Miracle-dz/UniFAR-FTUs huggingface dataset:
Levodopa-induced dyskinesia in Parkinson disease

Objective To assess dyskinesia frequency in a population-based cohort of patients with Parkinson disease (PD). Dyskinesia complicates levodopa treatment and affects quality of life. Methods Utilizing the 1991–2010 population-based, parkinsonism-incident cohort of Olmsted County, MN (n = 669), accessed via the Rochester Epidemiology Project, we identified patients with PD and abstracted levodopa-related dyskinesia information. Results Of 309 patients with PD (46.2% with parkinsonisms), 279 (90.3%) received levodopa. Most (230/279; 82.4%) had been treated by a Mayo Clinic neurologist. Median age of the 309 patients with PD at the time of diagnosis was 74.1 years (range 33.1–97.8 years). Median-age levodopa initiation in this cohort was 75 years (range 37–98 years), and median-duration levodopa treatment was 6 years (range 2 months to 19.8 years). Dyskinesia was documented in 84 of 279 patients (30.1%). Median time from levodopa initiation to dyskinesia onset was 4 years (range 2 months to 20 years); those with dyskinesia (65.5%; 55/84) developed it within 5 years of levodopa initiation (9 within the first year). Dyskinesia was mild in 57/84 (67.9%), moderate in 16/84 (19.1%), and severe in 9/84 (10.7%); severity was not reported in 2 cases. Dyskinesia severity led to levodopa adjustments or amantadine initiation in 60.7% (51/84 of those with dyskinesia), with improvement in 23/51 (45.1%). Thirteen patients with dyskinesia underwent deep brain stimulation, reporting marked improvement. Postmortem examination confirmed Lewy body disease in 7 autopsied cases. Conclusions Levodopa-induced dyskinesia affected 30% of the patients with PD in our cohort. Mayo neurologists favoring levodopa dosage optimization treated most patients. Dyskinesia was severe in 3.2% of all levodopa-treated patients with PD (10.7% of all patients with dyskinesia) with marked improvement among those treated with deep brain stimulation.
The prognostic value of long noncoding RNA ZEB1-AS1 on clinical outcomes in human cancer

Background: Although growing evidence have demonstrated that long non-coding RNA ZEB1-AS1 was aberrantly expressed in various types of cancers and can be used as a prognostic marker in cancer, the results remain inconclusive. Therefore, we performed this meta-analysis to evaluate the prognostic value of ZEB1-AS1 in human cancer. Methods: A literature survey was conducted for all eligible studies by searching the following online databases: PubMed and Embase. The pooled odds ratios (ORs) or hazard ratios (HRs) with a 95 % confidence interval (95 % Cl) were computed to demonstrate its prognostic value. Results: A total of 14 studies with 1096 individuals were included to evaluate the association of ZEB1-AS1 with clinicopathological features and overall survival (OS). In the pooled analyses stratified by clinicopathological features, ZEB1-AS1 expression was significantly related to depth of tumor (OR=2.92, 95% CI 1.22-7.02), poor histological differentiation (OR=2.72, 95% CI: 1.92-3.86), lymph node metastasis (OR=3.93, 95% CI: 2.65-5.84), distant metastasis (OR=5.34, 95% CI: 2.85-10.02) and tumor stage (OR=2.46, 95% CI 1.42-4.24), but not to tumor size (OR=1.25, 95% CI 0.79-1.96). Altered ZEB1-AS1 expression was found to be an indicator of worse prognosis in OS (HR = 1.94, 95% CI: 1. 66-2.22) among tumor patients. Conclusions: High ZEB1-AS1expression was associated poor clinical outcome and it can serve as a novel predictive biomarker in various cancers.
High-Speed and High-Resolution Interrogation of a Strain and Temperature Random Grating Sensor

