Miracle-dz/UniFAR-FTUs huggingface dataset:
Socioeconomic Disparities in Mortality Among Women With Incident Breast Cancer Before and After Implementation of Medicare Part D

Background: Breast cancer patients exhibit survival disparities based on socioeconomic status (SES). Disparities may be attributable to access to expensive oral endocrine agents. Objectives: Define recent socioeconomic disparities in breast cancer survival and determine whether these improved after implementation of the Medicare Part D program. Design: Difference-in-difference natural experiment of women diagnosed and treated before or after implementation of Medicare Part D. Subjects: Female Medicare beneficiaries with early-stage breast cancer: 54,772 diagnosed in 2001 and 46,371 in 2007. Measures: SES was based on Medicaid enrollment and zip code per capita income, all-cause mortality from Medicare, and cause of death from National Death Index. Results: Among women diagnosed pre-Part D, 40.5% of poor beneficiaries had died within 5 years compared with 20.3% of high-income women (P<0.0001). Post-Part D, 33.6% of poor women and 18.4% of high-income women died by 5 years. After adjustment for potential confounders, improvement in all-cause mortality post-Part D was greater for poorer women compared with more affluent women (P=0.002). However, absolute improvement in breast cancer-specific mortality was 1.8%, 1.2%, and 0.8% (P=0.88 for difference in improvement by SES), respectively for poor, near-poor, and high-income women, whereas analogous improvement in mortality from other causes was 5.1%, 3.8%, and 0.9% (P=0.067 for difference in improvement by SES). Conclusions: Large survival disparities by SES exist among breast cancer patients. The Part D program successfully ameliorated SES disparities in all-cause mortality. However, improvement was concentrated in causes of death other than breast cancer, suggesting remaining gaps in care.
Multichannel Detrended Fluctuation Analysis Reveals Synchronized Patterns of Spontaneous Spinal Activity in Anesthetized Cats

The analysis of the interaction and synchronization of relatively large ensembles of neurons is fundamental for the understanding of complex functions of the nervous system. It is known that the temporal synchronization of neural ensembles is involved in the generation of specific motor, sensory or cognitive processes. Also, the intersegmental coherence of spinal spontaneous activity may indicate the existence of synaptic neural pathways between different pairs of lumbar segments. In this study we present a multichannel version of the detrended fluctuation analysis method (mDFA) to analyze the correlation dynamics of spontaneous spinal activity (SSA) from time series analysis. This method together with the classical detrended fluctuation analysis (DFA) were used to find out whether the SSA recorded in one or several segments in the spinal cord of the anesthetized cat occurs either in a random or in an organized manner. Our results are consistent with a non-random organization of the sets of neurons involved in the generation of spontaneous cord dorsum potentials (CDPs) recorded either from one lumbar segment (DFA- mean = 1.040.09) or simultaneously from several lumbar segments (mDFA- mean = 1.010.06), where = 0.5 indicates randomness while 0.5 indicates long-term correlations. To test the sensitivity of the mDFA method we also examined the effects of small spinal lesions aimed to partially interrupt connectivity between neighboring lumbosacral segments. We found that the synchronization and correlation between the CDPs recorded from the L5 and L6 segments in both sides of the spinal cord were reduced when a lesion comprising the left dorsal quadrant was performed between the segments L5 and L6 (mDFA- = 0.992 as compared to initial conditions mDFA- = 1.186). The synchronization and correlation were reduced even further after a similar additional right spinal lesion (mDFA- = 0.924). In contrast to the classical methods, such as correlation and coherence quantification that define a relation between two sets of data, the mDFA method properly reveals the synchronization of multiple groups of neurons in several segments of the spinal cord. This method is envisaged as a useful tool to characterize the structure of higher order ensembles of cord dorsum spontaneous potentials after spinal cord or peripheral nerve lesions.
Transient analysis of multi‐state dc–dc converters using system energy characteristics

The study of multi‐state dc–dc power conversion techniques is restricted by the complicated inner switching behaviors. This paper presents a general and unified transient analysis for various sorts of multi‐state dc–dc converters from a viewpoint of their system energy characteristics. With the applications to the boost converters, the proposed analytical method has indicated its advantages of high convenience and practicability to the multi‐state converters. The generalized concepts of system energy parameters of dc–dc converters are introduced and applied to the transient analysis. Consequently, the expressions of system model parameters of multi‐state dc–dc converters are deduced. The new 2nd order transfer functions are obtained to describe the large‐ and small‐signal mathematical models accurately. The model simulation and experimental results are provided to support the theoretical analysis. Copyright © 2007 John Wiley & Sons, Ltd.
Newtonian Viscosity of Amorphous Silicon Carbonitride at High Temperature

