Determining biological roles of four unique Vernicia fordii acyl-CoA Binding Proteins

High-value industrial oils are essential for many processes and have great economic and environmental impacts. The tung tree produces a high-value seed oil. Approximately 80% of tung oil is α-eleostearic acid, which has a high degree of unsaturation thus giving it properties as a drying oil. The identification of the biological components in tung is imperative to further the knowledge of its processes. Four unique tung acyl-CoA binding proteins, VfACBP3a, VfACBP3b, VfACBP4, and VfACBP6 were identified and the genes encoding them were cloned and analyzed to determine their biological roles. The VfACBPs were observed to be similar to other organisms\u27 ACBPs, especially Arabidopsis thaliana. In addition, each gene was expressed in all tung tissues. They were shown to interact with VfDGAT1 and VfDGAT2, two known components of tung lipid metabolism. Finally, VfACBP3a and VfACBP6 were expressed in the seeds of transgenic plants to study the effects of VfACBP expression on seed lipid fatty acid content

Flame filtering and perimeter localization of wildfires using aerial thermal imagery

Airborne thermal infrared (TIR) imaging systems are being increasingly used for wild fire tactical monitoring since they show important advantages over spaceborne platforms and visible sensors while becoming much more affordable and much lighter than multispectral cameras. However, the analysis of aerial TIR images entails a number of difficulties which have thus far prevented monitoring tasks from being totally automated. One of these issues that needs to be addressed is the appearance of flame projections during the geo-correction of off-nadir images. Filtering these flames is essential in order to accurately estimate the geographical location of the fuel burning interface. Therefore, we present a methodology which allows the automatic localisation of the active fire contour free of flame projections. The actively burning area is detected in TIR georeferenced images through a combination of intensity thresholding techniques, morphological processing and active contours. Subsequently, flame projections are filtered out by the temporal frequency analysis of the appropriate contour descriptors. The proposed algorithm was tested on footages acquired during three large-scale field experimental burns. Results suggest this methodology may be suitable to automatise the acquisition of quantitative data about the fire evolution. As future work, a revision of the low-pass filter implemented for the temporal analysis (currently a median filter) was recommended. The availability of up-to-date information about the fire state would improve situational awareness during an emergency response and may be used to calibrate data-driven simulators capable of emitting short-term accurate forecasts of the subsequent fire evolution.Postprint (author's final draft

Social inertia in collaboration networks

This work is a study of the properties of collaboration networks employing the formalism of weighted graphs to represent their one-mode projection. The weight of the edges is directly the number of times that a partnership has been repeated. This representation allows us to define the concept of "social inertia" that measures the tendency of authors to keep on collaborating with previous partners. We use a collection of empirical datasets to analyze several aspects of the social inertia: 1) its probability distribution, 2) its correlation with other properties, and 3) the correlations of the inertia between neighbors in the network. We also contrast these empirical results with the predictions of a recently proposed theoretical model for the growth of collaboration networks.Comment: 7 pages, 5 figure

TFAP2C regulates transcription in human naive pluripotency by opening enhancers.

Naive and primed pluripotent human embryonic stem cells bear transcriptional similarity to pre- and post-implantation epiblast and thus constitute a developmental model for understanding the pluripotent stages in human embryo development. To identify new transcription factors that differentially regulate the unique pluripotent stages, we mapped open chromatin using ATAC-seq and found enrichment of the activator protein-2 (AP2) transcription factor binding motif at naive-specific open chromatin. We determined that the AP2 family member TFAP2C is upregulated during primed to naive reversion and becomes widespread at naive-specific enhancers. TFAP2C functions to maintain pluripotency and repress neuroectodermal differentiation during the transition from primed to naive by facilitating the opening of enhancers proximal to pluripotency factors. Additionally, we identify a previously undiscovered naive-specific POU5F1 (OCT4) enhancer enriched for TFAP2C binding. Taken together, TFAP2C establishes and maintains naive human pluripotency and regulates OCT4 expression by mechanisms that are distinct from mouse

High-Throughput Single-Molecule Telomere Characterization

We have developed a novel method that enables global subtelomere and haplotype-resolved analysis of telomere lengths at the single-molecule level. An in vitro CRISPR/Cas9 RNA-directed nickase system directs the specific labeling of human (TTAGGG) n DNA tracts in genomes that have also been barcoded using a separate nickase enzyme that recognizes a 7bp motif genome-wide. High-throughput imaging and analysis of large DNA single molecules from genomes labeled in this fashion using a nanochannel array system permits mapping through subtelomere repeat element (SRE) regions to unique chromosomal DNA while simultaneously measuring the (TTAGGG) n tract length at the end of each large telomere- terminal DNA segment. The methodology also permits subtelomere and haplotype-resolved analyses of SRE organization and variation, providing a window into the population dynamics and potential functions of these complex and structurally variant telomere-adjacent DNA regions. At its current stage of development, the assay can be used to identify and characterize telomere length distributions of 30-35 discrete telomeres simultaneously and accurately. The assay\u27s utility is demonstrated using early versus late passage and senescent human diploid fibroblasts, documenting the anticipated telomere attrition on a global telomere-by-telomere basis as well as identifying subtelomere-specific biases for critically short telomeres. Similarly, we present the first global single-telomere-resolved analyses of two cancer cell lines

Secondary Education Teacher Training and Emotional Intelligence: Ingredients for Attention to Diversity in anInclusive School for All

