Miracle-dz/UniFAR-FTUs huggingface dataset:
Simpler, Faster, More Accurate Melanocytic Lesion Segmentation Through MEDS

We present a new technique for melanocytic lesion segmentation, Mimicking Expert Dermatologists' Segmentations (MEDS), and extensive tests of its accuracy, speed, and robustness. MEDS combines a thresholding scheme reproducing the cognitive process of dermatologists with a number of optimizations that may be of independent interest. MEDS is simple, with a single parameter tuning its “tightness”. It is extremely fast, segmenting medium-resolution images in a fraction of a second even with the modest computational resources of a cell phone-an improvement of an order of magnitude or more over state-of-the-art techniques. And it is extremely accurate: very experienced dermatologists disagree with its segmentations less than they disagree with the segmentations of state-of-the-art techniques, and in fact less than they disagree with the segmentations of dermatologists of moderate experience.
Improving Accuracy and Precision of Glucose Sensor Profiles: Retrospective Fitting by Constrained Deconvolution

Frequent and accurate reference measurements of blood-glucose (BG) concentration are key for modeling and for computing outcome metrics in clinical trials but difficult, invasive, and costly to collect. Continuous glucose monitoring (CGM) is a minimally-invasive technology that has the requested temporal resolution to substitute BG references for such a scope, but still lacks of precision and accuracy. In this paper, we propose an algorithm that retrospectively reconstructs a reliable continuous-time BG profile for the aforementioned purposes, by simultaneously exploiting the high accuracy of (possibly sparse) BG references and the high temporal resolution of CGM data. The algorithm performs a constrained semiblind deconvolution in two steps: first, it estimates the unknown parameters of a model accounting for plasma-interstitum diffusion and sensor inaccurate calibration; then, it estimates BG performing a regularized deconvolution of CGM data, subject to the additional constraint that the reconstructed BG profile has to lay within the confidence interval of the available BG references. The algorithm was tested on 24 datasets collected in a 20 h clinical trial where CGM records and a median of 13 BG samples per day were available. Mean absolute relative deviation was reduced (from 15.71% to 8.84%) with respect to unprocessed CGM and so did the error in the evaluation of the outcomes metrics (e.g., halved the error in the time-in-hypo assessment). The reconstructed BG profile, in view of its improved accuracy and precision, is suitable for clinical trial assessment, modeling and other offline applications.
Creep, hysteresis, and vibration effects attenuation in an AFM PTS

There is a need, in the wide ranging scientific community, to perform fast scans using atomic force microscope (AFM) with nanoscale accuracy. The performance of AFM at high scanning speeds is limited due to some serious limitations of its scanning unit; i.e, the piezoelectric tube scanner (PTS). In order to increase the imaging speed of an AFM, a multi-input multi-output (MIMO) model predictive control (MPC) scheme is applied to the axes of the PTS to reduce its creep, hysteresis, and vibration effects. The design of this controller is based on an identified MIMO model of the AFM PTS. Also, a vibration compensator is designed and included in the feedback loop with the plant to suppress the vibration of the PTS at the resonant frequency. To evaluate the performance improvement achieved using the proposed control scheme, experimental comparisons of the scanned images obtained using open-loop and the in-built proportional-integral (PI) controller is made. The comparison demonstrates the usefulness of the proposed controller.
CLIMATE CHANGE. Bumblebees aren't keeping up with a warming planet.

As the climate changes, plants and animals are on the move. So far, many are redistributing in a similar pattern: As habitat that was once too cold warms up, species are expanding their ranges toward the poles, while boundaries closer to the equator have remained more static. Bumblebees, however, appear to be a disturbing exception, according to a new study in Science. A comprehensive look at dozens of species, it finds that many North American and European bumblebees are failing to "track" warming by colonizing new habitat north of their historic range. Simultaneously, they are disappearing from the southern portions of their range. "Climate change is crushing (bumblebee) species in a vice," says ecologist Jeremy Kerr of the University of Ottawa in Canada, the study9s lead author. Where bumblebees vanish, the wild plants and crops they pollinate could also suffer.
Robotic Adherent Cell Injection for Characterizing Cell–Cell Communication

