Miracle-dz/UniFAR-FTUs huggingface dataset:
Nacre-inspired design of graphene oxide–polydopamine nanocomposites for enhanced mechanical properties and multi-functionalities

Inspired by the hierarchical structure of nacre and the robust adhesive ability of mussel threads, graphene oxide–polydopamine (GO–PDA) nanocomposites are designed and synthesized to achieve enhanced mechanical properties and to provide additional functionalities. Here we report a joint experimental/computational investigation of GO–PDA nanocomposites, proposing a probable chemical reduction mechanism of PDA to convert GO to reduced GO (rGO), which helps increase the electrical conductivity. The most stable chemical connection between PDA and GO is also proposed. Our artificial nacre-like GO–PDA nanocomposites are shown to have higher tensile strength and toughness compared to natural nacre. The pulling tests conducted by molecular dynamics simulations, which are supported by our experiments, reveal that the enhanced mechanical strength of GO–PDA nanocomposites mainly originates from the additional non-covalent interactions provided by PDA. The humidity-driven shrinking mechanism of GO–PDA nanocomposites due to non-uniform stresses on the GO–PDA sheets is also discovered in our simulations and supported by our experiments. The findings in this work can help improve and tune the properties of GO–PDA nanocomposites and might also apply to other 2D materials.
A network-based asynchronous architecture for cryptographic devices

The traditional model of cryptography examines the security of the cipher as a mathematical function. However, ciphers that are secure when specified as mathematical functions are not necessarily secure in real-world implementations. The physical implementations of ciphers can be extremely difficult to control and often leak socalled side-channel information. Side-channel cryptanalysis attacks have shown to be especially effective as a practical means for attacking implementations of cryptographic algorithms on simple hardware platforms, such as smart-cards. Adversaries can obtain sensitive information from side-channels, such as the timing of operations, power consumption and electromagnetic emissions. Some of the attack techniques require surprisingly little side-channel information to break some of the best known ciphers. In constrained devices, such as smart-cards, straightforward implementations of cryptographic algorithms can be broken with minimal work. Preventing these attacks has become an active and a challenging area of research. Power analysis is a successful cryptanalytic technique that extracts secret information from cryptographic devices by analysing the power consumed during their operation. A particularly dangerous class of power analysis, differential power analysis (DPA), relies on the correlation of power consumption measurements. It has been proposed that adding non-determinism to the execution of the cryptographic device would reduce the danger of these attacks. It has also been demonstrated that asynchronous logic has advantages for security-sensitive applications. This thesis investigates the security and performance advantages of using a network-based asynchronous architecture, in which the functional units of the datapath form a network. Non-deterministic execution is achieved by exploiting concurrent execution of instructions both with and without data-dependencies; and by forwarding register values between instructions with data-dependencies using randomised routing over the network. The executions of cryptographic algorithms on different architectural configurations are simulated, and the obtained power traces are subjected to DPA attacks. The results show that the proposed architecture introduces a level of non-determinism in the execution that significantly raises the threshold for DPA attacks to succeed. In addition, the performance analysis shows that the improved security does not degrade performance.
Lysosomal Cholesterol Hydrolysis Couples Efferocytosis to Anti-Inflammatory Oxysterol Production

Rationale: Macrophages face a substantial amount of cholesterol after the ingestion of apoptotic cells, and the LIPA (lysosomal acid lipase) has a major role in hydrolyzing cholesteryl esters in the endocytic compartment. Objective: Here, we directly investigated the role of LIPA-mediated clearance of apoptotic cells both in vitro and in vivo. Methods and Results: We show that LIPA inhibition causes a defective efferocytic response because of impaired generation of 25-hydroxycholesterol and 27-hydroxycholesterol. Reduced synthesis of 25-hydroxycholesterol after LIPA inhibition contributed to defective mitochondria-associated membrane leading to mitochondrial oxidative stress–induced NLRP3 (NOD-like receptor family, pyrin domain containing) inflammasome activation and caspase-1–dependent Rac1 (Ras-related C3 botulinum toxin substrate 1) degradation. A secondary event consisting of failure to appropriately activate liver X receptor–mediated pathways led to mitigation of cholesterol efflux and apoptotic cell clearance. In mice, LIPA inhibition caused defective clearance of apoptotic lymphocytes and stressed erythrocytes by hepatic and splenic macrophages, culminating in splenomegaly and splenic iron accumulation under hypercholesterolemia. Conclusions: Our findings position lysosomal cholesterol hydrolysis as a critical process that prevents metabolic inflammation by enabling efficient macrophage apoptotic cell clearance.
Automatic segmentation of retinal vasculature

