Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of filamentary structures in the environment of superclusters of galaxies in the Local Universe

Context.Characterization of the internal structure of the superclusters of galaxies (walls, filaments, and knots where the clusters are located) is crucial for understanding the formation of the large-scale structure and for outlining the environment where galaxies evolved in the last few gigayears.Aims. We aim to detect the compact regions of high relative density (clusters and rich groups of galaxies), to map the elongated structures of low relative density (filaments, bridges, and tendrils of galaxies), and to characterize the galaxies that populate the filaments and study the environmental effects they are subject to.Methods. We used optical galaxies with spectroscopic redshifts from the SDSS-DR13 inside rectangular boxes encompassing the volumes of a sample of 46 superclusters of galaxies up toz= 0.15. A virial approximation was applied to correct the positions of the galaxies in the redshift space for the “finger of God” projection effect. Our methodology implements different classical pattern recognition and machine-learning techniques (Voronoi tessellation, hierarchical clustering, graph-network theory, and minimum spanning trees, among others), pipelined in the Galaxy System-Finding algorithm and the Galaxy Filament-Finding algorithm.Results. In total, we detected 2705 galaxy systems (clusters and groups, of which 159 are new) and 144 galaxy filaments in the 46 superclusters of galaxies. The filaments we detected have a density contrast of above 3, with a mean value of around 10, a radius of about 2.5 h70−1Mpc, and lengths of between 9 and 130 h70−1Mpc. Correlations between the galaxy properties (mass, morphology, and activity) and the environment in which they reside (systems, filaments, and the dispersed component) suggest that galaxies closer to the skeleton of the filaments are more massive by up to 25% compared to those in the dispersed component; 70% of the galaxies in the filament region present early-type morphologies and the fractions of active galaxies (both AGNs and star-forming galaxies) seem to decrease as galaxies approach the filament.Conclusions. Our results support the idea that galaxies in filaments are subject to environmental effects leading them to be more massive (probably due to larger rates of both merging and gas accretion), less active both in star formation and nuclear activity, and prone to the density–morphology relation. These results suggest that preprocessing in large-scale filaments could have significant effects on galaxy evolution.
Snippext: Semi-supervised Opinion Mining with Augmented Data

Online services are interested in solutions to opinion mining, which is the problem of extracting aspects, opinions, and sentiments from text. One method to mine opinions is to leverage the recent success of pre-trained language models which can be fine-tuned to obtain high-quality extractions from reviews. However, fine-tuning language models still requires a non-trivial amount of training data. In this paper, we study the problem of how to significantly reduce the amount of labeled training data required in fine-tuning language models for opinion mining. We describe , an opinion mining system developed over a language model that is fine-tuned through semi-supervised learning with augmented data. A novelty of is its clever use of a two-prong approach to achieve state-of-the-art (SOTA) performance with little labeled training data through: (1) data augmentation to automatically generate more labeled training data from existing ones, and (2) a semi-supervised learning technique to leverage the massive amount of unlabeled data in addition to the (limited amount of) labeled data. We show with extensive experiments that performs comparably and can even exceed previous SOTA results on several opinion mining tasks with only half the training data required. Furthermore, it achieves new SOTA results when all training data are leveraged. By comparison to a baseline pipeline, we found that extracts significantly more fine-grained opinions which enable new opportunities of downstream applications.
Stochastic Online Optimization using Kalman Recursion

We study the Extended Kalman Filter in constant dynamics, offering a bayesian perspective of stochastic optimization. We obtain high probability bounds on the cumulative excess risk in an unconstrained setting. The unconstrained challenge is tackled through a two-phase analysis. First, for linear and logistic regressions, we prove that the algorithm enters a local phase where the estimate stays in a small region around the optimum. We provide explicit bounds with high probability on this convergence time. Second, for generalized linear regressions, we provide a martingale analysis of the excess risk in the local phase, improving existing ones in bounded stochastic optimization. The EKF appears as a parameter-free O(d^2) online algorithm that optimally solves some unconstrained optimization problems.
Be Like Water: Robustness to Extraneous Variables Via Adaptive Feature Normalization

Extraneous variables are variables that are irrelevant for a certain task, but heavily affect the distribution of the available data. In this work, we show that the presence of such variables can degrade the performance of deep-learning models. We study three datasets where there is a strong influence of known extraneous variables: classification of upper-body movements in stroke patients, annotation of surgical activities, and recognition of corrupted images. Models trained with batch normalization learn features that are highly dependent on the extraneous variables. In batch normalization, the statistics used to normalize the features are learned from the training set and fixed at test time, which produces a mismatch in the presence of varying extraneous variables. We demonstrate that estimating the feature statistics adaptively during inference, as in instance normalization, addresses this issue, producing normalized features that are more robust to changes in the extraneous variables. This results in a significant gain in performance for different network architectures and choices of feature statistics.
Derivative Manipulation for General Example Weighting.

