Wirespeed: Extending the AFF4 forensic container format for scalable acquisition and live analysis

BackgroundPain is common in most diseases and is usually treated by medical and physical approaches (medications, exercise). Limited attention has been given to whether non-medical approaches (such as emotion regulation abilities including self-compassion) can help further reduce the pain experienced. Scleroderma (systemic sclerosis), a painful and complex autoimmune connective tissue and vascular disease, was examined in this study in relation to psychological aspects of pain and the links of pain to (1) early life experiences, (2) current insecure attachment style, (3) physiological arousal (negative, hyper-arousal), and (4) personal emotion regulation (self-compassion). Knowledge about these relationships may help in the treatment of pain.Participants and procedureParticipants (120) from Australia (39) and the United Kingdom (81) completed an online or a hard copy survey; 78 participants remained for analysis after screening.ResultsWe found that poor or limited positive early life experiences and a current insecure (dismissive) attachment style contributed significantly to experiencing elevated levels of pain. We also found that negative early life experiences and limited emotion regulation (low self-compassion) predicted hyper-arousal, which in turn was associated with experienced pain. Insecure dismissive attachment style and hyper-arousal were significantly correlated.ConclusionsThese findings suggest that negative childhood experiences and an insecure dismissive attachment style may determine how emotional experiences are regulated or managed by individuals in disease-related pain. Implications include that interventions addressing attachment style and self-compassion may help reduce pain in the individuals concerned, thus helping supplement the effectiveness of current medication and physical approaches.

Nonlinear optical imaging techniques have been widely used to reveal biological structures for accurate diagnosis at the cellular as well as the tissue level. In the present study, polarization-dependent second-harmonic generation (PSHG) was used to determine collagen orientation in breast cancer biopsy tissues (grades 0, I, II and III). The obtained data were processed using fast Fourier transform (FFT) analysis, while second-harmonic generation (SHG) anisotropy and the "ratio parameter" values were also calculated. Such measurements were shown to be able to distinguish collagen structure modifications in different cancer grades tested. The analysis presented herein suggests that PSHG imaging could provide a quantitative evaluation of the tumor state and the distinction of malignant from benign breast tissues. The obtained results also allowed the development of a biophysical model, which can explain the aforementioned differentiations and is in agreement with the simulations relating the SHG anisotropy values with the mechanical tension applied to the collagen during cancer progression. The current approach could be a step forward for the development of new, nondestructive, label free optical diagnostic tools for cancer reducing the need of recalls and unnecessary biopsies, while potentially improving cancer detection rates.

Large scale storage data networks are difficult to conFigure and tricky to maintain [1][2][3]. As storage data volumes grow and the pace of change accelerates, it can be a struggle to keep up the integrity of a large scale storage network. Software Defined Networking (SDN) [5][6][7][8][10][11] provides a method to centrally conFigure and manage physical and virtual network devices such as routers, switches, and gateways in HPC datacenter. Current HPC computer cluster and data centers are using homogeneous data network technologies such as the Infiniband network (QDR, FDR, EDR, and HDR). Due to the connector, cable, and bandwidth backward compatibility issues, once an aged HPC computing system was retired, we have to demolish all established computer clusters, data network, and data storage. Eventually we lost all costly investment on the data network and storage. Those current deployment approaches do not provide a feasible and compatible growing path and cannot meet the need for future extreme scale HPC computing systems [4][12][13].

The emerging area of (smart) Cyber Physical Systems (sCPS) triggers demand for new methods of design, development, and deployment of architecturally dynamic distributed systems. Current approaches (e.g. Component-Based Software Engineering and Agent-Based Development) become insufficient since they fail in addressing challenges specific to sCPS such as mobility, heterogeneous and unreliable deployment infrastructure, and architectural dynamicity. The strong dependence on the underlying communication infrastructure, often combining ad-hoc established links typical for wireless connectivity with more reliable connections of infrastructural networks, requires a novel method to optimize system deployment. In this paper we propose such a method based on the domain knowledge elicited from design level specification. As a proof of concept, we have provided an extension to the DEECo (Dependable Emergent Ensembles of Components) model and validated it on a scenario from the domain of Vehicular Area Networks.

