Miracle-dz/UniFAR-FTUs huggingface dataset:
An autopsy case of early‐stage amyotrophic lateral sclerosis with TDP‐43 immunoreactive neuronal, but not glial, inclusions

Phosphorylated transactivation response DNA‐binding protein 43 kDa (p‐TDP‐43)‐immunoreactive neuronal and glial cytoplasmic inclusions are a histopathological hallmark of sporadic amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration with TDP‐43. We report an autopsy case of lower motor neuron‐predominant ALS in a 47‐year‐old Japanese man who committed suicide 5 months after onset. Histopathologically, neuronal loss was restricted to the anterior horn of the spinal cord, and no obvious neuronal loss was noted in the motor cortex or brainstem motor nuclei. Bunina bodies were found in the spinal anterior horn cells and the facial and hypoglossal nuclei. Immunohistochemically, p‐TDP‐43‐immunoreactive neuronal, but not glial, cytoplasmic inclusions were frequently found in the spinal anterior horn and facial and hypoglossal nuclei, and rarely in the motor cortex. We considered the present case to be an example of lower motor neuron‐predominant ALS. p‐TDP‐43‐immunoreactive aggregates in neurons, but not in glial cells, may be an early‐stage pathology of ALS.
Compressive Multidimensional Harmonic Retrieval with Prior Knowledge

This paper concerns the problem of estimating multidimensional (MD) frequencies using prior knowledge of the signal spectral sparsity from partial time samples. In many applications, such as radar, wireless communications, and super-resolution imaging, some structural information about the signal spectrum might be known beforehand. Suppose that the frequencies lie in given intervals, the goal is to improve the frequency estimation performance by using the prior information. We study the MD Vandermonde decomposition of block Toeplitz matrices in which the frequencies are restricted to given intervals. We then propose to solve the frequency-selective atomic norm minimization by converting them into semidefinite program based on the MD Vandermonde decomposition. Numerical simulation results are presented to illustrate the good performance of the proposed method.
Low Interest Rates and Housing Booms: The Role of Capital Inflows, Monetary Policy and Financial Innovation

A number of OECD countries experienced an environment of low interest rates and a rapid increase in housing market activity during the last decade. Previous work suggests three potential explanations for these events: expansionary monetary policy, capital inflows due to a global savings glut and excessive financial innovation combined with inappropriately lax financial regulation. In this study we examine the effects of these three factors on the housing market. We estimate a panel VAR for a sample of OECD countries and identify monetary policy and capital inflows shocks using sign restrictions. To explore how these effects change with the structure of the mortgage market and the degree of securitisation, we augment the VAR to let the coefficients vary with mortgage market characteristics. Our results suggest that both types of shocks have a significant and positive effect on real house prices, real credit to the private sector and real residential investment. The responses of housing variables to both types of shocks are stronger in countries with more developed mortgage markets, roughly doubling the responses to a monetary policy shock. The amplification effect of mortgage-backed securitisation is particularly strong for capital inflows shocks, increasing the response of real house prices, residential investment and real credit by a factor of two, three and five, respectively.
6. Conclusions and Future Work

Temporal video segmentation using unsupervised clustering and semantic object tracking",A fuzzy k-nearest neighbor algorithm", IEEE Transactions on systems man, cybernetics. 12. Internal object-part structural information was not used in these tests, but detailed results are provided in [14]. The results above suggest that the framework presented here is suitable for performing visual classification when the elements in the class present a strong consistency in terms of regions (e.g., elephants). In cases where that consistency is limited to a very small subset of the conceptual definition of a class (e.g., skater), we say that our approach is suitable for detection-The Visual Apprentice learns from the examples it is provided, therefore, the definition of skater in the system is limited to skaters with very similar visual characteristics to the one in the examples. In other classes, such as cocktails, for instance, there is a wide variation in terms of the color, size and location of the regions. Our approach is not suitable in such cases. We have presented a different approach to content-based retrieval in which distinct users can utilize an object definition hierarchy to define their own visual classes. Classes are defined in The Visual Apprentice, an implementation of our approach, which learns classifiers that can be used to automatically organize the contents of a database. Our novel framework for classification of visual information incorporates multiple fuzzy classifiers and learning techniques according to the levels of the object definition hierarchy: (1) region, (2) perceptual, (3) object-part, (4) object and (5) scene. The use of generic and task-specific classifiers is possible, and our approach is flexible since each user can have his own visual classifiers. Future research directions include placing a feedback loop in the The Visual Apprentice so that learning is performed incrementally, allowing the user to place additional restrictions on the visual classes. This will require more sophisticated interface tools. In addition, we are working on integrating visual classification results with information provided by other media accompanying the visual information. In particular, we are working on using The Visual Apprentice to build classifiers for news images/video making use of text information. Motion information is also being included in our model and we plan to integrate the work from [26] to assist labeling of video objects. Our system forms part of Columbia's Mpeg-7 testbed [20].
Association Between Blood Glucose Variability and the Characteristics of Vulnerable Plaque in Elderly Non-ST Segment Elevation Acute Coronary Syndrome Patients.

