Showing papers by "Xiang Zhang published in 2019"

Two-dimensional magnetic crystals and emergent heterostructure devices

Abstract: Magnetism, originating from the moving charges and spin of elementary particles, has revolutionized important technologies such as data storage and biomedical imaging, and continues to bring forth new phenomena in emergent materials and reduced dimensions. The recently discovered two-dimensional (2D) magnetic van der Waals crystals provide ideal platforms for understanding 2D magnetism, the control of which has been fueling opportunities for atomically thin, flexible magneto-optic and magnetoelectric devices (such as magnetoresistive memories and spin field-effect transistors). The seamless integration of 2D magnets with dissimilar electronic and photonic materials opens up exciting possibilities for unprecedented properties and functionalities. We review the progress in this area and identify the possible directions for device applications, which may lead to advances in spintronics, sensors, and computing.

Double-negative-index ceramic aerogels for thermal superinsulation

Abstract: Ceramic aerogels are attractive for thermal insulation but plagued by poor mechanical stability and degradation under thermal shock. In this study, we designed and synthesized hyperbolic architectured ceramic aerogels with nanolayered double-pane walls with a negative Poisson’s ratio (−0.25) and a negative linear thermal expansion coefficient (−1.8 × 10 −6 per °C). Our aerogels display robust mechanical and thermal stability and feature ultralow densities down to ~0.1 milligram per cubic centimeter, superelasticity up to 95%, and near-zero strength loss after sharp thermal shocks (275°C per second) or intense thermal stress at 1400°C, as well as ultralow thermal conductivity in vacuum [~2.4 milliwatts per meter-kelvin (mW/m·K)] and in air (~20 mW/m·K). This robust material system is ideal for thermal superinsulation under extreme conditions, such as those encountered by spacecraft.

Multiferroicity in atomic van der Waals heterostructures.

Abstract: Materials that are simultaneously ferromagnetic and ferroelectric - multiferroics - promise the control of disparate ferroic orders, leading to technological advances in microwave magnetoelectric applications and next generation of spintronics. Single-phase multiferroics are challenged by the opposite d-orbital occupations imposed by the two ferroics, and heterogeneous nanocomposite multiferroics demand ingredients' structural compatibility with the resultant multiferroicity exclusively at inter-materials boundaries. Here we propose the two-dimensional heterostructure multiferroics by stacking up atomic layers of ferromagnetic Cr2Ge2Te6 and ferroelectric In2Se3, thereby leading to all-atomic multiferroicity. Through first-principles density functional theory calculations, we find as In2Se3 reverses its polarization, the magnetism of Cr2Ge2Te6 is switched, and correspondingly In2Se3 becomes a switchable magnetic semiconductor due to proximity effect. This unprecedented multiferroic duality (i.e., switchable ferromagnet and switchable magnetic semiconductor) enables both layers for logic applications. Van der Waals heterostructure multiferroics open the door for exploring the low-dimensional magnetoelectric physics and spintronic applications based on artificial superlattices.

Immuno-subtyping of breast cancer reveals distinct myeloid cell profiles and immunotherapy resistance mechanisms.

Abstract: Cancer-induced immune responses affect tumour progression and therapeutic response. In multiple murine models and clinical datasets, we identified large variations of neutrophils and macrophages that define 'immune subtypes' of triple-negative breast cancer (TNBC), including neutrophil-enriched (NES) and macrophage-enriched subtypes (MES). Different tumour-intrinsic pathways and mutual regulation between macrophages (or monocytes) and neutrophils contribute to the development of a dichotomous myeloid compartment. MES contains predominantly macrophages that are CCR2-dependent and exhibit variable responses to immune checkpoint blockade (ICB). NES exhibits systemic and local accumulation of immunosuppressive neutrophils (or granulocytic myeloid-derived suppressor cells), is resistant to ICB, and contains a minority of macrophages that seem to be unaffected by CCR2 knockout. A MES-to-NES conversion mediated acquired ICB resistance of initially sensitive MES models. Our results demonstrate diverse myeloid cell frequencies, functionality and potential roles in immunotherapies, and highlight the need to better understand the inter-patient heterogeneity of the myeloid compartment.

