Impact of a wearable-based physical activity and sleep intervention in multimorbidity patients: protocol for a randomized controlled trial.

BACKGROUND: The benefits of physical activity (PA) and adequate sleep are well documented, and their importance strengthens with the increasing prevalence of chronic diseases and multimorbidity (MM). Interventions to promote physical activity and sleep that use commercial activity trackers may be useful non-pharmacological approaches to managing individual health; however, limited evidence exists on their use to improve physical activity in older adult patients with MM. METHODS: This study aims to measure the effects of behavioral change techniques (BCTs) delivered by a wearable device on physical activity and quality of sleep (QS) in older adult patients with MM. We designed an open-label randomized controlled trial with participants recruited through primary care and a specialist outpatient clinic. Participants must be more than 65 years old, have MM, and have access to smartphones. All eligible participants will receive PA promotion content and will be randomly assigned to wear a smartwatch. The primary outcome will be the participants' PA measurement at baseline and at six months using the International Physical Activity Questionnaire - Short Form (IPAQ-SF). Secondary outcomes will include changes in the participants' frailty status, biometric measurements, quality of life, and biopsychosocial assessments. A sample size of 40 participants per arm was calculated to detect group differences, with 50 participants planned to recruit and randomize into each arm. DISCUSSION: This study aims to contribute to a better understanding of PA patterns and the impact of wearable-based PA interventions in patients with MM. In addition, we aim to contribute to more knowledge about the relationship between PA patterns, Patient Reported Outcomes Measures (PROMs), and healthcare resource utilization in patients with MM. To achieve this, the study will leverage a locally developed PROMs registry and assess data from participants' medical records, in order to understand the added impact of wearable data and medical information data on predicting PROMs and unplanned hospital admissions. TRIAL REGISTRATION: NCT05777291.

Fill in the blank for fashion complementary outfit product Retrieval: VISUM summer school competition.

Every year, the VISion Understanding and Machine intelligence (VISUM) summer school runs a competition where participants can learn and share knowledge about Computer Vision and Machine Learning in a vibrant environment. 2021 VISUM's focused on applying those methodologies in fashion. Recently, there has been an increase of interest within the scientific community in applying computer vision methodologies to the fashion domain. That is highly motivated by fashion being one of the world's largest industries presenting a rapid development in e-commerce mainly since the COVID-19 pandemic. Computer Vision for Fashion enables a wide range of innovations, from personalized recommendations to outfit matching. The competition enabled students to apply the knowledge acquired in the summer school to a real-world problem. The ambition was to foster research and development in fashion outfit complementary product retrieval by leveraging vast visual and textual data with domain knowledge. For this, a new fashion outfit dataset (acquired and curated by FARFETCH) for research and benchmark purposes is introduced. Additionally, a competitive baseline with an original negative sampling process for triplet mining was implemented and served as a starting point for participants. The top 3 performing methods are described in this paper since they constitute the reference state-of-the-art for this particular problem. To our knowledge, this is the first challenge in fashion outfit complementary product retrieval. Moreover, this joint project between academia and industry brings several relevant contributions to disseminating science and technology, promoting economic and social development, and helping to connect early-career researchers to real-world industry challenges.

Embedded Sensors for Structural Health Monitoring: Methodologies and Applications Review.

Sensing Technology (ST) plays a key role in Structural Health-Monitoring (SHM) systems. ST focuses on developing sensors, sensory systems, or smart materials that monitor a wide variety of materials' properties aiming to create smart structures and smart materials, using Embedded Sensors (ESs), and enabling continuous and permanent measurements of their structural integrity. The integration of ESs is limited to the processing technology used to embed the sensor due to its high-temperature sensitivity and the possibility of damage during its insertion into the structure. In addition, the technological process selection is dependent on the base material's composition, which comprises either metallic or composite parts. The selection of smart sensors or the technology underlying them is fundamental to the monitoring mode. This paper presents a critical review of the fundaments and applications of sensing technologies for SHM systems employing ESs, focusing on their actual developments and innovation, as well as analysing the challenges that these technologies present, in order to build a path that allows for a connected world through distributed measurement systems.

"Hitchhicking with invertebrates": two reports of epibiosis by peritrich ciliates on ostracods and hydrachnid mites in tanks of epiphytic bromeliads from south Brazil.

