Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Coding Aided Collaborative Real-Time Scalable Video Transmission in D2D Communications

There has been an increasing demand for providing real-time video streaming services in the next-generation cellular networks. To improve the quality of such services without additional infrastructure, neighboring devices can recover missing packets by using network coding aided collaborative transmission via device-to-device (D2D) communication. However, most existing work in this area had not considered the issue of how to schedule such coding aided collaborative transmissions effectively for supporting real-time scalable video applications in such environment. In this paper, we study how to improve the quality of real-time scalable video services by efficiently scheduling coding aided collaborative transmissions. We first formulate the problem of optimal collaborative transmission scheduling that determines the optimal transmitting sequence and coding pattern at each transmitting device, which is shown to be NP-hard. To address this problem, we propose a new weight function for measuring the quality of a coding pattern by considering packet recovery gain and potential video decoding gain at receivers. Based on this new weight function, we propose a low complexity centralized algorithm using global state information and an efficient distributed mechanism supporting localized operations in dynamic environment. We deduce their computational complexities. Simulation results verify that the proposed solution outperforms the representative work in the literature.
It’s Not Just about Accuracy

Design sketching is an important skill for designers, engineers, and creative professionals, as it allows them to express their ideas and concepts in a visual medium. Being a critical and versatile skill for many different disciplines, courses on design sketching are often taught in universities. Courses today predominately rely on pen and paper; however, this traditional pedagogy is limited by the availability of human instructors, who can provide personalized feedback. Using a stylus-based intelligent tutoring system called SketchTivity, we aim to eventually mimic the feedback given by an instructor and assess student-drawn sketches to give students insight into areas for improvement. To provide effective feedback to users, it is important to identify what aspects of their sketches they should work on to improve their sketching ability. After consulting with several domain experts in sketching, we came up with several classes of features that could potentially differentiate expert and novice sketches. Because improvement on one metric, such as speed, may result in a decrease in another metric, such as accuracy, the creation of a single score may not mean much to the user. We attempted to create a single internal score that represents overall drawing skill so that the system can track improvement over time and found that this score correlates highly with expert rankings. We gathered over 2,000 sketches from 20 novices and four experts for analysis. We identified key metrics for quality assessment that were shown to significantly correlate with the quality of expert sketches and provide insight into providing intelligent user feedback in the future.
Baicalin Depresses the Sympathoexcitatory Reflex Induced by Myocardial Ischemia via the Dorsal Root Ganglia

Myocardial ischemia (MI) is one of the major causes of death in cardiac diseases. Purinergic signaling is involved in bidirectional neuronal-glial communication in the primary sensory ganglia. The sensory neuritis of cardiac afferent neurons in cervical dorsal root ganglion (cDRG) interacts with cardiac sympathetic efferent postganglionic neurons, forming feedback loops. The P2Y12 receptor is expressed in satellite glial cells (SGCs) of DRG. Baicalin is a major active ingredient extracted from natural herbal medicines, which has anti-inflammatory and strong anti-oxidation properties. In this study we investigated the effect of baicalin on P2Y12 receptor in the cervical DRG SGC-mediated sympathoexcitatory reflex, which is increased during MI. The results showed that the expression of P2Y12 receptor mRNA and protein in DRG, and the co-localization values of P2Y12 receptor and glial fibrillary acidic protein (GFAP) in cDRG SGCs were increased after MI. The activated SGCs increased IL-1β protein expression and elevated Akt phosphorylation in cDRG. Baicalin treatment inhibited the upregulation of the P2Y12 receptor, GFAP protein and Akt phosphorylation in cDRG neurons/SGCs. The stellate ganglia (SG) affect cardiac sympathetic activity. Baicalin treatment also decreased the upregulation of the P2Y12 receptor, GFAP protein in the SG. The P2Y12 agonist, 2Me-SADP, increased [Ca2+]i in HEK293 cells transfected with the P2Y12 receptor plasmid and SGCs in cDRG. These results indicate that application of baicalin alleviates pathologic sympathetic activity induced by MI via inhibition of afferents in the cDRG.
Influence of Acyl Chain Saturation on the Membrane-Binding Activity of a Short Antimicrobial Peptide

