Miracle-dz/UniFAR-FTUs huggingface dataset:
Voltage tuning of vibrational mode energies in single-molecule junctions

Significance Like guitar strings, molecules have characteristic vibrational frequencies, set by the strength of chemical bonding between the atoms. In an experiment using a special antenna for light, we have found that applying an electrical voltage to a single buckyball molecule systematically lowers its vibrational frequencies, indicating that the bonds are weakened. We can explain this observation in terms of a very simple, general model in which the applied voltage slightly increases the amount of negative charge on the molecule, thus tuning the chemical bond strength. This may be generally useful in understanding and controlling the mechanical properties of molecules. Vibrational modes of molecules are fundamental properties determined by intramolecular bonding, atomic masses, and molecular geometry, and often serve as important channels for dissipation in nanoscale processes. Although single-molecule junctions have been used to manipulate electronic structure and related functional properties of molecules, electrical control of vibrational mode energies has remained elusive. Here we use simultaneous transport and surface-enhanced Raman spectroscopy measurements to demonstrate large, reversible, voltage-driven shifts of vibrational mode energies of C60 molecules in gold junctions. C60 mode energies are found to vary approximately quadratically with bias, but in a manner inconsistent with a simple vibrational Stark effect. Our theoretical model instead suggests that the mode shifts are a signature of bias-driven addition of electronic charge to the molecule. These results imply that voltage-controlled tuning of vibrational modes is a general phenomenon at metal–molecule interfaces and is a means of achieving significant shifts in vibrational energies relative to a pure Stark effect.
Combinatorial Ant Optimization and the Flowshop Problem

Researchers have developed efficient techniques, meta-heuristics to solve many Combinatorial Optimization (CO) problems, e.g., Flow shop Scheduling Problem, Travelling Salesman Problem (TSP) since the early 60s of the last century. Ant Colony Optimization (ACO) and its variants were introduced by Dorigo et al. [DBS06] in the early 1990’s which is a technique to solve CO problems. In this thesis, we used the ACO technique to find solutions to the classic Flow shop Scheduling Problem and proposed a novel method for solution improvement. Our solution is composed of two phases; in the first phase, we solved TSP using ACO technique which gave us an initial permutation or tour. We used the same trip as an initial solution for our problem and then improved it by using 2-opt exchanges which yielded in a promising result. Furthermore, we introduced another improvement technique which gave us a more promising result. We have compared our results with the best (optimal) and worst solution known till date. A comprehensive experimental study using existing dataset proves that our approach remarkably gives good results.
Medigap Reform Legislation of 1990: A 10-Year Review

The 1990 Medigap reform legislation sought to make it easier for consumers to compare policies, provide market stability, promote competition, and avoid adverse selection. Evidence is that the standardization of benefits has simplified consumer choice and is strongly supported by consumers and State regulators. The 1990 reforms also decreased carrier and agent abuses. However, loss ratios (the proportion of premiums paid in benefits versus being retained for administration and profit) have changed little since 1990, bringing into question whether price competition has been enhanced. The prescription drug benefit, which is included in 3 of the 10 standardized plans, provides only limited financial protection yet is expensive, one reason being adverse selection. Access to coverage for Medicare disabled beneficiaries is problematic in most States.
Hybrid Transmission Scheme for MIMO Relay Channels

To improve the achievable rate for the MIMO channel s, we propose a hybrid transmission (HT) scheme that mixes half-duplex decode-and-forward cooperative relaying transmission （ DFRH ） with direct transmission (DT). In the HT scheme, the source message is divided into two parts: one is transmitted by DFRH scheme and another is transmitted by DT scheme. Precoding and decoding are considered to convert the original MIMO relay channel into several parallel subchannels so that resource allocation can be easily performed . We focus on the spatial subchannel and power allocation problem . T he objective of this problem is to maximize the total achievable rate under the constraints of joint total transmission power . Simulation results show that significant capacity gain can be achieved by the HT scheme compared to t he DT scheme and the pure DFRH scheme.
Anticipating the “Silver Tsunami”: Prevalence Trajectories and Comorbidity Burden among Older Cancer Survivors in the United States

