Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Depression and Cervical Spondylosis: A Nationwide Retrospective Propensity Score-Matched Cohort Study

Objective: Depression is a psychiatric disorder associated with poorer health outcomes. Inappropriate mechanical stress and aging are factors associated with developing cervical spondylosis. The connection between cervical spondylosis and depression is not developed. Methods: From the health insurance claims data of Taiwan, we identified 34,166 persons newly diagnosed with depression in 2000–2010 and 34,166 persons without the disorder frequency matched by sex, age and diagnosis year. Both cohorts were followed up to the end of 2013 to estimate incident cervical spondylosis. We further examined the risk of cervical spondylosis in depressed people taking antidepressants. Results: The incidence of cervical spondylosis was 1.8-fold greater in the depression cohort than in comparison cohort (9.46 vs. 5.36 per 1000 person-years), with an adjusted hazard ratio (aHR) of 1.79 (95% confidence interval (CI) = 1.66–1.92). The incidence of cervical spondylosis increased in patients who had taken medications of serotonin-specific reuptake inhibitors (SSRIs) or of non-SSRIs than in those without these medicines (9.13 or 11.5 vs. 6.54 per 1000 person-years, respectively). Conclusions: Patients with depression are at an increased risk of developing cervical spondylosis. Additional efforts in reducing the risk of cervical spondylosis might be required in depressed individuals undergoing anti-depressive therapy.
Chirality Plasmonic Lens Induced Terahertz Super-Focusing

We propose a planar chiral spiral pIsmonic lens consisting of two columns of orthogonal chirality aperture array. When a concentric groove is introduced into the center of the lens, the convergent SPPs are emitted into the free space, resulting in a polarization-controlled terahertz super-focusing. The full wave at half maximum (FWHM) of the hot-spot is almost 0. 38λ (beyond the diffraction limit), and the intensity of focal spot is about 2 times than that of the conventional in-plane plasmonic lens. The flexible and controllable of the THz super-focusing is of practical applications in many areas like super-resolution imaging, data storage and lithography.
An Improved Double-a Model for Millimeter wave CMOS On-Chip Inductor

An improved double-a model for CMOS on-chip inductor is presented in this paper. The series resistor- inductor (R-L) network is used to characterize the loss and phase-shift through the transmission line. And the second order cascaded parallel R-L network is induced to model the skin and proximity effects. Besides, the resistor-capacitor(R-C) networks are used to characterize the loss and coupling in the substrate. Finally, to validate the model, test structures were fabricated by 40-nm CMOS process. The model calculation results are compared with the EM simulation and measurement results, and good agreements are achieved.
Global Attention for Name Tagging

Many name tagging approaches use local contextual information with much success, but can fail when the local context is ambiguous or limited. We present a new framework to improve name tagging by utilizing local, document-level, and corpus-level contextual information. For each word, we retrieve document-level context from other sentences within the same document and corpus-level context from sentences in other documents. We propose a model that learns to incorporate document-level and corpus-level contextual information alongside local contextual information via document-level and corpus-level attentions, which dynamically weight their respective contextual information and determines the influence of this information through gating mechanisms. Experiments on benchmark datasets show the effectiveness of our approach, which achieves state-of-the-art results for Dutch, German, and Spanish on the CoNLL-2002 and CoNLL-2003 datasets. We will make our code and pre-trained models publicly available for research purposes.
Informative Features for Model Comparison

Given two candidate models, and a set of target observations, we address the problem of measuring the relative goodness of fit of the two models. We propose two new statistical tests which are nonparametric, computationally efficient (runtime complexity is linear in the sample size), and interpretable. As a unique advantage, our tests can produce a set of examples (informative features) indicating the regions in the data domain where one model fits significantly better than the other. In a real-world problem of comparing GAN models, the test power of our new test matches that of the state-of-the-art test of relative goodness of fit, while being one order of magnitude faster.
Invasive Optical Pacing in Perfused, Optogenetically Modified Mouse Heart Using Stiff Multi-LED Optical Probes

