Atomically engineered ferroic layers yield a room-temperature magnetoelectric multiferroic.

Materials that exhibit simultaneous order in their electric and magnetic ground states hold promise for use in next-generation memory devices in which electric fields control magnetism. Such materials are exceedingly rare, however, owing to competing requirements for displacive ferroelectricity and magnetism. Despite the recent identification of several new multiferroic materials and magnetoelectric coupling mechanisms, known single-phase multiferroics remain limited by antiferromagnetic or weak ferromagnetic alignments, by a lack of coupling between the order parameters, or by having properties that emerge only well below room temperature, precluding device applications. Here we present a methodology for constructing single-phase multiferroic materials in which ferroelectricity and strong magnetic ordering are coupled near room temperature. Starting with hexagonal LuFeO3-the geometric ferroelectric with the greatest known planar rumpling-we introduce individual monolayers of FeO during growth to construct formula-unit-thick syntactic layers of ferrimagnetic LuFe2O4 (refs 17, 18) within the LuFeO3 matrix, that is, (LuFeO3)m/(LuFe2O4)1 superlattices. The severe rumpling imposed by the neighbouring LuFeO3 drives the ferrimagnetic LuFe2O4 into a simultaneously ferroelectric state, while also reducing the LuFe2O4 spin frustration. This increases the magnetic transition temperature substantially-from 240 kelvin for LuFe2O4 (ref. 18) to 281 kelvin for (LuFeO3)9/(LuFe2O4)1. Moreover, the ferroelectric order couples to the ferrimagnetism, enabling direct electric-field control of magnetism at 200 kelvin. Our results demonstrate a design methodology for creating higher-temperature magnetoelectric multiferroics by exploiting a combination of geometric frustration, lattice distortions and epitaxial engineering.

GAN for synthesizing CT from T2-weighted MRI data towards MR-guided radiation treatment.

OBJECTIVE: In medical domain, cross-modality image synthesis suffers from multiple issues , such as context-misalignment, image distortion, image blurriness, and loss of details. The fundamental objective behind this study is to address these issues in estimating synthetic Computed tomography (sCT) scans from T2-weighted Magnetic Resonance Imaging (MRI) scans to achieve MRI-guided Radiation Treatment (RT). MATERIALS AND METHODS: We proposed a conditional generative adversarial network (cGAN) with multiple residual blocks to estimate sCT from T2-weighted MRI scans using 367 paired brain MR-CT images dataset. Few state-of-the-art deep learning models were implemented to generate sCT including Pix2Pix model, U-Net model, autoencoder model and their results were compared, respectively. RESULTS: Results with paired MR-CT image dataset demonstrate that the proposed model with nine residual blocks in generator architecture results in the smallest mean absolute error (MAE) value of [Formula: see text], and mean squared error (MSE) value of [Formula: see text], and produces the largest Pearson correlation coefficient (PCC) value of [Formula: see text], SSIM value of [Formula: see text] and peak signal-to-noise ratio (PSNR) value of [Formula: see text], respectively. We qualitatively evaluated our result by visual comparisons of generated sCT to original CT of respective MRI input. DISCUSSION: The quantitative and qualitative comparison of this work demonstrates that deep learning-based cGAN model can be used to estimate sCT scan from a reference T2 weighted MRI scan. The overall accuracy of our proposed model outperforms different state-of-the-art deep learning-based models.

Truthful online double auction based dynamic resource provisioning for multi-objective trade-offs in IaaS clouds.

