The latest Internet of Things (IoT) edge-centric architectures allow for unburdening higher layers from part of their computational and data processing requirements. In the specific case of fog computing systems, they reduce greatly the requirements of cloud-centric systems by processing in fog gateways part of the data generated by end devices, thus providing services that were previously offered by a remote cloud. Thanks to recent advances in System-on-Chip (SoC) energy efficiency, it is currently possible to create IoT end devices with enough computational power to process the data generated by their sensors and actuators while providing complex services, which in recent years derived into the development of the mist computing paradigm. To allow mist computing nodes to provide the previously mentioned benefits and guarantee the same level of security as in other architectures, end-to-end standard security mechanisms need to be implemented. In this paper, a high-security energy-efficient fog and mist computing architecture and a testbed are presented and evaluated. The testbed makes use of Transport Layer Security (TLS) 1.2 Elliptic Curve Cryptography (ECC) and Rivest-Shamir-Adleman (RSA) cipher suites (that comply with the yet to come TLS 1.3 standard requirements), which are evaluated and compared in terms of energy consumption and data throughput for a fog gateway and two mist end devices. The obtained results allow a conclusion that ECC outperforms RSA in both energy consumption and data throughput for all the tested security levels. Moreover, the importance of selecting a proper ECC curve is demonstrated, showing that, for the tested devices, some curves present worse energy consumption and data throughput than other curves that provide a higher security level. As a result, this article not only presents a novel mist computing testbed, but also provides guidelines for future researchers to find out efficient and secure implementations for advanced IoT devices. The rise of the Internet of Things (IoT) paradigm is expected to connect to the Internet more than 30 billion devices by 2020 . This unNowadays, IoT devices are present in numerous environments, such as in agriculture , home auTo guarantee a secure and energy-efficient deployment of IoT networks, three main elements must be considered and addressed as a whole: IoT architecture, IoT hardware and the required security mechanisms of all the involved devices. Due to the advances in System-on-Chip (SoC) efficiency, current IoT nodes have enough computational capabilities for processing most of the data and unburden the devices of higher layers from data processing. This movement of data processing capabilities from the gateways to the end devices is called mist computing . More poThis article includes three main contributions aimed at creating a testbed for determining the impact of securing fog and mist communications. First, in order to establish the basics, it presents a high-security fog and mist computing architecture and an analysis of the main and the latest hardware platforms to be used as mist end devices. Second, it thoroughly explains the design and implementation of a testbed that allows for obtaining energy and throughput measurements when securing the communications of fog and mist devices. To maintain the interoperability between all the layers, Hypertext Transport Protocol Secure (HTTPS) was selected as the communication protocol. The latest Elliptic Curve Cryptography (ECC) and Rivest-Shamir-Adleman (RSA)-based Transport Layer Security (TLS) cipher suites are analyzed and only the ones aligned with the requirements of the next TLS standard  are selected. Third, multiple tests are conducted to determine the performance and energy-consumption impact of using TLS with different RSA and ECC implementations in a real-world scenario. It is performed an analysis in terms of security level, scalability, energy consumption and data throughput in two different scenarios: a fog gateway to mist end-device communication and a direct mist end-device to mist end-device communication.The rest of this paper is structured as follows. Cloud-centric architectures allow for providing complex services with high demands on computational power, data storage and network bandwidth that are difficult to accommodate on personal computers or Small and Medium-sized Enterprise (SMEs) mainframes. Although the emergence of cloud computing solutions democratized the access to High Performance Computing (HPC) capabilities, its centralized nature comes with restrictions that are difficult to accommodate on real-time IoT systems. To alleviate the inherent high latency of cloud computing, edge-centric approaches ,33 move An example of generic IoT edge architecture with fog and mist computing layers is shown in In a mist computing deployment, the selected end-device hardware platform is essential. To allow for implementing standard Internet communications protocols such as Hypertext Transport Protocol (HTTP) or Message Queuing Telemetry Transport (MQTT) , IoT harDue to the resource-constrained nature of these platforms, it is usually not possible to execute the necessary cryptographic algorithms on the main SoC processor for securing mist devices. In addition, hardware acceleration is often required for maintaining acceptable energy consumption and throughput values when executing algorithms involved in public-key cryptography such as Elliptic Curve Digital Signature Algorithm (ECDSA) . Speed-uIt is important to note that even if a platform embeds a hardware-acceleration module into the SoC for performing certain cryptographic functions, software implementations that make use of the available hardware acceleration are also needed. Software optimizations to tackle specific hardware accelerators can impact significantly both the algorithm execution time and the energy consumption .The ESP32 is the only hardware board in Confidentiality: it disallows unwanted access to sensitive information.Integrity: it protects the transmitted data for unwanted modification or tampering.Availability: it is related to the ability of accessing the data when they are needed.The main information security characteristics are confidentiality, integrity and availability, also known as the CIA triad. Each one of them grants different properties to a communications system:–Node tampering: it consists on altering the mist device itself or the sensors or actuators connected to it to access or modify sensitive information or the device behavior .–Jamming: an attacker blocks mist device wireless communications by transmitting with a jammer device .–Radio Frequency (RF) interference: it consists in introducing devices on the network that generate signals or noise that interfere legitimate communications .–Malicious node injection: it consists on adding a malicious mist node to the mist layer with the intention of introducing tampered data or to access the data being transmitted between the victim mist devices .–Physical damage: an attacker can prevent a mist node from working properly or can neutralize it completely by totally or partially destroying it.–Malicious code injection: by physically accessing the mist node an attacker could change the code controlling the node and achieve total access to the mist layer .–Sleep deprivation attack: an attacker tries to maximize the energy consumption of a node, which can have a massive effect on the overall energy consumption of the mist layer and thus reduce the life of battery-operated nodes .Physical attacks: they target the physical device or the physical communication channel:–Traffic analysis attacks: the attacker can somehow intercept the traffic transmitted over the mist network and analyze it to infer any type of useful information about how the network and the mist devices work .–Man-in-the-Middle (MitM) attacks: one of the most common network attacks where a third party captures the communications between two legitimate mist nodes and accesses or tampers the transmitted data .–Denial of Service (DoS) attack: an attacker tries to prevent legitimate access to mist nodes by flooding the mist layer communication network with fake traffic or by tampering with the normal functioning of the network .–Message replaying attack: an attacker can capture a command sent to a mist device and replay it at its convenience .–Sybil attack: a malicious mist node can spoof the identity of other legitimate nodes and act as them .Network attacks: they try to exploit some vulnerability on the way the communications are established on the mist layer:–Virus and Worms: attacks that consist on some type of code that can take partial or total control of the mist end-device firmware, and that can be easily replicated to other mist nodes in an autonomous way .–Malicious scripts: mist nodes are usually capable of executing arbitrary pieces of code. By introducing a malicious script, an attacker can gain control of a mist node while the node seems unaltered to the rest of the mist network.–DoS attacks: a DoS attack can be also performed at a software level, by disallowing the access to a service at an application level. It is usually performed by gaining control of mist nodes or fog gateways that provide such a service.Software attacks: malware attacks using viruses, worms or malicious scripts targeting mist devices:–Side-channel attacks: the attacker uses behavioral information of the device that is applying the cryptographic algorithms. By measuring the elapsed time required for signing or verifying on a public-key cryptographic scheme, or the energy consumption of a mist device during certain phases of the cryptographic protocol, an attacker can infer information and guess the encryption key being used without having a direct knowledge of the plaintext or the ciphertext .–Cryptanalysis attacks: an attacker can obtain the encryption key by obtaining the plaintext or the ciphertext .–MitM attacks: the attacker can intercept the communications between two legitimate parties when the key negotiation phase is performed. At such a point, the attacker would be able either to establish a spoofed shared secret or to obtain the negotiated shared secret and decrypt or tamper any messages transmitted between the two parties .Encryption attacks: the last type of attacks that can be performed against mist devices are the ones that try to break the encryption of the communications (in case encryption techniques are used to secure communications) by obtaining the private key of one of the involved parties:Different types of attacks can be performed to break one or several of these security characteristics when targeting mist deployments. Depending on which part of the communications architecture is targeted by the attack, they can be classified as :PhysicaAs of writing, no references were found in the literature about the analysis on the impact of security on a mist computing environment. As it was indicated previously in Regarding energy efficiency, some authors conclude that moving the processing power closer to the data origin could yield energy efficiency improvements , but theThere are other works focused on improving energy efficiency of IoT systems ,66 or onWhen the strength of a cryptographic algorithm is assessed, the main parameter that is usually considered is its key size in bits. Using the same algorithm, larger key lengths make brute force attacks more difficult, thus creating more secure systems. One important consideration is that key size defines an upper-bound on the security an algorithm can provide, but the actual strength of the algorithm can be lower: the security level that a certain algorithm provides is a way of measuring the effort needed to break its trapdoor function . GeneralThe relationship between key size and security level of different asymmetric-key algorithms varies greatly, as it can be observed in p is a prime number, and the binary field p and The ECC curves used by modern cryptosystems are implemented over two number fields. The prime field ocessors , althougocessors . They caocessors . Some coocessors  make theocessors ) lack thocessors .Regarding prime field curves, there are two main subgroups that differ on how the domain parameters of the curve are generated. There are curves generated with verifiable random domain parameters  and curves that are generated with domain parameters over a prime field associated with a Koblitz curve . A detailed explanation on how the domain parameters are obtained for each curve and its implications for both prime and binary fields can be found in .The most extended curves available for TLS communications are the ones defined by the National Institute of Standards and Technology (NIST) . The proAnother largely available set of curves is the one defined by the Standards for Efficient Cryptography Group (SECG) , which uFinally, another option is to use the so-called Brainpool curves . This InThe general architecture of the designed testbed is shown in It is important to note that, although the testbed architecture was designed to measure the energy consumption and throughput of devices of the fog and mist layers, it can be easily extended to cover other