Miracle-dz/UniFAR-FTUs huggingface dataset:
The Drosophila Dot Chromosome: Where Genes Flourish Amidst Repeats

The F element of the Drosophila karyotype (the fourth chromosome in Drosophila melanogaster) is often referred to as the “dot chromosome” because of its appearance in a metaphase chromosome spread. This chromosome is distinct from other Drosophila autosomes in possessing both a high level of repetitious sequences (in particular, remnants of transposable elements) and a gene density similar to that found in the other chromosome arms, ∼80 genes distributed throughout its 1.3-Mb “long arm. ” The dot chromosome is notorious for its lack of recombination and is often neglected as a consequence. This and other features suggest that the F element is packaged as heterochromatin throughout. F element genes have distinct characteristics (e.g., low codon bias, and larger size due both to larger introns and an increased number of exons), but exhibit expression levels comparable to genes found in euchromatin. Mapping experiments show the presence of appropriate chromatin modifications for the formation of DNaseI hypersensitive sites and transcript initiation at the 5′ ends of active genes, but, in most cases, high levels of heterochromatin proteins are observed over the body of these genes. These various features raise many interesting questions about the relationships of chromatin structures with gene and chromosome function. The apparent evolution of the F element as an autosome from an ancestral sex chromosome also raises intriguing questions. The findings argue that the F element is a unique chromosome that occupies its own space in the nucleus. Further study of the F element should provide new insights into chromosome structure and function.
Resistance Training Volume Load with and without Exercise Displacement

Monitoring the resistance training volume load (VL) (sets × reps × load) is essential to managing resistance training and the recovery–adaptation process. Subjects: Eight trained weightlifters, seven of which were at national level, participated in the study. Methods: VL was measured both with (VLwD) and without (VL) the inclusion of barbell displacement, across twenty weeks of training, in order to allow for comparisons to be made of these VL calculating methods. This consisted of recording the load, repetition count, and barbell displacement for every set executed. Comparisons were made between VL and VLwD for individual blocks of training, select training weeks, and select training days. Results: Strong, statistically significant correlations (r ≥ 0.78, p < 0.001) were observed between VL and VLwD between all training periods analyzed. t-tests revealed statistically significant (p ≤ 0.018) differences between VL and VLwD in four of the seven training periods analyzed. Conclusion: The very strong relationship between VL and VLwD suggest that a coach with time constraints and a large number of athletes can potentially spare the addition of displacement. However, differences in percent change indicate that coaches with ample time should include displacement in VL calculations, in an effort to acquire more precise workload totals.
The Flex End-to-End Simulator: From Concept Phase (A/B1) to Ground Segment and Operations (C/D)

ESA's FLEX/Sentinel-3 tandem mission aims at mapping Sun-induced fluorescence (SIF) as a proxy to quantify photosynthetic activity of terrestrial vegetation. Due to the complexity of the mission concept and stringent requirements for the data processing algorithms, ESA developed a Phase A/B1 End-to-End Mission Performance Simulator (E2ES) tool to reproduce the expected mission performance and check the mission and instrument concepts. In the current Phase C/D, the E2ES concept must evolve to consolidate the whole data processing chain, providing an accurate figures of the whole mission error budget and serving as a roadmap for the future development of FLEX Ground Segment. This paper gives an overview of the activities involving the FLEX E2ES, from the conceptual development phase (Phase A/B1) to the implementation of the Ground Segment and operations (Phase C/D). Particularly, we will focus on the high-level description of the Level-2 data processing chain from an architecture and software design point of view.
MicroRNAs are involved in the hypothalamic leptin sensitivity

