Miracle-dz/UniFAR-FTUs huggingface dataset:
Perfusion Changes at the Forehead Measured by Photoplethysmography during a Head-Down Tilt Protocol

Photoplethysmography (PPG) signals from the forehead can be used in pulse oximetry as they are less affected by vasoconstriction compared to fingers. However, the increase in venous blood caused by the positioning of the patient can deteriorate the signals and cause erroneous estimations of the arterial oxygen saturation. To date, there is no method to measure this venous presence under the PPG sensor. This study investigates the feasibility of using PPG signals from the forehead in an effort to estimate relative changes in haemoglobin concentrations that could reveal these posture-induced changes. Two identical reflectance PPG sensors were placed on two different positions on the forehead (above the eyebrow and on top of a large vein) in 16 healthy volunteers during a head-down tilt protocol. Relative changes in oxygenated (ΔHbO2), reduced (ΔHHb) and total (ΔtHb) haemoglobin were estimated from the PPG signals and the trends were compared with reference Near Infrared Spectroscopy (NIRS) measurements. Also, the signals from the two PPG sensors were analysed in order to reveal any difference due to the positioning of the sensor. ΔHbO2, ΔHHb and ΔtHb estimated from the forehead PPGs trended well with the same parameters from the reference NIRS. However, placing the sensor over a large vasculature reduces trending against NIRS, introduces biases as well as increases the variability of the changes in ΔHHb. Forehead PPG signals can be used to measure perfusion changes to reveal venous pooling induced by the positioning of the subject. Placing the sensor above the eyebrow and away from large vasculature avoids biases and large variability in the measurements.
A network medicine approach to psychiatric genetics

The major psychiatric disorders are complex in nature, meaning that they are influenced by multiple environmental and genetic exposures that perturb the intricate cellular network, resulting in disease. In general, psychiatric diseases are highly heritable but also have important environmental etiologies. Environmental influences include neonatal exposures, social environments, psychological mechanisms, and abnormal functioning of the neurotransmitter system. Molecular influences can be identified using many data types including genomics, epigenomics, transcriptomics, metabolomics, and proteomics. The emerging field of network medicine offers a new approach to explore the complexities of disease development in a framework that considers a holistic, rather than a reductionist viewpoint. In this review we explain a general framework of how the network medicine approach can provide valuable insight into understanding important molecular mechanisms that contribute to the pathogenesis of psychiatric disorders. © 2013 Wiley Periodicals, Inc.
Two new methods to estimate the IPFM model

In this paper, I present two new methods to estimate the modulating function of IPFM Model. As we know the heart rate variability (HRV) is important to assessment the heart health or classify heart disorder [1]. And there were some methods the estimate the modulating function of IPFM (to model the heart rate variability) like using sink functions [2] or using of Fourier transform [3] or using of new functions like heart timing [4]. but their precision is finite. It means their precision is limited to the number of Fourier statements or the number of sinks also the heart rate variability (HRV) and IPFM model is an extended tool to analyze the mechanisms controlling the cardiovascular system. In this paper, the integral pulse frequency modulation model (IPFM) is assumed.
Prognostic Value of Semen Analyses in infertility Evaluation (Male fertility/Life‐table analysis): Der prognostische Wert der Sperma‐Analyse bei der Infertilitäts‐Bewertung

Summary: The aim of the present study was to evaluate which of the various parameters of semen analysis that are most significantly related to the obtainment of pregnancy. A follow up questionnaire was sent to 1953 couples, who had been examined for infertility, and 1480 (76%) replied. We focused on 713 of these couples, in whom the female partner had a normal fertility set‐up (potentially fertile).
Identification of Alzheimer's Disease Autoantibodies and Their Target Biomarkers by Phage Microarrays.