scenarios. For instance, if it is required to measure a higher number of devices it would only be necessary to deploy more SBCs with their corresponding current sensors. Moreover, in case of needing to perform the same measurements when the network presents long or unpredictable latencies between the SBCs , advanced time synchronization approaches can be implemented. In the case of not being able to use Network Time Protocol (NTP) servers, Global Positioning System (GPS) time synchronization (with the simple addition of a GPS module to the SBCs) could be used to provide a common time reference to all the SBCs. When time synchronized, each of the SBCs could start and stop the tests and the energy measurement procedure in the same way as the implemented testbed presented in this paper.To perform the designed experiments, the devices in the fog layer and the mist layer  must be able to support the same cipher suites, ECC curves and RSA key sizes. For the sake of fairness, the selected devices for the hardware testbed must be constrained in terms of energy consumption, but, at the same time, be able to execute the required cryptographic algorithms. The traffic and data throughput requirements for a fog gateway usually imply the use of a device powerful enough to run a Linux distribution, thus supporting well known and extensively used TLS implementations such as OpenSSL . As it wDue to the previously mentioned requirements, an ESP32 module was selected as mist end-device. It is an IoT platform that embeds an Xtensa dual-core 32-bit LX6 microprocessor  that runs at 240 MHz and has 520 KB of Static RAM (SRAM). With a peak energy consumption of only 1.65 W, it presents a great balance between computational power and energy efficiency. It features both Wi-Fi and Bluetooth v4.2 BR/EDR and Bluetooth Low Energy (BLE) interfaces. The Wi-Fi interface supports IEEE 802.11 b/g/n modes of operation and WPA/WPA2 authentication. It provides several buses and communication interfaces that are ideal for IoT sensor nodes, such as I2C, UART and programmable GPIOs. Moreover, a crypto-engine is integrated into its SoC, thus providing hardware acceleration for different cryptographic algorithms such as ECC, RSA, AES and SHA, which are all used by the cipher suites tested in A SBC was chosen to implement the fog gateway, since it presents a good trade-off between energy efficiency, computational capabilities and software implementation flexibility. Specifically, Orange Pi PCs  . A second Orange Pi PC was also used as the device in charge of obtaining the energy consumption values from the INA-219 current sensors during the tests. The INA219 can operate in different modes: with 0.1 mA steps for a maximum of ±400 mA or 0.8 mA steps and a maximum of ±3.2 A. The maximum voltage allowed by the sensor is 26 V, far beyond the 5 V of the power supplies used for powering up the mist device and the fog gateway. The different values measured by the sensor can be accessed through the I2C bus, which eases the communication with the Orange Pi PC in charge of obtaining the energy consumption measurements.This Section details the main software used for implementing the testbed and executing the experiments. For programing the ESP32 modules, the ESP32-IDF  release/The Linux distribution ARMBian  was instFinally, a virtual machine with Debian was deployed on the laptop. The purpose of this machine is to allow for accessing the testbed during the execution of the tests through an SSH connection. The same virtual machine was used to download all the generated data to analyze them and to generate the different charts presented in CBC-SHA256ECDHE-ECDSA-AES128-CBC-SHA256ECDHE-RSA-AES128-andGCM-SHA256ECDHE-ECDSA-AES128-GCM-SHA256ECDHE-RSA-AES128-Two different cipher suites were selected for the tests: One based on RSA and another one based on ECDSA (for signing certificates). The remaining algorithms involved in the cipher suite ) were identical in order to perform a fair comparison. Taking this issue into account and following the NIST guidelines for the selection of TLS implementations , only twECDSA-AES128-GCM-SHA256 and ECDHE-RSA-AES128-GCM-SHA256.The main difference between these two options is the block cipher mode of operation: one uses Cipher Block Chaining (CBC), while the other one uses Galois/Counter Mode (GCM). Since the aim of the experiments is to provide future-proof conclusions, the latest TLS 1.3 draft  was evalAfter selecting the cipher suites to be used and before generating the certificates, ECC curves and RSA key sizes have to be selected. The limiting factor for such a selection is the available ECC curves and the RSA key sizes supported by the ESP32 hardware and the ESP-IDF SDK. According to the official documentation, the hardware crypto-engine of the ESP32 SoC supports up to 512-bit ECC curves and up to 4096-bit RSA keys.To take advantage of the hardware-acceleration capabilities of the ESP32, an ECC software implementation for each of the selected curves has to be available and ESP-IDF supports the ECC curves shown in To evaluate the performance of the selected devices, different tests were performed using both configurations of the testbed previously described in Regarding the hardware-acceleration options available on the ESP32, both SHA and AES were enabled for all the tests, since both algorithms are used by the selected cipher suites. Multi-precision integer (MPI) acceleration was turned on only when using the RSA cipher suite. Although the official ESP32 documentation states that its crypto-engine supports RSA operations for a key size of up to 4096 bits, it was only possible to keep MPI acceleration enabled when using the 1024-bit and the 2048-bit RSA certificates for the HTTPS server implementation. When using the certificates generated with 3072-bit and 4096-bit RSA keys, it was only possible to establish an HTTPS communication with the MPI acceleration disabled. This is due to the fact that when the MPI acceleration was enabled, the ESP32 server rebooted itself as soon as the HTTPS communication was initiated. The exact reason of this issue remains unknown, being the most probable cause a software-related bug on the hardware-acceleration implementation. In contrast, the ESP32 client implementation allowed for maintaining MPI acceleration enabled for all the RSA key sizes.p optimizations for ECC were enabled for both cipher suites. It is important to note that, as stated in [The NIST modulo-tated in , the ECCA high-level sequence diagram of the testing procedure is depicted in It is important to note that, although it can be considered that only two scenarios are evaluated  the use of different cipher suites and security levels gives as a result multiple combinations that can be seen as individual scenarios. For instance, for each of the testbed configurations, two cipher suites are used, and the selected RSA key sizes and ECC curves compare a total of 6 different security levels provided by 13 cipher suites configurations. Those security levels can be associated with different levels of data protection  and target different mist end devices, depending on their computational capabilities. Likewise, the obtained throughput values can be translated into different service needs, giving priority either to real-time actuation or to higher security assurance, depending on the application requirements.As it was previously indicated, payloads from 32 bytes to 8 kilobytes were transmitted a total of 100 times for each of the tested RSA key sizes and ECC curves. As explained in It can be observed that the average time difference percentage considering all tests is 0.011%, which means that the current measurement period is only 0.011% larger than the actual test, so its impact is negligible in the experiments presented in this article.To determine the impact of the payload transmitted when using the selected cipher suites, they were compared for the lowest security level of the ones evaluated in this article (80 bits). Such a security level was selected since it requires the lowest computational resources, thus allowing for determining in a clearer way than with higher security levels the impact on energy consumption and data throughput related exclusively to payload size.It is important to note that most of the time and computational effort of the transmission are dedicated to the TLS Handshake, specifically to the signing and verification processes . During X-axis indicates the different security levels provided by each RSA key size and ECC curve as indicated in The aim of this section is to present the results obtained by comparing the provided security level, the energy consumption and the data throughput of all the tested alternatives. As explained in the previous section, the payload effect on the energy consumption was constant through all the performed tests, being noticeable only for payloads larger than 4 kB. For this reason, the results presented in this section are only shown for a 512-byte payload, since the obtained results for different payloads are almost identical. Moreover, in the Figures shown in this section, the In this scenario two mist end devices communicate to each other, one acting as client and the other one as server. As it can be observed in The higher energy consumption in this scenario is due to the slower response of mist end devices in comparison to fog gateways. To measure the impact of these delays on the communications, the network sniffer tool Wireshark was used in two different tests for recording all the timestamped transmitted packets. The first test consisted on downloading the 512-byte JSON file from a Python server deployed on a virtual machine using the ESP32 client. On the second test, a Python client was used to download the same file from the ESP32 server. As it can be seen in In order evaluate the real-time performance of the presented testbed, the average time per request was measured on three scenarios: fog gateway to fog gateway, fog gateway to mist end-device, and mist end-device to mist end-device. As an example of evaluation of secure cipher suites, RSA 2048 and secp256r1 performance were compared. The obtained results are shown in To determine the reliability of the obtained results, it is first necessary to analyze the reliability of the testbed. In this regard, the implemented software allowed for obtaining 500 samples per second from each sensor, which is a high enough sample rate to detect any energy consumption spikes or fast variations that occur during the tests. Moreover, the time synchronization between the two Orange Pi PCs of the testbed allowed for determining that the current measurement period was on average only 0.011% larger than the actual test. That means that for the longest test (of 581.41 s), the measurement procedure only required roughly 63 ms.When analyzing the energy consumption and throughput results for the fog gateway to the mist end-device scenario, the BP256R1 curve presented the worst results of all the tested alternatives. As already discussed in modulo p optimizations enabled in the ESP32  were chosen following the NIST guidelines and following the restrictions of the latest TLS draft. For each cipher suite different alternatives for providing a range of security levels were analyzed and selected. Certificates were generated and tested using different ECC curves and RSA key sizes.After analyzing the results, it can be concluded that, in the performed tests, ECC is a better alternative than RSA for mist deployments, since it presents less energy consumption and better throughput. The energy consumption values obtained for the mist end devices and the fog gateway are always lower for the ECC certificates than for the RSA ones when compared at the same security level. The only exception for the previously conclusion was the brainpool ECC curve, which presented the worst results of all the tested alternatives. When comparing the secp ECC curves, they achieve similar performance at the same security level. An important finding regarding ECC curves is the platform and curve dependency on the implemented optimizations. In this scenario, the most secure secp256r1 curve presented better results than the secp224r1 curve. This fact reinforces the idea of the need for real-world testing when throughput and energy consumption are critical for a deployment. Thanks to the performed experiments, it was demonstrated empirically that relying on the size of the curve alone could end up on selecting less secure curves with lower throughput and higher energy consumption values.Regarding the evaluation of the communications between two mist end devices, the performed tests showed higher energy consumption and lower throughput values than in the case of the fog gateway to mist end-device communication scenario. Specifically, the reason behind this degradation in performance is communications delay, which derived into up to three times larger energy consumption values and up to five times lower throughput results. Therefore, the results of the tests performed on the mist layer indicate that further work must be done by future mist computing developers in order to improve the mist layer communications performance.


