Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting phonon-phonon scattering in single-layer graphene

Understanding the mechanisms of thermal conduction in graphene is a long-lasting research topic, due to its high thermal conductivity. Peierls-Boltzmann transport equation (PBTE) based studies have revealed many unique phonon transport properties in graphene, but most previous works only considered three-phonon scatterings and relied on interatomic force constants (IFCs) extracted at 0 K. In this paper, we explore the roles of four-phonon scatterings and the temperature dependent IFCs on phonon transport in graphene through our PBTE calculations. We demonstrate that the strength of four-phonon scatterings would be severely overestimated by using the IFCs extracted at 0 K compared with those corresponding to a finite temperature, and four-phonon scatterings are found to significantly reduce the thermal conductivity of graphene even at room temperature. In order to reproduce the prediction from molecular dynamics simulations, phonon frequency broadening has to be taken into account when determining the phonon scattering rates. Our study elucidates the phonon transport properties of graphene at finite temperatures, and could be extended to other crystalline materials.
Sustained NPY signaling enables AgRP neurons to drive feeding

Artificial stimulation of Agouti-Related Peptide (AgRP) neurons promotes intense food consumption, yet paradoxically during natural behavior these cells are inhibited before feeding begins. Previously, to reconcile these observations, we showed that brief stimulation of AgRP neurons can generate hunger that persists for tens of minutes, but the mechanisms underlying this sustained hunger drive remain unknown (Chen et al., 2016). Here we show that Neuropeptide Y (NPY) is uniquely required for the long-lasting effects of AgRP neurons on feeding behavior. We blocked the ability of AgRP neurons to signal through AgRP, NPY, or GABA, and then stimulated these cells using a paradigm that mimics their natural regulation. Deletion of NPY, but not AgRP or GABA, abolished optically-stimulated feeding, and this was rescued by NPY re-expression selectively in AgRP neurons. These findings reveal a unique role for NPY in sustaining hunger in the interval between food discovery and consumption.
THE SPLASH SURVEY: QUIESCENT GALAXIES ARE MORE STRONGLY CLUSTERED BUT ARE NOT NECESSARILY LOCATED IN HIGH-DENSITY ENVIRONMENTS

We use the stellar-mass-selected catalog from the Spitzer Large Area Survey with Hyper-Suprime-Cam (SPLASH) in the COSMOS field to study the environments of galaxies via galaxy density and clustering analyses up to z ∼ 2.5 . > The clustering strength of quiescent galaxies exceeds that of star-forming galaxies, implying that quiescent galaxies are preferentially located in more massive halos. When using local density measurement, we find a clear positive quiescent fraction–density relation at z < 1 , ? > consistent with earlier results. However, the quiescent fraction–density relation reverses its trend at intermediate redshifts ( 1 < z < 1.5 ? > ) with marginal significance (<1.8σ) and is found to be scale dependent (1.6σ). The lower fraction of quiescent galaxies seen in large-scale dense environments, if confirmed to be true, may be associated with the fact that the star formation can be more easily sustained via cold stream accretion in “large-scale” high-density regions, preventing galaxies from permanent quenching. Finally, at z > 1.5 , ? > the quiescent fraction depends little on the local density, even though clustering shows that quiescent galaxies are in more massive halos. We argue that at high redshift the typical halo size falls below 1013 , where intrinsically the local density measurements are so varied that they do not trace the halo mass. Our results thus suggest that in the high-redshift universe, halo mass may be the key in quenching the star formation in galaxies, rather than the conventionally measured galaxy density.
An Elementary Proof of a Matrix Tree Theorem for Directed Graphs

We present an elementary proof of a generalization of Kirchoff's matrix tree theorem to directed, weighted graphs. The proof is based on a specific factorization of the Laplacian matrices associated to the graphs, which only involves the two incidence matrices that capture the graph's topology. We also point out how this result can be used to calculate principal eigenvectors of the Laplacian matrices.
The effects of three different exercise modalities on markers of male reproduction in healthy subjects: a randomized controlled trial.

