Miracle-dz/UniFAR-FTUs huggingface dataset:
Lateral Gene Transfer Drives Metabolic Flexibility in the Anaerobic Methane-Oxidizing Archaeal Family Methanoperedenaceae

AOM by microorganisms limits the atmospheric release of the potent greenhouse gas methane and has consequent importance for the global carbon cycle and climate change modeling. While the oxidation of methane coupled to sulfate by consortia of anaerobic methanotrophic (ANME) archaea and bacteria is well documented, several other potential electron acceptors have also been reported to support AOM. In this study, we identify a number of novel respiratory strategies that appear to have been laterally acquired by members of the Methanoperedenaceae, as they are absent from related archaea and other ANME lineages. Expanding the known metabolic potential for members of the Methanoperedenaceae provides important insight into their ecology and suggests their role in linking methane oxidation to several global biogeochemical cycles. ABSTRACT Anaerobic oxidation of methane (AOM) is an important biological process responsible for controlling the flux of methane into the atmosphere. Members of the archaeal family Methanoperedenaceae (formerly ANME-2d) have been demonstrated to couple AOM to the reduction of nitrate, iron, and manganese. Here, comparative genomic analysis of 16 Methanoperedenaceae metagenome-assembled genomes (MAGs), recovered from diverse environments, revealed novel respiratory strategies acquired through lateral gene transfer (LGT) events from diverse archaea and bacteria. Comprehensive phylogenetic analyses suggests that LGT has allowed members of the Methanoperedenaceae to acquire genes for the oxidation of hydrogen and formate and the reduction of arsenate, selenate, and elemental sulfur. Numerous membrane-bound multiheme c-type cytochrome complexes also appear to have been laterally acquired, which may be involved in the direct transfer of electrons to metal oxides, humic substances, and syntrophic partners. IMPORTANCE AOM by microorganisms limits the atmospheric release of the potent greenhouse gas methane and has consequent importance for the global carbon cycle and climate change modeling. While the oxidation of methane coupled to sulfate by consortia of anaerobic methanotrophic (ANME) archaea and bacteria is well documented, several other potential electron acceptors have also been reported to support AOM. In this study, we identify a number of novel respiratory strategies that appear to have been laterally acquired by members of the Methanoperedenaceae, as they are absent from related archaea and other ANME lineages. Expanding the known metabolic potential for members of the Methanoperedenaceae provides important insight into their ecology and suggests their role in linking methane oxidation to several global biogeochemical cycles.
Antibioresistance of Microbial Strains Implicated in the Etiology of Sepsis with Oro-Maxillofacial Portal of Entry

SIMONA CONSTANTINESCU1, CLAUDIA ELENA PLESCA2*, PARASCHIVA POSTOLACHE3*, MIHAELA CATALINA LUCA2 1 Grigore T . Popa University of Medicine and Pharmacy, Microbiology Department, 16 Universitatii Str. , 700115, Iasi, Romania 2 GrigoreT. Popa University of Medicine and Pharmacy, Infectious Diseases Department, 16 Universitatii Str. , 700115, Iasi, Romania 3 Grigore T. Popa University of Medicine and Pharmacy, 1st Medical Department, Respiratory Rehabilitation Clinic, 16 Universitatii Str. , 700115, Iasi, Romania
The Prognostic Value of Body Fat Components in Metastasis Renal Cell Carcinoma Patients Treated with TKIs