Temporary waters are common environments found in physical and biological substrates. Among them, some bromeliads species are known to hold water in their tanks, in a habitat called phytotelmata. Phytotelmata serve as habitats for several organisms, from bacteria and protists to arthropods and anurans. Peritrich ciliates are often found as epibionts on aquatic invertebrates in these environments. Here, we report two cases of epibiosis involving Lagenophrys sp. attached to ostracods (Elpidium spp.) and Rhabdostyla sp. colonizing hydrachnid mites in the tanks of two bromeliad species. In our analysis, we measured the frequency of epibiosis considering the presence of both basibiont and epibiont in the samples. The results shown a significant difference between Elpidium sp. and Lagenophrys sp. compared to hydrachnid mites and Rhabdostyla sp. (87.5% and 19%, respectively), supported by the Kruskal-Walis test (p = 0.0003, Chi-square = 9.687). These reports are important since the knowledge of phytotelmata communities from tropical and subtropical areas is incipient, although it has been increasing over the last years. It also shows that epibiosis doesn't always represent a beneficial relationship. These two epibiosis systems found in bromeliad tanks raise questions about organism's dispersal throughout other phytotelmata and other temporary water habitats.

Optimized Design of Neural Networks for a River Water Level Prediction System.

In this paper, a Multi-Objective Genetic Algorithm (MOGA) framework for the design of Artificial Neural Network (ANN) models is used to design 1-step-ahead prediction models of river water levels. The design procedure is a near-automatic method that, given the data at hand, can partition it into datasets and is able to determine a near-optimal model with the right topology and inputs, offering a good performance on unseen data, i.e., data not used for model design. An example using more than 11 years of water level data (593,178 samples) of the Carrión river collected at Villoldo gauge station shows that the MOGA framework can obtain low-complex models with excellent performance on unseen data, achieving an RMSE of 2.5 × 10-3, which compares favorably with results obtained by alternative design.

Evaluation of LoRa Technology in Flooding Prevention Scenarios.

Global climate change originates frequent floods that may cause severe damage, justifying the need for real-time remote monitoring and alerting systems. Several works deal with LoRa (Long Range) communications over land and in the presence of obstacles, but little is known about LoRa communication reliability over water, as it may happen in real flooding scenarios. One aspect that is known to influence the communication quality is the height at which nodes are placed. However, its impact in water environments is unknown. This is an important aspect that may influence the location of sensor nodes and the network topology. To fill this gap, we conducted several experiments using a real LoRa deployment to evaluate several features related to data communication. We considered two deployment scenarios corresponding to countryside and estuary environments. The nodes were placed at low heights, communicating, respectively, over the ground and over the water. Measurements for packet loss, received signal strength indicator (RSSI), signal-to-noise ratio (SNR) and round-trip time (RTT) were collected during a period of several weeks. Results for both scenarios are presented and compared in this paper. One important conclusion is that the communication distance and reliability are significantly affected by tides when the communication is done over the water and nodes are placed at low heights. Based on the RTT measurements and on the characteristics of the hardware, we also derive a battery lifetime estimation model that may be helpful for the definition of an adequate maintenance plan.

Long-term ecological research in southern Brazil grasslands: Effects of grazing exclusion and deferred grazing on plant and arthropod communities.

Grazing exclusion may lead to biodiversity loss and homogenization of naturally heterogeneous and species-rich grassland ecosystems, and these effects may cascade to higher trophic levels and ecosystem properties. Although grazing exclusion has been studied elsewhere, the consequences of alleviating the disturbance regime in grassland ecosystems remain unclear. In this paper, we present results of the first five years of an experiment in native grasslands of southern Brazil. Using a randomized block experimental design, we examined the effects of three grazing treatments on plant and arthropod communities: (i) deferred grazing (i.e., intermittent grazing), (ii) grazing exclusion and (iii) a control under traditional continuous grazing, which were applied to 70 x 70 m experimental plots, in six regionally distributed blocks. We evaluated plant community responses regarding taxonomic and functional diversity (life-forms) in separate spatial components: alpha (1 x 1 m subplots), beta, and gamma (70 x 70 m plots), as well as the cascading effects on arthropod high-taxa. By estimating effect sizes (treatments vs. control) by bootstrap resampling, both deferred grazing and grazing exclusion mostly increased vegetation height, plant biomass and standing dead biomass. The effect of grazing exclusion on plant taxonomic diversity was negative. Conversely, deferred grazing increased plant taxonomic diversity, but both treatments reduced plant functional diversity. Reduced grazing pressure in both treatments promoted the break of dominance by prostrate species, followed by fast homogenization of vegetation structure towards dominance of ligneous and erect species. These changes in the plant community led to increases in high-taxa richness and abundance of vegetation-dwelling arthropod groups under both treatments, but had no detectable effects on epigeic arthropods. Our results indicate that decision-making regarding the conservation of southern Brazil grasslands should include both intensive and alleviated levels of grazing management, but not complete grazing exclusion, to maximize conservation results when considering plant and arthropod communities.

An Intelligent Weather Station.