Touch driven controller and tactile features for physical interactions

Many scientific workflows access data derived from specialized instruments. When the data is analyzed, it is accessed over wide area networks, creating bottlenecks from long access latencies. We ask the question: assuming that data must be accessed remotely, can latencies be hidden without application change? We present TAZeR, a remote I/O framework that reduces effective data access latency. TAZeR transparently converts POSIX I/O into operations that interleave application work with data transfer, i.e., read prefetching and write stage-out. TAZeR ensures read data moves directly to application memory without synchronous intervention (soft zero-copy). TAZeR uses distributed bandwidth-aware staging to exploit data reuse across application tasks and to manage the capacity constraints of fast hierarchical storage. We evaluate TAZeR on a High Energy Physics workflow where two 1 Gb/s WAN links request remote data at 48 Gb/s using non-streaming access patterns. TAZeR is $12 \times $ and $22 \times $ faster than XRootD (state-of-the-art) and file copies (current approach), respectively; and within 7% of optimal. We explore conditions under which TAZeR can hide I/O accesses by showing performance as effective staging sizes change.

In real-world applications, transactions are typically represented by quantitative data. Thus, fuzzy association rule mining algorithms have been proposed to handle these quantitative transactions. In addition, items generally have certain lifespans or temporal periods in which they exist in a database. Therefore, fuzzy temporal association rule mining algorithms have also been proposed in the literature. A key factor in the acquisition of fuzzy temporal association rules (FTARs) is the design of appropriate membership functions. Because current approaches have been designed to generate membership functions for mining fuzzy association rules (FARs) in market-basket analysis, in this paper, we propose a membership function tuning mechanism for a fuzzy temporal association rule mining algorithm. The proposed approach modifies an existing cluster-based method to generate unique membership functions that are specifically tailored to each item in a dataset. Two factors are utilized to decide the appropriate membership functions of each item: (1) the density similarity among intervals corresponding to the density similarity within intervals, and (2) the information closeness within an interval corresponding to the similarity in the number of data points between intervals. A parameter θ is used to indicate the relative importance of these two factors. As a result, the membership functions are generated based on the quantitative ranges of individual items, and the generated membership functions of items are different in terms of the values of each interval and the number of intervals. The generated membership functions are subsequently used in a fuzzy temporal association rule mining algorithm. Computational experiments were conducted on both a synthetic dataset and a real-world one to demonstrate the effectiveness of the proposed approach.

The advent of additive manufacturing (AM), commonly known as 3D printing, has enabled the rapid fabrication of complex structures previously unrealizable with traditional manufacturing techniques. Current approaches, however, are limited to single materials or single methodologies greatly limiting the potential scope of manufacturable products and components. Recently, our group has developed a novel multi-material multi-method (m4) 3D printer which integrates four AM technologies and two complementary technologies into one single platform. This allows for the fabrication of complex devices able to provide a wide range of functionalities ranging from stretchable electronics to self-sensing devices. To demonstrate these functionalities in the realm of printable electronics, multiple proof of concept printed circuit boards (PCBs) were fabricated which solve issues commonly encountered in 3D printed electronics such as high resolution or vertically integrated access (VIA) circuits. In addition, 3D printed smart structures able to respond to external stimulus, such as light or heat, have become highly desirable for applications ranging from soft robotics to implantable medical devices. Recently, our group has turned to liquid crystal elastomers (LCE), a class of active material able to generate large, rapid, and reversible actuations. Therefore, using the m4 3D printer, LCE-based smart structures requiring complex electronics were fabricated which can change their shape in response to an applied current. To demonstrate this, a smart, reconfigurable radio frequency (RF) antenna was 3D printed which can change its shape and operating frequency as a function of the applied current. These examples demonstrate the vast potential of m4 3D printing for creating smart, reconfigurable, and multi-functional structures.