Purpose To assess the association between body fat components and survival status and tumor response for metastatic renal cell carcinoma (mRCC) patients treated with tyrosine kinase inhibitors (TKIs). Patients and Methods Patients with pathologically diagnosed and radiologically indicated mRCC were enrolled into the retrospective study. Three body fat components: total fat accumulation (TFA), visceral fat accumulation (VFA) and subcutaneous fat accumulation (SFA) were measured using standard CT scans. The clinical outcomes included progression-free survival (PFS), overall survival (OS), and tumor response rates. Univariate analysis and multivariate Cox proportion hazard regression models were used to find associated parameters and to calculate the adjusted hazard ratio (HR). Results A total of 146 patients were enrolled and the average age of patients was 56.5 years old. According to the univariate analysis, patients with an increased SFA and TFA had a longer PFS and OS. A similar phenomenon was observed among patients with ≥2 increasing body fat components about PFS and OS. As for multivariate analysis, SFA change (p=0.014) or the number of increasing body fat components (p=0.040) were independent indicators to predict PFS. In addition, SFA change (p=0.022) or the number of increasing body fat components (p=0.008) could independently predict OS. Moreover, a better disease control rate (p=0.028) was founded in patients with ≥2 increasing components. In the subgroup of patients with ≥2 metastasis sites, improved OS (p=0.017) and PFS (p=0.027) were found compared to those with <2 increasing components. Further multivariate analysis identified the number of increasing body fat components was an independent factor in predicting PFS (p=0.018) and OS (p=0.029). Conclusion Body fat accumulation, such as high SFA or TFA at progression, could improve the survival of patients with mRCC treated with TKIs, especially patients with higher tumor burden. It should be considered as an important parameter to predict the survival status of patients with mRCC.
metaVaR: Introducing metavariant species models for reference-free metagenomic-based population genomics

Motivation The availability of large metagenomic data offers great opportunities for the population genomic analysis of uncultured organisms, especially for small eukaryotes that represent an important part of the unexplored biosphere while playing a key ecological role. However, the majority of these species lacks reference genome or transcriptome which constitutes a technical barrier for classical population genomic analyses. Results We introduce the metavariant species (MVS) model, a representation of the species only by intra-species nucleotide polymorphism. We designed a method combining reference-free variant calling, multiple density-based clustering and maximum weighted independent set algorithms to cluster intra-species variant into MVS directly from multisample metagenomic raw reads without reference genome or reads assembly. The frequencies of the MVS variants are then used to compute population genomic statistics such as FST in order to estimate genomic differentiation between populations and to identify loci under natural selection. The MVSs construction was tested on simulated and real metagenomic data. MVs showed the required quality for robust population genomics and allowed an accurate estimation of genomic differentiation (ΔFST < 0.0001 and < 0.03 on simulated and real data respectively). Loci predicted under natural selection on real data were all found by MVSs. MVSs represent a new paradigm that may simplify and enhance holistic approaches for population genomics and evolution of microorganisms. Availability The method was implemented in a R package, metaVaR. https://github.com/madoui/MetaVaR Contact amadoui@genoscope.cns. fr
Dynamics of coherent activity between cortical areas defines a two-stage process of selective attention

Analysing a visual scene requires the brain to briefly keep in memory potentially relevant parts and then direct attention to their locations for detailed processing. To reveal the neuronal basis of the underlying working memory and top-down attention processes, we trained macaques to match two patterns presented with a delay between them. As the above processes are likely to require communication between brain regions, and the parietal cortex is involved in spatial attention, we simultaneously recorded neuronal activities from the interconnected parietal and middle temporal areas. We found that mnemonic information about the first pattern was retained in coherent oscillating activity between the areas in high-frequency bands, followed by coherent activity in low-frequency bands that mediate top-down attention on the relevant location. ONE SENTENCE SUMMARY Gamma coherence allows retaining object features in a saliency map while lower frequency coherence facilitates attention.
TAX COMPLIANCE WITH TAXPAYER AWARENESS AS AN INTERVENING VARIABLE