Segmentation of retinal vessels from retinal fundus images is the key step in the automatic retinal image analysis. In this paper we propose a new unsupervised automatic method to segment the retinal vessels from retinal fundus images. Contrast enhancement and illumination correction are carried out through a series of image processing steps followed by adaptive histogram equalization and anisotropic diffusion filtering. This image is then converted to a gray scale using weighted scaling. The vessel edges are enhanced by boosting the detail curvelet coefficients. Optic disk pixels are removed before applying fuzzy C-mean classification to avoid the misclassification. Morphological operations and connected component analysis are applied to obtain the segmented retinal vessels. The performance of the proposed method is evaluated using DRIVE database to be able to compare with other state-of-art supervised and unsupervised methods. The overall segmentation accuracy of the proposed method is 95.18% which outperforms the other algorithms.
How new concepts become universal scientific approaches: insights from citation network analysis of agent-based complex systems science

Progress in understanding and managing complex systems comprised of decision-making agents, such as cells, organisms, ecosystems or societies, is—like many scientific endeavours—limited by disciplinary boundaries. These boundaries, however, are moving and can actively be made porous or even disappear. To study this process, I advanced an original bibliometric approach based on network analysis to track and understand the development of the model-based science of agent-based complex systems (ACS). I analysed research citations between the two communities devoted to ACS research, namely agent-based (ABM) and individual-based modelling (IBM). Both terms refer to the same approach, yet the former is preferred in engineering and social sciences, while the latter prevails in natural sciences. This situation provided a unique case study for grasping how a new concept evolves distinctly across scientific domains and how to foster convergence into a universal scientific approach. The present analysis based on novel hetero-citation metrics revealed the historical development of ABM and IBM, confirmed their past disjointedness, and detected their progressive merger. The separation between these synonymous disciplines had silently opposed the free flow of knowledge among ACS practitioners and thereby hindered the transfer of methodological advances and the emergence of general systems theories. A surprisingly small number of key publications sparked the ongoing fusion between ABM and IBM research. Beside reviews raising awareness of broad-spectrum issues, generic protocols for model formulation and boundary-transcending inference strategies were critical means of science integration. Accessible broad-spectrum software similarly contributed to this change. From the modelling viewpoint, the discovery of the unification of ABM and IBM demonstrates that a wide variety of systems substantiate the premise of ACS research that microscale behaviours of agents and system-level dynamics are inseparably bound.
Learning Deep Generative Models of Graphs

Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative models over graphs, which can capture both their structure and attributes. Our approach uses graph neural networks to express probabilistic dependencies among a graph's nodes and edges, and can, in principle, learn distributions over any arbitrary graph. In a series of experiments our results show that once trained, our models can generate good quality samples of both synthetic graphs as well as real molecular graphs, both unconditionally and conditioned on data. Compared to baselines that do not use graph-structured representations, our models often perform far better. We also explore key challenges of learning generative models of graphs, such as how to handle symmetries and ordering of elements during the graph generation process, and offer possible solutions. Our work is the first and most general approach for learning generative models over arbitrary graphs, and opens new directions for moving away from restrictions of vector- and sequence-like knowledge representations, toward more expressive and flexible relational data structures.
Inkjet Printing of Functional and Structural Materials: Fluid Property Requirements, Feature Stability, and Resolution

Inkjet printing is viewed as a versatile manufacturing tool for applications in materials fabrication in addition to its traditional role in graphics output and marking. The unifying feature in all these applications is the dispensing and precise positioning of very small volumes of fluid (1–100 picoliters) on a substrate before transformation to a solid. The application of inkjet printing to the fabrication of structures for structural or functional materials applications requires an understanding as to how the physical processes that operate during inkjet printing interact with the properties of the fluid precursors used. Here we review the current state of understanding of the mechanisms of drop formation and how this defines the fluid properties that are required for a given liquid to be printable. The interactions between individual drops and the substrate as well as between adjacent drops are important in defining the resolution and accuracy of printed objects. Pattern resolution is limited by the extent to which a liquid drop spreads on a substrate and how spreading changes with the overlap of adjacent drops to form continuous features. There are clearly defined upper and lower bounds to the width of a printed continuous line, which can be defined in terms of materials and process variables. Finer-resolution features can be achieved through appropriate patterning and structuring of the substrate prior to printing, which is essential if polymeric semiconducting devices are to be fabricated. Low advancing and receding contact angles promote printed line stability but are also more prone to solute segregation or “coffee staining” on drying.
Patient reported health status and all-cause mortality in patients with coronary heart disease

