Miracle-dz/UniFAR-FTUs huggingface dataset:
rRNA depletion for holobiont metatranscriptome profiling across demosponges

The pervasive association of demosponges with diverse bacterial communities has been a research topic since the late 1970s. Studies characterizing these bacterial communities have detected over 40 bacterial phyla associated with sponges and determined that sponge microbiomes differ from the bacterioplankton of the surrounding water and include sponges-specific bacterial associates. Despite the large body of information available on the diversity of the sponge microbiome, the molecular mechanisms used by sponges to interact with their bacterial symbionts and vice versa remain poorly understood. Yet, studying how sponges interact with their microbiomes is pivotal to understanding how these systems react to environmental changes and when these changes lead to a disruption of sponge-microbe associations. An important factor hampering the characterization of the sponge-microbiome molecular cross-talk mechanisms is the lack of broadly applicable molecular methods to, for instance, efficiently sequence meta-transcriptomes across a broad range of sponge species. To overcome this limitation, we implemented a hybrid-capture strategy capable of depleting both sponge and bacterial rRNA from total RNA extracts of highly divergent demosponges with microbiomes of different complexity. Our pan-demosponge rRNA depletion strategy allows for the efficient characterization of the metatranscriptome of diverse demosponge holobionts and the simultaneous quantification of gene expression in both the host and its microbiome.
Analysis of context-specific KRAS–effector (sub)complexes in Caco-2 cells

We used affinity purification of KRAS in different genetic and growth contexts to determine KRAS-mediated complexes, their rewiring in specific contexts, and their link to functional outcomes. Ras is a key switch controlling cell behavior. In the GTP-bound form, Ras interacts with numerous effectors in a mutually exclusive manner, where individual Ras–effectors are likely part of larger cellular (sub)complexes. The molecular details of these (sub)complexes and their alteration in specific contexts are not understood. Focusing on KRAS, we performed affinity purification (AP)–mass spectrometry (MS) experiments of exogenously expressed FLAG-KRAS WT and three oncogenic mutants (“genetic contexts”) in the human Caco-2 cell line, each exposed to 11 different culture media (“culture contexts”) that mimic conditions relevant in the colon and colorectal cancer. We identified four effectors present in complex with KRAS in all genetic and growth contexts (“context-general effectors”). Seven effectors are found in KRAS complexes in only some contexts (“context-specific effectors”). Analyzing all interactors in complex with KRAS per condition, we find that the culture contexts had a larger impact on interaction rewiring than genetic contexts. We investigated how changes in the interactome impact functional outcomes and created a Shiny app for interactive visualization. We validated some of the functional differences in metabolism and proliferation. Finally, we used networks to evaluate how KRAS–effectors are involved in the modulation of functions by random walk analyses of effector-mediated (sub)complexes. Altogether, our work shows the impact of environmental contexts on network rewiring, which provides insights into tissue-specific signaling mechanisms. This may also explain why KRAS oncogenic mutants may be causing cancer only in specific tissues despite KRAS being expressed in most cells and tissues.
Remote sensing-based forest modeling reveals positive effects of functional diversity on productivity at local spatial scale

Forest biodiversity is critical for many ecosystem functions and services at plot scale, but it is uncertain how biodiversity influences ecosystem functioning across environmental gradients and contiguous larger areas. We used remote sensing and process-based terrestrial biosphere modeling to explore functional diversity–productivity relationships at multiple scales for a heterogeneous forest site in Switzerland. We ran the biosphere model with empirical data about forest structure and composition derived from ground-based surveys, airborne laser scanning and imaging spectroscopy for the years 2006–2015 at 10×10-m spatial resolution. We then related the model outputs forest productivity to functional diversity under observed and experimental model conditions. Functional diversity increased productivity significantly (p < 0.001) across all simulations at 20×20-m to 30×30-m scale, but at 100×100-m scale positive relationships disappeared under homogeneous soil conditions. Whereas local functional diversity was an important driver of productivity, environmental context (especially soil depth, texture and water availability) underpinned the variation of productivity (and functional diversity) at larger spatial scales. Integration of remotely-sensed information on canopy composition and structure into terrestrial biosphere models helps fill the knowledge gap about how plant biodiversity affects carbon cycling and biosphere feedbacks onto climate over large contiguous areas.
CGIAR Barley Breeding Toolbox: A diversity panel to facilitate breeding and genomic research in the developing world