This study, which is part of a broader research project, aims to investigate the impact of the initial training received by students in the Master?s Degree in Secondary Education and Baccalaureate, Vocational Training, and Language Teaching (MDSE) on their future teaching development in the current educational and social framework. The main goal is to understand their concerns, attitudes, and level of acquired competencies and knowledge for their professional development as inclusive teachers. Additionally, the study aims to explore the relationship between their assessments and experiences with the perceived level of Emotional Intelligence (EI), given its importance as a facilitating element, which is teachable from formal education, in socio-educational inclusion processes and quality attention to diversity in classrooms. A total of 218 MDSE students (Mage = 31.5; SD = 6; males = 33%; females = 67%) participated in the study, coming from various Spanish universities, and having either completed their studies or being in the final stages after having completed the generic module and practices in secondary education centers. The information was collected through the ?Teacher Training in Secondary Education: Key Elements for Teaching in an Inclusive School for All? (TTSE-IN) questionnaire, which included five validated and relevant instruments, of which three were used for the study?s purpose (Questionnaire for Future Secondary Education Teachers about Perceptions of Diversity Attention, Scale of Feelings, Attitudes, and Concerns about Inclusive Education, Revised SACIE-R andWong and Law Emotional Intelligence Scale WLEIS-S). The main results indicate that future teachers show a positive attitude towards diversity but have significant training gaps. Additionally, the EI variable, along with regular contact with people in situations of special vulnerability and experience in teaching people in situations of special vulnerability in non-formal contexts, has a positive effect on both teacher well-being and the facilitation of inclusive education processes and diversity attention

A probabilistic deep learning model of inter-fraction anatomical variations in radiotherapy

In radiotherapy, the internal movement of organs between treatment sessions causes errors in the final radiation dose delivery. Motion models can be used to simulate motion patterns and assess anatomical robustness before delivery. Traditionally, such models are based on principal component analysis (PCA) and are either patient-specific (requiring several scans per patient) or population-based, applying the same deformations to all patients. We present a hybrid approach which, based on population data, allows to predict patient-specific inter-fraction variations for an individual patient. We propose a deep learning probabilistic framework that generates deformation vector fields (DVFs) warping a patient's planning computed tomography (CT) into possible patient-specific anatomies. This daily anatomy model (DAM) uses few random variables capturing groups of correlated movements. Given a new planning CT, DAM estimates the joint distribution over the variables, with each sample from the distribution corresponding to a different deformation. We train our model using dataset of 312 CT pairs from 38 prostate cancer patients. For 2 additional patients (22 CTs), we compute the contour overlap between real and generated images, and compare the sampled and ground truth distributions of volume and center of mass changes. With a DICE score of 0.86 and a distance between prostate contours of 1.09 mm, DAM matches and improves upon PCA-based models. The distribution overlap further indicates that DAM's sampled movements match the range and frequency of clinically observed daily changes on repeat CTs. Conditioned only on a planning CT and contours of a new patient without any pre-processing, DAM can accurately predict CTs seen during following treatment sessions, which can be used for anatomically robust treatment planning and robustness evaluation against inter-fraction anatomical changes

Image similarity metrics suitable for infrared video stabilization during active wildfire monitoring : a comparative analysis

Aerial Thermal Infrared (TIR) imagery has demonstrated tremendous potential to monitor active forest fires and acquire detailed information about fire behavior. However, aerial video is usually unstable and requires inter-frame registration before further processing. Measurement of image misalignment is an essential operation for video stabilization. Misalignment can usually be estimated through image similarity, although image similarity metrics are also sensitive to other factors such as changes in the scene and lighting conditions. Therefore, this article presents a thorough analysis of image similarity measurement techniques useful for inter-frame registration in wildfire thermal video. Image similarity metrics most commonly and successfully employed in other fields were surveyed, adapted, benchmarked and compared. We investigated their response to different camera movement components as well as recording frequency and natural variations in fire, background and ambient conditions. The study was conducted in real video from six fire experimental scenarios, ranging from laboratory tests to large-scale controlled burns. Both Global and Local Sensitivity Analyses (GSA and LSA, respectively) were performed using state-of-the-art techniques. Based on the obtained results, two different similarity metrics are proposed to satisfy two different needs. A normalized version of Mutual Information is recommended as cost function during registration, whereas 2D correlation performed the best as quality control metric after registration. These results provide a sound basis for image alignment measurement and open the door to further developments in image registration, motion estimation and video stabilization for aerial monitoring of active wildland fires

Thermal infrared video stabilization for aerial monitoring of active wildfires

Measuring wildland fire behavior is essential for fire science and fire management. Aerial thermal infrared (TIR) imaging provides outstanding opportunities to acquire such information remotely. Variables such as fire rate of spread (ROS), fire radiative power (FRP), and fireline intensity may be measured explicitly both in time and space, providing the necessary data to study the response of fire behavior to weather, vegetation, topography, and firefighting efforts. However, raw TIR imagery acquired by unmanned aerial vehicles (UAVs) requires stabilization and georeferencing before any other processing can be performed. Aerial video usually suffers from instabilities produced by sensor movement. This problem is especially acute near an active wildfire due to fire-generated turbulence. Furthermore, the nature of fire TIR video presents some specific challenges that hinder robust interframe registration. Therefore, this article presents a software-based video stabilization algorithm specifically designed for TIR imagery of forest fires. After a comparative analysis of existing image registration algorithms, the KAZE feature-matching method was selected and accompanied by pre- and postprocessing modules. These included foreground histogram equalization and a multireference framework designed to increase the algorithm's robustness in the presence of missing or faulty frames. The performance of the proposed algorithm was validated in a total of nine video sequences acquired during field fire experiments. The proposed algorithm yielded a registration accuracy between 10 and 1000x higher than other tested methods, returned 10x more meaningful feature matches, and proved robust in the presence of faulty video frames. The ability to automatically cancel camera movement for every frame in a video sequence solves a key limitation in data processing pipelines and opens the door to a number of systematic fire behavior experimental analyses. Moreover, a completely automated process supports the development of decision support tools that can operate in real time during an emergency