How can we enhance elderly health and well-being through various forms of game-based activities?

The health of an aging population is gradually turning into a topic of focus not only domestically but also internationally. The target group of this study is older Americans who are 65 and older. More specifically, elders who lack the motivation to exercise. Currently, related research has shown regular physical exercise is critical for elders to keep fit. However, participating in regular exercise can be challenging for older adults with physical limitations, and it’s often difficult to motivate oneself. There has been an expanded focus on the game design intended to motivate elders to contribute to their overall well-being.Through qualitative research, this project aims to understand which parts of the body require routine exercise and which game-based activities elders prefer. My goal is to design a motivational game that keeps the aging population physically fit, mentally sharp, and socially engaged. In conjunction with this research, I’ve designed a physical large-scale puzzle called “CubeX” as a method to motivate elders to incorporate exercise with a leisurely experience, and this activity might also serve to keep the brain active

A Novel Semisupervised Contrastive Regression Framework for Forest Inventory Mapping with Multisensor Satellite Data

Accurate mapping of forests is critical for forest management and carbon stocks monitoring. Deep learning is becoming more popular in Earth Observation (EO), however, the availability of reference data limits its potential in wide-area forest mapping. To overcome those limitations, here we introduce contrastive regression into EO based forest mapping and develop a novel semisupervised regression framework for wall-to-wall mapping of continuous forest variables. It combines supervised contrastive regression loss and semi-supervised Cross-Pseudo Regression loss. The framework is demonstrated over a boreal forest site using Copernicus Sentinel-1 and Sentinel-2 imagery for mapping forest tree height. Achieved prediction accuracies are strongly better compared to using vanilla UNet or traditional regression models, with relative RMSE of 15.1% on stand level. We expect that developed framework can be used for modeling other forest variables and EO datasets

Deep Learning Model Transfer in Forest Mapping Using Multi-Source Satellite SAR and Optical Images

Deep learning (DL) models are gaining popularity in forest variable prediction using Earth observation (EO) images. However, in practical forest inventories, reference datasets are often represented by plot- or stand-level measurements, while high-quality representative wall-to-wall reference data for end-to-end training of DL models are rarely available. Transfer learning facilitates expansion of the use of deep learning models into areas with sub-optimal training data by allowing pretraining of the model in areas where high-quality teaching data are available. In this study, we perform a “model transfer” (or domain adaptation) of a pretrained DL model into a target area using plot-level measurements and compare performance versus other machine learning models. We use an earlier developed UNet based model (SeUNet) to demonstrate the approach on two distinct taiga sites with varying forest structure and composition. The examined SeUNet model uses multi-source EO data to predict forest height. Here, EO data are represented by a combination of Copernicus Sentinel-1 C-band SAR and Sentinel-2 multispectral images, ALOS-2 PALSAR-2 SAR mosaics and TanDEM-X bistatic interferometric radar data. The training study site is located in Finnish Lapland, while the target site is located in Southern Finland. By leveraging transfer learning, the SeUNet prediction achieved root mean squared error (RMSE) of (Formula presented.) m and R2 of 0.882, considerably more accurate than traditional benchmark methods. We expect such forest-specific DL model transfer can be suitable also for other forest variables and other EO data sources that are sensitive to forest structure.</p

SWI3 subunits of SWI/SNF complexes in Sweet Orange (Citrus sinensis): genome-wide identification and expression analysis of CsSWI3 family genes

SWI3 proteins as the core accessory subunits of SWI/SNF chromatin remodeling complexes (CRCs) could jointly take part in the genome epigenetic regulation upon disrupting the interaction between DNA and histones, ulteriorly regulating the accessibility of DNA-binding proteins or TFs to DNA. Research on chromatin remodeling complexes in plants lags behind yeast and animals, however, the last decade has witnessed an intensive effort to enhance our understanding of identification, characterization and molecular mechanisms of CRCs in Arabidopsis which provided the information for further studies in other plant species. So far, genome-wide identification of SWI3 family in citrus has not been reported. Here, four CsSWI3 genes based on Citrus sinensis genome were identified and clustered into four subfamilies. According to conserved domains and motifs analysis, we found that each CsSWI3 protein contained three conserved domains and the members in the same subfamily showed strong similarity with those in Arabidopsis. All of the CsSWI3 members were localized in the cell nucleus, which was consistent with the role as the subunit of CRCs. Analysis of promoter cis-regulatory elements indicated that CsSWI3 genes may be involved in stress response, phytohormone response and growth and development of citrus. Meanwhile, they were expressed extensively in citrus tissues and disparate development stages in fruit. We found that the expression level of CsSWI3A, CsSWI3B and CsSWI3C are positively correlated with sugar content during fruit development, especially for CsSWI3B. This study provides comprehensive information for the CsSWI3 gene family and sets a basis for its function identification in citrus