Compared to robotic injection of suspended cells (e.g., embryos and oocytes), fewer attempts were made to automate the injection of adherent cells (e.g., cancer cells and cardiomyocytes) due to their smaller size, highly irregular morphology, small thickness (a few micrometers thick), and large variations in thickness across cells. This paper presents a robotic system for automated microinjection of adherent cells. The system is embedded with several new capabilities: automatically locating micropipette tips; robustly detecting the contact of micropipette tip with cell culturing surface and directly with cell membrane; and precisely compensating for accumulative positioning errors. These new capabilities make it practical to perform adherent cell microinjection truly via computer mouse clicking in front of a computer monitor, on hundreds and thousands of cells per experiment (versus a few to tens of cells as state of the art). System operation speed, success rate, and cell viability rate were quantitatively evaluated based on robotic microinjection of over 4000 cells. This paper also reports the use of the new robotic system to perform cell-cell communication studies using large sample sizes. The gap junction function in a cardiac muscle cell line (HL-1 cells), for the first time, was quantified with the system.
Adhesive RFID Sensor Patch for Monitoring of Sweat Electrolytes

Wearable digital health devices are dominantly found in rigid form factors such as bracelets and pucks. An adhesive radio-frequency identification (RFID) sensor bandage (patch) is reported, which can be made completely intimate with human skin, a distinct advantage for chronological monitoring of biomarkers in sweat. In this demonstration, a commercial RFID chip is adapted with minimum components to allow potentiometric sensing of solutes in sweat, and surface temperature, as read by an Android smartphone app with 96% accuracy at 50 mM Na+ (in vitro tests). All circuitry is solder-reflow integrated on a standard Cu/polyimide flexible-electronic layer including an antenna, but while also allowing electroplating for simple integration of exotic metals for sensing electrodes. Optional paper microfluidics wick sweat from a sweat porous adhesive allowing flow to the sensor, or the sensor can be directly contacted to the skin. The wearability of the patch has been demonstrated for up to seven days, and includes a protective textile which provides a feel and appearance similar to a standard Band-Aid. Applications include hydration monitoring, but the basic capability is extendable to other mM ionic solutes in sweat (Cl-, K+, Mg2+, NH4+, and Zn2+). The design and fabrication of the patch are provided in full detail, as the basic components could be useful in the design of other wearable sensors.
Coffee Consumption Is Associated With Higher Plasma Adiponectin Concentrations in Women With or Without Type 2 Diabetes

Kotani et al. reported results on the association between coffee consumption and serum adiponectin concentrations in Japanese men and women (1). They concluded that their study does not confirm our recent observation that frequent consumption of coffee is associated with higher adiponectin concentrations in a population of U.S. women (2) and suggest that ethnic differences may have contributed to this difference in results. However, one could offer several …
Ballistocardiogram as Proximal Timing Reference for Pulse Transit Time Measurement: Potential for Cuffless Blood Pressure Monitoring

Goal: We tested the hypothesis that the ballistocardiogram (BCG) waveform could yield a viable proximal timing reference for measuring pulse transit time (PTT). Methods: From 15 healthy volunteers, we measured PTT as the time interval between BCG and a noninvasively measured finger blood pressure (BP) waveform. To evaluate the efficacy of the BCG-based PTT in estimating BP, we likewise measured pulse arrival time (PAT) using the electrocardiogram (ECG) as proximal timing reference and compared their correlations to BP. Results: BCG-based PTT was correlated with BP reasonably well: the mean correlation coefficient (r ) was 0.62 for diastolic (DP), 0.65 for mean (MP), and 0.66 for systolic (SP) pressures when the intersecting tangent method was used as distal timing reference. Comparing four distal timing references (intersecting tangent, maximum second derivative, diastolic minimum, and systolic maximum), PTT exhibited the best correlation with BP when the systolic maximum method was used (mean r value was 0.66 for DP, 0.67 for MP, and 0.70 for SP). PTT was more strongly correlated with DP than PAT regardless of the distal timing reference: mean r value was 0.62 versus 0.51 (p = 0.07) for intersecting tangent, 0.54 versus 0.49 (p = 0.17) for maximum second derivative, 0.58 versus 0.52 (p = 0.37) for diastolic minimum, and 0.66 versus 0.60 (p = 0.10) for systolic maximum methods. The difference between PTT and PAT in estimating DP was significant (p = 0.01) when the r values associated with all the distal timing references were compared altogether. However, PAT appeared to outperform PTT in estimating SP ( p = 0.31 when the r values associated with all the distal timing references were compared altogether). Conclusion: We conclude that BCG is an adequate proximal timing reference in deriving PTT, and that BCG-based PTT may be superior to ECG-based PAT in estimating DP. Significance: PTT with BCG as proximal timing reference has potential to enable convenient and ubiquitous cuffless BP monitoring.
S1 and S2 Heart Sound Recognition Using Deep Neural Networks

