per second with latency lower than 10μs per track. Current algorithms are implemented on ASIC chips or FPGAs and scale O(N)or worse. Thus the track trigger system at HL-LHC will have to process O(10x) more tracks than they currently do while providing the same efficiency and purity and operating with the same bandwidth and latency requirements. Simultaneously, deep learning has become a standard technique in computer vision. The HEP.TrkkX project[1], among others, is exploring the applicability of Deep Neural Networks (DNNs) to the tracking problem. Current popular deep learning libraries are all heavily reliant on Graphics Processing Units (GPUs) to shoulder the bulk of heavy computation. Unfortunately, current and projected GPU architectures do not meet the stringent latency requirements (10μs) of LHC L1 triggers. To address this problem, we have started investigating the feasibility and performance of DNN inference implemented on FPGAs. We demonstrated a pipelined CNN inference in firmware which Preprint submitted to Elsevier April 4, 2018 can be scaled to maximize FPGA resource usage, along with an OpenCL implementation of the same network. We found that Digital Signal Processors (DSPs), a resource on the FPGA which can be used as a multiplier, are a limiting resource. Our firmware implementation allows larger FPGAs with more DSPs available to maximize resource usage in order to further parallelize individual convolution and dense layers. We implemented and benchmarked LeNet[2] on an Altera Cyclone V, a small FPGA with 112 DSPs, as compared to over 5000 in high end FPGAs. Finally, we analyzed the feasibility of DNN architectures used for tracking with very stringent latency requirements.
Fairness-Aware Resource Allocation in Multi-Hop Wireless Powered Communication Networks with User Cooperation

In wireless powered communication networks (WPCNs), the harvested energy varies greatly among user nodes (UNs), resulting in throughput unfairness. Since the harvested energy is limited, each UN must strategically allocate the energy used for forwarding the other nodes’ information and for transmitting its own information, which further aggravates the global unfairness in terms of throughput. In this paper, we leverage user cooperation in multi-hop transmission to improve the throughput fairness. We formulate the fairness problem as the max-min throughput with resource allocation, which is NP-hard. We design an approximate algorithm to address this problem. The theoretical proof and the simulation results both show that the proposed algorithm provides tight upper and lower bounds for the optimal solution. Compared with the benchmark methods, our proposed method significantly enhances the throughput fairness for WPCNs.