Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation of growth factor levels at the fusion site of diabetic patients undergoing hindfoot arthrodesis and clinical outcome

BackgroundThe purpose of this study was to quantify the levels of growth factors (platelet-derived growth factor [PDGF] AB, vascular endothelial growth factor [VEGF], insulin-like growth factor [IFG] 1, and transforming growth factor [TGF]- &bgr;1) within bone samples at the fusion site of diabetic patients undergoing hindfoot arthrodesis to determine any correlation with successful fusion. MethodsThe study included 10 adult diabetic patients from one US center, with an average age of 57.9 years (range 49–71 years). Interventions included hindfoot arthrodeses with a fixator (n=8) and pantalar arthrodeses with fixator (n=2). During each procedure, a bony bed sample was taken from the fusion site and frozen. This local bone bed was analyzed for growth factors using ELISA kits. A platelet-rich plasma (PRP) concentration was applied to each fusion site during the procedure. ResultsThree of the ten patients had a nonunion. After normalizing to bicinchoninic acid assay (BCA) total protein levels, a 70% decrease in PDGF-AB (0.044 pg/&mgr;g non-union to 0.149 pg/&mgr;g union; P=0.016) and a 44% decrease in VEGF (0.522 pg/&mgr;g non-union to 0.924 pg/&mgr;g union; P=0.031) were observed in the three nonunion bone samples compared with the arthrodesis group. No difference existed in the levels of IGF-I between the groups. Insignificantly higher levels of TGF-&bgr;1 were detected in the nonunion group (0.199 ng/&mgr;g non-union to 0.142 ng/&mgr;g union; P=0.544). ConclusionsSignificant differences in local growth factor (PDGF, VEGF) levels exist between diabetic patients who achieve union and non-union. Potentially, growth factor levels, PDGF-AB and VEGF, in bone may affect the outcome of successful arthrodesis in diabetic patients. This study supports the concept that growth factor levels within the fusion site of a diabetic patient may affect the outcome of a successful hindfoot arthrodesis.
Differences in Risk of Insulin-Dependent Diabetes in Offspring of Diabetic Mothers and Diabetic Fathers

Abstract To examine the role of heritable factors in insulin-dependent diabetes mellitus (IDDM), we studied the incidence of IDDM in the offspring of patients with the disease who were identified by the medical records of the Joslin Diabetes Center from 1928 to 1939. We found 187 survivors who, after the diagnosis of IDDM, had produced 419 offspring for whom information about diabetes status was available. By the age of 20, 6.1 per cent of the offspring of the 88 men had diabetes; in contrast, only 1.3 per cent of the offspring of the 99 women had the disease by the age of 20 (P<0.05). Daughters and sons of the men with IDDM were affected equally (there were insufficient numbers of affected offspring of diabetic women to permit determination of whether the sexes were equally affected). We conclude that IDDM is transmitted less frequently to the offspring of diabetic women than to those of diabetic men. More study is required to determine whether this difference reflects a genetic mechanism or, instead, se.
Cushing Disease Caused By an Ectopic Intracavernous Pituitary Microadenoma: Case Report and Review of the Literature

&NA; Despite increasing performance of pituitary imaging, adrenocorticotropic hormone (ACTH)‐secreting pituitary adenomas are often difficult to identify because of their small size and variable location. We present a case of ACTH‐dependent Cushing syndrome in which the demonstration of the cavernous sinus localization of a microadenoma was made by magnetic resonance imaging (MRI). Ectopic pituitary microadenomas have been described only in a few reports, and the current case‐report is the first to our knowledge to illustrate clearly the intracavernous localization of the microadenoma.
An Evidential Framework for Localization of Sensors in Indoor Environments

Indoor localization has several applications ranging from people tracking and indoor navigation, to autonomous robot navigation and asset tracking. We tackle the problem as a zoning localization where the objective is to determine the zone where the mobile sensor resides at any instant. The decision-making process in localization systems relies on data coming from multiple sensors. The data retrieved from these sensors require robust fusion approaches to be processed. One of these approaches is the belief functions theory (BFT), also called the Dempster–Shafer theory. This theory deals with uncertainty and imprecision with a theoretically attractive evidential reasoning framework. This paper investigates the usage of the BFT to define an evidence framework for estimating the most probable sensor’s zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.
Brain and Body Emotional Responses: Multimodal Approximation for Valence Classification

