Miracle-dz/UniFAR-FTUs huggingface dataset:
Radioresistance, DNA Damage and DNA Repair in Cells With Moderate Overexpression of RPA1

Molecular responses to genotoxic stress, such as ionizing radiation, are intricately complex and involve hundreds of genes. Whether targeted overexpression of an endogenous gene can enhance resistance to ionizing radiation remains to be explored. In the present study we take an advantage of the CRISPR/dCas9 technology to moderately overexpress the RPA1 gene that encodes a key functional subunit of the replication protein A (RPA). RPA is a highly conserved heterotrimeric single-stranded DNA-binding protein complex involved in DNA replication, recombination, and repair. Dysfunction of RPA1 is detrimental for cells and organisms and can lead to diminished resistance to many stress factors. We demonstrate that HEK293T cells overexpressing RPA1 exhibit enhanced resistance to cell killing by gamma-radiation. Using the alkali comet assay, we show a remarkable acceleration of DNA breaks rejoining after gamma-irradiation in RPA1 overexpressing cells. However, the spontaneous rate of DNA damage was also higher in the presence of RPA1 overexpression, suggesting alterations in the processing of replication errors due to elevated activity of the RPA protein. Additionally, the analysis of the distributions of cells with different levels of DNA damage showed a link between the RPA1 overexpression and the kinetics of DNA repair within differentially damaged cell subpopulations. Our results provide knew knowledge on DNA damage stress responses and indicate that the concept of enhancing radioresistance by targeted alteration of the expression of a single gene is feasible, however undesired consequences should be considered and evaluated.
Deciphering the Biotic and Climatic Factors That Influence Floral Scents: A Systematic Review of Floral Volatile Emissions

Currently, a global analysis of the information available on the relative composition of the floral scents of a very diverse variety of plant species is missing. Such analysis may reveal general patterns on the distribution and dominance of the volatile compounds that form these mixtures, and may also allow measuring the effects of factors such as the phylogeny, pollination vectors, and climatic conditions on the floral scents of the species. To fill this gap, we compiled published data on the relative compositions and emission rates of volatile organic compounds (VOCs) in the floral scents of 305 plant species from 66 families. We also gathered information on the groups of pollinators that visited the flowers and the climatic conditions in the areas of distribution of these species. This information allowed us to characterize the occurrence and relative abundances of individual volatiles in floral scents and the effects of biotic and climatic factors on floral scent. The monoterpenes trans-β-ocimene and linalool and the benzenoid benzaldehyde were the most abundant floral VOCs, in both ubiquity and predominance in the floral blends. Floral VOC richness and relative composition were moderately preserved traits across the phylogeny. The reliance on different pollinator groups and the climate also had important effects on floral VOC richness, composition, and emission rates of the species. Our results support the hypothesis that key compounds or compounds originating from specific biosynthetic pathways mediate the attraction of the main pollinators. Our results also indicate a prevalence of monoterpenes in the floral blends of plants that grow in drier conditions, which could link with the fact that monoterpene emissions protect plants against oxidative stresses throughout drought periods and their emissions are enhanced under moderate drought stress. Sesquiterpenes, in turn, were positively correlated with mean annual temperature, supporting that sesquiterpene emissions are dominated mainly by ambient temperature. This study is the first to quantitatively summarise data on floral-scent emissions and provides new insights into the biotic and climatic factors that influence floral scents.
A Reliable Weighting Scheme for the Aggregation of Crowd Intelligence to Detect Fake News

Social networks play an important role in today’s society and in our relationships with others. They give the Internet user the opportunity to play an active role, e.g., one can relay certain information via a blog, a comment, or even a vote. The Internet user has the possibility to share any content at any time. However, some malicious Internet users take advantage of this freedom to share fake news to manipulate or mislead an audience, to invade the privacy of others, and also to harm certain institutions. Fake news seeks to resemble traditional media to establish its credibility with the public. Its seriousness pushes the public to share them. As a result, fake news can spread quickly. This fake news can cause enormous difficulties for users and institutions. Several authors have proposed systems to detect fake news in social networks using crowd signals through the process of crowdsourcing. Unfortunately, these authors do not use the expertise of the crowd and the expertise of a third party in an associative way to make decisions. Crowds are useful in indicating whether or not a story should be fact-checked. This work proposes a new method of binary aggregation of opinions of the crowd and the knowledge of a third-party expert. The aggregator is based on majority voting on the crowd side and weighted averaging on the third-party side. An experimentation has been conducted on 25 posts and 50 voters. A quantitative comparison with the majority vote model reveals that our aggregation model provides slightly better results due to weights assigned to accredited users. A qualitative investigation against existing aggregation models shows that the proposed approach meets the requirements or properties expected of a crowdsourcing system and a voting system.
Survey and Preliminary Results on the Design of a Visual Light Communication System for Radioactive and Underwater Scenarios