The characterization of the humoral response in Alzheimer's disease (AD) patients might aid in detecting the disease at early stages. We have combined phage display and protein microarrays to identify AD autoantibodies and their target biomarkers. After enrichment of T7 phage display libraries from AD and healthy brain tissue mRNA in AD-specific phages, 1536 monoclonal phages were printed on microarrays to probe them with 8 AD and 8 healthy controls sera. 57 phages showed higher seroreactivity in AD. 13 out of the 44 unique sequences displayed on phages were selected for validation using 68 AD and 52 healthy controls sera. Peptides from Anthrax toxin receptor 1, Nuclear protein 1, Glycogen phosphorylase and Olfactory receptor 8J1 expressed in bacteria as HaloTag fusion proteins showed statistically significant ability to discriminate between AD patients and controls. The identified panel of AD autoantibodies might provide new insights into the blood-based diagnosis of the disease.
Secrecy Outage Probability for MRC Receiver in Presence of Multiple Antenna Eavesdropper

In this paper, we consider a hybrid fading model in which line of sight (LOS) component is available between the transmitter and legitimate receiver and assume that eavesdropper is not in LOS from the transmitter. We analyze probability of existence of secrecy and secrecy outage probability for the case that the channels to the legitimate receiver are $\kappa -\mu $ faded and the channels to eavesdropper are Rayleigh faded. We present closed form expressions for existence of secrecy and a lower bound on secrecy outage probability when legitimate receiver and eavesdropper uses maximal ratio combining (MRC) diversity scheme to improve their performances in fading conditions. Results of analytical expressions are verified by an agreement of the results from simulation. Finally, it is concluded that the presence of LOS component improves secrecy of the system and stronger LOS component betters the secrecy.
An Optimized Parameter Design Method of WPT System for EV Charging Based on Optimal Operation Frequency Range

Commonly, the variable frequency phase shift control strategy (VFPSC) is usually used to realize the required output and ZVS operation simultaneously without extra dc/dc converters. However, its corresponding resonant parameter design method has not been studied, especially combining with the EV’s battery. In this paper, the characteristics of the SS type WPT system have been studied in detail, which include trans-conductance gain, voltage gain of the system, inductor current and capacitor voltage of resonant network, and quality factor. Moreover, an optimized electrical parameter design method based on VFPSC is proposed with the multiple boundary conditions. Finally, a 500W WPT system is built to verify the rationality of this design method. The experimental results show that a very high overall efficiency is achieved in the whole charging process and the maximum efficiency can achieve 94.9% with k=0.2.
The Systemin Signaling Cascade As Derived from Time Course Analyses of the Systemin-responsive Phosphoproteome*

The early phosphorylation signaling cascade of the peptide hormone systemin was defined through comparing systemin-induced responses with non-specific responses induced by the inactive analogon systemin-A17 or mock treatment. Systemin-responsive phosphorylation of kinases and phosphatases was identified over a time course of 2 to 45 minutes, as well as typical substrate phosphorylation sites. We identified the C-terminal threonine of H+-ATPase LHA1 as a substrate of phosphatase PLL5 and MAP-Kinase MPK2, thereby defining a signaling circuit of rapid dephosphorylation and re-phosphorylation after 15 minutes. Graphical Abstract Highlights Definition of early systemin-responsive phosphorylation time course. Reconstruction of kinase-substrate relationships from phosphorylation time profiles. Phosphatase PLL5 rapidly dephosphorylated H+-ATPase LHA1 inducing alkalinization of the medium. MAP-Kinase MPK2 re-phosphorylated LHA1 after 15 minutes. Systemin is a small peptide with important functions in plant wound response signaling. Although the transcriptional responses of systemin action are well described, the signaling cascades involved in systemin perception and signal transduction at the protein level are poorly understood. Here we used a tomato cell suspension culture system to profile phosphoproteomic responses induced by systemin and its inactive Thr17Ala analog, allowing us to reconstruct a systemin-specific kinase/phosphatase signaling network. Our time-course analysis revealed early phosphorylation events at the plasma membrane, such as dephosphorylation of H+-ATPase, rapid phosphorylation of NADPH-oxidase and Ca2+-ATPase. Later responses involved transient phosphorylation of small GTPases, vesicle trafficking proteins and transcription factors. Based on a correlation analysis of systemin-induced phosphorylation profiles, we predicted substrate candidates for 44 early systemin-responsive kinases, which includes receptor kinases and downstream kinases such as MAP kinases, as well as nine phosphatases. We propose a regulatory module in which H+-ATPase LHA1 is rapidly de-phosphorylated at its C-terminal regulatory residue T955 by phosphatase PLL5, resulting in the alkalization of the growth medium within 2 mins of systemin treatment. We found the MAP kinase MPK2 to have increased phosphorylation level at its activating TEY-motif at 15 min post-treatment. The predicted interaction of MPK2 with LHA1 was confirmed by in vitro kinase assays, suggesting that the H+-ATPase LHA1 is re-activated by MPK2 later in the systemin response. Our data set provides a resource of proteomic events involved in systemin signaling that will be valuable for further in-depth functional studies in elucidation of systemin signaling cascades.
A Lightweight, Soft Wearable Sleeve for Rehabilitation of Forearm Pronation and Supination

