High-Speed Rail opportunities around metropolitan regions: Madrid and London

The original main aim of high-speed rail (HSR) was to link big metropolitan areas 400–600 km apart. Recently, intermediate HSR stations have also been created in suburban areas or small cities within the limits of metropolitan areas (up to 100 km), opening up new metropolitan transportation opportunities, notably the strengthening of inward and outward commuting and through traffic across the metropolis. The argument advanced in this paper is under what conditions HSR could facilitate the development of small HSR suburban cities as special subcenters of the metropolitan area with particularly good connections to the metropolitan center and to other distant metropolises. The paper focuses on a comparative study of this new type of metropolitan HSR by analyzing the Madrid (Toledo, Segovia, and Guadalajara stations), Spain, and the London (Ashford, Ebbsfleet, and Stratford stations) cases. Infrastructure layout, station typologies, and rail services are compared together with each city's territorial contexts, activities, and connections with other transport modes. This case-study approach, taking account of specific circumstances and contexts, facilitates the understanding of the HSR impact on metropolitan development, offering new transport alternatives

Objective ADHD diagnosis using convolutional neural networks over daily-life activity records

Attention Deficit/Hyperactivity Disorder (ADHD) is the most common neurobehavioral disorder in children and adolescents. However, its etiology is still unknown, and this hinders the existence of reliable, fast and inexpensive standard diagnostic methods. Objective: This paper proposes an end-to-end methodology for automatic diagnosis of the combined type of ADHD. Methods: Diagnosis is based on the analysis of 24 hour-long activity records using Convolutional Neural Networks to classify spectrograms of activity windows. Results: We achieve up to 97.62% average sensitivity, 99.52% specificity and AUC values over 99%. Overall, our figures overcome those obtained by actigraphy-based methods reported in the literature as well as others based on more expensive (and not so convenient) acquisition methods. Conclusion: These results reinforce the idea that combining deep learning techniques together with actimetry can lead to a robust and efficient system for objective ADHD diagnosis. Significance: Reliance on simple activity measurements leads to an inexpensive and non-invasive objective diagnostic method, which can be easily implemented with daily devices

Planeamiento estratégico de la estación de servicio Gar Oil S.A.C.

La empresa Gar Oil actualmente se encuentra en una situación crítica económica y financieramente, debido a una importante inversión en activos fijos en el ejercicio económico del 2018, por ello se ha desarrollado un plan estratégico. Se propone la visión: Para el 2023 ser la primera Estación de Servicio en ventas de la corporación PECSA en la Ciudad del Cusco, logrando a su vez crecer en rentabilidad y llegar al 5% después de impuestos buscando siempre el bienestar de los accionistas, de los trabajadores y de la comunidad. Con 4 objetivos a largo plazo: (a) Al 2023 incrementar la cartera de clientes corporativos a 22 actualmente al 2019 es de 11., (b) Al 2023 ser reconocida como una empresa con estándares de calidad, ambiental, salud y seguridad ocupacional obteniendo una certificación por cada concepto mencionado, en la actualidad no se cuenta con ninguna certificación (c) Al 2022 Incrementar los puestos de trabajo a 21 colaboradores con mano de obra calificada y potenciar a la alta gerencia y colaboradores de nuestra actual fuerza laboral, en la tenemos 14 colaboradores y actualidad solo existen programas de capacitación obligatorios por las entidades reguladoras. (d) Al 2023 Incrementar a 4.5% ROE con la capacitación a la alta gerencia y colaboradores sobre optimización del uso de recursos de manera eficientes actualmente es de 1.5% negativo. Toda esta información se representa en las matrices que ayudaran al desarrollo del plan estratégico que son plan estratégico integral y el tablero control balanceado. Y así desarrollar nuestra estrategia de desarrollo de mercado para el incremento de participación de la empresa Gar Oil.The Gar Oil company is currently in a critical situation economically and financially, due to a significant investment in fixed assets in the fiscal year of 2018, therefore a strategic plan has been developed. The vision is proposed: By 2023, it will be the first Sales Service Station of the PECSA corporation in the City of Cusco, in turn, increasing profitability and reaching 5% after taxes, always seeking the well-being of shareholders, of The workers and the community. With 4 long-term objectives: (a) By 2023, increasing the portfolio of corporate clients to 22 currently by 2019 is 11., (b) By 2023 being recognized as a company with standards of quality, environmental, health and occupational safety obtaining one certification for each mentioned concept, currently there is no certification (c) By 2022 Increase jobs to 21 employees with skilled labor and empower senior management and employees of our current workforce, in the We have 14 employees and currently there are only mandatory training programs for regulatory entities. (d) By 2023 Increase to 4.5% ROE with training to senior management and collaborators on optimizing the use of resources efficiently is currently 1.5% negative. All this information is represented in the matrices that will help the development of the strategic plan that are integral strategic plan and the balanced control board. And thus develop our market development strategy to increase the participation of the company Gar Oil.Tesi

