Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical spectroscopy of X‐Mega targets – IV. CPD – 59°2636: a new O‐type multiple system in the Carina Nebula

High-resolution optical spectroscopy of CPD −59 ◦ 2636, one of the O-type stars in the open cluster Trumpler 16 in the Carina Nebula, reveals this object to be a multiple system displaying triple lines which we label as components A, B and C of spectral types O7 V, O8 V and O9 V, respectively. From our radial velocity measurements we find that the components A and B form a close binary with a period of 3.6284 d, and we obtain the first circular radial velocity orbit for this system with semi-amplitudes of 184 and 192 km s −1 , leading to minimum masses of 10.1 and 9.7 M� . We find that the component C is a single lined binary with a period of 5.034 d and semi-amplitude of 48 km s −1 . We also analyse the X-ray radiation from CPD −59 ◦ 2636, finding neither appreciable overluminosity nor phase-related X-ray flux variations.
DAVO:A Monocular Visual Odometry Method Based on Dual Attention

In recent years, Visual odometry(VO) has been widely used in fields such as autonomous driving and virtual reality. Traditional methods for solving visual odometry rely on complex processes such as feature extraction, feature matching and camera calibration, and have low robustness and serious accuracy deficiency problems in challenging environments. In this paper, we propose a dual attention monocular visual odometry model that integrates Deep Learning(DL) with Reinforcement Learning(RL), named DAVO (Dual Attention Visual Odometry). The model combines a recurrent attention network model with a self-attentive mechanism to solve the relative poses of six degrees of freedom(6-DoF) by learning the image region locations that are favorable for the model pose estimation through a reinforcement learning algorithm. Finally, the model is evaluated and compared on the publicly available dataset KITTI. Compared with other mainstream models, DAVO only inputs 14.04% of the data in the image preprocessing stage, runs faster and outperforms most of the mainstream models.
Raman Scattering from Nonequilibrium LO Phonons with Picosecond Resolution

A novel technique is described for time-resolved Raman scattering for studying the dynamics of nonequilibrium excitations on a picosecond time scale. The generation and the decay of nonthermal LO phonons in GaAs is measured, and ${\ensuremath{\tau}}^{\ensuremath{'}}=7\ifmmode\pm\else\textpm\fi{}1$ ps is obtained for the relaxation time of the phonon population at 77 K.
Heat Transfer and Friction Factors for Flows Inside Circular Tubes With Concavity Surfaces

Heat transfer and friction coefficients measurements have been obtained for fully developed, turbulent internal flows in circular tubes with six different concavity (dimple) surface array geometries. Two different concavity depths and three different concavity array densities were tested using tube bulk flow Reynolds numbers from 20,000 to 90,000. Liquid-crystal thermography was used to measure the temperature distributions on the outside of the concavity tubes. Using the average heat transfer coefficient for the fully developed region, the overall heat transfer enhancements are compared to baseline smooth tube results. Friction coefficients are also compared to values for a smooth tube. Dimple depths of 0.2-0.4 relative to the dimple surface diameter were used, with surface area densities ranging from 0.3 to 0.7. Dimple arrays were all in-line geometries. The results showed that heat transfer enhancements for dimpled internal surfaces of circular passages can reach factors of 2 or more when the relative dimple depth is greater than 0.3 and the dimple array density is about 0.5 or higher. The associated friction factor multipliers for such configurations are in the range of 4-6. The present study provides a first insight into the heat transfer and friction effects of various concavity arrays for turbulent flows.
Altered Lateralization of Gamma Oscillations and Theta-gamma Coupling in Major Depression: An EEG Study