High-speed and high-resolution interrogation of a random fiber grating sensor based on spectral shaping and wavelength-to-time (SS-WTT) mapping, and pulse compression for simultaneous measurement of strain and temperature is proposed and demonstrated. In the proposed system, an ultrashort pulse is spectrum shaped by a high-birefringence (Hi-Bi) random grating (HBRG) to generate two orthogonally polarized spectra with a wavelength difference determined by the birefringence of the HBRG, which are then fed to a dispersive optical loop in which a linearly chirped fiber Bragg grating (LCFBG) is incorporated, to perform linear WTT mapping, to generate two temporally separated optical pulses, which are converted to two random electrical waveforms at a photodetector). Random pulse compression is then performed to increase the interrogation resolution. By measuring the time shifts of the temporally compressed pulses, the strain and temperature information is retrieved. An experiment is performed. The experimental results show that the proposed random grating sensor and its interrogation system can provide a strain and temperature resolution of 7.1 μϵ and 0.79 °C at an ultrahigh speed of 20 MHz.
Inhibition of Pyramidal Neurons in the Basal Amygdala Promotes Fear Learning

Abstract The basolateral amygdala complex, which contains the lateral (LA) and basal (BA) subnuclei, is a critical substrate of associative learning related to reward and aversive stimuli. Auditory fear conditioning studies in rodents have shown that the excitation of LA pyramidal neurons, driven by the inhibition of local GABAergic interneurons, is critical to fear memory formation. Studies examining the role of the BA in auditory fear conditioning, however, have yielded divergent outcomes. Here, we used a neuron-specific chemogenetic approach to manipulate the excitability of mouse BA neurons during auditory fear conditioning. We found that chemogenetic inhibition of BA GABA neurons, but not BA pyramidal neurons, impaired fear learning. Further, either chemogenetic stimulation of BA GABA neurons or chemogenetic inhibition of BA pyramidal neurons was sufficient to generate the formation of an association between a behavior and a neutral auditory cue. This chemogenetic memory required presentation of a discrete cue, and was not attributable to an effect of BA pyramidal neuron inhibition on general freezing behavior, locomotor activity, or anxiety. Collectively, these data suggest that BA GABA neuron activation and the subsequent inhibition of BA pyramidal neurons play important role in fear learning. Moreover, the roles of inhibitory signaling differ between the LA and BA, with excitation of pyramidal neurons promoting memory formation in the former, and inhibition of pyramidal neurons playing this role in the latter.
Individual-, maternal- and household-level factors associated with stunting among children aged 0–23 months in Bangladesh

Abstract Objective Childhood stunting remains a major public health concern in Bangladesh. To accelerate the reduction rate of stunting, special focus is required during the first 23 months of a child’s life when the bulk of growth takes place. Therefore the present study explored individual-, maternal- and household-level factors associated with stunting among children under 2 years of age in Bangladesh. Design Data were collected through a nationwide cross-sectional survey conducted between October 2015 and January 2016. A two-stage cluster random sampling procedure was applied to select 11 428 households. In the first stage, 210 enumerations areas (EA) were selected with probability proportional to EA size (180 EA from rural areas, thirty EA from urban slums). In the second stage, an average of fifty-four households were selected from each EA through systematic random sampling. Setting Rural areas and urban slums of Bangladesh. Participants A total of 6539 children aged 0–23 months. Results Overall, 29·9 % of the children were stunted. After adjusting for all potential confounders in the modified Poisson regression model, child’s gender, birth weight (individual level), maternal education, age at first pregnancy, nutrition (maternal level), administrative division, place of residence, socio-economic status, food security status, access to sanitary latrine and toilet hygiene condition (household level) were significantly associated with stunting. Conclusions The study identified a number of potentially addressable multilevel risk factors for stunting among young children in Bangladesh that should be addressed through comprehensive multicomponent interventions.
An Ultralow-Power RF Wireless Receiver With RF Blocker Energy Recycling for IoT Applications