Viruses are obligatory parasites that take advantage of intracellular niches to replicate. During infection, their genomes are carried in capsids across the membranes of host cells to sites of virion production by exploiting cellular behaviour and resources to guide and achieve all aspects of delivery and the downstream virus manufacturing process. Successful entry hinges on execution of a precisely tuned viral uncoating program where incoming capsids disassemble in consecutive steps to ensure that genomes are released at the right time, and in the right place for replication to occur. Each step of disassembly is cell-assisted, involving individual pathways that transmit signals to regulate discrete functions, but at the same time, these signalling pathways are organized into larger networks, which communicate back and forth in complex ways in response to the presence of virus. In this review, we consider the elegant strategy by which adenoviruses (AdVs) target and navigate cellular networks to initiate the production of progeny virions. There are many remarkable aspects about the AdV entry program; for example, the virus gains targeted control of a large well-defined local network neighbourhood by coupling several interacting processes  around a collective reference state centred on the interactional topology and multifunctional nature of protein VI. Understanding the network targeting activity of protein VI, as well as other built-in mechanisms that allow AdV particles to be efficient at navigating the subsystems of the cell, can be used to improve viral vectors, but also has potential to be incorporated for use in entirely novel delivery systems. Structural biology has dispelled this notion, especially as cryo-electron microscopy (cryo-EM) has matured into a method that can routinely determine near-atomic-resolution 2–4 Å) structures of viral assemblies . The var–4 Å struThe technical ability to correlate changes in viral structure with discrete steps in cell entry has largely been beyond reach, and even trying to simulate the process in a computer is too difficult due to the context, size and time scale of capsid uncoating. Nevertheless, researchers are making remarkable progress by shifting from using a single technology to embracing hybrid approaches that span different resolution scales ,6. In th2.A typical mammalian cell contains tens of thousands of molecular components that are architecturally organized into networks to execute integrated functions such as growth, movement and communication . BiologiSelf-defence against infection begins with the organization of the superstructure of the cell . ComplexThe first barrier to entry is the plasma membrane itself, which is heterogeneous by nature, featuring distinct subcompartments that differ in biophysical properties and composition . Only smTransport across the spatially embedded networks of the cell requires sharing of intracellular spaces and molecular components. Security is high priority and the cell has an impressive multilayered defence strategy mediated by effector molecules and compartment-specific sensory receptors that detect so-called pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs). Incorporation of pattern recognition receptors (PRRs) into the diverse spaces of the superstructure of the cell, such as in the membranes or the cytoplasm, endows the cell with many ‘informed’ surfaces on which detection can occur . These i3.in vivo given that these systems do not fully capture all of the biological driving forces that exert selective pressures on the virus during entry.Viruses thrive only to the extent that they are able to transmit their genomes from an infected host to a noninfected recipient. Once inside the new host, virions bind to permissive target cells followed by entry and replication, after which they can spread to neighbouring cells and repeat the process . Entry ca). These building blocks when assembled adopt an ancient underlying structure common to many viruses, the icosahedron: each of the 20 faces are composed of 12 copies of a trimer with pseudo-hexagonal character called hexon, and the vertices are formed by pentamers of penton base, each having one or more copies of a non-covalently associated trimeric spike referred to as a fibre (b). The fibres for a given AdV serotype vary in length depending on the number of β spiral repeats in the shaft [The AdV capsid (not including the fibres) has an overall diameter of about 900 Å and is composed of major  and minor  proteins  figure a. These  a fibre b. The fihe shaft . Furtherhe shaft . The phyhe shaft .Figure c). These proteins play important roles in cell entry and infection. The structures and organization of three of these minor proteins  has been a source of confusion and debate ever since the first atomic structures of intact AdV5 were independently derived by cryo-EM and X-ray crystallography [d). Below the capsid surface at each of the 12 vertices, five copies of protein IIIa cement the gaps between each penton base and its five surrounding hexons. Also, IIIa molecules bind to adjacently positioned protein VIII, which has the effect of extending the cement to hexons beyond the peripentonal region (e). Multiple copies (120 per virion) of protein VIII glue neighbouring hexons together at threefold and fivefold sites on the capsid interior. Finally, protein VI can occupy up to three binding sites in the cavity of trimeric hexon , but must compete for attachment with core protein VII, and this explains why only approximately 350 copies of protein VI are found in the assembled virion (f).In addition to hexon, penton base and fibre, there are four minor proteins intricately positioned in the virion that affect capsid assembly, disassembly and stability, which are often referred to as cementing or glue proteins c. These lography ,38. Conflography ,40, but lography –43. At tl region e. Multipd virion f.μ, IVa2, terminal protein and viral protease. During assembly, this region of the virion condenses into a fluid-like unstructured state in the immature particle [During cell entry, environmental cues progressively induce conformational changes in the structural proteins of the incoming virion to promote stepwise disassembly, and to direct the virus to the site of replication. Once the uncoating program is completed, the virus is able to release the core that contains all the necessary information to initiate a productive infection, as well as to carry out synthesis and assembly of virus particles. The core itself consists of the genome, which is approximately 36 kb of double-stranded DNA, and is accompanied by core proteins V, VII, particle , which tparticle . At thisparticle , and at particle ,48.4.The goal of AdV entry is to enter into the cell, bypass the security features and send the message for temporal programming of the production of virions. The overall process is instructed (target-driven) and dynamic, and combines both molecular and supramolecular events in such a way that the target with help from the cell ultimately performs assembly of the optimal parts to make new mature particles . Furtherav integrins. Here, attachment is a two-step process where different ligands on the virus particle are targeted by multiple receptors on the same cell. First, the higher-affinity receptor CAR displays recognition towards the knobs of the fibres protruding from the capsid, a step that anchors the virus to the cell. Second, av integrins bind to penton base loops bearing RGD motifs. Impressive is the fact that the AdV fibre shaft flexes dramatically so that the virus can simultaneously engage both CAR and av integrin receptors to form the tethered ligand assembly [To begin the entry process, AdVs must first initiate contact at the plasma membrane of the target cell, and coordinate transport services to gain access to the internal networks , which proceeds under the control of cellular cues in a stepwise, irreversible manner as virus particles move through the cell until the eventual release of the genome at the site of replication . The countegrins . This tyntegrins ,77. Thus5.av integrins by AdV penton base RGD loops triggers the transient formation of endocytic machinery on the plasma membrane to package the virus into a clathrin-coated vesicle for cellular internalization , which the virus can manipulate using its built-in uncoating program. Uncoating is a triggered dynamic process that results in the production of important constitutional changes: fluctuations, conformational alterations and releases of mechanically maintained components of the assembled virus. The strategy depends on both the cell and the viral capsid, with the latter relaying cues from the environment to induce controlled structural modifications . In the av integrin binding to penton base, generates excessive disruptive motions strong enough to cause a vertex-specific tearing event, which results in the formation of a fibreless leaky capsid at the outset of endocytosis. Exposure of protein VI at the site of uptake on the surface of the cell causes membrane piercing, which activates another cellular network, namely a lysosomal exocytosis repair pathway that works to maintain cellular membrane integrity . The NE serves not only as a physical bilayer partition, but also as an information-processing centre for regulated exchange of matter between the nucleus and cytoplasm. The latter function is made possible by incorporation of dynamic channel-forming nuclear pore complexes (NPCs), each consisting of hundreds of proteins called nucleoporins (Nups), which include both structural Nups that build the scaffolding structure of the pore and phenylalanine-glycine-rich (FG-rich) intrinsically disordered Nups that emanate as fibrils into the cytoplasm and line the central channel to provide a selective passageway for receptor-mediated active transport ,115. Lar the NPC . Followi the NPC .10.In summary, transport in complex networks is a problem of much interest in many aspects of biology. In this regard, the study of virus entry has been crucial for gaining a deeper understanding of how cells regulate and coordinate various events across their internal networks. Also, viruses provide examples of how to achieve targeted control of intracellular subsystems, which may be interesting for a variety of applications. Here, we have looked at how AdVs navigate the connected pathways to deliver their genomes for production of progeny virions with particular emphasis placed on the multifaceted role of viral protein VI during the entry process. Protein VI contains an N-terminal amphipathic helix that fragments the endosomal membrane and an adjacent PPXY motif that is exposed upon membrane lysis to help escape the endocytic network and tune autophagy and microtubule trafficking to a proviral state. The target control efficiency of protein VI is impressive given that intracellular networks display a high degree of robustness, an attribute due in part to the redundant wiring of functions within the integrated system as a whole. Local failures rarely result in the loss of normal functional states; however, as research on protein VI has demonstrated, error tolerance comes at a high price in that the system is extremely vulnerable to attacks, especially when the target is a few critical nodes that play a key role in maintaining the network's connectivity. Future work is needed to help clarify the network targeting activity of protein VI, as well as other built-in mechanisms that allow AdV particles to be efficient at navigating the intracellular space for temporal programming of the production of virions. In undertaking such work, we will learn more about how cells and viruses work, and at the same time, the knowledge can be used to improve vectors for basic research and therapeutic applications, either by optimizing AdVs for delivery or by transferring certain properties (like protein VI targeted control capacity) to other types of delivery systems.


