Miracle-dz/UniFAR-FTUs huggingface dataset:
New approaches for interpreting projected bone densitometry data

Bone densitometry using dual‐photon absorptiometry (DPA) or dual‐energy x‐ray absorptiometry (DXA) has become a standard method for assessing bone mineral content in the spine and other skeletal regions. A projected areal density, referred to as bone mineral density (BMD,g/cm2), is normally calculated to assess regional bone density and strength. We demonstrate that this measure can be misleading when used to compare bones of different sizes due to inherent biases caused by bone thickness differences. For example, assuming that volumetric bone density remains constant and bony linear dimensions are proportional to height, a 20% increase in height would result in a 20% increase in both the thickness and the BMD of any bone. We describe new analysis methods to reduce the confounding effect of bone size, and we introduce a parameter, bone mineral apparent density (BMAD, g/cm3), that better reflects bone apparent density. Using this parameter, we calculate a quantity that serves as an index of bone strength (IBS, g2/cm4) for whole vertebral bodies. These analyses were applied to lumbar spine (L2–4) DXA measurements in a population of women 17–40 years old and appear to offer advantages to conventional techniques.
Visualizing the loss of diversity in genetic programming

This work introduces visualization techniques that allow for a multivariate approach in understanding the dynamics that underlie genetic programming (GP). Emphasis is given toward understanding the relationship between problem difficulty and the loss of diversity. The visualizations raise questions about diversity and problem solving efficacy, as well as the role of the initial population in determining solution outcomes.
Essential oil-loaded lipid nanoparticles for wound healing

Chronic wounds and severe burns are diseases responsible for severe morbidity and even death. Wound repair is a crucial process and tissue regeneration enhancement and infection prevention are key factors to minimize pain, discomfort, and scar formation. The aim of this work was the development of lipid nanoparticles (solid lipid nanoparticles and nanostructured lipid carriers [NLC]), to be loaded with eucalyptus or rosemary essential oils and to be used, as medical devices, to enhance healing of skin wounds. Lipid nanoparticles were based on natural lipids: cocoa butter, as solid lipid, and olive oil or sesame oil, as liquid lipids. Lecithin was chosen as surfactant to stabilize nanoparticles and to prevent their aggregation. The systems were prepared by high shear homogenization followed by ultrasound application. Nanoparticles were characterized for physical–chemical properties, bioadhesion, cytocompatibility, in vitro proliferation enhancement, and wound healing properties toward normal human dermal fibroblasts. Antimicrobial activity of nanoparticles was evaluated against two reference microbial strains, one of Staphylococcus aureus, the other of Streptococcus pyogenes. Finally, the capability of nanoparticles to promote wound healing in vivo was evaluated on a rat burn model. NLC based on olive oil and loaded with eucalyptus oil showed appropriate physical–chemical properties, good bioadhesion, cytocompatibility, in vitro proliferation enhancement, and wound healing properties toward fibroblasts, associated to antimicrobial properties. Moreover, the in vivo results evidenced the capability of these NLC to enhance the healing process. Olive oil, which is characterized by a high content of oleic acid, proved to exert a synergic effect with eucalyptus oil with respect to antimicrobial activity and wound repair promotion.
GABA Expression and Regulation by Sensory Experience in the Developing Visual System

The developing retinotectal system of the Xenopus laevis tadpole is a model of choice for studying visual experience-dependent circuit maturation in the intact animal. The neurotransmitter gamma-aminobutyric acid (GABA) has been shown to play a critical role in the formation of sensory circuits in this preparation, however a comprehensive neuroanatomical study of GABAergic cell distribution in the developing tadpole has not been conducted. We report a detailed description of the spatial expression of GABA immunoreactivity in the Xenopus laevis tadpole brain at two key developmental stages: stage 40/42 around the onset of retinotectal innervation and stage 47 when the retinotectal circuit supports visually-guided behavior. During this period, GABAergic neurons within specific brain structures appeared to redistribute from clusters of neuronal somata to a sparser, more uniform distribution. Furthermore, we found that GABA levels were regulated by recent sensory experience. Both ELISA measurements of GABA concentration and quantitative analysis of GABA immunoreactivity in tissue sections from the optic tectum show that GABA increased in response to a 4 hr period of enhanced visual stimulation in stage 47 tadpoles. These observations reveal a remarkable degree of adaptability of GABAergic neurons in the developing brain, consistent with their key contributions to circuit development and function.
Budgeted Distribution Learning of Belief Net Parameters

