The nutrient games - Plasmodium metabolism during hepatic development

© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Malaria is a febrile illness caused by species of the protozoan parasite Plasmodium and is characterized by recursive infections of erythrocytes, leading to clinical symptoms and pathology. In mammals, Plasmodium parasites undergo a compulsory intrahepatic development stage before infecting erythrocytes. Liver-stage parasites have a metabolic configuration to facilitate the replication of several thousand daughter parasites. Their metabolism is of interest to identify cellular pathways essential for liver infection, to kill the parasite before onset of the disease. In this review, we summarize the current knowledge on nutrient acquisition and biosynthesis by liver-stage parasites mostly generated in murine malaria models, gaps in knowledge, and challenges to create a holistic view of the development and deficiencies in this field.This work was also financed by la Caixa Foundation (HR17/52150010) to M.M.M.info:eu-repo/semantics/publishedVersio

Intelligent Sensors for Real-Time Decision-Making

The simultaneous integration of information from sensors with business data and how to acquire valuable information can be challenging. This paper proposes the simultaneous integration of information from sensors and business data. The proposal is supported by an industrial imple mentation, which integrates intelligent sensors and real-time decision-making, using a combination of PLC and PC Platforms in a three-level architecture: cloud-fog-edge. Automatic identification intelligent sensors are used to improve the decision-making of a dynamic scheduling tool. The proposed platform is applied to an industrial use-case in analytical Quality Control (QC) laborato ries. The regulatory complexity, the personalized production, and traceability requirements make QC laboratories an interesting use case. We use intelligent sensors for automatic identification to improve the decision-making of a dynamic scheduling tool. Results show how the integration of intelligent sensors can improve the online scheduling of tasks. Estimations from system processing times decreased by over 30%. The proposed solution can be extended to other applications such as predictive maintenance, chemical industry, and other industries where scheduling and rescheduling are critical factors for the production.This work was supported by FCT, through IDMEC, under LAETA, project UIDB/50022/2020

Dual-resource Constrained Scheduling for Quality Control Laboratories

This work presents a novel formulation for quality control laboratory scheduling considering both equipment and analysts as constraints. The problem is modelled as a dualresource constrained exible job shop problem. The formulation considers analyst's tasks in multiple time points during the processing of samples. The mathematical model is implemented as a mixed integer linear programming model (MILP) aiming to minimize makespan. Two sets of instances for the scheduling problem are developed and solved. The rst instance consists on a small example that illustrates the proposed formulation and is solved to optimality. The second instance mimics the real industrial problem and shows the challenges resulting from growing complexity

The impact of intelligent automation in internal supply chains

Nowadays, industry is being forced to produce smaller and more diverse batches, increasing the complexity of internal supply chains. Data has become a valuable asset, supporting the development of intelligent automation solutions. Decision support systems, which leverage data, require the automation pyramid to be more flexible, as information needs to be exchanged simultaneously and in real-time with all automation layers. This paper proposes a framework for intelligent automation to deal with current challenges in acquisition and management of data in industrial settings, towards feeding decision support systems. It frames the topic within the scope of internal supply chains, addressing the framework impact on work practices within the organisation. Two real industrial implementation cases are examined, in the wood and chemical industries. Results help practitioners address the most impactful challenges affecting the performance of internal supply chains, by developing systems which are faster, more flexible, efficient and with improved quality.This work was supported by FCT, through IDMEC, under LAETA, project UIDB/50022/2020

Reinforcement Learning for Dual-Resource Constrained Scheduling

This paper proposes using reinforcement learning to solve scheduling problems where two types of resources of limited availability must be allocated. The goal is to minimize the makespan of a dual-resource constrained flexible job shop scheduling problem. Efficient practical implementation is very valuable to industry, yet it is often only solved combining heuristics and expert knowledge. A framework for training a reinforcement learning agent to schedule diverse dual-resource constrained job shops is presented. Comparison with other state-of-theart approaches is done on both simpler and more complex instances that the ones used for training. Results show the agent produces competitive solutions for small instances that can outperform the implemented heuristic if given enough time. Other extensions are needed before real-world deployment, such as deadlines and constraining resources to work shifts

Temporal Analysis of Measured LOS Massive MIMO Channels with Mobility

The first measured results for massive multiple-input, multiple-output (MIMO) performance in a line-of-sight (LOS) scenario with moderate mobility are presented, with 8 users served by a 100 antenna base Station (BS) at 3.7 GHz. When such a large number of channels dynamically change, the inherent propagation and processing delay has a critical relationship with the rate of change, as the use of outdated channel information can result in severe detection and precoding inaccuracies. For the downlink (DL) in particular, a time division duplex (TDD) configuration synonymous with massive MIMO deployments could mean only the uplink (UL) is usable in extreme cases. Therefore, it is of great interest to investigate the impact of mobility on massive MIMO performance and consider ways to combat the potential limitations. In a mobile scenario with moving cars and pedestrians, the correlation of the MIMO channel vector over time is inspected for vehicles moving up to 29 km/h. For a 100 antenna system, it is found that the channel state information (CSI) update rate requirement may increase by 7 times when compared to an 8 antenna system, whilst the power control update rate could be decreased by at least 5 times relative to a single antenna system.Comment: Accepted for presentation at the 85th IEEE Vehicular Technology Conference in Sydney. 5 Pages. arXiv admin note: substantial text overlap with arXiv:1701.0881