Different bacterial types and their living environments can lead to different saturations in the chains of their membrane lipids. Such structural differences may influence the efficacy of antibiotics that target bacterial membranes. In this work, the effects of acyl chain saturation on the binding of an antimicrobial peptide G4 have been examined as a function of the packing density of lipid monolayers by combining external reflection Fourier transform infrared (ER-FTIR) spectroscopy and neutron reflection (NR) measurements. Langmuir monolayers were formed from 1,2-dipalmitoyl-sn-glycero-3-phospho-(1′-rac-glycerol) (DPPG) and 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-(1′-rac-glycerol) (POPG), respectively, with the initial surface pressures controlled at 8 and 28 mN/m. A reduction in the order of the acyl chains associated with the increase in the layer thickness upon G4 binding was revealed from ER-FTIR spectroscopy, with peptide binding reaching equilibration faster in POPG than in DPPG monolayers. Whereas the dynamic DPPG-binding process displayed a steady increase in the amide I band area, the POPG-binding process showed little change in the amide area after the initial period. The peptide amide I area from ER-FTIR spectroscopy could be linearly correlated with the adsorbed G4 amount from NR, irrespective of time, initial pressure, or chain saturation, with clearly more peptide incorporated into the DPPG monolayer. Furthermore, NR revealed that although the peptide was associated with both POPG and DPPG lipid monolayers, it was more extensively distributed in the latter, showing that acyl chain saturation clearly promoted peptide binding and structural disruption.
Deep Neural Network Based Speech Separation Optimizing an Objective Estimator of Intelligibility for Low Latency Applications

Mean square error (MSE) has been the preferred choice as loss function in the current deep neural network (DNN) based speech separation techniques. In this paper, we propose a new cost function with the aim of optimizing the extended short time objective intelligibility (ESTOI) measure. We focus on applications where low algorithmic latency (≤ 10 ms) is important. We use long short-term memory networks (LSTM) and evaluate our proposed approach on four sets of two-speaker mixtures from extended Danish hearing in noise (HINT) dataset. We show that the proposed loss function can offer improved or at par objective intelligibility (in terms of ESTOI) compared to an MSE optimized baseline while resulting in lower objective separation performance (in terms of the source to distortion ratio (SDR)). We then proceed to propose an approach where the network is first initialized with weights optimized for MSE criterion and then trained with the proposed ESTOI loss criterion. This approach mitigates some of the losses in objective separation performance while preserving the gains in objective intelligibility.
Pedagogical mediation using the virtual learning environment and the new generation: A search for improved performance in medical education

Introduction: Medical schools face increasing challenges to accommodate new health care needs. However, little has changed regarding the students' education. In addition, educators face the challenge of improving their teaching approach, making it suitable and effective for the new generation. The purpose of this work was to describe and contextualize the application of active pedagogical methodologies using the virtual learning environment (VLE) as a tool to improve the students’ performance. Methods: This research is a quantitative and qualitative case study, carried out in one faculty of medicine in the state of Amazonas. Given a total population of senior students in this faculty, the number of participants corresponds to a proportion of 75% in the period of two years. This work required a paradigm shift, by focusing on interactive and technological teaching, professionalism, mentoring, communication, and feedback. Courses using virtual learning environment (VLE) should be prone to interaction, dialogue, collaboration and, asynchronously, autonomy. One of the key aspects of VLE content production is to select appropriate subject matters and texts to be discussed. It is through the VLE contents that the mediation of knowledge construction process and the participants' interaction takes place. Results: The improvement over the pre-test evaluation was evident, ranging between 12.59 and 50.9%, depending on the subject matter, to 81.31% in the final evaluation. In addition, the students expressed consistently positive opinions regarding the use of VLE. Conclusion: The use of VLE as a pedagogical tool, in an interactive format and appropriate to the transmission of information and knowledge construction, was adequate for an evident improvement in the cognitive performance of the senior students of medicine. In addition, the positive evaluation of the students regarding the pedagogical methodology used constitutes an added value of this approach.
Effect of Comprehensive Surgical Safety System on Patients’ Outcome: A Prospective Clinical Study

Background Patient safety has become an integral part of hospital management to prevent catastrophic events which adversely affects the patients, care providers and the hospital. Surgical Checklists are an easy and simple way to prevent surgical errors and complications. Objective This prospective study is to evaluate the effect of SURPASS (Surgical Patient Safety System) checklist on the outcome of the patients who underwent surgery in our hospital. Methods All the patients who underwent surgery in the sixth unit of Department of Surgery from April 2014 to May 2015 were included in the study excluding those aged above 13 years and day care surgery cases. For the control group (initial six months) no checklist was implemented whereas for the study group (next six months) SURPASS checklist was implemented. Data collected on age, sex, diagnosis, surgical procedure, type of anaesthesia, number and type of postoperative complications, need of second surgery because of complications, length of hospital stay and outcome (discharge, disability or death). Mann–Whitney U test and Fisher’s exact test were used for analysis. Results Of the total 372 patients operated, 200 were before and 172 were after implementation of SURPASS checklist. Before implementation of the checklist, complications were noticed in 66.66% of elective and 77.23% of emergency cases. Whereas after implementation of checklist the complications in elective cases were found to be 51.09% (p-value = 0.008) and 67.50% (p-value = 0.024) in emergency cases. Conclusion Implementation of SURPASS checklist is effective in reducing the rate of postoperative complications in both elective and emergency surgeries.
Spiking Elementary Motion Detector in Neuromorphic Systems