In low/intermediate-level waste (L/ILW) repositories, anaerobic corrosion of metals and degradation of organic materials produce hydrogen, methane, and carbon dioxide. Gas migration in a L/ILW repository is one of the processes evaluated in the safety assessment of deep geological disposal in low-permeability formations, in particular with respect to the development of gas pressures in the repository caverns which could negatively affect the host rock or the engineered barrier system (EBS). In order to restrict build-up of gas overpressures in the emplacement caverns, Nagra (National Cooperative for the Disposal of Radioactive Waste, Switzerland) has proposed design options aimed at increasing the gas transport capacity of the backfilled underground structures, compromising neither the low hydraulic conductivity nor the radionuclide retention capacity of the EBS (Nagra, 2008). They involve specially designed backfill and sealing materials such as high porosity mortars as backfill materials for the emplacement caverns and sand/bentonite (S/B) mixtures with a bentonite content of 20% to 30% for the seals themselves and for backfilling other underground structures. These increased gas permeability materials can supplement the gas flow that is expected to occur through the excavation damaged zone (EDZ) and avoid the creation of overpressures. Preliminary experimental studies have confirmed the gas transport capacity of the S/B mixtures and demonstrated the ability to design mixtures with specific target permeabilities for water and gas flow (Nagra, 2008). Two-phase flow modelling studies have shown that the gas transport capacity of seals is largely dependent on their permeability and length. More detailed models of sealing elements show a rather complex history of seal saturation during the early saturation phase and the later gas escape phase (Gaus et al., 2010). Note, however, that current modelling approaches are based on parameters and conceptual understanding of small-scale laboratory experiments. Two large(r) scale experiments which aim at validating and, if necessary, improving current conceptual models for the resaturation and gas invasion processes into S/B seals and the determination of up-scaled gas / water permeabilities of S/B seals (i.e. two-phase flow parameters for large-scale models) have been initiated and will be highlighted in the paper. The first one, a mock-up experiment, was set up in 2010 as part of the EU 7th FP project FORGE, aiming at demonstrating seal performance on an intermediate (decimetre scale). The second one is a large-scale experiment (metre-scale), the Gas-Permeable Seal Test (GAST), which was also initiated in 2010 at the Grimsel Test Site (GTS). For GAST, a seal will be emplaced at the GTS to demonstrate the effective functioning of gas-permeable seals on a realistic scale and with realistic boundary conditions (‘proof of concept’).

In acute myeloid leukemia (AML), malignant stem cells hijack the normal bone marrow niche where they are largely protected from the current therapeutic approaches. Thus, eradicating these progenitors is the ultimate challenge in the treatment of this disease. Specifically, the development of chimeric antigen receptors (CARs) against distinct mesenchymal stromal cell subpopulations involved in the maintenance of leukemic stem cells within the malignant bone marrow microenvironment could represent a new strategy to improve CAR T-cell therapy efficacy, which is still unsuccessful in AML. As a proof of concept, we generated a novel prototype of Tandem CAR, with one specificity directed against the leukemic cell marker CD33 and the other against the mesenchymal stromal cell marker CD146, demonstrating its capability of simultaneously targeting two different cell types in a 2D co-culture system. Interestingly, we could also observe an in vitro inhibition of CAR T cell functionality mediated by stromal cells, particularly in later effector functions, such as reduction of interferon-gamma and interleukin-2 release and impaired proliferation of the CAR+ effector Cytokine-Induced Killer (CIK) cells. Taken together, these data demonstrate the feasibility of a dual targeting model against two molecules, which are expressed on two different target cells, but also highlight the immunomodulatory effect on CAR CIK cells exerted by stromal cells, confirming that the niche could be an obstacle to the efficacy of CAR T cells. This aspect should be considered in the development of novel CAR T cell approaches directed against the AML bone marrow niche.

Multispecies biofilms are an important healthcare problem and may lead to persistent infections. These infections are difficult to treat, as cells in a biofilm are highly resistant to antimicrobial agents. While increasingly being recognized as important, the properties of multispecies biofilms remain poorly studied. In order to do so, the quantification of the individual species is needed. The current cultivation-based approaches can lead to an underestimation of the actual cell number and are time-consuming. In the present study we set up a culture-independent approach based on propidium monoazide qPCR (PMA-qPCR) to quantify Pseudomonas aeruginosa in a multispecies biofilm. As a proof of concept, we explored the influence of the combined presence of Staphylococcus aureus, Streptococcus anginosus and Burkholderia cenocepacia on the antimicrobial susceptibility of P. aeruginosa using this PMA-qPCR approach.

The ability to read has become an indispensable skill in modern ages, and any reading difficulty, such as dyslexia, can seriously impair the aspirations of the individual. Orthographically opaque languages such as English lay a heavy burden on learners. In this article, a gamified intervention program for the remediation of dyslexia is proposed for opaque orthographies. Current technology-based approaches of reading acquisition in the literature lack sophistication in terms of training design, game design, and adaptivity. This approach is based on the principles of automaticity acquisition and the gamification of learning as well as intelligent instruction. For the latter, an optimization model is proposed to maximize the educational value of each training session while respecting the capabilities of each individual.

With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional “physics-based” approach, deep-learning-enabled optical metrology is a kind of “data-driven” approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally, the directions for future research are outlined.

Cancer stem cells (CSCs) have been identified as the main center of tumor therapeutic resistance. They are highly resistant against current cancer therapy approaches particularly radiation therapy (RT). Recently, a wide spectrum of physical methods has been proposed to treat CSCs, including high energetic particles, hyperthermia (HT), nanoparticles (NPs) and combination of these approaches. In this review article, the importance and benefits of the physical CSCs therapy methods such as nanomaterial-based heat treatments and particle therapy will be highlighted.

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