Accurate measurements of global solar radiation, atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors (NEN) algorithm and artificial neural network (ANN) models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead.

PH² - a dermoscopic image database for research and benchmarking.

The increasing incidence of melanoma has recently promoted the development of computer-aided diagnosis systems for the classification of dermoscopic images. Unfortunately, the performance of such systems cannot be compared since they are evaluated in different sets of images by their authors and there are no public databases available to perform a fair evaluation of multiple systems. In this paper, a dermoscopic image database, called PH², is presented. The PH² database includes the manual segmentation, the clinical diagnosis, and the identification of several dermoscopic structures, performed by expert dermatologists, in a set of 200 dermoscopic images. The PH² database will be made freely available for research and benchmarking purposes.

Evaluation and cross-cultural adaptation of the Hendrich II Fall Risk Model to Portuguese.

BACKGROUND: Several tools for the assessment of the risk of falling are used commonly by clinical nurses, but none have been validated in Portuguese. AIMS: To adapt and evaluate the Hendrich II Fall Risk Model (HIIFRM) for use with elderly Portuguese inpatients. METHOD: We conducted a prospective study of 586 older inpatients in acute care hospitals, from November 2007 to May 2010. RESULTS: The study involved 270 men and 316 women. The most frequent risk factor on admission and at discharge was a score ≥3 on the 'Get Up and Go' test. The adapted HIIFRM showed a sensitivity of 93.2% and 75.7%, and a specificity of 35% and 46.7%, on admission and at discharge, respectively. A positive predictive value of 17.2% on admission and 17% at discharge and a negative predictive value of 97.3% and 93%, respectively, were estimated. CONCLUSIONS: The HIIFRM was shown to be a useful tool in predicting falls by patients. Nevertheless, the research model suggested that only four risk factors affected the occurrence of falls significantly on admission and two risk factors at discharge. Further research is required in Portuguese hospital settings.

A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature.

Accurate measurements of global solar radiation and atmospheric temperature,as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature.

Potential reflection of distinct ecological units in plant endemism categories.

The level of endemism at a site may indicate species richness of the site. Nevertheless, assessing endemism levels in taxonomic groups such as plants may be difficult because the species richness of plants is high relative to species richness of other taxonomic groups (e.g., vertebrates). A major problem in determining whether plant species are endemic is the lack of standardization of the geographic extent of endemism: species are considered endemic to, for example, countries, continents, or states. We compiled a history of the concept of endemism as it applies to plants. The application of the concept to geographic distribution dates from the 19th century, when European explorers discovered many taxa exclusive to regions outside Europe. Two types of endemism, paleoendemism and neoendemism, were then acknowledged, according to evolutionary age, and these categories are still in use. In the 20th century, most of the research on endemism focused on explaining range restriction on the basis of cytological data, edaphic and geological factors, and phylogeny. This research led to a vast number of concepts, of which only edaphic endemism remains relatively well accepted. More recently, researchers suggest that competition may determine endemism in some cases. We suggest that plants be labeled as endemic only if their distribution occurs in a distinct ecological unit, such as a biome. On the basis of a literature review of the factors that cause range restriction, we categorized endemic taxa as paleoendemic, neoendemic, edaphically endemic, or suppressed endemic. For example, Schlechtendalia luzulifolia, is a rare forb that is a paleoendemic species of the granite and sandstone-based grasslands of the Pampa. Levels of endemism in southern Brazilian grasslands are poorly known. We emphasize the importance of recognizing these grasslands as a single transnational biome so that levels of endemism of species therein can be assessed correctly.

Amodiaquine resistant Plasmodium falciparum malaria in vivo is associated with selection of pfcrt 76T and pfmdr1 86Y.

The choice of partner drug is critical for artemisinine-based combination therapy (ACT) to remain effective and amodiaquine (AQ) is one important candidate to evaluate. We treated 81 children <5 years with uncomplicated Plasmodium falciparum malaria with AQ alone and related the treatment outcome to the possible selection of pfcrt 76T, 152T, 163S, 326S, pfmdr1 86Y and pfmrp 191H, 437S in recurrent infections (recrudescenses and re-infections) and to the blood concentration of desethylamodiaquine (DEAQ). During 21 days follow-up 28 children had a recurrent infection (9 recrudescenses, 13 re-infections and 6 mixed). Neither genotyping of the polymorphisms before treatment nor DEAQ blood concentrations could predict treatment outcome. pfcrt 76T was however significantly selected for in recurrent infections (p=0.020). pfmdr1 86Y was also selected for, but only in recrudescent infections (p=0.048). The study showed high prevalence of AQ resistant parasites in vivo, which appeared to be associated to pfcrt 76T and pfmdr1 86Y.