Purpose Patients with coronary heart disease (CHD) experience reduced quality of life which may be associated with mortality in the longer term. This study explores whether patient-rated physical and mental health status was associated with mortality at 6-year follow-up among patients with CHD attending primary care in Ireland and Northern Ireland. Methods This study is a secondary data analysis of patients with CHD recruited to a cluster randomized controlled trial from 2004 to 2010. Data collected included patient-rated physical component summary (PCS) and mental component summary (MCS) scores of health status (from the 12-Item Short-Form Health Survey (SF-12)), demographics and clinical parameters at baseline, and all-cause mortality at 6-year follow-up. Multivariate regression was conducted using generalized estimating equations (GEE) with a log-link function. Results are presented as odds ratios (ORs) and 95% confidence intervals (CIs). Results The study consisted of 762 individuals with mean age 67.6 years [standard deviation (SD): 9.8], and was 29% female. Mean baseline SF-12 mental (MCS) and physical (PCS) component scores were 50.0 (SD: 10.8) and 39.6 (SD: 11.2), respectively. At 6-year follow-up, the adjusted OR for the baseline MCS for mortality was 0.97 (95% CI: 0.95-0.99) and for the PCS 0.97 (95% CI: 0.95-0.99). For every five-point increase in MCS and PCS scores, there was a 14% reduction in the likelihood of all-cause mortality. Conclusions Overall, the magnitude of effect for both mental health status and physical health status was similar; higher scores were significantly associated with a lower risk of mortality at 6-year follow-up.
Fair and Throughput-Optimal Routing in Multimodal Underwater Networks

While acoustic communications are still considered the most prominent technology to communicate under water, other technologies are being developed based, e.g., on optical and radio-frequency electromagnetic waves. Each technology has its own advantages and drawbacks: for example, acoustic signals achieve long communication ranges at order-of-kbit/s rates, whereas optical signals offer order-of-Mbit/s transmission rates, but only over short ranges. Such diversity can be leveraged by multimodal systems, which integrate different technologies and provide the intelligence required to decide which one should be used at any given time. In this paper, we address a fundamental part of this intelligence by proposing optimal multimodal routing (OMR), a novel routing protocol for underwater networks of multimodal nodes. OMR makes distributed decisions about the flow in each link and over each technology, at any given time, in order to advance a packet toward its destination; in doing so, it prevents bottlenecks and allocates resources fairly to different nodes. We analyze the performance of OMR via simulations and in a field experiment. The results show that OMR successfully leverages all technologies to deliver data, even in the presence of imperfect topology information. To permit the reproduction of our results, we share our simulation code.
HyperENTM: Evolving Scalable Neural Turing Machines through HyperNEAT

Recent developments within memory-augmented neural networks have solved sequential problems requiring long-term memory, which are intractable for traditional neural networks. However, current approaches still struggle to scale to large memory sizes and sequence lengths. In this paper we show how access to memory can be encoded geometrically through a HyperNEAT-based Neural Turing Machine (HyperENTM). We demonstrate that using the indirect HyperNEAT encoding allows for training on small memory vectors in a bit-vector copy task and then applying the knowledge gained from such training to speed up training on larger size memory vectors. Additionally, we demonstrate that in some instances, networks trained to copy bit-vectors of size 9 can be scaled to sizes of 1,000 without further training. While the task in this paper is simple, these results could open up the problems amendable to networks with external memories to problems with larger memory vectors and theoretically unbounded memory sizes.
The Inositol Polyphosphate 5-Phosphatase Ship Is a Crucial Negative Regulator of B Cell Antigen Receptor Signaling

Ship is an Src homology 2 domain containing inositol polyphosphate 5-phosphatase which has been implicated as an important signaling molecule in hematopoietic cells. In B cells, Ship becomes associated with Fcγ receptor IIB (FcγRIIB), a low affinity receptor for the Fc portion of immunoglobulin (Ig)G, and is rapidly tyrosine phosphorylated upon B cell antigen receptor (BCR)–FcγRIIB coligation. The function of Ship in lymphocytes was investigated in Ship−/− recombination-activating gene (Rag)−/− chimeric mice generated from gene-targeted Ship−/− embryonic stem cells. Ship−/−Rag−/− chimeras showed reduced numbers of B cells and an overall increase in basal serum Ig. Ship−/− splenic B cells displayed prolonged Ca2+ influx, increased proliferation in vitro, and enhanced mitogen-activated protein kinase (MAPK) activation in response to BCR–FcγRIIB coligation. These results demonstrate that Ship plays an essential role in FcγRIIB-mediated inhibition of BCR signaling, and that Ship is a crucial negative regulator of Ca2+ flux and MAPK activation.
Body Size Evolution in Insular Speckled Rattlesnakes (Viperidae: Crotalus mitchellii)