Blood glucose variability is considered to be one of the risk factors for coronary heart disease, and there is growing evidence that blood glucose fluctuation is closely related to the characteristics of plaques. The aim of the study was to investigate the influence of blood glucose variability on the vulnerability of culprit plaques in elderly non-ST segment elevation acute coronary syndrome (NSTE-ACS) patients.Coronary angiography and VH-IVUS were applied to evaluate the components of culprit plaque in NSTE-ACS patients. CGMS monitoring was performed for 72 hours and blood glucose variability was assessed by glycemic excursions (MAGE), absolute means of daily differences (MODD), postprandial glycemic excursions (PPGE), and the largest amplitude of glycemic excursions (LAGE). An oxidative stress indicator (urinary 8-iso-PGF2α) was also tested.Eighty two elderly NSTE-ACS patients were enrolled in this study. Higher glucose variability was associated with the increased culprit plaque instability. MODD was positively correlated with urinary 8-iso-PGF2α. PPGE and urinary 8-iso-PGF2α were independent risk factors for percent fibrous and necrotic volume in culprit plaques (PPGE: β = -0.340, P = 0.024; urinary 8-iso-PGF2α: β = -0.294, P = 0.013). Blood glucose variability is positively related to oxidative stress. With an increase in blood glucose variability, the instability of criminal plaques in elderly NSTE-ACS patients increased.
The Impact of an Educational Video on Clinical Trial Enrollment and Knowledge in Ethnic Minorities: A Randomized Control Trial

Introduction: Innovative methods to increase awareness about clinical trials and address barriers associated with low participation among racial/ethnic minorities are desperately needed. African Americans comprise 5% of all clinical trial participants, and Hispanics make up 1%. Use of multimedia educational material has shown promise as an effective strategy to increase minority clinical trial enrollment. However, this approach has not been broadly implemented. We tested the effect of a video educational program on clinical trial knowledge and enrollment in a sample of oncology outpatients. Methods: A randomized controlled trial was conducted with 63 oncology patients without previous history of clinical trial participation. Participants were randomly assigned to the intervention, to watch a clinical trial educational video in the office, or to the control group which did not receive in-office education. The Clinical Trial Knowledge survey was administered before the intervention and 1 week after the intervention. Participation in clinical trials was assessed 1-year post study participation. Results for white participants and ethnic minorities were compared. Ethnicity was self-reported through the electronic health record and confirmed by self-reporting on questionnaire. Results: Sixty-three participants were recruited in this study. At 1-year follow-up, 3 participants enrolled in clinical trials in the study group which had received office-based video intervention and 2 participants enrolled in the control group (Z = 0.39, p = 0.69). These results were not statistically significant. Impact of the intervention by ethnicity could not be assessed due to low total clinical trial enrollment. The video intervention did not change knowledge, attitudes, or barriers as measured by the Clinical Trial Knowledge Survey. Minority participants did report significantly more negative beliefs and barriers to participation than white participants. Conclusions: Increasing awareness and knowledge about clinical trials in underrepresented communities is an important step to providing opportunities for participation. Future studies should focus on how to address the negative expectations of clinical trials and the greater information needs in minority populations. Tailored or personalized messaging may address negative perceptions of clinical trial participation.
Target Community Detection With User’s Preference and Attribute Subspace

Community detection in the network has become an invaluable tool to explore and reveal the internal organization of nodes. In particular, the target community detection focuses on discovering the “local” links within and connecting to the target community related to user’s preference, which refers to a limited number of nodes in the whole network. A few works in the literature discuss the target community detection. In this paper, we propose a target community detection with user’s preference and attribute subspace. Our method utilizes not only network structure but also node attributes within a certain subspace to quantify both internal consistency and external separability, which is able to capture a user preferred target community. First, the similarity between nodes is calculated via both attributes and structures, and the center node set of the target community can be obtained by extending the sample node given by the user with its neighbors. Second, an attribute subspace calculation method with entropy weights is established based on the center node set, and the attribute subspace of the target community can thus be deduced. Finally, the target community quality, which is the combination of internal connectivity and external separability, is defined, based on which the target community with a user’s preference can be detected. The experimental results on both synthetic network and real-world network datasets demonstrated the efficiency and effectiveness of the proposed algorithm.
Mechanical Properties Prediction for Hot Rolled Alloy Steel Using Convolutional Neural Network

