Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved sleep scoring in mice reveals human-like stages

Rodents are the main animal model to study sleep. Yet, in spite of a large consensus on the distinction between rapid-eye-movements sleep (REM) and non-REM sleep (NREM) in both humans and rodent, there is still no equivalent in mice of the NREM subdivision classically described in humans. Here we propose a classification of sleep stages in mice, inspired by human sleep scoring. By using chronic recordings in medial prefrontal cortex (mPFC) and hippocampus we can classify three NREM stages with a stage N1 devoid of any low oscillatory activity and N3 with a high density of delta waves. These stages displayed the same evolution observed in human during the whole sleep or within sleep cycles. Importantly, as in human, N1 in mice is the first stage observed at sleep onset and is increased after sleep fragmentation in Orexin-/- mice, a mouse model of narcolepsy. We also show that these substages are associated to massive modification of neuronal activity. Moreover, considering these stages allows to predict mPFC neurons evolution of firing rates across sleep period. Notably, neurons preferentially active within N3 decreased their activity over sleep while the opposite is seen for those preferentially active in N1 and N2. Overall this new approach shows the feasibility of NREM sleep sub-classification in rodents, and, in regard to the similarity between sleep in both species, will pave the way for further studies in sleep pathologies given the perturbation of specific sleep substages in human pathologies such as insomnia, somnambulism, night terrors, or fibromyalgia.
Regulation of Parent-of-Origin Allelic Expression in Arabidopsis thaliana endosperm

Genomic imprinting is an epigenetic phenomenon set in the gametes prior to fertilization that causes differential expression of parental alleles mainly in the endosperm of flowering plants. The overlap between previously identified panels of imprinted genes is limited. In order to achieve high resolution sequencing data we have used sequence capture technology to investigate imprinting. Here, we present a bioinformatics pipeline to assay parent-of-origin allele specific expression and report more than 300 loci with parental expression bias. We find that the level of expression from maternal and paternal alleles in most cases is not binary, instead favouring a differential dosage hypothesis for the evolution of imprinting in plants. To address imprinting regulation, we systematically employed mutations in regulative epigenetic pathways suggested to be major players in the process. We establish the mechanistic mode of imprinting for more than 50 loci regulated by DNA methylation and Polycomb-dependent histone methylation. However, the imprinting patterns of the majority of genes were not affected by these mechanisms. To this end we also demonstrate that the RNA-directed DNA methylation pathway alone does not influence imprinting patterns in a substantial manner, suggesting more complex epigenetic regulation pathways for the majority of identified imprinted genes. Author summary Expression of gene copies only from the mother or the father’s genome, also termed imprinting, is a specialized epigenetic phenomenon that is found to be enriched at some genes expressed in the mammalian placenta and in the endosperm of the plant seed. Although several studies have reported on imprinted genes in plants, the identified loci are at large non-overlapping between reports. This motivated us to investigate in detail the expression pattern of imprinted genes in the endosperm and to determine how imprinting patterns are established at various imprinted loci. Although several underlying epigenetic regulation mechanisms have been demonstrated to establish imprinting patterns at certain genes, the majority of imprinted genes have not been linked to such mechanisms. In the present study we systematically investigated the mechanisms that are involved in establishing imprinting, by employing mutants of epigenetic regulators and high-throughput sequencing. In our high resolution study, we report more than 300 imprinted genes and demonstrate that the biological phenomenon imprinting involves gradual expression of parental gene copies rather than switching gene copies on or off. Notably, for the majority of imprinted genes, the mechanisms previously believed to be major to establish their imprinting patterns, are not responsible for mediating imprinting.
hilldiv: an R package for the integral analysis of diversity based on Hill numbers

Hill numbers provide a powerful framework for measuring, comparing and partitioning the diversity of biological systems as characterised using high throughput DNA sequencing approaches. In order to facilitate the implementation of Hill numbers into such analyses, whether focusing on diet reconstruction, microbial community profiling or more general ecosystem characterisation analyses, we present a new R package. ‘Hilldiv’ provides a set of functions to assist analysis of diversity based on Hill numbers, using count tables (e.g. OTU, ASV) and associated phylogenetic trees as inputs. Multiple functionalities of the library are introduced, including diversity measurement, diversity profile plotting, diversity comparison between samples and groups, multi-level diversity partitioning and (dis)similarity measurement. All of these are grounded in abundance-based and incidence-based Hill numbers, and can accommodate phylogenetic or functional correlation among OTUs or ASVs. The package can be installed from CRAN or Github, and tutorials and example scripts can be found in the package’s page (https://github.com/anttonalberdi/hilldiv).
Multi-omic analysis supports a developmental hierarchy of molecular subtypes in high-grade serous ovarian carcinoma

Background: The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis and limited treatment options. Several studies have identified gene expression-based subtypes of high-grade serous ovarian carcinoma (HGSOC) as a basis for targeted therapy, yet extensive ambiguity in subtype classification impairs translation of these subtypes into clinical practice. Furthermore, although HGSOC tumors are known to be frequently polyclonal, it is unknown whether clones within the same tumor share the same subtype. Results: We investigate whether ambiguity in subtype classification can be attributed to the polyclonal composition of HGSOC tumors, addressing the currently unresolved question whether proposed subtypes are early or late events in tumorigenesis. This hypothesis is first tested in The Cancer Genome Atlas HGSOC cases by (i) analyzing recurrent somatic copy number alterations for their association with subtypes, (ii) inferring per-alteration clonality from complementary analysis of SNP arrays and whole-exome sequencing, and (iii) testing whether subtype-associated alterations tend to predominantly occur clonally (early events) or subclonally (late events). As opposed to the genomically distinct evolution of soft-tissue sarcoma subtypes, we find that subtype association of HGSOC alterations significantly correlate with subclonality. This correlation is particularly evident for the high-purity proliferative subtype spectrum, which is also characterized by extreme genomic instability, absence of immune infiltration, and increased patient age. This is in stark contrast to the high-purity differentiated subtype spectrum, which is characterized by largely intact genome integrity, high immune infiltration, and younger patient age. Other subtypes showed intermediate levels for these characteristics. From single cell sequencing of an independent HGSOC tumor, we demonstrate that ambiguity in subtype classification extends to individual tumor epithelial cells, further supporting a developmental transition from one subtype spectrum to another. Conclusion: We propose a novel model of HGSOC tumor development that complements the subtype perspective. In this model, individual tumors develop from an early differentiated spectrum to a late proliferative spectrum, and may exhibit characteristics of different previously defined "subtypes" at different points along a timeline characterized by increasing genomic instability and subclonal expansion. This model is more consistent with available bulk and single-cell data, and provides an explanation for ambiguity in subtype classification as the result of assigning discrete, mutually exclusive subtypes to a genomically complex process of tumor evolution.
The Association of Body Mass Index with Pancreatic Cancer: Variation by Age at Body Mass Index Assessment.

Higher body mass index (BMI) is associated with increased risk of pancreatic cancer in epidemiologic studies but BMI has usually been assessed at older ages, potentially underestimating the full impact of excess weight. We examined the association between BMI and pancreatic cancer mortality among 963,317 adults aged 30-89 years at enrollment into Cancer Prevention Study-II in 1982. During follow-up through 2014, 8,354 participants died of pancreatic cancer. Hazard ratios (HRs) per 5 BMI-units, calculated using proportional hazards regression, declined steadily with age at BMI assessment, from 1.25 (95% confidence interval (CI) 1.18, 1.33) in those aged 30-49 at enrollment to 1.13 (95% CI 1.02, 1.26) in those aged 70-89 (p-trend=0.005). Based on a HR of 1.25 per 5 BMI-units at age 45, we estimate 28% of US pancreatic cancer deaths in those born from 1970-74 will be attributable to BMI>25, nearly twice the equivalent proportion in those born in the 1930s, a birth cohort with much lower BMI in middle age. These results suggest BMI before age 50 is more strongly associated with pancreatic cancer risk than BMI at older ages and underscore the importance of avoiding excess weight gain before middle age for preventing this highly fatal cancer.
Obstacles influencing The obstacles influencing sports activities in Saudi universities

Objectives: The aim of this study to evaluate the sports activities in Saudi universities from the students perspective also, to investigate the obstacles influencing sports activities in Saudi universities. Methods: This study is used the descriptive approach to study 114 respondents were randomly selected from in some Saudi university. The questionnaire was used study comprises five questions. An online questionnaire was designed by Google forms and was distributed by email and WhatsApp. Results: The study revealed some obstacles that hinder the success of sports activities in Saudi Arabia universities for males are: Sports injuries, Lack of facilities, Lack of tools, Non updated programs, admission conditions are not clear, Lack of student interest to do sport, Lack of time in university time table to do sport, There is no scheduled for the sport activities in Saudi universities, lack of fund and lack of human recourse. Conclusion: Can see the importance to investigate the most important factors to enhance sports activities. As well most of the university's students agree with the important need to enhance the sports activities. In addition, all of the obstacles consider by the student are easy to solve if universities implement important the factors.
Compact Authenticated Key Exchange in the Quantum Random Oracle Model

Several quantum-resistant authenticated key exchange protocols (AKEs) have been proposed from supersingular isogeny and lattice. Most of their security analyses are conducted in the classical random oracle model, leaving their securities in the quantum random oracle model (QROM) as open problems. In this paper, we propose a generic construction of two-message AKE in QROM. It can be regarded as a QROM-secure version of X3LH [Xue et al. ASIACRYPT 2018], a generic AKE based on double-key PKE. We prove that, with some modiﬁcation, the QROM security of X3LH can be reduced to the one-way security of double-key PKE. Aside from answering open problems on the QROM security of prior AKEs, such as SIAKE [Xu et al. ASIACRYPT 2019] based on supersingular isogeny, 2Kyber-AKE based on Module-LWE, and FSXY , we propose a new construction, CSIAKE , based on commutative supersingular isogeny. Our framework enjoys the following desirable features. First of all, it supports PKEs with non-perfect correctness. Secondly, the basic building block is compact and only requires one-wayness. Finally, the resulting AKE achieves the security in CK + model as strong as X3LH , and the transformation overhead is low.
Negative regulation of ACE2 by interferons in vivo and its genetic control

The SARS-CoV-2 pandemic has resulted in widespread morbidity and mortality globally. ACE2 is a receptor for SARS-CoV-2 and differences in expression may affect susceptibility to COVID-19. Using HCV-infected liver tissue from 195 individuals, we discovered that among genes negatively correlated with ACE2, interferon signalling pathways were highly enriched and observed down-regulation of ACE2 after interferon-alpha treatment. Negative correlation was also found in the gastrointestinal tract and in lung tissue from a murine model of SARS-CoV-1 infection suggesting conserved regulation of ACE2 across tissue and species. Performing a genome-wide eQTL analysis, we discovered that polymorphisms in the interferon lambda (IFNL) region are associated with ACE2 expression. Increased ACE2 expression in the liver was also associated with age and presence of cirrhosis. Polymorphisms in the IFNL region may impact not only antiviral responses but also ACE2 with potential consequences for clinical outcomes in distinct ethnic groups and with implications for therapeutic interventions.
End-to-End Text-to-Speech Synthesis with Unaligned Multiple Language Units Based on Attention

This paper presents the use of unaligned multiple language units for end-to-end text-to-speech (TTS). End-to-end TTS is a promising technology in that it does not require intermediate representation such as prosodic contexts. However, it causes mispronunciation and unnatural prosody. To alleviate this problem, previous methods have used multiple language units, e.g., phonemes and characters, but required the units to be hard-aligned. In this paper, we propose a multi-input attention structure that simultaneously accepts multiple language units without alignments among them. We consider using not only traditional phonemes and characters but also subwords tokenized in a language-independent manner. We also propose a progressive training strategy to deal with the unaligned multiple language units. The experimental results demonstrated that our model and training strategy improve speech quality.
Neural Entrainment to Natural Speech Envelope Based on Subject Aligned EEG Signals

Reconstruction of speech envelope from neural signal is a general way to study neural entrainment, which helps to understand the neural mechanism underlying speech processing. Previous neural entrainment studies were mainly based on single-trial neural activities, and the reconstruction accuracy of speech envelope is not high enough, probably due to the interferences from diverse noises such as breath and heartbeat. Considering that such noises independently emerge in the consistent neural processing of the subjects responding to the same speech stimulus, we proposed a method to align and average electroen-cephalograph (EEG) signals of the subjects for the same stimuli to reduce the noises of neural signals. Pearson correlation of constructed speech envelops with the original ones showed a great improvement comparing to the single-trial based method. Our study improved the correlation coefﬁcient in delta band from around 0.25 to 0.5, where 0.25 was obtained in previous leading studies based on single-trial. The speech tracking phenomenon not only occurred in the commonly reported delta and theta band, but also occurred in the gamma band of EEG. More-over, the reconstruction accuracy for regular speech was higher than that for the time-reversed speech, suggesting that neural entrainment to natural speech envelope reﬂects speech semantics.
Aggregate Signature with Detecting Functionality from Group Testing

In this paper, we comprehensively study aggregate signatures with detecting functionality, that have functionality of both key-less aggregation of multiple signatures and identifying an invalid message from the aggregate signature, in order to reduce a total amount of signature-size for lots of messages. Our contribution is (i) to formalize strong security notions for both non-interactive and interactive protocols by taking into account related work such as fault-tolerant aggregate signatures and (non-)interactive aggregate MACs with detecting functionality (i.e., symmetric case); and (ii) to construct aggregate signatures with the functionality from group testing-protocols in a generic and comprehensive way. As instantiations, pairing-based constructions are provided.
Utterance Invariant Training for Hybrid Two-Pass End-to-End Speech Recognition

In this paper, we propose an utterance invariant training (UIT) speciﬁcally designed to improve the performance of a two-pass end-to-end hybrid ASR. Our proposed hybrid ASR solution uses a shared encoder with a monotonic chunkwise attention (MoChA) decoder for streaming capabilities, while using a low-latency bidirectional full-attention (BFA) decoder for enhancing the overall ASR accuracy. A modiﬁed sequence summary network (SSN) based utterance invariant training is used to suit the two-pass model architecture. The input feature stream self-conditioned by scaling and shifting with its own sequence sum-mary is used as a concatenative conditioning on the bidirectional encoder layers sitting on top of the shared encoder. In effect, the proposed utterance invariant training combines three different types of conditioning namely, concatenative, multiplicative and additive. Experimental results show that the proposed approach shows reduction in word error rates up to 7% relative on Librispeech, and 10-15% on a large scale Korean end-to-end two-pass hybrid ASR model.
Comparative study on the bio-activity of hemoglobin and myoglobin as recognition materials in biosensors

Apart from the huge physiological importance of hemo-, myo- globin and hemin, they play also an important role as robust and effective recognition agents in chemical and biological sensors. In this aspect the key factor in developing a reliable biosensor is the immobilization on the transducer. Usually immobilization can be produced by chemical methods, but there is frequently a need for homogeneous films of well-controlled thickness or films which can be deposited in a dry environment. The film thickness is of main importance for the optical transducer detected by Surface Plasmon Resonance (SPR) what is used in our study. Hundreds of immobilization protocols have been developed in an effort to ensure high performance sensing. All of them are focused on finding and deposition of appropriate matrices in which the recognition medium can be incorporated. However, the matrix always deteriorates the effectiveness of recognition. It seems that the best approach is to perform direct immobilization of the recognition medium. However, this is not always possible regarding the organic materials – the problem is whether the deposition retains the bioactivity of the recognition agent. On the other hand, the type of the transducer also imposes constrains. For example, the direct immobilization of the proteins is not possible for electrochemical sensors, because of the distance between the redox center and electrodes is too long. Evaluating the pros and cons of organic (protein) film deposition we have considered to study the possibility for direct immobilization of myoglobin, hemoglobin and hemin on SPR transducer. To best of our knowledge, SPR biochip with immobilized myoglobin, hemoglobin and hemin has never been constructed before. We have used spin coating, for direct immobilization and matrix-assisted pulsed-laser evaporation (MAPLE) for elaboration of the SPR biochip. The performance of both SPR chips – direct and MAPLE immobilized, was studied by SPR registration of the binding activity of myo- and hemo- globin ligands with carbon monoxide (CO), carbon dioxide (CO2) and nitride oxide (NO).
Black-Box Attacks on Spoofing Countermeasures Using Transferability of Adversarial Examples

Spooﬁng countermeasure systems protect Automatic Speaker Veriﬁcation (ASV) systems from spooﬁng attacks such as re-play, synthesis, and conversion. However, research has shown spooﬁng countermeasures are vulnerable to adversarial attacks. Previous literature mainly uses adversarial attacks on spooﬁng countermeasures under a white-box scenario, where attackers could access all the information of the victim networks. Black-box attacks would be a more serious threat than white-box attacks. In this paper, our objective is to black-box attack spooﬁng countermeasures using adversarial examples with high transferability. We used MI-FGSM to improve the transferability of adversarial examples. We propose an iterative ensemble method (IEM) to further improve the transferability. Comparing with previous ensemble-based attacks, our proposed IEM method, combined with MI-FGSM, could effectively generate adversarial examples with higher transferability. In our experiments, we evaluated the attacks on four black-box networks. For each black-box model, we used the other three as a white-box ensemble to generate the adversarial examples. The proposed IEM with MI-FGSM improved attack success rate by 4-30% relative (depending on black-box model) w.r. t. the baseline logit ensemble. Therefore, we conclude that spooﬁng countermeasure models are also vulnerable to black-box attacks.
Attention-Driven Projections for Soundscape Classification

Acoustic soundscapes can be made up of background sound events and foreground sound events. Many times, either the background (or the foreground) may provide useful cues in discriminating one soundscape from another. A part of the background or a part of the foreground can be suppressed by us-ing subspace projections. These projections can be learnt by utilising the framework of robust principal component analysis. In this work, audio signals are represented as embeddings from a convolutional neural network, and meta-embeddings are derived using an attention mechanism. This representation enables the use of class-speciﬁc projections for effective suppression, leading to good discrimination. Our experimental evaluation demonstrates the effectiveness of the method on standard datasets for acoustic scene classiﬁcation.
Empirical Interpretation of Speech Emotion Perception with Attention Based Model for Speech Emotion Recognition

Speech emotion recognition is essential for obtaining emotional intelligence which affects the understanding of context and meaning of speech. Harmonically structured vowel and consonant sounds add indexical and linguistic cues in spoken information. Previous research argued whether vowel sound cues were more important in carrying the emotional context from a psychological and linguistic point of view. Other research also claimed that emotion information could exist in small over-lapping acoustic cues. However, these claims are not corroborated in computational speech emotion recognition systems. In this research, a convolution-based model and a long-short-term memory-based model, both using attention, are applied to investigate these theories of speech emotion on computational models. The role of acoustic context and word importance is demonstrated for the task of speech emotion recognition. The IEMOCAP corpus is evaluated by the proposed models, and 80.1% unweighted accuracy is achieved on pure acoustic data which is higher than current state-of-the-art models on this task. The phones and words are mapped to the attention vectors and it is seen that the vowel sounds are more important for deﬁning emotion acoustic cues than the consonants, and the model can assign word importance based on acoustic context.