Miracle-dz/UniFAR-FTUs huggingface dataset:
Methylation at CpG sites in genomes of aphid Acyrtosiphon pisum and its endosymbiont Buchnera

Pea aphid Acyrtosiphon pisum, a sap-feeding insect, has established a mutualistic relationship with an endosymbiotic bacteria (Buchnera aphidicola) that constitutes an evolutionary successful symbiosis to synthetize complex chemical compounds from a nutrient deprived diet. In this study, led by the presence of DNMT1 and a putative DNMT3 methylase in the aphid genome, we investigated the distribution of the methyl groups on 5’cytosine in CpG motifs on the whole genomes of host and endosymbiont, and looked into their correlation with gene expression. The DNA methylation turned to be present at low level in aphid (around 3% of total genomic cytosine) compared to mammals and plants, but increased to ∼9% in genes. Interestingly, the reduced genome of the endosymbiont Buchnera also shows global low level of methyl cytosine despite the fact that its genome does not shelter any DNA methylase. This finding argues for the import of DNA methylase from the host to the endosymbiont. The observed differences in methylation patterns between two clonal variants (host plus endosymbiont) are reported along with the differences in their transcriptome profiles. Our data allowed to decipher a dynamic combinatorial DNA methylation and epigenetic cross talk between host and symbiont in a clonality context that might count for the aphid adaptation to environment.
What do we gain when tolerating loss? The information bottleneck wrings out recombination

Most microbes have the capacity to acquire genetic material from their environment. Recombination of foreign DNA yields genomes that are, at least in part, incongruent with the vertical history of their species. Dominant approaches for detecting these transfers are phylogenetic, requiring a painstaking series of analyses including alignment and phylogenetic tree reconstruction. These traditional pan-genomic methods do not scale. Here we propose an unsupervised, alignment-free and tree-free technique based on the sequential information bottleneck (SIB), an optimization procedure designed to extract some portion of relevant information from one random variable conditioned on another. In our case, this joint probability distribution tabulates occurrence counts of k-mers against their genomes of origin with the expectation that recombination will create a strong signal that unifies certain sets of co-occuring k-mers. We conceptualize the technique as a rate-distortion problem, measuring distortion in the relevance information as k-mers are compressed into clusters based on their co-occurrence in the source genomes. The result is fast, model-free, lossy compression of k-mers into groups that learns tracts of shared genome sequence differentiating recombined elements from the vertically inherited core. We show that the technique yields a new recombination measure based purely on information, divorced from any biases and limitations inherent to alignment and phylogeny. Significance The information bottleneck, a lossy compression technique borrowed from the information theoretic and Natural Langauge Processing literature, is well suited to detecting evolutionary patterns in sets of co-occuring k-mers. Here we show that we can detect simulated and real recombination events while highlighting a core set of k-mers that comprise the vertically inherited portion of any set of genomes. Moreover, the compressibility of any given set of genomes offers a new way to compare the pangenomes of clades across the microbial tree of life. In our application, the bottleneck is informed by genome origin, our relevance variable, but the technique is general. The information bottleneck can be used for any biological contingency matrix where the goal is to learn groups from unstructured data.
Friend or Foe: Hybrid proline-rich proteins determine how plants interact with and respond to beneficial and pathogenic microbes

Plant plastids generate signals, including some derived from lipids, that need to be mobilized to effect signaling. We used informatics to discover potential plastid membrane proteins involved in microbial responses. Among these are proteins co-regulated with the systemic immunity component AZI1, a hybrid proline-rich protein (HyPRP) and HyPRP superfamily members. HyPRPs have a transmembrane domain, a proline-rich region (PRR) and a lipid transfer protein domain. The precise subcellular location(s) and function(s) is unknown for most HyPRP family members. As predicted by informatics, a subset of HyPRPs have a pool of protein that targets plastid outer envelope membranes (OEMs) via a mechanism that requires the PRR. Additionally, two HyPRPs may be associated with thylakoid membranes. Most of the plastid and non-plastid localized family members also have pools that localize to endoplasmic reticulum, plasma membrane or plasmodesmata. HyPRPs with plastid pools regulate, positively or negatively, systemic immunity against the pathogen Pseudomonas syringae. HyPRPs also regulate the interaction with the plant growth promoting rhizobacteria Pseudomonas simiae WCS417 in the roots to influence colonization, root system architecture and/or biomass. Thus, HyPRPs have broad and distinct roles in immune, development and growth responses to microbes and reside at sites that may facilitate signal molecule transport.
Predicting and measuring decision rules for social recognition in a Neotropical frog