Most learning algorithms assume that a training dataset is given initially. We address the common situation where data is not available initially, but can be obtained, at a cost. We focus on learning Bayesian belief networks (BNs) over discrete variables. As such BNs are models of probabilistic distributions, we consider the "generative" challenge of learning the parameters for a fixed structure, that best match the true distribution. We focus on the budgeted learning setting, where there is a known fixed cost ci for acquiring the value of the ith feature for any specified instance, and a known total budget to spend acquiring all information. After formally defining this problem from a Bayesian perspective, we first consider non-sequential algorithms that must decide, before seeing any results, which features of which instances to probe. We show this is NP-hard, even if all variables are independent, then prove that the greedy allocation algorithm IGA is optimal here when the costs are uniform, but can otherwise be sub-optimal. We then show that general (sequential) policies perform better than non-sequential, and explore the challenges of learning the parameters for general belief networks in this sequential setting, describing conditions for when the obvious round-robin algorithm will, versus will not, work optimally. We also explore the effectiveness of this and various other heuristic algorithms.
Dysregulated miRNA biogenesis downstream of cellular stress and ALS‐causing mutations: a new mechanism for ALS

Interest in RNA dysfunction in amyotrophic lateral sclerosis (ALS) recently aroused upon discovering causative mutations in RNA‐binding protein genes. Here, we show that extensive down‐regulation of miRNA levels is a common molecular denominator for multiple forms of human ALS. We further demonstrate that pathogenic ALS‐causing mutations are sufficient to inhibit miRNA biogenesis at the Dicing step. Abnormalities of the stress response are involved in the pathogenesis of neurodegeneration, including ALS. Accordingly, we describe a novel mechanism for modulating microRNA biogenesis under stress, involving stress granule formation and re‐organization of DICER and AGO2 protein interactions with their partners. In line with this observation, enhancing DICER activity by a small molecule, enoxacin, is beneficial for neuromuscular function in two independent ALS mouse models. Characterizing miRNA biogenesis downstream of the stress response ties seemingly disparate pathways in neurodegeneration and further suggests that DICER and miRNAs affect neuronal integrity and are possible therapeutic targets.
Information Filtering in Sparse Online Systems: Recommendation via Semi-Local Diffusion

With the rapid growth of the Internet and overwhelming amount of information and choices that people are confronted with, recommender systems have been developed to effectively support users’ decision-making process in the online systems. However, many recommendation algorithms suffer from the data sparsity problem, i.e. the user-object bipartite networks are so sparse that algorithms cannot accurately recommend objects for users. This data sparsity problem makes many well-known recommendation algorithms perform poorly. To solve the problem, we propose a recommendation algorithm based on the semi-local diffusion process on the user-object bipartite network. The simulation results on two sparse datasets, Amazon and Bookcross, show that our method significantly outperforms the state-of-the-art methods especially for those small-degree users. Two personalized semi-local diffusion methods are proposed which further improve the recommendation accuracy. Finally, our work indicates that sparse online systems are essentially different from the dense online systems, so it is necessary to reexamine former algorithms and conclusions based on dense data in sparse systems.
124I PET-Based 3D-RD Dosimetry for a Pediatric Thyroid Cancer Patient: Real-Time Treatment Planning and Methodologic Comparison

