Showing papers by "Xiang Zhang published in 2020"

Enhanced ferroelectricity in ultrathin films grown directly on silicon.

Abstract: Ultrathin ferroelectric materials could potentially enable low-power logic and nonvolatile memories1,2. As ferroelectric materials are made thinner, however, the ferroelectricity is usually suppressed. Size effects in ferroelectrics have been thoroughly investigated in perovskite oxides—the archetypal ferroelectric system3. Perovskites, however, have so far proved unsuitable for thickness scaling and integration with modern semiconductor processes4. Here we report ferroelectricity in ultrathin doped hafnium oxide (HfO2), a fluorite-structure oxide grown by atomic layer deposition on silicon. We demonstrate the persistence of inversion symmetry breaking and spontaneous, switchable polarization down to a thickness of one nanometre. Our results indicate not only the absence of a ferroelectric critical thickness but also enhanced polar distortions as film thickness is reduced, unlike in perovskite ferroelectrics. This approach to enhancing ferroelectricity in ultrathin layers could provide a route towards polarization-driven memories and ferroelectric-based advanced transistors. This work shifts the search for the fundamental limits of ferroelectricity to simpler transition-metal oxide systems—that is, from perovskite-derived complex oxides to fluorite-structure binary oxides—in which ‘reverse’ size effects counterintuitively stabilize polar symmetry in the ultrathin regime. Enhanced switchable ferroelectric polarization is achieved in doped hafnium oxide films grown directly onto silicon using low-temperature atomic layer deposition, even at thicknesses of just one nanometre.

Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images

Abstract: Background A novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Materials and Methods We collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the collected dataset, a deep learning-based CT diagnosis system (DeepPneumonia) was developed to identify patients with COVID-19. Results The experimental results showed that our model can accurately identify the COVID-19 patients from others with an excellent AUC of 0.99 and recall (sensitivity) of 0.93. In addition, our model was capable of discriminating the COVID-19 infected patients and bacteria pneumonia-infected patients with an AUC of 0.95, recall (sensitivity) of 0.96. Moreover, our model could localize the main lesion features, especially the ground-glass opacity (GGO) that is of great help to assist doctors in diagnosis. The diagnosis for a patient could be finished in 30 seconds, and the implementation on Tianhe-2 supercompueter enables a parallel executions of thousands of tasks simultaneously. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/server/Ncov2019. Conclusions The established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.

Non-invasive early detection of cancer four years before conventional diagnosis using a blood test.

Abstract: Early detection has the potential to reduce cancer mortality, but an effective screening test must demonstrate asymptomatic cancer detection years before conventional diagnosis in a longitudinal study. In the Taizhou Longitudinal Study (TZL), 123,115 healthy subjects provided plasma samples for long-term storage and were then monitored for cancer occurrence. Here we report the preliminary results of PanSeer, a noninvasive blood test based on circulating tumor DNA methylation, on TZL plasma samples from 605 asymptomatic individuals, 191 of whom were later diagnosed with stomach, esophageal, colorectal, lung or liver cancer within four years of blood draw. We also assay plasma samples from an additional 223 cancer patients, plus 200 primary tumor and normal tissues. We show that PanSeer detects five common types of cancer in 88% (95% CI: 80–93%) of post-diagnosis patients with a specificity of 96% (95% CI: 93–98%), We also demonstrate that PanSeer detects cancer in 95% (95% CI: 89–98%) of asymptomatic individuals who were later diagnosed, though future longitudinal studies are required to confirm this result. These results demonstrate that cancer can be non-invasively detected up to four years before current standard of care. Patients whose disease is diagnosed in its early stages have better outcomes. In this study, the authors develop a non invasive blood test based on circulating tumor DNA methylation that can potentially detect cancer occurrence even in asymptomatic patients.