This paper proposes a novel infrared-inertial navigation method for the precise landing of commercial aircraft in low visibility and Global Position System (GPS)-denied environments. Within a Square-root Unscented Kalman Filter (SR_UKF), inertial measurement unit (IMU) data, forward-looking infrared (FLIR) images and airport geo-information are integrated to estimate the position, velocity and attitude of the aircraft during landing. Homography between the synthetic image and the real image which implicates the camera pose deviations is created as vision measurement. To accurately extract real runway features, the current results of runway detection are used as the prior knowledge for the next frame detection. To avoid possible homography decomposition solutions, it is directly converted to a vector and fed to the SR_UKF. Moreover, the proposed navigation system is proven to be observable by nonlinear observability analysis. Last but not least, a general aircraft was elaborately equipped with vision and inertial sensors to collect flight data for algorithm verification. The experimental results have demonstrated that the proposed method could be used for the precise landing of commercial aircraft in low visibility and GPS-denied environments. Landing is the most accident-prone phase of flight for both military and civil aircraft. This is due to the manoeuvring sequence required to exhaust a large amount of aircraft kinetic energy in a relatively small area. Fixed-wing aircraft usually descend smoothly at a constant angle, pointing in the direction of the runway centerline, and touch down at the beginning of the runway. If low visibility conditions  are encountered, the pilots have no choice but to manipulate the aircraft to land using navigation instruments. If the conventional radio navigation systems are disturbed or disabled, they can mislead the pilots and cause a Controlled Flight Into Terrain (CFIT) accident. In addition, most airports are equipped with simple and coarse radio beacons rather than expensive and precise ground-based guidance systems. Nowadays neither avionics systems, nor airport infrastructures are perfectly designed to support precision landing. Faced with these challenges, an autonomous, accurate and affordable landing navigation mechanism is extremely necessary for most fixed-wing aircrafts.The traditional landing aid systems include the Instrument Landing System (ILS), and Global Position System (GPS). However, these systems themselves have deficiencies for fixed-wing aircraft precision landing. ILS can only guide an aircraft to the decision height  and cannot guide it onto the runway. Besides, ILS, with its high cost and complicated maintenance is not suitable for general aviation airports. Although GPS can meet the needs of class I and II landings for most aircraft, its signal is vulnerable to jamming or disabling [Recently vision-based landing navigation, which has the benefits of accuracy, autonomy and low cost, is becoming a central research topic ,3. ExistOnboard-based vision landing navigation based on looking-forward images and computer vision algorithms can be divided into two types, namely moving platform-based and airport runway-based methods. For landing on a moving platform, the core solution is to track a known target, e.g., an aircraft carrier, and compute its relative position and orientation. Coutard et al. at the French INRIA ,12 propoAlthough the above algorithms have achieved remarkable progress in vision-aided landing navigation, there are four main problems which need to be coped with. Firstly, image sensors cannot satisfy the requirements of high-speed landing navigation because of their low image update rates, whereas, IMUs can measure accelerations and rotational velocities at high update rates. The two types of sensors can complement each other in nature and integrate well in an optimized framework . SecondlThe main contributions presented in this paper are as follows: we propose a visual-inertial landing navigation method based on SR_UKF in which inertial data, infrared images and geo-referenced information are fused to estimate the landing kinetic states of the aircraft. Firstly, a short-wave infrared (SWIR) camera is used to capture a forward-looking infrared (FLIR) image to meet the requirement of precise landing under low visibility. Moreover, the homography that contains the measured pose deviation of the FLIR camera is directly created as the vision observation instead of its decomposition, because it implies the pose deviation between the measured camera and the true camera. Furthermore, an improved runway detection algorithm based on FLIR images is proposed to reach more robustness and accuracy. Specially, a non-linear observability analysis based on Lie derivatives ,37,38 isThis section explicitly details the framework of the proposed visual-inertial navigation approach. Vision observations with the camera pose deviations are designed elaborately, then the visual-inertial fusion based on SR-UKF is constructed, and its observability is analyzed.In general, a complete landing procedure of a commercial aircraft includes two parts: an instrument flight segment and a natural vision segment. The instrument portion of an instrument landing procedure ends at the Decision Altitude (DA), and the visual segment begins just below DA and continues to the runway. Prior to reaching DA, the pilot’s primary references for maneuvering the airplane are the aircraft instruments and onboard navigation system. As the pilot approaches the DA, he or she looks for the approach lighting system, if there is one, as well as the runway threshold and touchdown zone lights, markings, surfaces. These visual references help the pilot align the aircraft with the runway and provide position and distance remaining information. At 100 feet above the Threshold Elevation (THRE), the visual transition point, the pilot makes a determination about whether the flight visibility is sufficient to continue the approach and distinctly identify the required visual references using natural vision. If the requirements identified above are met, the pilot may continue descending below DA down to 100 feet height above THRE . OtherwiAmong several flight parameters, the flight height is one of the most important ones for the pilot’s decision in a landing procedure. Usually the height measured by barometer or radio altimeter is inaccurate, while the altitude captured by GPS is unreliable. In this paper, the FLIR camera and IMU can complement each other in nature and fuse well in a filtering framework. This paper proposes a novel visual-inertial landing navigation approach based on the SR-UKF, in which visual observation and inertial measurements are integrated to estimate aircraft landing motion. This novel visual-inertial navigation system (VINS) is composed of a FLIR camera, an IMU, a barometer (BARO), a radio altimeter (RALT) and a processing unit that is in charge of motion estimation of the aircraft.As shown in This method involves three key issues: process modeling, measurement modeling, and its observability. Firstly, this novel vision observation is designed in Before proposing the measurement model, we need to analyze the vision measurement mechanism. During the landing, the aircraft descends along the glide slope, and the optical axis of the FLIR camera is aligned with the airport runway. The camera pose is composed of the calibrated IMU/camera relative pose and the measured IMU pose. Ideally the measured camera pose should be equal to the real camera pose. The synthetic image is derived by the terrain data and the measured camera pose, and the real image is captured by the FLIR camera. Therefore, in the image plane the synthetic runway features should be in coincidence with the real detected features accurately. However, the random errors of inertial sensors bring a deviation between the measured camera pose and the real camera pose and further lead to the mismatch between the synthetic runway features and the real runway features. The relationships between the measured camera pose t the transformation from the synthetic image to the real image satisfies the homography As shown in Note that the matrix nd plane ,41. To iIt is notable that the terms In the proposed VINS, a FLIR camera and an IMU are installed on the aircraft. As shown in (1) Transformation between geodetic and Cartesian coordinates in the ECEF reference frameThe geodetic coordinate that contains longitude (2) From f on the runway surface, Any known point in the ECEF can be projected into the geographic coordinate system with the IMU center as its origin: (3) From The navigation coordinate system (4) From The rigid connection between aircraft body and camera contains a relative rotation (5) From cZ is the normalization coefficient, dx and dy represent the pixel sizes in image u and v axes respectively,  are the coordinates of the principal point, s is the skew parameter, and f is the focal length of the FLIR camera. According to the pinhole imaging model , the homEquations (10)–(14) give a complete transformation from the runway plane to the pixel plane of airborne FLIR camera, as shown in Line features of airport runway can be generated by the projection model combining IMU pose and runway geo-information. Consequently, the pixel coordinate of line features can be described as:Visible images have high spatial resolution and rich texture details, but these images can be easily influenced by severe conditions, such as poor illumination, fog, and other effects of bad weather. Visible images capture reflected light, whereas infrared images capture thermal radiation. In general, infrared images are resistant to these disturbances. In the present paper, we adopt the SWIR camera to capture FLIR images with important airfield features in low visibility. However, infrared images typically have defects of low resolution and poor texture . ExistinThis improved method adopts a coarse-to-fine hierarchical idea in which the runway region of interest (ROI) is preliminarily estimated in the FLIR image and the runway edges are finely extracted from the ROI. At the coarse layer, the runway ROI can be calculated by the aircraft pose parameters and airport geo-information in the first few frames. Then, the detected runway is used as the prior knowledge of the next image. Meanwhile, considering the errors of aircraft pose parameters, the runway ROI based on special confidence interval can be estimated. The higher the confidence level is, the larger the runway ROI will be. Therefore, surrounding useless objects and complex background texture can be excluded from ROI so as to reduce interference and image processing time. Especially the errors transfer equations of vision projection model can be given as follows:At the fine layer, EDLines detector  is used The UKF adopts a deterministic sampling technique to estimate the state and covariance of the non-linear models directly. Compared with the EKF, the UKF can predict the state of the non-linear system more accurately rather than calculate the Jacobian and Hessian matrices of the process and measurement models. However, the UKF need calculate the square root of state covariance matrix during sigma points update, it may occasionally generate a negative