Ischemia Elicits a Coordinated Expression of Pro-Survival Proteins in Mouse Myocardium

Cardiomyocytes are post-mitotic, long-lived cells until disruptions to pro-survival factors occur after myocardial ischemia. To gain an understanding of the factors involved with ischemic injury, we examined expression changes in pro-survival and opposing pro-apoptotic signals at early and chronic periods of ischemia using an in vivo murine model. Alterations of pro-survival proteins such as the inhibitor of apoptosis protein on chromosome X (xIAP) and the apoptotic repressor protein (ARC) have not been evaluated in a murine model of cardiac ischemia. Early ischemia (1 day) resulted in a 50% reduction in ARC protein levels relative to sham-operated left ventricles, without significant changes in the expression of xIAP or other pro-survival factors. In contrast, a deficiency of xIAP expression was found in cardiac infarcts starting after 1 week, concomitant with significant evidence of apoptotic cell death and an up-regulation of pro-apoptotic signals including Bax, tumor necrosis factor-a, and caspase-8 activation. Chronic ischemia (after 2 weeks) was associated with elevated levels of other pro-survival factors such as Bcl-xL and the phosphorylated form of Akt, as part of the adaptive remodeling of the myocardium. Altogether, these findings suggest that strategies to increase IAP expression may promote myocyte survival after chronic ischemia

Global tropospheric ozone trends, attributions, and radiative impacts in 1995–2017: an integrated analysis using aircraft (IAGOS) observations, ozonesonde, and multi-decadal chemical model simulations

Quantification and attribution of long-term tropospheric ozone trends are critical for understanding the impact of human activity and climate change on atmospheric chemistry but are also challenged by the limited coverage of long-term ozone observations in the free troposphere where ozone has higher production efficiency and radiative potential compared to that at the surface. In this study, we examine observed tropospheric ozone trends, their attributions, and radiative impacts from 1995–2017 using aircraft observations from the In-service Aircraft for a Global Observing System database (IAGOS), ozonesondes, and a multi-decadal GEOS-Chem chemical model simulation. IAGOS observations above 11 regions in the Northern Hemisphere and 19 of 27 global ozonesonde sites have measured increases in tropospheric ozone (950–250 hPa) by 2.7 ± 1.7 and 1.9 ± 1.7 ppbv per decade on average, respectively, with particularly large increases in the lower troposphere (950–800 hPa) above East Asia, the Persian Gulf, India, northern South America, the Gulf of Guinea, and Malaysia/Indonesia by 2.8 to 10.6 ppbv per decade. The GEOS-Chem simulation driven by reanalysis meteorological fields and the most up-to-date year-specific anthropogenic emission inventory reproduces the overall pattern of observed tropospheric ozone trends, including the large ozone increases over the tropics of 2.1–2.9 ppbv per decade and above East Asia of 0.5–1.8 ppbv per decade and the weak tropospheric ozone trends above North America, Europe, and high latitudes in both hemispheres, but trends are underestimated compared to observations. GEOS-Chem estimates an increasing trend of 0.4 Tg yr−1 of the tropospheric ozone burden in 1995–2017. We suggest that uncertainties in the anthropogenic emission inventory in the early years of the simulation (e.g., 1995–1999) over developing regions may contribute to GEOS-Chem's underestimation of tropospheric ozone trends. GEOS-Chem sensitivity simulations show that changes in global anthropogenic emission patterns, including the equatorward redistribution of surface emissions and the rapid increases in aircraft emissions, are the dominant factors contributing to tropospheric ozone trends by 0.5 Tg yr−1. In particular, we highlight the disproportionately large, but previously underappreciated, contribution of aircraft emissions to tropospheric ozone trends by 0.3 Tg yr−1, mainly due to aircraft emitting NOx in the mid-troposphere and upper troposphere where ozone production efficiency is high. Decreases in lower-stratospheric ozone and the stratosphere–troposphere flux in 1995–2017 contribute to an ozone decrease at mid-latitudes and high latitudes. We estimate the change in tropospheric ozone radiative impacts from 1995–1999 to 2013–2017 is +18.5 mW m−2, with 43.5 mW m−2 contributed by anthropogenic emission changes (20.5 mW m−2 alone by aircraft emissions), highlighting that the equatorward redistribution of emissions to areas with strong convection and the increase in aircraft emissions are effective for increasing tropospheric ozone's greenhouse effect.</p