Purpose of the study: This research explores the role of taxpayer awareness as an intervening variable in analyzing the effect of tax penalties and tax audits on taxpayer compliance. Methodology: The Research to use a purposive sampling method with the type of sample selection based on consideration the judgment sampling. Main Findings: The results of the SmartPLS Version 3.0 test provide evidence that tax penalties and tax audits carried out by professional auditors proved to be able to increase taxpayer compliance in paying their taxes in a timely manner. Applications of this study: The population of this study is individual taxpayers who carry out free work recorded at Tax Offices (KPP) Pratama Makassar (KPP Pratama Makassar Utara, KPP Pratama Selatan, and KPP Pratama Makassar Barat). whose status is still active, until January 2017 there were 234,104 effective taxpayers. Novelty/Originality of this study: The nationalism of the people as citizens and as high taxpayers creates a high sense of tax awareness that has an impact on their high compliance in paying taxes.
In silico analysis of maize HDACs with an emphasis on their response to biotic and abiotic stresses

Histone deacetylases (HDACs) are key epigenetic factors in regulating chromatin structure and gene expression in multiple aspects of plant growth, development, and response to abiotic or biotic stresses. Many studies on systematic analysis and molecular function of HDACs in Arabidopsis and rice have been conducted. However, systematic analysis of HDAC gene family and gene expression in response to abiotic and biotic stresses has not yet been reported. In this study, a systematic analysis of the HDAC gene family in maize was performed and 18 ZmHDACs distributed on nine chromosomes were identified. Phylogenetic analysis of ZmHDACs showed that this gene family could be divided into RPD3/HDA1, SIR2, and HD2 groups. Tissue-specific expression results revealed that ZmHDACs exhibited diverse expression patterns in different tissues, indicating that these genes might have diversified functions in growth and development. Expression pattern of ZmHDACs in hormone treatment and inoculation experiment suggested that several ZmHDACs might be involved in jasmonic acid or salicylic acid signaling pathway and defense response. Interestingly, HDAC genes were downregulated under heat stress, and immunoblotting results demonstrated that histones H3K9ac and H4K5ac levels were increased under heat stress. These results provide insights into ZmHDACs, which could help to reveal their functions in controlling maize development and responses to abiotic or biotic stresses.
Silicon modulates multi-layered defense against powdery mildew in Arabidopsis

Silicon (Si) has been widely employed in agriculture to enhance resistance against pathogens in many crop plants. However, the underlying molecular mechanisms of Si-mediated resistance remain elusive. In this study, the Arabidopsis-powdery mildew pathosystem was employed to investigate possible defense mechanisms of Si-mediated resistance. Because Arabidopsis lacks efficient Si transporters and thus is a low Si-accumulator, two heterologous Si influx transporters (from barley and muskmelon) were individually expressed in wild-type Arabidopsis Col-0 and a panel of mutants defective in different immune signaling pathways. Results from infection tests showed that while very low leaf Si content slightly induced salicylic acid (SA)-dependent resistance, high Si promoted PAD4-dependent but largely EDS1- and SA-independent resistance against the adapted powdery mildew isolate Golovinomyces cichoracearum UCSC1. Intriguingly, our results also showed that high Si could largely reboot non-host resistance in an immune-compromised eds1/pad4/sid2 triple mutant background against a non-adapted powdery mildew isolate G. cichoracearum UMSG1. Taken together, our results suggest that assimilated Si modulates distinct, multi-layered defense mechanisms to enhance plant resistance against adapted and no-adapted powdery mildew pathogens, possibly via synergistic interaction with defense-induced callose.
Health literacy in Israel - from measurement to intervention: Two case studies

This report focuses on opportunities, challenges and outcomes of health literacy related interventions in Israel, based on health literacy measurement. The importance of a system’s and community approaches are discussed, as is cultural appropriateness. Two case studies are highlighted - the first on childhood immunization and the second on self-management of chronic health situations. In the second example, a combination of community, media, digital, and face-to-face interventions comprise a broad approach to intervention. The impact and some findings are presented, including conclusions derived from each initiative.
Self-Supervised Learning for Audio-Visual Speaker Diarization

Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems. In this paper, we propose a self-supervised audio-video synchronization learning method to address the problem of speaker diarization without massive labeling effort. We improve the previous approaches by introducing two new loss functions: the dynamic triplet loss and the multinomial loss. We test them on a real-world human-computer interaction system and the results show our best model yields a remarkable gain of +8% F1-scores as well as diarization error rate reduction. Finally, we introduce a new large scale audio-video corpus designed to fill the vacancy of audio-video dataset in Chinese.
Variational Autoencoders with Riemannian Brownian Motion Priors

Variational Autoencoders (VAEs) represent the given data in a low-dimensional latent space, which is generally assumed to be Euclidean. This assumption naturally leads to the common choice of a standard Gaussian prior over continuous latent variables. Recent work has, however, shown that this prior has a detrimental effect on model capacity, leading to subpar performance. We propose that the Euclidean assumption lies at the heart of this failure mode. To counter this, we assume a Riemannian structure over the latent space, which constitutes a more principled geometric view of the latent codes, and replace the standard Gaussian prior with a Riemannian Brownian motion prior. We propose an efficient inference scheme that does not rely on the unknown normalizing factor of this prior. Finally, we demonstrate that this prior significantly increases model capacity using only one additional scalar parameter.
Modelling Fog Offloading Performance

Fog computing has emerged as a computing paradigm aimed at addressing the issues of latency, bandwidth and privacy when mobile devices are communicating with remote cloud services. The concept is to offload compute services closer to the data. However many challenges exist in the realisation of this approach. During offloading, (part of) the application underpinned by the services may be unavailable, which the user will experience as down time. This paper describes work aimed at building models to allow prediction of such down time based on metrics (operational data) of the underlying and surrounding infrastructure. Such prediction would be invaluable in the context of automated Fog offloading and adaptive decision making in Fog orchestration. Models that cater for four container-based stateless and stateful offload techniques, namely Save and Load, Export and Import, Push and Pull and Live Migration, are built using four (linear and non-linear) regression techniques. Experimental results comprising over 42 million data points from multiple lab-based Fog infrastructure are presented. The results highlight that reasonably accurate predictions (measured by the coefficient of determination for regression models, mean absolute percentage error, and mean absolute error) may be obtained when considering 25 metrics relevant to the infrastructure.
Improving Efficiency in Neural Network Accelerator using Operands Hamming Distance Optimization

Neural network accelerator is a key enabler for the on-device AI inference, for which energy efficiency is an important metric. The datapath energy, including the computation energy and the data movement energy among the arithmetic units, claims a significant part of the total accelerator energy. By revisiting the basic physics of the arithmetic logic circuits, we show that the datapath energy is highly correlated with the bit flips when streaming the input operands into the arithmetic units, defined as the hamming distance (HD) of the input operand matrices. Based on the insight, we propose a post-training optimization algorithm and a HD-aware training algorithm to co-design and co-optimize the accelerator and the network synergistically. The experimental results based on post-layout simulation with MobileNetV2 demonstrate on average 2.85X datapath energy reduction and up to 8.51X datapath energy reduction for certain layers.
Consumer Privacy Protection using Flexible Thermal Loads

Due to the increasing adoption of smart meters, there are growing concerns about consumer privacy risks stemming from the high resolution metering data. To counter these risks, there have been various works in shaping the grid-visible energy consumption profile using controllable loads. However, most works have focused on using energy storage systems (ESSs). In this paper, we explore the use of flexible thermal loads (FTLs) for consumer privacy protection. The theoretical limitations of using FTLs are compared to systems using ESSs. It is shown that due to the limitations in the operation of FTLs, without significant over-sizing of systems, and sacrifices in consumer comfort, FTLs of much higher equivalent energy storage capacity are required to afford the same level of protection as ESSs. Nonetheless, given their increasing ubiquity, controllable FTLs should be considered for use in consumer privacy protection.
Imaging Spatiotemporal Hong-Ou-Mandel Interference of Biphoton States of Extremely High Schmidt Number