Parameterized Explainer for Graph Neural Network

Abstract: Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by GNNs remains a challenging open problem. The leading method independently addresses the local explanations (i.e., important subgraph structure and node features) to interpret why a GNN model makes the prediction for a single instance, e.g. a node or a graph. As a result, the explanation generated is painstakingly customized for each instance. The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to a lack of generalizability and hindering it from being used in the inductive setting. Besides, as it is designed for explaining a single instance, it is challenging to explain a set of instances naturally (e.g., graphs of a given class). In this study, we address these key challenges and propose PGExplainer, a parameterized explainer for GNNs. PGExplainer adopts a deep neural network to parameterize the generation process of explanations, which enables PGExplainer a natural approach to explaining multiple instances collectively. Compared to the existing work, PGExplainer has better generalization ability and can be utilized in an inductive setting easily. Experiments on both synthetic and real-life datasets show highly competitive performance with up to 24.7\% relative improvement in AUC on explaining graph classification over the leading baseline.

Ten years of spasers and plasmonic nanolasers.

Abstract: Ten years ago, three teams experimentally demonstrated the first spasers, or plasmonic nanolasers, after the spaser concept was first proposed theoretically in 2003. An overview of the significant progress achieved over the last 10 years is presented here, together with the original context of and motivations for this research. After a general introduction, we first summarize the fundamental properties of spasers and discuss the major motivations that led to the first demonstrations of spasers and nanolasers. This is followed by an overview of crucial technological progress, including lasing threshold reduction, dynamic modulation, room-temperature operation, electrical injection, the control and improvement of spasers, the array operation of spasers, and selected applications of single-particle spasers. Research prospects are presented in relation to several directions of development, including further miniaturization, the relationship with Bose-Einstein condensation, novel spaser-based interconnects, and other features of spasers and plasmonic lasers that have yet to be realized or challenges that are still to be overcome.

Hypokalemia and Clinical Implications in Patients with Coronavirus Disease 2019 (COVID-19)

Abstract: BACKGROUND SARS-CoV-2 has caused a series of COVID-19 globally. SARS-CoV-2 binds angiotensin I converting enzyme 2 (ACE2) of renin–angiotensin system (RAS) and causes prevalent hypokalemia METHODS The patients with COVID-19 were classified into severe hypokalemia, hypokalemia, and normokalemia group. The study aimed to determine the relationship between hypokalemia and clinical features, the underlying causes and clinical implications of hypokalemia. RESULTS By Feb 15, 2020, 175 patients with COVID-19 (92 women and 83 men; median age, 46 [IQR, 34–54] years) were admitted to hospital in Wenzhou, China, consisting 39 severe hypokalemia-, 69 hypokalemia-, and 67 normokalemia patients. Gastrointestinal symptoms were not associated with hypokalemia among 108 hypokalemia patients (P > 0.05). Body temperature, CK, CK-MB, LDH, and CRP were significantly associated with the severity of hypokalemia (P CONCLUSIONS Hypokalemia is prevailing in patients with COVID-19. The correction of hypokalemia is challenging because of continuous renal K+ loss resulting from the degradation of ACE2. The end of urine K+ loss indicates a good prognosis and may be a reliable, in-time, and sensitive biomarker directly reflecting the end of adverse effect on RAS system.

Production of charged pions, kaons, and (anti-)protons in Pb-Pb and inelastic pp collisions at √sNN = 5.02 TeV

Abstract: Mid-rapidity production of $\pi^{\pm}$, $\rm{K}^{\pm}$ and ($\bar{\rm{p}}$)p measured by the ALICE experiment at the LHC, in Pb-Pb and inelastic pp collisions at $\sqrt{s_{\rm{NN}}}$ = 5.02 TeV, is presented. The invariant yields are measured over a wide transverse momentum ($p_{\rm{T}}$) range from hundreds of MeV/$c$ up to 20 GeV/$c$. The results in Pb-Pb collisions are presented as a function of the collision centrality, in the range 0$-$90%. The comparison of the $p_{\rm{T}}$-integrated particle ratios, i.e. proton-to-pion (p/$\pi$) and kaon-to-pion (K/$\pi$) ratios, with similar measurements in Pb-Pb collisions at $\sqrt{s_{\rm{NN}}}$ = 2.76 TeV show no significant energy dependence. Blast-wave fits of the $p_{\rm{T}}$ spectra indicate that in the most central collisions radial flow is slightly larger at 5.02 TeV with respect to 2.76 TeV. Particle ratios (p/$\pi$, K/$\pi$) as a function of $p_{\rm{T}}$ show pronounced maxima at $p_{\rm{T}}$ $\approx$ 3 GeV/$c$ in central Pb-Pb collisions. At high $p_{\rm{T}}$, particle ratios at 5.02 TeV are similar to those measured in pp collisions at the same energy and in Pb-Pb collisions at $\sqrt{s_{\rm{NN}}}$ = 2.76 TeV. Using the pp reference spectra measured at the same collision energy of 5.02 TeV, the nuclear modification factors for the different particle species are derived. Within uncertainties, the nuclear modification factor is particle species independent for high $p_{\rm{T}}$ and compatible with measurements at $\sqrt{s_{\rm{NN}}}$ = 2.76 TeV. The results are compared to state-of-the-art model calculations, which are found to describe the observed trends satisfactorily.