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

In this study, we examine the potential of several self-supervised deep learning models in predicting forest attributes and detecting forest changes using ESA Sentinel-1 and Sentinel-2 images. The performance of the proposed deep learning models is compared to established conventional machine learning approaches. Studied use-cases include mapping of forest disturbance (windthrown forests, snowload damages) using deep change vector analysis, forest height mapping using UNet+ based models, Momentum contrast and regression modeling. Study areas were represented by several boreal forest sites in Finland. Our results indicate that developed methods allow to achieve superior classification and prediction accuracies compared to traditional methodologies and mimimize the amount of necessary in-situ forestry data

Real-time tracking and in vivo visualization of β-galactosidase activity in colorectal tumor with a ratiometric near-infrared fluorescent probe

Development of “smart” noninvasive bioimaging probes for trapping specific enzyme activities is highly desirable for cancer therapy in vivo. Given that β-galactosidase (β-gal) is an important biomarker for cell senescence and primary ovarian cancers, we design an enzyme-activatable ratiometric near-infrared (NIR) probe (DCM-βgal) for the real-time fluorescent quantification and trapping of β-gal activity in vivo and in situ. DCM-βgal manifests significantly ratiometric and turn-on NIR fluorescent signals simultaneously in response to β-gal concentration, which makes it favorable for monitoring dynamic β-gal activity in vivo with self-calibration in fluorescent mode. We exemplify DCM-βgal for the ratiometric tracking of endogenously overexpressed β-gal distribution in living 293T cells via the <i>lacZ</i> gene transfection method and OVCAR-3 cells, and further realize real-time in vivo bioimaging of β-gal activity in colorectal tumor-bearing nude mice. Advantages of our system include light-up ratiometric NIR fluorescence with large Stokes shift, high photostability, and pH independency under the physiological range, allowing for the in vivo real-time evaluation of β-gal activity at the tumor site with high-resolution three-dimensional bioimaging for the first time. Our work provides a potential tool for in vivo real-time tracking enzyme activity in preclinical applications

Production of a video tutorial on a multipurpose medical camera for basic examinations

Over 72% of population in Finland lives in urban area which covers 5% of the land surface, while nearly 30% of population is distributing in the rest of 95% areas all over Finland. An evaluation on the social and health services in Finland shows that, the inadequate equal access to medical care is hindered by the traveling and waiting time. It had also been approved that distance to access health care has negative impact on health outcome. How to improve the equal access to all populations becomes a big challenge for each municipal health services providers. Telemedicine, sometimes described as telehealth, eHealth, designed to overcome the obstacles of time and space to deliver medical care, had been highlighted and adopted deeper into practical use by health services providers. Registered nurses being the most front-line healthcare providers with physical contacts in the community, play a critical role in telehealth services. In the above mentioned situation, portability and mobility are especially important in the set up of telemedicine when the aim is to provide patient-based medical services. Based on various consideration, a Multipurpose Medical Camera, a portable hand-piece which performs different type of basic examination digitally, was selected as an exploratory product by the RoboSote project managed by Centria University of Applied Sciences HealthLab. This portable medical camera would allow the operators to capture images or videos during medical examination and transfer to medical providers who work remotely on the background for diagnosis and draw up treatment plans. It could be a tool to improve healthcare access for patients living in remote areas or with decreased mobility. The purpose of this thesis is to produce a video tutorial on the use of this Multipurpose Medical Camera. This video is aimed to be as a training material for nursing students studying in Centria University of Applied Sciences, as well as registered nurses who will be working related to the use of this camera. This video was presented in English, with subtitles in English, Finnish and Chinese.https://www.youtube.com/watch?v=5st4L9H_pu