Background: Cancer survivors are a growing population, due in large part to the aging of the baby boomer generation and the related “silver tsunami” facing the U.S. health care system. Understanding the impact of a graying nation on cancer prevalence and comorbidity burden is critical in informing efforts to design and implement quality cancer care for this population. Methods: Incidence and survival data from 1975 to 2011 were obtained from the Surveillance, Epidemiology, and End Results (SEER) Program to estimate current cancer prevalence. SEER-Medicare claims data were used to estimate comorbidity burden. Prevalence projections were made using U.S. Census Bureau data and the Prevalence Incidence Approach Model, assuming constant future incidence and survival trends but dynamic projections of the U.S. population. Results: In 2016, there were an estimated 15.5 million cancer survivors living in the United States, 62% of whom were 65 years or older. The prevalent population is projected to grow to 26.1 million by 2040, and include 73% of survivors who are 65 years and older. Comorbidity burden was highest in the oldest survivors (those ≥85 years) and worst among lung cancer survivors. Conclusions: Older adults, who often present with complex health needs, now constitute the majority of cancer survivors and will continue to dominate the survivor population over the next 24 years. Impact: The oldest adults (i.e., those >75 years) should be priority populations in a pressing cancer control and prevention research agenda that includes expanding criteria for clinical trials to recruit more elderly participants and developing relevant supportive care interventions. Cancer Epidemiol Biomarkers Prev; 25(7); 1029–36. ©2016 AACR.
A Distributed Framework for Dimensionality Reduction and Denoising

With the recent upsurge in the amount of data transmission and use in networks, a lot of emphasis is placed on designing efficient methods to extract useful information by reducing the size of large data sets. A major approach in data dimensionality reduction relies on the estimation of the principal data covariance eigenspace. In this paper a distributed algorithmic framework is put forth for finding the principal eigenspace of spatially scattered sensor data. Toward this end, the standard principal component analysis framework is reformulated as a separable constrained minimization problem which is solved by utilizing coordinate descent techniques combined with the alternating direction method of multipliers. Computationally simple local updating recursions are obtained that involve only single-hop intersensor communications. The proposed distributed algorithm is shown to be robust even in the presence of inter-sensor communication noise and converge to the principal eigenspace when inter-sensor links are ideal. The proposed distributed framework is used in a data denoising setting, while extensive numerical tests using both synthetic and real data demonstrate a fast convergence rate, better steady-state performance of the novel algorithm over existing alternatives and communication noise resilience.
DExD-box RNA-helicases in Listeria monocytogenes are important for growth, ribosomal maturation, rRNA processing and virulence factor expression

RNA-helicases are proteins required for the unwinding of occluding secondary RNA structures, especially at low temperatures. In this work, we have deleted all 4 DExD-box RNA helicases in various combinations in the Gram-positive pathogen Listeria monocytogenes. Our results show that 3 out of 4 RNA-helicases were important for growth at low temperatures, whereas the effect was less prominent at 37°C. Over-expression of one RNA-helicase, Lmo1450, was able to overcome the reduced growth of the quadruple mutant strain at temperatures above 26°C, but not at lower temperatures. The maturation of ribosomes was affected in different degrees in the various strains at 20°C, whereas the effect was marginal at 37°C. This was accompanied by an increased level of immature 23S rRNA precursors in some of the RNA-helicase mutants at low temperatures. Although the expression of the PrfA regulated virulence factors ActA and LLO decreased in the quadruple mutant strain, this strain showed a slightly increased infection ability. Interestingly, even though the level of the virulence factor LLO was decreased in the quadruple mutant strain as compared with the wild-type strain, the hly-transcript (encoding LLO) was increased. Hence, our results could suggest a role for the RNA-helicases during translation. In this work, we show that DExD-box RNA-helicases are involved in bacterial virulence gene-expression and infection of eukaryotic cells.
In Vivo 19F NMR Imaging of the Cardiovascular System

A technique is described that permits the 19F nuclear magnetic resonance (NMR) imaging of the vascular system in animals that have been infused with certain perfluorocarbon blood substitutes. The essence of the technique is the selection for imaging of a narrow group of spectral lines from the fluorocarbon using a combination of selective excitation and selective spin echo. For perfluorotributylamine (FTBA), this uses 569f of the available fluorine signal. Both slice and whole body projection images were obtained. The method was studied on an experimental NMR imaging system based on a 1.4 T. 31 cm diameter magnet. Phantom tests showed that misregistration artifacts, due to the imaging of unwanted spectral lines, were negligible. The spatial resolution obtained in a scan time of 8.5 min was ∼l mm. The technique was tested with living rats in which 56% of the blood volume was replaced with an emulsion of FTBA. Short echo times, less than 15 ms, were essential to avoid losing signal from flow effects in the larger vessels. Structures identified included the heart, liver, spleen, lungs, kidneys, and major veins. A diffuse fluorine signal seen in most tissues is indicative of tissue perfusion. The problems and possibilities of potential clinical applications are discussed.
Pediatric brain extraction from T2‐weighted MR images using 3D dual frame U‐net and human connectome database