A convolutional neural network (CNN)-based method for predicting the mechanical properties of hot rolled steel using chemical composition and process parameters is proposed. The novel contribution of this research is to introduce the prediction method of CNN into the steel properties prediction field by converting the production data into two-dimensional data images. Compared with the traditional artificial neural network method, the CNN adopts the idea of local connection and weight sharing, which reduces the complexity of the network model, and uses convolution and pooling operations to extract local features for better prediction precision. The experiments in this paper show that the proposed CNN model with the optimal structure provides higher prediction accuracy and higher robustness when compared with other prediction model reported in the literature. Finally, the metallurgical phenomena in the steel rolling processes are verified by sensitivity analysis using the proposed CNN model. The results are consistent with the metallurgical properties of the steel materials used in the experiments. Therefore, the proposed CNN model has a guiding significance in predicting the mechanical properties of hot rolled steel products in practical applications.
Enhanced DOA Estimation Using Linearly Predicted Array Expansion for Automotive Radar Systems

In this paper, we propose an enhanced direction-of-arrival (DOA) estimation method using linearly predicted array expansion to improve the angular resolution for automotive radar systems with a small number of antenna elements. The proposed method extracts the linear relation among signals received in the array antenna and uses it to generate extrapolated signals outside the array antenna. Finally, we apply the DOA estimation methods, such as the Bartlett and multiple signal classification algorithms, to both the original and extrapolated signals, and verify its performance through simulations and field experiments. From the simulation results, in terms of root-mean-square error and resolution probability, we observed that the proposed method had a higher angular resolution and estimation accuracy than conventional interpolation and extrapolation methods. Moreover, with the experiment results, we verified that the proposed array expansion method can be suitably applied to commercial automotive radar systems.
Safety and Tolerability, Pharmacokinetics, and Pharmacodynamics of ACT017, an Antiplatelet GPVI (Glycoprotein VI) Fab: First-in-Human Healthy Volunteer Trial

Objective— ACT017 is a novel, first in class, therapeutic antibody to platelet GPVI (glycoprotein VI) with potent and selective antiplatelet effects. This first-in-human, randomized, placebo-controlled phase 1 study was conducted to evaluate the safety, tolerability, pharmacokinetics, and pharmacodynamics of ACT017 in healthy subjects. Approach and Results— Six cohorts of 8 healthy male and female subjects each received ascending single doses of ACT017 (n=6) or placebo (n=2) as a 6-hour intravenous infusion, with ¼ of the total dose administered within 15 minutes and the rest of the dose (¾ of the total dose) administered within 5 hours and 45 minutes. The 6 investigated doses ranged from 62.5 to 2000 mg. All doses of ACT017 were well tolerated, and no serious adverse events occurred during the study. None of the subjects reported an infusion site reaction. Template bleeding time was not affected in a clinically significant manner by any of the ACT017 doses. Plasma concentrations, determined by liquid chromatography-tandem mass spectrometry, increased linearly with the dose received as were the established pharmacokinetics values. There was no change in the platelet count, platelet GPVI expression assessed by flow cytometry, or plasma levels of soluble GPVI assessed by ELISA. In contrast, administration of ACT017 inhibited collagen-induced platelet aggregation measured by light transmission aggregometry on platelet-rich plasma, and the extent and duration of the effect were dose-dependent. Conclusions— The novel antiplatelet agent ACT017 has consistent pharmacokinetic/pharmacodynamic properties and favorable safety and tolerability profiles warranting further clinical development.
Insomnia as a Partial Mediator of the Relationship Between Personality and Future Symptoms of Anxiety and Depression Among Nurses

Background: This study investigates insomnia as a partial mediator in the relationship between personality and symptoms of anxiety and depression. Methods: The study is based on partly longitudinal data from the ongoing cohort study “Survey of Shift work, Sleep, and Health” (SUSSH) among Norwegian nurses, a survey examining the work situation and health status of Norwegian nurses measured with annual questionnaires. The present study uses data collected in 2012 (Wave 4), 2013 (Wave 5), and 2014 (Wave 6). The final sample at Wave 6 consisted of 2002 participants, of which 91% were females. The questionnaires included items measuring, among others, demographic variables, work time schedule, insomnia (Bergen Insomnia Scale), personality (Mini-IPIP) and anxiety and depression (Hospital Anxiety and Depression Scale). Results: Extraversion and conscientiousness had no significant direct or indirect association with insomnia, anxiety or depression. Neuroticism and insomnia had direct associations to future symptoms of anxiety and depression. Insomnia was also a significant partial mediator of the relationship between both neuroticism and anxiety, and neuroticism and depression, meaning that neuroticism had an indirect relation to symptoms of anxiety and depression through insomnia. When adjusting for previous symptoms of anxiety and depression at Wave 5, insomnia was no longer a significant mediator between neuroticism and anxiety, and only marginally mediated the relationship between neuroticism and depression. Conclusion: The results showed that insomnia may act as a mediator between neuroticism and symptoms of anxiety and depression, but the indirect relationship between neuroticism and anxiety and depression through insomnia is considerably weaker than the direct association. Hence, the mediating effect of insomnia should be interpreted with caution. The sample mainly consisted of female nurses, and the generalizability of the findings to male dominated occupations is limited. Findings from the present study highlight the importance of an integrated approach and strengthen the understanding of how personality and psychopathology are connected.
Aging changes in protective balance and startle responses to sudden drop perturbations.