In order to develop more precise and functional affective applications, it is necessary to achieve a balance between the psychology and the engineering applied to emotions. Signals from the central and peripheral nervous systems have been used for emotion recognition purposes, however, their operation and the relationship between them remains unknown. In this context, in the present work, we have tried to approach the study of the psychobiology of both systems in order to generate a computational model for the recognition of emotions in the dimension of valence. To this end, the electroencephalography (EEG) signal, electrocardiography (ECG) signal and skin temperature of 24 subjects have been studied. Each methodology has been evaluated individually, finding characteristic patterns of positive and negative emotions in each of them. After feature selection of each methodology, the results of the classification showed that, although the classification of emotions is possible at both central and peripheral levels, the multimodal approach did not improve the results obtained through the EEG alone. In addition, differences have been observed between cerebral and peripheral responses in the processing of emotions by separating the sample by sex; though, the differences between men and women were only notable at the peripheral nervous system level.
Sprint Interval Exercise for Fat Loss: Good Return on Investment

ABSTRACT THIS STUDY SHOWED THAT AN ALL-OUT INTERVAL EXERCISE PROTOCOL RESULTED IN 24-HOUR OXYGEN USE AND, THEREFORE, CALORIC EXPENDITURE EQUAL TO THAT OF CONTINUOUS EXERCISE. THE RESULTS SUPPORT REPEAT SPRINT EXERCISE AS A VIABLE FAT LOSS OPTION, ESPECIALLY FITTING FOR "POWER ENDURANCE" TRAINING NEEDS. SHORTER IN DURATION, IT CAN ALSO SAVE TRAINING TIME FOR FITNESS CLIENTS.
Neighbor Joining Approaches for Reconstructing Tandem Duplication History

Motivation: Genomes are replete with short sequences repeated consecutively called tandem repeats. Reconstructing duplication histories for tandem repeats may yield valuable insights into their functions and the biological mechanisms of tandem repeat creation and extension. Results: we design and implement a set of heuristic algorithms for reconstructing tandem duplication history with neighbor-joining approaches. We prove that the algorithms are able to infer the correct duplication history under some condition. Extensive simulations show that some of our algorithms work very well. We also analyze some real data sets using our algorithms. Availability: NJtandem is available at http://www.cs. cityu.edu. hk/̃lwang/tandem.
Efficient convolutional neural networks for multi-planar lung nodule detection: improvement on small nodule identification

Objective: In clinical practice, small lung nodules can be easily overlooked by radiologists. The paper aims to provide an efficient and accurate detection system for small lung nodules while keeping good performance for large nodules. Methods: We propose a multi-planar detection system using convolutional neural networks. The 2-D convolutional neural network model, U-net++, was trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are combined. For false positive reduction, we apply 3-D multi-scale dense convolutional neural networks to efficiently remove false positive candidates. We use the public LIDC-IDRI dataset which includes 888 CT scans with 1186 nodules annotated by four radiologists. Results: After ten-fold cross-validation, our proposed system achieves a sensitivity of 94.2% with 1.0 false positive/scan and a sensitivity of 96.0% with 2.0 false positives/scan. Although it is difficult to detect small nodules (i.e. < 6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall false positive rate of 1.0 (2.0) false positives/scan. At the nodule candidate detection stage, results show that a multi-planar method is capable to detect more nodules compared to using a single plane. Conclusion: Our approach achieves good performance not only for small nodules, but also for large lesions on this dataset. This demonstrates the effectiveness and efficiency of our developed CAD system for lung nodule detection. Significance: The proposed system could provide support for radiologists on early detection of lung cancer.
Broadband Variability and Correlation Study of 3C 279 during Flares of 2017–2018

A multiwavelength temporal and spectral analysis of flares of 3C 279 during 2017 November–2018 July are presented in this work. Three bright gamma-ray flares were observed simultaneously in X-ray and optical/UV along with a prolonged quiescent state. A “harder-when-brighter” trend is observed in both gamma-rays and X-rays during the flaring period. The gamma-ray light curve for all the flares is binned in one day time bins and a day-scale variability is observed. Variability time constrains the size and location of the emission region to 2.1 × 1016 cm and 4.4 × 1017 cm, respectively. The fractional variability reveals that the source is more than 100% variable in gamma-rays and it decreases toward the lower energy. A cross-correlation study of the emission from different wavebands is done using the discrete correlations function method, which shows a strong correlation between them without any time lags. The zero time lag between different wavebands suggests their cospatial origin. This is the first time 3C 279 has shown a strong correlation between gamma-ray and X-ray emission with zero time lag. A single-zone emission model was adopted to model the multiwavelength spectral energy distributions by using the publicly available code GAMERA. The study reveals that a higher jet power in electrons is required to explain the gamma-ray flux during the flaring state, as much as 10 times that required for the quiescent state. However, more jet power in the magnetic field has been observed during the quiescent state compared to the flaring state.
A bibliometric analysis of the top 100 most cited papers and research trends in breast cancer related BRCA1 and BRCA2 genes