GNNGuard: Defending Graph Neural Networks against Adversarial Attacks

Abstract: Deep learning methods for graphs achieve remarkable performance on many tasks. However, despite the proliferation of such methods and their success, recent findings indicate that small, unnoticeable perturbations of graph structure can catastrophically reduce performance of even the strongest and most popular Graph Neural Networks (GNNs). Here, we develop GNNGuard, a general defense approach against a variety of training-time attacks that perturb the discrete graph structure. GNNGuard can be straightforwardly incorporated into any GNN. Its core principle is to detect and quantify the relationship between the graph structure and node features, if one exists, and then exploit that relationship to mitigate negative effects of the attack. GNNGuard uses network theory of homophily to learn how best assign higher weights to edges connecting similar nodes while pruning edges between unrelated nodes. The revised edges then allow the underlying GNN to robustly propagate neural messages in the graph. GNNGuard introduces two novel components, the neighbor importance estimation, and the layer-wise graph memory, and we show empirically that both components are necessary for a successful defense. Across five GNNs, three defense methods, and four datasets, including a challenging human disease graph, experiments show that GNNGuard outperforms existing defense approaches by 15.3% on average. Remarkably, GNNGuard can effectively restore the state-of-the-art performance of GNNs in the face of various adversarial attacks, including targeted and non-targeted attacks.

Learning to Drop: Robust Graph Neural Network via Topological Denoising

Abstract: Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics. The key idea is to recursively propagate and aggregate information along edges of the given graph. Despite their success, however, the existing GNNs are usually sensitive to the quality of the input graph. Real-world graphs are often noisy and contain task-irrelevant edges, which may lead to suboptimal generalization performance in the learned GNN models. In this paper, we propose PTDNet, a parameterized topological denoising network, to improve the robustness and generalization performance of GNNs by learning to drop task-irrelevant edges. PTDNet prunes task-irrelevant edges by penalizing the number of edges in the sparsified graph with parameterized networks. To take into consideration of the topology of the entire graph, the nuclear norm regularization is applied to impose the low-rank constraint on the resulting sparsified graph for better generalization. PTDNet can be used as a key component in GNN models to improve their performances on various tasks, such as node classification and link prediction. Experimental studies on both synthetic and benchmark datasets show that PTDNet can improve the performance of GNNs significantly and the performance gain becomes larger for more noisy datasets.

Lung mesenchymal cells elicit lipid storage in neutrophils that fuel breast cancer lung metastasis

Abstract: Acquisition of a lipid-laden phenotype by immune cells has been defined in infectious diseases and atherosclerosis but remains largely uncharacterized in cancer. Here, in breast cancer models, we found that neutrophils are induced to accumulate neutral lipids upon interaction with resident mesenchymal cells in the premetastatic lung. Lung mesenchymal cells elicit this process through repressing the adipose triglyceride lipase (ATGL) activity in neutrophils in prostaglandin E2-dependent and -independent manners. In vivo, neutrophil-specific deletion of genes encoding ATGL or ATGL inhibitory factors altered neutrophil lipid profiles and breast tumor lung metastasis in mice. Mechanistically, lipids stored in lung neutrophils are transported to metastatic tumor cells through a macropinocytosis-lysosome pathway, endowing tumor cells with augmented survival and proliferative capacities. Pharmacological inhibition of macropinocytosis significantly reduced metastatic colonization by breast tumor cells in vivo. Collectively, our work reveals that neutrophils serve as an energy reservoir to fuel breast cancer lung metastasis.