ABSTRACT The central nervous system monitors modifications in metabolic parameters or hormone levels (leptin) and elicits adaptive responses such as food intake and glucose homeostasis regulation. Particularly, within the hypothalamus, pro-opiomelanocortin (POMC) neurons are crucial regulators of energy balance. Consistent with a pivotal role of the melanocortin system in the control of energy homeostasis, disruption of the Pomc gene causes hyperphagia and obesity. Pomc gene expression is tightly controlled by different mechanisms. Interestingly, recent studies pointed to a key role for micro ribonucleic acid (miRNAs) in the regulation of gene expression. However, the role of miRNAs in the leptin sensitivity in hypothalamic melanocortin system has never been assessed. We developed a transgenic mouse model (PDKO) with a partial deletion of the miRNA processing enzyme DICER specifically in POMC neurons. PDKO mice exhibited a normal body weight but a decrease of food intake. Interestingly, PDKO mice had decreased metabolic rate by reduction of VO2 consumption and CO2 production which could explain that PDKO mice have normal weight while eating less. Interestingly, we observed an increase of leptin sensitivity in the POMC neurons of PDKO mice which could explain the decrease of food intake in this model. We also observed an increase in the expression of genes involved in the function of brown adipose tissue that is in polysynaptic contact with the POMC neurons. In summary, these results support the hypothesis that Dicer-derived miRNAs may be involved in the effect of leptin on POMC neurons activity.
Metformin reveals a mitochondrial copper addiction of mesenchymal cancer cells

The clinically approved drug metformin has been shown to selectively kill persister cancer cells through mechanisms that are not fully understood. To provide further mechanistic insights, we developed a drug surrogate that phenocopies metformin and can be labeled in situ by means of click chemistry. Firstly, we found this molecule to be more potent than metformin in several cancer cell models. Secondly, this technology enabled us to provide visual evidence of mitochondrial targeting with this class of drugs. A combination of fluorescence microscopy and cyclic voltammetry indicated that metformin targets mitochondrial copper, inducing the production of reactive oxygen species in this organelle, mitochondrial dysfunction and apoptosis. Importantly, this study revealed that mitochondrial copper is required for the maintenance of a mesenchymal state of human cancer cells, and that metformin can block the epithelial-to-mesenchymal transition, a biological process that normally accounts for the genesis of persister cancer cells, through direct copper targeting.
Mapping Urbanization in the United States for 2020

Population is often projected as a bulk number without details about its spatial distribution. We use remote sensing and Census Bureau data to disaggregate the projected U.S. population and assess the urban expansion for year 2020 compared to 2011. The results show that population growth in California's urban centers will surpass land consumption. In the eastern U.S., as well as in the Midwest, urban expansion is expected to increase significantly faster than population. This study reveals that urbanization in the U.S. appears to implicitly include a ‘cultural character’ whereby depending on the region, urban centers are either built horizontally using large area development per capita or becoming denser and possibly using vertical structures with minimal footprints. These are considerations for the United Nations (U.N.) Sustainable Development Goal (SDG) Target 11.3 to ensure future design of safe, resilient, and sustainable cities and human settlements. The method developed here supports the U.N. SDG effort.
Toward an Operational Forest Monitoring Using Copernicus Data: A Case Study in Central Germany

In the Free State of Thuringia, in Central Germany, the ThüringenForst develops new remote sensing products using open and free data. For this purpose, Copernicus data as well as LiDAR data, and Thuringian GIS database provided by the Agency for Surveying and Geodata (Federal State Thuringia) have been used. Copernicus services combined with auxiliary data can provide at no cost timely and objective information on forest resources that complement and update the existing forest geoinformation. Until now, three products have been available for operational use by ThüringenForst, namely afforestation monitoring, terrain accessibility maps, and tree height maps. Another important forest parameter to monitor is tree species. In this paper, a workflow for classifying tree species has been presented that can be utilized as a future remote sensing local forest product. Using the Sentinel-2 data, five dominant tree species were classified with an overall accuracy of more than 83%.
Using a Cosmic-Ray Neutron Sensor (CRNS) to Monitor Vegetation