Patient-specific 3-dimensional radiobiologic dosimetry (3D-RD) was used for 131I treatment planning for an 11-y-old girl with differentiated papillary thyroid cancer, heavy lung involvement, and cerebral metastases. 124I PET was used for pharmacokinetics. Calculation of the recommended administered activity, based on lung toxicity constraints, was performed in real time (i.e., during the data-acquisition interval). The results were available to the physician in time to influence treatment; these estimates were compared with conventional dosimetry methodologies. In subsequent, retrospective analyses, the 3D-RD calculations were expanded to include additional tumor dose estimates, and the conventional methodologies were reexamined to reveal the causes of the differences observed. A higher recommended administered activity than by an S-value–based method with a favorable clinical outcome was obtained. This approach permitted more aggressive treatment while adhering to patient-specific lung toxicity constraints. A retrospective analysis of the conventional methodologies with appropriate corrections yielded absorbed dose estimates consistent with 3D-RD.
Rasch Analysis of the Premature Ejaculation Diagnostic Tool (PEDT) and the International Index of Erectile Function (IIEF) in an Iranian Sample of Prostate Cancer Patients

Background Male sexual dysfunction is an increasing problem across a variety of general and clinical populations, such as cancer populations; especially among prostate cancer patients who tend to receive treatments that often result in erectile dysfunction (ED) and/or premature ejaculation (PE). Therefore, in order to diagnose ED and PE in these populations, adequate and efficient instruments such as the International Index of Erectile Function 5-item version (IIEF-5) and the Premature Ejaculation Diagnostic Tool (PEDT) are needed. However, since this is an important topic additional evidence of psychometric properties of the IIEF-5 and the PEDT in such samples are required. Thus the aim of the present study was to use Rasch models to investigate the construct validity, local dependency, score order, and differential item functioning (DIF) of both questionnaires in a sample of prostate cancer patients. Methods Prostate cancer patients (n = 1058, mean±SD age = 64.07±6.84 years) who visited urology clinics were invited to fill out the IIEF-5 and the PEDT. Construct validity was examined using infit and outfit mean square (MnSq) and local dependency using correlations between each two residual Rasch scores. Score order was investigated using step and average measures of difficulty and DIF using DIF contrast. Results All IIEF-5 and PEDT items had acceptable infit and outfit MnSq. Step measures revealed that all but two items had disordered categories in terms of scores 1 to 3. Only one local dependency was found, and no items displayed DIF across age, educational level, and help seeking. Conclusions The results showed that both the IIEF-5 and the PEDT had sound psychometric properties in the Rasch analyses, although some score disordering could be detected in both instruments. The results of no DIF items in both instruments suggest using them to compare ED and PE across age and educational level is adequate.
User Identification Across Social Media

People use various social media sites for different purposes. The information on each site is often partial. When sources of complementary information are integrated, a better profile of a user can be built. This profile can help improve online services such as advertising across sites. To integrate these sources of information, it is necessary to identify individuals across social media sites. This paper aims to address the cross-media user identification problem. We provide evidence on the existence of a mapping among identities of individuals across social media sites, study the feasibility of finding this mapping, and illustrate and develop means for finding this mapping. Our studies show that effective approaches that exploit information redundancies due to users’ unique behavioral patterns can be utilized to find such a mapping. This study paves the way for analysis and mining across social networking sites, and facilitates the creation of novel online services across sites. In particular, recommending friends and advertising across networks, analyzing information diffusion across sites, and studying specific user behavior such as user migration across sites in social media are one of the many areas that can benefit from the results of this study.
Predictive Control of Container Flows in Maritime Intermodal Terminals

Predictive control is investigated as a paradigm for the allocation of handling resources to transfer containers inside intermodal terminals. The decisions on the allocation of such resources are derived from the minimization of performance cost functions that measure the lay times of carriers over a forward horizon basing on a model of the container flows. Such a model allows one to take advantage of the information available in real time on the arrival or departure of carriers with the corresponding amounts of containers scheduled for loading or unloading. The resulting strategy of resource allocation can be regarded as a feedback control law and is obtained by solving nonlinear programming problems online. Since the computation may be too expensive, a technique based on the idea of approximating offline such a law is proposed. The approximation is performed by using neural networks, which allow one to construct an approximate feedback controller and generate the corresponding online control actions with a negligible computational burden. The effectiveness of the approach is shown via simulations in a case study.
Performance bounds for analog network coding based two-way relaying with multiuser selection diversity