We present the first invasive use of a stiff, multiLED optical probe for intramural optical stimulation of cardiac tissue. We demonstrate that optical pacing is possible with high spatial and temporal resolution in transgenic mice expressing channelrhodopsin-2. The technical implementation of this study builds on optical probes recently developed and tested ex vivo in cerebral tissue of mice. The probes comprise LEDs integrated on flexible substrates stiffened by silicon-based MEMS structures enabling the successful penetration into the cardiac tissue. The probe technology is extended to allow dual-sided illumination for directional tissue stimulation. Implantation trials affirm the ability to optically pace the isolated perfused heart at stimulation frequencies between 4Hz and 12Hz with experimentally determined emittance levels of 10mW mm-2 Rapid activation of two distant LEDs could reliably be used to induce short runs of ventricular fibrillation, while simultaneous activation of all LEDs allowed termination of re-entrant rhythm disturbances (optical defibrillation). Thus, spatially-resolved intramural pacing and rhythm control of the isolated heart is possible using stiff, multi-LED optical probes.
Wi-Fi Halow Signal Coverage Estimation in Collapsed Structures

With the growing research for IoTs particularly wireless signals as sensors, Wi-Fi has witnessed an increasing trend for numerous applications. In this study, we investigate the feasibility of Wi-Fi Halow signals for collapsed structured environments. The objectives of this paper are first to consider a complex debris scenario and then wireless signal coverage under this debris has to be computed. A modified path loss model known as PL-Collapsed along with complex collapsed structure layout has been used for proper reception of echo which provides the probable coverage for aforementioned scenario. Objectives have been realized with proper simulations. Then, we compare the results for various signal intensities, antenna gains and debris layers. Comparison of results show that Wi-Fi Halow at low power with higher antenna gains has better coverage. It is therefore paving way for post-disaster rescue through IoTs in comparison with traditional radar techniques.
On Using Sampling Bloom Filter for Unknown Tag Identification in Large-Scale RFID Systems

Radio Frequency Identification (RFID) is one of the key technologies for Internet of things. In RFID systems, the tags without registering in advance are called unknown tags, which usually appear in the scenarios, where the tag-attached objects are moved into or misplaced in the reader’s interrogating area. Consequently, unknown tag identification is significant for RFID-based applications, which is the concentration of this paper. We first propose a basic efficient unknown tag identification protocol based on sampling Bloom filter called UTI-SBF, which consists of known tag deactivation phase and unknown tag identification phase. The idea behind the UTI-SBF protocol is to deactivate the known tags to counteract their interference on the unknown tag identification. Then, we propose an enhanced protocol called EUTI-SBF, which eliminates the non-homogeneous slots based on the UTI-SBF protocol to improve the time efficiency. The parameters of the two protocols are theoretically analyzed to maximize the efficiency. We conduct extensive simulations to evaluate the proposed UTI-SBF and EUTI-SBF protocols and the simulation results illustrate that the UTI-SBF and EUTI-SBF protocols outperform the BUIP protocol. In particular, the EUTI-SBF protocol only consumes about 70% of deactivation time compared with the BUIP protocol in the known tag deactivation phase.
Osimertinib (AZD9291) increases radio-sensitivity in EGFR T790M non-small cell lung cancer

Osimertinib (AZD9291) is a third generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that has demonstrated significant clinical benefits in patients with EGFR-sensitizing mutations or the T790M mutation. However, the potential therapeutic effect of osimertinib combined with ionizing irradiation (IR) is not well understood. The present study investigated treatment with osimertinib combined with IR in EGFR T790M non-small cell lung cancer (NCI-H1975) in vitro and in vivo. The results revealed that osimertinib inhibited proliferation and clonogenic survival following irradiation, decreased G2/M phase arrest in irradiated cells, and delayed DNA damage repair in a concentration- and time-dependent manner. Furthermore, osimertinib alone or in combination with IR, blocked the phosphorylation of EGFR (Tyr1068/Tyr1173), protein kinase B and extracellular signal-regulated kinase. Osimertinib also enhanced the antitumor activity of IR in tumor-bearing nude mice. The results of the present study indicated that osimertinib has therapeutic potential as a radiation-sensitizer in lung cancer cells harboring the EGFR T790M mutation, providing a rationale for clinically combining osimertinib with irradiation in EGFR T790M non-small cell lung cancer.
Substituting Missing Values in End-to-End Internet Performance Measurements Using k-Nearest Neighbors