A Survey on Deep Learning based Brain Computer Interface: Recent Advances and New Frontiers.

Abstract: Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has lifted the performance of brain-computer interface systems significantly in recent years. In this article, we systematically investigate brain signal types for BCI and related deep learning concepts for brain signal analysis. We then present a comprehensive survey of deep learning techniques used for BCI, by summarizing over 230 contributions most published in the past five years. Finally, we discuss the applied areas, opening challenges, and future directions for deep learning-based BCI.

Exploiting Coarse-to-Fine Task Transfer for Aspect-Level Sentiment Classification

Abstract: Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT). However, due to the especially expensive and labor-intensive labeling, existing public corpora in AT-level are all relatively small. Meanwhile, most of the previous methods rely on complicated structures with given scarce data, which largely limits the efficacy of the neural models. In this paper, we exploit a new direction named coarse-to-fine task transfer, which aims to leverage knowledge learned from a rich-resource source domain of the coarse-grained AC task, which is more easily accessible, to improve the learning in a low-resource target domain of the fine-grained AT task. To resolve both the aspect granularity inconsistency and feature mismatch between domains, we propose a Multi-Granularity Alignment Network (MGAN). In MGAN, a novel Coarse2Fine attention guided by an auxiliary task can help the AC task modeling at the same finegrained level with the AT task. To alleviate the feature false alignment, a contrastive feature alignment method is adopted to align aspect-specific feature representations semantically. In addition, a large-scale multi-domain dataset for the AC task is provided. Empirically, extensive experiments demonstrate the effectiveness of the MGAN.

An experimental study of residual stress and direction-dependence of fatigue crack growth behaviour in as-built and stress-relieved selective-laser-melted Ti6Al4V

Abstract: Selective-laser-melting (SLM) is a powder-bed fusion additive-manufacturing process that has the potential to deliver three-dimensional complex parts with mechanical properties comparable or superior to parts produced via traditional manufacturing using cast and wrought alloys. Concerns for metallic parts built via SLM are the process-induced residual stresses, and anisotropic mechanical properties. This paper investigates the effect of residual stresses on the fatigue crack growth rate of SLM Ti6Al4V in as-built and stress-relieved conditions. Neutron diffraction and the contour method are employed to measure residual stresses in compact-tension samples. Neutron diffraction results are in good agreement with the contour method. It was found that tensile stresses are present at the notch root and the free edge areas, and compressive stress is seen in the middle of the sample. The tensile stresses in the as-built condition resulted in a higher fatigue crack growth rate. After stress relieving by heat treatment, the tensile residual stress diminished by around 90%, resulting in decreased crack growth rate. The build direction was seen to affect the crack growth rate, although the trend was different between the as-built and stress-relieved conditions.

Phonon heat transfer across a vacuum through quantum fluctuations

Abstract: Heat transfer in solids is typically conducted through either electrons or atomic vibrations known as phonons. In a vacuum, heat has long been thought to be transferred by radiation but not by phonons because of the lack of a medium1. Recent theory, however, has predicted that quantum fluctuations of electromagnetic fields could induce phonon coupling across a vacuum and thereby facilitate heat transfer2–4. Revealing this unique quantum effect experimentally would bring fundamental insights to quantum thermodynamics5 and practical implications to thermal management in nanometre-scale technologies6. Here we experimentally demonstrate heat transfer induced by quantum fluctuations between two objects separated by a vacuum gap. We use nanomechanical systems to realize strong phonon coupling through vacuum fluctuations, and observe the exchange of thermal energy between individual phonon modes. The experimental observation agrees well with our theoretical calculations and is unambiguously distinguished from other effects such as near-field radiation and electrostatic interaction. Our discovery of phonon transport through quantum fluctuations represents a previously unknown mechanism of heat transfer in addition to the conventional conduction, convection and radiation. It paves the way for the exploitation of quantum vacuum in energy transport at the nanoscale. Conventionally, heat transfer occurs by conduction, convection or radiation, but has also been theoretically predicted to occur through quantum fluctuations across a vacuum; this prediction has now been confirmed experimentally.

