Telerehabilitation in Multiple Sclerosis: Results of a Randomized Feasibility and Efficacy Pilot Study

A prospective, randomized, three-arm, evaluator blinded study to demonstrate the feasibility of a telerehabilitation (TR) program in individuals with ambulatory deficits secondary to Multiple Sclerosis (MS) and evaluate its efficacy when compared to conventional on-site physical therapy (PT) was completed. Thirty participants were evaluated at baseline and randomized to one of three groups with intervention lasting 8 weeks: Group 1 (control)- customized unsupervised home-based exercise program (HEP) 5 days a week; Group 2 (TR)- remote PT supervised via audio/visual real-time telecommunication twice weekly; Group 3 (PT)- in-person PT at the medical facility twice weekly. Outcomes included patient reported outcomes (PROs) obtained through questionnaires, and measurements of gait and balance performed with bedside tests and a computerized system. Functional gait assessment improved from baseline in all three groups. There were no significant differences between the TR and the conventional PT groups for a variety of outcome measures. TR is a feasible method to perform PT in persons with MS and has comparable efficacy to conventional in-person PT as measured by patient reported outcomes and objective outcomes of gait and balance

Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography

Tesis por compendio[ES] Esta tesis presenta soluciones de vanguardia basadas en algoritmos de computer vision (CV) y machine learning (ML) para ayudar a los expertos en el diagnóstico clínico. Se centra en dos áreas relevantes en el campo de la imagen médica: la patología digital y la oftalmología. Este trabajo propone diferentes paradigmas de machine learning y deep learning para abordar diversos escenarios de supervisión en el estudio del cáncer de próstata, el cáncer de vejiga y el glaucoma. En particular, se consideran métodos supervisados convencionales para segmentar y clasificar estructuras específicas de la próstata en imágenes histológicas digitalizadas. Para el reconocimiento de patrones específicos de la vejiga, se llevan a cabo enfoques totalmente no supervisados basados en técnicas de deep-clustering. Con respecto a la detección del glaucoma, se aplican algoritmos de memoria a corto plazo (LSTMs) que permiten llevar a cabo un aprendizaje recurrente a partir de volúmenes de tomografía por coherencia óptica en el dominio espectral (SD-OCT). Finalmente, se propone el uso de redes neuronales prototípicas (PNN) en un marco de few-shot learning para determinar el nivel de gravedad del glaucoma a partir de imágenes OCT circumpapilares. Los métodos de inteligencia artificial (IA) que se detallan en esta tesis proporcionan una valiosa herramienta de ayuda al diagnóstico por imagen, ya sea para el diagnóstico histológico del cáncer de próstata y vejiga o para la evaluación del glaucoma a partir de datos de OCT.[CA] Aquesta tesi presenta solucions d'avantguarda basades en algorismes de *computer *vision (CV) i *machine *learning (ML) per a ajudar als experts en el diagnòstic clínic. Se centra en dues àrees rellevants en el camp de la imatge mèdica: la patologia digital i l'oftalmologia. Aquest treball proposa diferents paradigmes de *machine *learning i *deep *learning per a abordar diversos escenaris de supervisió en l'estudi del càncer de pròstata, el càncer de bufeta i el glaucoma. En particular, es consideren mètodes supervisats convencionals per a segmentar i classificar estructures específiques de la pròstata en imatges histològiques digitalitzades. Per al reconeixement de patrons específics de la bufeta, es duen a terme enfocaments totalment no supervisats basats en tècniques de *deep-*clustering. Respecte a la detecció del glaucoma, s'apliquen algorismes de memòria a curt termini (*LSTMs) que permeten dur a terme un aprenentatge recurrent a partir de volums de tomografia per coherència òptica en el domini espectral (SD-*OCT). Finalment, es proposa l'ús de xarxes neuronals *prototípicas (*PNN) en un marc de *few-*shot *learning per a determinar el nivell de gravetat del glaucoma a partir d'imatges *OCT *circumpapilares. Els mètodes d'intel·ligència artificial (*IA) que es detallen en aquesta tesi proporcionen una valuosa eina d'ajuda al diagnòstic per imatge, ja siga per al diagnòstic histològic del càncer de pròstata i bufeta o per a l'avaluació del glaucoma a partir de dades d'OCT.[EN] This thesis presents cutting-edge solutions based on computer vision (CV) and machine learning (ML) algorithms to assist experts in clinical diagnosis. It focuses on two relevant areas at the forefront of medical imaging: digital pathology and ophthalmology. This work proposes different machine learning and deep learning paradigms to address various supervisory scenarios in the study of prostate cancer, bladder cancer and glaucoma. In particular, conventional supervised methods are considered for segmenting and classifying prostate-specific structures in digitised histological images. For bladder-specific pattern recognition, fully unsupervised approaches based on deep-clustering techniques are carried out. Regarding glaucoma detection, long-short term memory algorithms (LSTMs) are applied to perform recurrent learning from spectral-domain optical coherence tomography (SD-OCT) volumes. Finally, the use of prototypical neural networks (PNNs) in a few-shot learning framework is proposed to determine the severity level of glaucoma from circumpapillary OCT images. The artificial intelligence (AI) methods detailed in this thesis provide a valuable tool to aid diagnostic imaging, whether for the histological diagnosis of prostate and bladder cancer or glaucoma assessment from OCT data.García Pardo, JG. (2022). Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182400Compendi