An engineered exosome for delivering sgRNA:Cas9 ribonucleoprotein complex and genome editing in recipient cells.

Abstract: CRISPR-Cas9 is a versatile genome-editing technology that is a promising gene therapy tactic. However, the delivery of CRISPR-Cas9 is still a major obstacle to its broader clinical application. Here, we confirm that the components of CRISPR-Cas9—sgRNA and Cas9 protein—can be packaged into exosomes, where sgRNA and Cas9 protein exist as a sgRNA:Cas9 ribonucleoprotein complex. Although exosomal CRISPR-Cas9 components can be delivered into recipient cells, they are not adequate to abrogate the target gene in recipient cells. To solve this, we engineered a functionalized exosome (M-CRISPR-Cas9 exosome) that could encapsulate CRISPR-Cas9 components more efficiently. To improve the loading efficiency of Cas9 proteins into exosomes, we artificially engineered exosomes by fusing GFP and GFP nanobody with exosomal membrane protein CD63 and Cas9 protein, respectively. Therefore, Cas9 proteins could be captured selectively and efficiently loaded into exosomes due to the affinity of GFP-GFP nanobody rather than random loading. sgRNA and Cas9 protein exist as a complex in functionalized exosomes and can be delivered into recipient cells. To show the function of modified exosomes-delivered CRISPR-Cas9 components in recipient cells visually, we generated a reporter cell line (A549stop-DsRed) that produced a red fluorescent signal when the stop element was deleted by the sgRNA-guided endonuclease. Using A549stop-DsRed reporter cells, we showed that modified exosomes loaded with CRISPR-Cas9 components abrogated the target gene more efficiently in recipient cells. Our study reports an alternative tactic for CRISPR-Cas9 delivery.

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

Abstract: Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous seizures, which affects about one percent of the worlds population. Most of the current seizure detection approaches strongly rely on patient history records and thus fail in the patient-independent situation of detecting the new patients. To overcome such limitation, we propose a robust and explainable epileptic seizure detection model that effectively learns from seizure states while eliminates the inter-patient noises. A complex deep neural network model is proposed to learn the pure seizure-specific representation from the raw non-invasive electroencephalography (EEG) signals through adversarial training. Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure. The proposed approach is evaluated over the Temple University Hospital EEG (TUH EEG) database. The experimental results illustrate that our model outperforms the competitive state-of-the-art baselines with low latency. Moreover, the designed attention mechanism is demonstrated ables to provide fine-grained information for pathological analysis. We propose an effective and efficient patient-independent diagnosis approach of epileptic seizure based on raw EEG signals without manually feature engineering, which is a step toward the development of large-scale deployment for real-life use.

The bone microenvironment invigorates metastatic seeds for further dissemination

Abstract: Metastasis has been considered as the terminal step of tumor progression. However, recent clinical studies suggest that many metastases are seeded from other metastases, rather than primary tumors. Thus, some metastases can further spread, but the corresponding pre-clinical models are lacking. By using several approaches including parabiosis and an evolving barcode system, we demonstrated that the bone microenvironment facilitates breast and prostate cancer cells to further metastasize and establish multi-organ secondary metastases. Importantly, dissemination from the bone microenvironment appears to be more aggressive compared to that from mammary tumors and lung metastases. We further uncovered that this metastasis-promoting effect is independent from genetic selection, as single cell-derived cancer cell populations (SCPs) exhibited enhanced metastasis capacity after being extracted from the bone microenvironment. Taken together, our work revealed a previously unappreciated effect of the bone microenvironment on metastasis evolution, and suggested a stable reprogramming process that engenders cancer cells more metastatic.