We propose derivative manipulation (DM) for training accurate and robust softmax-based deep neural networks, for two reasons: (1) In gradient-based optimisation, manipulating the derivative directly is more straightforward than designing loss functions, and it has a direct impact on the update of a model. (2) A loss function's derivative magnitude function can be understood as a weighting scheme; the loss's derivative of an example defines how much impact it has on the update of a model. Therefore, manipulating the derivative is to adjust the weighting scheme. DM simply modifies the derivative magnitude, including transformation and normalisation, after which the derivative magnitude function is termed emphasis density function (EDF). An EDF is a formula expressing an example weighting scheme and we may deduce many options for EDFs from common probability density functions (PDFs). We demonstrate the effectiveness of the DM formulation empirically by extensive experiments on both vision and language tasks, especially when adverse conditions exist, e.g., noisy data and sample imbalance.
Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling

We aim to develop off-policy DRL algorithms that not only exceed state-of-the-art performance but are also simple and minimalistic. For standard continuous control benchmarks, Soft Actor Critic (SAC), which employs entropy maximization, currently provides state-of-the-art performance. We first demonstrate that the entropy term in SAC addresses action saturation due to the bounded nature of the action spaces. With this insight, we propose a streamlined algorithm with a simple normalization scheme or with inverted gradients. We show that both approaches can match SAC's sample efficiency performance without the need of entropy maximization. We then propose a simple non-uniform sampling method for selecting transitions from the replay buffer during training. Extensive experimental results demonstrate that our proposed sampling scheme leads to state of the art sample efficiency on challenging continuous control tasks. We combine all of our findings into one simple algorithm, which we call Streamlined Off Policy with Emphasizing Recent Experience, for which we provide robust public-domain code.
Molecular gas and star formation activity in luminous infrared galaxies in clusters at intermediate redshifts

We investigate the role of dense megaparsec-scale environments in processing molecular gas of cluster galaxies as they fall into the cluster cores. We selected a sample of ∼20 luminous infrared galaxies (LIRGs) belonging to intermediate-redshift clusters, mainly from the Herschel Lensing Survey and the Local Cluster Substructure Survey. These galaxies include MACS J0717.5+3745 at z = 0.546 and Abell 697, 963, 1763, and 2219 at z = 0.2 − 0.3. We performed spectral energy distribution modeling from the far-infrared to ultraviolet of the LIRGs, which span cluster-centric distances within r/r200 ≃ 0.2 − 1.6. We observed the LIRGs in CO(1→0) or CO(2→1) with the Plateau de Bure interferometer and its successor NOEMA, as part of five observational programs carried out between 2012 and 2017. We compared the molecular gas to stellar mass ratio M(H2)/M⋆, star formation rate (SFR), and depletion time (τdep) of the LIRGs with those of a compilation of cluster and field star-forming galaxies from the literature. The targeted LIRGs have SFR, M(H2)/M⋆, and τdep that are consistent with those of both main-sequence (MS) field galaxies and star-forming galaxies from the comparison sample. However we find that the depletion time, normalized to the MS value, tentatively increases with increasing r/r200, with a significance of 2. 8σ, which is ultimately due to a deficit of cluster-core LIRGs with τdep ≳ τdep, MS. We suggest that a rapid exhaustion of the molecular gas reservoirs occurs in the cluster LIRGs and is indeed effective in suppressing their star formation and ultimately quenching them. This mechanism may explain the exponential decrease of the fraction of cluster LIRGs with cosmic time. The compression of the gas in LIRGs, possibly induced by intra-cluster medium shocks, may be responsible for the short timescales that are observed in a large fraction of cluster-core LIRGs. Some of our LIRGs may also belong to a population of infalling filament galaxies.
Preclinical Models of Malignant Mesothelioma

Rodent models of malignant mesothelioma help facilitate the understanding of the biology of this highly lethal cancer and to develop and test new interventions. Introducing the same genetic lesions as found in human mesothelioma in mice results in tumors that show close resemblance with the human disease counterpart. This includes the extensive inflammatory responses that characterize human malignant mesothelioma. The relatively fast development of mesothelioma in mice when the appropriate combination of lesions is introduced, with or without exposure to asbestos, make the autochthonous models particularly useful for testing new treatment strategies in an immunocompetent setting, whereas Patient-Derived Xenograft models are particularly useful to assess effects of inter- and intra-tumor heterogeneity and human-specific features of mesothelioma. It is to be expected that new insights obtained by studying these experimental systems will lead to new more effective treatments for this devastating disease.
Fzr/Cdh1 Promotes the Differentiation of Neural Stem Cell Lineages in Drosophila

