Circulating cell-free DNA (ccfDNA) sequencing for low-burden cancer monitoring is limited by sparsity of circulating tumor DNA (ctDNA), the abundance of genomic material within a plasma sample, and pre-analytical error rates due to library preparation, and sequencing errors. Sequencing costs have historically favored the development of deep targeted sequencing approaches for overcoming sparsity in ctDNA detection, but these techniques are limited by the abundance of ccfDNA in samples, which imposes a ceiling on the maximal depth of coverage in targeted panels. Whole genome sequencing (WGS) is an orthogonal approach to ctDNA detection that can overcome the low abundance of ccfDNA by supplanting sequencing depth with breadth, integrating signal across the entire tumor mutation landscape. However, the higher cost of WGS limits the practical depth of coverage and hinders broad adoption. Lower sequencing costs may thus allow for enhanced ctDNA cancer monitoring via WGS. We therefore applied emerging lower-cost WGS (Ultima Genomics, 1USD/Gb) to plasma samples at ∼120x coverage. Copy number and single nucleotide variation profiles were comparable between matched Ultima and Illumina datasets, and the deeper WGS coverage enabled ctDNA detection at the parts per million range. We further harnessed these lower sequencing costs to implement duplex error-corrected sequencing at the scale of the entire genome, demonstrating a ∼1,500x decrease in errors in the plasma of patient-derived xenograft mouse models, and error rates of ∼10−7 in patient plasma samples. We leveraged this highly de-noised plasma WGS to undertake cancer monitoring in the more challenging context of resectable melanoma without matched tumor sequencing. In this context, duplex-corrected WGS allowed us to harness known mutational signature patterns for disease monitoring without matched tumors, paving the way for de novo cancer monitoring.

Background A potential benefit of intravenous thrombolysis (IVT) before mechanical thrombectomy (MT) is pre-interventional reperfusion. Currently, there are few data on the occurrence of pre-interventional reperfusion in patients randomized to IVT or no IVT before MT. Methods SWIFT DIRECT (Solitaire With the Intention For Thrombectomy Plus Intravenous t-PA vs DIRECT Solitaire Stent-retriever Thrombectomy in Acute Anterior Circulation Stroke) was a randomized controlled trial including acute ischemic stroke IVT eligible patients being directly admitted to a comprehensive stroke center, with allocation to IVT with MT versus MT alone. The primary endpoint of this analysis was the occurrence of pre-interventional reperfusion, defined as a pre-interventional expanded Thrombolysis in Cerebral Infarction score of ≥2a. The effect of IVT and potential treatment effect heterogeneity were analyzed using logistic regression analyses. Results Of 396 patients, pre-interventional reperfusion occurred in 20 (10.0%) patients randomized to IVT with MT, and in 7 (3.6%) patients randomized to MT alone. Receiving IVT favored the occurrence of pre-interventional reperfusion (adjusted OR 2.91, 95% CI 1.23 to 6.87). There was no IVT treatment effect heterogeneity on the occurrence of pre-interventional reperfusion with different strata of Randomization-to-Groin-Puncture time (p for interaction=0.33), although the effect tended to be stronger in patients with a Randomization-to-Groin-Puncture time >28 min (adjusted OR 4.65, 95% CI 1.16 to 18.68). There were no significant differences in rates of functional outcomes between patients with and without pre-interventional reperfusion. Conclusion Even for patients with proximal large vessel occlusions and direct access to MT, IVT resulted in an absolute increase of 6% in rates of pre-interventional reperfusion. The influence of time strata on the occurrence of pre-interventional reperfusion should be studied further in an individual patient data meta-analysis of comparable trials. Trial registration number clinicaltrials.gov NCT03192332.