It is believed that the cosmic-ray neutron sensor can be used to measure vegetation. We compare biomass water equivalent (BWE) to neutron counts from a cosmic-ray neutron sensor. We believe that the thermal neutrons will have a highly correlated inverse relationship with BWE. This is due to the ambient energy of thermal neutrons and the ability of hydrogen to capture them easily. Our results for soybean in 2017 showed that there was better correlation between raw fast neutron count $(\mathbf{R}^{2}= \mathbf{0.61})$ and BWE than raw thermal neutron count $(\mathbf{R}^{2}= \mathbf{0.21})$. It would be ideal to explore this relationship of fast neutrons to BWE, with the addition of other hydrogen pools at the surface.
Satellite-Borne and Iss-Borne Remote Sensing in School Lessons: Lessons Learned and New Mediation Ways

The contribution demonstrates the application possibilities of remote sensing in secondary STEM education. It is shown how propaedeutic learning and pupils' methodological competences are emphasized by applying digital image processing techniques and real data of earth observation satellites and experimental photography onboard the International Space Station (ISS). E- and m-leaming developments are presented focusing on the multispectral Sentinel-2 data as well as the footage of the High Definition Earth Viewing (HDEV) experiment. It is shown how digital learning modules and augmented reality tools are integrated into classrooms in order to communicate the knowledge about and the handling of natural and man-made phenomena in times of global change. The learning material presented here is found at coloumbuseye.uni-bonn.de (for ISS / HDEV) and fis.uni-bonn.de (for remote sensing in general).
Weighted Matchings via Unweighted Augmentations

We design a generic method to reduce the task of finding weighted matchings to that of finding short augmenting paths in unweighted graphs. This method enables us to provide efficient implementations for approximating weighted matchings in the massively parallel computation (MPC) model and in the streaming model. For the MPC and the multi-pass streaming model, we show that any algorithm computing a (1-δ)-approximate unweighted matching in bipartite graphs can be translated into an algorithm that computes a (1-(ε(δ))-approximate maximum weighted matching. Furthermore, this translation incurs only a constant factor (that depends on ε > 0) overhead in the complexity. Instantiating this with the current best MPC algorithm for unweighted matching yields a (1 - ε)-approximation algorithm for maximum weighted matching that uses Oε(log log n) rounds, O(m/n) machines per round, and O(npoly(logn)) memory per machine. This improves upon the previous best approximation guarantee of (1/2-ε) for weighted graphs. In the context of single-pass streaming with random edge arrivals, our techniques yield a (1/2+c)-approximation algorithm thus breaking the natural barrier of 1/2.
Covariance Matrix Based Feature Fusion for Scene Classification

In this paper, a covariance matrix based feature fusion (CMF-F) framework is proposed to combine two low-level visual features i.e., the Gabor feature and color feature for scene classification. Generally, the proposed method consists of following three steps. Firstly, the Gabor feature and color feature are extracted from original image and stacked together. Then, a covariance matrix is extracted to fuse these two low-level visual features. Each nondiagonal entry in the covariance matrix stands for the correlation of two different feature dimensions. Finally, the obtained covariance matrix is handled by a kernel linear discriminative analysis algorithm followed with nearest neighboring classifier for label assignment. The proposed method is tested on a public 21-classes UC Merced land use data set and compared with mid-level visual feature oriented method and the high-level feature oriented methods. The experimental results demonstrate that the proposed CMFF framework can not only improve the classification performance of the low-level visual feature (the Gabor feature and the color feature), but also can outperform the conventional mid-level visual feature oriented methods.
Aliens: A Novel Hybrid Architecture for Resistive Random-Access Memory