The use of radio-frequency communication systems is very well known and also it is broadly used in the design of mobile robotics. In fact, it can be very well applied in rescue robotic systems, such as the ones that present smoke and fire. In radioactivity scenarios the robot might get problems to communicate, in the presence for example of magnets. Also, in underwater fields radio-frequency solutions need to improve the communication distance, while sonar systems present variable delays and limited bandwidth, which are difficulties to provide remote visual feedback to the operator. This paper states that field robotic systems, such as the ones in radioactivity and underwater scenarios, need to complement the current communication systems with multi-modal solutions, in order to enhance operation safety and reliability, while better adapting to the mission unexpected situations. For this, Visual Light Communication solutions have been studied in detail and a preliminary prototype, which is presented in this paper, has been designed. This prototype would need further work to be applied successfully in real radioactive and underwater scenarios, as stated in the conclusions.
Mapping Coastal Dune Landscape through Spectral Rao's Q Temporal Diversity

Coastal dunes are found at the boundary between continents and seas representing unique transitional mosaics hosting highly dynamic habitats undergoing substantial seasonal changes. Here, we implemented a land cover classification approach specifically designed for coastal landscapes accounting for the within-year temporal variability of the main components of the coastal mosaic: vegetation, bare surfaces and water surfaces. Based on monthly Sentinel-2 satellite images of the year 2019, we used hierarchical clustering and a Random Forest model to produce an unsupervised land cover map of coastal dunes in a representative site of the Adriatic coast (central Italy). As classification variables, we used the within-year diversity computed through Rao’s Q index, along with three spectral indices describing the main components of the coastal mosaic (i.e., Modified Soil-adjusted Vegetation Index 2—MSAVI2, Normalized Difference Water Index 2—NDWI2 and Brightness Index 2—BI2). We identified seven land cover classes with high levels of accuracy, highlighting different covariates as the most important in differentiating them. The proposed framework proved effective in mapping a highly seasonal and heterogeneous landscape such as that of coastal dunes, highlighting Rao’s Q index as a sound base for natural cover monitoring and mapping. The applicability of the proposed framework on updated satellite images emphasizes the procedure as a reliable and replicable tool for coastal ecosystems monitoring.
Assessing the Temporal Response of Tropical Dry Forests to Meteorological Drought

Due to excessive human disturbances, as well as predicted changes in precipitation regimes, tropical dry forests (TDFs) are susceptible to meteorological droughts. Here, we explored the response of TDFs to meteorological drought by conducting temporal correlations between the MODIS-derived normalized difference vegetation index (NDVI) and land surface temperature (LST) to a standardized precipitation index (SPI) between March 2000 and March 2017 at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS), Guanacaste, Costa Rica. We conducted this study using monthly and seasonal scales. Our results indicate that the NDVI and LST are largely influenced by seasonality, as well as the magnitude, duration, and timing of precipitation. We find that greenness and evapotranspiration are highly sensitive to precipitation when TDFs suffer from long-term water deficiency, and they tend to be slightly resistant to meteorological drought in the wet season. Greenness is more resistant to short-term rainfall deficiency than evapotranspiration, but greenness is more sensitive to precipitation after a period of rainfall deficiency. Precipitation can still strongly influence evapotranspiration on the canopy surface, but greenness is not controlled by the rainfall, but rather phenological characteristics when leaves begin to senesce.
Attention-Based SeriesNet: An Attention-Based Hybrid Neural Network Model for Conditional Time Series Forecasting

Traditional time series forecasting techniques can not extract good enough sequence data features, and their accuracies are limited. The deep learning structure SeriesNet is an advanced method, which adopts hybrid neural networks, including dilated causal convolutional neural network (DC-CNN) and Long-short term memory recurrent neural network (LSTM-RNN), to learn multi-range and multi-level features from multi-conditional time series with higher accuracy. However, they didn’t consider the attention mechanisms to learn temporal features. Besides, the conditioning method for CNN and RNN is not specific, and the number of parameters in each layer is tremendous. This paper proposes the conditioning method for two types of neural networks, and respectively uses the gated recurrent unit network (GRU) and the dilated depthwise separable temporal convolutional networks (DDSTCNs) instead of LSTM and DC-CNN for reducing the parameters. Furthermore, this paper presents the lightweight RNN-based hidden state attention module (HSAM) combined with the proposed CNN-based convolutional block attention module (CBAM) for time series forecasting. Experimental results show our model is superior to other models from the viewpoint of forecasting accuracy and computation efficiency.
LOWER WAIST-TO-HIP, WAIST-TO-STATURE, AND WAIST-TO-BUST RATIOS PREDICT HIGHER RANKINGS OF PLUS-SIZE MODELS IN A NATURALISTIC CONDITION