A wireless RF receiver system suitable for Internet-of-Things (IoT) applications is presented. The system is able to simultaneously harvest energy from out-of-band blockers with normal receiver operation; thus, the battery life for IoT applications can be extended. The system has only a single antenna for simultaneous RF energy harvesting and wireless reception. The receiver is a mixer-first quadrature receiver to tolerate large unavoidable blockers. The system is implemented in 180-nm CMOS technology and operates at a 900-MHz industrial, scientific, and medical band. The receiver gain is 41.5 dB. Operating from a 1-V supply, the receiver core consumes 430 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>. Furthermore, this power can be reduced to <inline-formula> <tex-math notation="LaTeX">$220~\mu \text{W}$ </tex-math></inline-formula> in the presence of a large blocker (<inline-formula> <tex-math notation="LaTeX">${\approx} 0$ </tex-math></inline-formula> dBm) by the power provided by the blocker RF energy harvesting where the power conversion efficiency is 30%.
Convergence Analysis of a Fixed Point Algorithm Under Maximum Complex Correntropy Criterion

With the emergence of complex correntropy, the maximum complex correntropy criterion (MCCC) has been applied to the complex-domain adaptive filtering. The MCCC uses the fixed point method to find the optimal solution, which provides good robustness in the non-Gaussian noise environment, especially for the impulse noise. However, the convergence analysis for the fixed point method is limited to the real-domain filtering. In this letter, we provide the convergence analysis of fixed point based MCCC algorithm in complex-domain filtering. First, by using the matrix inversion lemma, we rewrite the MCCC algorithm to a gradient-like version. In addition, we provide two computationally efficient versions of MCCC. Then, we provide the stability analysis and obtain the excess mean square error for MCCC. Finally, simulation results confirm the correctness of the convergence analysis in this letter.
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training

Distributed training of deep nets is an important technique to address some of the present day computing challenges like memory consumption and computational demands. Classical distributed approaches, synchronous or asynchronous, are based on the parameter server architecture, i.e., worker nodes compute gradients which are communicated to the parameter server while updated parameters are returned. Recently, distributed training with AllReduce operations gained popularity as well. While many of those operations seem appealing, little is reported about wall-clock training time improvements. In this paper, we carefully analyze the AllReduce based setup, propose timing models which include network latency, bandwidth, cluster size and compute time, and demonstrate that a pipelined training with a width of two combines the best of both synchronous and asynchronous training. Specifically, for a setup consisting of a four-node GPU cluster we show wall-clock time training improvements of up to 5.4x compared to conventional approaches.
HDAC inhibitor apicidin suppresses murine oral squamous cell carcinoma cell growth in vitro and in vivo via inhibiting HDAC8 expression

Apicidin, a cyclic peptide histone deacetylase (HDAC) inhibitor, has been demonstrated to exhibit antitumor activity in a number of human cancer types. The present study examined the antitumor activity of apicidin in murine oral squamous cell carcinoma (OSCC) cells. Inhibition of cell proliferation and the expression of selective HDACs were determined in apicidin-treated AT-84 murine OSCC cells. A C3H mouse model with subcutaneous injection of AT-84 cells was used to assess the in vivo effect of apicidin on tumor growth. Apicidin-induced cell growth inhibition and selectively reduced HDAC8 expression in AT-84 cells. Induction of apoptosis and autophagy was observed in apicidin-treated AT-84 cells. Apicidin notably inhibited tumor growth by up to 46% relative to the control group at the end of a 14-day period in a murine tumor model. The immunohistochemistry results in tumor tissues indicated that apicidin inhibited cell proliferation and induced apoptosis and autophagy in AT-84 cell-derived tumor tissues. Overexpression of HDAC8 was observed in the nucleus and cytoplasm in tumor tissues and apicidin significantly inhibited the level of HDAC8 expression, compared with the vehicle group. These results indicated that apicidin inhibited cell proliferation through HDAC8 inhibition in murine OSCC cells in vitro and in vivo. The present study indicated that apicidin may be an effective therapeutic agent for OSCC.
A 1.8–3.8-GHz Power Amplifier With 40% Efficiency at 8-dB Power Back-Off