Apparent motion of the surroundings on an agent's retina can be used to navigate through cluttered environments, avoid collisions with obstacles, or track targets of interest. The pattern of apparent motion of objects, (i.e., the optic flow), contains spatial information about the surrounding environment. For a small, fast-moving agent, as used in search and rescue missions, it is crucial to estimate the distance to close-by objects to avoid collisions quickly. This estimation cannot be done by conventional methods, such as frame-based optic flow estimation, given the size, power, and latency constraints of the necessary hardware. A practical alternative makes use of event-based vision sensors. Contrary to the frame-based approach, they produce so-called events only when there are changes in the visual scene. We propose a novel asynchronous circuit, the spiking elementary motion detector (sEMD), composed of a single silicon neuron and synapse, to detect elementary motion from an event-based vision sensor. The sEMD encodes the time an object's image needs to travel across the retina into a burst of spikes. The number of spikes within the burst is proportional to the speed of events across the retina. A fast but imprecise estimate of the time-to-travel can already be obtained from the first two spikes of a burst and refined by subsequent interspike intervals. The latter encoding scheme is possible due to an adaptive nonlinear synaptic efficacy scaling. We show that the sEMD can be used to compute a collision avoidance direction in the context of robotic navigation in a cluttered outdoor environment and compared the collision avoidance direction to a frame-based algorithm. The proposed computational principle constitutes a generic spiking temporal correlation detector that can be applied to other sensory modalities (e.g., sound localization), and it provides a novel perspective to gating information in spiking neural networks.
CosMIC: A Consistent Metric for Spike Inference from Calcium Imaging

In recent years, the development of algorithms to detect neuronal spiking activity from two-photon calcium imaging data has received much attention. Meanwhile, few researchers have examined the metrics used to assess the similarity of detected spike trains with the ground truth. We highlight the limitations of the two most commonly used metrics, the spike train correlation and success rate, and propose an alternative, which we refer to as CosMIC. Rather than operating on the true and estimated spike trains directly, the proposed metric assesses the similarity of the pulse trains obtained from convolution of the spike trains with a smoothing pulse. The pulse width, which is derived from the statistics of the imaging data, reflects the temporal tolerance of the metric. The final metric score is the size of the commonalities of the pulse trains as a fraction of their average size. Viewed through the lens of set theory, CosMIC resembles a continuous Sorensen-Dice coefficient — an index commonly used to assess the similarity of discrete, presence/absence data. We demonstrate the ability of the proposed metric to discriminate the precision and recall of spike train estimates. Unlike the spike train correlation, which appears to reward overfitting, the proposed metric score is maximised when the correct number of spikes have been detected. Furthermore, we show that CosMIC discriminates the temporal precision of estimates more consistently than either the success rate or the spike train correlation.
Mobile Edge Computing Empowered Energy Efficient Task Offloading in 5G

Mobile edge computing has been proposed in recent years to offload computation tasks from user equipments (UEs) to the network edge to break hardware limitations and resource constraints at UEs. Although there have been some existing works on computation offloading in 5G, most of them fail to take into account the unique property of 5G in their scheme design. In this paper, we consider small-cell network architecture for task offloading. In order to achieve energy efficiency, we model the energy consumption of offloading from both task computation and communication aspects. Besides, transmission scheduling are carried over both the fronthaul and backhaul links. We first formulate an energy optimization problem of offloading, which aims at minimizing the overall energy consumption at all system entities and takes into account of the constraints from both computation capabilities and service delay requirement. We then develop an artificial fish swarm algorithm based scheme to solve the energy optimization problem. Besides, the global convergence property of the our scheme is formally proven. Finally, various simulation results demonstrate the efficiency of our scheme.
Re-Defining Stem Cell-Cardiomyocyte Interactions: Focusing on the Paracrine Effector Approach