definite state covariance matrix which will cause the program to abort. The SR-UKF requires less numerical computations and has more accuracy by using a Cholesky factorization of the error covariance matrix in propagation directly . The proFirstly, we define the system state as: Considering the discrete-time, the model can be written as follows:Because the homography matrix contains the deviation of aircraft pose, four groups of possible solutions can be obtained by decomposing the homography matrix according to the traditional method ,41, and Suppose that (1) The homography (2) Besides the above visual measurements, the proposed landing navigation can integrate with other common observations such as air pressure height and radio altitude. These measurement models can be written as follows: The multi-source information fusion framework based on SR_UKF consists of the process model and measurement model, which realizes the integration of inertial measurements, infrared image, airport geo-reference, air data and radio altitude. Observability is an inherent characteristic of the proposed VINS; it provides an understanding of how well states of a system can be inferred from the system output measurements. Recently there has been many works in studying the observability of VINSs ,37,38. WConsidering the state space as an infinitely smooth manifold The recursive Lie derivative is defined as:k-th derivative of The Based on the preceding expression for the Lie derivative, the observability matrix is defined as:O is full rank, the system is locally weakly observable.If the observability matrix In order to reveal the observability of our proposed system, we use the motion state instead of the state errors. The state errors are approximations where second and higher order terms are omitted under the assumption of a small error state . HoweverFirst, we define the system state vector of the core system as follows:Then the nonlinear kinematic equations of the core system for computing the Lie derivatives is rearranged as:A well-known result that we will use in the observability analysis of (31) is the following: when four and more known features are detected in FLIR image frame, the infrared camera pose is observable. According to Equation (2), the measurements can be summarized as:(1) Zeroth-Order Lie Derivatives: Define the zeroth-order Lie derivative of Their gradients are:(2) First-Order Lie Derivatives: The first-order Lie derivatives of Their gradients are:(3) Second-Order Lie Derivatives: The second-order Lie derivative of The gradient is:O by stacking the gradient matrices above: O, these matrices O that consist entirely of zeros, a row-reduced form of the matrix O having the same rank is given by: We obtain the observability matrix In this section, we designed a flight data acquisition platform and adopted real flight data to verify the accuracy and robustness of the proposed method. The flight data was gathered at a general aviation airport  under different weather conditions such as fog, haze, cloud and sunny. As shown in To get accurate motion estimations, precise FLIR camera parameters and camera/INS relative pose are needed. Classical calibration method based on chessboard pattern  is adoptThe flight data is stored in a flight data simulator (FDS), which can play back the whole flight process for the algorithm design and verification. Moreover, the geographic data of the airport and its surrounding has been surveyed accurately. In this paper, experiments are run on an embedded computer board (Nvidia Jetson TX2) with six ARM CPU cores, 256 Pascal GPU cores, 8 GB memory. The block diagram of the experimental platform is shown in An ideal line segment detector could process any images regardless of its orientation or size, and extract line segments in real-time without parameters tuning. Among existing algorithms, EDLines detector  and LineIn this paper, a complete landing process in fog is used for verifying the proposed algorithm. Experimental results contain two parts: runway detection and motion estimation. Some runway detection results are shown in At the coarse layer, the ROI is marked in red at the left column. At the fine layer of our improved method, some line segments in ROI are detected and highlighted in red, and the trapezoid of runway contour is labeled in green at the middle column. These line segments are fitted into the final runway features which are shown in red at the right column. In addition, the statistics of runway detection listed in As shown in The proposed algorithm is compared with three other methods such as INS/GPS integration , EPnP baMeanwhile, the data update rate is limited by the camera frame rate, which is lower than the INS update rate. In addition, the accuracy of motion estimation based on INS/GPS/BARO/RALT cannot be further improved due to the larger measurement errors of barometer and radio altimeter. However, this paper improves the existing runway detection algorithm to avoid the problem that the features of runway terminal are difficult to accurately detect, which can obtain accurate runway features. After the integration of vision measurements and inertial data, the update rate of motion estimation is also improved. Even if in low-visibility environments the motion estimations of the proposed method are still accurate enough, which is benefited from the accurate visual observations. In addition, the RMS errors of different motion estimation are listed in Among several flight parameters, the height observation is one of the most important for flight safety in landing phase. The flight altitude from GPS is usually inaccurate and unreliable, while the height channel of INS trends to diverges caused by the absence of damping. In general, air pressure height or radio altitude is adopted to damp the height channel of INS, but their accuracy is too low to meet the precision landing. The proposed algorithm that absorbs the advantages of vision and inertial sensors can not only improves the estimation accuracy but also guarantees high update rate.In Although INS/GPS mode has high update rate of height data, its accuracy is poor compared with DGPS/INS. The EPnP-based method has higher accuracy than INS/GPS mode, but EPnP has lower update rate than INS/GPS mode due to its use of pure vision navigation. Obviously, the height precision obtained from the proposed INS/FLIR method is the smallest, it can replace INS/DGPS mode to meet precision landing demands. The proposed method has high precision up to the DGPS/INS level in low visibility. Firstly, the homography can be served as an ideal visual observation without error accumulation. Meanwhile, owing to the improved runway detection method, it can efficiently overcome the defects of infrared images and smoothly run in a landing scene with large scale and less texture. Compared to ILS and GPS, our method merely takes advantage of an infrared camera to cooperate with airborne navigation sensors, e.g., IMUs, to achieve autonomous motion estimation with low cost, robustness and accuracy. In particular, the accuracy of our method has reached the level of the DGPS/INS for precision approach and landing. In the proposed method, the main factors that affect the accuracy of aircraft motion estimation include sensors calibration errors, terrain database precision, spatiotemporal consistency, and runway detection quality. The errors can be partially eliminated by strict sensors calibration ,45, highThe paper proposed a novel visual-inertial navigation method to provide drift-free pose estimation for fixed-wing aircrafts landing, in which inertial measurements, infrared observations and geo-information are organically fused in the UKF. In addition, the proposed method has been proven to be observable by nonlinear observability. Comprehensive experiments with real flight data have verified the accuracy and robustness of the proposed method. In the future, there are still some research tasks to do for further improvement. (1) For stronger adaptability, we will adopt a multispectral image fusion method ,49 to en


Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance.We demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations.As such, HTBAC advances the state of the art of binding affinity calculations and protocols. HTBAC provides the platform to enable scientists to study a wide range of cancer drugs and candidate ligands in order to support personalized clinical decision making based on genome sequencing and drug discovery. In recent years, chemotherapy based on targeted kinase inhibitors (TKIs) has played an increasingly prominent role in the treatment of cancer. TKIs have been developed to selectively inhibit kinases involved in the signaling pathways that control growth and proliferation, which often become dysregulated in cancers. This targeting of specific cancers reduces the risk of damage to healthy cells and increases treatment success. Currently, 35 FDA-approved small molecule TKIs are in clinical use, and they represent a significant fraction of the $37 billion U.S. market for oncology drugs , 2. ImatUnfortunately, the development of resistance to these drugs limits the amount of time that patients can derive benefits from their treatment. Resistance to therapeutics is responsible for more than 90% of deaths in patients with metastatic cancer . While dThere are two major strategies for countering the threat to treatment efficacy posed by resistance: tailoring the drug regimen received by a patient according to the mutations present in their particular cancer, and developing more advanced therapies that retain potency for known resistance mutations. In both cases, future developments require insight into the molecular changes produced by mutations, as well as ways to predict their impact on drug binding on a timescale much shorter than is typically experimentally feasible. This represents a grand challenge for computational approaches.precision cancer therapy. While several common (recurrent) mutations have been cataloged for their ability to induce resistance or for their susceptibility to particular kinase inhibitors, the vast majority of clinically observed mutations are rare [The rapidly decreasing cost of next-generation sequencing has led many cancer centers to begin deep sequencing of patient tumors to identify the genetic alterations driving individual cancers. The ultimate goal is to make individualized therapeutic decisions based upon these data—an approach termed are rare , 7. EsseFortunately, concurrent improvements in computational power and algorithm design are enabling the use of molecular simulations to reliably quantify differences in binding strength. This provides the opportunity to use advances in molecular simulations to supplement existing inductive decision support systems with deductive predictive modeling and drug ranking , 9. WherThe same molecular simulation technologies that can be employed to investigate the origins of drug resistance can also be used to design new therapeutics. Creating simulation protocols which have well defined uncertainty and produce statistically meaningful results represents a significant computational challenge. Furthermore, it is highly likely that differences among investigated systems will demand different protocols as studies