Multi-agent system for dynamic scheduling

This paper proposes a flexible manufacturing system based on intelligent computational agents. A Multi-Agent System composed of 4 types of reactive agents was designed to control the operation of a real implementation in the Intelligent Automation Lab at Instituto Superior Tecnico. This implementation was ´ based and constructed analogously to a known benchmark, AIPPRIMECA. The agents were modelled using Petri nets and agent communications were defined through the combination of FIPA Interaction Protocols. The system was tested under the conditions of static and dynamic scenarios, having its performance validated whenever possible by comparison with results from a Potential Fields Approach in the same benchmark. Overall, the performance exhibited by the proposed MAS was slightly better and it is worth highlighting the simple behaviour of each agent and ability to respond in real-time to all the dynamic scenarios tested

Insights into the posttranslational structural heterogeneity of thyroglobulin and its role in the development, diagnosis, and management of benign and malignant thyroid diseases

Thyroglobulin (Tg) is the major glycoprotein produced by the thyroid gland, where it serves as a template for thyroid hormone synthesis and as an intraglandular store of iodine. Measurement of Tg levels in serum is of great practical importance in the follow-up of differentiated thyroid carcinoma (DTC), a setting in which elevated levels after total thyroidectomy are indicative of residual or recurrent disease. The most recent methods for serum Tg measurement are monoclonal antibody-based and are highly sensitive. However, major challenges remain regarding the interpretation of the results obtained with these immunometric methods, particularly in patients with endogenous antithyroglobulin antibodies or in the presence of heterophile antibodies, which may produce falsely low or high Tg values, respectively. The increased prevalence of antithyroglobulin antibodies in patients with DTC, as compared with the general population, raises the very pertinent possibility that tumor Tg may be more immunogenic. This inference makes sense, as the tumor microenvironment (tumor cells plus normal host cells) is characterized by several changes that could induce posttranslational modification of many proteins, including Tg. Attempts to understand the structure of Tg have been made for several decades, but findings have generally been incomplete due to technical hindrances to analysis of such a large protein (660 kDa). This review article will explore the complex structure of Tg and the potential role of its marked heterogeneity in our understanding of normal thyroid biology and neoplastic processes.FapespCNPqCapesUniv Fed Sao Paulo EPM Unifesp, Escola Paulista Med, Lab Endocrinol Mol & Translac, Div Endocrinol & Metab,Dept Med, Sao Paulo, SP, BrazilUniv Fed Mato Grosso do Sul UFMS, Fac Med Famed, Dept Med, Clin Integrada 5,Endocrinol & Metab, Campo Grande, MS, BrazilUniv Fed Sao Paulo, EPM, Dept Bioquim, Div Mol Biol, Sao Paulo, SP, BrazilUniv Fed Sao Paulo EPM Unifesp, Escola Paulista Med, Lab Endocrinol Mol & Translac, Div Endocrinol & Metab,Dept Med, Sao Paulo, SP, BrazilUniv Fed Sao Paulo, EPM, Dept Bioquim, Div Mol Biol, Sao Paulo, SP, BrazilWeb of Scienc

Quinoxaline, its derivatives and applications: a state of the art review

Quinoxaline derivatives are an important class of heterocycle compounds, where N replaces some carbon atoms in the ring of naphthalene. Its molecular formula is C8H6N2, formed by the fusion of two aromatic rings, benzene and pyrazine. It is rare in natural state, but their synthesis is easy to perform. In this review the State of the Art will be presented, which includes a summary of the progress made over the past years in the knowledge of the structure and mechanism of the quinoxaline and quinoxaline derivatives, associated medical and biomedical value as well as industrial value. Modifying quinoxaline structure it is possible to obtain a wide variety of biomedical applications, namely antimicrobial activities and chronic and metabolic diseases treatment

Assessing the impact of automation in pharmaceutical quality control labs using a digital twin

Nowadays, pharmaceutical Quality Control (QC) laboratories have complex workflows where analysts test different samples simultaneously. Tests ensure the physical properties of drugs are expected and within guidelines. Each test follows an analytical procedure containing tasks. Cyber-Physical Production System (CPPS) improves tasks/operations; however, accurate cost analysis with reasoned data is challenging. Theoretical estimation of impacts requires a high level of abstraction and fails to capture the proper behavior of the workflow. This paper proposes a method for evaluating the introduction of automation in a pharmaceutical QC laboratory. In the proposed methodology, this paper developed a simulation model of the analytical workflow of the tests. The impact assessment compares the current As-Is and future To-Be workflows, reworking the affected tasks. The model of the new resource is a hybrid parallel process with an initial buffer. The paper analyses several scenarios on parameters such as throughput, resource occupation, and annual man-hours gained. The simulation model was validated against actual historical data and compared to theoretical projections on the impact of automation. From our results, we found the available equipment has a high impact. Using production data, we project an increase in the analyst availability of 4,7% and equipment availability of 1,2%