Accurate extraction of brain tissues from magnetic resonance (MR) images is important in neuroradiology. However, brain extraction is more difficult for pediatric brains than for adult brains due to several factors including smaller brain sizes and lower tissue contrasts. In this work, we propose a brain extraction technique that utilizes dual frame (DF) 3D U‐net deep learning architecture and the human connectome project (HCP) database for multislice 2D pediatric T2‐weighted MR images with diseases. To improve segmentation accuracy in small pediatric brains with detailed boundary regions, DF 3D U‐net architecture was used. We pretrained networks with the HCP database to compensate for the limited amount of MR images and manual segmentation masks of pediatric patients. For quantitative analysis, we compared the brain extraction results of brain extraction tool, DF, and conventional 3D U‐net using the dice similarity coefficient (DSC), intersection of union (IoU), and boundary F1 (BF) scores; each deep learning architecture was evaluated with and without pretraining using the HCP. This study included 10 patients with diseases and all images were acquired using a PROPELLER MR sequence. Pretraining using the HCP database enhanced segmentation performance of the network, and the skip connections in the DF 3D U‐net could enhance the contour similarity of segmentation results. Experimental results showed that the proposed method increased the DSC, IoU, and BF scores by 0.8%, 1.6%, and 1.5%, respectively, compared with those of the conventional 3D U‐net without pretraining.
Assessing intonation and prosody in children with atypical language development: the PEPS‐C test and the revised version

A procedure for assessing prosody and intonation in children (PEPS‐C: Profiling Elements of Prosodic Systems—Children), suitable for use by clinicians with both children and adults, is described. The procedure includes testing of four communication areas in which intonation/prosody has a crucial role: interaction, affect, boundary (chunking) and focus. Each area has parallel tasks for assessing understanding and expression of the functions and ability to discriminate and articulate the prosodic forms involved. The original and revised forms of the test are compared, with some discussion of procedural considerations. Past and present uses of the test and future applications are considered.
Signal attenuation and localization in optical coherence tomography studied by Monte Carlo simulation

A Monte Carlo model has been developed for optical coherence tomography (OCT). A geometrical optics implementation of the OCT probe with low-coherence interferometric detection was combined with three-dimensional stochastic Monte Carlo modelling of photon propagation in the homogeneous sample medium. Optical properties of the sample were selected to simulate intralipid and blood, representing moderately ( g = 0.7) and highly ( g = 0.99) anisotropic scattering respectively. For shallow optical depths in simulated intralipid (<3 scattering mean free path (mfp) units), the number of detected backscattered photons followed the extinction-single-backscatter model, and OCT was found to detect only minimally scattered photons. Within this depth range the backscatter positions of detected photons corresponded well with the nominal focus position of the probe. For propagation to deeper positions in intralipid, localization of backscattering was quickly lost due to detection of stray photons, and the number of detected photons remained constant with increasing depth in the non-absorbing medium. For strongly forward-directed scattering in simulated blood, the number of detected photons approached the extinction-single-backscatter model only for very shallow depths (<2 mfp units). However, backscattering positions for detected photons correlated well with the nominal focus position of the probe even for optical depths greater than 40 mfp units.
Prevalence and Molecular Analysis of Novel Newly Identified Periodontal Pathogens in Subgingival Plaque Samples of Saudi Patients with Chronic Periodontitis

Objective: To evaluate the prevalence of novel newly identified periodontal pathogenic strains in subgingival plaque samples and relate it with bleeding on probing, probing pocket depth and age. Material and Methods: 268 chronic periodontitis patients with a mean age of 46.0 ± 6.0 years were included. The following microorganisms were evaluated: Campylobacter gracilis (Cg), Fusobacterium nucleatum (Fn), Porphyromonas gingivalis (Pg), and Tannerella forsythia (Tf). Full mouth examination was registered; the probing pocket depth and clinical attachment level were assessed at six sites per tooth. Dental subgingival plaque samples were taken in the deepest pocket per arch in the maxilla and mandible. DNA analysis was performed using DNA-strip technology. Pearson’s correlation coefficient was used for statistical analysis. Results: Porphyromonas gingivalis and Tannerella forsythia were detected at high level of 80% and 82% respectively. F nucleatum revealed a rate of 94%. Bacterial load significantly increased by increasing mean probing depth of the examined sites. Pearson’s coefficient was the highest for Pg (r=0.62, p=0.0001) and the lowest for Cg (r=0.08, p=0.04). The bacterial load significantly increased by increasing the number of bleeding sites; Pearson’s coefficient varied from r= 0.14 for Pg (p=0.01) to r=0.39 for Tf (p=0.001). Conclusion: It was confirmed the presence of main putative periodontal pathogens detectable in Saudi periodontal subjects, also providing a comprehensive view for correlation of these putative periodontal pathogens with the increase in probing pocket depth to the presence and load of all the bacterial species.
Add-On Module of Active Disturbance Rejection for Set-Point Tracking of Motion Control Systems

