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The ever increasing demands placed upon machine performance have resulted in the need for more comprehensive particle accelerator modeling. Computer simulations are key to the success of particle accelerators. Many aspects of particle accelerators rely on computer modeling at some point, sometimes requiring complex simulation tools and massively parallel supercomputing. Examples include the modeling of beams at extreme intensities and densities (toward the quantum degeneracy limit), and with ultra-fine control (down to the level of individual particles). In the future, adaptively tuned models might also be relied upon to provide beam measurements beyond the resolution of existing diagnostics. Much time and effort has been put into creating accelerator software tools, some of which are highly successful. However, there are also shortcomings such as the general inability of existing software to be easily modified to meet changing simulation needs. In this paper possible mitigating strategies are discussed for issues faced by the accelerator community as it endeavors to produce better and more comprehensive modeling tools. This includes lack of coordination between code developers, lack of standards to make codes portable and/or reusable, lack of documentation, among others.

In particle accelerators, RF cavities are used to accelerate charged particle beams to designed high energy for physical applications. In a typical accelerator design, the optimization of RF cavities and the optimization of beam dynamics are carried out in separate studies. For a more general and unrestricted accelerator design, a coupled optimization of the RF cavities and the beam parameters is required. For this coupled optimization problem, we have developed an integrated electromagnetics and beam dynamics workflow management system. Within this system, the geometries for a set of cavity components are first adjusted; the field modes are then computed with an electromagnetics program, and imported into a beam dynamics program for beam dynamics simulation. This workflow is encapsulated into a parallel multi-objective optimizer to achieve the integrated accelerator design optimization. A multi fidelity strategy is developed to improve the speed of the optimizer. This integrated global optimization capability is illustrated using a photoinjector design example and yields an improved design.

We recently characterised the NUP98-HOXD13 (NHD13) mouse as a model of T-cell pre-leukaemia, featuring thymocytes that can engraft in recipient animals and progress to T-cell acute lymphoblastic leukaemia (T-ALL). However, loss of this engraftment ability by deletion of Lyl1 did not result in any loss of leukemogenesis activity. In the present study, we observe that NHD13 thymocytes overexpress EPHA3, and we characterise thymocyte behaviour in NHD13 mice with deletion of EphA3, which show a markedly reduced incidence of T-ALL. Deletion of EphA3 from the NHD13 mice does not prevent the abnormal accumulation or transplantation ability of these thymocytes. However, upon transplantation, these cells are unable to block the normal progression of recipient wild type (WT) progenitor cells through the normal developmental pathway. This is in contrast to the EphA3+/+ NHD13 thymocytes, which block the progression of incoming WT progenitors past the DN1 stage. Therefore, EphA3 is not critical for classical self-renewal, but is essential for mediating an interaction between the abnormally self-renewing cells and healthy progenitors-an interaction that results in a failure of the healthy cells to differentiate normally. We speculate that this may orchestrate a loss of healthy cell competition, which in itself has been demonstrated to be oncogenic, and that this may explain the decrease in T-ALL incidence in the absence of EphA3. We suggest that pre-leukaemic self-renewal in this model is a complex interplay of cell-intrinsic and -extrinsic factors, and that multiple redundant pathways to leukaemogenesis are active.