Resistance to natural killer cell immunosurveillance confers a selective advantage to polyclonal metastasis

Abstract: Polyclonal metastases frequently arise from clusters of circulating tumor cells (CTCs). CTC clusters metastasize better than single CTCs, but the underlying molecular mechanisms are poorly understood. Here, we show that polyclonal metastatic seeds exhibit higher resistance to natural killer (NK) cell killing. Using breast cancer models, we observed higher proportions of polyclonal lung metastasis in immunocompetent mice compared with mice lacking NK cells. Depleting NK cells selectively increased monoclonal but not polyclonal metastases, suggesting that CTC clusters are less sensitive to NK-mediated suppression. Transcriptional analyses revealed that clusters have elevated expression of cell–cell adhesion and epithelial genes, which is associated with decreased expression of NK cell activating ligands. Furthermore, perturbing tumor cell epithelial status altered NK ligand expression and sensitivity to NK-mediated killing. Collectively, our findings show that NK cells can determine the fate of CTCs of different epithelial and mesenchymal states, and impact metastatic clonal evolution by favoring polyclonal seeding. Zhang and colleagues report that multicellular clusters of circulating tumor cells are more resistant to killing by natural killer (NK) cells through altered NK ligand expression, resulting in polyclonal metastases.

Measurements of inclusive jet spectra in pp and central Pb-Pb collisions at sNN =5.02 TeV

Abstract: This article reports measurements of the pT- differential inclusive jet cross section in pp collisions at √s=5 .02TeV and the pT- differential inclusive jet yield in Pb-Pb 0–10% central collisions at√sNN =5.02TeV. Jets were reconstructed at midrapidity with the ALICE tracking detectors and electromagnetic calorimeter using the anti-kT algorithm. For ppcollisions, we report jet cross sections for jet resolution parameters R =0.1–0.6 over the range 20 < pT, jet < 140 GeV/c, as well as the jet cross-section ratios of different R and comparisons to two next-to-leading-order (NLO)– based theoretical predictions. For Pb-Pb collisions, we report the R=0.2 and R=0.4 jet spectra for 40 < pT, jet < 140 GeV/c and 60 < pT, jet < 140 GeV/c, respectively. The scaled ratio of jet yields observed in Pb-Pb to ppcollisions, RAA, is constructed, and exhibits strong jet quenching and a clear pT dependence for R=0.2. No significant R dependence of the jet RAA is observed within the uncertainties of the measurement. These results are compared to several theoretical predictions.

A Thermal Radiation Modulation Platform by Emissivity Engineering with Graded Metal-Insulator Transition.

Abstract: Thermal radiation from a black body increases with the fourth power of absolute temperature (T4 ), an effect known as the Stefan-Boltzmann law. Typical materials radiate heat at a portion of this limit, where the portion, called integrated emissivity (eint ), is insensitive to temperature (|deint /dT| ≈ 10-4 °C-1 ). The resultant radiance bound by the T4 law limits the ability to regulate radiative heat. Here, an unusual material platform is shown in which eint can be engineered to decrease in an arbitrary manner near room temperature (|deint /dT| ≈ 8 × 10-3 °C-1 ), enabling unprecedented manipulation of infrared radiation. As an example, eint is programmed to vary with temperature as the inverse of T4 , precisely counteracting the T4 dependence; hence, thermal radiance from the surface becomes temperature-independent, allowing the fabrication of flexible and power-free infrared camouflage with unique advantage in performance stability. The structure is based on thin films of tungsten-doped vanadium dioxide where the tungsten fraction is judiciously graded across a thickness less than the skin depth of electromagnetic screening.

Direct observation of Klein tunneling in phononic crystals.

Abstract: Tunneling plays an essential role in many branches of physics and has found important applications. It is theoretically proposed that Klein tunneling occurs when, under normal incidence, quasiparticles exhibit unimpeded penetration through potential barriers independent of their height and width. We created a phononic heterojunction by sandwiching two types of artificial phononic crystals with different Dirac point energies. The direct observation of Klein tunneling as shown by the key feature of unity transmission is demonstrated. Our experiment reveals that Klein tunneling occurs over a broad band of acoustic frequency. The direct observation of Klein tunneling in phononic crystals could find applications in signal processing, supercollimated beams, and communications.