Introduction: Dementia prevention trials have so far shown little benefit of multidomain interventions against cognitive decline. Recruitment strategies in these trials often centre around dementia risk or cardiovascular risk profile, but it is uncertain whether this leads to inclusion of individuals who may benefit most from the intervention. We determined the effects of eligibility criteria on the recruitment of potential trial participants in the general population. Methods: In a systematic search until January 1, 2022, we identified all published and ongoing large (≥500 participants), phase-3 multidomain trials for the prevention of cognitive decline or dementia. We applied trial eligibility criteria to 5,381 participants of the population-based Rotterdam Study (mean age: 72 years, 58% women), to compare participant characteristics, predicted risk of cardiovascular disease, and dementia risk, between trial eligible and ineligible persons. Results: We identified 10 trials, of which 5 had been published (DR’s EXTRA, FINGER, preDIVA, MAPT, and HATICE) and 5 are ongoing (US-POINTER, MIND-CHINA, MYB, AgeWell.de, and J-Mint). Among all Rotterdam Study participants, eligibility across published trials ranged from 48% for MAPT to 87% for preDIVA, in line with original trial reports. Variability in eligibility was wider for ongoing trials, from 1% for US-POINTER to over 94% for MYB trial. Over 70% of trial eligible individuals are recommended preventive intervention in routine care based on their cardiovascular risk, similar for lipid-lowering (71%) and blood pressure-lowering treatment (73%). Ten-year risks of dementia were similar for eligible compared to ineligible individuals (12 vs. 11%). Conclusion: Multidomain dementia prevention trials fail to preferentially include those at the highest risk of dementia and mostly include individuals who qualify for interventions already on the basis of cardiovascular prevention guidelines. These findings call for better targeted enrolment of individuals for whom trial results can improve clinical decision-making.

Abstract Some recent findings suggest that metformin, an oral antidiabetic drug, may have antitumor properties. Studies have shown that metformin can alter cell metabolism, both tumor and immune cells, which can greatly influence disease outcome. In this review, we discuss the potential mechanisms in which metformin can directly induce apoptosis of tumor cells as well as mechanisms in which metformin can elicit or enhance antitumor immune response.

Today's extensive requirements for the storage, management, and analysis of complex, dynamic, evolving, distributed, and heterogeneous data from different sources and platforms, e.g., Big data, generate enormous challenges for IT, especially database applications. That is why the demand for data reduction is increasingly coming from the world of databases, intending to reduce the costs of storing, processing, and querying Big data. There is a large number of different techniques for Big data reduction that can cause confusion and complicate this process. Because of that, the authors proposed a Big data reduction framework to structure and present both data reduction techniques and necessary components essential for a better understanding of the process. The importance and the components of the proposed framework are explained in this paper.

This paper presents the results of detailed geological investigations of the Middle Triassic dolomite deposit of Nikolin Potok, which is located west of Bugojno. Based on the established borders of surface distribution and research results, geological reserves of about 4.6 million m3 have been determined in the wider area of the deposit. The dolomite reserves that have been established so far in this area are at a low level of geological exploration. For this reason, the level of research should be significantly increased, because the calculated and confirmed reserves are very modest compared to the potential possibilities. The paper contains a description of the geological characteristics of the area and the qualitative-quantitative characteristics of dolomite. The results of the conducted research point that the general geological and technical-exploitation factors are favorable and indicate profitable exploitation of the deposit in the coming period as well. Taking into account the significant raw material potential, and the possibility of expanding the existing raw material base, this area has a special significance for the perspective development of dolomite exploitation and its use in the production of technical-building stone.

Air pollution is a major problem in developing countries and around the world causing lung diseases such as asthma, chronic bronchitis, emphysema, and chronic obstructive pulmonary disease. Therefore, innovative methods and systems for predicting air pollution are needed to reduce such risks. Some Internet of Things (IoT) technologies have been developed to assess and monitor various air quality parameters. In the context of IoT, Artificial intelligence is one of the main segments of smart cities that enables collecting a large amount of data to make recommendations, predict future events and help make decisions. Big data, as part of artificial intelligence, greatly contributes to making further decisions, determining the necessary resources, and identifying critical places thanks to the large amount of data it collects. This paper proposes a solution, with the integration of the Internet of Things (IoT), to predict pollution for any given day. This paper aims to show how sensor-derived data in smart air pollution monitoring solutions can be used for intelligent pollution management. By collecting data from the air pollution sensor that sends the data to the server via. NET 6 REST API endpoint and places it in a SQL Server database together with additional weather data that is collected from REST API for that part of the day, a dataset is created through the ETL process in Jupyter notebook. Linear regression algorithms will be used for making predictions. By detecting the largest sources of air pollution, artificial intelligence solutions can proactively reduce pollution and thus improve health conditions and reduce health costs.

In order to find the optimal solution for the drainage of rainwater from roads in urban areas, as well as for the evaluation and ranking of conceptual solutions, appropriate mathematical models and software packages were used in this research. For relevant rain episodes, i.e. rainfall of appropriate duration and intensity, runoff coefficients and flows were taken into account and analyzed according to the rational method, all for the purpose of obtaining data on the amount of rainwater entering the sewage system. Through this research, very good correlations and regressions were established between the cross slope of the road and the parameters of rainwater drainage from the road, as well as the correlation and regression relationships of the cross slope of the road and the efficiency of the drain. Likewise, the dependences of the drainage parameters, the efficiency of the drains and the cross slope of the road were determined, expressed through mathematical functions.

This paper describes the pilot implementation of blockchain technology (Ethereum) for smart grid data management in the IT environment of electrical distribution company Elektro Celje. The work focuses on the decentralised notation of smart meter data to enable secure access and prevent data misuse. The procedure for setting up the pilot project and the operational functions as well as the results of the performance tests are presented and discussed.

Image segmentation has an important role in image processing and computer vision and it is widely used in numerous applications, including feature extraction, pattern recognition, scene analysis, object tracking. Due to its simplicity and effectiveness, multilevel thresholding approach to image segmentation has gained increased research attention in recent years. In this paper, the ability of two recently proposed metaheuristic algorithms, Honey badger algorithm and Chef-based optimization algorithm to ascertain the optimal threshold values based on Kapur’s entropy is systematically examined. The performance of the two multilevel thresholding image segmentation methods are assessed on a dataset of nine standard benchmark images. Based on a fixed number of independent runs, for each test image and a given number of thresholds, the multilevel thresholding performance is reported using the mean and standard deviation of Kapur’s entropy as well as the best objective function value and the associated threshold values.

Compared to conventional fire detection techniques, high-precision computer vision-based fire detection systems have a number of desirable characteristics, such as the ability to monitor large areas, provide a bountiful amount of information, and are easy to maintain. This paper extensively and systematically investigates the use of simple color-based rules for pixel-wise flame recognition in still images. The rules are evaluated on a hundred and nineteen test images that correspond to fires in urban environments. The performances of the considered flame recognition rules are reported in terms of Recall, Balanced accuracy, Accuracy, F1 score, and Matthews correlation coefficient. The best-performing rule is identified. More complex classifiers are formed by combining two or more simple rules. The experimental results show that simple color-based rules and some of their combinations can offer effective fire recognition performance.

Abstract In armed conflicts and crises, children with disabilities face serious threats to their lives and safety, including those related to their inability to flee attacks, risk of abandonment, lack of access to assistive devices, lack of access to basic services and denial of education as well as experiences of stigma, abuse, psychological harm and poverty. Children with disabilities experience multiple and intersecting forms of human rights violations based on their disability and age. Since 2015, Human Rights Watch has documented the impact of armed conflict on children with disabilities in Afghanistan, Cameroon, the Central African Republic, the Gaza Strip in the Occupied Palestinian Territory, South Sudan, Syria and Yemen. While international human rights specifically call for the protection of children with disabilities in situations of armed conflict, the United Nations, governments, parties to the conflict and humanitarian actors have long neglected their specific rights and needs. There is an urgent need for the United Nations and governments to increase efforts to protect children with disabilities as part of their international commitments to protect all children impacted by hostilities. Their attention and investment in those most at risk of violence during armed conflicts will in turn enhance protection measures for everyone.

Interaction channels are unique opportunities to improve customer satisfaction by offering a consistent problem-solving experience. The role of employees in the contact center is to maintain an appropriate relationship between the company and the customer, thus they are personally responsible for the customer experience. In this paper, an objective evaluation method for evaluating customer-agent interaction, i.e. evaluating calls is proposed. The motivation for evaluating calls stems from the key performance characteristics of a contact center.