Miracle-dz/UniFAR-FTUs huggingface dataset:
Mouse Models for Deciphering the Impact of Homologous Recombination on Tumorigenesis

Simple Summary Homologous recombination (HR) is a DNA repair pathway essential to genome stability and mutations in many HR genes are correlated with cancer predisposition. Transgenic mouse models are critical to establish HR factors as tumor suppressor genes. However, investigating the effects of suppressing HR genes in vivo is challenging because invalidation of most of them leads to embryonic lethality in mammals. To tackle this issue, elaborated alternative strategies have been developed. Here we review these alternative HR-defective mouse models and reveal the impact of HR defects on tumorigenesis. We highlight that the central HR factor, RAD51, has yet to be well characterized in vivo and, unlike most HR factors, its inactivation has not been associated with cancer predisposition, revealing what we call the “RAD51 paradox”. Finally, we discuss the use of mouse models to develop targeted cancer therapies as well as to understand the mechanisms of HR inactivation-driven tumorigenesis in vivo. Abstract Homologous recombination (HR) is a fundamental evolutionarily conserved process that plays prime role(s) in genome stability maintenance through DNA repair and through the protection and resumption of arrested replication forks. Many HR genes are deregulated in cancer cells. Notably, the breast cancer genes BRCA1 and BRCA2, two important HR players, are the most frequently mutated genes in familial breast and ovarian cancer. Transgenic mice constitute powerful tools to unravel the intricate mechanisms controlling tumorigenesis in vivo. However, the genes central to HR are essential in mammals, and their knockout leads to early embryonic lethality in mice. Elaborated strategies have been developed to overcome this difficulty, enabling one to analyze the consequences of HR disruption in vivo. In this review, we first briefly present the molecular mechanisms of HR in mammalian cells to introduce each factor in the HR process. Then, we present the different mouse models of HR invalidation and the consequences of HR inactivation on tumorigenesis. Finally, we discuss the use of mouse models for the development of targeted cancer therapies as well as perspectives on the future potential for understanding the mechanisms of HR inactivation-driven tumorigenesis in vivo.
Nitrogen Starvation-Responsive MicroRNAs Are Affected by Transgenerational Stress in Durum Wheat Seedlings

Stress events have transgenerational effects on plant growth and development. In Mediterranean regions, water-deficit and heat (WH) stress is a frequent issue that negatively affects crop yield and quality. Nitrogen (N) is an essential plant macronutrient and often a yield-limiting factor for crops. Here, the response of durum wheat seedlings to N starvation under the transgenerational effects of WH stress was investigated in two genotypes. Both genotypes showed a significant reduction in seedling height, leaf number, shoot and root weight (fresh and dry), primary root length, and chlorophyll content under N starvation stress. However, in the WH stress-tolerant genotype, the percentage reduction of most traits was lower in progeny from the stressed parents than progeny from the control parents. Small RNA sequencing identified 1534 microRNAs in different treatment groups. Differentially expressed microRNAs (DEMs) were characterized subject to N starvation, parental stress and genotype factors, with their target genes identified in silico. GO and KEGG enrichment analyses revealed the biological functions, associated with DEM-target modules in stress adaptation processes, that could contribute to the phenotypic differences observed between the two genotypes. The study provides the first evidence of the transgenerational effects of WH stress on the N starvation response in durum wheat.
Audiovisual looming signals are not always prioritised: evidence from exogenous, endogenous and sustained attention

ABSTRACT The majority of studies showing multisensory attention benefits have focused on brief audiovisual events. Here we examined whether attention benefits can occur for auditory and visual signals that change in synchrony over time. In the first two experiments, we found no evidence that attention was captured more by synchronous compared to asynchronous audiovisual and visual looming signals. The results of our third experiment suggested that attention was not preferentially oriented towards synchronous compared to asynchronous audiovisual and visual looming signals. In the fourth experiment, we found no evidence for better sustained attention for synchronous compared to asynchronous audiovisual looming and unisensory signals. Together, these findings indicate that synchronous multisensory looming signals are not always prioritised by our information processing system. Future multisensory research should focus on more conceptual clarity and deepen our understanding of the specific stimulus, task and contextual features affecting the strength and quality of multisensory integration.
Whether and How Knowledge Moderates Linkages between Parent–Child Conversations and Children’s Reflections about Tinkering in a Children’s Museum