The aim of this study was to investigate the effects of moderate-intensity continuous training (MICT), high-intensity continuous training (HICT) and high-intensity interval training (HIIT) on markers of male reproduction including seminal markers of oxidative stress and inflammation as well as semen quality and sperm DNA integrity in healthy human subjects. A total of 397 healthy male volunteers were screened and 280 were randomly assigned to one of the MICT (n = 70), HICT (n = 70), HIIT (n = 70) and non-exercise (NON-EX, n = 70) groups. Subjects had inflammatory markers (IL-1β, IL-6, IL-8 and TNF-α), oxidants (ROS, MDA and 8-isoprostane), antioxidants (SOD, catalase and TAC), semen parameters and sperm DNA damage measured at baseline (T1), the end of week 12 (T2), the end of week 24 (T3), and 7 (T4) and 30 days (T5) after training. Chronic MICT, HICT and HIIT attenuated seminal markers of oxidative stress and inflammation with different kinetics for the three types of exercise (P < 0.05), and these changes were correlated with favorable improvements in semen quality parameters and sperm DNA integrity (P < 0.05). MICT was superior to HICT and HIIT in the improvements of markers of male reproductive function (P < 0.05). In conclusion, different exercise modalities favorably affect markers of male reproduction with different kinetics, suggesting intensity-, duration- and type-dependent adaptations to exercise training in healthy human subjects.
Bohr's Complementarity: Completed with Entanglement

Ninety years ago in 1927, at an international congress in Como, Italy, Niels Bohr gave an address which is recognized as the first instance in which the term "complementarity", as a physical concept, was spoken publicly [1], revealing Bohr's own thinking about Louis de Broglie's "duality". Bohr had very slowly accepted duality as a principle of physics: close observation of any quantum object will reveal either wave-like or particle-like behavior, one or the other of two fundamental and complementary features. Little disagreement exists today about complementarity's importance and broad applicability in quantum science. Book-length scholarly examinations even provide speculations about the relevance of complementarity in fields as different from physics as biology, psychology and social anthropology, connections which were apparently of interest to Bohr himself (see Jammer [2], Murdoch [3] and Whitaker [4]). Confusion evident in Como following his talk was not eliminated by Bohr's article [1], and complementarity has been subjected to nine decades of repeated examination ever since with no agreed resolution. Semi-popular treatments [5] as well as expert examinations [6-9] show that the topic cannot be avoided, and complementarity retains its central place in the interpretation of quantum mechanics. However, recent approaches by our group [10-13] and others [14-20] to the underlying notion of coherence now allow us to present a universal formulation of complementarity that may signal the end to the confusion. We demonstrate a new relationship that constrains the behavior of an electromagnetic field (quantum or classical) in the fundamental context of two-slit experiments. We show that entanglement is the ingredient needed to complete Bohr's formulation of complementarity, debated for decades because of its incompleteness.
Spontaneous Eye Blink Rate (EBR) Is Uncorrelated with Dopamine D2 Receptor Availability and Unmodulated by Dopamine Agonism in Healthy Adults

Abstract Spontaneous eye blink rate (EBR) has been proposed as a noninvasive, inexpensive marker of dopamine functioning. Support for a relation between EBR and dopamine function comes from observations that EBR is altered in populations with dopamine dysfunction and EBR changes under a dopaminergic manipulation. However, the evidence across the literature is inconsistent and incomplete. A direct correlation between EBR and dopamine function has so far been observed only in nonhuman animals. Given significant interest in using EBR as a proxy for dopamine function, this study aimed to verify a direct association in healthy, human adults. Here we measured EBR in healthy human subjects whose dopamine D2 receptor (DRD2) availability was assessed with positron emission tomography (PET)-[18F]fallypride to examine the predictive power of EBR for DRD2 availability. Effects of the dopamine agonist bromocriptine on EBR also were examined to determine the responsiveness of EBR to dopaminergic stimulation and, in light of the hypothesized inverted-U profile of dopamine effects, the role of DRD2 availability in EBR responsivity to bromocriptine. Results from 20 subjects (age 33.6 ± 7.6 years, 9F) showed no relation between EBR and DRD2 availability. EBR also was not responsive to dopaminergic stimulation by bromocriptine, and individual differences in DRD2 availability did not modulate EBR responsivity to bromocriptine. Given that EBR is hypothesized to be particularly sensitive to DRD2 function, these findings suggest caution in using EBR as a proxy for dopamine function in healthy humans.
Embedding the stationary spacetimes into Brans-Dicke cosmology via conformal transformations