This study aimed to identify, characterize, and map the important attributes of the top 100 most cited papers on BRCA1 and BRCA2 genes. The scientific literature on BRCA1 and BRCA2 was searched in the Web of Science Core Collection database using the keywords “BRCA1” OR “BRCA2” (Title). The top 100 most cited papers were selected based on citations. The obtained data were exported into HistCiteTM, RStudio, and VOSviewer software for prerequisite analysis. The top 100 most cited papers on BRCA1 and BRCA2 were authored by 932 authors from 24 countries and published in 27 journals. These papers were cited 79,713 times, ranging from 441 to 4671 citations. The highly cited paper was cited 4671 times and published in Science (1994). The leading author, journal, publication year, institution, and country were Easton DF (n = 16), Nature Genetics (n = 11), 2002 (n = 11), University of Pennsylvania (n = 17), and the USA (n = 76), respectively. The results show that all the top 100 papers were produced in developed countries. The collaboration index among the authors was 9.49. The most frequently appeared keywords were ovarian-cancer, breast-cancer, mutations, gene, and familial breast. In recent times, the trend topics were patients, mutations, carriers, ovarian, and risk.
LoCuSS: exploring the connection between local environment, star formation, and dust mass in Abell 1758

We explore the connection between dust and star formation, in the context of environmental effects on galaxy evolution. In particular, we exploit the susceptibility of dust to external processes to assess the influence of dense environment on star-forming galaxies. We have selected cluster Abell 1758 from the Local Cluster Substructure Survey (LoCuSS). Its complex dynamical state is an ideal test-bench to track dust removal and destruction in galaxies due to merger and accretion shocks. We present a systematic panchromatic study (from 0.15 $\rm \mu$m with GALEX to 500 $\rm \mu$m with Herschel) of spectroscopically confirmed star-forming cluster galaxies at intermediate redshift. We observe that the main subclusters (A1758N and A1758S) belong to two separate large-scale structures, with no overlapping galaxy members. Star-forming cluster members are distributed preferentially outside cluster central regions, and are not grouped in substructures. Rather, these galaxies are being funneled towards the main subclusters along separate accretion filaments. Additionally, we present the first study of dust-to-stellar (DTS) mass ratio used as indicator for local environmental influence on galaxy evolution. Star-forming cluster members show lower mean values (32% at 2.4$\rm \sigma$) of DTS mass ratio and lower levels of infrared emission from birth clouds with respect to coeval star-forming field galaxies. This picture is consistent with the majority of star-forming cluster members infalling in isolation. Upon accretion, star-formation is observed to decrease and warm dust is destroyed due to heating from the intracluster medium radiation, ram-pressure stripping and merger shocks.
Learning Transformation-Aware Embeddings for Image Forensics

A dramatic rise in the flow of manipulated image content on the Internet has led to an aggressive response from the media forensics research community. New efforts have incorporated increased usage of techniques from computer vision and machine learning to detect and profile the space of image manipulations. This paper addresses Image Provenance Analysis, which aims at discovering relationships among different manipulated image versions that share content. One of the main sub-problems for provenance analysis that has not yet been addressed directly is the edit ordering of images that share full content or are near-duplicates. The existing large networks that generate image descriptors for tasks such as object recognition may not encode the subtle differences between these image covariates. This paper introduces a novel deep learning-based approach to provide a plausible ordering to images that have been generated from a single image through transformations. Our approach learns transformation-aware descriptors using weak supervision via composited transformations and a rank-based quadruplet loss. To establish the efficacy of the proposed approach, comparisons with state-of-the-art handcrafted and deep learning-based descriptors, and image matching approaches are made. Further experimentation validates the proposed approach in the context of image provenance analysis.
The high-energy environment and atmospheric escape of the mini-Neptune K2-18 b

K2-18 b is a transiting mini-Neptune that orbits a nearby (38 pc), cool M3 dwarf and is located inside its region of temperate irradiation. We report on the search for hydrogen escape from the atmosphere K2-18 b using Lyman-α transit spectroscopy with the Space Telescope Imaging Spectrograph instrument installed on the Hubble Space Telescope. We analyzed the time-series of fluxes of the stellar Lyman-α emission of K2-18 in both its blue- and redshifted wings. We found that the average blueshifted emission of K2-18 decreases by 67% ± 18% during the transit of the planet compared to the pre-transit emission, tentatively indicating the presence of H atoms escaping vigorously and being blown away by radiation pressure. This interpretation is not definitive because it relies on one partial transit. Based on the reconstructed Lyman-α emission of K2-18, we estimate an EUV irradiation in the range 101 − 102 erg s−1 cm−2 and a total escape rate on the order of 108 g s−1. The inferred escape rate suggests that the planet will lose only a small fraction (< 1%) of its mass and retain its volatile-rich atmosphere during its lifetime. More observations are needed to rule out stellar variability effects, confirm the in-transit absorption, and better assess the atmospheric escape and high-energy environment of K2-18 b.
Companion Unmanned Aerial Vehicles: A Survey