ABSTRACT This study is a collaborative project between university researchers and museum practitioners investigating ways to advance STEM learning opportunities for young children and their families in museums. The focus of the work is on how the knowledge resources that are available to families (e.g., prior knowledge, information provided by the museum staff) can support talk about STEM during and after hands-on problem-solving activities in a museum exhibit. More specifically, we asked whether and how associations between parent–child talk about STEM during tinkering, and the STEM content of the children’s reflections on their learning afterward, might vary based on the knowledge that is available to families to make sense of their experiences. One hundred and eleven families with 5- to 10-year-old children (M = 7.23 years) were observed in a tinkering exhibit in a children’s museum. Results indicated that the more parents and children talked about STEM during tinkering, the more the children talked about STEM in their reflections. This association was moderated by children’s knowledge based on prior tinkering experiences, and knowledge that was acquired through a pre-tinkering, facilitated orientation by museum staff about engineering, but not by children’s age or parents’ educational attainment. Implications for museum research and practice are discussed.
Antarctic-wide annual ice flow maps from Landsat 8 imagery between 2013 and 2019

ABSTRACT Ice velocity constitutes a key parameter for quantifying ice-sheet discharge rates and is thus crucial for improving the coupled models of the Antarctic ice sheet towards accurately predict its contribution to future global sea-level rise. However, in Antarctica, high-resolution and continuous ice velocity estimates remain elusive, which is key to unravel Antarctica’s present-day ice mass balance processes. Here, we present a suite of newly estimated Antarctic-wide, annually-sampled ice velocity products at 105-m grid-spacing observed by Landsat 8 optical images data. We first describe a procedure that can automatically calibrate and integrate ice displacement maps to generate Antarctic-wide seamless ice velocity products. The annual ice velocity mosaics are assembled using a total of 250,000 displacement maps inferred from more than 80,000 Landsat 8 images acquired between December 2013 and April 2019. The new annual Antarctic ice velocity data product exhibits an improved quantification of near-decadal Antarctic-wide ice flow, and an opportunity to investigate ice dynamics at a higher spatial resolution and annual sampling, as compared to existing data products. Validation studies confirmed improved accuracy and consistency of this new data product, when compared with independently estimated optical and radar ice velocity data products, as well as in situ data.
Formation and anion exchange reactions of layered transition metal hydroxides [Ni1―xMx] (OH)2(CO3)x/2(H2O)z(M=Fe,Co)

Les composes du type hydrotalcite (Ni 1―x Fe x ) (OH) 2 (CO 3 ) x/2 (H 2 O) z et (Ni 1―x Co x ) (OH) 2 (CO 3 ) x/2 (H 2 O) z , sont obtenus par coprecipitation des hydroxydes correspondants en milieu fortement basique. Le vieillissement des precipites a 80 o C, pendant 15 h donne un materiau cristallise. Les proprietes d'echange de ces materiaux sont etudiees avec les anions: Cl ― , NO 3 ― , SO 4 2― , ClO 4 ― et CH 3 COO ―
Emerging Selectivity: Group Membership and Early Prosociality