Forearm disabilities can result from a number of diseases or injuries, such as stroke, spinal cord injuries (SPI), and acquired/traumatic brain injuries (ABI/TBI). Patients with forearm disabilities suffer from daily activities and follow-up care and rehabilitation are critical to recovery of the patients in this case. However, they have to make personal trips to clinics or hospitals for follow-up treatments since most of existing devices available are bulky, heavy and even expensive in general, discouraging the patients from having them home. This paper proposes a pneumatic inflatable rehabilitation device designed with bio-inspiration. The device is lightweight, safe, and affordable compared to conventional devices. When the actuators of the device are inflated, the contractions of the actuators mimic the motions of the human muscles of the forearm, assisting pronation and supination. The actuators are made of heat-sealable fabrics using a custom-built patterning system. This system facilitates rapid fabrication of repetitive and complex sealing patterns. Also, the proposed rehabilitation device, which can be conjointly used with typical clothing materials, provides comfort for the user by easily conforming the three-dimensional geometries of the forearm. To characterize the system, the angles and the torques of the wrist generated by the device were measured. The result of simple closed-loop control using a vision feedback was also presented.
Cross-Talk Compensation in Low-Cost Resistive Pressure Matrix Sensors

For socially assistive robots in close contact to people, a tactile sensor can be useful for gathering feedback and inputs in the form of touch gestures. In this paper, we concentrate on low-cost textile pressure matrix sensors since they are easy to manufacture and due to their flexibility can be adopted to the curved shape of a robot's outer cover. Due to the matrix principle for reading out, the setup suffers from artifacts when it comes to activation of multiple sensor elements. We present a machine learning approach for preprocessing the raw measurements from the pressure sensitive array in order to get reliable pressure patterns which can be used for gesture classification later on. By means of that, an expensive hardware solution for capturing the pressure values can be avoided.
Automated Mechanism Design via Neural Networks

Using AI approaches to automatically design mechanisms has been a central research mission at the interface of AI and economics. Previous approaches that attempt to design revenue optimal auctions for the multi-dimensional settings fall short in at least one of the three aspects: 1) representation --- search in a space that probably does not even contain the optimal mechanism; 2) exactness --- finding a mechanism that is either not truthful or far from optimal; 3) domain dependence --- need a different design for different environment settings. To resolve the three difficulties, in this paper, we put forward a unified neural network based framework that automatically learns to design revenue optimal mechanisms. Our framework consists of a mechanism network that takes an input distribution for training and outputs a mechanism, as well as a buyer network that takes a mechanism as input and output an action. Such a separation in design mitigates the difficulty to impose incentive compatibility constraints on the mechanism, by making it a rational choice of the buyer. As a result, our framework easily overcomes the previously mentioned difficulty in incorporating IC constraints and always returns exactly incentive compatible mechanisms. We then applied our framework to a number of multi-item auction design settings, for a few of which the theoretically optimal mechanisms are unknown. We then go on to theoretically prove that the mechanisms found by our framework are indeed optimal.
Machine Learning based Joint Torque calculations of Industrial Robots