Abnormal gamma oscillations in major depressive disorder (MDD) have been repeatedly reported. Auditory steady-state response (ASSR), induced by the periodic auditory stimuli with the frequency and phase synchronized response, is the most common brain response to gamma oscillations in mental disorders. However, such researches in MDD remain scarce. Here, we attempted to explored the abnormal neural oscillations in MDD by analyzing gamma Inter-trial phase coherence (ITC) and theta-phase/gamma-amplitude coupling (TGC) under the ASSR task. 40Hz-ASSR electroencephalography data were recorded from 30 MDD and 33 healthy controls (HC). Then we calculated gamma-ITC and TGC in bilateral temporal regions, finally confirmed the separability of these indicators with inter-group significance for MDD by Support Vector Machine (SVM). Compared with HC, our results demonstrated MDD exhibited a decreased right-lateralization of gamma-ITC, and a decreased right temporal TGC with a reversed hemispheric asymmetry, which may reveal temporal impairments of MDD, particularly the reduced right-side dominance, when they performing the auditory task. Besides, the joint feature of gamma-ITC and TGC showed the information complementarity with the classification sensitivity 90.0%, specificity 70.1% and accuracy 81.8%, comparing to the single feature. Our findings suggested the altered hemispheric lateralization under 40Hz-ASSR may be a neurophysiological feature of MDD, providing novel insights into pathological mechanisms and the diagnosis of MDD.
Research on the application of artificial intelligence algorithms in drought prediction

With the aggravation of global warming, drought has caused more and more serious impact on society and national economy, so the accurate prediction of drought is also the most important thing to deal with the frequent drought problem. On the other hand, artificial intelligence technology has shown its advantages in various fields and has a broad application prospects, so it is gradually being used in drought prediction. In this paper, drought prediction algorithms in recent years are reviewed, and existing cases of drought prediction using different arithmetic models for different drought indices are studied. The advantages and disadvantages of different algorithms are compared, the challenges of drought prediction are summarized, and the future of drought prediction is prospected.
The angular correlation function of galaxies with B ~ 25 mag

We investigate the angular two-point correlation functions, ω(θ), for a sample of 4540 faint galaxies observed in the blue passband to a magnitude limit B ccd = 25.0, and for a sample of 3627 galaxies observed in the red passband to R ccd = 23.5. Our results are in reasonable agreement with those from earlier photographic surveys to B ≃ 24 and R ≃ 23. It is found that, for both the blue-selected and the red-selected samples, the correlation amplitude is significantly lower at our faint magnitude limits than would be expected for a model in which clustering is stable in proper coordinates and the redshift distribution maintains a no-evolution form
Transients and the Extratropical Response to El Niño

Abstract A baroclinic stationary wave model linearized about a zonally symmetric flow is used to interpret the extra-tropical atmospheric response to El Nino produced by a general circulation model. When forced by the anomalous diabatic beating and tendency due to transients, the linear model provides a useful simulation of this response. The direct response to anomalous diabatic heating is found to be small in the extratropics; the dominant term is the response to the anomalous transients, particularly the anomalous upper tropospheric transients in the vorticity equation. These results are complementary to those obtained with a nonlinear barotropic model by Held and Kang, and indicate that the anomalous subtropical convergence which plays a key role in that study is itself primarily forced by the anomalous transients. One can distinguish between two distinct parts of the response of the transients to the tropical heating: the movement of the Pacific storm track associated with the anomalous extratropical.
Study of functional and metabolic changes in subregions of precuneus in mild cognitive impairment patients using hybrid FDG-PET/fMRI

Characterization of the pathological changes in mild cognitive impairment (MCI) might provide useful biomarkers for early detection of individuals at high risk for MCI to Alzheimer's disease (AD) conversion. Previous studies have shown that the disrupted functional connectivities (FCs) between the precuneus (PCu) and large-scale brain functional networks are involved in early-stage AD pathology. Yet the findings in MCI are various, which might be due to the heterogeneous functions of the subregions of PCu. Also, the underlying metabolic mechanism remains unclear. In this study, using a hybrid 18F-2-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) and functional magnetic resonance imaging (fMRI) approach, we investigated the FC changes between the functional subregions of PCu, i.e., the dorsal-anterior (DA_pcu), dorsal-posterior (DP_pcu), and ventral precuneus (V_pcu), to large-scale brain functional networks, i.e., the default mode network (DMN), the frontoparietal network (FPN) and the visual network (VIS), in relation to the local glucose metabolism in healthy controls and MCI patients. For DP_pcu, the MCI patients showed reduced FC strength (FCS) to DMN and FPN and increased FCS to VIS, while increased FCS was found in the MCI group between V_pcu and VIS. Significant correlations were found between the DP_pcu local glucose metabolism and functional connections to DMN and VIS in the HC group, which was absent in the MCI group. For V_pcu, a positive correlation was found between glucose metabolism and FCS with VIS in the HC group, while a negative correlation was shown in the MCI group. Moreover, the FCs between DP_pcu and FPN and VIS were associated with subject cognitive function. These findings shed light on our understandings about the functional-metabolic signal changes in the subregions of PCu in MCI using an integrated PET/fMRI imaging approach.
Sales Demand Forecast based on Recurrent Neural Network∗