ABSTRACT Engaging in prosocial behavior is costly. By selectively directing prosocial behavior toward individuals with a high probability of reciprocating, we are able to offset these potential costs and maintain a sustainable prosocial system. Often, we determine whether an individual will make a good prosocial partner through the observation of their social behaviors. In the absence of such behavioral information, however, group membership can be an effective heuristic for guiding selectivity. Older children and adults show ingroup bias when engaging in selective social behavior, but studies with samples of children under the age of 4 years have been inconclusive. In part, this may be because studies have often relied on measures of prosocial behavior such as sharing or subjective liking, which are reliant on additional cognitive processes apart from selectivity considerations and which may still be undergoing development early in childhood. These tasks may mask younger children’s capacity for selectivity in the ingroup context. The present study recruited 65 2-year-old children with the aim to test the capacity for toddlers to demonstrate group-based selective behavior in a minimal group task. Results suggest that children of this age preference both minimal group members and unlabeled similar others over dissimilar individuals when directing their selective helping, but not sharing, behavior. In sum, the findings suggest that even toddlers have a developing sense of ingroup bias in selective prosocial behavior, and that helping, as opposed to sharing, may be a more sensitive measure of children’s group-oriented selective behavior at this age.
Determination of Strategic Spreads in East Asia

ABSTRACT This study develops a simple spread model to explain whether market dealers behave strategically when using electronic broking services. Our spread model stresses the role of an unexpected liquidity imbalance and its volatility for inventory risk, which contrasts sharply with previous studies that emphasised price volatility as the inventory risk. To capture a dealer’s strategic behaviour, we introduce a new concept of a strategic weighted spread and test this new spread using the full information maximum likelihood method with the GARCH (1,1) process. Daily spread data from 1 January 2006 to 20 December 2016 is used to explore strategic spreading at the end of a trading day in East Asia. Different effects on strategic spreads of liquidity depth in Asian financial markets are also investigated by comparing strategic spreads between the thin and deep markets. The evidence provides support for our hypothesis that a dealer behaves strategically to avoid the unexpected inventory risk, and that the magnitude of this influence depends on the depth of the financial market.
A remote sensing-based approach to investigate changes in land use and land cover in the lower uMfolozi floodplain system, South Africa

The goal of this study was to understand land use and land cover (LULC) changes within the lower uMfolozi floodplain system, South Africa, and relate those changes to wetland loss. Changes in LULC were assessed using a geographic object-based image analysis (GEOBIA) algorithm to classify multi-date Landsat images into eight cover types over a period of 20 years, between 1997 and 2017. Post-classification accuracy assessment of all map-outputs was conducted by compiling confusion matrixes and calculating producer, user, and global accuracies and kappa coefficients (K) for each map-output. Levels of accuracy for all map-outputs were within acceptable limits, ranging between 79% and 88% (K = 0.76 and 0.86, respectively). Thereafter, paired t-tests were applied to determine whether the changes in LULC over the study period were significant. Results of this investigation showed a significant (p-value, < 0.01) conversion of wetland to cultivation, by 14%. This finding is important because it demonstrates that in this environment, human agency is one of the major drivers of a persistent decrease in the wetland ecosystem. The major insight from this observation is that there is an urgent need to formulate and implement objectively informed interventions to enhance the sustainability of the uMfolozi floodplain system and that of others elsewhere.
Anti-icing capability of textured silicone rubber surfaces via laser processing

ABSTRACT It is an undisputable fact that surface texture has a profound influence on the functional behavior of products. A method is proposed for manufacturing an anti-icing surface on silicone rubber composite material. Using a multi-function laser engraving machine, cylindrical micro-textures with different size and depth parameters are processed on the umbrella skirt surface of composite insulator samples. The optimal dimensional parameter of the surface micro-texture was determined by the best superhydrophobic property. By analyzing the freezing time of droplets, the influence of texture depth parameters on the anti-icing performance of the silicone rubber surface was investigated, which clearly confirmed that the contact angle affected anti-icing performance. Studying on adhesion of the ice on the sample surface and changing of the water contact angle after ice coating demonstrated the influence of texture-depth parameter on the deicing performance of silicone rubber surface. Thus, the characteristics and optimality of the texture specimen were obtained.
Fishers’ response to temperature change reveals the importance of integrating human behavior in climate change analysis