The creep viscosity of chemical-precursor-derived silicon carbonitride (SiCN), which is known to remain predominantly amorphous at temperatures below 1400°C, was measured in the temperature range 1090-1280°C. Experiments were done in uniaxial compression at constant loads in pure nitrogen atmosphere. The creep behavior exhibited three stages. In stage I the strain rate decreased rapidly with time and deformation was accompanied by densification. In stage II the samples exhibited a steady-state creep rate. In stage III, which commenced after long-term deformation, creep gradually declined to rates that were below the sensitivity of our apparatus. The relative density of the specimens during stage II and stage III remained constant at ≅2.3 g/cm3. The shear viscosity in stage II was nearly Newtonian and was measured to be 1.3 × 1013-5.0 1013 Pa·s at 1280°C, which is approximately 103 times the value for fused silica. The creep-hardened as well as uncrept specimens contained silicon nitride crystallites. The volume fraction of these crystals was variable but always less than 5%. Such a small volume fraction of crystals does not explain the dramatic creep-hardening behavior in stage III, even if it is assumed that the crystals formed during creep deformation in stage II.
Modeling and Design of “Smart Braid” Inductance Sensors for Fiber-Reinforced Elastomeric Enclosures

Fiber-reinforced soft actuators use inextensible fibers to shape the expansion of elastomeric fluid-filled chambers. The classic example, McKibben muscles, is part of a broader class of cylindrical soft actuators known as fiber-reinforced elastomeric enclosures (FREEs). These actuators can be designed to twist while extending or contracting. Using a circuit of conductive, electrically insulated wire as the reinforcing fibers allows one to measure the motion of the actuator with the circuit inductance. These sensors, developed previously for McKibben muscles, are known as “Smart Braids. ” This paper extends the concept of Smart Braids to the broader class of two-fiber-family cylindrical FREEs. A dimensionless model for the inductance of Smart Braid FREEs is presented that can be scaled to specific sensor geometries. This model depends only on the ratio of the fiber lengths in the two families and the angle of one of the fibers. The model was validated both numerically and experimentally, predicting the sensitivity of experimental sensors accurately, the largest errors being −11% and 9%. The model presented in this paper will enable high-level design decisions for Smart Braid FREES and optimization without computationally expensive direct numerical simulation.
Practical Block-Wise Neural Network Architecture Generation

Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset.
Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme

Background Despite recent discoveries of new molecular targets and pathways, the search for an effective therapy for Glioblastoma Multiforme (GBM) continues. A newly emerged field, radiogenomics, links gene expression profiles with MRI phenotypes. MRI-FLAIR is a noninvasive diagnostic modality and was previously found to correlate with cellular invasion in GBM. Thus, our radiogenomic screen has the potential to reveal novel molecular determinants of invasion. Here, we present the first comprehensive radiogenomic analysis using quantitative MRI volumetrics and large-scale gene- and microRNA expression profiling in GBM. Methods Based on The Cancer Genome Atlas (TCGA), discovery and validation sets with gene, microRNA, and quantitative MR-imaging data were created. Top concordant genes and microRNAs correlated with high FLAIR volumes from both sets were further characterized by Kaplan Meier survival statistics, microRNA-gene correlation analyses, and GBM molecular subtype-specific distribution. Results The top upregulated gene in both the discovery (4 fold) and validation (11 fold) sets was PERIOSTIN (POSTN). The top downregulated microRNA in both sets was miR-219, which is predicted to bind to POSTN. Kaplan Meier analysis demonstrated that above median expression of POSTN resulted in significantly decreased survival and shorter time to disease progression (P<0.001). High POSTN and low miR-219 expression were significantly associated with the mesenchymal GBM subtype (P<0.0001). Conclusion Here, we propose a novel diagnostic method to screen for molecular cancer subtypes and genomic correlates of cellular invasion. Our findings also have potential therapeutic significance since successful molecular inhibition of invasion will improve therapy and patient survival in GBM.
How Predation and Landscape Fragmentation Affect Vole Population Dynamics