Targeting the Interplay between Epithelial-to-Mesenchymal-Transition and the Immune System for Effective Immunotherapy.

Abstract: Over the last decade, both early diagnosis and targeted therapy have improved the survival rates of many cancer patients. Most recently, immunotherapy has revolutionized the treatment options for cancers such as melanoma. Unfortunately, a significant portion of cancers (including lung and breast cancers) do not respond to immunotherapy, and many of them develop resistance to chemotherapy. Molecular characterization of non-responsive cancers suggest that an embryonic program known as epithelial-mesenchymal transition (EMT), which is mostly latent in adults, can be activated under selective pressures, rendering these cancers resistant to chemo- and immunotherapies. EMT can also drive tumor metastases, which in turn also suppress the cancer-fighting activity of cytotoxic T cells that traffic into the tumor, causing immunotherapy to fail. In this review, we compare and contrast immunotherapy treatment options of non-small cell lung cancer (NSCLC) and triple negative breast cancer (TNBC). We discuss why, despite breakthrough progress in immunotherapy, attaining predictable outcomes in the clinic is mostly an unsolved problem for these tumors. Although these two cancer types appear different based upon their tissues of origin and molecular classification, gene expression indicate that they possess many similarities. Patient tumors exhibit activation of EMT, and resulting stem cell properties in both these cancer types associate with metastasis and resistance to existing cancer therapies. In addition, the EMT transition in both these cancers plays a crucial role in immunosuppression, which exacerbates treatment resistance. To improve cancer-related survival we need to understand and circumvent, the mechanisms through which these tumors become therapy resistant. In this review, we discuss new information and complementary perspectives to inform combination treatment strategies to expand and improve the anti-tumor responses of currently available clinical immune checkpoint inhibitors.

Abstract GS5-05: Resistance to neoadjuvant chemotherapy in triple negative breast cancer mediated by a reversible drug-tolerant state