Fishers’ response to temperature change reveals the importance of integrating human behavior in climate change analysis. Climate change will reshape ecological dynamics. Yet, how temperature increases alter the behavior and resource use of people reliant on natural resources remains underexplored. Consequent behavior shifts have the potential to mitigate or accelerate climate impacts on livelihoods and food security. Particularly within the small-scale inland fisheries that support approximately 10% of the global population, temperature changes likely affect both fish and fishers. To analyze how changing temperatures alter households’ fishing behavior, we examined fishing effort and fish catch in a major inland fishery. We used longitudinal observational data from households in Cambodia, which has the highest per-capita consumption of inland fish in the world. Higher temperatures caused households to reduce their participation in fishing but had limited net effects on fish catch. Incorporating human behavioral responses to changing environmental conditions will be fundamental to determining how climate change affects rural livelihoods, food production, and food access.
Precise correction of Duchenne muscular dystrophy exon deletion mutations by base and prime editing

Base and prime genome editing correct Duchenne muscular dystrophy mutations to restore dystrophin in mice and human cells. Duchenne muscular dystrophy (DMD) is a fatal muscle disease caused by the lack of dystrophin, which maintains muscle membrane integrity. We used an adenine base editor (ABE) to modify splice donor sites of the dystrophin gene, causing skipping of a common DMD deletion mutation of exon 51 (∆Ex51) in cardiomyocytes derived from human induced pluripotent stem cells, restoring dystrophin expression. Prime editing was also capable of reframing the dystrophin open reading frame in these cardiomyocytes. Intramuscular injection of ∆Ex51 mice with adeno-associated virus serotype-9 encoding ABE components as a split-intein trans-splicing system allowed gene editing and disease correction in vivo. Our findings demonstrate the effectiveness of nucleotide editing for the correction of diverse DMD mutations with minimal modification of the genome, although improved delivery methods will be required before these strategies can be used to sufficiently edit the genome in patients with DMD.
Implementing Clinical Decision Support Tools and Pharmacovigilance to Reduce the Use of Potentially Harmful Medications and Health Care Costs in Adults With Heart Failure

Heart failure (HF) is associated with significant morbidity, mortality, compromised quality of life and socioeconomic burden worldwide. This chronic condition is becoming an increasingly important concern given the increased prevalence of HF among aging populations. Significant contributors toward escalating health care costs are emergency room visits and hospitalizations associated with HF. An important strategy to improve health care outcomes and reduce unnecessary costs is to identify and reduce the prescribing of potentially harmful medications (PHMs) among adults with HF. Previous studies in patients with HF found roughly 10–50% of them were prescribed at least one PHM in ambulatory care and inpatient health care settings. This opinion highlights recent findings from studies assessing prevalence of PHMs, associations between PHM prescribing and characteristics, and what can be done to improve patient outcomes and reduce the use of PHMs and associated health care costs in adults with HF.
A Review of Detection and Removal of Raindrops in Automotive Vision Systems

Research on the effect of adverse weather conditions on the performance of vision-based algorithms for automotive tasks has had significant interest. It is generally accepted that adverse weather conditions reduce the quality of captured images and have a detrimental effect on the performance of algorithms that rely on these images. Rain is a common and significant source of image quality degradation. Adherent rain on a vehicle’s windshield in the camera’s field of view causes distortion that affects a wide range of essential automotive perception tasks, such as object recognition, traffic sign recognition, localization, mapping, and other advanced driver assist systems (ADAS) and self-driving features. As rain is a common occurrence and as these systems are safety-critical, algorithm reliability in the presence of rain and potential countermeasures must be well understood. This survey paper describes the main techniques for detecting and removing adherent raindrops from images that accumulate on the protective cover of cameras.
Hand Motion-Aware Surgical Tool Localization and Classification from an Egocentric Camera