This paper evaluates the performance of an opportunistic scheduling scheme for a multiuser two-way relay network, wherein an analog network coding (ANC)-based relay assists the communication between multiple pairs of users. To ensure fairness for each user pair, we consider a scheduler based on maximizing the normalized minimum of the instantaneous signal-to-noise ratios (SNRs) at each user of the pair. Under a high SNR assumption, we derive upper and lower bound expressions for the outage probability and the average sum-rate in a Rayleigh fading environment and establish that a multiuser diversity of order equal to the number of user pairs is harnessed by the scheme. Numerical and simulation results are presented to confirm the performance gain of the proposed scheme.
Analyzing Mobile Instant Messaging User Satisfaction And Loyalty: An Integrated Perspective

Mobile instant messaging (mobile IM) is a cutting edge technology of the P2P communication. The prevalence of the mobile IM boosts the research of the perceived satisfaction and loyalty towards mobile IM long-term usage. In the era of 4G, individuals use mobile IM to communicate with their friends in every-day life. However, prior studies neglected a holistic view of users’ roles and then yielded insufficient explanation about the usage satisfaction of mobile IM. Based on the integrated users’ role framework, this research-in-progress explores the determinants of mobile IM satisfaction and loyalty with the consideration of the triangulation perspective of users’ multiple roles: the technology user role, the service consumer role, and the network member role. To this end, a survey is designed to collect data from mobile IM users. The data analysis results will validate the proposed hypotheses.
Interleukin interrupted: A new strategy for the treatment of pancreatic cancer

[Figure][1] Insight from Steven Leach Pancreatic cancer is an aggressively metastatic cancer with a high mortality rate, but the mechanisms underlying this aggression are not yet fully understood. In this issue, [Wu et al.][2] shed some light on this subject. They report that pancreatic
A gold-standard social media corpus for urban issues

This paper introduces a gold-standard corpus extracted from manually labeled tweets concerning urban issues. The main contribution is to provide a labeled tweet dataset which can be useful for building machine-learning classifiers in the urban issues domain, including geographical features. Thus, this corpus can also be useful for improving geoparsers to correctly identify place names in urban such as Points-of-Interest (POI), Streets/Roads and Districts. Our method for building the corpus includes human-volunteer quality assessment and human-driven labeling using an ad hoc web application, the Tweet Annotator. The volunteers were asked to complete a feedback survey in order to identify the main difficulties during the labeling task. In this paper, we also report the findings from a case study carried out to analyze the spatial relationships in the generated corpus for the locations which a tweet may refer to: the geocoded, the user home and the mentioned ones.
Cough and Asthma

Cough is the most common complaint for which patients seek medical attention. Cough variant asthma (CVA) is a form of asthma, which presents solely with cough. CVA is one of the most common causes of chronic cough. More importantly, 30 to 40% of adult patients with CVA, unless adequately treated, may progress to classic asthma. CVA shares a number of pathophysiological features with classic asthma such as atopy, airway hyper-responsiveness, eosinophilic airway inflammation and various features of airway remodeling. Inhaled corticosteroids remain the most important form of treatment of CVA as they improve cough and reduce the risk of progression to classic asthma most likely through their prevention of airway remodeling and chronic airflow obstruction.
Treebanks in Machine Translation

We present an approach using treebanks in machine translati on. Our experiment in Czech-English machine translation is an attempt to develop a full machine translation system based on dependency trees (Dependency Based Mac hine Translation, DBMT). We use the following resources: Prague Dependency Tr eebank, a newly created Czech-English parallel corpus of Penn Treebank, En glish monolingual corpus, and translation lexicons. The fully automatic process includes analysis of the Czech input into tectogrammatical (semantic) representat io , lexical and structural transfer, a simple rule-based system for generation to Engl ish surface realization, and an -gram language model for scoring and choosing from translat ion hypotheses. The results are evaluated quantitatively with BLEU score.
On the Identification and Estimation of Partially Nonstationary ARMAX Systems