Recent technological advancements in small-scale unmanned aerial vehicles (UAVs) have led to the development of companion UAVs. Similar to conventional companion robots, companion UAVs have the potential to assist us in our daily lives and to help alleviating social loneliness issue. In contrast to ground companion robots, companion UAVs have the capability to fly and possess unique interaction characteristics. Our goals in this work are to have a bird's-eye view of the companion UAV works and to identify lessons learned and guidelines for the design of companion UAVs. We tackle two major challenges towards these goals, where we first use a coordinated way to gather top-quality human-drone interaction (HDI) papers from three sources, and then propose to use a perceptual map of UAVs to summarize current research efforts in HDI. While being simple, the proposed perceptual map can cover the efforts have been made to realize companion UAVs in a comprehensive manner and lead our discussion coherently. We also discuss patterns we noticed in the literature and some lessons learned throughout the review. In addition, we recommend several areas that are worth exploring and suggest a few guidelines to enhance HDI researches with companion UAVs.
Reference-Driven Compressed Sensing MR Image Reconstruction Using Deep Convolutional Neural Networks without Pre-Training

Deep learning has proven itself to be able to reduce the scanning time of Magnetic Resonance Imaging (MRI) and to improve the image reconstruction quality since it was introduced into Compressed Sensing MRI (CS-MRI). However, the requirement of using large, high-quality, and patient-based datasets for network training procedures is always a challenge in clinical applications. In this paper, we propose a novel deep learning based compressed sensing MR image reconstruction method that does not require any pre-training procedure or training dataset, thereby largely reducing clinician dependence on patient-based datasets. The proposed method is based on the Deep Image Prior (DIP) framework and uses a high-resolution reference MR image as the input of the convolutional neural network in order to induce the structural prior in the learning procedure. This reference-driven strategy improves the efficiency and effect of network learning. We then add the k-space data correction step to enforce the consistency of the k-space data with the measurements, which further improve the image reconstruction accuracy. Experiments on in vivo MR datasets showed that the proposed method can achieve more accurate reconstruction results from undersampled k-space data.
An adaptive Douglas-Rachford dynamic system for finding the zero of sum of two operators

The Douglas-Rachford splitting method is a popular method for finding a zero of the sum of two operators, and has been well studied as a numerical method. Nevertheless, the convergence theory for its continuous versions is still in infancy. In this paper, we develop an adaptive Douglas-Rachford dynamic system with perturbations or computational errors for finding a zero of the sum of two operators, one of which is strongly monotone while the other one is weakly monotone, in a real Hilbert space. With proper tuning the parameters, we demonstrate that % the adaptive Douglas-Rachford operator is quasi-nonexpansive, and the trajectory of the adaptive dynamic system converges weakly to a fixed point of the adaptive Douglas-Rachford operator. Even if there is a perturbation, we show that its shadow trajectory strongly converges to a solution of the original problem, and the rate of asymptotic regularity of the adaptive Douglas-Rachford operator is $O\left(\frac{1}{\sqrt{t}}\right)$. Furthermore, global exponential-type rates of the trajectory of the adaptive dynamic system are achieved under a metric subregularity or Lipschitz assumption.
Quantized orbital-chasing liquid metal heterodimers directed by an integrated pilot-wave field

A millimetric bouncing droplet sustained on a vibrating bath becomes a moving wave source (particle) through periodically interacting with the local wave field it generates during the droplet-bath impact. By virtue of such particle-wave duality, the macroscopic hydrodynamic system imitates enigmatic behaviors of the quantum realm. Here we show that it is possible to create an integrated pilot-wave field to better prescribe the droplet trajectories, via amplified bath capillarity. This is demonstrated with a liquid metal droplet-bath system in which the local wave field generated by droplet bouncing is superposed by the global wave field induced by bath meniscus oscillation. The resulted dual pilot-wave configuration enables a class of directional chasing motions of two bound dissimilar droplets (heterodimers) in multilevel hydrodynamic traps (orbits), featuring two quantized regime parameters, namely the inter-droplet binding level and the orbit level. We investigate the dynamics of the vibrating liquid metal bath, with its level-split ring wave field and its peculiar vortex field being highlighted. We also rationalize the exotic droplet motions by considering the inter-droplet particle-wave interactions mediated by the integrated pilot-wave field. It is revealed that a temporal bouncing phase shift between the two droplets in the heterodimers, due to size mismatch, gives rise to their horizontal propulsion, while their spatial binding regime exclusively determines the collective chasing direction. It is further evidenced that the horizontal in-orbit chasing motion is directly related to vertical droplet bouncing. Our findings unveil integrated pilot-wave field as a trail towards improved droplet guiding, thereby extending the hydrodynamic particle-wave analogy to optical systems and beyond.