Detecting surgical tools is an essential task for the analysis and evaluation of surgical videos. However, in open surgery such as plastic surgery, it is difficult to detect them because there are surgical tools with similar shapes, such as scissors and needle holders. Unlike endoscopic surgery, the tips of the tools are often hidden in the operating field and are not captured clearly due to low camera resolution, whereas the movements of the tools and hands can be captured. As a result that the different uses of each tool require different hand movements, it is possible to use hand movement data to classify the two types of tools. We combined three modules for localization, selection, and classification, for the detection of the two tools. In the localization module, we employed the Faster R-CNN to detect surgical tools and target hands, and in the classification module, we extracted hand movement information by combining ResNet-18 and LSTM to classify two tools. We created a dataset in which seven different types of open surgery were recorded, and we provided the annotation of surgical tool detection. Our experiments show that our approach successfully detected the two different tools and outperformed the two baseline methods.
Sprachvarietäten- abhängige Terminologie in der neuronalen maschinellen Übersetzung: Eine Analyse in der Sprachrichtung Englisch-Deutsch mit Schwerpunkt auf der österreichischen Varietät der deutschen Sprache

zur Konferenz Digital Humanities im deutschsprachigen Raum 2020 Sprachvarietätenabhängige Terminologie in der neuronalen maschinellen Übersetzung: Eine Analyse in der Sprachrichtung Englisch-Deutsch mit Schwerpunkt auf der österreichischen Varietät der deutschen Sprache
Performance Overview of the Latest Video Coding Proposals: HEVC, JEM and VVC

The audiovisual entertainment industry has entered a race to find the video encoder offering the best Rate/Distortion (R/D) performance for high-quality high-definition video content. The challenge consists in providing a moderate to low computational/hardware complexity encoder able to run Ultra High-Definition (UHD) video formats of different flavours (360°, AR/VR, etc.) with state-of-the-art R/D performance results. It is necessary to evaluate not only R/D performance, a highly important feature, but also the complexity of future video encoders. New coding tools offering a small increase in R/D performance at the cost of greater complexity are being advanced with caution. We performed a detailed analysis of two evolutions of High Efficiency Video Coding (HEVC) video standards, Joint Exploration Model (JEM) and Versatile Video Coding (VVC), in terms of both R/D performance and complexity. The results show how VVC, which represents the new direction of future standards, has, for the time being, sacrificed R/D performance in order to significantly reduce overall coding/decoding complexity.
Domain Adaptation for Medical Image Segmentation: A Meta-Learning Method

Convolutional neural networks (CNNs) have demonstrated great achievement in increasing the accuracy and stability of medical image segmentation. However, existing CNNs are limited by the problem of dependency on the availability of training data owing to high manual annotation costs and privacy issues. To counter this limitation, domain adaptation (DA) and few-shot learning have been extensively studied. Inspired by these two categories of approaches, we propose an optimization-based meta-learning method for segmentation tasks. Even though existing meta-learning methods use prior knowledge to choose parameters that generalize well from few examples, these methods limit the diversity of the task distribution that they can learn from in medical image segmentation. In this paper, we propose a meta-learning algorithm to augment the existing algorithms with the capability to learn from diverse segmentation tasks across the entire task distribution. Specifically, our algorithm aims to learn from the diversity of image features which characterize a specific tissue type while showing diverse signal intensities. To demonstrate the effectiveness of the proposed algorithm, we conducted experiments using a diverse set of segmentation tasks from the Medical Segmentation Decathlon and two meta-learning benchmarks: model-agnostic meta-learning (MAML) and Reptile. U-Net and Dice similarity coefficient (DSC) were selected as the baseline model and the main performance metric, respectively. The experimental results show that our algorithm maximally surpasses MAML and Reptile by 2% and 2.4% respectively, in terms of the DSC. By showing a consistent improvement in subjective measures, we can also infer that our algorithm can produce a better generalization of a target task that has few examples.