Previous research suggests that waist-to-hip ratio (WHR), waist-to-stature ratio (WSR), and waist-to-bust ratio (WBR) serve as cues of health and fertility in women, influencing the viewers’ perception of attractiveness. However, it is unclear to what extent these findings can be applied to the perception of female attractiveness in a naturalistic condition or in women with a higher body mass index. In this study, we tested whether lower WHR, WSR, and WBR increased the perceived attractiveness of plus-size models in a naturalistic condition. The WHR, WSR, and WBR were computed via biometric data (height, bust, waist, and hip measurements) of 49 U.S. plus-size models who have been listed on ranker.com. The photographs of these models have been viewed 2.60 million times and voted 146,000 times. The perception of attractiveness was operationalized as rankings, generated from the relative number of upvotes and downvotes from site visitors. Spearman correlations showed that lower WHR, WSR, and WBR were all positively correlated with higher rankings. In a subsequent ordinal logistic regression, only WSR and WBR remained as significant predictors of rankings. The principal component regression also revealed that the latent body component of WHR, WSR, and WBR predicted rankings of the models.These findings cannot be accounted by the models’ general popularity or their anthropometric measures being similar to other types of models’ (e.g., fashion, glamor, playboy, and adult film models). Our findings suggest that smaller WHR, WSR, and WBR influence the perception of female attractiveness in a naturalistic condition, even among plus-size models.
Improving Urban Land Cover/Use Mapping by Integrating A Hybrid Convolutional Neural Network and An Automatic Training Sample Expanding Strategy

Moderate spatial resolution (MSR) satellite images, which hold a trade-off among radiometric, spectral, spatial and temporal characteristics, are extremely popular data for acquiring land cover information. However, the low accuracy of existing classification methods for MSR images is still a fundamental issue restricting their capability in urban land cover mapping. In this study, we proposed a hybrid convolutional neural network (H-ConvNet) for improving urban land cover mapping with MSR Sentinel-2 images. The H-ConvNet was structured with two streams: one lightweight 1D ConvNet for deep spectral feature extraction and one lightweight 2D ConvNet for deep context feature extraction. To obtain a well-trained 2D ConvNet, a training sample expansion strategy was introduced to assist context feature learning. The H-ConvNet was tested in six highly heterogeneous urban regions around the world, and it was compared with support vector machine (SVM), object-based image analysis (OBIA), Markov random field model (MRF) and a newly proposed patch-based ConvNet system. The results showed that the H-ConvNet performed best. We hope that the proposed H-ConvNet would benefit for the land cover mapping with MSR images in highly heterogeneous urban regions.
Phytoliths from loess in Southland, New Zealand

Abstract This pilot study establishes and quantifies phytoliths in five cores of loess from different sites in Southland, New Zealand. A total of 48 Samples were processed and phytoliths were present in the upper portions of all cores. Phytolith analysis provides information on vegetation changes in Southland in response to climate for about the 1st 24 000 years. High numbers of both grass and tree phytoliths at about the Oxygen Isotope Stage 213 boundary suggest an open tree‐land. This abruptly changes to a landscape dominated by cold‐climate tussock grasses. Problems associated with phytolith preservation in the bottom of all cores have been identified and possible solutions suggested.
The theory of limiting similarity

The theory of limiting similarity is an outgrowth of the competitive exclusion principle. Until recently [i.e. before the work of Armstrong & McGehee (13, 14)], the competitive exclusion principle, as usually formulated, stated that two species with identical niches could not coexist. The ease with which ecologists discovered differences between species when they looked for them must have suggested that a stronger principle might apply. At the risk of oversimplifica­ tion, one may say that limiting similarity means that there is some maximum level of similarity (i.e. similar use of a set of resources that are in short supply) between competing species short of complete identity that will allow these species to coexist. D. Lack apparently thought of the competitive exclu sion principle in these terms during the 1940s (41) and G. E. Hutchinson discussed the idea in 1959 (35). MacArthur & Levins were responsible for introducing the term limiting similarity in their 1967 paper (47). Since then, there have been dozens of papers on the topic, and the idea that there is such a thing as a limiting similarity between competitors has found its way into most of the major textbooks [e.g. Krebs (40), Ricklefs (57), McNaughton & Wolf (54)]. The logical point at which to begin a review article about a concept is with its definition. In the present case, however, the concept is particularly difficult to define. Similarity is limited in the sense that too great a degree of similarity can result in the exclusion of a species by one or more others. The term exclusion is vague, because extinction as a result of competition may take a very long time. Similarity is even more difficult to define. [Hurlbert (34) gives a partial catalogue of proposed definitions. ] Deciding between mathematical definitions of similarity is only part of the problem. One must decide whether the similarity in question is one of resources actually used in a given environment or
BIM-Based Registration and Localization of 3D Point Clouds of Indoor Scenes Using Geometric Features for Augmented Reality