Passive crossbar arrays of resistive random-access memory (RRAM) have shown great potential to meet the demands of future memory. By eliminating transistor per cell, the crossbar array possesses a higher memory density but introduces sneak currents which incur extra energy waste and reliability issues. The complementary resistive switch (CRS), consisting of two anti-serially stacked memristors, is considered as a promising solution to the sneak current problem. However, the destructive read of the CRS results in an additional recovery write operation which strongly restricts its further promotion. Exploiting the dual CRS/memristor mode of CRS devices, we propose Aliens, a novel hybrid architecture for resistive random-access memory which introduces one alien cell (memristor mode) for each wordline in the crossbar to provide a practical hybrid memory without operating system's intervention. Aliens draws advantages from both modes: restrained sneak current of CRS mode and non-destructive read of memristor mode. The simple and regular cell mode organization of Aliens enables an energy-saving read method and an effective mode switching strategy called Lazy-Switch. By exploiting memory access locality, Lazy-Switch delays and merges the recovery write operations of the CRS mode. Due to fewer recovery write operations and negligible sneak currents, Aliens achieves improvement in energy, overall endurance, and access performance. The experiment results show that our design offers average energy savings of 13.9× compared with memristor-only memory, a memory lifetime 5.3× longer than CRS-only memory, and a competitive performance compared with memristor-only memory.
Non-Convex Low-Rank Approximation for Hyperspectral Image Recovery with Weighted Total Varaition Regularization

Low-rank representation has been widely used as a powerful tool in hyperspectral image (HSI) recovery. The existing studies involving low-rank problems are commonly under the nuclear norm penalization. However, nuclear norm minimization tends to over-shrink the components of rank, which leads to modeling bias. In this paper, a new nonconvex penalty is introduced to obtain an unbiased low-rank approximation. In Addition, local spatial neighborhood weighted spectral-spatial total variation (TV) regularization is introduced to preserve spatial structural information. And sparse l1-norm is used as a constraint to sparse noise. Finally, a novel HSI non-convex low-rank relaxation restoration model is proposed. A number of experiments show that the proposed method can effectively remove the mixed-noise, and result in an unbiased estimate with better robustness.
CREBBP gene mutations are frequently detected in in situ follicular neoplasia.

TO THE EDITOR: Follicular lymphoma (FL) is characterized by the t(14;18)(q32;q21) chromosomal translocation,[1][1] found in ∼85% of manifest FL (mFL) cases. The t(14;18) is also present in early precursor lesions of FL and in a significant fraction of healthy individuals. [2][2],[3][3] This
Hyperspectral Image Denoising via Nonnegative Matrix Factorization and Convolutional Neural Networks

Hyperspectral image (HSI) denoising plays an important role to enhance the image quality for subsequent applications. This paper proposes a novel denoising framework for the HSI, in which the denoising issue is modeled as a convolutional neural network (CNN) constrained non-negative matrix factorization problem. Then, by adopting the proximal alternating linearized minimization, the proposed approach can be decomposed into two iterative steps: In the first step, the spectral matrix is updated using a proximal operator to guarantee the non-negativity; in the second step, the abundance matrix is updated using a designed CNN. Finally, we propose a transfer learning scheme to train the design CNN with a natural gray image dataset. Exhaustive experiments show that the proposed approach outperforms the comparison state-of-the-art methods on two different test HSI datasets.
The impact of keels and tails on turtle swimming performance and their potential as models for biomimetic design

Stability and turning performance are two key metrics of locomotor performance in animals, and performance in both of these metrics can be improved through a variety of morphological structures. Aquatic vehicles are often designed with keels and rudders to improve their stability and turning performance, but how keels and rudders function in rigid-bodied animals is less understood. Aquatic turtles are a lineage of rigid-bodied animals that have the potential to function similarly to engineered vehicles, and also might make use of keels and rudders to improve their stability and turning performance. To test these possibilities, we trained turtles to follow a mechanically controlled prey stimulus under three sets of conditions: with no structural modifications, with different sized and shaped keels, and with restricted tail use. We predicted that keels in turtles would function similarly to those in aquatic vehicles to reduce oscillations, and that turtles would use the tail like a rudder to reduce oscillations and improve turning performance. We found that the keel designs we tested did not reduce oscillations in turtles, but that the tail was used similarly to a rudder, with benefits to both the magnitude of oscillations they experienced and turning performance. These data show how variation in the accessory structures of rigid-bodied animals can impact swimming performance, and suggest that such variation among turtles could serve as a biomimetic model in designing aquatic vehicles that are stable as well as maneuverable and agile.
Characterizing Focused Attention and Working Memory Using EEG