A conformal transformation of a static or stationary metric by a time dependent conformal scale factor $S(\tau)^2$ is one of the methods of producing a cosmological spacetime. Using this knowledge and Brans-Dicke (BD) field equations, we investigate two cosmological metrics including Friedmann-Lemaitre-Robertson-Walker (FLRW) spacetime and cosmological Kerr-Newmann black hole and we obtain solutions which allow different expansion rates for each geometries. These expansion rates depend on the matter content of the untransformed parts of geometry. From where, we obtain that an accelerated expansion of the universe can only arise from a vacuum energy and the BD scalar field is the main factor causing this expansion.
Psychological and Environmental Correlates of Moderate-to-Vigorous Physical Activity and Step Counts Among Older Adults With Cognitive Decline

Promoting physical activity (PA) among older adults with cognitive decline is important for maintaining and improving their health. Identifying psychological and environmental PA correlates in this population can help develop effective strategies for PA promotion. Since past findings with healthy older adults may not generalize to those with cognitive decline, this study offers data on how self-efficacy, self-regulation, and social-environmental factors were associated with PA among a group of older adults with cognitive decline. We report secondary analysis of baseline data from a randomized control study of 262 older participants (M = 76.2 years) who showed a decline in global cognitive function as defined by Mini-Mental State Examination scores between 21 and 24. The participants’ PA was measured by an accelerometer, and participants completed questionnaires measuring self-efficacy, self-regulation, social support, social network, and demographic variables. In this study, we evaluated the participants' neighborhood environment with a geographic information system and found through stepwise multiple regression analyses that self-efficacy, gender, and age were associated with PA variables, while self-regulation and environmental factors were not. Thus, perceived self-efficacy was an important psychological correlate of PA in this group of older adults with cognitive decline. PA promotion interventions for this population should attend to self-efficacy, gender, and age.
Species-richness patterns of the living collections of the world's botanic gardens: a matter of socio-economics?

BACKGROUND AND AIMS The botanic gardens of the world are now unmatched ex situ collections of plant biodiversity. They mirror two biogeographical patterns (positive diversity-area and diversity-age relationships) but differ from nature with a positive latitudinal gradient in their richness. Whether these relationships can be explained by socio-economic factors is unknown. METHODS Species and taxa richness of a comprehensive sample of botanic gardens were analysed as a function of key ecological and socio-economic factors using (a) multivariate models controlling for spatial autocorrelation and (b) structural equation modelling. KEY RESULTS The number of plant species in botanic gardens increases with town human population size and country Gross Domestic Product (GDP) per person. The country flora richness is not related to the species richness of botanic gardens. Botanic gardens in more populous towns tend to have a larger area and can thus host richer living collections. Botanic gardens in richer countries have more species, and this explains the positive latitudinal gradient in botanic gardens' species richness. CONCLUSIONS Socio-economic factors contribute to shaping patterns in the species richness of the living collections of the world's botanic gardens.
Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition

Action recognition with skeleton data has recently attracted much attention in computer vision. Previous studies are mostly based on fixed skeleton graphs, only capturing local physical dependencies among joints, which may miss implicit joint correlations. To capture richer dependencies, we introduce an encoder-decoder structure, called A-link inference module, to capture action-specific latent dependencies, i.e. actional links, directly from actions. We also extend the existing skeleton graphs to represent higher-order dependencies, i.e. structural links. Combing the two types of links into a generalized skeleton graph, We further propose the actional-structural graph convolution network (AS-GCN), which stacks actional-structural graph convolution and temporal convolution as a basic building block, to learn both spatial and temporal features for action recognition. A future pose prediction head is added in parallel to the recognition head to help capture more detailed action patterns through self-supervision. We validate AS-GCN in action recognition using two skeleton data sets, NTU-RGB+D and Kinetics. The proposed AS-GCN achieves consistently large improvement compared to the state-of-the-art methods. As a side product, AS-GCN also shows promising results for future pose prediction.
Line-items and table understanding in structured documents