Despite the reduced cost of the new genotyping technologies, many public and private breeding programs, mostly in developing countries, still cannot afford them, hindering their research. The objective of the present study was to identify and assemble an Association Mapping panel of widely diverse barley genotypes to serve as a barley breeding toolbox, especially for the Developing World. The main criteria were: i) to be representative of the germplasm grown in the Developing World; ii) to cover a wide range of genetic variability and iii) to be of public domain. For it, we assembled and genotyped a Global Barley Panel (GBP) of 530 genotypes representing a wide range of row-types, end-uses, growth habits, geographical origins and environmental conditions. The GBP accessions were genotyped using the barley Infinium iSelect 50K chip. A total of 40,342 SNP markers were polymorphic and displayed an average polymorphism information content (PIC) of 0.35, with 66% of them exceeding PIC=0.25. The analysis of the population structure identified 8 sub-populations mostly linked to the geographical origin of the lines (Europe, Australia, USA, Canada, Africa, Latin-America, ICARDA and others), four of them of significant ICARDA origin. The 16 allele combinations at 4 major flowering genes (HvVRN-H3, HvPPD-H1, HvVRN-H1 and HvCEN) explained 11.07% genetic variation and were linked to the geographic origins of the lines studied. Among origins, ICARDA material (n= 257) showed wide diversity as revealed by the highest number of polymorphic loci (99.76% of all polymorphic SNPs in GBP), number of private alleles and Nei’s gene diversity and the fact that ICARDA lines were present in all 8 sub-populations and carried all 16 allelic combinations at phenology genes. Due to their genetic diversity and their representativity of the germplasm adapted to the Developing World, 312 ICARDA lines and cultivated landraces were pre-selected to form the CGIAR Barley Breeding Toolbox (CBBT). Using the genotypic data and the Mean of Transformed Kinships method, we assembled the CBBT, an Association Mapping panel of 250 accessions capturing most of the allelic diversity in the global panel. The CBBT preserves a good balance between row types as well as a good representation of both allelic combinations identified at most important phenological loci and sub-populations of the GBP. The CBBT lines together with their genotypic data will be made available to breeders and researchers worldwide to serve as a collaborative tool to underpin the genetic mechanisms of traits of interest for barley cultivation.
Dynamics of the cyclotron instability in a mirror system

When there is a source of energetic particles in a magnetic mirror system, the cyclotron instability is accompanied by distinctive relaxation oscillations, during which the intensity of cyclotron waves and the fluxes of electrons leaving the system turn out to be amplitude-modulated. If there are periodic external perturbations, which lead to a modulation of the parameters of the mirror system and of the electromagnetic resonator at a frequency near the frequency of the relaxation oscillations, there can be resonances, at which a slight perturbation can lead to pronounced variations in the electromagnetic radiation and the fluxes of escaping particles. A theory is worked out which explains the origin of the ''quasiperiodic emission'' in the earth's magnetosphere. (AIP)
Structure and thermodynamics of a UGG motif interacting with Ba2+ and other metal ions: accommodating changes in the RNA structure and the presence of a G(syn)–G(syn) pair

The self-complementary triplet 5’UGG3’/5’UGG3’ is a particular structural motif containing noncanonical G-G pair and two U·G wobble pairs. It constitutes a specific structural and electrostatic environment attracting metal ions, particularly Ba2+ ions. Crystallographic research has shown that two Ba2+ cations are located in the major groove of the helix and interact directly with the UGG triplet. A comparison with the unliganded structure has revealed global changes in the RNA structure in the presence of metal ions, whereas thermodynamic measurements have shown increased stability. Moreover, in the structure with Ba2+, an unusual noncanonical G(syn)-G(syn) pair is observed instead of the common G(anti)-G(syn). We further elucidate the metal binding properties of the UGG/UGG triplet by performing crystallographic and thermodynamic studies using DSC and UV melting with other metal ions. The results explain the preferences of the UGG sequence for Ba2+ cations and point to possible applications of this metal-binding propensity.
Revisiting a pre-inflationary radiation era and its effect on the CMB power spectrum