Many animals use signals, such as vocalizations, to recognize familiar individuals. However, animals risk making recognition mistakes because the signal properties of different individuals often overlap due to within-individual variation in signal production. To understand the relationship between signal variation and decision rules for social recognition, we studied male golden rocket frogs, which recognize the calls of territory neighbors and respond less aggressively to a neighbor’s calls than to the calls of strangers. We quantified patterns of individual variation in acoustic properties of calls and predicted optimal discrimination thresholds using a signal detection theory model of receiver utility that incorporated signal variation, the payoffs of correct and incorrect decisions, and the rates of encounters with neighbors and strangers. We then experimentally determined thresholds for discriminating between neighbors and strangers using a habituation-discrimination experiment with territorial males in the field. Males required a threshold difference between 9% and 12% to discriminate between calls differing in temporal properties; this threshold matched those predicted by a signal detection theory model under ecologically realistic assumptions of infrequent encounters with strangers and relatively costly missed detections of strangers. We demonstrate empirically that receivers group continuous variation in vocalizations into discrete social categories and show that signal detection theory can be applied to investigate evolved decision rules.
Effect of mycorrhization and variety on the chemical composition and antioxidant activity of sea buckthorn berries

As well as being a rich source of nutrients, sea buckthorn berries contain large amounts of bio- logically active substances. Numerous studies have confirmed that the plants colonized by arbuscular mycorrhizal fungi have greater resistance to a shortage of nutrients, soil salinity, high temperature and prolonged drought. Currently, there is little information on the influence of mycorrhization on the chemical composition and antioxidant properties of these plants. This study was conducted in 2014 and 2015 at the Experimental Station in Lipnik, Poland. The levels of essential nutrients, dietary fiber fractions, minerals, antioxidants, and the ability to neutra - lize ABTS radical cations in sea buckthorn berries were investigated. The berries of mycorrhized sea buckthorns had more crude fiber (15%) and total carbohydrates (2%), K (19%), Ca (28%), Na (16%), Zn (2%), and Cu (31%), they contained lower levels of antioxidants (polyphenols 6%, carotenoids 25%, flavonoids 14%), and had a lower ability to neutralize the ABTS radical cation (12%) compared to the untreated control. The variety Hergo had more favourable chemical com -position, containing more protein (16%), crude fiber (18%), ash (1%) and total carbohydrates (14%) as well as a better mineral composition than the variety Habego. This study did not con firm unambiguously the beneficial effect of mycorrhization on the quality of sea buckthorn ber - ries. Mycorrhization increased the levels of various nutrients in the berries, yet at the same time it decreased the antioxidant potential.
Multi-Platform Intelligent System for Multimodal Human-Computer Interaction

We present a flexible human–robot interaction architecture that incorporates emotions and moods to provide a natural experience for humans. To determine the emotional state of the user, information representing eye gaze and facial expression is combined with other contextual information such as whether the user is asking questions or has been quiet for some time. Subsequently, an appropriate robot behaviour is selected from a multi-path scenario. This architecture can be easily adapted to interactions with non-embodied robots such as avatars on a mobile device or a PC. We present the outcome of evaluating an implementation of our proposed architecture as a whole, and also of its modules for detecting emotions and questions. Results are promising and provide a basis for further development
Myocyte Androgen Receptor Modulates Body Composition and Metabolic Parameters

Androgens (such as testosterone) have been shown to increase lean body mass and reduce fat body mass in men through activation of androgen receptors (AR). While this suggests a potential clinical use for androgens, attempts at utilization of this class of hormones as a therapeutic are limited by side effects due to indiscriminate AR activation in various tissues. Thus, a greater understanding of the tissues and cells involved in promoting these changes would be beneficial. Here we show that selective overexpression of AR in muscle cells of transgenic (HSA-AR) rodents both increases lean muscle mass and significantly reduces fat mass in males. Similar effects can be induced in HSA-AR females treated with testosterone. Metabolic analyses of HSA-AR males show that these animals demonstrate increased O2 consumption and hypermetabolism. Thus, targeted activation of AR in muscle regulates body composition and metabolism, suggesting a novel target for drug development.
Multiscale and Multitemporal Road Detection from High Resolution SAR Images Using Attention Mechanism