Abstract: Approximately 50% of patients with localized triple negative breast cancer (TNBC) have substantial residual cancer burden following treatment with neoadjuvant chemotherapy (NACT), resulting in distant metastasis and death for most of these patients. While genomic and phenotypic intra-tumor heterogeneity are pervasive features of TNBCs at the time of diagnosis, the functional contributions of heterogeneous tumor cell populations to chemoresistance have not been elucidated. To investigate tumor evolution accompanying NACT, we employed orthotopic patient-derived xenograft (PDX) models of treatment-naive TNBC, which retain intra-tumor heterogeneity characteristic of human TNBC. We discovered that some PDX models initially exhibited partial sensitivity to standard front-line NACT (Adriamycin plus Cytoxan, AC). Following AC, residual tumors were resistant to chemotherapy but repopulated tumors with chemo-sensitive cells if left untreated, indicating that tumor cells possessed inherent plasticity. To identify the tumor cell subpopulation(s) conferring chemoresistance, we conducted barcode-mediated clonal tracking in three independent PDX models by introducing a high-complexity pooled lentiviral barcode library into PDX tumor cells which were then orthotopically engrafted into recipient mice. Strikingly, residual tumors maintained the same heterogeneous clonal architecture as naive tumors. Concordantly, whole-exome sequencing revealed conservation of genomic subclonal architecture throughout treatment. These results were corroborated by genomic sequencing of serial biopsies pre- and post-AC obtained directly from TNBC patients enrolled on an ongoing clinical trial at MD Anderson (ARTEMIS; NCT02276443). Together, these studies revealed that genomically distinct pre-treatment subclones were equally capable of surviving AC to reconstitute tumors after treatment. To identify functional addictions of residual tumor cells, we conducted histologic and transcriptomic profiling. Residual tumors following AC-treatment exhibited extensive fibrotic desmoplasia and tumor cell pleomorphism in both PDX models and in serial biopsies obtained from TNBC patients enrolled on the ARTEMIS trial. Strikingly, these AC-induced features were reverted upon regrowth of residual tumors in PDXs and in patients9 tumors. Similarly, residual tumors exhibited unique transcriptomic features, many of which are also de-regulated in cohorts of human TNBCs undergoing chemotherapy treatment. These features were nearly completely reverted after tumors regrew, suggesting that the residual tumor state may be a unique and transient therapeutic window. Gene set enrichment analyses revealed that residual tumors had increased activation of oxidative phosphorylation and decreased glycolytic signaling. Pharmacologic targeting of oxidative phosphorylation with a small-molecule inhibitor of mitochondrial electron transport chain complex I (IACS-010759) significantly delayed the regrowth of AC-treated residual tumors in three independent PDX models. Collectively, these studies reveal that a reversible phenotypic state can confer chemoresistance in the absence of genomic selection and that the residual tumor state is a novel therapeutic window for chemo-refractory TNBC. Citation Format: Echeverria GV, Ge Z, Seth S, Jeter-Jones SL, Zhang X, Zhou X, Cai S, Tu Y, McCoy A, Peoples M, Lau R, Shao J, Sun Y, Bristow C, Carugo A, Ma X, Harris A, Wu Y, Moulder S, Symmans WF, Marszalek JR, Heffernan TP, Chang JT, Piwnica-Worms H. Resistance to neoadjuvant chemotherapy in triple negative breast cancer mediated by a reversible drug-tolerant state [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr GS5-05.

Observation of acoustic spin

Abstract: Unlike optical waves, acoustic waves in fluids are described by scalar pressure fields, and therefore are considered spinless. Here, we demonstrate experimentally the existence of spin in acoustics. In the interference of two acoustic waves propagating perpendicularly to each other, we observed the spin angular momentum in free space as a result of the rotation of local particle velocity. We successfully measured the acoustic spin, and spin-induced torque acting on a designed lossy acoustic probe that results from absorption of the spin angular momentum. The acoustic spin is also observed in the evanescent field of a guided mode traveling along a metamaterial waveguide. We found spin-momentum locking in acoustic waves whose propagation direction is determined by the sign of spin. The observed acoustic spin could open a new door in acoustics and its applications for the control of wave propagation and particle rotation.

Stable Casimir equilibria and quantum trapping

Abstract: The Casimir interaction between two parallel metal plates in close proximity is usually thought of as an attractive interaction. By coating one object with a low-refractive index thin film, we show that the Casimir interaction between two objects of the same material can be reversed at short distances and preserved at long distances so that two objects can remain without contact at a specific distance. With such a stable Casimir equilibrium, we experimentally demonstrate passive Casimir trapping of an object in the vicinity of another at the nanometer scale, without requiring any external energy input. This stable Casimir equilibrium and quantum trapping can be used as a platform for a variety of applications such as contact-free nanomachines, ultrasensitive force sensors, and nanoscale manipulations.

Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning

Abstract: Deep learning algorithms have achieved excellent performance lately in a wide range of fields (e.g., computer version). However, a severe challenge faced by deep learning is the high dependency on hyper-parameters. The algorithm results may fluctuate dramatically under the different configuration of hyper-parameters. Addressing the above issue, this paper presents an efficient Orthogonal Array Tuning Method (OATM) for deep learning hyper-parameter tuning. We describe the OATM approach in five detailed steps and elaborate on it using two widely used deep neural network structures (Recurrent Neural Networks and Convolutional Neural Networks). The proposed method is compared to the state-of-the-art hyper-parameter tuning methods including manually (e.g., grid search and random search) and automatically (e.g., Bayesian Optimization) ones. The experiment results state that OATM can significantly save the tuning time compared to the state-of-the-art methods while preserving the satisfying performance.