The rise in coronavirus variants has resulted in surges of the disease across the globe. The mutations in the spike protein on the surface of the virion membrane not only allow for greater transmission but also raise concerns about vaccine effectiveness. Preventing the spread of SARS-CoV-2, its variants, and other viruses from person to person via airborne or surface transmission requires effective inactivation of the virus. Here, we report a water-borne spray-on coating for the complete inactivation of viral particles and degradation of their RNA. Our nanoworms efficiently bind and, through subsequent large nanoscale conformational changes, rupture the viral membrane and subsequently bind and degrade its RNA. Our coating completely inactivated SARS-CoV-2 (VIC01) and an evolved SARS-CoV-2 variant of concern (B.1.1.7 (alpha)), influenza A, and a surrogate capsid pseudovirus expressing the influenza A virus attachment glycoprotein, hemagglutinin. The polygalactose functionality on the nanoworms targets the conserved S2 subunit on the SARS-CoV-2 virion surface spike glycoprotein for stronger binding, and the additional attachment of guanidine groups catalyze the degradation of its RNA genome. Coating surgical masks with our nanoworms resulted in complete inactivation of VIC01 and B.1.1.7, providing a powerful control measure for SARS-CoV-2 and its variants. Inactivation was further observed for the influenza A and an AAV-HA capsid pseudovirus, providing broad viral inactivation when using the nanoworm system. The technology described here represents an environmentally friendly coating with a proposed nanomechanical mechanism for inactivation of both enveloped and capsid viruses. The functional nanoworms can be easily modified to target viruses in future pandemics, and is compatible with large scale manufacturing processes.

The ever increasing demands placed upon machine performance have resulted in the need for more comprehensive particle accelerator modeling. Computer simulations are key to the success of particle accelerators. Many aspects of particle accelerators rely on computer modeling at some point, sometimes requiring complex simulation tools and massively parallel supercomputing. Examples include the modeling of beams at extreme intensities and densities (toward the quantum degeneracy limit), and with ultra-fine control (down to the level of individual particles). In the future, adaptively tuned models might also be relied upon to provide beam measurements beyond the resolution of existing diagnostics. Much time and effort has been put into creating accelerator software tools, some of which are highly successful. However, there are also shortcomings such as the general inability of existing software to be easily modified to meet changing simulation needs. In this paper possible mitigating strategies are discussed for issues faced by the accelerator community as it endeavors to produce better and more comprehensive modeling tools. This includes lack of coordination between code developers, lack of standards to make codes portable and/or reusable, lack of documentation, among others.

In particle accelerators, RF cavities are used to accelerate charged particle beams to designed high energy for physical applications. In a typical accelerator design, the optimization of RF cavities and the optimization of beam dynamics are carried out in separate studies. For a more general and unrestricted accelerator design, a coupled optimization of the RF cavities and the beam parameters is required. For this coupled optimization problem, we have developed an integrated electromagnetics and beam dynamics workflow management system. Within this system, the geometries for a set of cavity components are first adjusted; the field modes are then computed with an electromagnetics program, and imported into a beam dynamics program for beam dynamics simulation. This workflow is encapsulated into a parallel multi-objective optimizer to achieve the integrated accelerator design optimization. A multi fidelity strategy is developed to improve the speed of the optimizer. This integrated global optimization capability is illustrated using a photoinjector design example and yields an improved design.

© 2019 John Wiley & Sons, Ltd. Malware refers to any software, programs, or files that are intentionally utilised to compromise the system and cause unexpected losses to end-users such as economical losses or privacy breaches. The rapid growth of malware makes it impossible to keep up with its progress merely via human interventions or manual analysis. One of the challenges for the human-oriented approaches is they will cause backlog and inability to keep up with the development traces of the malware. Hence, an efficient method is needed urgently to analyse effectively and identify accurately the malware in their domain. Malware clustering has been extensively studied in the machine learning area with regards to distance functions, grouping algorithm and cluster validation. A large number of research studies have been done via behavioral analysis for clustering to achieve high performance of malware detections. However, there is a trade-off for better detection performance between behaviorial approaches and high computational forces. Up to date, little work focuses on the deep learning representations for malware clustering. Therefore, in this paper, we propose an enhanced deep embedded clustering method to facilitate an effective and efficient malware clustering process. The new method takes advantage of linear dimensionality reduction and a customised deep neural network to learn malware representations in an orthogonal space and performs cluster assignments. Our experimental results demonstrate that the proposed clustering model outperforms the traditional K-means method with regards to the enhanced features using various auto-encoder, pre-trained weight and principle component analysis (PCA).