Detecting the cognitive profiles of learners is an important step towards personalized and adaptive learning. Electroencephalograms (EEG) have been used to detect the subject’s emotional and cognitive states. In this paper, an approach for detecting two cognitive skills, focused attention and working memory, using EEG signals is proposed. The proposed approach consists of the following main steps: first, subjects undergo a scientifically-validated cognitive assessment test that stimulates and measures their full cognitive profile while putting on a 14-channel wearable EEG headset. Second, the scores of focused attention and working memory are extracted and encoded for a classification problem. Third, the collected EEG data are analyzed and a total of 280 time- and frequency-domain features are extracted. Fourth, several classifiers were trained to correctly classify and predict three levels (low, average, and high) of the two cognitive skills. The classification accuracies that were obtained on 86 subjects were 84% and 81% for the focused attention and working memory, respectively. In comparison with similar approaches, the obtained results indicate the generalizability and suitability of the proposed approach for the detection of these two skills. Thus, the presented approach can be used as a step towards adaptive learning where real-time adaptation is to be done according to the predicted levels of the measured cognitive skills.
Overcoming CDK4/6 inhibitor resistance in ER positive breast cancer.

Three inhibitors of CDK4/6 kinases were recently FDA approved for use in combination with endocrine therapy, and they significantly increase the progression-free survival of patients with advanced Estrogen Receptor positive (ER+) breast cancer in the first-line treatment setting. As the new standard-of-care in some countries, there is the clinical emergence of patients with breast cancer that is both CDK4/6 inhibitor and endocrine therapy resistant. The strategies to combat these cancers with resistance to multiple treatments are not yet defined and represent the next major clinical challenge in ER+ breast cancer. In this review we discuss how the molecular landscape of endocrine therapy resistance may affect the response to CDK4/6 inhibitors, and how this intersects with biomarkers of intrinsic insensitivity. We identify the handful of pre-clinical models of acquired resistance to CDK4/6 inhibitors and discuss whether the molecular changes in these models are likely to be relevant or modified in the context of endocrine therapy resistance. Finally, we consider the crucial question of how some of these changes are potentially amenable to therapy.
Crop Type Mapping Based on Sentinel-1 Backscatter Time Series

The high revisit time of Sentinel-1 (S1) observations enables the design of crop type mapping approaches exploiting the backscatter time series observed for the different crops. The objective of this study is to propose a supervised crop classification methodology based on the temporal signature of crops. With this aim 29 dual-pol S1 observations acquired over an agricultural area of Spain, where ground truth was available, were processed. The classification approach was based on the temporal signatures obtained for each polarization channel (VH, VV and the cross-pol ratio) for the different crops. Highest accuracies were obtained when fields were assigned to the class that minimized the RMSE, with an overall accuracy of 79% and best results for rapeseed, sunflower, alfalfa and barley.
Efficient Detection of Intensively Parked Vehicles Form Satellite Image with 0.5-Meter Spatial Resolution

It is challenging to distinguish the intensive parked vehicles in the parking lot for the high-resolution satellite images even at a spatial resolution of 0.5 meter. A vehicle segmentation and detection algorithm is proposed to address this problem based on the improved Mean Shift algorithm with the adaptive parameter adjustment. The suspected vehicle areas are first located using the regional growth with the seed point determined by the simplified Itti algorithm and non-maximal suppression. An adaptive parameter adjustment based Mean Shift method is presented to divide each suspected vehicle area into the suspected vehicle slices. To reduce the false alarms, feature vectors contained spectral and texture features are constructed and used in the classification based on support vector machine to distinguish between vehicles and non-vehicles. Experimental results on several remote sensing images at a spatial resolution of 0.5 meter present the accurate detection results for the intensive parked vehicles.