Getting closed-form inverse dynamics solution for a manipulating robot is desirable for real-time torque computation. Some powerful and well-established mechanics based tools like Newton Euler method (N-E), Lagrangian methods are available for developing a mathematical model for manipulating robots. However, the coupled equation being highly non-linear and incomplete (joint friction, dimensional inaccuracies arising due to manufacturing error are very difficult to model) and hence difficult to apply in real life situation which requires accurate joint torque computation. We believe learning based machine intelligence tools can more efficiently and appropriately be utilized for joint torque computations in the inverse dynamics paradigm. In this paper, K-nearest neighbor (KNN) algorithm has been proposed for finding joint torques from the dataset created by solving forward dynamics equation for the manipulating robots, which is comparatively straight forward and rather less complex. However, since computational complexity of KNN is high, we used K-dimensional tree (K-D tree) for decreasing the computational complexity. The simulation result for two-link manipulator shows the proposed method of KNN based joint torque calculation coupled with K-D tree is simple, robust and accurate, which can be emulated for a further higher degrees of freedom robot having six links.
Temporal-order iconicity bias in narrative event understanding and memory

ABSTRACT Incongruence between the narrated (encoded) order and the actual chronological order of events is ubiquitous in various kinds of narratives and information modalities. The iconicity assumption in text comprehension proposes that readers will by default assume the chronological order to match the narrated order. However, it is not clear whether this iconicity assumption would directly bias inferred chronology of events and memory of their narrated order. In the current study, using non-linearly narrated video narratives as encoding materials, we dissociated the narrated order and the underlying chronological order of events. In Experiment 1, we found that participants’ judgments of the chronological order of events were biased by the narrated order, but not vice versa. In Experiment 2, when the chronological positions of events were provided during encoding, participants’ judgments of the chronological order were not biased by the narrated order, rather, their memory of the narrated order of events was biased by the chronological order. Interpreting the bias under a descriptive Bayesian framework, we offer a new perspective on the role of the iconicity assumption as prior belief, apart from prior knowledge about event sequences, in event understanding as well as memory.
Acceleration of LSTM With Structured Pruning Method on FPGA

This paper focuses on accelerating long short-term memory (LSTM), which is one of the popular types of recurrent neural networks (RNNs). Because of the large number of weight memory accesses and high computation complexity with the cascade-dependent structure, it is a big challenge to efficiently implement the LSTM on field-programmable gate arrays (FPGAs). To speed up the inference on FPGA, considering its limited resource, a structured pruning method that can not only reduce the LSTM model’s size without loss of prediction accuracy but also eliminate the imbalance computation and irregular memory accesses is proposed. Besides that, the hardware architecture of the compressed LSTM is designed to pursue high performance. As a result, the implementation of an LSTM language module on Stratix V GXA7 FPGA can achieve 85.2 GOPS directly on the sparse LSTM network by our method, corresponding to 681.6-GOPS effective throughput on the dense one, which shows that the proposed structured pruning algorithm makes 7.82 times speedup when only 1/8 parameters are reserved. We hope that our method can give an efficient way to accelerate the LSTM and similar recurrent neural networks when the resource-limited environment is emphasized.
Feature Learning for Bird Call Clustering

In this paper, a supervised algorithm is proposed for the identification and segmentation of bird calls with K-means clustering using features learnt by matrix factorization. Singular value decomposition is applied on pooled time-frequency vocalization in a class-wise manner to learn a class-specific feature representation. These representations show discriminative behavior even when unseen classes are represented. By combining the proposed feature representation with K-means clustering, we are able to effectively cluster and segment bird calls from multiple species, which are present in an input recording. Experimental results are provided on a small dataset of birdsong.
Selective Harmonic Compensation of Three Phase Grid tied SEPIC based Differential inverter

three phase SEPIC based Differential inverter topology provides cost effective solution which can effectively connect PV resources with electric grid. This paper presents new technique for compensating second order harmonic component at AC output current of three-phase grid-tie DC-AC SEPIC-based converter that uses high-frequency transformer (HFT) for galvanic isolation. Parallel connection of three SEPIC converters results in negative sequence harmonic component (NSHC) at grid current due to circulating current between SEPIC modules. The direct relationship between circulating current and leakage inductance of the HFT with the NSHC is investigated. Also, a three-stage low pass filter (LPF) is proposed and designed for accurately detecting the NSHC, which is then compensated from the grid current. The proposed technique has been verified using 1.6Kw differential inverter Setup.
Trajectories of Blood Lipid Concentrations Over the Adult Life Course and Risk of Cardiovascular Disease and All‐Cause Mortality: Observations From the Framingham Study Over 35 Years

Background Elevated total cholesterol (TC), low‐density lipoprotein cholesterol (LDL‐C), triglycerides, and non‐high‐density lipoprotein cholesterol (non‐HDL‐C) and low high‐density lipoprotein cholesterol (HDL‐C) concentrations correlate with atherosclerotic cardiovascular disease (ASCVD) and mortality. Therefore, understanding how lipid trajectories throughout adulthood impact ASCVD and mortality risk is essential. Methods and Results We investigated 3875 Framingham Offspring participants (54% women, mean age 48 years) attending ≥1 examination between 1979 and 2014. We evaluated longitudinal correlates of each lipid subtype using mixed‐effects models. Next, we clustered individuals into trajectories through group‐based modeling. Thereafter, we assessed the prospective association of lipid trajectories with ASCVD and mortality. Male sex, greater body mass index, and smoking correlated with higher TC, LDL‐C, triglycerides, non‐HDL‐C, and lower HDL‐C concentrations. We identified 5 TC, HDL‐C, and LDL‐C trajectories, and 4 triglycerides and non‐HDL‐C trajectories. Upon follow‐up (median 8.2 years; 199 ASCVD events; 256 deaths), elevated TC (>240 mg/dL), LDL‐C (>155 mg/dL), or non‐HDL‐C (>180 mg/dL) concentrations conferred >2.25‐fold ASCVD and mortality risk compared with concentrations <165 mg/dL, <90 mg/dL, and <115 mg/dL, respectively ([TC hazard ratio (HR)ASCVD=4.17, 95% CI 1.94–8.99; TC HR death=2.47, 95% CI 1.28–4.76] [LDL‐C HRASCVD=5.09, 95% CI 1.54–16.85; LDL‐C HR death=4.04, 95% CI 1.84–8.89] [non‐HDL‐C HRASCVD=4.60, 95% CI 1.98–10.70; LDL‐C HR death=3.74, 95% CI 2.03–6.88]). Consistent HDL‐C concentrations <40 mg/dL were associated with greater ASCVD and mortality risk than concentrations >70 mg/dL (HRASCVD=3.81, 95% CI 2.04–7.15; HR death=2.88, 95% CI 1.70–4.89). Triglycerides trajectories were unassociated with outcomes. Conclusions Using a longitudinal modeling technique, we demonstrated that unfavorable lipid trajectories over 35 years confer higher ASCVD and mortality risk later in life.
Toward Robust Policy Summarization

AI agents are being developed to help people with high stakes decision-making processes from driving cars to prescribing drugs. It is therefore becoming increasingly important to develop "explainable AI" methods that help people understand the behavior of such agents. Summaries of agent policies can help human users anticipate agent behavior and facilitate more effective collaboration. Prior work has framed agent summarization as a machine teaching problem where examples of agent behavior are chosen to maximize reconstruction quality under the assumption that people do inverse reinforcement learning to infer an agent's policy from demonstrations. We compare summaries generated under this assumption to summaries generated under the assumption that people use imitation learning. We show through simulations that in some domains, there exist summaries that produce high-quality reconstructions under different models, but in other domains, only matching the summary extraction model to the reconstruction model produces high-quality reconstructions. These results highlight the importance of assuming correct computational models for how humans extrapolate from a summary, suggesting human-in-the-loop approaches to summary extraction.