Augmented reality can improve construction and facility management by visualizing an as-planned model on its corresponding surface for fast, easy, and correct information retrieval. This requires the localization registration of an as-built model in an as-planned model. However, the localization and registration of indoor environments fail, owing to self-similarity in an indoor environment, relatively large as-planned models, and the presence of additional unplanned objects. Therefore, this paper proposes a computer vision-based method to (1) homogenize indoor as-planned and as-built models, (2) reduce the search space of model matching, and (3) localize the structure (e.g., room) for registration of the scanned area in its as-planned model. This method extracts a representative horizontal cross section from the as-built and as-planned point clouds to make these models similar, restricts unnecessary transformation to reduce the search space, and corresponds the line features for the estimation of the registration transformation matrix. The performance of this method, in terms of registration accuracy, is evaluated on as-built point clouds of rooms and a hallway on a building floor. A rotational error of 0.005 rad and a translational error of 0.088 m are observed in the experiments. Hence, the geometric feature described on a representative cross section with transformation restrictions can be a computationally cost-effective solution for indoor localization and registration.
Zero-Shot Classification Based on Multitask Mixed Attribute Relations and Attribute-Specific Features

Zero-shot classification is a hot topic in computer vision and pattern recognition. Most zero-shot classification methods are based on the intermediate level representation of attributes to achieve knowledge transfer from the training classes to the unseen test classes. Recently, multitask learning (MTL) has been shown as one of state-of-the-art approaches for attribute learning and zero-shot classification. Aiming at the attribute relation learning, features shared by attributes learning and attribute heterogeneity, we propose a zero-shot classification based on multitask mixed attribute relations and attribute-specific features. First, considering the relationship between attribute–attribute and attribute–features, a second-order attribute relation and attribute-specific features learning model is constructed from training samples based on MTL. Second, second-order attribute relation is extended to high-order attribute relation and multiple attribute classifiers are learned. Finally, zero-shot classification is completed based on the maximum posterior probability. Experimental results on AWA and PubFig data sets show that the proposed method can yield more accurate attribute prediction and zero-shot classification compared with several multitask attribute learning methods.
Whitecap Observations by Microwave Radiometers: With Discussion on Surface Roughness and Foam Contributions

Ocean surface whitecaps manifest surface wave breaking. Most of the whitecap data reported in the literature are based on optical observations through photographic or video recording. The air in whitecaps modifies the dielectric properties of microwave emissions and scattering. Therefore, whitecap information is intrinsic to microwave signals. This paper discusses a method to retrieve the ocean surface whitecap coverage from microwave radiometer signals.
Covariance-adapting algorithm for semi-bandits with application to sparse outcomes

We investigate stochastic combinatorial semi-bandits, where the entire joint distribution of outcomes impacts the complexity of the problem instance (unlike in the standard bandits). Typical distributions considered depend on specific parameter values, whose prior knowledge is required in theory but quite difficult to estimate in practice; an example is the commonly assumed sub-Gaussian family. We alleviate this issue by instead considering a new general family of sub-exponential distributions, which contains bounded and Gaussian ones. We prove a new lower bound on the regret on this family, that is parameterized by the unknown covariance matrix, a tighter quantity than the sub-Gaussian matrix. We then construct an algorithm that uses covariance estimates, and provide a tight asymptotic analysis of the regret. Finally, we apply and extend our results to the family of sparse outcomes, which has applications in many recommender systems.
Tracking Rates of Forest Disturbance and Associated Carbon Loss in Areas of Illegal Amber Mining in Ukraine Using Landsat Time Series

Mapping forest disturbance is crucial for many applications related to decision-making for sustainable forest management. This study identified the effect of illegal amber mining on forest change and accumulated carbon stock across a study area of 8125.5 ha in northern Ukraine. Our method relies on the Google Earth Engine (GEE) implementation of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) temporal segmentation algorithm of Landsat time-series (LTS) to derive yearly maps of forest disturbance and recovery in areas affected by amber extraction operations. We used virtual reality (VR) 360 interactive panoramic images taken from the sites to attribute four levels of forest disturbance associated with the delta normalized burn ratio (dNBR) and then calculated the carbon loss. We revealed that illegal amber extraction in Ukraine has been occurring since the middle of the 1990s, yielding 3260 ha of total disturbed area up to 2019. This study indicated that the area of forest disturbance increased dramatically during 2013–2014, and illegal amber operations persist. As a result, regrowth processes were mapped on only 375 ha of total disturbed area. The results were integrated into the Forest Stewardship Council® (FSC®) quality management system in the region to categorize Forest Management Units (FMUs) conforming to different disturbance rates and taking actions related to their certification status. Moreover, carbon loss evaluation allows the responsible forest management systems to be streamlined and to endorse ecosystem service assessment.