We report the experimental observation of a spatio-temporal Hong-Ou-Mandel (HOM) interference of bi-photon states of extremely high Schmidt number. Two-photon interference of 1500 spatial modes and a total of more than 3x10^6 spatio-temporal modes is evidenced by measuring momentum spatial coincidences between the pixels of the far-field images of two strongly multimode spontaneous parametric down conversion (SPDC) beams propagating through a HOM interferometer. The outgoing SPDC beams are recorded onto two separate detectors arrays operating in the photon-counting regime. The properties of HOM interference are investigated both in the time and space domains. We show that the two-photon interferences exhibit temporal and two-dimensional spatial HOM dips with visibilities of 30% and widths in good agreement with the spatio-temporal coherence properties of the bi-photon state. Moreover, we demonstrate that a peak of momentum spatial coincidences is evidenced inside each image, in correspondence with this dip.
Work-Efficient Batch-Incremental Minimum Spanning Trees with Applications to the Sliding-Window Model

Algorithms for dynamically maintaining minimum spanning trees (MSTs) have received much attention in both the parallel and sequential settings. While previous work has given optimal algorithms for dense graphs, all existing parallel batch-dynamic algorithms perform polynomial work per update in the worst case for sparse graphs. In this paper, we present the first work-efficient parallel batch-dynamic algorithm for incremental MST, which can insert l edges in O(l log(1+n/l) work in expectation and O(polylog(n)) span w.h. p. The key ingredient of our algorithm is an algorithm for constructing a compressed path tree of an edge-weighted tree, which is a smaller tree that contains all pairwise heaviest edges between a given set of marked vertices. Using our batch-incremental MST algorithm, we demonstrate a range of applications that become efficiently solvable in parallel in the sliding-window model, such as graph connectivity, approximate MSTs, testing bipartiteness, k-certificates, cycle-freeness, and maintaining sparsifiers.
Exponential Step Sizes for Non-Convex Optimization

Stochastic Gradient Descent (SGD) is a popular tool in large scale optimization of machine learning objective functions. However, the performance is greatly variable, depending on the choice of the step sizes. In this paper, we introduce the exponential step sizes for stochastic optimization of smooth non-convex functions which satisfy the Polyak-Łojasiewicz (PL) condition. We show that, without any information on the level of noise over the stochastic gradients, these step sizes guarantee a convergence rate for the last iterate that automatically interpolates between a linear rate (in the noisy-free case) and a $O(\frac{1}{T})$ rate (in the noisy case), up to poly-logarithmic factors. Moreover, if without the PL condition, the exponential step sizes still guarantee optimal convergence to a critical point, up to logarithmic factors. We also validate our theoretical results with empirical experiments on real-world datasets with deep learning architectures.
Radiative Transitions and Relaxation Pathways in Plasmon-Based Cavity Quantum Electrodynamics Systems

Molecular aggregates on plasmonic nanoparticles have emerged as attractive systems for the studies of cavity quantum electrodynamics. They are highly tunable, scalable, easy to synthesize and offer sub-wavelength confinement, all while giving access to the ultrastrong light-matter coupling regime at room temperature and promising a plethora of applications. However, the complexity of both the molecular aggregate and plasmonic nanoparticle introduces many more processes affecting the excitation and its relaxation, than are present in atom-cavity systems. Here, we follow the complex relaxation pathways of the photoexcitation of such hybrid systems and conclude that while the metal is responsible for destroying the coherence of the excitation, the molecular aggregate significantly participates in dissipating the energy. We rely on two-dimensional electronic spectroscopy in a combined theory-experiment approach, which allows us to ascribe the different timescales of relaxation to processes inside the molecules or the metal nanoparticle. We show that the dynamics beyond a few femtoseconds has to be cast in the language of hot electron distributions and excitons instead of the accepted lower and upper polariton branches, and furthermore set the framework for delving deeper into the photophysics of excitations that could be used in hot electron transfer, for example to drive photocatalytic reactions.