We revisit the scenario where inflation is preceded by a radiation era by considering that the inflaton too could have been in thermal equilibrium early in the radiation era. Hence we take into account not only the effect of a pre-inflationary era on the inflaton mode functions but also that of a frozen thermal distribution of inflaton quanta. We initially discuss in detail the issues relevant to our scenario of a pre-inflationary radiation dominated era and then obtain the scalar power spectrum for this scenario. We find that the power spectrum is free from infrared divergences. We then use the WMAP and Planck data to determine the constraints on the inflaton comoving 'temperature' and on the duration of inflation. We find that the best fit value of the duration of inflation is less than 1 e-folding more than what is required to solve cosmological problems, while only an upper bound on the inflaton temperature can be obtained.
Omni-Dimensional Dynamic Convolution

Learning a single static convolutional kernel in each convolutional layer is the common training paradigm of modern Convolutional Neural Networks (CNNs). Instead, recent research in dynamic convolution shows that learning a linear combination of $n$ convolutional kernels weighted with their input-dependent attentions can significantly improve the accuracy of light-weight CNNs, while maintaining efficient inference. However, we observe that existing works endow convolutional kernels with the dynamic property through one dimension (regarding the convolutional kernel number) of the kernel space, but the other three dimensions (regarding the spatial size, the input channel number and the output channel number for each convolutional kernel) are overlooked. Inspired by this, we present Omni-dimensional Dynamic Convolution (ODConv), a more generalized yet elegant dynamic convolution design, to advance this line of research. ODConv leverages a novel multi-dimensional attention mechanism with a parallel strategy to learn complementary attentions for convolutional kernels along all four dimensions of the kernel space at any convolutional layer. As a drop-in replacement of regular convolutions, ODConv can be plugged into many CNN architectures. Extensive experiments on the ImageNet and MS-COCO datasets show that ODConv brings solid accuracy boosts for various prevailing CNN backbones including both light-weight and large ones, e.g., 3.77%~5.71%|1.86%~3.72% absolute top-1 improvements to MobivleNetV2|ResNet family on the ImageNet dataset. Intriguingly, thanks to its improved feature learning ability, ODConv with even one single kernel can compete with or outperform existing dynamic convolution counterparts with multiple kernels, substantially reducing extra parameters. Furthermore, ODConv is also superior to other attention modules for modulating the output features or the convolutional weights.
Medico Multimedia Task at MediaEval 2021: Transparency in Medical Image Segmentation

The Medico Multimedia Task focuses on providing multimedia researchers with the opportunity to contribute to different areas of medicine using multimedia data to solve several subtasks. This year, the task focuses on transparency within machine learning-based medical segmentation systems, where the use case is gastrointestinal endoscopy. In this paper, we motivate the organization of this task, describe the development and test dataset, and present the evaluation process used to assess the participants’ submissions
A Review: Antimicrobial Activity and Photochemical Analysis of Marigold Flower (Tagetes erecta)