Road detection from images has emerged as an important way to obtain road information, thereby gaining much attention in recent years. However, most existing methods only focus on extracting road information from single temporal intensity images, which may cause a decrease in image resolution due to the use of spatial filter methods to avoid coherent speckle noises. Some newly developed methods take into account the multi-temporal information in the preprocessing stage to filter the coherent speckle noise in the SAR imagery. They ignore the temporal characteristic of road objects such as the temporal consistency for the road objects in the multitemporal SAR images that cover the same area and are taken at adjacent times, causing the limitation in detection performance. In this paper, we propose a multiscale and multitemporal network (MSMTHRNet) for road detection from SAR imagery, which contains the temporal consistency enhancement module (TCEM) and multiscale fusion module (MSFM) that are based on attention mechanism. In particular, we propose the TCEM to make full use of multitemporal information, which contains temporal attention submodule that applies attention mechanism to capture temporal contextual information. We enforce temporal consistency constraint by the TCEM to obtain the enhanced feature representations of SAR imagery that help to distinguish the real roads. Since the width of roads are various, incorporating multiscale features is a promising way to improve the results of road detection. We propose the MSFM that applies learned weights to combine predictions of different scale features. Since there is no public dataset, we build a multitemporal road detection dataset to evaluate our methods. State-of-the-art semantic segmentation network HRNetV2 is used as a baseline method to compare with MSHRNet that only has MSFM and the MSMTHRNet. The MSHRNet(TAF) whose input is the SAR image after the temporal filter is adopted to compare with our proposed MSMTHRNet. On our test dataset, MSHRNet and MSMTHRNet improve over the HRNetV2 by 2.1% and 14.19%, respectively, in the IoU metric and by 3.25% and 17.08%, respectively, in the APLS metric. MSMTHRNet improves over the MSMTHRNet(TAF) by 8.23% and 8.81% in the IoU metric and APLS metric, respectively.
Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning

Hyperspectral imagery has been widely used in precision agriculture due to its rich spectral characteristics. With the rapid development of remote sensing technology, the airborne hyperspectral imagery shows detailed spatial information and temporal flexibility, which open a new way to accurate agricultural monitoring. To extract crop types from the airborne hyperspectral images, we propose a fine classification method based on multi-feature fusion and deep learning. In this research, the morphological profiles, GLCM texture and endmember abundance features are leveraged to exploit the spatial information of the hyperspectral imagery. Then, the multiple spatial information is fused with the original spectral information to generate classification result by using the deep neural network with conditional random field (DNN+CRF) model. Specifically, the deep neural network (DNN) is a deep recognition model which can extract depth features and mine the potential information of data. As a discriminant model, conditional random field (CRF) considers both spatial and contextual information to reduce the misclassification noises while keeping the object boundaries. Moreover, three multiple feature fusion approaches, namely feature stacking, decision fusion and probability fusion, are taken into account. In the experiments, two airborne hyperspectral remote sensing datasets (Honghu dataset and Xiong’an dataset) are used. The experimental results show that the classification performance of the proposed method is satisfactory, where the salt and pepper noise is decreased, and the boundary of the ground object is preserved.
This Time Really Is Different: The Effect of COVID-19 on Independent K-12 School Enrollments

ABSTRACT The COVID-19 pandemic caused a severe economic recession in 2020. Historically, during economic recessions, there have been large declines in independent (private) school enrollments in the United States. Thus, it would be expected that the American independent school sector would have experienced significant enrollment declines between fall 2019 and fall 2020. But this time was different. In our sample, 70% of independent schools experienced increases in enrollment or level enrollments during the Pandemic Recession. In our multiple regression analyses, the main driver of this beneficial change in enrollments for independent schools was whether the public school districts that served their home county were open for only virtual instruction to start the 2020–21 academic year. We find suggestive evidence that a higher prevalence of county-level COVID-19, measured as deaths per capita, was associated with higher independent school enrollments as well. Economic conditions do not appear to have played a major role in changes in independent school enrollments.
Value Creation in Business Ecosystems - A Design Theory for a Reference Model

Business ecosystems are dynamic network structures of autonomous actors who contribute individual resources and capabilities towards a shared purpose. This study proposes a design theory to develop a model that describes the fundamental value creation activities in business ecosystems. This research endeavor is embedded in a consortium research project following design science and builds on both the current body of academic knowledge and the experience of. The paper makes three main contributions. Firstly, the study defines a specific problem space, identifies justificatory knowledge, and develops design requirements and principles as the theory’s core. Secondly, a reference model is developed that describes value creation, providing a conceptual grid for further theoretical analysis. Thirdly, the model provides an analytical framework for practitioners, who need to understand which activities should be performed and how these activates should be configured to generate advantages relative to others.
'The Best of Two Worlds' - Combining Classifier Fusion and Ecological Models to Map and Explain Landscape Invasion by an Alien Shrub

