Distributed Model Predictive Consensus via the Alternating Direction Method of Multipliers

We propose a distributed optimization method for solving a distributed model predictive consensus problem. The goal is to design a distributed controller for a network of dynamical systems to optimize a coupled objective function while respecting state and input constraints. The distributed optimization method is an augmented Lagrangian method called the Alternating Direction Method of Multipliers (ADMM), which was introduced in the 1970s but has seen a recent resurgence in the context of dramatic increases in computing power and the development of widely available distributed computing platforms. The method is applied to position and velocity consensus in a network of double integrators. We find that a few tens of ADMM iterations yield closed-loop performance near what is achieved by solving the optimization problem centrally. Furthermore, the use of recent code generation techniques for solving local subproblems yields fast overall computation times.Comment: 7 pages, 5 figures, 50th Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, 201

Current Status and Emerging Trends in Colorectal Cancer Screening and Diagnostics

Colorectal cancer (CRC) is a prevalent and potentially fatal disease categorized based on its high incidences and mortality rates, which raised the need for effective diagnostic strategies for the early detection and management of CRC. While there are several conventional cancer diagnostics available, they have certain limitations that hinder their effectiveness. Significant research efforts are currently being dedicated to elucidating novel methodologies that aim at comprehending the intricate molecular mechanism that underlies CRC. Recently, microfluidic diagnostics have emerged as a pivotal solution, offering non-invasive approaches to real-time monitoring of disease progression and treatment response. Microfluidic devices enable the integration of multiple sample preparation steps into a single platform, which speeds up processing and improves sensitivity. Such advancements in diagnostic technologies hold immense promise for revolutionizing the field of CRC diagnosis and enabling efficient detection and monitoring strategies. This article elucidates several of the latest developments in microfluidic technology for CRC diagnostics. In addition to the advancements in microfluidic technology for CRC diagnostics, the integration of artificial intelligence (AI) holds great promise for further enhancing diagnostic capabilities. Advancements in microfluidic systems and AI-driven approaches can revolutionize colorectal cancer diagnostics, offering accurate, efficient, and personalized strategies to improve patient outcomes and transform cancer management

Unattended network operations technology assessment study. Technical support for defining advanced satellite systems concepts

The results are summarized of an unattended network operations technology assessment study for the Space Exploration Initiative (SEI). The scope of the work included: (1) identified possible enhancements due to the proposed Mars communications network; (2) identified network operations on Mars; (3) performed a technology assessment of possible supporting technologies based on current and future approaches to network operations; and (4) developed a plan for the testing and development of these technologies. The most important results obtained are as follows: (1) addition of a third Mars Relay Satellite (MRS) and MRS cross link capabilities will enhance the network's fault tolerance capabilities through improved connectivity; (2) network functions can be divided into the six basic ISO network functional groups; (3) distributed artificial intelligence technologies will augment more traditional network management technologies to form the technological infrastructure of a virtually unattended network; and (4) a great effort is required to bring the current network technology levels for manned space communications up to the level needed for an automated fault tolerance Mars communications network

Blocked recombinase polymerase amplification for mutation analysis of PIK3CA gene

[EN] A blocked recombinase polymerase amplification (blocked-RPA) approach has been developed for the enrichment of mutated templates in heterogeneous specimens as tumor tissues. This isothermal amplification technique opens alternative solutions for meeting the technological demand of physician office laboratories. Herein, the detection of mutations in PIK3CA gene, such as p.E545K, and p.H1047L, is presented. The main element was an oligonucleotide (dideoxycytidine functionalized at 3'-end) which matched with wild-type sequence in the target locus. The amplification was performed operating at 37¿°C during 40¿min. The results demonstrated that the competition between the upstream primer and the blocker reduced the percentage of amplified wild-type allele, making the detection of the present mutation easier. For mutation discrimination, a fast hybridization assay was performed in microarray format on plastic chip and colorimetric detection. This approach enabled the reliable discrimination of specific mutations against a background of up to 95% wild-type DNA. The applicability of the method, based on the combination of blocked-RPA and low-cost chip hybridization, was successfully proven for the genotyping of various cancer cell lines as well as tumor tissues. The assignations agreed with those provided by next-generation sequencing. Therefore, these investigations would support a personalized approach to patient care based on the molecular signature of human cancers.PROJECT ONCOMARKER (MINECO RTC-2015-3625-1), CTQ 2013–45875-R, FEDER and GVAPROMETEO 2014/40.Martorell-Tejedor, S.; Palanca, S.; Maquieira Catala, Á.; Tortajada-Genaro, LA. (2018). Blocked recombinase polymerase amplification for mutation analysis of PIK3CA gene. Analytical Biochemistry. 544:49-56. https://doi.org/10.1016/j.ab.2017.12.013S495654

Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing

We review some emerging trends in transduction, connectivity and data analytics for Point-of-Care Testing (POCT) of infectious and non-communicable diseases. The patient need for POCT is described along with developments in portable diagnostics, specifically in respect of Lab-on-chip and microfluidic systems. We describe some novel electrochemical and photonic systems and the use of mobile phones in terms of hardware components and device connectivity for POCT. Developments in data analytics that are applicable for POCT are described with an overview of data structures and recent AI/Machine learning trends. The most important methodologies of machine learning, including deep learning methods, are summarised. The potential value of trends within POCT systems for clinical diagnostics within Lower Middle Income Countries (LMICs) and the Least Developed Countries (LDCs) are highlighted

Tumor Drug Penetration Measurements Could Be the Neglected Piece of the Personalized Cancer Treatment Puzzle

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150514/1/cpt1211.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150514/2/cpt1211_am.pd