progress. For example, drug design programmes often require the rapid screening of thousands of candidate compounds to filter out the worst binders before using more sensitive methods to refine the structure. Not all changes induced in protein shape or behavior are local to the drug binding site and, in some cases, simulation protocols will need to adjust to account for complex interactions between drugs and their targets within individual studies.Recent work that used molecular simulations to provide input to machine learning models  requiredFor molecular simulations to make the necessary impact, the dual challenge of scale (thousands of concurrent multi-stage pipelines) and sophistication  will need to be tackled. Tools that facilitate the scalable and automated computation of varied binding free energy calculations on high-performance computing resources are necessary. To achieve that goal, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC applies recent advances in workflow system building blocks to the accurate calculation of binding affinities, executing hundreds of concurrent calculations on a leadership class machine . This peIn the next section, we provide details of ensemble molecular dynamics approach and its advantages over the single trajectory approach. We also introduce the ESMACS and related protocols to compute binding affinities using ensemble-based approaches. In “The strength of drug binding is determined by a thermodynamic property known as the binding free energy (or binding affinity). One promising technology for estimating binding free energies and the influence of protein composition on them is molecular dynamics (MD) . Our preAtomistically detailed models of the drug and target protein can be used as the starting point for MD simulations to study the influence of mutations on drug binding. The chemistry of the system is encoded in what is known as a potential . In the The potentials used in the simulations are completely under the control of the user. This allows the user to manipulate the system in ways which would not be possible in experiments. A particularly powerful example of this are the so called “alchemical” simulations in which the potential used in the simulation changes, from representing a particular starting system into one describing a related target system during execution. This allows for the calculation of free energy differences between the two systems, such as those induced by a protein mutation.MD simulations can reveal how interactions change as a result of mutations, and account for the molecular basis of drug efficacy. This understanding can form the basis for structure-based drug design as well as helping to target existing therapies based on protein composition. However, correctly capturing the system behavior poses at least two major challenges: The approximations made in the potential must be accurate enough representations of the real system chemistry; and sufficient sampling of phase space is also required.In order for MD simulations to be used as part of clinical decision support systems, it is necessary that results can be obtained in a timely fashion. Typically, interventions are made on a timescale of a few days or, at most, a week. The necessity for rapid turn around times places additional demands on simulation protocols which need to be optimized to gain results with a short turn around time. Further to the scientific and practical considerations outlined above, it is vital that reliable uncertainty estimates are provided alongside all quantitative results for simulations to provide actionable predictions.We have developed a number of free energy calculation protocols based on the use of ensemble molecular dynamics simulations with the aim of meeting these requirements , 17–19. −1 for small ligands (flexible ligands can vary even more) [We have demonstrated the lack of reproducibility of single trajectory approaches in both HIV-1 protease and MHC systems, with calculations for the same protein-ligand combination, with identical initial structure and force field, shown to produce binding affinities varying by up to 12 kcal mol en more) , 20, 21.−1, less than a 20th of the range observed in the original ensemble.Our work using ensemble simulations have also reliably produced results in agreement with previously published experimental findings , 21, 22,We designed two free energy calculation protocols with the demands of clinical decision support and drug design applications in mind: ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent)  and TIESIn both ESMACS and TIES, ensembles of MD simulations are employed to perform averaging and to obtain tight control of error bounds in our estimates. In addition, the ability to run replica simulations concurrently means that, as long as sufficient compute resources are available, turn around times can be significantly reduced compared to the generation of single long trajectories. The common philosophy behind the two protocols entails similar middleware requirements: In this work we focus on the ESMACS protocol but all results are applicable also to TIES. In  we focusEach replica within the ESMACS protocol consists of a sequence of simulation stages followed by post production analysis. Generally, an ESMACS replica will contain between 3 and 12 equilibration simulation stages followed by a production MD run, all of which are conducted in the NAMD package . The fir−1  for a varied range of systems including small molecules bound to kinases and more flexible peptide ligands binding to MHC proteins [The ESMACS protocol is highly customizable. Both the number of simulation replicas in the ensemble and the lengths of their runs can be varied to obtain optimal performance for any given system. Using replicas that only vary in the initial velocities assigned to the atoms of the system we have defined a standard protocol which prescribes a 25 replica ensemble, each run consisting of 2 ns of equilibration and 4 ns of production simulation. Our protocol has produced bootstrap errors of below 1.5 kcal mol proteins , 20, 21.The ESMACS protocol can also be extended to account for adaptation energies involved in altering the conformation of the protein or ligand during binding. Almost all MMPBSA studies in the literature use the so-called 1-trajectory method, in which the energies of protein-inhibitor complexes, receptor proteins and ligands are extracted from the MD trajectories of the complexes alone. The ESMACS protocol can additionally use separate ligand and receptor trajectories to account for adaptation energies, providing further motivation to deploy the protocol via flexible and scalable middleware.A common target of kinase inhibitors is the epidermal growth factor receptor (EGFR) which regulates important cellular processes including proliferation, differentiation and apoptosis. EGFR is frequently over expressed in a range of cancers, and is associated with disease progression and treatment. Clinical studies have shown that EGFR mutations confer tumor sensitivity to tyrosine kinase inhibitors in patients with non-small-cell lung cancer  environments were designed to primarily support the execution of single simulations. Current HPC platforms enable the strong and weak scaling of single tasks (hitherto mostly simulations), with limited software and systems support for the concurrent execution of multiple heterogeneous tasks as part of a single application (or workflow). As the nature of scientific inquiry and the applications to support that inquiry evolve, there is a critical need to support the scalable and concurrent execution of a large number of heterogeneous tasks.Sets of tasks with dependencies that determine the order of their execution are usually referred to as “workflows”. Often times, the structure of the task dependencies is simple and adheres to discernible patterns, even though the individual tasks and their duration are non-trivially distinct. Put together, it is a challenge to support the scalable execution of workflows on HPC resources due to the existing software ecosystem and runtime systems typically found.Many workflow systems have emerged in response to the aforementioned problem. Each workflow system has its strengths and unique capability, however each system typically introduces its problems and challenges. In spite of the many successes of workflow systems, there is a perceived high barrier-to-entry, integration overhead and limited flexibility.Interestingly, many commonly used workflow systems in high-performance and distributed computing emerged from an era when the software landscape supporting distributed computing was fragile, missing features and services. Not surprisingly, initial workflow systems had a monolithic design that included the end-to-end capabilities needed to execute workflows on heterogeneous and distributed cyberinfrastructures. Further, these workflow systems were typically designed by a set of specialists to support large “big science” projects such as those carried out at the LHC  or LIGO Executing biomolecular applications on HPC systems require specific knowledge of resource, data, and execution management. Several middleware frameworks  have beeHowever as the nature, number and usage of workflows has evolved so have the requirements: scale remains important but only when delivered with the ability to prototype quickly and flexibly. Furthermore, there are also new performance requirements that arise from the need to support concurrent execution of heterogeneous tasks. For example, when executing multiple homogeneous pipelines of heterogeneous tasks, for reasons of efficient resource utilization there is a need to ensure that the individual pipelines have similar execution times. The pipeline-to-pipeline fluctuation must be minimal while also managing the task-to-task runtime fluctuation across concurrently executing pipelines.Thus the flexible execution of heterogeneous ensembles MD simulations face both system software and middleware challenges: existing system software that is typically designed to support the execution of single large simulations on the one hand, and workflow systems that are designed to support specific use cases or ‘locked-in’ end-to-end executions. In the next sections, we discuss the design and implementation of the RADICAL-Cybertools, a set of software building blocks that can be composed to design, implement and execute domain specific workflows rapidly and at scale.We have designed RADICAL-Cybertools (RCT) to be functionally delineated middleware building blocks and to address some of the challenges in developing and executing workflows on HPC platforms. HTBAC uses two RCT components, mainly the Ensemble Toolkit (EnTK) and RADICAL-Pilot (RP). EnTK provides the ability to create and execute ensemble-based workflows/applications with complex coordination and communication but without the need for explicit resource management. EnTK uses RP as a runtime system which provides resource management and task execution capabilities.RCT eschew the concept of a monolithic workflow systems and uses “building blocks”. RCT provide scalable implementations of building blocks in Python that are used to support dozens of scientific applications on high-performance and distributed systems –43. In tThe