How stem cells and progenitors balance between self-renewal and differentiation is a central issue of stem cell biology. Here, we describe a novel and essential function of Drosophila Fzr/Cdh1, an evolutionary conserved protein, during the differentiation of neural stem cell (NSC) lineages in the central nervous system. We show that Fzr, a known co-activator of Anaphase Promoting Complex/Cyclosome (APC/C) ubiquitin ligase, promotes the production of neurons from neural progenitors called ganglion mother cells (GMCs). However, knockdown of APC/C subunit Ida or another APC/C co-activator CDC20 does not similarly impair GMC-neuron transition. We also observe a concomitant loss of differentiation factor Prospero expression and ectopic accumulation of mitotic kinase Polo in fzr mutant clones, strongly supporting the impairment of GMC to neuron differentiation. Besides functioning in GMCs, Fzr is also present in NSCs to facilitate the production of intermediate neural progenitors from NSCs. Taken together, Fzr plays a novel function in promoting differentiation programs during Drosophila NSC lineage development. Given that human Fzr is inactivated in multiple types of human cancers including brain tumors and that Fzr regulates neurotoxicity in various models of neurodegenerative diseases, our study on the role of Fzr in turning off proliferation in neuronal cells may provide insights into how Fzr deficits may contribute to human neurodegenerative diseases and tumors.
Hypofractionated Radiotherapy Dose Scheme and Application of New Techniques Are Associated to a Lower Incidence of Radiation Pneumonitis in Breast Cancer Patients

Purpose: Radiation pneumonitis (RP) is one of the most severe toxicities experienced by patients with breast cancer after radiotherapy (RT). RT fractionation schemes and techniques for breast cancer have undergone numerous changes over the past decades. This study aimed to investigate the incidence of RP as a function of such changes in patients with breast cancer undergoing RT and to identify dosimetric markers that predict the risk of this adverse event. Methods and Materials: We identified 1,847 women with breast cancer who received adjuvant RT at our institution between 2015 and 2017. The RT technique was individually tailored based on each patient's clinicopathological features. Deep inspiration breath hold technique or prone positioning were used for patients who underwent left whole-breast irradiation for cardiac sparing, while those requiring regional lymph node irradiation underwent volumetric-modulated arc therapy (VMAT). Results: Of 1,847 patients who received RT, 21.2% received the conventional dose scheme, while 78.8% received the hypofractionated dose scheme (mostly 40 Gy in 15 fractions). The median follow-up period was 14.5 months, and the overall RP rate was 2.1%. The irradiated organ at risk was corrected concerning biologically equivalent dose. The ipsilateral lung V30 in equivalent dose in 2 Gy (EQD2) was the most significant dosimetric factor associated with RP development. Administering RT using VMAT, and hypofractionated dose scheme significantly reduced ipsilateral lung V30. Conclusions: Application of new RT techniques and hypofractionated scheme significantly reduce the ipsilateral lung dose. Our data demonstrated that ipsilateral lung V30 in EQD2 is the most relevant dosimetric predictor of RP in patients with breast cancer.
Mediated Non-geminate Recombination in Ternary Organic Solar Cells Through a Liquid Crystal Guest Donor

The approach via ternary blends prompts the increase of absorbed photon density and resultant photocurrent enhancement in organic solar cells (OSCs). In contrast to actively reported high efficiency ternary OSCs, little is known about charge recombination properties and carrier loss mechanisms in these emerging devices. Here, through introducing a small molecule donor BTR as a guest component to the PCE-10:PC71BM binary system, we show that photocarrier losses via recombination are mitigated with respect the binary OSCs, owing to a reduced bimolecular recombination. The gain of the fill factor in ternary devices are reconciled by the change in equilibrium between charge exaction and recombination in the presence of BTR toward the former process. With these modifications, the power conversion efficiency in ternary solar cells receives a boost from 8.8 (PCE-10:PC71BM) to 10.88%. We further found that the voltage losses in the ternary cell are slightly suppressed, related to the rising charge transfer-state energy. These benefits brought by the third guest donor are important for attaining improvements on key photophysical processes governing the photovoltaic efficiencies in organic ternary solar cells.
Flavivirus-Mediating B Cell Differentiation Into Antibody-Secreting Cells in Humans Is Associated With the Activation of the Tryptophan Metabolism