Immune Checkpoint Profiles in Luminal B Breast Cancer (Alliance).

Abstract: BACKGROUND Unlike estrogen receptor (ER)-negative breast cancer, ER-positive breast cancer outcome is less influenced by lymphocyte content, indicating the presence of immune tolerance mechanisms that may be specific to this disease subset. METHODS A supervised analysis of microarray data from the ACOSOG Z1031 (Alliance) neoadjuvant aromatase inhibitor (AI) trial identified upregulated genes in Luminal (Lum) B breast cancers that correlated with AI-resistant tumor proliferation (percentage of Ki67-positive cancer nuclei, Pearson r > 0.4) (33 cases Ki67 > 10% on AI) vs LumB breast cancers that were more AI sensitive (33 cases Ki67 < 10% on AI). Overrepresentation analysis was performed using WebGestalt. All statistical tests were two-sided. RESULTS Thirty candidate genes positively correlated (r ≥ 0.4) with AI-resistant proliferation in LumB and were upregulated greater than twofold. Gene ontologies identified that the targetable immune checkpoint (IC) components IDO1, LAG3, and PD1 were overrepresented resistance candidates (P ≤ .001). High IDO1 mRNA was associated with poor prognosis in LumB disease (Molecular Taxonomy of Breast Cancer International Consortium, hazard ratio = 1.43, 95% confidence interval = 1.04 to 1.98, P = .03). IDO1 also statistically significantly correlated with STAT1 at protein level in LumB disease (Pearson r = 0.74). As a composite immune tolerance signature, expression of IFN-γ/STAT1 pathway components was associated with higher baseline Ki67, lower estrogen, and progesterone receptor mRNA levels and worse disease-specific survival (P = .002). In a tissue microarray analysis, IDO1 was observed in stromal cells and tumor-associated macrophages, with a higher incidence in LumB cases. Furthermore, IDO1 expression was associated with a macrophage mRNA signature (M1 by CIBERSORT Pearson r = 0.62 ) and by tissue microarray analysis. CONCLUSIONS Targetable IC components are upregulated in the majority of endocrine therapy-resistant LumB cases. Our findings provide rationale for IC inhibition in poor-outcome ER-positive breast cancer.

Tumor-Associated Neutrophils and Macrophages-Heterogenous but Not Chaotic.

Abstract: Tumor-associated macrophages (TAMs) and tumor-associated neutrophils (TANs) have been extensively studied. Their pleotropic roles were observed in multiple steps of tumor progression and metastasis, and sometimes appeared to be inconsistent across different studies. In this review, we collectively discussed many lines of evidence supporting the mutual influence between cancer cells and TAMs/TANs. We focused on how direct interactions among these cells dictate co-evolution involving not only clonal competition of cancer cells, but also landscape shift of the entire tumor microenvironment (TME). This co-evolution may take distinct paths and contribute to the heterogeneity of cancer cells and immune cells across different tumors. A more in-depth understanding of the cancer-TAM/TAN co-evolution will shed light on the development of TME that mediates metastasis and therapeutic resistance.

Effect of pulsed metal inert gas (pulsed-MIG) and cold metal transfer (CMT) techniques on hydrogen dissolution in wire arc additive manufacturing (WAAM) of aluminium

Abstract: Aluminium is one of the most experimented metals in the WAAM field owing to a wide range of applications in the automotive sector. Due to concerns over reduction of strength, elimination of porosity from wire arc additive manufactured aluminium is one of the major challenges. In line with this, the current investigation presents findings on hydrogen dissolution in solid aluminium and hydrogen consumed to form porosity along with its distribution as a function of heat inputs and interlayer temperatures in a WAAM 5183 aluminium alloy. Two varieties of WAAM, pulsed metal inert gas (MIG) and cold metal transfer (CMT), were explored. Samples made with pulsed metal inert gas (pulsed-MIG) process picked up more hydrogen compared to samples produced by cold metal transfer technique. Correspondingly, pulsed-MIG samples showed increased number of pores and volume fraction of porosity than samples manufactured using the cold metal transfer (CMT) technique for different heat input and interlayer temperature conditions. However, CMT samples exhibited higher amount of dissolved hydrogen in solid solution compared to pulsed-MIG process. In addition, heat input, interlayer temperature, and interlayer dwell time also played a key role in pore formation and distribution in WAAM-produced aluminium 5183 alloy.