PingER (Ping End-to-end Reporting) is a worldwide end-to-end Internet performance measurement framework running for the last 20 years and led by the SLAC National Accelerator Laboratory USA. The objective of the project is to monitor the performance of the Internet links around the world using the ubiquitous ping facility. Currently, the framework comprises of about 50 active Monitoring Agents (MAs) in 20 countries of the world. These MAs probe 700 remote sites located in 170 countries of the world. They are covering an area containing over 98% of the world's population. Currently, the size of the PingER data is about 60 GB stored in 100,000 flat files with a compression ratio of 5:1. The data is of an historical nature and very useful for fine-grained Internet performance analysis. However, the data contains missing values due to congestion, queuing overflow, faulty hardware or software and unavailability of MAs & remote sites. These missing values affect the quality of the Internet performance analysis. The objective of this paper is to substitute the missing values using the k-Nearest Neighbors algorithm (k-NN) and compare the estimation with the statistical method. Therefore, PingER historical data is first transformed into CSV format using a PingER data dimensional model. Afterward, missing values are imputed, using the statistical method and the k-NN algorithm, on data containing the different percentages of missing values. The results conclude that the k-NN algorithm is best suited for the substitution of missing values in the PingER data as compared to the method based on the statistical procedure.
A Multicenter Phase II Study of AMG 337 in Patients with MET-Amplified Gastric/Gastroesophageal Junction/Esophageal Adenocarcinoma and Other MET-Amplified Solid Tumors

Purpose: MET gene amplification is associated with poor prognosis in gastric/gastroesophageal junction/esophageal (G/GEJ/E) cancers. We determined antitumor activity, safety, and pharmacokinetics of the small-molecule MET inhibitor AMG 337 in MET-amplified G/GEJ/E adenocarcinoma or other solid tumors. Patients and Methods: In this phase II, single-arm study, adults with MET-amplified G/GEJ/E adenocarcinoma (cohort 1) or other MET-amplified solid tumors (cohort 2) received AMG 337 300 mg/day orally in 28-day cycles. The primary endpoint was objective response rate (ORR; cohort 1). Secondary endpoints included ORR (cohort 2), progression-free survival (PFS), overall survival (OS), and safety. Results: Of 2101 patients screened for MET amplification, 132 were MET-amplified and 60 were enrolled: 45 in cohort 1, and 15 in cohort 2. Fifty-six patients (97%) had metastatic disease; 57 had prior lines of therapy (1 prior line, 29%; ≥2 prior lines, 69%). A protocol-permitted review showed efficacy that was lower-than-expected based on preliminary data from a first-in-human study, and enrollment was stopped. Fifty-eight patients received ≥1 AMG 337 dose. ORR in cohort 1 was 18% (8 partial responses). No responses were observed in cohort 2. Of 54 evaluable patients, median (95% CI) PFS and OS were 3.4 (2.2–5.0) and 7.9 (4.8–10.9) months, respectively. The most frequent adverse events (AEs) were headache (60%), nausea (38%), vomiting (38%), and abdominal pain, decreased appetite, and peripheral edema (33% each); 71% had grade ≥3 AEs and 59% had serious AEs. Conclusions: AMG 337 showed antitumor activity in MET-amplified G/GEJ/E adenocarcinoma but not in MET-amplified non–small-cell lung cancer. See related commentary by Ma, p. 2375
Acylated Aminooligosaccharides with Inhibitory Effects against α-Amylase from Streptomyces sp. HO1518

Five new acylated aminooligosaccharides (1–5), together with one known related analogue (6), were isolated from Streptomyces sp. HO1518. Their structure was identified by extensive spectroscopic analysis, including 1D and 2D NMR data and high resolution electrospray ionization mass spectrometry (HRESIMS), and by comparison with those reported in the literature. All of the new compounds showed more promising porcine pancreatic α-amylase (PPA) inhibitory activities than the clinical drug acarbose, indicating them as potential pharmaceutical drug leads toward type II diabetes.
Improving Myoelectric Pattern Recognition Robustness to Electrode Shift by Autoencoder