Oblique-plane single-molecule localization microscopy for tissues and small intact animals.

Abstract: Single-molecule localization microscopy (SMLM), while well established for cultured cells, is not yet fully compatible with tissue-scale samples. We introduce single-molecule oblique-plane microscopy (obSTORM), which by directly imaging oblique sections of samples with oblique light-sheet illumination offers a deep and volumetric SMLM platform that is convenient for standard tissue samples and small intact animals. We demonstrate super-resolution imaging at depths of up to 66 µm for cells, Caenorhabditis elegans gonads, Drosophila melanogaster larval brain, mouse retina and brain sections, and whole stickleback fish. Single-molecule oblique-plane microscopy (obSTORM) enables deep volumetric super-resolution imaging in a light-sheet microscopy platform that is convenient for standard tissues and small intact animals.

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 correlation. Electrical stimuli can influence quasiparticle interactions and the free-energy landscape, 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 nonvolatile memory based on Berry curvature in few-layer WTe$_2$. 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 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.

Spatio-temporal attentive RNN for node classification in temporal attributed graphs

Abstract: Node classification in graph-structured data aims to classify the nodes where labels are only available for a subset of nodes. This problem has attracted considerable research efforts in recent years. In real-world applications, both graph topology and node attributes evolve over time. Existing techniques, however, mainly focus on static graphs and lack the capability to simultaneously learn both temporal and spatial/structural features. Node classification in temporal attributed graphs is challenging for two major aspects. First, effectively modeling the spatio-temporal contextual information is hard. Second, as temporal and spatial dimensions are entangled, to learn the feature representation of one target node, it’s desirable and challenging to differentiate the relative importance of different factors, such as different neighbors and time periods. In this paper, we propose STAR, a spatio-temporal attentive recurrent network model, to deal with the above challenges. STAR extracts the vector representation of neighborhood by sampling and aggregating local neighbor nodes. It further feeds both the neighborhood representation and node attributes into a gated recurrent unit network to jointly learn the spatio-temporal contextual information. On top of that, we take advantage of the dual attention mechanism to perform a thorough analysis on the model interpretability. Extensive experiments on real datasets demonstrate the effectiveness of the STAR model.

Bone Metastasis: Find Your Niche and Fit in.

Abstract: Metastasis to bones is determined by both intrinsic traits of metastatic tumor cells and properties appertaining to the bone microenvironment. Bone marrow niches are critical for all major steps of metastasis, including the seeding of disseminated tumor cells (DTCs) to bone, the survival of DTCs and microscopic metastases under dormancy, and the eventual outgrowth of overt metastases. In this review, we discuss the role of bone marrow niches in bone colonization. The emphasis is on complicated and dynamic nature of cancer cells–niche interaction, which may underpin the long-standing mystery of metastasis dormancy, and represent a therapeutic target for elimination of minimal residue diseases and prevention of life-taking, overt metastases.

Strong optical response and light emission from a monolayer molecular crystal

Abstract: Excitons in two-dimensional (2D) materials are tightly bound and exhibit rich physics. So far, the optical excitations in 2D semiconductors are dominated by Wannier-Mott excitons, but molecular systems can host Frenkel excitons (FE) with unique properties. Here, we report a strong optical response in a class of monolayer molecular J-aggregates. The exciton exhibits giant oscillator strength and absorption (over 30% for monolayer) at resonance, as well as photoluminescence quantum yield in the range of 60-100%. We observe evidence of superradiance (including increased oscillator strength, bathochromic shift, reduced linewidth and lifetime) at room-temperature and more progressively towards low temperature. These unique properties only exist in monolayer owing to the large unscreened dipole interactions and suppression of charge-transfer processes. Finally, we demonstrate light-emitting devices with the monolayer J-aggregate. The intrinsic device speed could be beyond 30 GHz, which is promising for next-generation ultrafast on-chip optical communications.