Table detection and extraction has been studied in the context of documents like scientific papers, where tables are clearly outlined and stand out from the visual document structure. We study this topic in a rather more challenging domain of layout-heavy business documents, particularly invoices. Invoices present the novel challenges of tables being often without outlines - either in the form of borders or surrounding text flow - with ragged columns and widely varying data content. We will also show, that we can extract different structural information from different table-like structures. We present a comprehensive representation of a page using graph over word boxes, positional embeddings, trainable textual features and rephrase the table detection as a text box labeling problem. We will work on a new dataset of invoices using this representation and propose multiple baselines to solve this labeling problem. We then propose a novel neural network model that achieves strong, practical results on the presented dataset and analyze the model performance and effects of graph convolutions and self-attention in detail.
HAXMLNet: Hierarchical Attention Network for Extreme Multi-Label Text Classification

Extreme multi-label text classification (XMTC) addresses the problem of tagging each text with the most relevant labels from an extreme-scale label set. Traditional methods use bag-of-words (BOW) representations without context information as their features. The state-ot-the-art deep learning-based method, AttentionXML, which uses a recurrent neural network (RNN) and the multi-label attention, can hardly deal with extreme-scale (hundreds of thousands labels) problem. To address this, we propose our HAXMLNet, which uses an efficient and effective hierarchical structure with the multi-label attention. Experimental results show that HAXMLNet reaches a competitive performance with other state-of-the-art methods.
Tumor‐derived exosomal lncRNA GAS5 as a biomarker for early‐stage non‐small‐cell lung cancer diagnosis

Diagnosis and treatment at an early stage may improve survival of non‐small‐cell lung cancer (NSCLC). Previous studies have found that long noncoding RNA growth arrest‐specific transcript 5 (GAS5) is essential to cancer progression. However, the expression and diagnostic value of GAS5 in exosomes (Exo‐GAS5) remain unclear. One hundred and four participants were enrolled, including subjects with NSCLC (n = 64) and healthy subjects ( n = 40). The total Exosome Isolation Kit was applied to isolate exosomes from serum. Total RNA was extracted and the AS5 expression was analyzed using quantitative reverse transcription polymerase chain reaction. Receiver operating characteristic (ROC) curve analysis was applied to evaluate the diagnostic value of Exo‐GAS5 in NSCLC. Our data indicated that the Exo‐GAS5 was downregulated in patients with NSCLC compared with healthy controls ( p < 0.001). Furthermore, patients with NSCLC with larger tumor size ( p = 0.025) and advanced TNM (T: extent of the primary tumor; N: lymph node involvement; M: metastatic disease) classification ( p = 0.047) showed lower Exo‐GAS5 expression. ROC curve analysis using Exo‐GAS5 combined with carcinoembryonic antigen showed an area under curve (AUC) of 0.929. Exo‐GAS5 could be used to distinguish patients with Stage I NSCLC with an AUC of 0.822. In conclusion, Exo‐GAS5 may function as an ideal noninvasive serum‐based marker for identifying patients with early NSCLC.
PD‐1 Blockade Cellular Vesicles for Cancer Immunotherapy

Cancer cells resist to the host immune antitumor response via multiple suppressive mechanisms, including the overexpression of PD‐L1 that exhausts antigen‐specific CD8+ T cells through PD‐1 receptors. Checkpoint blockade antibodies against PD‐1 or PD‐L1 have shown unprecedented clinical responses. However, limited host response rate underlines the need to develop alternative engineering approaches. Here, engineered cellular nanovesicles (NVs) presenting PD‐1 receptors on their membranes, which enhance antitumor responses by disrupting the PD‐1/PD‐L1 immune inhibitory axis, are reported. PD‐1 NVs exhibit a long circulation and can bind to the PD‐L1 on melanoma cancer cells. Furthermore, 1‐methyl‐tryptophan, an inhibitor of indoleamine 2,3‐dioxygenase can be loaded into the PD‐1 NVs to synergistically disrupt another immune tolerance pathway in the tumor microenvironment. Additionally, PD‐1 NVs remarkably increase the density of CD8+ tumor infiltrating lymphocytes in the tumor margin, which directly drive tumor regression.
Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising

Click-through rate (CTR) prediction is a critical task in online display advertising. The data involved in CTR prediction are typically multi-field categorical data, i.e., every feature is categorical and belongs to one and only one field. One of the interesting characteristics of such data is that features from one field often interact differently with features from different other fields. Recently, Field-aware Factorization Machines (FFMs) have been among the best performing models for CTR prediction by explicitly modeling such difference. However, the number of parameters in FFMs is in the order of feature number times field number, which is unacceptable in the real-world production systems. In this paper, we propose Field-weighted Factorization Machines (FwFMs) to model the different feature interactions between different fields in a much more memory-efficient way. Our experimental evaluations show that FwFMs can achieve competitive prediction performance with only as few as 4% parameters of FFMs. When using the same number of parameters, FwFMs can bring 0.92% and 0.47% AUC lift over FFMs on two real CTR prediction data sets.
Comparison of the treatment efficacy between tigecycline plus high-dose cefoperazone-sulbactam and tigecycline monotherapy against ventilator-associated pneumonia caused by extensively drug-resistant Acinetobacter baumannii.

OBJECTIVE The present study examined the effect of high-dose cefoperazone-sulbactam combined with tigecycline against ventilator-associated pneumonia (VAP) caused by extensively drug-resistant Acinetobacter baumannii(XDR-AB). MATERIALS AND METHODS 42 patients with VAP due to XDR-AB infection were randomized into two groups: the TIG group (received tigecycline injection) and the TIG+CFS group (received tigecycline and cefoperazone-sulbactam (1 : 1) injection). Pulsed field gel electrophoresis (PFGE) was used for genotyping the isolated XDR-AB. The microdilution method was used to test the minimum inhibitory concentration (MIC) of cefoperazone-sulbactam or tigecycline in vitro and the combined effect was determined with the checkerboard method. RESULTS The total combined effectiveness rate (including all patients who demonstrated an improved condition) was significantly higher in the TIG+CFS group (85.7%) compared with the TIG group (47.6%) (p = 0.010). No significant differences were noted with regard to the adverse reactions between the two groups. The 42 isolated XDR-AB strains were classified into four types. The MIC of the two drugs in combination was significantly lower than that of each drug used alone (p < 0.05). CONCLUSION High dose of cefoperazone-sulbactam can improve the antimicrobial activity of tigecycline against XDR-AB.
Trastuzumab-Resistant HER2+ Breast Cancer Cells Retain Sensitivity to Poly (ADP-Ribose) Polymerase (PARP) Inhibition

HER2-targeted therapies, such as trastuzumab, have increased the survival rates of HER2+ breast cancer patients. However, despite these therapies, many tumors eventually develop resistance to these therapies. Our lab previously reported an unexpected sensitivity of HER2+ breast cancer cells to poly (ADP-ribose) polymerase inhibitors (PARPi), agents that target homologous recombination (HR)–deficient tumors, independent of a DNA repair deficiency. In this study, we investigated whether HER2+ trastuzumab-resistant (TR) breast cancer cells were susceptible to PARPi and the mechanism behind PARPi induced cytotoxicity. We demonstrate that the PARPi ABT-888 (veliparib) decreased cell survival in vitro and tumor growth in vivo of HER2+ TR breast cancer cells. PARP-1 siRNA confirmed that cytotoxicity was due, in part, to PARP-1 inhibition. Furthermore, PARP-1 silencing had variable effects on the expression of several NF-κB–regulated genes. In particular, silencing PARP-1 inhibited NF-κB activity and reduced p65 binding at the IL8 promoter, which resulted in a decrease in IL8 mRNA and protein expression. Our results provide insight in the potential mechanism by which PARPi induces cytotoxicity in HER2+ breast cancer cells and support the testing of PARPi in patients with HER2+ breast cancer resistant to trastuzumab. Mol Cancer Ther; 17(5); 921–30. ©2018 AACR.