This paper presents a novel power combining architecture, referred to as distributed efficient power amplifier (DEPA) that mitigates the inherent bandwidth limiting factors in a Doherty power amplifier (DPA), namely, impedance inversion and load modulation, by distributing the conventional auxiliary amplifier of the DPA to multiple auxiliary subamplifiers along a broadband multisection impedance transformer. The proposed technique is demonstrated through the design, implementation, and measurement of a wideband DEPA having 8-dB output power back-off and 72% fractional bandwidth. The experimental results show a Doherty-like efficiency enhancement from 1.8 to 3.8 GHz. For the output power levels of 37.4±1.1 and 45.4±1.1 dBm, corresponding to 8-dB OBO and peak output power, the efficiency levels are between 41%–51% and 42%–62%, respectively. Furthermore, linearized modulated measurements show that the DEPA is linearizable and meets the spectral requirements for modern wireless communication systems.
FEZF1 is an Independent Predictive Factor for Recurrence and Promotes Cell Proliferation and Migration in Cervical Cancer

The Fez family zinc finger protein 1 (FEZF1), a critical transcription factor in nervous system development, has been implicated in cancer progression recently. However, its clinical significance remains unknown. By analyzing gene expression data of eight most common cancer types from The Cancer Genome Atlas (TCGA), we found that FEZF1 prominently associated with the recurrence-free survival of cervical cancer patients (P<0.001) and was an independent diagnostic factor for cervical cancer recurrence (P=0.002). Moreover, FEZF1 expression was significantly higher in the tumor samples from cervical cancer patients with relapse in TCGA(P=0.015). By RNA interference, we knocked down FEZF1 and found that cell proliferation, growth and migration were significantly decreased in C33A and SiHa cells. Meanwhile, FEZF1 knockdown also attenuated the growth of C33A cells in nude mice. In contrast, expression of FEZF1 promoted cell proliferation, growth and migration in HeLa cells. Using chromatin immunoprecipitation (ChIP) assay, we revealed that FEZF1 could bind to multiple key genes in the Wnt signaling pathway in HeLa cells. Furthermore, analysis of the levels of β-catenin protein, the core component of the Wnt pathway, and downstream effector genes of the pathway showed that FEZF1 could activate the Wnt pathway. Together, these results suggest that FEZF1 promotes cell proliferation and migration possibly by acting as a transcriptional activator of the Wnt signaling pathway in cervical cancer, and also provide a valuable molecular predictive marker for cervical cancer recurrence.
A Translational Parallel Manipulator with Three Horizontal-Axial Pneumatic Actuators for 3-D Path Tracking

This paper aims to propose a novel translational parallel manipulator (TPM) with three degrees of freedom (3-DOF), driven by rodless pneumatic actuators (RPA). The developed parallel manipulator is composed of a fixed base frame, a moving platform, and three sets of parallel kinematic chains. In addition, three identical rodless pneumatic cylinders are employed as the linear actuators of the manipulator. To allow more working space for the manipulator, the RPAs are configured into horizontal positions. A mobility analysis of the parallel mechanism verifies the manipulator to possess three translational degrees of freedom. In this study, a geometric method is introduced to solve the kinematic relation between the actuated joints and the moving platform: A vector-loop closure equation is first established for each limb of the manipulator; by solving the equation, solutions for both the inverse and forward kinematics are obtained. For the control of the 3-DOF TPM, a Fuzzy-PID controller with feedback loops is applied. To improve control precision against nonlinearities and uncertainties in the pneumatic plant, a Back-propagation Neural Network (BPNN) is trained to implement effective pre-compensation. Finally, the real-time experiment is conducted to verify the path tracking control of the three-axial manipulator end-effector.
A Novel Cost Function for Decision-Making Strategies in Automotive Collision Avoidance Systems

Nowadays, the automotive advanced driver assistance systems have the ability to detect surrounding objects, predict impending collisions and initiate automatic emergency braking. However, by a late detection of objects at higher speeds, the collision is hardly avoidable by braking only, hence an evasive steering maneuver shall be performed simultaneously to cure this deficiency. This paper presents a novel approach that utilizes a dedicated cost function to make the appropriate maneuver decision in an imminent collision avoidance situation. By taking into account the host vehicle and the collision target motions as well as other moving or stationary objects in the near vicinity, diverse aspects and criteria are analyzed and discussed to evaluate possible maneuver candidates. After that, the cost functions of different maneuvers are calculated by summarizing the results of all the evaluation criteria and aspects. In both simulated and measured critical situations, the cost function is validated and the maneuver with the best (i.e., lowest) cost is selected to avoid the impending collision and the endangerment of any other road users aside.
The Relationship between Running Power and Running Economy in Well-Trained Distance Runners