Background Microtine species in Fennoscandia display a distinct north-south gradient from regular cycles to stable populations. The gradient has often been attributed to changes in the interactions between microtines and their predators. Although the spatial structure of the environment is known to influence predator-prey dynamics of a wide range of species, it has scarcely been considered in relation to the Fennoscandian gradient. Furthermore, the length of microtine breeding season also displays a north-south gradient. However, little consideration has been given to its role in shaping or generating population cycles. Because these factors covary along the gradient it is difficult to distinguish their effects experimentally in the field. The distinction is here attempted using realistic agent-based modelling. Methodology/Principal Findings By using a spatially explicit computer simulation model based on behavioural and ecological data from the field vole (Microtus agrestis), we generated a number of repeated time series of vole densities whose mean population size and amplitude were measured. Subsequently, these time series were subjected to statistical autoregressive modelling, to investigate the effects on vole population dynamics of making predators more specialised, of altering the breeding season, and increasing the level of habitat fragmentation. We found that fragmentation as well as the presence of specialist predators are necessary for the occurrence of population cycles. Habitat fragmentation and predator assembly jointly determined cycle length and amplitude. Length of vole breeding season had little impact on the oscillations. Significance There is good agreement between our results and the experimental work from Fennoscandia, but our results allow distinction of causation that is hard to unravel in field experiments. We hope our results will help understand the reasons for cycle gradients observed in other areas. Our results clearly demonstrate the importance of landscape fragmentation for population cycling and we recommend that the degree of fragmentation be more fully considered in future analyses of vole dynamics.
Glycophenotypic Alterations Induced by Pteridium aquilinum in Mice Gastric Mucosa: Synergistic Effect with Helicobacter pylori Infection

The bracken fern Pteridium aquilinum is a plant known to be carcinogenic to animals. Epidemiological studies have shown an association between bracken fern exposure and gastric cancer development in humans. The biological effects of exposure to this plant within the gastric carcinogenesis process are not fully understood. In the present work, effects in the gastric mucosa of mice treated with Pteridium aquilinum were evaluated, as well as molecular mechanisms underlying the synergistic role with Helicobacter pylori infection. Our results showed that exposure to Pteridium aquilinum induces histomorphological modifications including increased expression of acidic glycoconjugates in the gastric mucosa. The transcriptome analysis of gastric mucosa showed that upon exposure to Pteridium aquilinum several glycosyltransferase genes were differently expressed, including Galntl4, C1galt1 and St3gal2, that are mainly involved in the biosynthesis of simple mucin-type carbohydrate antigens. Concomitant treatment with Pteridium aquilinum and infection with Helicobacter pylori also resulted in differently expressed glycosyltransferase genes underlying the biosynthesis of terminal sialylated Lewis antigens, including Sialyl-Lewisx. These results disclose the molecular basis for the altered pattern of glycan structures observed in the mice gastric mucosa. The gene transcription alterations and the induced glycophenotypic changes observed in the gastric mucosa contribute for the understanding of the molecular mechanisms underlying the role of Pteridium aquilinum in the gastric carcinogenesis process.
Clinical Application of 3D-CISS MRI Sequences for Diagnosis and Surgical Planning of Spinal Arachnoid Diverticula and Adhesions in Dogs

Abstract Objective Abnormalities within the spinal arachnoid space are often treated surgically, but they can be challenging to detect with conventional magnetic resonance imaging (MRI) sequences. 3D-CISS sequences are considered superior in evaluating structures surrounded by cerebrospinal fluid (CSF) due to the high signal-to-noise ratio, high contrast-to-noise ratio and intrinsic insensitivity to motion with minimal signal loss due to CSF pulsations. Our objective was to describe findings and advantages in adding 3D-CISS sequences to routine MRI in patients affected by spinal arachnoid diverticula (SAD) or arachnoid adhesions. Material and Methods This article is a retrospective review of medical records of 19 dogs admitted at Fitzpatrick Referrals between 2013 and 2017 that were diagnosed with SAD and confirmed surgically. Inclusion criterions were the presence of clinical signs compatible with compressive myelopathy and an MRI diagnosis, which included the 3D-CISS sequence. Our database was searched for additional 19 dogs diagnosed with other spinal lesions other than SAD that had the same MR sequences. All MR images were anonymized and evaluated by two assessors. Conclusion and Clinical Relevance 3D-CISS sequence appears to improve confidence in diagnosing and surgical planning (Mann–Whitney U-test: p < 0.0005), delineating SAD from other changes associated with abnormal CSF hydrodynamics and providing more anatomical details than conventional MRI sequences. The clinical data in combination with imaging findings would limit over interpretation, when concurrent pathology within the arachnoid space is present.
An optimized lung cancer classification system for computed tomography images