Berry curvature memory through electrically driven stacking transitions

Abstract: In two-dimensional layered quantum materials, the stacking order of the layers determines both the crystalline symmetry and electronic properties such as the Berry curvature, topology and electron correlation1–4. Electrical stimuli can influence quasiparticle interactions and the free-energy landscape5,6, making it possible to dynamically modify the stacking order and reveal hidden structures that host different quantum properties. Here, we demonstrate electrically driven stacking transitions that can be applied to design non-volatile memory based on Berry curvature in few-layer WTe2. The interplay of out-of-plane electric fields and electrostatic doping controls in-plane interlayer sliding and creates multiple polar and centrosymmetric stacking orders. In situ nonlinear Hall transport reveals that such stacking rearrangements result in a layer-parity-selective Berry curvature memory in momentum space, where the sign reversal of the Berry curvature and its dipole only occurs in odd-layer crystals. Our findings open an avenue towards exploring coupling between topology, electron correlations and ferroelectricity in hidden stacking orders and demonstrate a new low-energy-cost, electrically controlled topological memory in the atomically thin limit. A memory device is proposed that uses a dynamical modification of the stacking order of few-layer WTe2 to encode information. The change in stacking modifies both the Berry curvature and the Hall transport, allowing two states to be distinguished.

Local Community Detection in Multiple Networks

Abstract: Local community detection aims to find a set of densely-connected nodes containing given query nodes. Most existing local community detection methods are designed for a single network. However, a single network can be noisy and incomplete. Multiple networks are more informative in real-world applications. There are multiple types of nodes and multiple types of node proximities. Complementary information from different networks helps to improve detection accuracy. In this paper, we propose a novel RWM (Random Walk in Multiple networks) model to find relevant local communities in all networks for a given query node set from one network. RWM sends a random walker in each network to obtain the local proximity w.r.t. the query nodes (i.e., node visiting probabilities). Walkers with similar visiting probabilities reinforce each other. They restrict the probability propagation around the query nodes to identify relevant subgraphs in each network and disregard irrelevant parts. We provide rigorous theoretical foundations for RWM and develop two speeding-up strategies with performance guarantees. Comprehensive experiments are conducted on synthetic and real-world datasets to evaluate the effectiveness and efficiency of RWM.

UDP-glucose 6-dehydrogenase regulates hyaluronic acid production and promotes breast cancer progression

Abstract: An improved understanding of the biochemical alterations that accompany tumor progression and metastasis is necessary to inform the next generation of diagnostic tools and targeted therapies. Metabolic reprogramming is known to occur during the epithelial-mesenchymal transition (EMT), a process that promotes metastasis. Here, we identify metabolic enzymes involved in extracellular matrix remodeling that are upregulated during EMT and are highly expressed in patients with aggressive mesenchymal-like breast cancer. Activation of EMT significantly increases production of hyaluronic acid, which is enabled by the reprogramming of glucose metabolism. Using genetic and pharmacological approaches, we show that depletion of the hyaluronic acid precursor UDP-glucuronic acid is sufficient to inhibit several mesenchymal-like properties including cellular invasion and colony formation in vitro, as well as tumor growth and metastasis in vivo. We found that depletion of UDP-glucuronic acid altered the expression of PPAR-gamma target genes and increased PPAR-gamma DNA-binding activity. Taken together, our findings indicate that the disruption of EMT-induced metabolic reprogramming affects hyaluronic acid production, as well as associated extracellular matrix remodeling and represents pharmacologically actionable target for the inhibition of aggressive mesenchymal-like breast cancer progression.