It is evident that the electrode shift will result in a degradation of myoelectric pattern recognition classification accuracy, which is inevitable during the prosthetic socket donning and doffing. To cope with this limitation, we propose an unsupervised feature extraction method called sparse autoencoder (SAE) to extract the robust spatial structure and correlation of high density (HD) electromyography (EMG). The algorithm is evaluated on nine intact-limbed subjects and one amputee. The experimental results show that SAE achieves lower classification error without shift, and significantly decrease the sensitivity to electrode shift with ±1 cm compared with the timedomain and autoregressive features (TDAR). Furthermore, SAE is not sensitive to the shift direction that is perpendicular to the muscle fibers. The promising results of this study make great contribution to promoting the applications of pattern recognition based myoelectric control system in real-world condition.
Dual Latent Variable Model for Low-Resource Natural Language Generation in Dialogue Systems

Recent deep learning models have shown improving results to natural language generation (NLG) irrespective of providing sufficient annotated data. However, a modest training data may harm such models’ performance. Thus, how to build a generator that can utilize as much of knowledge from a low-resource setting data is a crucial issue in NLG. This paper presents a variational neural-based generation model to tackle the NLG problem of having limited labeled dataset, in which we integrate a variational inference into an encoder-decoder generator and introduce a novel auxiliary auto-encoding with an effective training procedure. Experiments showed that the proposed methods not only outperform the previous models when having sufficient training dataset but also demonstrate strong ability to work acceptably well when the training data is scarce.
LASSO-based Cox-PH model identifies an 11-lncRNA signature for prognosis prediction in gastric cancer

The present study aimed to identify a long non-coding (lnc) RNAs-based signature for prognosis assessment in gastric cancer (GC) patients. By integrating gene expression data of GC and normal samples from the National Center for Biotechnology Information Gene Expression Omnibus, the EBI ArrayExpress and The Cancer Genome Atlas (TCGA) repositories, the common RNAs in Genomic Spatial Event (GSE) 65801, GSE29998, E-MTAB-1338, and TCGA set were screened and used to construct a weighted correlation network analysis (WGCNA) network for mining GC-related modules. Consensus differentially expressed RNAs (DERs) between GC and normal samples in the four datasets were screened using the MetaDE method. From the overlapped lncRNAs shared by preserved WGCNA modules and the consensus DERs, an lncRNAs signature was obtained using L1-penalized (lasso) Cox-proportional hazard (PH) model. LncRNA-mRNA networks were constructed for these signature lncRNAs, followed by functional annotation. A total of 14,824 common mRNAs and 2,869 common lncRNAs were identified in the 4 sets and 5 GC-associated WGCNA modules were preserved across all sets. MetaDE method identified 1,121 consensus DERs. A total of 50 lncRNAs were shared by preserved WGCNA modules and the consensus DERs. Subsequently, an 11-lncRNA signature was identified by LASSO-based Cox-PH model. The lncRNAs signature-based risk score could divide patients into 2 risk groups with significantly different overall survival and recurrence-free survival times. The predictive capability of this signature was verified in an independent set. These signature lncRNAs were implicated in several biological processes and pathways associated with the immune response, the inflammatory response and cell cycle control. The present study identified an 11-lncRNA signature that could predict the survival rate for GC.
Nonrigid Point Set Registration With Robust Transformation Learning Under Manifold Regularization

This paper solves the problem of nonrigid point set registration by designing a robust transformation learning scheme. The principle is to iteratively establish point correspondences and learn the nonrigid transformation between two given sets of points. In particular, the local feature descriptors are used to search the correspondences and some unknown outliers will be inevitably introduced. To precisely learn the underlying transformation from noisy correspondences, we cast the point set registration into a semisupervised learning problem, where a set of indicator variables is adopted to help distinguish outliers in a mixture model. To exploit the intrinsic structure of a point set, we constrain the transformation with manifold regularization which plays a role of prior knowledge. Moreover, the transformation is modeled in the reproducing kernel Hilbert space, and a sparsity-induced approximation is utilized to boost efficiency. We apply the proposed method to learning motion flows between image pairs of similar scenes for visual homing, which is a specific type of mobile robot navigation. Extensive experiments on several publicly available data sets reveal the superiority of the proposed method over state-of-the-art competitors, particularly in the context of the degenerated data.
CZ2HF mitigates β-amyloid 25-35 fragment-induced learning and memory impairment through inhibition of neuroinflammation and apoptosis in rats.