We present an efficient and reliable algorithm for solving a class of nonlinear eigenvalue problems arising from the modeling of particle accelerator cavities. The eigenvalue nonlinearity in these problems results from the use of waveguides to couple external power sources or to allow certain excited electromagnetic modes to exit the cavity. We use a rational approximation to reduce the nonlinear eigenvalue problem first to a rational eigenvalue problem. We then apply a special linearization procedure to turn the rational eigenvalue problem into a larger linear eigenvalue problem with the same eigenvalues, which can be solved by existing iterative methods. By using a compact scheme to represent both the linearized operator and the eigenvectors to be computed, we obtain a numerical method that only involves solving linear systems of equations of the same dimension as the original nonlinear eigenvalue problem. We refer to this method as a compact rational Krylov (CORK) method. We implemented the CORK method in the Omega3P module of the Advanced Computational Electromagnetic 3D Parallel (ACE3P) simulation suite and validated it by comparing the computed cavity resonant frequencies and damping Q factors of a small model problem to those obtained from a fitting procedure that uses frequency responses computed by another ACE3P module called S3P. We also used the CORK method to compute trapped modes damped in an ideal eight 9-cell SRF cavity cryomodule. This was the first time it was possible to compute these modes directly. The damping Q factors of the computed modes match well with those measured in experiments and the difference in resonant frequencies is within the range introduced by cavity imperfection. Therefore, the CORK method is an extremely valuable tool for computational cavity design.

We present an efficient and reliable algorithm for solving a class of nonlinear eigenvalue problems arising from the modeling of particle accelerator cavities. The eigenvalue nonlinearity in these problems results from the use of waveguides to couple external power sources or to allow certain excited electromagnetic modes to exit the cavity. We use a rational approximation to reduce the nonlinear eigenvalue problem first to a rational eigenvalue problem. We then apply a special linearization procedure to turn the rational eigenvalue problem into a larger linear eigenvalue problem with the same eigenvalues, which can be solved by existing iterative methods. By using a compact scheme to represent both the linearized operator and the eigenvectors to be computed, we obtain a numerical method that only involves solving linear systems of equations of the same dimension as the original nonlinear eigenvalue problem. We refer to this method as a compact rational Krylov (CORK) method. We implemented the CORK method in the Omega3P module of the Advanced Computational Electromagnetic 3D Parallel (ACE3P) simulation suite and validated it by comparing the computed cavity resonant frequencies and damping Q factors of a small model problem to those obtained from a fitting procedure that uses frequency responses computed by another ACE3P module called S3P. We also used the CORK method to compute trapped modes damped in an ideal eight 9-cell SRF cavity cryomodule. This was the first time it was possible to compute these modes directly. The damping Q factors of the computed modes match well with those measured in experiments and the difference in resonant frequencies is within the range introduced by cavity imperfection. Therefore, the CORK method is an extremely valuable tool for computational cavity design.

The NGLS collaboration is developing design concepts for a multi-beamline soft x-ray FEL array powered by a superconducting linear accelerator, operating with a high bunch repetition rate of approximately 1 MHz. The CW superconducting linear accelerator design is based on developments of TESLA and ILC technology, and is supplied by an injector based on a high-brightness, highrepetition- rate photocathode electron gun. Electron bunches from the linac are distributed by RF deflecting cavities to the array of independently configurable FEL beamlines with nominal bunch rates of ∼100 kHz in each FEL, with uniform pulse spacing, and some FELs capable of operating at the full linac bunch rate. Individual FELs may be configured for different modes of operation, including self-seeded and external-laser-seeded, and each may produce high peak and average brightness x-rays with a flexible pulse format, and with pulse durations ranging from femtoseconds and shorter, to hundreds of femtoseconds. In this paper we describe current design concepts, and progress in RandD activities. Copyright © 2013 CC-BY-3.0 and by the respective authors.