scalable execution of applications with large ensembles of tasks is challenging. Traditionally, two methods are used to execute multiple tasks on a resource: each task is scheduled as an individual job, or message-passing interface (MPI) capabilities are used to execute multiple tasks as part of a single job. The former method suffers from unpredictable queue time: each task independently awaits in the resource’s queue to be scheduled. The latter method relies on the fault tolerance of MPI, and is suitable to execute tasks that are homogeneous and have no interdependencies.The Pilot abstraction  solves tRADICAL-Pilot is an implementation of the pilot abstraction, engineered to support scalable and efficient launching of heterogeneous tasks across different platforms.workflow, i.e. tasks with dependencies that determine the order of their execution. Subsets of these tasks can be workloads, i.e., tasks whose dependencies have been satisfied at a particular time and may be executed concurrently. Ensemble-based application vary in the type of coupling between tasks, the frequency and volume of information exchanged between these tasks, and the executable of each task. This type of applications requires specific coordination, orchestration and execution protocols, posing both domain-specific and engineering challenges.An ensemble-based application is a Ensemble Toolkit (EnTK), the topmost layer of RCT, simplifies the process of creating and executing ensemble-based applications with complex coordination and communication requirements. EnTK decouples the description of ensemble-based applications from their execution by separating three orders of concern: specification of task and resource requirements; resource selection and acquisition; and task execution management. Domain scientists retain full control of the implementation of their algorithms, programming ensemble-based applications by describing what, when and where should be executed. EnTK uses a runtime system, like RADICAL-Pilot, to acquire the resources needed by applications to manage task execution.Application Manager, Pipeline, Stage and Task. Users describe ensembles in terms of pipelines, stages and tasks, and pass this description to an instance of Application Manager, specifying what resource to use for executing the application  with four components: see Fig. .Fig. 2EThe Task component is used to encapsulate an executable and its software environment and data dependencies. The Stage component contains a set of tasks without mutual dependencies and that can therefore be executed concurrently. The Pipeline component is used to describe a sequence of stages, i.e., sets of tasks that need to be executed sequentially, not concurrently.The use of the Task, Stage, and Pipeline components, implemented as set and list data structures, avoids the need to express explicitly relationship among tasks. These relationships are insured by design, depending on the formal properties of the lists and sets used to partition tasks into stages and group stages into pipelines. Further, EnTK enables an explicit definition of pre and post conditions on the execution of tasks, enabling a fine grained adaptivity, both a local and global level. Conveniently, this does not require the codification of a directed acyclic graph (DAG), a process that imposes a rigid representation model on the domain scientists .Application Manager component of EnTK enables users to specify target resources for the execution of the ensemble-based application. This includes properties like walltime, number of nodes and credentials for resource access. Users can also define execution setup parameters such as the number of processes or messaging queues that should be used by EnTK. This allows to size and tune the performance of EnTK, depending on the number of tasks, stages and pipelines, but also on the resources available to the toolkit.The WF Processor is responsible for the transformation of the application workflow into workloads, i.e., set of tasks, that can be submitted to the indicated resources for execution. Internally, the Resource Manager and Execution Manager components enable the acquisition of resources and the management of execution of these workloads  understanding the invariance of HTBAC execution time for a single protocol instance over the number of workflow pipelines executed; and (2) studying how the performance of EnTK and RP varies in relation to the size of workflow.We designed two experiments to measure weak scalability properties using the ESMACS protocol when executing an increasing number of concurrent pipelines. According to the use case described in “Experiment 1 measures the baseline behavior of EnTK and RP with the workflow of the ESMACS protocol and a null workload (/bin/sleep 0). The goal is to isolate the overheads of EnTK and RP from the specifics of the executables of the workflow and the overheads of the resources. The null workload does not require data staging, I/O on both memory and disk, or communication over network.Experiment 2 replicates the design of Experiment 1 but it executes the workflow of the ESMACS protocol with the actual simulation and data for the EGFR kinase workload. The comparison between the two experiments enables performance analysis of EnTK and RP to understand whether and how the size of the executed workflow affects its overheads. Further, Experiment 2 shows also whether HTBAC execution time is sensitive to the number of concurrent pipelines executed.Both experiments measure the weak scalability properties of a single ESMACS protocol instance, executed with RCT. This means that the ratio between the number of pipelines and cores is kept constant by design. While an investigation of strong scalability would contribute to a better understanding of the behavior of a single ESMACS protocol instance, it is of limited interest for the current use case. The driving goal of this study is to increase throughput by a means of concurrency of pipelines, not in the number of sequential executions per core. This is a driving motivation to target large HPC machines instead of so-called high-throughput computing (HTC) infrastructures.We perform both Experiment 1 and 2 on NCSA’s Blue Waters—a 13.3 petaFLOPS Cray, with 32 Interlago cores/50 GB RAM per node, Cray Gemini, Lustre shared file system. Currently, we exclusively use CPUs on Blue Waters as GPUs are not required by our use case.We perform our experiments from a virtual machine, which maintains a stable network connection. This helps to simulate the conditions in which the experimental campaign will be performed by the research group at UCL, where users run experiments from local machines.To this end, we perform our experiments from a virtual machine. This helps to simulate the conditions in which the experimental campaign will be performed from a local machine. To allow this, RCT support gsissh for authentication and authorization. The virtual machine serves as the client from which the user provides the description of the experiments including the physical systems, and resource requirements. Once the experiment is submitted, RCT establish a gsissh connection from the virtual machine to the remote resource i.e. Blue Waters and maintain a tunnel for the duration of the experiment.Table EnTK uses RP to acquire resources via a single pilot. The size of the pilot is contingent upon characterization of performance, in this case, weak scalability. Accordingly, we request the maximum number of cores required by the workload as the number of cores in a pilot. We use between 64 and 1024 cores in Experiment 2 as the NAMD executable used in stages 3, 4, 5, and 6 requires 8 cores. Stages 1, 2 and 7 require instead 1 core. The null workload of Experiment 1 requires only 1 core per stage but we request the same number of cores as for Experiment 2 to be able to compare the overheads of both EnTK and RP across experiments.All experiments use EnTK version 0.4.7 and RP version 0.42. The MD engine used is NAMD-MPI. The equilibration tasks of stage 4 and 6 are assigned 5000 timesteps while the task of stage 5 requires 55000 timesteps. We ran two trials of both the null and MD workload at each pipeline configurations.First we characterize the overhead of EnTK and RP in the null workload, where we isolate the overhead introduced by the two systems Fig. . We see RP overhead is also, on average, superlinear but with a much greater variance. This variance is due to mainly two factors: Network latency and filesystem latency on the HPC resource. EnTK submits CU descriptions to the MongoDB used by RP, and the RP pilot pulls these descriptions from the same database. As described in “TTX=TTC−Tq where TTC is time-to-completion and Tq is time spent queuing on the HPC machine. We further break down TTX into the time-to-completion per stage, where stages 1,2, and 7 perform file movements, while stages 3,4,5, and 6 execute NAMD tasks. At this level, we notice that the time-to-completion of the NAMD stages are essentially invariant across pipelines of different size while file movement stages exhibit linearly increasing behavior. In addition, when accounting for variance, RP overheads also show linear weak scaling behavior. As expected, EnTK overhead remains superlinear and comparable to the one measured in Experiment 1. This is because in both experiments EnTK overhead depends on the number of tasks translated to CU descriptions.When the workload includes the EGFR kinase, we see Fig.  that theWe designed experiments in Table It is necessary to move beyond the prevailing paradigm of running individual MD simulations, which provide irreproducible results and cannot provide meaningful error bars . FurtherHTBAC employs the RADICAL-Cybertools to build ensemble-based applications for executing protocols like ESMACS at scale. We show how the implementation of the ESMACS protocol scales almost perfectly to hundreds of concurrent pipelines of binding affinity calculations on Blue Waters. This permits a time-to-solution that is essentially invariant of the size of candidate ligands, as well as the type and number of protocols concurrently employed.The use of software implementing well-defined abstractions like that of “building blocks”, future proofs users of HTBAC to evolving hardware platforms, while providing immediate benefits of scale and support for a range of different application workflows. Thus, HTBAC represents an important advance towards the use of molecular dynamics based free energy calculations to the point where they can produce actionable results both in the clinic and industrial drug discovery.In the short term, the development of HTBAC will allow a significant increase in the size of study. Much of the literature on MD-based free calculations is limited to a few tens of systems, usually of similar drugs bound to the same protein target. By facilitating the investigation of much larger set of systems, HTBAC contributes to solve the grand challenge in drug design and precision medicine: understanding the influences on binding strength for hundreds or thousands of drug-protein variant combinations. While contributing to reach this ambitious goal, we reveal the limits of existing simulation technology and of the potentials used to approximate the chemistry of the real systems.