San Adrian: un nuevo yacimiento de la Edad del Bronce en el Norte de la Peninsula Iberica

Bronze Age studies carried out in the Cantabrian Region have traditionally focused on prestige goods and funerary contexts. As a result of this, the lack of information about daily activities, subsistence strategies, and human settlement on a regional scale is evident in the state of art. However, current research has achieved new discoveries in recent years, allowing a reconstruction of some aspects of the economic structure, settlements, material culture and the palaeoenvironment during the Bronze Age. Indeed, besides the funerary practices discovered in 1983 in San Adrian (Parztuergo Nagusia, Gipuzkoa), research has now revealed the presence of Upper Palaeolithic and Early Bronze Age occupations. This paper presents a first characterization of the retrieved evidence and a preliminary evaluation of the archaeological site and its environment. San Adrian is a tunnel-shaped cave located at 1,000 meters a.s.l. in the Aizkorri mountain range, opening a passage beneath the Atlantic-Mediterranean watershed in northern Iberia. The strategic character of this mountain site is demonstrated by the presence of Upper Palaeolithic and Bronze Age occupations, and by the construction of a road passing through it and the fortification of both its entrances in the Middle Ages. The aim of the archaeological survey started in 2008 was to identify, describe and evaluate the heritage potential of the cave, because previous fieldwork had only managed to make surface finds in the side galleries, including a medieval hoard and Bronze Age human remains. The work carried out by our research group at San Adrian includes a series of test pits and the excavation of an area nine square metres in size following stratigraphic criteria. In the current state, we identified at least two contexts corresponding to Late Upper Palaeolithic and Bronze Age occupations in the cave. Fieldwork included the sieving and flotation of sediment and the collection of samples for different types of analysis: palynology, carpology, sedimentology, and radiocarbon dating. The evidence is being studied by a multidisciplinary team according to expertise requirements for each topic: palaeobotany and environment, archaeozoology, sedimentology, geology, physical anthropology, prehistoric industries (lithics, pottery and bone) and archaeological and historical documentation. Because of its recent discovery, Upper Palaeolithic evidence remains still under study, but first results on Bronze Age layers can be presented. The ongoing archaeobotanical and archaeozoological studies reveal the exploitation of domestic plants and fauna complemented by hunting and foraging of wild species. At the same time, the archaeological artefacts and their production sequences show the exploitation of nearby resources on both sides of the mountain range, while prestige goods are absent. This evidence is also used to estimate the regularity of cave occupations and to propose a model of seasonal exploitation of the mountain environment. The results obtained reveal the exploitation of resources from both the Mediterranean and Atlantic basins, and contribute towards an understanding of the daily activities of Bronze Age societies. In addition, the evidence shows the exchange and circulation of quotidian products between the Cantabrian region and inland Iberia in other networks than those of prestige goods

Prognostic implications of comorbidity patterns in critically ill COVID-19 patients: A multicenter, observational study

Background The clinical heterogeneity of COVID-19 suggests the existence of different phenotypes with prognostic implications. We aimed to analyze comorbidity patterns in critically ill COVID-19 patients and assess their impact on in-hospital outcomes, response to treatment and sequelae. Methods Multicenter prospective/retrospective observational study in intensive care units of 55 Spanish hospitals. 5866 PCR-confirmed COVID-19 patients had comorbidities recorded at hospital admission; clinical and biological parameters, in-hospital procedures and complications throughout the stay; and, clinical complications, persistent symptoms and sequelae at 3 and 6 months. Findings Latent class analysis identified 3 phenotypes using training and test subcohorts: low-morbidity (n=3385; 58%), younger and with few comorbidities; high-morbidity (n=2074; 35%), with high comorbid burden; and renal-morbidity (n=407; 7%), with chronic kidney disease (CKD), high comorbidity burden and the worst oxygenation profile. Renal-morbidity and high-morbidity had more in-hospital complications and higher mortality risk than low-morbidity (adjusted HR (95% CI): 1.57 (1.34-1.84) and 1.16 (1.05-1.28), respectively). Corticosteroids, but not tocilizumab, were associated with lower mortality risk (HR (95% CI) 0.76 (0.63-0.93)), especially in renal-morbidity and high-morbidity. Renal-morbidity and high-morbidity showed the worst lung function throughout the follow-up, with renal-morbidity having the highest risk of infectious complications (6%), emergency visits (29%) or hospital readmissions (14%) at 6 months (p<0.01). Interpretation Comorbidity-based phenotypes were identified and associated with different expression of in-hospital complications, mortality, treatment response, and sequelae, with CKD playing a major role. This could help clinicians in day-to-day decision making including the management of post-discharge COVID-19 sequelae. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort

Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery