bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bone mineral density in women newly diagnosed with breast cancer: a prospective cohort study
Authors: Fraenkel Merav , Novack Victor , Mizrakli Yuval , Koretz Michael , Siris Ethel , Norton Larry , Shafat Tali , Geffen David B. 
Published: February 2022

AbstractEstrogen may have opposing effects on health, namely increasing the risk of breast cancer and improving bone health by increasing bone mineral density (BMD). The objective of this study was to compare dual-energy X-ray absorptiometry (DXA) BMD between women newly diagnosed with breast cancer and matched controls without breast cancer. Women newly diagnosed with breast cancer treated between April 2012 and October 2017 were prospectively enrolled. A control group was established of women with negative mammography or breast ultrasound, matched 1:1 by age, body mass index, parity, and the use of hormone replacement therapy. All those included had DXA BMD, and lab assessments at enrollment. Of 869 women with newly diagnosed breast cancer, 464 signed informed consent. Of the 344 who completed the study protocol, 284 were matched to controls. Overall, the mean age was 58 years. Compared to the control group, for the breast cancer group, the mean vitamin D level was lower (48.9 ± 19.0 vs. 53.8 ± 28.8 nmol/L, p = 0.022); and mean values were higher of total hip BMD (0.95 ± 0.14 vs. 0.92 ± 0.12 g/cm2, p = 0.002), T score (−0.38 ± 1.17 vs. −0.68 ± 0.98, p = 0.002), and Z score (0.32 ± 1.09 vs. 0.01 ± 0.88, p < 0.001). Among the women with breast cancer, no correlations were found of baseline BMD with tumor size or grade, nodal involvement, or breast cancer stage. We concluded that women with newly diagnosed breast cancer tend to have higher BMD than women with similar characteristics but without breast cancer. This implies that BMD might be considered a biomarker for breast cancer risk.
Reconfigurable quantum phononic circuits via piezo-acoustomechanical interactions
Authors: Taylor Jeffrey C. , Chatterjee Eric , Kindel William F. , Soh Daniel , Eichenfield Matt 
Published: February 2022

AbstractWe show that piezoelectric strain actuation of acoustomechanical interactions can produce large phase velocity changes in an existing quantum phononic platform: aluminum nitride on suspended silicon. Using finite element analysis, we demonstrate a piezo-acoustomechanical phase shifter waveguide capable of producing ±π phase shifts for GHz frequency phonons in 10s of μm with 10s of volts applied. Then, using the phase shifter as a building block, we demonstrate several phononic integrated circuit elements useful for quantum information processing. In particular, we show how to construct programmable multi-mode interferometers for linear phononic processing and a dynamically reconfigurable phononic memory that can switch between an ultra-long-lifetime state and a state strongly coupled to its bus waveguide. From the master equation for the full open quantum system of the reconfigurable phononic memory, we show that it is possible to perform read and write operations with over 90% quantum state transfer fidelity for an exponentially decaying pulse.
The jam session between muscle stem cells and the extracellular matrix in the tissue microenvironment
Authors: Loreti Mafalda , Sacco Alessandra 
Published: February 2022

AbstractSkeletal muscle requires a highly orchestrated coordination between multiple cell types and their microenvironment to exert its function and to maintain its homeostasis and regenerative capacity. Over the past decades, significant advances, including lineage tracing and single-cell RNA sequencing, have contributed to identifying multiple muscle resident cell populations participating in muscle maintenance and repair. Among these populations, muscle stem cells (MuSC), also known as satellite cells, in response to stress or injury, are able to proliferate, fuse, and form new myofibers to repair the damaged tissue. These cells reside adjacent to the myofiber and are surrounded by a specific and complex microenvironment, the stem cell niche. Major components of the niche are extracellular matrix (ECM) proteins, able to instruct MuSC behavior. However, during aging and muscle-associated diseases, muscle progressively loses its regenerative ability, in part due to a dysregulation of ECM components. This review provides an overview of the composition and importance of the MuSC microenvironment. We discuss relevant ECM proteins and how their mutations or dysregulation impact young and aged muscle tissue or contribute to diseases. Recent discoveries have improved our knowledge about the ECM composition of skeletal muscle, which has helped to mimic the architecture of the stem cell niche and improved the regenerative capacity of MuSC. Further understanding about extrinsic signals from the microenvironment controlling MuSC function and innovative technologies are still required to develop new therapies to improve muscle repair.
Robustness of the thermal Hall effect close to half-quantization in α-RuCl3
Authors: Bruin J. A. N. , Claus R. R. , Matsumoto Y. , Kurita N. , Tanaka H. , Takagi H. 
Published: February 2022

AbstractA key feature of quantum spin liquids is the predicted formation of fractionalized excitations. They are expected to produce changes in the physical response, providing a way to observe the quantum spin liquid state1. In the honeycomb magnet α-RuCl3, a quantum spin liquid has been proposed to explain the behaviour observed on applying an in-plane magnetic field H||. Previous work reported that the thermal Hall conductivity took on a half-integer quantized value and suggested this as a signature of a fractionalized Majorana edge mode predicted to exist in Kitaev quantum spin liquids2. However, the temperature and magnetic-field range of the half-quantized signal2–4 and its association with Majorana edge modes are still under debate5,6. Here we present a comprehensive study of the thermal Hall conductivity in α-RuCl3 showing that approximately half-integer quantization exists in an extended region of the phase diagram, particularly across a plateau-like parameter regime for H|| exceeding 10 T and temperature below 6.5 K. At lower fields, the thermal Hall conductivity exhibits correlations with complex anomalies in the longitudinal thermal conductivity and magnetization, and is suppressed by cooling to low temperatures. Our results can be explained by the existence of a topological state in magnetic fields above 10 T.
Quantum phase transition from superconducting to insulating-like state in a pressurized cuprate superconductor
Authors: Zhou Yazhou , Guo Jing , Cai Shu , Zhao Jinyu , Gu Genda , Lin Chengtian , Yan Hongtao , Huang Cheng , Yang Chongli , Long Sijin , Gong Yu , Li Yanchun , Li Xiaodong , Wu Qi , Hu Jiangping , Zhou Xingjiang , Xiang Tao , Sun Liling 
Published: February 2022

AbstractCopper oxide superconductors continue to fascinate the communities of condensed matter physics and material sciences because they host the highest ambient-pressure superconducting transition temperature and unconventional electronic behaviour that are not fully explained1–3. Searching for universal links between the superconducting state and its normal metallic state is believed to be an effective approach to elucidate the underlying mechanism of superconductivity. One of the common expectations for copper oxide superconductors is that a metallic phase will appear after the superconductivity is entirely suppressed by chemical doping4–8 or the application of a magnetic field9. Here we report the first observation of a quantum phase transition from a superconducting state to an insulating-like state as a function of pressure in Bi2Sr2CaCu2O8+δ (Bi2212) superconductors with two CuO2 planes in a unit cell for doping below, at and above a level that achieves the highest transition temperature. We also find the same phenomenon in related compounds with a single CuO2 plane as well as three CuO2 planes in a unit cell. This apparently universal phenomenon poses a challenge for achieving a unified understanding of the mechanism of high-temperature superconductivity.
Tile-based massively scalable MIMO and phased arrays for 5G/B5G-enabled smart skins and reconfigurable intelligent surfaces
Authors: He Xuanke , Cui Yepu , Tentzeris Manos M. 
Published: February 2022

AbstractThis work presents a novel tile based approach to constructing, in a modular fashion, massively scalable MIMO and phased arrays for 5G/B5G millimeter-wave smart skins and large-area reconfigurable intelligent surfaces for Smart Cities and IoT applications. A proof-of-concept 29 GHz 32 elements phased array utilizing $$2 \times 2$$

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“8-element subarray” tiles was fabricated and measured and demonstrates $$+/-$$

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30beamsteering capability. The unique benefits of the proposed tile approach utilizes the fact that tiles of identical sizes can be manufactured in large quantities rather than have arrays of multiple sizes serve various user capacity coverage areas. It has to be stressed that the proof-of-concept flexible $$2 \times 2$$

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tile array features no performance degradation when it is wrapped around a 3.5 cm radius curvature. This topology can be easily scaled up to massively large arrays by simply adding more tiles and extending the feeding network on the mounting tiling layer. The tiles are assembled onto a single flexible substrate which interconnects the RF, DC and digital traces, allowing for the easy realization of on-demand very large antenna arrays on virtually any practical conformal platform for frequencies up to sub-THz frequency range.
Simulation of emergency evacuation from construction site of prefabricated buildings
Authors: Xiao Mufeng , Zhou Xihua , Pan Xinxin , Wang Yanan , Wang Jue , Li Xianlin , Sun Yashengnan , Wang Yumeng 
Published: February 2022

AbstractTo ensure the safe construction of prefabricated buildings and improve the efficiency of the safe evacuation of construction personnel after a fire caused by improper operation during construction, this study used the PyroSim software to numerically simulate a fire situation based on the size and volume of a prefabricated building construction site. The variation rules of smoke visibility, CO concentration, and ambient temperature in the construction site of prefabricated buildings were analyzed and the available safe evacuation time was determined. Moreover, the Pathfinder software was used for simulation in combination with the physical attributes of personnel, evacuation speed, and personnel proportions. The time required for safe evacuation was determined and the factors influencing the evacuation time, such as the quantity and location of stacked prefabricated components, machinery, and appliances, and the number of on-site construction personnel, were analyzed. The data collected by the temperature sensor, CO concentration sensor, and visibility sensor reveal that the visibility and crash time are the key factors restricting the efficiency of personnel avoidance and evacuation. At 400 s, the visibility at the escape exit of the prefabricated apartment construction site was lower than 5 m. The crashing time of the building was 360 s, which is the critical point for casualties. The first emergency evacuation simulation took 398.7 s. The required safe evacuation time (TREST) > available safe evacuation time (TASET), and the original site layout cannot facilitate the safe evacuation of all construction workers. The evacuation time can be effectively reduced by re-planning the stacking positions of prefabricated construction site components, construction equipment, and other items, and reducing the number of personnel in the construction plane. The results of the second simulation reveal that the safe evacuation time (TREST) is 355.2 s. Because it is required that the safety evacuation time (TREST) < available safe evacuation time (TASET), the results are in line with the emergency evacuation requirements. The findings of this study can provide a theoretical basis for the rational planning of evacuation passages at the construction sites of prefabricated buildings and assist the management of construction site safety.
LIM Tracker: a software package for cell tracking and analysis with advanced interactivity
Authors: Aragaki Hideya , Ogoh Katsunori , Kondo Yohei , Aoki Kazuhiro 
Published: February 2022

AbstractCell tracking is one of the most critical tools for time-lapse image analysis to observe cell behavior and cell lineages over a long period of time. However, the accompanying graphical user interfaces are often difficult to use and do not incorporate seamless manual correction, data analysis tools, or simple training set design tools if it is machine learning based. In this paper, we introduce our cell tracking software “LIM Tracker”. This software has a conventional tracking function consisting of recognition processing and link processing, a sequential search-type tracking function based on pattern matching, and a manual tracking function. LIM Tracker enables the seamless use of these functions. In addition, the system incorporates a highly interactive and interlocking data visualization method, which displays analysis result in real time, making it possible to flexibly correct the data and reduce the burden of tracking work. Moreover, recognition functions with deep learning (DL) are also available, which can be used for a wide range of targets including stain-free images. LIM Tracker allows researchers to track living objects with good usability and high versatility for various targets. We present a tracking case study based on fluorescence microscopy images (NRK-52E/EKAREV-NLS cells or MCF-10A/H2B-iRFP-P2A-mScarlet-I-hGem-P2A-PIP-NLS-mNeonGreen cells) and phase contrast microscopy images (Glioblastoma-astrocytoma U373 cells). LIM Tracker is implemented as a plugin for ImageJ/Fiji. The software can be downloaded from https://github.com/LIMT34/LIM-Tracker.
Assessing systemic risk in financial markets using dynamic topic networks
Authors: So Mike K. P. , Mak Anson S. W. , Chu Amanda M. Y. 
Published: February 2022

AbstractSystemic risk in financial markets refers to the breakdown of a financial system due to global events, catastrophes, or extreme incidents, leading to huge financial instability and losses. This study proposes a dynamic topic network (DTN) approach that combines topic modelling and network analysis to assess systemic risk in financial markets. We make use of Latent Dirichlet Allocation (LDA) to semantically analyse news articles, and the extracted topics then serve as input to construct topic similarity networks over time. Our results indicate how connected the topics are so that we can correlate any abnormal behaviours with volatility in the financial markets. With the 2015–2016 stock market selloff and COVID-19 as use cases, our results also suggest that the proposed DTN approach can provide an indication of (a) abnormal movement in the Dow Jones Industrial Average and (b) when the market would gradually begin to recover from such an event. From a practical risk management point of view, this analysis can be carried out on a daily basis when new data come in so that we can make use of the calculated metrics to predict real-time systemic risk in financial markets.
Decoding Individual differences and musical preference via music-induced movement
Authors: Agrawal Yudhik , Carlson Emily , Toiviainen Petri , Alluri Vinoo 
Published: February 2022

AbstractMovement is a universal response to music, with dance often taking place in social settings. Although previous work has suggested that socially relevant information, such as personality and gender, are encoded in dance movement, the generalizability of previous work is limited. The current study aims to decode dancers’ gender, personality traits, and music preference from music-induced movements. We propose a method that predicts such individual difference from free dance movements, and demonstrate the robustness of the proposed method by using two data sets collected using different musical stimuli. In addition, we introduce a novel measure to explore the relative importance of different joints in predicting individual differences. Results demonstrated near perfect classification of gender, and notably high prediction of personality and music preferences. Furthermore, learned models demonstrated generalizability across datasets highlighting the importance of certain joints in intrinsic movement patterns specific to individual differences. Results further support theories of embodied music cognition and the role of bodily movement in musical experiences by demonstrating the influence of gender, personality, and music preferences on embodied responses to heard music.
Increase in landslide activity after a low-magnitude earthquake as inferred from DInSAR interferometry
Authors: Martino S. , Fiorucci M. , Marmoni G. M. , Casaburi L. , Antonielli B. , Mazzanti P. 
Published: February 2022

AbstractOn August 16th, 2018, a Mw 5.1 earthquake struck the Molise region (central Italy), inducing 84 earthquake-triggered landslides that predominantly involved soil covers of clayey materials and flysch on gently dipping slopes. To quantify the spatiotemporal landslide activity in the months immediately after the earthquake, a differential SAR interferometry (DInSAR) analysis was performed for a time span from 2 years before to one year after the earthquake, recognising both first-time and reactivated landslides. The results showed a clear increase in landslide activity following the low-magnitude earthquake with respect to the activities recorded in the same months of the previous years. Several coherent landslides (earth slides and earth flows) were observed following seasonally recurrent rainfall events. Such increases were observed for both reactivated and first-time landslides, showing decreases in inactive periods and activity over longer periods. Furthermore, the spatial density distribution of the landslides was investigated in the postseismic time interval along transects perpendicular and parallel to the direction of the tectonic element responsible for the seismic event. An asymmetrical distribution was deduced parallel to the fault strike with a higher number of landslides located inside the compressional sector according to a strike-slip faulting mechanism.
Platelet-activating factor (PAF) strongly enhances contractile mechanical activities in guinea pig and mouse urinary bladder
Authors: Liu Ge , Kaneko Mizuki , Yoshioka Kento , Obara Keisuke , Tanaka Yoshio 
Published: February 2022