A novel running wearable called the Stryd Summit footpod fastens to a runner’s shoe and estimates running power. The footpod separates power output into two components, Stryd power and form power. The purpose of this study was to measure the correlations between running economy and power and form power at lactate threshold pace. Seventeen well-trained distance runners, 9 male and 8 female, completed a running protocol. Participants ran two four-minute trials: one with a self-selected cadence, and one with a target cadence lowered by 10%. The mean running economy expressed in terms of oxygen cost at self-selected cadence was 201.6 ± 12.8 mL·kg−1·km−1, and at lowered cadence was 204.5 ± 11.5 mL·kg−1·km−1. Ventilation rate and rating of perceived exertion (RPE) were not significantly different between cadence conditions with one-tailed paired t-test analysis (ventilation, p = 0.77, RPE, p = 0.07). Respiratory exchange ratio and caloric unit cost were significantly greater with lower cadence condition (respiratory exchange ratio, p = 0.03, caloric unit cost, p = 0.03). Mean power at self-selected cadence was 4.4 ± 0.5 W·kg−1, and at lowered cadence was 4.4 ± 0.5 W·kg−1. Mean form power at self-selected cadence was 1.1 ± 0.1 W·kg−1, and at lowered cadence was 1.1 ± 0.1 W·kg−1. There were positive, linear correlations between running economy and power (self-selected cadence and lower cadence, r = 0.6; the 90% confidence interval was 0.2 to 0.8); running economy and form power (self-selected cadence and lower cadence r = 0.5; the 90% confidence interval was 0.1 to 0.8). The findings suggest running economy is positively correlated with Stryd’s power and form power measures yet the footpod may not be sufficiently accurate to estimate differences in the running economy of competitive runners.
Perceived helpfulness of peer-delivered trauma specific treatment: A randomized controlled trial.

Peer providers have been associated with positive outcomes in behavioral health, but little is known about the perceived helpfulness of their services. We used a mixed-methods randomized controlled trial to compare the perceived helpfulness of seeking safety (SS) led by peer providers compared to its delivery by licensed behavioral health clinicians (including both a licensed alcohol and drug abuse counselor and licensed clinical mental health counselor). Participants (N = 278) rated the helpfulness of 12 SS topics at the end of every session. A subset (n = 245) of participants also rated the helpfulness of SS overall and its treatment components; 3 and 6 months following their first SS group. We also collected qualitative data to inform our understanding of the ways perceived helpfulness varied among participants in peer-led (PL) and clinician-led (CL) groups. Ratings of overall and topic-specific helpfulness were high among both groups, and while ratings were slightly higher among participants in PL groups, the difference was not significant. Participants in PL-SS rated certain treatment components significantly more helpful compared to participants in CL-SS, including the focus on learning coping skills (81.6% PL vs. 64.9% CL, p = .020) and safety as a priority of treatment (81.6% PL vs. 61.5% CL, p = .006). Because of the homogeneity of helpfulness ratings, the relationship between perceived helpfulness and treatment retention and outcomes could not be examined. Future research on this association is warranted. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Large-Scale Visual Active Learning with Deep Probabilistic Ensembles

Annotating the right data for training deep neural networks is an important challenge. Active learning using uncertainty estimates from Bayesian Neural Networks (BNNs) could provide an effective solution to this. Despite being theoretically principled, BNNs require approximations to be applied to large-scale problems, where both performance and uncertainty estimation are crucial. In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scalable technique that uses a regularized ensemble to approximate a deep BNN. We conduct a series of large-scale visual active learning experiments to evaluate DPEs on classification with the CIFAR-10, CIFAR-100 and ImageNet datasets, and semantic segmentation with the BDD100k dataset. Our models require significantly less training data to achieve competitive performances, and steadily improve upon strong active learning baselines as the annotation budget is increased.