Synthetic Antimicrobial drugs are ineffectual against multidrug-resistant bacteria, resulting in a loss of funding for the treatment of infectious diseases around the world. As a result, alternative antimicrobial medicines for the treatment of infectious disorders are urgently needed. One method is to screen medicinal plants for antibacterial characteristics, which is innovative, low-cost, and effective against pathogenic microorganisms. Antimicrobial defenses have evolved in bacteria, and disease drug resistance is on the rise. This is due to the rapid evolution of multidrug resistance, the limited antibacterial spectrum, and the side effects of currently available antimicrobial medications. This entails the creation of novel antimicrobials with a wide range of structures and modes of action. Marigold (Tagetes erecta) is a common ornamental plant. Eastern countries use the flowers of this plant in garlands or loose for social and religious purposes. After their religious uses, the flowers are normally discarded. The Asteraceae family includes this plant (Composited). Marigold is its English name. Folk medicine makes use of several components of this plant, especially the flower. Skin complaints, wounds and burns, conjunctivitis and poor vision, monthly irregularities, varicose veins, hemorrhoids, duodenal ulcers, and other conditions have all been treated with it in traditional and homeopathic medicine. Dental caries is not an unusual place for continual oral contamination that affects children in particular. The most regular causal agent of dental caries is Streptococcus mutants. The antibacterial hobby of methanol and chloroform extracts of Tagetes erecta flowers have become tested using the agar diffusion method in competition to Streptococcus mutants in this study. Preliminary phytochemical screening of T. erecta L. flowers extracts in methanol and chloroform located the presence of alkaloids, carbohydrates, proteins, Anthraquinons, tannin, cardiac glycosides, fatty acids, and oils with inside the methanol and chloroform extracts. Saponin and amino acids, on the opportunity hand, had been now not observed in each extract. Flavonoids had been now not located in chloroform extract. The GCMS assessment of methanol extract identified seven chemicals.
Zero-CL: Instance and Feature decorrelation for negative-free symmetric contrastive learning

For self-supervised contrastive learning, models can easily collapse and generate trivial constant solutions. The issue has been mitigated by recent improvement on objective design, which however often requires square complexity either for the size of instances (O(N)) or feature dimensions (O(d)). To prevent such collapse, we develop two novel methods by decorrelating on different dimensions on the instance embedding stacking matrix, i.e., Instance-wise (ICL) and Featurewise (FCL) Contrastive Learning. The proposed two methods (FCL, ICL) can be combined synthetically, called Zero-CL, where “Zero” means negative samples are zero relevant, which allows Zero-CL to completely discard negative pairs i.e., with zero negative samples. Compared with previous methods, Zero-CL mainly enjoys three advantages: 1) Negative free in symmetric architecture. 2) By whitening transformation, the correlation of the different features is equal to zero, alleviating information redundancy. 3) Zero-CL remains original information to a great extent after transformation, which improves the accuracy against other whitening transformation techniques. Extensive experimental results on CIFAR-10/100 and ImageNet show that Zero-CL outperforms or is on par with state-of-the-art symmetric contrastive learning methods.
"Time to Take a Break"

Although often ignored, adult gamers, as with children and adolescents, can suffer from problematic gaming. This study explores the use of a built-in gaming gradual intervention system (G-GIS) designed to help adult gamers achieve their desired gaming habits in an autonomous and acceptable manner. In this study, we interviewed 26 heavy adult gamers (i.e., adult gamers who played frequently and for relatively long periods of time) of an online poker game using an in-built G-GIS. Then, we triangulated the interviewees' results with their real-world behavioral data collected over eight months to explore how demographics, attitudes, and contextual factors influenced their use of the G-GIS. The results indicate that family and occupation demographics play key roles in determining adult gamers' gaming habits and their self-control under G-GIS intervention. We also revealed that adult gamers' attitudes and contextual factors can facilitate or hinder the effectiveness of the G-GIS. The findings of this study extend our understanding of heavy adult gamers and reveal how a G-GIS influences adult gamers at the individual level, which can be applied in the design of future game intervention systems, particularly for adult gamers.
On the approximation properties of recurrent encoder-decoder architectures