Active disturbance rejection control (ADRC) as a standalone motion solution has been made available in recent years on various industrial platforms. The idea of ADRC, however, can be integrated with the existing control technologies seamlessly, as shown in this paper. A modularized ADRC design is proposed in this particular work for set-point tracking task in motion control, such that better uncertainty rejection can be obtained without any change in the existing proportional-derivative control with a linear observer. It is proven that a certain extended state of the observer, i.e., the integration of the observer error, can serve as an estimation for the “total disturbance” in low-frequency range by only tuning the augmented gain. This enables the estimation and cancellation of the “total disturbance” to be incorporated into the existing control loop. Also, a comparison between the methods with and without such “module” is discussed. The proposed ADRC is implemented and validated experimentally using a laboratory manipulator, where the desired set-point tracking performance in position control is achieved under unknown mass variations and sudden external disturbances.
Impact of Spectral Notch Width on Neurophysiological Plasticity and Clinical Effectiveness of the Tailor-Made Notched Music Training

Tinnitus, the ringing in the ears that is unrelated to any external source, causes a significant loss in quality of life, involving sleep disturbance and depression for 1 to 3% of the general population. While in the first place tinnitus may be triggered by damage to the inner ear cells, the neural generators of subjective tinnitus are located in central regions of the nervous system. A loss of lateral inhibition, tonotopical reorganization and a gain-increase in response to the sensory deprivation result in hypersensitivity and hyperactivity in certain regions of the auditory cortex. In the tailor-made notched music training (TMNMT) patients listen to music from which the frequency spectrum of the tinnitus has been removed. This evokes strong lateral inhibition from neurons tuned to adjacent frequencies onto the neurons involved in the tinnitus percept. A reduction of tinnitus loudness and tinnitus-related neural activity was achieved with TMNMT in previous studies. As the effect of lateral inhibition depends on the bandwidth of the notch, in the current study we altered the notch width to find the most effective notch width for TMNMT. We compared 1-octave notch width with ½-octave and ¼-octave. Participants chose their favorite music for the training that included three month of two hours daily listening. The outcome was measured by means of standardized questionnaires and magnetoencephalography. We found a general reduction of tinnitus distress in all administered tinnitus questionnaires after the training. Additionally, tinnitus-related neural activity was reduced after the training. Nevertheless, notch width did not have an influence on the behavioral or neural effects of TMNMT. This could be due to a non-linear resolution of lateral inhibition in high frequencies.
Correlation and Symmetrical Uncertainty-Based Feature Selection for Multivariate Time Series Classification

: Data relating to everyday phenomena can be recorded in the form of time series. Recently, modeling of time-series has become a topic of interest in data mining. Conducting an analysis of multivariate time series effectively is an essential task for decision-making activities in fields such as meteorology, medicine, and finance. Features selection is a key problem in the analysis of multivariate time series. Rainfall prediction, biomedical classification, pattern recognition, sensor network analysis and so on all have different input features. The problem is that these features have interdependencies and time-delay relationships. Currently, research on the selection of input features of these data still depends on whether they are linear or non-linear. In this paper, we propose a new integration strategy between Pearson Correlation and Symmetrical Uncertainty for relevant feature selection based on linear and non-linear relationships for multivariate time-series classification. We evaluated the goodness of fit of feature subsets using merit value. The meteorological data set was used to test the proposed method. The result showed that the method was able to reduce the number of features by 77.9% features and increase their merit value 2.25 times compared to no input features selection.
Classification of Research Papers into a Patent Classification System Using Two Translation Models

Classifying research papers into patent classification systems enables an exhaustive and effective invalidity search, prior art search, and technical trend analysis. However, it is very costly to classify research papers manually. Therefore, we have studied automatic classification of research papers into a patent classification system. To classify research papers into patent classification systems, the differences in terms used in research papers and patents should be taken into account. This is because the terms used in patents are often more abstract or creative than those used in research papers in order to widen the scope of the claims. It is also necessary to do exhaustive searches and analyses that focus on classification of research papers written in various languages. To solve these problems, we propose some classification methods using two machine translation models. When translating English research papers into Japanese, the performance of a translation model for patents is inferior to that for research papers due to the differences in terms used in research papers and patents. However, the model for patents is thought to be useful for our task because translation results by patent translation models tend to contain more patent terms than those for research papers. To confirm the effectiveness of our methods, we conducted some experiments using the data of the Patent Mining Task in the NTCIR-7 Workshop. From the experimental results, we found that our method using translation models for both research papers and patents was more effective than using a single translation model.