This study investigated aging changes in protective balance and startle responses to sudden drop perturbations and their effect on landing impact forces (vertical ground reaction forces, vGRF) and balance stability. Twelve healthy older (6 men; mean age = 72.5 ± 2.32 yr, mean ± SE) and 12 younger adults (7 men; mean age = 28.09 ± 1.03 yr) stood atop a moveable platform and received externally triggered drop perturbations of the support surface. Electromyographic activity was recorded bilaterally over the sternocleidomastoid (SCM), middle deltoid, biceps brachii, vastus lateralis (VL), biceps femoris (BF), medial gastrocnemius (MG), and tibialis anterior (TA). Whole body kinematics were recorded with motion analysis. Stability in the anteroposterior direction was quantified using the margin of stability (MoS). Incidence of early onset of bilateral SCM activation within 120 ms after drop onset was present during the first-trial response (FTR) for all participants. Co-contraction indexes during FTRs between VL and BF as well as TA and MG were significantly greater in the older group (VL/BF by 26%, P < 0.05; TA/MG by 37%, P < 0.05). Reduced shoulder abduction between FTR and last-trial responses, indicative of habituation, was present across both groups. Significant age-related differences in landing strategy were present between groups, because older adults had greater trunk flexion (P < 0.05) and less knee flexion (P < 0.05) that resulted in greater peak vGRFs and decreased MoS compared with younger adults. These findings suggest age-associated abnormalities of delayed, exaggerated, and poorly habituated startle/postural FTRs are linked with greater landing impact force and diminished balance stabilization. NEW & NOTEWORTHY This study investigated the role of startle as a pathophysiological mechanism contributing to balance impairment in aging. We measured neuromotor responses as younger and older adults stood on a platform that dropped unexpectedly. Group differences in landing strategies indicated age-associated abnormalities of delayed, exaggerated, and poorly habituated startle/postural responses linked with a higher magnitude of impact force and decreased balance stabilization. The findings have implications for determining mechanisms contributing to falls and related injuries.
Equalization Enhanced Phase Noise in Coherent Receivers: DSP-Aware Analysis and Shaped Constellations

We revisit the analysis of equalization-enhanced phase noise (EEPN) arising in coherent receivers from the interaction between the chromatic dispersion compensation by an electronic equalizer and the phase noise of the local oscillator. Through numerical simulations we highlight EEPN characteristics and investigate its impact on the behavior of the carrier phase recovery algorithm. We show that the blind phase search, which is usually used in practice to recover the carrier phase, partially mitigates the EEPN. We detail a numerical approach to predict the system performance including the phase recovery algorithm and show that taking into account EEPN characteristics relaxes the constraint on the system laser phase noise given by previous pessimistic analytical models. We present experimental validations of our claims, and address future advanced transoceanic systems using 98 GBd probabilistically shaped QAM formats.
Using RIPE Atlas for Geolocating IP Infrastructure

The vast majority of studies on IP geolocation focuses on localizing the end-users, and little attention has been devoted to localizing the elements of the Internet infrastructure, i.e., the routers and servers that make the Internet work. In this paper, we study the maximum theoretical accuracy that can be achieved by a geolocation approach aimed at geolocating the Internet infrastructure. In particular, we study the effects on localization accuracy produced by the position of landmarks and by the strategy followed for their enrollment. We compare two main approaches: the first is more centralized and controlled, and uses well-connected machines belonging to the infrastructure as landmarks; the second is more distributed and scalable and is based on landmarks positioned at the edge of the network. The study is based on an extensive set of measurements collected using the RIPE Atlas platform. The results show that the uniform and widespread diffusion of landmarks can be as important as their measurement accuracy. The study is carried out at both the worldwide and regional scale, including regions that were scarcely observed in the past. The results highlight that the geographical characteristics of the Internet paths are dependent on the considered region, thus suggesting the use of specifically calibrated models. Finally, the study shows that geolocating IP infrastructure with active measurements is feasible in terms of precision and scalability of the overall system.