This paper extends current theory on the identification and estimation of vector time series models to nonstationary processes. It examines the structure of dynamic simultaneous equations systems or ARMAX processes that start from a given set of initial conditions and evolve over a given, possibly infinite, future time horizon. The analysis proceeds by deriving the echelon canonical form for such processes. The results are obtained by amalgamating ideas from the theory of stochastic difference equations with adaptations of the Kronecker index theory of dynamic systems. An extension of these results to the analysis of unit-root, partially nonstationary (cointegrated) time series models is also presented, leading to straightforward identification conditions for the error correction, echelon canonical form. An innovations algorithm for the evaluation of the exact Gaussian likelihood is given and the asymptotic properties of the approximate Gaussian estimator and the exact maximum likelihood estimator based upon the algorithm are derived. Examples illustrating the theory are discussed and some experimental evidence is also presented.
Statistical Automatic Summarization in Organic Chemistry

We present an oriented numerical summarizer algorithm, applied to producing automatic summaries of scientific documents in Organic Chemistry. We present its implementation named Yachs (Yet Another Chemistry Summarizer) that combines a specific document pre-processing with a sentence scoring method relying on the statistical properties of documents. We show that Yachs achieves the best results among several other summarizers on a corpus of Organic Chemistry articles.
An efficient approach to directly compute the exact Hausdorff distance for 3D point sets

Hausdorff distance measure is very important in CAD/CAE/CAM related applications. This manuscript presents an efficient framework and two complementary subalgorithms to directly compute the exact Hausdorff distance for general 3D point sets. The first algorithm of Nonoverlap Hausdorff Distance (NOHD) combines branch-and-bound with early breaking to cut down the Octree traversal time in case of spatial nonoverlap. The second algorithm of Overlap Hausdorff Distance (OHD) integrates a point culling strategy and nearest neighbor search to reduce the number of points traversed in case of spatial overlap. The two complementary subalgorithms can achieve a highly efficient and balanced result. Both NOHD and OHD compute the exact Hausdorff distance directly for arbitrary 3D point sets. We conduct a number of experiments on benchmark models and CAD application models, and compare the proposed approach with other state-of-the-art algorithms. The results demonstrate the effectiveness of our method.
Adaptive Erasure Resilient Coding in Distributed Storage

A challenge of peer-to-peer (P2P) storage is to build reliable data storage from inherently unreliable P2P network, and to do so efficiently. In this paper, we investigate the use of adaptive erasure resilient code (ERC) which changes ERC fragment size according to the size of the file distributed. We show that the adaptive ERC may greatly improve the efficiency and reliability of P2P storage. A number of design policies and strategies for the P2P storage application are also investigated
Prediction of novel ultra-incompressibility compounds TM2B (TM=Mo, W, Re and Os) by first-principles calculations

Abstract Several novel ultra-incompressibility compounds TM2B (TM = Mo, W, Re and Os) have been predicted by means of the first-principles calculations. Those novel compounds were assumed to have a ReB2-type structure [P63/mmc space group (No.194, Z = 2), atomic sites: TM 4f (2/3, 1/3, z), B 2c (1/3, 2/3, 1/4)]. We calculated the mechanical properties of the TM2B, and the results reveal that they exhibit brittle behaviour and mechanically stable. The hardness values are 23.8 GPa, 23.3 GPa, 26.6 GPa and 26.3 GPa for Mo2B, W2B, Re2B and Os2B, respectively, which suggests that they are hard materials. Additionally, we found that the anisotropy of Re2B is weaker than the others. Finally, the Mo2B has the highest Debye temperature (905.8 K), while Os2B has the lowest Debye temperature (615.5 K). We hoped that our results can help to offer a theoretical data for future experimental work and application of TM2B.