Amongst diverse cancers, lung cancer is measured to be the foremost reason of cancer demise with utmost demise pace. Nodules lying on lungs have distinct structures, they could be either circle or coil shaped which under various circumstances composes the recognition complex. In this work a system has been urbanized for detection of lung cancer in its early stages and classification between malignant and benign tumors via images from Computerized Tomography (CT) scanner. Lung cancer detection process has four steps which include pre-processing phase, segmentation, feature extraction and lung cancer cell classification. BAT Algorithm is applied to provide considerable optimization results which improve the performance of system. The classification between malignant nodules and benign has been done through Artificial Neural Network Ensemble to provide results of higher accuracy. The overall accuracy, sensitivity and specificity of 98.5%, 100% and 91% respectively is acquired in the system.
Effects of vertex insertion on local rescheduling in wireless sensor networks

Rescheduling has abundant issues yet to be explored. The local rescheduling problem in wireless sensor networks (WSNs) have been firstly addressed and investigated in this paper. The algorithms of local rescheduling have been proposed and evaluates the performance of reschedule solutions with different metrics. The solutions have to be under the limitation that the network should stay connected after the process of rescheduling. This paper introduces a theoretical bound of maximum degree after node insertion. Along with empirical results in real world settings, the results motivate the design of algorithms and give possible reasons why existing rescheduling algorithms do not work efficiently. Two local link rescheduling algorithms and one local broadcast rescheduling algorithm are developed as improvements. With different node densities and other critical parameters, simulations show that the developed algorithms greatly improve the ratio of finding proper solutions successfully in both types of scheduling compared with other existing simple algorithms.
Maximum mutual information design for amplify-and-forward multi-hop MIMO relaying systems under channel uncertainties

In this paper, we investigate maximum mutual information design for multi-hop amplify-and-forward (AF) multiple-input multiple-out (MIMO) relaying systems with imperfect channel state information, i.e., Gaussian distributed channel estimation errors. The robust design is formulated as a matrix-variate optimization problem. Exploiting the elegant properties of Majorization theory and matrix-variate functions, the optimal structures of the forwarding matrices at the relays and precoding matrix at the source are derived. Based on the derived structures, a water-filling solution is proposed to solve the remaining unknown variables.
Spectral Unmixing Imaging for Differentiating Brown Adipose Tissue Mass and Its Activation

Recent large-scale clinical analysis indicates that brown adipose tissue (BAT) mass levels inversely correlate with body-mass index (BMI), suggesting that BAT is associated with metabolic disorders such as obesity and diabetes. PET imaging with 18F-FDG is the most commonly used method for visualizing BAT. However, this method is not able to differentiate between BAT mass and BAT activation. This task, in fact, presents a tremendous challenge with no currently existing methods to separate BAT mass and BAT activation. Our previous results indicated that BAT could be successfully imaged in mice with near infrared fluorescent (NIRF) curcumin analogues. However, the results from conventional NIRF imaging could not reflect what portion of the NIRF signal from BAT activation contributed to the signal observed. To solve this problem, we used spectral unmixing to separate/unmix NIRF signal from oil droplets in BAT, which represents its mass and NIRF signal from blood, which represents BAT activation. In this report, results from our proof-of-concept investigation demonstrated that spectral unmixing could be used to separate NIRF signal from BAT mass and BAT activation.
Non-branching tree-decompositions

We prove that if a graph has a tree-decomposition of width at most w, then it has a tree-decomposition of width at most w with certain desirable properties. We will use this result in a subsequent paper to show that every 2-connected graph of large path-width has a minor isomorphic to either a large tree with a vertex attached to every vertex of the tree or a large outerplanar graph.
Healthy Coping, Negative Emotions, and Diabetes Management

PURPOSE The purpose of this systematic review is to assess the literature pertinent to healthy coping in diabetes management and to identify effective or promising interventions and areas needing further investigation. METHODS A PubMed search identified 186 articles in English published between January 1, 1990, and July 31, 2006, addressing diabetes and emotion, quality of life, depression, adjustment, anxiety, coping, family therapy, behavior therapy, psychotherapy, problem solving, couples therapy, or marital therapy. RESULTS Connections among psychological variables, behavioral factors, coping, metabolic control, and quality of life are appreciable and multidirectional. Interventions for which well-controlled studies indicate benefits for quality of life and/or metabolic control include general self-management, coping/problem-solving interventions, stress management, support groups, cognitive-behavioral therapy, behavioral family systems therapy, cognitive-analytic therapy, multisystemic therapy, medications for depression, and the Pathways intervention integrating case management, support of medication, and problem-solving counseling. CONCLUSIONS Psychological, emotional, related behavioral factors, and quality of life are important in diabetes management, are worthy of attention in their own right, and influence metabolic control. A range of interventions that achieve benefits in these areas provide a base for developing versatile programs to promote healthy coping.