Confocal Microwave Imaging (CMI) for the early detection of breast cancer has been under development for over two decades and is currently going through early-phase clinical evaluation. The image reconstruction algorithm is a key signal processing component of any CMI-based breast imaging system and impacts the efficacy of CMI in detecting breast cancer. Several image reconstruction algorithms for CMI have been developed since its inception. These image reconstruction algorithms have been previously evaluated and compared, using both numerical and physical breast models, and healthy volunteer data. However, no study has been performed to evaluate the performance of image reconstruction algorithms using clinical patient data. In this study, a variety of imaging algorithms, including both data-independent and data-adaptive algorithms, were evaluated using data obtained from a small-scale patient study conducted at the University of Calgary. Six imaging algorithms were applied to reconstruct 3D images of five clinical patients. Reconstructed images for each algorithm and each patient were compared to the available clinical reports, in terms of abnormality detection and localisation. The imaging quality of each algorithm was evaluated using appropriate quality metrics. The results of the conventional Delay-and-Sum algorithm and the Delay-Multiply-and-Sum (DMAS) algorithm were found to be consistent with the clinical information, with DMAS producing better quality images compared to all other algorithms. Confocal Microwave Imaging (CMI) is an emerging imaging modality for the detection of breast cancer. One important signal processing challenge in reconstructing high quality breast images using CMI is the image reconstruction algorithm itself. An effective image reconstruction algorithm provides an accurate localisation of tumours, while suppressing clutter due to healthy breast tissues and any residual artifacts from preprocessing.Several image reconstruction algorithms for CMI have been developed over the last two decades ,11,12,13Delay-And-Sum (DAS)Improved Delay-And-Sum (IDAS)Delay-Multiply-And-Sum (DMAS)Coherence Factor Based DAS (CF-DAS)Channel Ranked DAS (CR-DAS)Robust Capon Beamformer (RCB)In this study, the performance of a variety of DI and DA algorithms is evaluated using clinical patient data. The following algorithms are examined in this study:The novelty of this study is two-fold: firstly, a comprehensive set of both DI and DA beamforming algorithms was examined; and, secondly, these algorithms were evaluated together for the first time on both clinical patient data and with a non-idealised artifact removal algorithm. The patient data used in this study were obtained from a first patient study conducted at the University of Calgary . The patThe remainder of this paper is organised as follows: The imaging algorithms evaluated in this study are described in this section.M is the total number of channels, i, and i.The conventional DAS beamformer equation is given as:i is calculated based on the assumed average propagation speed in the breast and the round trip distance between the focal point i. Next, the time-aligned signals are simply summed together. Reflections from any tumour present are expected to add coherently. Conversely, reflections from healthy tissues and any artifacts from preprocessing are expected to add incoherently. The energy of the summed signal (often computed over a time-window) is assigned to the intensity of the focal point under consideration and this process is repeated for all focal points within the 3D breast volume to create a 3D breast image.In the DAS beamformer, firstly, the signals received at individual channels are time-aligned for each focal point The DMAS beamformer is similar to the conventional DAS  with an M to M signals). All the In the DMAS algorithm, the signals are multiplied in pairs after time-alignment for each focal point The IDAS beamformer modifies the conventional DAS by introducing an additional weighting factor for each focal point . The weia is chosen as the QF of focal point The QF for each focal point In the CF-DAS beamformer, a different coherence based weighting factor is introduced in the conventional DAS algorithm . The CohThen, the intensity at focal point M is the number of signals, M, assigned to each signal based on that signal’s round trip propagation distance . The weighting factor is applied to each signal prior to coherent summation and formation of the energy image as given below:In the CR-DAS beamformer, each individual signal is scaled with a weighting factor . The weiThe RCB algorithm is based on the Standard Capon Beamformer (SCB) ,12. The Consider the following preprocessed signal vector for a focal point Each snapshot RCB assumes that the true steering vector of a^(t) ,12. The Given lated as:minwwTR^wThe beamformer output can be written as a vectorFinally, the backscattered energy from location In this section, details of patients, the patient scanning process, and the preprocessing of scanned data is detailed.The patient data used in this study came from the microwave imaging patient study conducted at the University of Calgary . This stIn this study, five patient cases are presented in detail. Three of these five patients had clearly identified benign or malignant lesions, while two patients had no disease. The first generation TSAR prototype used for the patient scanning is described in . The proDuring the patient scan, patient lay in a prone position with one breast extended into the examination table of the prototype. Underneath the table, the BAVA-D antenna was vertically moved in a step of 5–10 mm between the nipple and chest wall of the breast. For each vertical position, multiple measurements were collected around the breast. The number of vertical positions (rows) for each patient was decided based on the vertical extent of the breast, which was estimated using the images acquired from the digital camera. For each patient, measurements were collected at up to 200 locations, as detailed in The frequency-domain data acquired from the VNA were preprocessed prior to imaging. Firstly, the calibration scan was subtracted from the patient scan. Next, the frequency-domain data were weighted with a differentiated Gaussian pulse, and converted to the time-domain using an inverse Chirp Z-transform . The cenIn the current study, the preprocessed patient data were used to reconstruct the breast images of patients using the DAS, IDAS, CF-DAS, DMAS, CR-DAS and RCB imaging algorithms. An average breast permittivity of  of ϵr=9 . The recThe first column in each table lists the imaging algorithm used to image the patient.The second column lists clinically identified (CI) regions of interest (ROI) that were detected through either clinical imaging or clinical examination and are expected to be detected in the microwave images. Any additional high intensity (HI) regions identified in microwave images (MI) are also listed in this column.The third column indicates whether CI-ROIs were detected in microwave images (MI).The fourth column of the table lists the Signal-to-Mean Ratio (SMR) of each high intensity region detected in the microwave images. The SMR is a measure of the quality of the beamformed image that provides a measure of separation between the ROI and the background clutter. It is defined as the ratio of the average intensity of the ROI to the average intensity of the overall 3D image.The fifth column lists the Full-width Half Maximum (FWHM) of each high intensity region detected in the microwave images. The FWHM may be used to estimate the extent of the ROI in the image. The FWHM is defined as twice the distance from peak intensity in the ROI to the point where intensity of ROI drops by half. The FWHM is computed by growing a region around the centroid of the ROI until the ROI intensity drops by half. Twice the average Euclidean distance from the centroid of the ROI to the end of the region is estimated to be the FWHM.The last column ranks the performance of each algorithm in terms of the detection of the CI-ROI and the quality of the image.Microwave images obtained from each imaging algorithm are shown for each patient in Figures 3–9. The results of each individual patient are discussed in the following sections.The mammogram indicated the presence of a 10 mm lesion at the 4 o’clock radian in the right breast of this patient. The MRI report showed the lesion at 5 o’clock, and a second possibly benign lesion at 7 o’clock. A Magenetic Resonance (MR) image of the patient is shown in The image produced with DAS a shows tThe image obtained with the IDAS algorithm shows only one response In terms of image quality, the IDAS, CF-DAS and CR-DAS have improved the SMR of The mammogram of this patient reported a fibroadenolipoma of several centimetres in diameter, in the inner lower quadrant of the left breast . FibroadThe microwave images of this patient are shown in The images produced with the DAS, DMAS and CR-DAS are similar in terms of location of the dominant response and are also consistent with the clinical information. However, both the CR-DAS and the DMAS provide an improved SMR of the dominant response compared to the DAS algorithm, with the best SMR achieved with the DMAS algorithm. The IDAS, CF-DAS and the RCB algorithms suppress the average clutter in the images. However, the location of the dominant response is not consistent with the known location of the fibroadenolipoma.The MRI report of this patient showed enhancements from the 2 o’clock to 6 o’clock radians, and a focal mass located near the nipple . The mamThe microwave images of this patient are shown in The mammogram and the ultrasound report showed a lesion of 11 mm × 7 mm in size at the 10 o’clock position in the left breast. The lesion was later found to be benign with fat necrosis. The DAS images shows a dominant response Patient 5 had a heterogeneously dense breast, as indicated by the mammography report. The report also indicated a small concentration of fibroglandular tissue on the inner side of the breast and more glandular tissue on the outer side. However, no breast disease was found. The microwave image produced with the DAS a algoritBoth CF-DAS and DMAS significantly improved the SMR of the dominant response Analysis of the microwave images produced with a variety of imaging algorithms shown in  indicateDMAS provided the second highest average SMR value. This indicates a comparable clutter suppression to IDAS, and significantly better than DAS. The location of the dominant responses in DMAS images were consistent with the DAS images, as well as the actual location of lesions reported in clinical reports. In comparison to DAS, DMAS improved the SMR by 44%. The improved performance of the DMAS algorithm can be attributed to its ability to virtually increase the number of measurements, while improving the coherence of responses, using pairing multiplication. Both CR-DAS and RCB performed poorly across all patients in terms of clutter suppression and particularly in terms of the locations of the dominant responses. RCB is known to suffer performance degradations in the presence of coherence interferences . TherefoIt should also be noted that both DAS and DMAS detected a dominant response in the case of Patient 5, even though there was no disease present. This would have resulted in a false-positive detection in the absence of a mammogram. A clear distinction between true-positive and false-positive is a challenge for CMI, which requires further investigations. A number of methods can be employed to define a criterion to reject false-positives. These include comparison of responses from left and right breasts as proposed in  and clasIn this study, various data independent as well as data adaptive algorithms for confocal microwave breast imaging were applied to patient data. The algorithms were applied in monostatic configuration, however, the relative performance of the algorithms in multistatic configuration is expected to be same. The patient data were obtained from the first patient trials conducted at the University of Calgary. The patients were scanned with the TSAR prototype for microwave imaging, as well as with X-ray mammography and MRI to aid in the interpretation of microwave images.Results from this study indicate that the conventional DAS is able to detect most malignancies. However, a significant amount of clutter can be observed in the images produced with DAS. The coherence based imaging algorithms (IDAS and CF-DAS) improve the image quality by suppressing the clutter; however, they often fail to correctly localise the malignancy, particularly in the case of multiple lesions and heterogeneous breasts. CR-DAS does not provide significant improvements when compared to DAS. The DA algorithm RCB suffers severe performance degradations in patients, due to the presence of coherent interferences from multiple lesions and the heterogeneity of the breast. DMAS is the only algorithm that consistently demonstrated better clutter suppression and accurate localisation capability compared to all other algorithms.