Auction designs have recently been adopted for static and dynamic resource provisioning in IaaS clouds, such as Microsoft Azure and Amazon EC2. However, the existing mechanisms are mostly restricted to simple auctions, single-objective, offline setting, one-sided interactions either among cloud users or cloud service providers (CSPs), and possible misreports of cloud user's private information. This paper proposes a more realistic scenario of online auctioning for IaaS clouds, with the unique characteristics of elasticity for time-varying arrival of cloud user requests under the time-based server maintenance in cloud data centers. We propose an online truthful double auction technique for balancing the multi-objective trade-offs between energy, revenue, and performance in IaaS clouds, consisting of a weighted bipartite matching based winning-bid determination algorithm for resource allocation and a Vickrey-Clarke-Groves (VCG) driven algorithm for payment calculation of winning bids. Through rigorous theoretical analysis and extensive trace-driven simulation studies exploiting Google cluster workload traces, we demonstrate that our mechanism significantly improves the performance while promising truthfulness, heterogeneity, economic efficiency, individual rationality, and has a polynomial-time computational complexity.

Molecule generation toward target protein (SARS-CoV-2) using reinforcement learning-based graph neural network via knowledge graph.

AI-driven approaches are widely used in drug discovery, where candidate molecules are generated and tested on a target protein for binding affinity prediction. However, generating new compounds with desirable molecular properties such as Quantitative Estimate of Drug-likeness (QED) and Dopamine Receptor D2 activity (DRD2) while adhering to distinct chemical laws is challenging. To address these challenges, we proposed a graph-based deep learning framework to generate potential therapeutic drugs targeting the SARS-CoV-2 protein. Our proposed framework consists of two modules: a novel reinforcement learning (RL)-based graph generative module with knowledge graph (KG) and a graph early fusion approach (GEFA) for binding affinity prediction. The first module uses a gated graph neural network (GGNN) model under the RL environment for generating novel molecular compounds with desired properties and a custom-made KG for molecule screening. The second module uses GEFA to predict binding affinity scores between the generated compounds and target proteins. Experiments show how fine-tuning the GGNN model under the RL environment enhances the molecules with desired properties to generate 100 % valid and 100 % unique compounds using different scoring functions. Additionally, KG-based screening reduces the search space of generated candidate molecules by 96.64 % while retaining 95.38 % of promising binding molecules against SARS-CoV-2 protein, i.e., 3C-like protease (3CLpro). We achieved a binding affinity score of 8.185 from the top rank of generated compound. In addition, we compared top-ranked generated compounds to Indinavir on different parameters, including drug-likeness and medicinal chemistry, for qualitative analysis from a drug development perspective. Supplementary Information: The online version contains supplementary material available at 10.1007/s13721-023-00409-2.

Perpendicular magnetization and generic realization of the Ising model in artificial spin ice.

We have studied frustrated kagome arrays and unfrustrated honeycomb arrays of magnetostatically interacting single-domain ferromagnetic islands with magnetization normal to the plane. The measured pairwise spin correlations of both lattices can be reproduced by models based solely on nearest-neighbor correlations. The kagome array has qualitatively different magnetostatics but identical lattice topology to previously studied artificial spin ice systems composed of in-plane moments. The two systems show striking similarities in the development of moment pair correlations, demonstrating a universality in artificial spin ice behavior independent of specific realization in a particular material system.

Generating novel molecule for target protein (SARS-CoV-2) using drug-target interaction based on graph neural network.

The transmittable spread of viral coronavirus (SARS-CoV-2) has resulted in a significant rise in global mortality. Due to lack of effective treatment, our aim is to generate a highly potent active molecule that can bind with the protein structure of SARS-CoV-2. Different machine learning and deep learning approaches have been proposed for molecule generation; however, most of these approaches represent the drug molecule and protein structure in 1D sequence, ignoring the fact that molecules are by nature in 3D structure, and because of this many critical properties are lost. In this work, a framework is proposed that takes account of both tertiary and sequential representations of molecules and proteins using Gated Graph Neural Network (GGNN), Knowledge graph, and Early Fusion approach. The generated molecules from GGNN are screened using Knowledge Graph to reduce the search space by discarding the non-binding molecules before being fed into the Early Fusion model. Further, the binding affinity score of the generated molecule is predicted using the early fusion approach. Experimental result shows that our framework generates valid and unique molecules with high accuracy while preserving the chemical properties. The use of a knowledge graph claims that the entire generated dataset of molecules was reduced by roughly 96% while retaining more than 85% of good binding desirable molecules and the rejection of more than 99% of fruitless molecules. Additionally, the framework was tested with two of the SARS-CoV-2 viral proteins: RNA-dependent-RNA polymerase (RdRp) and 3C-like protease (3CLpro).

Purification and magnetic interrogation of hybrid Au-Fe3O4 and FePt-Fe3O4 nanoparticles.

Purifying heterodimers: differential magnetic catch and release separation is used to purify two important hybrid nanocrystal systems, Au-Fe(3)O(4) and FePt-Fe(3)O(4). The purified samples have substantially different magnetic properties compared to the as-synthesized materials: the magnetization values are more accurate and magnetic polydispersity is identified in morphologically similar hybrid nanoparticles.

Cytomorphological patterns of tubercular lymphadenitis and its comparison with Ziehl-Neelsen staining and culture in eastern up. (Gorakhpur region): Cytological study of 400 cases.

BACKGROUND: Tuberculous lymphadenitis is most common cause of lymphadenopathy in developing countries. Although enormous literature is available on various aspects of the disease including cytological patterns and its incidence in others parts of India and in other countries, only limited literature is available regarding its incidence and morphological spectrum on cytology in eastern parts of Uttar Pradesh in Gorakhpur region. AIM: The present study was undertaken to estimate the incidence of tuberculous lymphadenitis in our settings along with its morphological spectrum on cytology as well as to determine the utility of culture of fine needle aspirates in addition to cytology and Ziehl-Neelsen (ZN) staining. MATERIAL AND METHODS: Four hundred cases of superficial lymphadenopathy were subjected to fine needle aspiration cytology (FNAC), and in case, smears were stained with Hematoxylin and eosin (H and E), Giemsa, and ZN stain and categorized into three cytomorphological patterns. All the aspirates were inoculated on two sterile Lowenstein Jensen (LJ) medium. RESULT: Out of 400 cases of consecutive lymph nodes aspirated, 180 cases (45%) showed features of tuberculous lymphadenitis. Smears revealed epithelioid granulomas with caseous necrosis in maximum cases (40%). On statistical analysis, difference between group I and group II was found to be significant (P < 0.05); while comparison between groups II and III as well as between groups I and III was found to be statistically insignificant. Overall, acid fast bacilli positivity was seen in 51.6% of the cases. CONCLUSION: FNAC has been proved very safe, highly sensitive, and first line investigation in diagnosing tubercular lymphadenitis. The sensitivity can be further be increased by complementary cytomorphology with acid fast staining. Diagnostic accuracy can further be increased by culture.

Image-guided fine needle cytology with aspiration versus non-aspiration in retroperitoneal masses: is aspiration necessary?

BACKGROUND: Although using fine needle cytology with aspiration (FNC-A) for establishing diagnoses in the retroperitoneal region has shown promise, there is scant literature supporting a role of non-aspiration cytology (FNC-NA) for this region. We assessed the accuracy and reliability of FNC-A and FNC-NA as tools for preoperative diagnosis of retroperitoneal masses and compared the results of both techniques with each other and with histopathology. METHODS: Fifty-seven patients with retroperitoneal masses were subjected to FNC-A and FNC-NA. Smears were stained with May-Grunwald Giemsa and hematoxylin and eosin stain. An individual slide was objectively analysed using a point scoring system to enable comparison between FNC-A and FNC-NA. RESULTS: By FNC-A, 91.7% accuracy was obtained in cases of retroperitoneal lymph node lesions followed by renal masses (83.3%). The diagnostic accuracy of other sites by FNC-A varied from 75.0%-81.9%. By FNC-NA, 93.4% diagnostically accurate results were obtained in the kidney, followed by 75.0% in adrenal masses. The diagnostic accuracy of other sites by FNC-NA varied from 66.7%-72.8%. CONCLUSIONS: Although both techniques have their own advantages and disadvantages, FNC-NA may be a more efficient adjuvant method of sampling in retroperitoneal lesions.