Room-Temperature Giant Stark Effect of Single Photon Emitter in van der Waals Material.

Abstract: Single photon emitters (SPEs) are critical building blocks needed for quantum science and technology. For practical applications, room-temperature solid-state platforms are critically demanded. To ...

Lasing without population inversion in N2

Abstract: We experimentally demonstrate the existence of long-lived coherent polarizations coupling simultaneously ground state X (X2Σg+) to excited states A (A2Πu) and B (B2Σu+) of N2+ inside a plasma created by a short intense laser pulse at 800 nm. This three-level V-scheme arrangement is responsible for a strong optical gain without population inversion at the B–X transition at 391.4 nm. Simulations based on Maxwell-Bloch equations reproduce well the kinetics and the pressure dependence of the gain.

Observation of Rydberg exciton polaritons and their condensate in a perovskite cavity.

Abstract: The condensation of half-light half-matter exciton polaritons in semiconductor optical cavities is a striking example of macroscopic quantum coherence in a solid-state platform. Quantum coherence is possible only when there are strong interactions between the exciton polaritons provided by their excitonic constituents. Rydberg excitons with high principal value exhibit strong dipole-dipole interactions in cold atoms. However, polaritons with the excitonic constituent that is an excited state, namely Rydberg exciton polaritons (REPs), have not yet been experimentally observed. Here, we observe the formation of REPs in a single crystal CsPbBr3 perovskite cavity without any external fields. These polaritons exhibit strong nonlinear behavior that leads to a coherent polariton condensate with a prominent blue shift. Furthermore, the REPs in CsPbBr3 are highly anisotropic and have a large extinction ratio, arising from the perovskite's orthorhombic crystal structure. Our observation not only sheds light on the importance of many-body physics in coherent polariton systems involving higher-order excited states, but also paves the way for exploring these coherent interactions for solid-state quantum optical information processing.

Internet of Things Meets Brain–Computer Interface: A Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity

Abstract: A brain–computer interface (BCI) acquires brain signals, analyzes, and translates them into commands that are relayed to actuation devices for carrying out desired actions. With the widespread connectivity of everyday devices realized by the advent of the Internet of Things (IoT), BCI can empower individuals to directly control objects such as smart home appliances or assistive robots, directly via their thoughts. However, realization of this vision is faced with a number of challenges, most importantly being the issue of accurately interpreting the intent of the individual from the raw brain signals that are often of low fidelity and subject to noise. Moreover, preprocessing brain signals and the subsequent feature engineering are both time-consuming and highly reliant on human domain expertise. To address the aforementioned issues, in this paper, we propose a unified deep learning-based framework that enables effective human-thing cognitive interactivity in order to bridge individuals and IoT objects. We design a reinforcement learning-based selective attention mechanism (SAM) to discover the distinctive features from the input brain signals. In addition, we propose a modified long short-term memory to distinguish the interdimensional information forwarded from the SAM. To evaluate the efficiency of the proposed framework, we conduct extensive real-world experiments and demonstrate that our model outperforms a number of competitive state-of-the-art baselines. Two practical real-time human-thing cognitive interaction applications are presented to validate the feasibility of our approach.