Conservation agriculture in trouble? Estimating the economic impact of an eventual glyphosate prohibition in Spain

Weed control is a crucial aspect in many conservation agriculture systems given that costs and time savings from avoiding tillage are closely linked to the use of effective and environmental friendly herbicides. This has led to the widespread use of glyphosate in farms, as it is a broad-spectrum, easily degradable, low- cost herbicide. The recent debate on the safety of glyphosate and on the excessive use of chemical herbicides in food production has caused concern on farmers about the possible economic effects of a virtual ban on glyphosate. The aim of this paper is to estimate the costs associated with an eventual prohibition of glyphosate in Spanish conservation agriculture areas. The costs of different alternative weed control strategies for herbaceous and tree crops were calculated: i) substitution of glyphosate in chemical control; ii) minimum tillage; iii) conventional tillage; and iv) natural or planted vegetal groundcovers. The results show that banning glyphosate would increase the costs of chemical control by 40% for herbaceous and by 57% for tree crops. However, conventional tillage would be a cheaper option for herbaceous because costs increase by 10% compared to current techniques. Our estimations suggest that the ban on glyphosate would have a negative impact on the economic profitability of farms and also on other non-economic advantages derived from conservation farming techniques

Separation of refrigerant gas mixtures containing R32, R134a and R1234yf through poly(ether-block-amide) membranes