Stem cell research for treating or curing ischemic heart disease has, till date, culminated in three basic approaches: the use of induced pluripotent stem cell (iPSC) technology; reprogramming cardiac fibroblasts; and cardiovascular progenitor cell regeneration. As each approach has been shown to have its advantages and disadvantages, exploiting the advantages while minimizing the disadvantages has been a challenge. Using human germline pluripotent stem cells (hgPSCs) along with a modified version of a relatively novel cell-expansion culture methodology to induce quick, indefinite expansion of normally slow growing hgPSCs, it was possible to emphasize the advantages of all three approaches. We consistently found that unipotent germline stem cells, when removed from their niche and cultured in the correct medium, expressed endogenously, pluripotency genes, which induced them to become hgPSCs. These cells are then capable of producing cell types from all three germ layers. Upon differentiation into cardiac lineages, our data consistently showed that they not only expressed cardiac genes, but also expressed cardiac-promoting paracrine factors. Taking these data a step further, we found that hgPSC-derived cardiac cells could integrate into cardiac tissue in vivo. Note, while the work presented here was based on testes-derived hgPSCs, data from other laboratories have shown that ovaries contain very similar types of stem cells that can give rise to hgPSCs. As a result, hgPSCs should be considered a viable option for eventual use in patients, male or female, with ischemic heart disease
Information-based Variational Model Reduction of high-dimensional Reaction Networks

In this work we present new scalable, information theory-based variational methods for the efficient model reduction of high-dimensional deterministic and stochastic reaction networks. The proposed methodology combines, (a) information theoretic tools for sensitivity analysis that allow us to identify the proper coarse variables of the reaction network, with (b) variational approximate inference methods for training a best-fit reduced model. This approach takes advantage of both physicochemical modeling and data-based approaches and allows to construct optimal parameterized reduced dynamics in the number of variables, reactions and parameters, while controlling the information loss due to the reduction. We demonstrate the effectiveness of our model reduction method on several complex, high-dimensional chemical reaction networks arising in biochemistry.
A first study exploring the performance of the state-of-the art CNN model in the problem of breast cancer

During last decades, computer-assisted diagnosis systems for medical purposes have been highly developed. However, further research is still needed, especially for the diagnosis of very dangerous diseases such as breast cancer. For diagnosis, deep learning and more precisely Convolutional Neural Networks (CNNs) have shown a high potential in providing an automatic assistance to domain experts. This work explores and analyzes the performance of the state-of-the art CNN model in the problem of breast cancer histopathological images diagnosis.
Modular Algorithms for Computing Minimal Associated Primes and Radicals of Polynomial Ideals

In this paper, we propose algorithms for computing minimal associated primes of ideals in polynomial rings over Q and computing radicals of ideals in polynomial rings over a field. They apply Chinese Remainder Theorem (CRT) to Laplagne's algorithm which computes minimal associated primes without producing redundant components and computes radicals. CRT reconstructs an object in a ring from its modular images in the quotient rings modulo some ideals. In Laplagne's algorithm, ideals are decomposed over rational function fields by regarding some variables as parameters. In our new algorithms, we compute the minimal associated primes and the radical of < φ(G) > for a given ideal I= < G >, where φ is a substitution map for a parameter. Then we construct candidates of the minimal associated primes and the radical of I by applying CRT for those of < φ(G) >'s. In order for this method to work correctly, the shape of each modular component must coincide with that of the corresponding component of the ideal for computations of minimal associated primes, and radicals of modular images of given ideals must coincide with modular images of radicals of given ideals for radical computations. The former is realized with a high probability because a multivariate irreducible polynomial over Q remains irreducible after a substitution of integers for variables with a high probability and the latter is realized except for a finite number of moduli.
Genome-Wide Association Study Reveals Both Overlapping and Independent Genetic Loci to Control Seed Weight and Silique Length in Brassica napus

Seed weight (SW) is one of three determinants of seed yield, which positively correlates with silique length (SL) in Brassica napus (rapeseed). However, the genetic mechanism underlying the relationship between seed weight (SW) and silique length (SL) is largely unknown at present. A natural population comprising 157 inbred lines in rapeseed was genotyped by whole-genome re-sequencing and investigated for SW and SL over four years. The genome-wide association study identified 20 SNPs in significant association with SW on A01, A04, A09, C02, and C06 chromosomes and the phenotypic variation explained by a single locus ranged from 11.85% to 34.58% with an average of 25.43%. Meanwhile, 742 SNPs significantly associated with SL on A02, A03, A04, A07, A08, A09, C01, C03, C04, C06, C07, and C08 chromosomes were also detected and the phenotypic variation explained by a single locus ranged from 4.01 to 48.02% with an average of 33.33%, out of which, more than half of the loci had not been reported in the previous studies. There were 320 overlapping or linked SNPs for both SW and SL on A04, A09, and C06 chromosomes. It indicated that both overlapping and independent genetic loci controlled both SW and SL in B. napus. On the haplotype block on A09 chromosome, the allele variants of a known gene BnaA.ARF18. a controlling both SW and SL were identified in the natural population by developing derived cleaved amplified polymorphic sequence (dCAPS) markers. These findings are valuable for understanding the genetic mechanism of SW and SL and also for rapeseed molecular breeding programs.