Parameterized Explainer for Graph Neural Network

Abstract: Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by GNNs remains a challenging open problem. The leading method independently addresses the local explanations (i.e., important subgraph structure and node features) to interpret why a GNN model makes the prediction for a single instance, e.g. a node or a graph. As a result, the explanation generated is painstakingly customized for each instance. The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to a lack of generalizability and hindering it from being used in the inductive setting. Besides, as it is designed for explaining a single instance, it is challenging to explain a set of instances naturally (e.g., graphs of a given class). In this study, we address these key challenges and propose PGExplainer, a parameterized explainer for GNNs. PGExplainer adopts a deep neural network to parameterize the generation process of explanations, which enables PGExplainer a natural approach to explaining multiple instances collectively. Compared to the existing work, PGExplainer has better generalization ability and can be utilized in an inductive setting easily. Experiments on both synthetic and real-life datasets show highly competitive performance with up to 24.7\% relative improvement in AUC on explaining graph classification over the leading baseline.

Brain activity fluctuations propagate as waves traversing the cortical hierarchy

Abstract: The brain exhibits highly organized patterns of spontaneous activity as measured by resting-state fMRI fluctuations that are being widely used to assess the brain’s functional connectivity. Some evidence suggests that spatiotemporally coherent waves are a core feature of spontaneous activity that shapes functional connectivity, though this has been difficult to establish using fMRI given the temporal constraints of the hemodynamic signal. Here we investigated the structure of spontaneous waves in human fMRI and monkey electrocorticography. In both species, we found clear, repeatable, and directionally constrained activity waves coursed along a spatial axis approximately representing cortical hierarchical organization. These cortical propagations were closely associated with activity changes in distinct subcortical structures, particularly those related to arousal regulation, and modulated across different states of vigilance. The findings demonstrate a neural origin of spatiotemporal fMRI wave propagation at rest and link it to the principal gradient of resting-state fMRI connectivity.

Transcriptional Repression of SIRT3 Potentiates Mitochondrial Aconitase Activation to Drive Aggressive Prostate Cancer to the Bone.

Abstract: Metabolic dysregulation is a known hallmark of cancer progression, yet the oncogenic signals that promote metabolic adaptations to drive metastatic cancer remain unclear. Here we show that transcriptional repression of mitochondrial deacetylase sirtuin 3 (SIRT3) by androgen receptor (AR) and its coregulator steroid receptor coactivator (SRC-2) enhances mitochondrial aconitase (ACO2) activity to favor aggressive prostate cancer. ACO2 promoted mitochondrial citrate synthesis to facilitate de novo lipogenesis, and genetic ablation of ACO2 reduced total lipid content and severely repressed in vivo prostate cancer progression. A single acetylation mark lysine258 on ACO2 functioned as a regulatory motif, and the acetylation-deficient Lys258Arg-mutant was enzymatically inactive and failed to rescue growth of ACO2-deficient cells. Acetylation of ACO2 was reversibly regulated by SIRT3, which was predominantly repressed in many tumors including prostate cancer. Mechanistically, SRC-2 bound AR formed a repressive complex by recruiting histone deacetylase 2 (HDAC2) to the SIRT3 promoter, and depletion of SRC-2 enhanced SIRT3 expression and simultaneously reduced acetylated-ACO2. In human prostate tumors, ACO2 activity was significantly elevated and increased expression of SRC-2 with concomitant reduction of SIRT3 was found to be a genetic hallmark enriched in prostate cancer metastatic lesions. In a mouse model of spontaneous bone metastasis, suppression of SRC-2 reactivated SIRT3 expression and was sufficient to abolish prostate cancer colonization in the bone microenvironment, implying this nuclear-mitochondrial regulatory axis is a determining factor for metastatic competence.

Unidirectional Extraordinary Sound Transmission with Mode-Selective Resonant Materials

Abstract: Realizing direction-dependent energy responses greatly benefits the construction of switching and logic devices. This study proposes an archetype for an acoustic resonant-tunneling diode, made of mode-selective resonant metamaterial. Using this material, the authors experimentally demonstrate a broadband, high contrast ratio and single-mode unidirectional sound tunneling. This result is expected to impact control methodology in biomedical ultrasonography, acoustic communication, sound identification, and noise control.