Cu‑zhi‑2‑hao‑fang (CZ2HF), a traditional Chinese medicine, has been used clinically for the treatment of amnesia. However, whether CZ2HF is capable of alleviating learning and memory impairment in Alzheimer's disease (AD) remains to be elucidated. The present study was designed to explore the effect and mechanism of CZ2HF on β‑amyloid 25‑35 (Aβ25‑35)‑induced impairment in the learning and memory of rats. Morris water maze test was used to determine spatial learning and memory ability in Aβ25‑35‑induced AD rats and hippocampal neuronal damage and apoptosis were observed using hematoxylin and eosin staining, Nissl staining and terminal deoxynucleotidyltransferase‑mediated dUTP nick‑end labeling (TUNEL) assays, respectively. The levels of β‑amyloid 1‑42 (Aβ1‑42), pro‑inflammatory factors, such as cyclooxygenase‑2 (COX‑2), tumor necrosis factor‑α (TNF‑α) and interleukin‑1β (IL‑1β) and apoptosis‑associated genes including B cell leukemia/lymphoma 2 (Bcl‑2), Bcl-2‑associated X, apoptosis regulator (Bax), pro‑caspase‑3, inhibitor of κB (IκB‑α) degradation and phosphorylated‑nuclear factor‑κB p65 (p‑NF‑κB p65) activation were analyzed using western blotting. The findings of the present study revealed that CZ2HF treatment significantly attenuated Aβ25‑35‑induced cognitive impairments in rats. Subsequently, CZ2HF treatment markedly inhibited neuronal damage and deletions. Furthermore, CZ2HF reduced TNF‑α, IL‑1β, COX‑2 protein expression levels, Bax/Bcl‑2 ratio, and reduced Aβ1‑42 and active‑caspase‑3 levels. In addition, IκB‑α degradation and p‑NF‑κB p65 activation were reduced by CZ2HF. These findings suggested that CZ2HF treatment improved Aβ25‑35‑induced learning and memory impairment and hippocampal neuronal injury, and its underlying mechanism may be due to the inhibition of neuroinflammation and neuronal apoptosis. CZ2HF may be a potential agent for the treatment of AD.
Art as Sanctuary: A Four-Year Mixed-Methods Evaluation of a Visual Art Course Addressing Uncertainty Through Reflection

Purpose Engagement with visual art is a promising modality for addressing issues of uncertainty via reflective practice, one that is being increasingly used in health science education. An elective museum-based course for first-year medical students was created by two medical schools and led by an art educator experienced in medical education. The course, Observation and Uncertainty in Art and Medicine, sought to help students explore experiences of uncertainty and to develop reflective capacity through engagement with visual art. Method The course was run and evaluated from 2014 to 2017, with 47 students participating over the 4 years, with 12 students enrolled per year. Before and after the course, students were given the Groningen Reflection Ability Scale (GRAS) for reflective ability, the Tolerance for Ambiguity scale for ambiguity, and Best Intentions Questionnaire for personal bias awareness, and 35 students (74%) completed all of the scales. Focus group interviews and narrative postcourse evaluations were conducted, coded, and thematically analyzed. Results Statistically significant improvement was found in GRAS scores. Qualitative themes included student enhancement of observational skills, awareness of the subjectivity and uncertainty of perception, exploration of multiple points of view, and recognition of the course as a place for restoration and connection to classmates. Conclusions Incorporating visual art into medical education is an effective pedagogical method for addressing competencies central to training, including observation, reflection, and self-care.