Hydrofluorocarbons (HFCs) are powerful greenhouse gases whose production and consumption must be phased down in order to reach the reduction goals established by the Kigali Amendment to the Montreal Protocol. However, the share of recycled refrigerant gases remains very low owing to the extremely inefficient separation of refrigerant mixtures by cryogenic distillation. In this sense, the HFCs, difluoromethane (R32, GWP = 675) and 1,1,1,2-tetrafluoroethane (R134a, GWP = 1430), together with the hydrofluoroolefin (HFO) 2,3,3,3-tetrafluoropropene (R1234yf, GWP = 4), are among the most common constituents of HFC/HFO refrigerant mixtures currently employed in the refrigeration and air-conditioning sector. Therefore, the feasibility of using membrane technology for the selective separation of these compounds is assessed in this work for the first time. A comprehensive study of their gas permeation through several poly(ether-block-amide) (PEBA) membranes that differ on the content and type of backbone segments is performed. Results show that PEBA membranes exhibit superior permeability of R32 (up to 305 barrer) and R134a (up to 230 barrer) coupled with reasonably high selectivity for the gas pairs R32/R1234yf (up to 10) and R134a/R1234yf (up to 8). Moreover, for the blends R32/R1234yf and R32/R134a, the membrane separation performance is not significantly affected under the mixed gas conditions tested. Thus, results evidence that consideration should be given to membrane technology for the cost-efficient separation of HFC/HFO mixtures in order to boost the recycling of these compounds.This research is supported by Project KET4F-Gas-SOE2/ P1/P0823, which is co-inanced by the European Regional Development Fund within the framework of Interreg Sudoe Programme. The authors acknowledge the collaboration of Dr. Mar Lopez-Gonza ́ lez and Dr. Rosario Benavente (Institute of Polymer Science and Technology-CSIC) to perform the sorption and DSC experiments. F.P. acknowledges the postdoctoral fellowship (FJCI-2017-32884, ‘Juan de la Cierva Formacioń ’) from the Spanish Ministry of Science, Innovation and Universities

Branching processes with pairwise interactions

In this manuscript, we are interested on the long-term behaviour of branching processes with pairwise interactions (BPI-processes). A process in this class behaves as a pure branching process with the difference that competition and cooperation events between pairs of individual are also allowed. BPI-processes form a subclass of branching processes with interactions, which were recently introduced by Gonz\'alez Casanova et al. (2017), and includes the so-called logistic branching process which was studied by Lambert (2005). Here, we provide a series of integral tests that fully explains how competition and cooperation regulates the long-term behaviour of BPI-processes. In particular, we give necessary and sufficient conditions for the events of explosion and extinction, as well as conditions under which the process comes down from infinity. Moreover, we also determine whether the process admits, or not, a stationary distribution. Our arguments uses the moment dual of BPI-processes which turns out to be a family of diffusions taking values on $[0,1]$, that we introduce as generalised Wright-Fisher diffusions together with a complete understanding of the nature of their boundaries.Comment: 3 table

Vapor-liquid equilibria and diffusion coefficients of difluoromethane, 1,1,1,2-tetrafluoroethane, and 2,3,3,3-tetrafluoropropene in low-viscosity ionic liquids

The phase-down of hydrofluorocarbons (HFCs) established by the Kigali Amendment to the Montreal Protocol is leading to the formulation and commercialization of new refrigerant blends containing hydrofluoroolefins (HFOs), such as 2,3,3,3-tetrafluoropropene (R1234yf), and HFCs with moderate global warming potential, namely, difluoromethane (R32) and 1,1,1,2-tetrafluoroethane (R134a). Moreover, the recycling of refrigerants is attracting attention as a means to reduce the amount of new HFCs produced and their release to the environment. To that end, the use of ionic liquids has been proposed as entrainers to separate refrigerants with close-boiling points or azeotropic blends. Thus, the vapor?liquid equilibria and diffusion coefficients of the refrigerant?ionic liquid pairs formed by R32 + [C2mim][BF4], R134a + [C2mim][BF4], R134a+ [C2mim][OTf], R1234yf + [C2mim][OTf], and R1234yf + [C2mim][Tf2N] are studied using an isochoric saturation method at temperatures ranging from 283.15 to 323.15 K and pressures up to 0.9 MPa. In addition, the solubility behavior is successfully modeled using the nonrandom two-liquid activity-coefficient method, and the Henry?s law constants at infinite dilution, solvation energies, and infinite dilution activity coefficients are calculated.This research is supported by Project KET4F-Gas – SOE2/P1/P0823, which is co-financed by the European Regional Development Fund within the framework of Interreg Sudoe Programme. S. A-D. and F.P acknowledge the FPU grant (18/03939) and the post-doctoral fellowship (FJCI-2017-32884 Juan de la Cierva Formación), respectively, awarded by the Spanish Ministry of Science, Innovation and Universities