Encoder-decoder architectures have recently gained popularity in sequence to sequence modelling, featuring in state-of-the-art models such as trans-formers. However, a mathematical understanding of their working principles still remains limited. In this paper, we study the approximation properties of recurrent encoder-decoder architectures. Prior work established theoretical results for RNNs in the linear setting, where approximation capabilities can be related to smoothness and memory of target temporal relationships. Here, we uncover that the encoder and decoder together form a particular “temporal product structure” which determines the approximation eﬃciency. Moreover, the encoder-decoder architecture generalises RNNs with the capability to learn time-inhomogeneous relationships. Our results provide the theoretical understanding of approximation properties of the recurrent encoder-decoder architecture, which precisely characterises, in the considered setting, the types of temporal relationships that can be eﬃciently learned.
Distributional Reinforcement Learning with Monotonic Splines

Distributional Reinforcement Learning (RL) differs from traditional RL by estimating the distribution over returns to capture the intrinsic uncertainty of MDPs. One key challenge in distributional RL lies in how to parameterize the quantile function when minimizing the Wasserstein metric of temporal differences. Existing algorithms use step functions or piecewise linear functions. In this paper, we propose to learn smooth continuous quantile functions represented by monotonic rational-quadratic splines, which also naturally solve the quantile crossing problem. Experiments in stochastic environments show that a dense estimation for quantile functions enhances distributional RL in terms of faster empirical convergence and higher rewards in most cases.
Self-Joint Supervised Learning

Supervised learning is a fundamental framework used to train machine learning systems. A supervised learning problem is often formulated using an i.i. d. assumption that restricts model attention to a single relevant signal at a time when predicting. This contrasts with the human ability to actively use related samples as reference when making decisions. We hypothesize that the restriction to a single signal for each prediction in the standard i.i. d. framework contributes to well-known drawbacks of supervised learning: making overconfident predictions and vulnerability to overfitting, adversarial attacks, and out-of-distribution data. To address these limitations, we propose a new supervised learning paradigm called self-joint learning that generalizes the standard approach by modeling the joint conditional distribution of two observed samples, where each sample is an image and its label. Rather than assuming samples are independent, our models explicitly learn the sample-to-sample relation of conditional independence. Our framework can naturally incorporate auxiliary unlabeled data to further improve the performance. Experiments on benchmark image datasets show our method offers significant improvement over standard supervised learning in terms of accuracy, robustness against adversarial attacks, out-of-distribution detection, and overconfidence mitigation. Code: github.com/ndkn/Self-joint-Learning
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks

Spiking Neural Networks (SNNs) have gained great attraction due to their distinctive properties of low power consumption and fast inference on neuromorphic hardware. As the most effective method to get deep SNNs, ANN-SNN conversion has achieved comparable performance as ANNs on large-scale datasets. Despite this, it requires long time-steps to match the firing rates of SNNs to the activation of ANNs. As a result, the converted SNN suffers severe performance degradation problems with short time-steps, which hamper the practical application of SNNs. In this paper, we theoretically analyze ANN-SNN conversion error and derive the estimated activation function of SNNs. Then we propose the quantization clip-floor-shift activation function to replace the ReLU activation function in source ANNs, which can better approximate the activation function of SNNs. We prove that the expected conversion error between SNNs and ANNs is zero, enabling us to achieve high-accuracy and ultra-low-latency SNNs. We evaluate our method on CIFAR-10/100 and ImageNet datasets, and show that it outperforms the state-of-the-art ANN-SNN and directly trained SNNs in both accuracy and time-steps. To the best of our knowledge, this is the first time to explore high-performance ANN-SNN conversion with ultra-low latency (4 time-steps). Code is available at https://github.com/putshua/SNN\_conversion\_QCFS
Image-Text Rematching for News Items using Optimized Embeddings and CNNs in MediaEval NewsImages 2021

Finding a matching image for a news article is a core problem in the creation of traditional and online newspapers. The task of image-text matching has thus become a vibrant research area in computer science. The performance of state-of-the-art image retrieval systems on various benchmarks is excellent. However, they all rely on datasets with a detailed textual description of the images or on very large training collections. In this work, we optimize image-text matching algorithms for a small dataset based on the data of a single newspaper. Our optimized processing pipeline and the computed configurations reach precise results. The evaluation results obtained in the MediaEval NewsImages benchmark significantly outperforming the algorithms from previous years.