The NGLS collaboration is developing design concepts for a multi-beamline soft x-ray FEL array powered by a superconducting linear accelerator, operating with a high bunch repetition rate of approximately 1 MHz. The CW superconducting linear accelerator design is based on developments of TESLA and ILC technology, and is supplied by an injector based on a high-brightness, highrepetition- rate photocathode electron gun. Electron bunches from the linac are distributed by RF deflecting cavities to the array of independently configurable FEL beamlines with nominal bunch rates of ∼100 kHz in each FEL, with uniform pulse spacing, and some FELs capable of operating at the full linac bunch rate. Individual FELs may be configured for different modes of operation, including self-seeded and external-laser-seeded, and each may produce high peak and average brightness x-rays with a flexible pulse format, and with pulse durations ranging from femtoseconds and shorter, to hundreds of femtoseconds. In this paper we describe current design concepts, and progress in RandD activities. Copyright © 2013 CC-BY-3.0 and by the respective authors.

The application of paired-end next generation sequencing approaches has made it possible to systematically characterize rearrangements of the cancer genome to base-pair level. Utilizing this approach, we report the first detailed analysis of ovarian cancer rearrangements, comparing high-grade serous and clear cell cancers, and these histotypes with other solid cancers. Somatic rearrangements were systematically characterized in eight high-grade serous and five clear cell ovarian cancer genomes and we report here the identification of > 600 somatic rearrangements. Recurrent rearrangements of the transcriptional regulator gene, TSHZ3, were found in three of eight serous cases. Comparison to breast, pancreatic and prostate cancer genomes revealed that a subset of ovarian cancers share a marked tandem duplication phenotype with triple-negative breast cancers. The tandem duplication phenotype was not linked to BRCA1/2 mutation, suggesting that other common mechanisms or carcinogenic exposures are operative. High-grade serous cancers arising in women with germline BRCA1 or BRCA2 mutation showed a high frequency of small chromosomal deletions. These findings indicate that BRCA1/2 germline mutation may contribute to widespread structural change and that other undefined mechanism(s), which are potentially shared with triple-negative breast cancer, promote tandem chromosomal duplications that sculpt the ovarian cancer genome.

A novel noninvasive flexible periphery sensor for respiration detection has been developed. At low cost, it is lightweight and portable and is able to fit to human body without affecting user movement, suitable for different applications under various conditions. Real time data of a breathing profile provided by the sensor can be recorded and it is very useful for medical diagnosis. The working principle of the proposed sensor is presented and the developed prototype is described. Experimental testing of the sensor with different proposed materials has been performed. It was found that the sensor with carbon conductive grease that contains pure carbon black and dimethylpolysiloxane can extend approximately 10%. The change in voltage for the extension was 0.7V. The repeatability was +/-7.6% FS and the response time is less than 1s. The results demonstrate the feasibility of the proposed sensor for diagnosis of pulmonary disease.

A novel noninvasive flexible periphery sensor for respiration detection has been developed. At low cost, it is lightweight and portable and is able to fit to human body without affecting user movement, suitable for different applications under various conditions. Real time data of a breathing profile provided by the sensor can be recorded and it is very useful for medical diagnosis. The working principle of the proposed sensor is presented and the developed prototype is described. Experimental testing of the sensor with different proposed materials has been performed. It was found that the sensor with carbon conductive grease that contains pure carbon black and dimethylpolysiloxane can extend approximately 10%. The change in voltage for the extension was 0.7V. The repeatability was +/-7.6% FS and the response time is less than 1s. The results demonstrate the feasibility of the proposed sensor for diagnosis of pulmonary disease.

A novel noninvasive flexible periphery sensor for respiration detection has been developed. At low cost, it is lightweight and portable and is able to fit to human body without affecting user movement, suitable for different applications under various conditions. Real time data of a breathing profile provided by the sensor can be recorded and it is very useful for medical diagnosis. The working principle of the proposed sensor is presented and the developed prototype is described. Experimental testing of the sensor with different proposed materials has been performed. It was found that the sensor with carbon conductive grease that contains pure carbon black and dimethylpolysiloxane can extend approximately 10%. The change in voltage for the extension was 0.7V. The repeatability was +/-7.6% FS and the response time is less than 1s. The results demonstrate the feasibility of the proposed sensor for diagnosis of pulmonary disease.