Interrupted fatigue testing with periodic tomography to monitor porosity defects in wire + arc additive manufactured Ti-6Al-4V

Abstract: Porosity defects remain a challenge to the structural integrity of additive manufactured materials, particularly for parts under fatigue loading applications. Although the wire + arc additive manufactured Ti-6Al-4 V builds are typically fully dense, occurrences of isolated pores may not be completely avoided due to feedstock contamination. This study used contaminated wires to build the gauge section of fatigue specimens to purposely introduce spherical gas pores in the size range of 120–250 micrometres. Changes in the defect morphology were monitored via interrupted fatigue testing with periodic X-ray computed tomography (CT) scanning. Prior to specimen failure, the near surface pores grew by approximately a factor of 2 and tortuous fatigue cracks were initiated and propagated towards the nearest free surface. Elastic-plastic finite element analysis showed cyclic plastic deformation at the pore root as a result of stress concentration; consequently for an applied tension-tension cyclic stress (stress ratio 0.1), the local stress at the pore root became a tension-compression nature (local stress ratio −1.0). Fatigue life was predicted using the notch fatigue approach and validated with experimental test results.

Influence of Interpass Temperature on Wire Arc Additive Manufacturing (WAAM) of Aluminium Alloy Components

Abstract: Wire arc additive manufacturing (WAAM) technique has revealed the potential of replacing existing aerospace industry parts manufactured by traditional manufacturing routes. The reduced mechanical properties compared to wrought products, the porosity formation, and solidification cracking are the prime constraints that are restricting wide-spread applications of WAAM products using aluminium alloys. An interpass temperature is less studied in robotic WAAM and is the vital aspect affecting the properties of a formed product. This paper highlights the effects of change in interpass temperature on porosity content and mechanical properties of WAAM parts prepared using DC pulsed GMAW process, with 5356 aluminium consumable wire. The samples prepared with different interpass temperatures were studied for the distribution of pores with the help of computed tomography radiography (CT radiography) technique. A WAAM sample produced with higher interpass temperature revealed 10.41% less porosity than the sample prepared with lower interpass temperature. The pores with size less than 0.15mm3 were contributing over 95% of the overall porosity content. Additionally, on a volumetric scale, small pores (<0.15mm3) in the higher interpass temperature sample contributed 81.47% of overall volume of pores whereas only 67.92% volume was occupied in lower interpass temperature sample with same sized pores. The different solidification rates believed to have influence on the hydrogen evolution mechanism. Tensile properties of higher interpass temperature sample were comparatively better than lower interpass temperature sample. For the deposition pattern used in this study, horizontal specimens were superior to vertical specimens in tensile properties.

Achieving Pain Control in Rheumatoid Arthritis with Baricitinib or Adalimumab Plus Methotrexate: Results from the RA-BEAM Trial

Abstract: The purpose of the study was to assess the proportion of patients who achieve pain relief thresholds, the time needed to reach the thresholds, and the relationship between pain and inflammation among patients with rheumatoid arthritis (RA) and an inadequate response to methotrexate in RA-BEAM (NCT0170358). A randomized, double-blind trial was conducted, comparing baricitinib (N = 487), adalimumab (N = 330), and placebo (N = 488) plus methotrexate. Pain was evaluated by patient’s assessment on a 0–100 mm visual analog scale (VAS). The following were assessed through a 24-week placebo-controlled period: the proportion of patients who achieved ≥30%, ≥50%, and ≥70% pain relief, the time to achieve these pain relief thresholds, remaining pain (VAS ≤ 10 mm, ≤20 mm, or ≤40 mm), and the relationship between inflammation markers and pain relief. Baricitinib-treated patients were more likely (p < 0.05) to achieve ≥30%, ≥50%, and ≥70% pain relief than placebo- and adalimumab-treated patients, as early as Week 1 vs. placebo and at Week 4 vs. adalimumab. A greater proportion of baricitinib-treated patients achieved ≤20 mm or ≤40 mm remaining pain vs. placebo- and adalimumab-treated patients. Baricitinib-treated patients tended to demonstrate consistent pain relief independent of levels of inflammation control. In RA patients with an inadequate response to methotrexate, baricitinib provided greater and more rapid pain relief than adalimumab and placebo. Analyses suggest the relationship between inflammation and pain may be different for baricitinib and adalimumab treatments.