Miracle-dz/UniFAR-FTUs huggingface dataset:
Unequal Reproduction Early in a Social Transition: Insights from Invasive Wasps

In eusocial insects, nestmate queens can differ in their reproductive output, causing asymmetries in the distribution of mutual benefits. However, little is known about how reproductive success is partitioned in incipiently polygynous species, which would provide clues about the evolutionary forces shaping the emergence of polygyny. Here, we leverage a recent transition from predominantly single-queen (monogyne) to multiple-queen (polygyne) colonies in an invasive yellowjacket species to investigate whether queens in incipiently polygyne colonies invest equally in reproductive effort or vary in their relative investment in each caste. We excavated nine polygyne Vespula pensylvanica colonies in Hawaii and used restriction site–associated DNA sequencing to infer the parentage of worker, male, and gyne (daughter queen) pupae from each nest comb. In four colonies with at least eight gyne pupae, a single queen produced most or all gynes. These queens had no male offspring and few worker offspring, suggesting that a subset of nestmate queens might exploit the collective benefits of newly polygyne societies. In contrast to most queens, gyne producers had offspring distributed nonrandomly across nest combs. Nestmate queens generally exhibited low relatedness levels. Our results suggest that rapid, ecologically driven transitions to polygyny among unrelated queens may, at their onset, be vulnerable to reproductive asymmetries that are likely evolutionarily unstable. More broadly, this study contributes to the understanding of social evolution by uncovering asymmetric partitioning of reproduction in a population with newly evolved polygyny and raises questions about the future trajectories of introduced populations.
Clinico-Radiological Phenotype of UBTF c.628G>A Pathogenic Variant-Related Neurodegeneration in Childhood: A Case Report and Literature Review

Background: This work aims to describe the clinico-radiological phenotype of UBTF c.628G>A (p.Glu210Lys) pathogenic variant-related neurodegeneration in childhood. Methods: We describe the progress of clinical and neuroimaging features in a male individual who had childhood-onset neuroregression and carried the heterozygous UBTF c.628G>A (p.Glu210Lys) pathogenic variant. Clinical cases reported in the literature are reviewed. Results: Fifteen individuals, from 14 reported cases and the index case, were noted. The median age at onset of neurodegeneration was 3 years. Clinical phenotype was consistent among the affected individuals, with progressive motor, speech, cognitive, and social–emotional regression together with ataxia and prominent pyramidal and extrapyramidal symptoms and signs in early to middle childhood. All individuals had the same brain MRI features in terms of symmetric and diffuse T2 high signal intensity over the bilateral subcortical, periventricular, and peritrigonal white matter and progressive cortical and subcortical supratentorial atrophy. Two individuals were reported to have bilateral thalamic involvement. All individuals had profound intellectual disability with loss of verbal and/or ambulatory functions during follow-up. Conclusions: Individuals with the heterozygous UBTF c.628G>A (p.Glu210Lys) pathogenic variant had consistent clinical progress and neuroimaging features. Familiarity with this clinico-radiological phenotype may allow earlier diagnosis of this rare disease.
Convergence and superconvergence analysis for nonlinear delay reaction–diffusion system with nonconforming finite element

In this article, we propose and analyze several numerical methods for the nonlinear delay reaction–diffusion system with smooth and nonsmooth solutions, by using Quasi‐Wilson nonconforming finite element methods in space and finite difference methods (including uniform and nonuniform L1 and L2‐1σ schemes) in time. The optimal convergence results in the senses of L2‐norm and broken H1‐norm, and H1‐norm superclose results are derived by virtue of two types of fractional Grönwall inequalities. Then, the interpolation postprocessing technique is used to establish the superconvergence results. Moreover, to improve computational efficiency, fast algorithms by using sum‐of‐exponential technique are built for above proposed numerical schemes. Finally, we present some numerical experiments to confirm the theoretical correctness and show the effectiveness of the fast algorithms.
Improved Facial Emotion Recognition using Yolo and DeepFace for Music suggestion

The most ideal characteristic in the human being that differentiates one another is their face. Sometimes Opinion of a person can be estimated from their facial expressions. Emotion recognition is a research area that focuses on human face recognition and estimating the type of emotion expressed. This research study utilizes two well-known algorithms, namely Yoloface and Deepface, wherein the former extracts the face from the live image and the latter classifies the type of emotion expressed by the person. The output of the Deepface algorithm is provided as an input to the music recommendation application. This application plays songs based on the category of emotion predicted by the Deepface algorithm. (Abstract)
Parameterized Diesel Engine Heat Release Modeling for Combustion Phasing Analysis

Different Vibe function model structures for parameterized diesel engine heat release models are investigated. The work is motivated by the need of such models when closed-loop combustion control is implemented based on torque domain combustion phasing analysis. Starting from the studied model structures, models are created by estimating the model parameters using a separable least squares approach. After this, the models are evaluated according to two different performance criteria. The first criterion rates the ability of the estimated models to describe reference mass fraction burned traces. The second criterion assesses how accurately the models estimate the reference combustion phasing measure. As expected, the analysis shows that the models based on the most flexible model structure achieve the best results, both regarding mass fraction burned estimation and combustion phasing estimation. However, even if the less complex models describe the mass fraction burned traces with lower accuracy, these models are able to estimate the combustion phasing measure surprisingly well.
High-energy Ce-3d photoemission: Bulk properties of CeM{sub 2} (M=Fe,Co,Ni) and Ce{sub 7}Ni{sub 3}

We study the photon energy dependence of Ce-3d photoemission in CeM{sub 2} compounds (M=Fe,Co,Ni) and in Ce{sub 7}Ni{sub 3} with measurements taken with synchrotron radiation in the photon energy range 1050{endash}3850 eV. The results show a drastic increase of the bulk sensitivity at the higher energies due to the increase of the photoelectron escape depth. The spectra at 3850 eV are largely dominated by the bulk contribution, which is typically 85{percent} of the total intensity in the CeM{sub 2} compounds. The results allow us to obtain the bulk contribution to the spectra in CeM{sub 2} with a procedure presented here and giving the fractional weights of the bulk f{sup 0}, f{sup 1}, and f{sup 2} configurations. In the CeM{sub 2} compounds a correlation is found between these spectral weights and the crystallographic data showing that the hybridization is nonmonotonic versus the atomic number of the transition metal. We discuss the results both in connection with an impurity model and with the available calculations in an itinerant model. Moreover, the results suggest that the bulk hybridization in CeM{sub 2} systems with heavy transition metals has been probably underestimated up to now. {copyright} {ital 1997} {ital The American Physical Society}
Design and Implementation of Real-time Object Detection for Blind using Convolutional Neural Network

In 2017 the Indonesian Ministry of Health estimated that there were 3.75 million blind and visually impaired people living in Indonesia. In this paper, we propose a device in the form of smart glasses as an object detection system and as a system for detecting barriers / obstacles in front of blind that equipped with earphone as speakers to convey object information to support and facilitate blind people's activities. The system is composed of a Pi Noir V2 camera is used for taking pictures of objects, ultrasonic sensors to determine the distance an object from user. Data processing is carried out on the Raspberry Pi used and further gives a sound-shaped output about the results of image processing and its position, through headphones. The detecting system used the MobileNet SSD v2 model and the Convolutional Neural Network method. The accuracy of system is verified through experiments. The effectiveness is confirmed through experiments with the optimal mAP and loss values determined by the model parameter testing process.
Enabling Efficient and Strong Privacy-Preserving Truth Discovery in Mobile Crowdsensing

Mobile crowdsensing has emerged as a popular platform to solve many challenging problems by utilizing users’ wisdom and resources. Due to user diversity, the data provided by different individuals may vary significantly, and thus it is important to analyze data quality during data aggregation. Truth discovery is effective in capturing data quality and obtaining accurate mobile crowdsensing results. Existing works on truth discovery either cannot protect both task privacy and data privacy, or introduce tremendous computational costs. In this paper, we propose an efficient and strong privacy-preserving truth discovery scheme, named EPTD, to protect users’ task privacy and data privacy simultaneously in the truth discovery procedure. In EPTD, we first exploit the randomizable matrix to express users’ tasks and sensory data. Then, based on the matrix computation properties, we design key derivation and (re-)encryption mechanisms to enable truth discovery to be performed in an efficient and privacy-preserving manner. Through a detailed security analysis, we demonstrate that data privacy and task privacy are well preserved. Extensive experiments based on real-world and simulated mobile crowdsensing applications show EPTD has practical efficiency in terms of computational cost and communication overhead.
Novel Deep-Learning Modulation Recognition Algorithm Using 2D Histograms over Wireless Communications Channels

Modulation recognition (MR) has become an essential topic in today’s wireless communications systems. Recently, convolutional neural networks (CNNs) have been employed as a potent tool for MR because of their ability to minimize the feature’s susceptibility to its surroundings and reduce the need for human feature extraction and evaluation. In particular, these investigations rely on the unrealistic assumption that the channel coefficient is typically one. This motivates us to overcome the previous constraint by providing a novel MR suited to fading wireless channels. This paper proposes a novel MR algorithm that is capable of recognizing a broad variety of modulation types, including M-ary QAM and M-ary PSK, without enforcing any restrictions on the modulation size, M. The analysis has shown that each modulation choice has a distinct two-dimensional in-phase quadrature histogram. This property is beneficially utilized to design a convolutional neural-network-based MR algorithm. When compared to the existing techniques, Monte Carlo simulations demonstrated the success of the proposed design.
SIW based Self-diplexing Antenna

In this paper, novel design of self diplexing antenna based on SIW technology to operate at ISM/WLAN bands is proposed. The proposed antenna has a smaller rectangular cavity nested inside the larger hexagonal shaped cavity. By inserting nested cavity, a good isolation was achieved at proposed frequencies of 4.9 GHz and 5.8 GHz. Four different shaped slots are inserted and a performance comparison is made to choose the best out of them for the proposed cavity configuration. The higher frequencies are realized by exciting the cavity mode (TE110) and placing the different slots shapes in the outer cavity. Resonating frequencies controlled by varying position of slot away from feed line. The diplexing antenna with rectangular slots shows a high isolation greater than 48 dB between the ports. The designed model shows gain values better than 3 dB at the proposed frequencies.
Identidade e Autenticação de Serviços através de Ambientes e Protocolos de Descoberta Heterogêneos: Uma Solução baseada em Self-Certifying Names

Sistemas de descoberta de serviços tem sido utilizados para localizar serviços, de forma automatizada, em diferentes aplicações e ambientes de rede. Para cada ambiente ou aplicação, diferentes protocolos de descoberta (SDPs) podem ser utilizados, frequentemente incompatíveis entre si. A literatura descreve soluções para descoberta através de múltiplos ambientes e/ou SDPs, mas a falta de mecanismos para identificar e reconhecer os serviços localizados, termina impedindo a descoberta integrada através desses meios distintos. Para tratar essa questão, o presente trabalho propõe uma solução, baseada no princípio dos self-certifying names, para identificar e autenticar serviços, através de ambientes e protocolos de descoberta heterogêneos.
The “Yin and Yang” of Unfolded Protein Response in Cancer and Immunogenic Cell Death

Physiological and pathological burdens that perturb endoplasmic reticulum homeostasis activate the unfolded protein response (UPR), a conserved cytosol-to-nucleus signaling pathway that aims to reinstate the vital biosynthetic and secretory capacity of the ER. Disrupted ER homeostasis, causing maladaptive UPR signaling, is an emerging trait of cancer cells. Maladaptive UPR sustains oncogene-driven reprogramming of proteostasis and metabolism and fosters proinflammatory pathways promoting tissue repair and protumorigenic immune responses. However, when cancer cells are exposed to conditions causing irreparable ER homeostasis, such as those elicited by anticancer therapies, the UPR switches from a survival to a cell death program. This lethal ER stress response can elicit immunogenic cell death (ICD), a form of cell death with proinflammatory traits favoring antitumor immune responses. How UPR-driven pathways transit from a protective to a killing modality with favorable immunogenic and proinflammatory output remains unresolved. Here, we discuss key aspects of the functional dichotomy of UPR in cancer cells and how this signal can be harnessed for therapeutic benefit in the context of ICD, especially from the aspect of inflammation aroused by the UPR.
Atualização de Modelo baseado em Aumento de Dados e Transferência de Aprendizagem para Detecção de Intrusão em Redes

Neste artigo apresentamos uma abordagem para atualização do modelo de aprendizagem de máquina para detecção de intrusão. Inicialmente, o tráfego de rede é aumentando por uma Redes Adversárias Generativas (GANs). Depois, as atualizações de modelos são realizadas por meio de Transferência de Aprendizagem sobre o conjunto de dados aumentado. O número de instâncias a ser rotuladas e os custos computacionais das atualizações do modelo são diminuídas significativa na proposta. A experimentação foi feita num conjunto de dados de 8TB (tráfego de rede de 1 ano), demonstrando a ineficiência dos trabalhos da literatura para detectar mudanças de comportamento no tráfego na rede. No caso do nosso modelo a taxa de falsos positivos diminuiu em até 18,1% quando atualizações periódicas são realizadas. As atualizações contemplaram somente 2,3% das instâncias, com uma diminuição de 14% no custo computacional.
Paradigms of Complexity. Fractals and Structures in the Sciences

Abstract : This document contains all accepted papers to Fractal 2000, the 6th International Multidisciplinary Conference, held 16-19 April 2000 in Singapore. One of the aims of the conference was to review the current status in the field of fractals within realm of complexity and to explore future directions. There is a plenitude of examples in nature displaying complex patterns. The couplings responsible for these intricate patterns give rise to processes and phenomena that are absent when couplings are not present. The universality of such systems can be observed over a wide range of scales, from sub-atomic domain to that of cosmology. Furthermore, in addition to physics, mathematics, and chemistry, where these phenomena were observed first, the existence of complex systems has been confirmed in many disciplines such as medicine, biology, economics and sociology. The underlying geometry is frequently non-Euclidean and can be best analyzed using the tools of fractal analysis.
Transesophageal Endoscopic Ultrasound Fine Needle Biopsy for the Diagnosis of Mediastinal Masses: A Retrospective Real-World Analysis

Background: Endoscopic ultrasound (EUS) plays an important role in the diagnosis and staging of thoracic disease. Our report studies the diagnostic performance and clinical impact of EUS fine needle aspiration (FNA) in a homogenous cohort of patients according to the distribution of the enlarged MLNs or pulmonary masses. Methods: We retrospectively reviewed the diagnostic performance of 211 EUS-FNA in 200 consecutive patients with enlarged or PET-positive MLNs and para-mediastinal masses who were referred to our oncological center between January 2019 and May 2020. Results: The overall sensitivity of EUS-FNA was 85% with a corresponding negative predictive value (NPV) of 56% and an accuracy of 87.5%. The sensitivity and accuracy in patients with abnormal MLNs were 81.1% and 84.4%, respectively. In those with para-mediastinal masses, sensitivity and accuracy were 96.4% and 96.8%. The accuracy for both masses and lymph nodes was 100%, and in the LAG (left adrenal gland), it was 66.6%. Conclusions: Our results show that, in patients with suspected mediastinal masses, EUS-FNA is an accurate technique to evaluate all reachable mediastinal nodal stations, including station 5.
Comprehensive Transcriptome Analysis Reveals Sex-Specific Alternative Splicing Events in Zebrafish Gonads

Alternative splicing is an important way of regulating gene functions in eukaryotes. Several key genes involved in sex determination and gonadal differentiation, such as nr5a1 and ddx4, have sex-biased transcripts between males and females, suggesting a potential regulatory role of alternative splicing in gonads. Currently, the sex-specific alternative splicing events and genes have not been comprehensively studied at the genome-wide level in zebrafish. In this study, through global splicing analysis on three independent sets of RNA-seq data from matched zebrafish testes and ovaries, we identified 120 differentially spliced genes shared by the three datasets, most of which haven’t been reported before. Functional enrichment analysis showed that the GO terms of mRNA processing, mRNA metabolism and microtubule-based process were strongly enriched. The testis- and ovary-biased alternative splicing genes were identified, and part of them (tp53bp1, tpx2, mapre1a, kif2c, and ncoa5) were further validated by RT-PCR. Sequence characteristics analysis suggested that the lengths, GC contents, and splice site strengths of the alternative exons or introns may have different influences in different types of alternative splicing events. Interestingly, we identified an unexpected high proportion (over 70%) of non-frameshift exon-skipping events, suggesting that in these cases the two protein isoforms derived from alternative splicing may both have functions. Furthermore, as a representative example, we found that the alternative splicing of ncoa5 causes the loss of a conserved RRM domain in the short transcript predominantly produced in testes. Our study discovers novel sex-specific alternative splicing events and genes with high reliabilities in zebrafish testes and ovaries, which would provide attractive targets for follow-up studies to reveal the biological significances of alternative splicing events and genes in sex determination and gonadal differentiation.
Multiscale Encoding of Electrocardiogram Signals with a Residual Network for the Detection of Atrial Fibrillation

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias, and it is an indication of high-risk factors for stroke, myocardial ischemia, and other malignant cardiovascular diseases. Most of the existing AF detection methods typically convert one-dimensional time-series electrocardiogram (ECG) signals into two-dimensional representations to train a deep and complex AF detection system, which results in heavy training computation and high implementation costs. In this paper, a multiscale signal encoding scheme is proposed to improve feature representation and detection performance without the need for using any transformation or handcrafted feature engineering techniques. The proposed scheme uses different kernel sizes to produce the encoded signal by using multiple streams that are passed into a one-dimensional sequence of blocks of a residual convolutional neural network (ResNet) to extract representative features from the input ECG signal. This also allows networks to grow in breadth rather than in depth, thus reducing the computing time by using the parallel processing capability of deep learning networks. We investigated the effects of the use of a different number of streams with different kernel sizes on the performance. Experiments were carried out for a performance evaluation using the publicly available PhysioNet CinC Challenge 2017 dataset. The proposed multiscale encoding scheme outperformed existing deep learning-based methods with an average F1 score of 98.54%, but with a lower network complexity.
Childhood Cancer and the Family: A Pilot Proposal for Comprehensive Intervention at the Time of Diagnosis

Childhood cancer has a great impact on children and their environment. To minimize this, countries such as Canada and the USA have protocols in the field of social work, although these are scarce in Europe and especially in Spain. This paper aims to develop a pilot protocol in Aragon (Spain) for the practice of onco-pediatric social work in one of the hardest moments: the diagnosis. For its elaboration, a previous study was carried out in three phases, which provided data on the disease and its impact on the family and children and a methodological basis for the intervention from social work, all considering the participation of the agents involved as a fundamental element. Variables have been identified that influence the impact on the family support network and its quality of life at the time of diagnosis of childhood cancer. In addition, different indicators have been explored, based on the reality of these families. Finally, a pilot proposal for a comprehensive family intervention protocol in the diagnosis of childhood cancer has been elaborated. This work is intended to be a guide for intervention and delimitation of quality standards to be considered when dealing with the diagnosis of childhood cancer.