In this paper, we compare the performance of RNN, LSTM and GRU, which are the most popular cyclic neural networks, in predicting the total sales of products in each store next month. A dropout layer is added to the model to reduce over-fitting. The results show that the root mean square errors between the predicted value and the actual value for all the three models are less than 0.47, while the value of GRU model is 0.39, which has the best performance. At the same time, LSTM and GRU prove that they are more robust to the gradient disappearance and gradient explosion problems of RNN. Compared with LSTM, RNN and GRU still have some over fitting problems.
Exploring Entity-level User Preference on the Knowledge Graph for Recommender System

Knowledge graphs (KG) have attracted extensive attention in recommender systems since they contain rich external knowledge. The recent trend in KG-enhanced recommender systems is to employ graph neural networks (GNN) to learn the node representations of the involved graph structures in the recommender system to speculate user preferences. However, existing KG-enhanced recommendation models face two major issues: i) User preferences are mainly built at the relation level and fail to model preferences at the attribute entity level; ii) Implicit feedback lacks accurate user rating information and the data may contain noisy interactions. Such inaccurate preference modeling and imperfect interaction data hinder the capture of users’ actual preferences. To this end, we propose an Entity-level user Preference-aware model on Knowledge Graph (EPKG), which models user preferences at the attribute entity level. Specifically, we introduce the number of connections between attribute entities and user interaction items in the knowledge graph and establish a weight distribution on the number of connections to speculate user preferences for attribute entities. Furthermore, we devise user preference learning to model user preferences to the finer attribute entity level. Afterward, we design a preference-aware aggregation strategy that uses entity-level user preferences to guide the learning of item weights in user interaction history, which in turn alleviates the effects of lack of user rating information and noisy interactions. Experimental results on the three datasets show that EPKG achieves significant improvement compared to the state-of-the-art models. Especially for the Last-FM dataset, EPKG improves NDCG@20 and Recall@20 by 31.5% and 18.4%, respectively.
Focus on modern frontiers of matter wave optics and interferometry

The level of experimental control and the detailed theoretical understanding of matter wave physics have led to a renaissance of experiments testing the very foundations of quantum mechanics and general relativity, as well as to applications in metrology. A variety of interferometric quantum sensors surpasses, or will surpass, the limits of their classical counterparts, for instance in the measurement of frequency and time or forces such as accelerations due to rotation and gravity with applications in basic science, navigation and the search for natural resources. The collection of original articles published in this focus issue of New Journal of Physics is intended as a snapshot of the current research pursued by a number of leading teams working on the development of new matter wave physics, devices and techniques. A number of contributions also stress the close relation between the historic roots of quantum mechanics and aspects of modern quantum information science which are relevant for matter wave physics.
Numerical Analysis of Nonlinear Partial Differential-Algebraic Equations: A Coupled and an Abstract Systems Approach

