Type-Inference Based Short Cut Deforestation (nearly) without Inlining

Deforestation optimises a functional program by transforming it into another one that does not create certain intermediate data structures. In [ICFP'99] we presented a type-inference based deforestation algorithm which performs extensive inlining. However, across module boundaries only limited inlining is practically feasible. Furthermore, inlining is a non-trivial transformation which is therefore best implemented as a separate optimisation pass. To perform short cut deforestation (nearly) without inlining, Gill suggested to split definitions into workers and wrappers and inline only the small wrappers, which transfer the information needed for deforestation. We show that Gill's use of a function build limits deforestation and note that his reasons for using build do not apply to our approach. Hence we develop a more general worker/wrapper scheme without build. We give a type-inference based algorithm which splits definitions into workers and wrappers. Finally, we show that we can deforest more expressions with the worker/wrapper scheme than the algorithm with inlining

Modelling agronomic properties of Technosols constructed with urban wastes

International audienceThe greening of urban and suburban areas requires large amounts of arable earth that is a non-renewable resource. However, concentration of population in cities leads to the production of high amounts of wastes and by-products that are nowadays partly recycled as a resource and quite systematically exported out of urban areas. To preserve natural soil resources, a strategy of waste recycling as fertile substitutes is proposed. Eleven wastes are selected for their environmental harmlessness and their contrasted physico-chemical properties for their potential use in pedological engineering. The aim is (i) to demonstrate the feasibility of the formulation of fertile substrates exclusively with wastes and (ii) to model their physico-chemical properties following various types, number and proportions of constitutive wastes. Twenty-five binary and ternary combinations are tested at different ratios for total carbon, Olsen available phosphorus, cation exchange capacity, water pH, water retention capacity and bulk density. Dose-response curves describe the variation of physico-chemical properties of mixtures depending on the type and ratio of selected wastes. If these mixtures mainly mimic natural soils, some of them present more extreme urban soil features, especially for pH and P Olsen. The fertility of the new substrates is modelled by multilinear regressions for the main soil properties

Automatic system for personalised exercise recommendation in breast cancer care using mobile technologies and machine learning

[ES] Aliviar las secuelas del cáncer en general, y en particular del cáncer de mama, es uno de los mayores retos de nuestros tiempos, y precisamente el ejercicio terapéutico se plantea como una solución para paliar los efectos secundarios del cáncer y su tratamiento a corto y largo plazo. No obstante, para que las intervenciones del ejercicio físico sean más efectivas estas deben estar adaptadas a cada paciente según sus capacidades y necesidades de entrenamiento específicas. Dicha adaptación al entrenamiento utilizando tecnologías de salud móvil (mSalud) ya se ha llevado a cabo con éxito en entornos deportivos, y en este trabajo se plantea una aproximación similar para pacientes con cáncer de mama, donde se pretende ajustar de forma individual las dosis de entrenamiento a las necesidades de cada paciente. Para ello, se ha diseñado y desarrollado un sistema completo de mSalud que ha permitido extraer un conjunto de datos longitudinal con mediciones de la carga del ejercicio de pacientes de cáncer de mama. A partir de dichos datos se están utilizando técnicas de ciencia de datos y aprendizaje automático para extraer los diferentes estados de recuperación de las pacientes a lo largo de una intervención en ejercicio físico, lo cual nos permitirá plantear un sistema de ayuda a la toma de decisiones para prescribir dosis individualizadas de ejercicio terapéutico.[EN] Alleviating the sequelae of cancer in general, and breast cancer in particular, is one of the greatest challenges of our times, and therapeutic exercise is precisely one solution to alleviate the side effects of cancer and its treatment in the short and long term. However, in order to make exercise interventions more effective, they must be adapted to each patient according to their specific training needs and abilities. Such adaptation to training using mobile health technologies (mHealth) has already been successfully carried out in sports settings, and this work proposes a similar approach for breast cancer patients, where the aim is to individually adjust the training doses to the needs of each patient. To this end, a complete mHealth system has been designed and developed to extract a longitudinal dataset of exercise load measurements from breast cancer patients. To leverage these data, data science and machine learning techniques are being used to extract the different states of recovery of patients throughout a physical exercise intervention, which will allow us to propose a decision support system to prescribe individualized doses of therapeutic exercise

31. Biopsia aspirativa transtorácica por agulha fina para o diagnóstico de lesões pulmonares

Transthoracic Fine-Needle Aspiration contributes for the diagnosis of pulmonary malignant and benign lesions through cytologic analysis of the obtained material.The purpose of this study was to determine indications, accuracy and safety of transthoracic fine-needle aspiration (TNA) in the evaluation of patients with pulmonary lesions.The authors made a retrospective chart review of seven hundred and forty patients submitted to TNA in our hospital, between September 1, 1998 and June 30, 2003. Three hundred and seventy four (50,5%) were outpatients. TNA procedure was performed using an ultrathin needle, guided by fluoroscopy and cytopathologic evaluation of samples was immediate in all patients. TNA was diagnostic in 72.0% patients: a diagnosis of malignancy was achieved in 81.8% of those and benign pathology was identified in 18.2%. Complications occurred in 7.8%: pneumothorax in 5,9% patients (chest tube placement required in 2,1%); haemoptysis occurred in 2.5%.We concluded that TNA has an excellent diagnostic accuracy for malignant pulmonary lesions at a low complication rate, therefore it can be safely done in outpatients

Study on the performance of different craniofacial superimposition approaches (II): Best practices proposal