Objective: This study focuses on the first (S1) and second (S2) heart sound recognition based only on acoustic characteristics; the assumptions of the individual durations of S1 and S2 and time intervals of S1–S2 and S2–S1 are not involved in the recognition process. The main objective is to investigate whether reliable S1 and S2 recognition performance can still be attained under situations where the duration and interval information might not be accessible. Methods: A deep neural network (DNN) method is proposed for recognizing S1 and S2 heart sounds. In the proposed method, heart sound signals are first converted into a sequence of Mel-frequency cepstral coefficients (MFCCs). The K-means algorithm is applied to cluster MFCC features into two groups to refine their representation and discriminative capability. The refined features are then fed to a DNN classifier to perform S1 and S2 recognition. We conducted experiments using actual heart sound signals recorded using an electronic stethoscope. Precision, recall, F-measure, and accuracy are used as the evaluation metrics. Results: The proposed DNN-based method can achieve high precision, recall, and F-measure scores with more than 91% accuracy rate. Conclusion: The DNN classifier provides higher evaluation scores compared with other well-known pattern classification methods. Significance: The proposed DNN-based method can achieve reliable S1 and S2 recognition performance based on acoustic characteristics without using an ECG reference or incorporating the assumptions of the individual durations of S1 and S2 and time intervals of S1–S2 and S2–S1.
A delayed consensus algorithm in networks of anticipatory agents

We introduce and analyze a delayed consensus algorithm as a model for interacting agents using anticipation of their neighbors' states to improve convergence to consensus. We derive a necessary and sufficient condition for the system to reach consensus. Furthermore, we explicitly calculate the dominant characteristic root of the consensus problem as a measure of the speed of convergence. The results show that the anticipatory algorithm can improve the speed of consensus, especially in networks with poor connectivity. Hence, anticipation can improve performance in networks if the delay parameter is chosen judiciously, otherwise the system might diverge as agents try to anticipate too aggressively into the future.
On the Merits of Tetrapolar Impedance Spectroscopy for Monitoring Lithium Concentration Variations in Human Blood Plasma

Bipolar disorder is characterized as a manic-depressive psychiatric syndrome with life-threatening risks to the patient. Diagnosed individuals undergo long-term lithium therapy which has proven to be effective for mood stabilization. Maintaining blood lithium concentration levels within a narrow therapeutic window between 0.6 and 1.5 mM is vital for the patient as slightly elevated concentrations of the order of 0.1 mM can be toxic. This paper aims to evaluate the merits of tetrapolar electrical impedance spectroscopy as an alternative method in monitoring blood lithium levels. Measurements were performed using a custom-made tetrapolar probe in human blood plasma with lithium concentrations covering the therapeutic range. The results indicate a limit of detection less than 0.1 mM and a response time of less than 5 s. Prediction of lithium concentration levels using impedance values is in good agreement with conventional standard techniques to approximately 0.05 mM. This technique provides a basis for further development of instrumentation for point of care healthcare technologies.
An Automatic User-Adapted Physical Activity Classification Method Using Smartphones

In recent years, an increasing number of people have become concerned about their health. Most chronic diseases are related to lifestyle, and daily activity records can be used as an important indicator of health. Specifically, using advanced technology to automatically monitor actual activities can effectively prevent and manage chronic diseases. The data used in this paper were obtained from acceleration sensors and gyroscopes integrated in smartphones. We designed an efficient Adaboost-Stump running on a smartphone to classify five common activities: cycling, running, sitting, standing, and walking and achieved a satisfactory classification accuracy of 98%. We designed an online learning method, and the classification model requires continuous training with actual data. The parameters in the model then become increasingly fitted to the specific user, which allows the classification accuracy to reach 95% under different use environments. In addition, this paper also utilized the OpenCL framework to design the program in parallel. This process can enhance the computing efficiency approximately ninefold.
Estimation of Penetrated Bone Layers During Craniotomy via Bioimpedance Measurement

Objective: Craniotomy is the removal of a bone flap from the skull and is a first step in many neurosurgical interventions. During craniotomy, an efficient cut of the bone without injuring adjoining soft tissues is very critical. The aim of this study is to investigate the feasibility of estimating the currently penetrated cranial bone layer by means of bioimpedance measurement. Methods: A finite-element model was developed and a simulation study conducted. Simulations were performed at different positions along an elliptical cutting path and at three different operation areas. Finally, the validity of the simulation was demonstrated by an ex vivo experiment based on use of a bovine shoulder blade bone and a commercially available impedance meter. Results: The curve of the absolute impedance and phase exhibits characteristic changes at the transition from one bone layer to the next, which can be used to determine the bone layer last penetrated by the cutting tool. The bipolar electrode configuration is superior to the monopolar measurement. A horizontal electrode arrangement at the tip of the cutting tool produces the best results. Conclusion: This study successfully demonstrates the feasibility to detect the transition between cranial bone layers during craniotomy by bioimpedance measurements using electrodes located on the cutting tool. Significance: Based on the results of this study, bioimpedance measurement seems to be a promising option for intra operative ad hoc information about the bone layer currently penetrated and could contribute to patient safety during neurosurgery.
Geometric deformable colon model for polyp detection

Virtual Colonoscopy is a minimally invasive procedure to detect polyps in the colon using three dimensional tomographic imaging. An important step in analyzing the image data is accurate localization of the lumen surface. Polyps protruding into the colon lumen can then be identified by analyzing the surface either manually or using computer assisted techniques. We have developed a method for lumen segmentation based on a 3D geometric deformable model (GDM) to provide an improved representation of the lumen surface. The results show the GDM can remove the surface holes or tunnels that can be created with thresholding techniques and demonstrates the ability to accurately locate the lumen surface in high contrast air- tissue boundaries while preserving relatively lower contrast boundaries of the houstra and polyps.
Drug Use Among Street Children and Non—Street Children in the Philippines

This study characterizes the prevalence of drug use among Filipino street children compared with Filipino non—street children. A cross-sectional survey was administered to 311 street children and 528 non—street children aged 13 to 17 years. Participants were enrolled through 4 nonprofit organizations and 3 high schools located in Manila, Philippines. After adjustment for age and sex, street children with little or no contact with their families were 2.0 (95% confidence interval [CI] = 1.7-2.3) times more likely to smoke tobacco, 1.3 (95% CI = 1.2-1.5) times more likely to use alcohol, 36.7 (95% CI = 16.4-82.0) times more likely to use inhalants, and 5.5 (95% CI = 3.6-8.2) times more likely to use illegal drugs than their non—street counterparts. Street children who maintained contact with their families, compared with non—street children, were 8.7 (95% CI = 3.9-19.4) times more likely to use inhalants and 2.8 (95% CI = 1.7-4.6) times more likely to use illegal drugs. There was no significant difference in tobacco or alcohol use between street children who maintained contact with their families and non—street children. All street children were significantly more likely to have been given or sold a drug in the past 30 days and to have received drug education compared with non—street children. Filipino street children are at greater risk of abusing drugs than are non—street children, with street children who do not maintain family contact being at greatest risk.
A Method for Intertemporal Functional-Domain Connectivity Analysis: Application to Schizophrenia Reveals Distorted Directional Information Flow

Objective: We introduce a method for analyzing dynamically changing functional magnetic resonance imaging (fMRI) network connectivity estimates as they vary within and between broad functional domains. The method captures evidence of intertemporal directionality in cross joint functional-domain influence and extends standard whole-brain dynamic network connectivity approaches into additional functionally meaningful dimensions by evaluating transition probabilities between clustered intradomain and interdomain connectivity patterns. Results: In applying this method to a large (N = 314) multisite resting-state fMRI dataset balanced between schizophrenia patients and healthy controls, we find evidence of joint functional domains that are global catalyzers, broadly shaping downstream functional relationships throughout the brain. Multiple interesting differences between patients and controls in both time-varying joint functional-domain connectivity patterns and in cross joint functional-domain intertemporal information flow were identified. Conclusion and Significance: Our proposed approach, thus, unifies the concepts of brain connectivity and interdomain connectivity and provides a powerful new way to evaluate functional connectivity data in the context of both the healthy and diseased brain.
Quantifying and Reducing Motion Artifacts in Wearable Seismocardiogram Measurements During Walking to Assess Left Ventricular Health

Goal: Our objective is to provide a framework for extracting signals of interest from the wearable seismocardiogram (SCG) measured during walking at normal (subject's preferred pace) and moderately fast (1.34–1.45 m/s) speeds. Methods: We demonstrate, using empirical mode decomposition (EMD) and feature tracking algorithms, that the pre-ejection period (PEP) can be accurately estimated from a wearable patch that simultaneously measures electrocardiogram and sternal acceleration signals. We also provide a method to determine the minimum number of heartbeats required for an accurate estimate to be obtained for the PEP from the accelerometer signals during walking. Results: The EMD-based denoising approach provides a statistically significant increase in the signal-to-noise ratio of wearable SCG signals and also improves estimation of PEP during walking. Conclusion: The algorithms described in this paper can be used to provide hemodynamic assessment from wearable SCG during walking. Significance: A major limitation in the use of the SCG, a measure of local chest vibrations caused by cardiac ejection of blood in the vasculature, is that a user must remain completely still for high-quality measurements. The motion can create artifacts and practically render the signal unreadable. Addressing this limitation could allow, for the first time, SCG measurements to be obtained reliably during movement—aside from increasing the coverage throughout the day of cardiovascular monitoring, analyzing SCG signals during movement would quantify the cardiovascular system's response to stress (exercise), and thus provide a more holistic assessment of overall health.
Free Radicals, Cytokines, and Respiratory Muscles in COPD Patients

Respiratory muscles are responsible for alveolar ventilation. In chronic obstructive pulmonary disease (COPD), the function of these and other muscles is impaired as a result of different factors, including geometrical changes occurring in the thorax, metabolic mismatching, oxidative stress, and inflammation. The aim of the present review was to analyze the role of free radical stress and proinflammatory cytokines in muscle dysfunction observed in COPD. Local oxidative stress occurs when reactive oxygen species overcome the action of different antioxidant systems. Oxidative stress has been shown to modify proteins in the respiratory and peripheral muscles of COPD patients and is associated with diaphragmatic dysfunction. Although lung and systemic inflammation are commonly accepted as an integral part of COPD, evidence of local inflammatory events occurring in skeletal muscles has only recently emerged. Current hypotheses indicate that these phenomena, expressed by the presence of either inflammatory cells or cytokines, should be especially evident in limb muscles. However, there is increasing evidence of the up-regulation of proinflammatory cytokines in the respiratory muscles (the muscles that are most adapted) of COPD patients, whereas these substances are down regulated in peripheral muscles. To sum up, oxidative stress and overexpression of inflammatory cytokines are present in respiratory muscles of COPD patients. However, although the presence of free radical stress is clearly associated with respiratory muscle dysfunction, the role of cytokines remains controversial.