AbstractIn this study, we investigated the effects of platelet-activating factor (PAF) on the basal tone and spontaneous contractile activities of guinea pig (GP) and mouse urinary bladder (UB) smooth muscle (UBSM) tissues to determine whether PAF could induce UBSM tissue contraction. In addition, we examined the mRNA expression of the PAF receptor, PAF-synthesizing enzyme (lysophosphatidylcholine acyltransferase, LPCAT), and PAF-degrading enzyme (PAF acetylhydrolase, PAF-AH) in GP and mouse UB tissues using RT-qPCR. PAF (10−9–10−6 M) strongly enhanced the basal tone and spontaneous contractile activities (amplitude and frequency) of GP and mouse UBSM tissues in a concentration-dependent manner. The enhancing effects of PAF (10−6 M) on both GP and mouse UBSM contractile activities were strongly suppressed by pretreatment with apafant (a PAF receptor antagonist, GP: 10−5 M; mouse: 3 × 10−5 M). The PAF receptor (Ptafr), LPCAT (Lpcat1, Lpcat2), and PAF-AH (Pafah1b3, Pafah2) mRNAs were detected in GP and mouse UB tissues. These findings reveal that PAF strongly enhances the contractile mechanical activities of UBSM tissues through its receptor and suggest that the PAF-synthesizing and -degrading system exists in UBSM tissues. PAF may serve as both an endogenous UBSM constrictor and an endogenous mediator leading to detrusor overactivity.
Neural oscillations promoting perceptual stability and perceptual memory during bistable perception
Authors: Zhu Michael , Hardstone Richard , He Biyu J. 
Published: February 2022

AbstractAmbiguous images elicit bistable perception, wherein periods of momentary perceptual stability are interrupted by sudden perceptual switches. When intermittently presented, ambiguous images trigger a perceptual memory trace in the intervening blank periods. Understanding the neural bases of perceptual stability and perceptual memory during bistable perception may hold clues for explaining the apparent stability of visual experience in the natural world, where ambiguous and fleeting images are prevalent. Motivated by recent work showing the involvement of the right inferior frontal gyrus (rIFG) in bistable perception, we conducted a transcranial direct-current stimulation (tDCS) study with a double-blind, within-subject cross-over design to test a potential causal role of rIFG in these processes. Subjects viewed ambiguous images presented continuously or intermittently while under EEG recording. We did not find any significant tDCS effect on perceptual behavior. However, the fluctuations of oscillatory power in the alpha and beta bands predicted perceptual stability, with higher power corresponding to longer percept durations. In addition, higher alpha and beta power predicted enhanced perceptual memory during intermittent viewing. These results reveal a unified neurophysiological mechanism sustaining perceptual stability and perceptual memory when the visual system is faced with ambiguous input.
Machine learning methods to predict presence of residual cancer following hysterectomy
Authors: Ganguli Reetam , Franklin Jordan , Yu Xiaotian , Lin Alice , Heffernan Daithi S. 
Published: February 2022

AbstractSurgical management for gynecologic malignancies often involves hysterectomy, often constituting the most common gynecologic surgery worldwide. Despite maximal surgical and medical care, gynecologic malignancies have a high rate of recurrence following surgery. Current machine learning models use advanced pathology data that is often inaccessible within low-resource settings and are specific to singular cancer types. There is currently a need for machine learning models to predict non-clinically evident residual disease using only clinically available health data. Here we developed and tested multiple machine learning models to assess the risk of residual disease post-hysterectomy based on clinical and operative parameters. Data from 3656 hysterectomy patients from the NSQIP dataset over 14 years were used to develop models with a training set of 2925 patients and a validation set of 731 patients. Our models revealed the top postoperative predictors of residual disease were the initial presence of gross abdominal disease on the diaphragm, disease located on the bowel mesentery, located on the bowel serosa, and disease located within the adjacent pelvis prior to resection. There were no statistically significant differences in performances of the top three models. Extreme gradient Boosting, Random Forest, and Logistic Regression models had comparable AUC ROC (0.90) and accuracy metrics (87–88%). Using these models, physicians can identify gynecologic cancer patients post-hysterectomy that may benefit from additional treatment. For patients at high risk for disease recurrence despite adequate surgical intervention, machine learning models may lay the basis for potential prospective trials with prophylactic/adjuvant therapy for non-clinically evident residual disease, particularly in under-resourced settings.