Craniofacial superimposition, although existing for one century, is still a controversial technique within the scientific community. Objective and unbiased validation studies over a significant number of cases are required to establish a more solid picture on the reliability. However, there is lack of protocols and standards in the application of the technique leading to contradictory information concerning reliability. Instead of following a uniform methodology, every expert tends to apply his own approach to the problem, based on the available technology and deep knowledge on human craniofacial anatomy, soft tissues, and their relationships. The aim of this study was to assess the reliability of different craniofacial superimposition methodologies and the corresponding technical approaches to this type of identification. With all the data generated, some of the most representative experts in craniofacial identification joined in a discussion intended to identify and agree on the most important issues that have to be considered to properly employ the craniofacial superimposition technique. As a consequence, the consortium has produced the current manuscript, which can be considered the first standard in the field; including good and bad practices, sources of error and uncertainties, technological requirements and desirable features, and finally a common scale for the craniofacial matching evaluation. Such a document is intended to be part of a more complete framework for craniofacial superimposition, to be developed during the FP7-founded project MEPROCS, which will favour and standardize its proper application

Dementia in Africa: Current evidence, knowledge gaps, and future directions

\ua9 2021 the Alzheimer\u27s Association. In tandem with the ever-increasing aging population in low and middle-income countries, the burden of dementia is rising on the African continent. Dementia prevalence varies from 2.3% to 20.0% and incidence rates are 13.3 per 1000 person-years with increasing mortality in parts of rapidly transforming Africa. Differences in nutrition, cardiovascular factors, comorbidities, infections, mortality, and detection likely contribute to lower incidence. Alzheimer\u27s disease, vascular dementia, and human immunodeficiency virus/acquired immunodeficiency syndrome–associated neurocognitive disorders are the most common dementia subtypes. Comprehensive longitudinal studies with robust methodology and regional coverage would provide more reliable information. The apolipoprotein E (APOE) ε4 allele is most studied but has shown differential effects within African ancestry compared to Caucasian. More candidate gene and genome-wide association studies are needed to relate to dementia phenotypes. Validated culture-sensitive cognitive tools not influenced by education and language differences are critically needed for implementation across multidisciplinary groupings such as the proposed African Dementia Consortium

Identification of distinct pathological signatures induced by patient-derived -synuclein structures in nonhuman primates

©. This manuscript version is made available under the CC BY-NC 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Submitted, Accepted, Published, version of a Published Work that appeared in final form in Science Advances To access the final edited and published work see http://dx.doi.org/10.5281/zenodo.1240558Dopaminergic neuronal cell death, associated with intracellular -synuclein (-syn)–rich protein aggregates [termed “Lewy bodies” (LBs)], is a well-established characteristic of Parkinson’s disease (PD). Much evidence, accumulated from multiple experimental models, has suggested that -syn plays a role in PD pathogenesis, not only as a trigger of pathology but also as a mediator of disease progression through pathological spreading. Here, we have used a machine learning–based approach to identify unique signatures of neurodegeneration in monkeys induced by distinct -syn pathogenic structures derived from patients with PD. Unexpectedly, our results show that, in nonhuman primates, a small amount of singular -syn aggregates is as toxic as larger amyloid fibrils present in the LBs, thus reinforcing the need for preclinical research in this species. Furthermore, our results provide evidence supporting the true multifactorial nature of PD, as multiple causes can induce a similar outcome regarding dopaminergic neurodegeneration

The MPIfR-MeerKAT Galactic Plane Survey - I. System set-up and early results

Galactic plane radio surveys play a key role in improving our understanding of a wide range of astrophysical phenomena. Performing such a survey using the latest interferometric telescopes produces large data rates necessitating a shift towards fully or quasi-real-time data analysis with data being stored for only the time required to process them. We present here the overview and set-up for the 3000-h Max-Planck-Institut für Radioastronomie (MPIfR)-MeerKAT Galactic Plane Survey (MMGPS). The survey is unique by operating in a commensal mode, addressing key science objectives of the survey including the discovery of new pulsars and transients and studies of Galactic magnetism, the interstellar medium and star formation rates. We explain the strategy coupled with the necessary hardware and software infrastructure needed for data reduction in the imaging, spectral, and time domains. We have so far discovered 78 new pulsars including 17 confirmed binary systems of which two are potential double neutron star systems. We have also developed an imaging pipeline sensitive to the order of a few tens of micro-Jansky () with a spatial resolution of a few arcseconds. Further science operations with an in-house built S-band receiver operating between 1.7 and 3.5 GHz are about to commence. Early spectral line commissioning observations conducted at S-band, targeting transitions of the key molecular gas tracer CH at 3.3 GHz already illustrate the spectroscopic capabilities of this instrument. These results lay a strong foundation for future surveys with telescopes like the Square Kilometre Array (SKA)

II Congrés Internacional sobre Traducció : abril 1994 : actes

Machine learning-based approach unravels distinct pathological signatures induced by patient-derived α-synuclein seeds in monkeys. Dopaminergic neuronal cell death, associated with intracellular α-synuclein (α-syn)-rich protein aggregates [termed "Lewy bodies" (LBs)], is a well-established characteristic of Parkinson's disease (PD). Much evidence, accumulated from multiple experimental models, has suggested that α-syn plays a role in PD pathogenesis, not only as a trigger of pathology but also as a mediator of disease progression through pathological spreading. Here, we have used a machine learning-based approach to identify unique signatures of neurodegeneration in monkeys induced by distinct α-syn pathogenic structures derived from patients with PD. Unexpectedly, our results show that, in nonhuman primates, a small amount of singular α-syn aggregates is as toxic as larger amyloid fibrils present in the LBs, thus reinforcing the need for preclinical research in this species. Furthermore, our results provide evidence supporting the true multifactorial nature of PD, as multiple causes can induce a similar outcome regarding dopaminergic neurodegeneratio