Background Speckled rattlesnakes (Crotalus mitchellii) inhabit multiple islands off the coast of Baja California, Mexico. Two of the 14 known insular populations have been recognized as subspecies based primarily on body size divergence from putative mainland ancestral populations; however, a survey of body size variation from other islands occupied by these snakes has not been previously reported. We examined body size variation between island and mainland speckled rattlesnakes, and the relationship between body size and various island physical variables among 12 island populations. We also examined relative head size among giant, dwarfed, and mainland speckled rattlesnakes to determine whether allometric differences conformed to predictions of gape size (and indirectly body size) evolving in response to shifts in prey size. Methodology/Principal Findings Insular speckled rattlesnakes show considerable variation in body size when compared to mainland source subspecies. In addition to previously known instances of gigantism on Ángel de la Guarda and dwarfism on El Muerto, various degrees of body size decrease have occurred frequently in this taxon, with dwarfed rattlesnakes occurring mostly on small, recently isolated, land-bridge islands. Regression models using the Akaike information criterion (AIC) showed that mean SVL of insular populations was most strongly correlated with island area, suggesting the influence of selection for different body size optima for islands of different size. Allometric differences in head size of giant and dwarf rattlesnakes revealed patterns consistent with shifts to larger and smaller prey, respectively. Conclusions/Significance Our data provide the first example of a clear relationship between body size and island area in a squamate reptile species; among vertebrates this pattern has been previously documented in few insular mammals. This finding suggests that selection for body size is influenced by changes in community dynamics that are related to graded differences in area over what are otherwise similar bioclimatic conditions. We hypothesize that in this system shifts to larger prey, episodic saturation and depression of primary prey density, and predator release may have led to insular gigantism, and that shifts to smaller prey and increased reproductive efficiency in the presence of intense intraspecific competition may have led to insular dwarfism.
An environmentally benign dual action antimicrobial: quaternized chitosan/sodium alga acid multilayer films and silver nanoparticles decorated on magnetic nanoparticles

Abstract There is an urgent need to develop a puissant and environmentally benign antibacterial composite that act via multiple mechanisms to make response to the potentially daunting complexity of the microbial population and microbial antibiotic resistance. In this work, a facile and green approach, layer-by-layer self-assembly technology was applied to assemble polycation quaternized chitosan (QAC) and polyanion sodium alga acid onto magnetic nanoparticles (MNPs). Then silver nanoparticles (AgNPs) with stable and narrow-sized distribution in the range of 25–35 nm were immobilized on the surface of MNPs with L-ascorbic acid as reducing agent and organic multilayers as stabilizer. Through above modification on MNPs, we expected to achieve a green dual antibacterial and recyclable composite via the combined antibacterial action of QAC and AgNPs. Fourier transform infrared spectroscopy, X-ray diffraction, thermogravimetric analysis, transmission electron microscopy, zeta potentials, and dynamic light scattering were employed to confirm the success of the surface functionalization. Silver ion release process was detected by inductively coupled plasma mass spectrometry. Furthermore, the antibacterial properties of the biomaterials against Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus were studied. The modified MNPs exhibited rapid, efficient, and long-lasting biocidal abilities against E. coli and S. aureus. The magnetic antibacterial composite still showed excellent antibacterial efficiency during five exposure/collection/recycle procedures.
Resonant-state expansion of light propagation in nonuniform waveguides

A rigorous approach for precise and efficient calculation of light propagation along nonuniform waveguides is presented. Resonant states of a uniform waveguide, which satisfy outgoing-wave boundary conditions, form a natural basis for expansion of the local electromagnetic field. Using such an expansion at fixed frequency, we convert the wave equation for light propagation in a nonuniform waveguide into an ordinary second-order matrix differential equation for the expansion coefficients depending on the coordinate along the waveguide. We illustrate the method on several examples of nonuniform planar waveguides and evaluate its efficiency compared to the aperiodic Fourier modal method and the finite element method, showing improvements of one to four orders of magnitude. A similar improvement can be expected also for applications in other fields of physics showing wave phenomena, such as acoustics and quantum mechanics.
Optimized area and speed architectures for the mix column operation of the advanced encryption standard

The advancements of The Internet has invoked a large increase in internet transactions and is also associated with a spurt in sharing of confidential, sensitive and private data over the World Wide Web. As a resultant of this, existing cryptographic methodologies such as the Advanced Encryption Standard (AES), which are at present protecting such data, are required to be swift and highly secure. This paper discusses two novel approaches for addressing the above issue and this has been made possible by optimizing the architecture of a vital step of the AES — the mix column operation. In terms of speed efficacy, an improvisation of 1.27 times was attained in comparison to previous algorithms. In terms of hardware architecture, an area optimization of around 3 times was also achieved. All speed and area calculations were performed for the Artix-7 series of FPGA by Xilinx.