Patients infected with the Dengue virus (DENV) often present with a massive generation of DENV-specific antibody-secreting cells (ASCs) in the blood. In some cases, these ASCs represent more than 50% of the circulating B cells, a higher magnitude than those induced by other infections, vaccinations, and plasma cell lymphomas. However, it remains unclear how the DENV infection elicits this colossal response. To address this issue, we utilised an in vitro strategy to induce human PBMCs of healthy individuals incubated with DENV particles (DENV4 TVP/360) to differentiate into ASCs. As controls, PBMCs were incubated with a mitogen cocktail or supernatants of uninfected C6/36 cells (mock). The ASC phenotype and function were increasingly detected in the DENV and mitogen-cultured PBMCs as compared to mock-treated cells. In contrast to the in vivo condition, secreted IgG derived from the PBMC-DENV culture was not DENV-specific. Lower ASC numbers were observed when inactivated viral particles or purified B cells were added to the cultures. The physical contact was essential between B cells and the remaining PBMCs for the DENV-mediated ASC response. Considering the evidence for the activation of the tryptophan metabolism detected in the serum of Dengue patients, we assessed its relevance in the DENV-mediated ASC differentiation. For this, tryptophan and its respective metabolites were quantified in the supernatants of cell cultures through mass spectrophotometry. Tryptophan depletion and kynurenine accumulation were found in the supernatants of PBMC-DENV cultures, which presented enhanced detection of indoleamine 2,3-dioxygenase 1 and 2 transcripts as compared to controls. In PBMC-DENV cultures, tryptophan and kynurenine levels strongly correlated to the respective ASC numbers, while the kynurenine levels were directly proportional to the secreted IgG titers. Contrastingly, PBMCs incubated with Zika or attenuated Yellow Fever viruses showed no correlation between their kynurenine concentrations and ASC numbers. Therefore, our data revealed the existence of distinct pathways for the DENV-mediated ASC differentiation and suggest the involvement of the tryptophan metabolism in this cellular process triggered by flavivirus infections.
“You Have Reached Your Destination”: A Single Trial EEG Classification Study

Studies have established that it is possible to differentiate between the brain's responses to observing correct and incorrect movements in navigation tasks. Furthermore, these classifications can be used as feedback for a learning-based BCI, to allow real or virtual robots to find quasi-optimal routes to a target. However, when navigating it is important not only to know we are moving in the right direction toward a target, but also to know when we have reached it. We asked participants to observe a virtual robot performing a 1-dimensional navigation task. We recorded EEG and then performed neurophysiological analysis on the responses to two classes of correct movements: those that moved closer to the target but did not reach it, and those that did reach the target. Further, we used a stepwise linear classifier on time-domain features to differentiate the classes on a single-trial basis. A second data set was also used to further test this single-trial classification. We found that the amplitude of the P300 was significantly greater in cases where the movement reached the target. Interestingly, we were able to classify the EEG signals evoked when observing the two classes of correct movements against each other with mean overall accuracy of 66.5 and 68.0% for the two data sets, with greater than chance levels of accuracy achieved for all participants. As a proof of concept, we have shown that it is possible to classify the EEG responses in observing these different correct movements against each other using single-trial EEG. This could be used as part of a learning-based BCI and opens a new door toward a more autonomous BCI navigation system.
Stepwise Biogenesis of Subpopulations of Lipid Droplets in Nitrogen Starved Phaeodactylum tricornutum Cells

Diatoms are unicellular heterokonts, living in oceans and freshwaters, exposed to frequent environmental variations. They have a sophisticated membrane compartmentalization and are bounded by a siliceous cell-wall. Formation of lipid droplets (LDs), filled with triacylglycerol (TAG), is a common response to stress. The proteome of mature-LDs from Phaeodactylum tricornutum highlighted the lack of proteins involved in early-LD formation, TAG biosynthesis or LD-to-LD connections. These features suggest that cytosolic LDs might reach a size limit. We analyzed the dynamics of LD formation in P. tricornutum (Pt1 8.6; CCAP 1055/1) during 7 days of nitrogen starvation, by monitoring TAG by mass spectrometry-based lipidomics, and LD radius using epifluorescence microscopy and pulse field gradient nuclear magnetic resonance. We confirmed that mature LDs reach a maximal size. Based on pulse field gradient nuclear magnetic resonance, we did not detect any LD-LD fusion. Three LD subpopulations were produced, each with a different maximal size, larger-sized LDs (radius 0.675 ± 0.125 µm) being generated first. Mathematical modeling showed how smaller LDs are produced once larger LDs have reached their maximum radius. In a mutant line having larger cells, the maximal size of the first LD subpopulation was higher (0.941 ± 0.169 µm), while the principle of stepwise formation of distinct LD populations was maintained. Results suggest that LD size is determined by available cytosolic space and sensing of an optimal size reached in the previous LD subpopulation. Future perspectives include the unraveling of LD-size control mechanisms upon nitrogen shortage. This study also provides novel prospects for the optimization of oleaginous microalgae for biotechnological applications.