Various mathematical models in many application areas give rise to systems of partial differential equations and differential-algebraic equations (DAEs). These systems are called partial or abstract differential-algebraic equations (ADAEs). Being usually discretized by the method of lines the semi-discretized system yields in general a DAE. A substantial mathematical treatment of nonlinear ADAEs is still at an initial stage. We present two approaches treating nonlinear ADAEs. We investigate them with regard to the solvability and uniqueness of solutions and the convergence of solutions of semi-dicretized systems to the original solution. Furthermore we study the sensitivity of a solution with regard to perturbations on the right hand side and in the initial value. The first approach represents an extension of an approach by Tischendorf for the treatment of a specific class of linear ADAEs to the nonlinear case. It is based on the Galerkin approach and the theory of monotone operators for evolution equations. We prove unique solvability of the ADAE and strong convergence of the Galerkin solutions. Furthermore we prove that this class of ADAEs has Perturbation Index 1 and at most ADAE Index 1. In the second approach we formulate two prototypes of coupled systems, an elliptic and a parabolic one. Here a semi-explicit DAE is coupled to an infinite dimensional algebraic operator equation or an evolution equation. For both prototypes we prove unique solvability, strong convergence of Galerkin solutions and a Perturbation Index 1 result. Both prototypes are applied to concrete coupled systems in circuit simulation. In this context we also prove a global solvability result for the nonlinear equations of the Modified Nodal Analysis under suitable topological assumptions.
Evidence that common arbuscular mycorrhizal network alleviates phosphate shortage in interconnected walnut sapling and maize plants

Under agroforestry practices, inter-specific facilitation between tree rows and cultivated alleys occurs when plants increase the growth of their neighbours especially under nutrient limitation. Due to a coarse roots architecture limiting soil inorganic phosphate (Pi) uptake, walnut trees (Juglans spp.) exhibit dependency on soil-borne symbiotic arbuscular mycorrhizal fungi that extend extra-radical hyphae beyond the root Pi depletion zone. To investigate the benefits of mycorrhizal walnuts in alley cropping, we experimentally simulated an agroforestry system in which walnut rootstocks RX1 (J. regia x J. microcarpa) were connected or not by a common mycelial network (CMN) to maize plants grown under two contrasting Pi levels. Mycorrhizal colonization parameters showed that the inoculum reservoir formed by inoculated walnut donor saplings allowed the mycorrhization of maize recipient roots. Relative to non-mycorrhizal plants and whatever the Pi supply, CMN enabled walnut saplings to access maize Pi fertilization residues according to significant increases in biomass, stem diameter and expression of JrPHT1;1 and JrPHT1;2, two mycorrhiza-inducible phosphate transporter candidates here identified by phylogenic inference of orthologs. In the lowest Pi supply, stem height, leaf Pi concentration and biomass of RX1 were significantly higher than in non-mycorrhizal controls, showing that mycorrhizal connections between walnut and maize roots alleviated Pi deficiency in the mycorrhizal RX1 donor plant. Under Pi limitation, maize recipient plants also benefited from mycorrhization relative to controls, as inferred from larger stem diameter and height, biomass, leaf number, N content and Pi concentration. Mycorrhization-induced Pi uptake generated a higher carbon cost for donor walnut plants than for maize plants by increasing walnut plant photosynthesis to provide the AM fungus with carbon assimilate. Here we show for the first time that CMN alleviates Pi deficiency in co-cultivated walnut and maize plants, and may therefore contribute to limit the use of chemical P fertilizers in agroforestry systems.
Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers

Nervous system cancers contain a large spectrum of transcriptional cell states, reflecting processes active during normal development, injury response and growth. However, we lack a good understanding of these states’ regulation and pharmacological importance. Here, we describe the integrated reconstruction of such cellular regulatory programs and their therapeutic targets from extensive collections of single-cell RNA sequencing data (scRNA-seq) from both tumors and developing tissues. Our method, termed single-cell Regulatory-driven Clustering (scRegClust), predicts essential kinases and transcription factors in little computational time thanks to a new efficient optimization strategy. Using this method, we analyze scRNA-seq data from both adult and childhood brain cancers to identify transcription factors and kinases that regulate distinct tumor cell states. In adult glioblastoma, our model predicts that blocking the activity of PDGFRA, DDR1, ERBB3 or SOX6, or increasing YBX1 -activity, would potentiate temozolomide treatment. We further perform an integrative study of scRNA-seq data from both cancer and the developing brain to uncover the regulation of emerging meta-modules. We find a meta-module regulated by the transcription factors SPI1 and IRF8 and link it to an immune-mediated mesenchymal-like state. Our algorithm is available as an easy-to-use R package and companion visualization tool that help uncover the regulatory programs underlying cell plasticity in cancer and other diseases.
A Consensus Model of Glucose-Stimulated Insulin Secretion in the Pancreatic β-Cell

The pancreas plays a critical role in maintaining glucose homeostasis through the secretion of hormones from the islets of Langerhans. Glucose-stimulated insulin secretion (GSIS) by the pancreatic β-cell is the main mechanism for reducing elevated plasma glucose. Here we present a systematic modeling workflow for the development of kinetic pathway models using the Systems Biology Markup Language (SBML). Steps include retrieval of information from databases, curation of experimental and clinical data for model calibration and validation, integration of heterogeneous data including absolute and relative measurements, unit normalization, data normalization, and model annotation. An important factor was the reproducibility and exchangeability of the model, which allowed the use of various existing tools. The workflow was applied to construct the first consensus model of GSIS in the pancreatic β-cell based on experimental and clinical data from 39 studies spanning 50 years of pancreatic, islet, and β-cell research in humans, rats, mice, and cell lines. The model consists of detailed glycolysis and equations for insulin secretion coupled to cellular energy state (ATP/ADP ratio). Key findings of our work are that in GSIS there is a glucose-dependent increase in almost all intermediates of glycolysis. This increase in glycolytic metabolites is accompanied by an increase in energy metabolites, especially ATP and NADH. One of the few decreasing metabolites is ADP, which, in combination with the increase in ATP, results in a large increase in ATP/ADP ratios in the β-cell with increasing glucose. Insulin secretion is dependent on ATP/ADP, resulting in glucose-stimulated insulin secretion. The observed glucose-dependent increase in glycolytic intermediates and the resulting change in ATP/ADP ratios and insulin secretion is a robust phenomenon observed across data sets, experimental systems and species. Model predictions of the glucose-dependent response of glycolytic intermediates and insulin secretion are in good agreement with experimental measurements. Our model predicts that factors affecting ATP consumption, ATP formation, hexokinase, phosphofructokinase, and ATP/ADP-dependent insulin secretion have a major effect on GSIS. In conclusion, we have developed and applied a systematic modeling workflow for pathway models that allowed us to gain insight into key mechanisms in GSIS in the pancreatic β-cell.
ATF4 Expression in Thermogenic Adipocytes is Required for Cold-Induced Thermogenesis in Mice via FGF21-Independent Mechanisms

In brown adipose tissue (BAT), short-term cold exposure induces the activating transcription factor 4 (ATF4), and its downstream target fibroblast growth factor 21 (FGF21). Induction of ATF4 in BAT in response to mitochondrial stress is required for thermoregulation, partially via upregulation of FGF21. In the present study, we tested the hypothesis that Atf4 and Fgf21 induction in BAT are both required for BAT thermogenesis by generating mice selectively lacking either Atf4 (ATF4 BKO) or Fgf21 (FGF21 BKO) in UCP1-expressing adipocytes. After 3 days of cold exposure, core body temperature was significantly reduced in ad-libitum-fed ATF4 BKO mice, which correlated with Fgf21 downregulation in brown and beige adipocytes, and impaired browning of white adipose tissue (WAT). Conversely, despite having reduced browning, FGF21 BKO mice had preserved core body temperature after cold exposure. Mechanistically, ATF4, but not FGF21, regulates amino acid import and metabolism in response to cold, likely contributing to BAT thermogenic capacity under ad libitum-fed conditions. Importantly, under fasting conditions, both ATF4 and FGF21 were required for thermogenesis in cold-exposed mice. Thus, ATF4 regulates BAT thermogenesis by activating amino acid metabolism in BAT in a FGF21-independent manner. Graphical Abstract