The spread of invasive alien species promotes ecosystem structure and functioning changes, with detrimental effects on native biodiversity and ecosystem services, raising challenges for local management authorities. Predictions of invasion dynamics derived from modeling tools are often spatially coarse and therefore unsuitable for guiding local management. Accurate information on the occurrence of invasive plants and on the main factors that promote their spread is critical to define successful control strategies. For addressing this challenge, we developed a dual framework combining satellite image classification with predictive ecological modeling. By combining data from georeferenced invaded areas with multispectral imagery with 10-meter resolution from Sentinel-2 satellites, a map of areas invaded by the woody invasive Acacia longifolia in a municipality of northern Portugal was devised. Classifier fusion techniques were implemented through which eight statistical and machine-learning algorithms were ensembled to produce accurate maps of invaded areas. Through a Random Forest (RF) model, these maps were then used to explore the factors driving the landscape-level abundance of A. longifolia. RF models were based on explanatory variables describing hypothesized environmental drivers, including climate, topography/geomorphology, soil properties, fire disturbance, landscape composition, linear structures, and landscape spatial configuration. Satellite-based maps synoptically described the spatial patterns of invaded areas, with classifications attaining high accuracy values (True Skill Statistic, TSS: 0.895, Area Under the Receiver Operating Curve, ROC: 0.988, Kappa: 0.857). The predictive RF models highlighted the primary role of climate, followed by landscape composition and configuration, as the most important drivers explaining the species abundance at the landscape level. Our innovative dual framework—combining image classification and predictive ecological modeling—can guide decision-making processes regarding effective management of invasions by prioritizing the invaded areas and tackling the primary environmental and anthropogenic drivers of the species’ abundance and spread.
Improving Intent Detection Accuracy Through Token Level Labeling

Intent detection is traditionally modeled as a sequence classification task where the role of the models is to map the users’ utterances to their class. In this paper, however, we show that the classification accuracy can be improved with the use of token level intent annotations and introducing new annotation guidelines for labeling sentences in the intent detection task. What is more, we introduce a method for training the network to predict joint sentence level and token level annotations. We also test the effects of different annotation schemes (BIO, binary, sentence intent) on the model’s accuracy.
Global Land Cover Assessment Using Spatial Uniformity Validation Dataset

The Degree Confluence Project (DCP) is a volunteer-based validation dataset that comprises useful information for global land cover map validation. However, there is a problem with using DCP points as validation data for the accuracy assessment of land cover maps. While resolutions of typical global land cover maps are several hundred meters to several kilometers, DCP points can only guarantee an area of several tens of meters that can be confirmed by ground photographs. So, the objective of this study is to create a land cover map validation dataset with added spatial uniformity information using satellite images and DCP points. For this, we devised a new method to semiautomatically guarantee the spatial uniformity of DCP validation data points at any resolution. This method can judge the validation data with guaranteed uniformity with a user’s accuracy of 0.954. Furthermore, we conducted the accuracy assessment for the existing global land cover maps by the DCP validation data with guaranteed spatial uniformity and found that the trends differed by class and region.
A Building Roof Identification CNN Based on Interior-Edge-Adjacency Features Using Hyperspectral Imagery

Hyperspectral remote sensing can obtain both spatial and spectral information of ground objects. It is an important prerequisite for a hyperspectral remote sensing application to make good use of spectral and image features. Therefore, we improved the convolutional Neural Network (CNN) model by extracting interior-edge-adjacency features of building roof and proposed a new CNN model with a flexible structure: Building Roof Identification CNN (BRI-CNN). Our experimental results demonstrated that the BRI-CNN can not only extract interior-edge-adjacency features of building roof, but also change the weight of these different features during the training process, according to selected samples. Our approach was tested using the Indian Pines (IP) data set and our comparative study indicates that the BRI-CNN model achieves at least 0.2% higher overall accuracy than that of the capsule network model, and more than 2% than that of CNN models.
In-Reservoir Waveform Retrieval for Monitoring at Groningen - Seismic Interferometry with Active and Passive Deep Borehole Data

The Groningen gas field in the Netherlands is an ideal test bed for in-situ reservoir monitoring techniques because of the availability of both active and passive in-reservoir seismic data. In this study, we use deconvolution interferometry to estimate the reflection and transmission response using active and passive borehole data within the reservoir at ∼3-km depth and separate up- and downgoing P- and S-wave fields by f-k filtering. We validate the results using synthetic data of a 1D elastic model built from sonic logs recorded in the well. The estimated full-waveform reflection response for a virtual source at the top geophone is consistent with the synthetic response. For the virtual source at the bottom geophone, the reflection response appears to be phase delayed, though its arrivals are consistent with the local subsurface geology. Similarly, the first-order estimated local transmission response successfully approximates that of the P-wave velocity in the reservoir. The study shows that reliable subsurface information can be obtained from borehole interferometry without detailed knowledge of the medium parameters. In addition, the method could be used for passive reservoir monitoring to detect velocity, attenuation, and/or interface time-lapse variations.