Multiscale Geometric Methods for Isolating Exercise Induced Morphological Adaptations in the Proximal Femur

The importance of skeletal bone in the functioning of the human body is well-established and acknowledged. Less pervasive among the populace, is the understanding of bone as an adaptive tissue which modulates itself to achieve the most construction sufficient for the role it is habituated to. These mechanisms are more pronounced in the long load bearing bones such as the femur. The proximal femur especially, functions under significant loads and does so with high degree of articulation, making it critical to mobility. Thus, exercising to buttress health and reinforce tissue quality is just as applicable to bone as it is to muscles. However, the efficiency of the adaptive (modelling/remodelling) processes is subdued after maturity, which makes the understanding of its potential even more important. Classically, studies have translated the evaluation of strength in terms of its material and morphology. While the morphology of the femur is constrained within a particular phenotype, minor variations can have a significant bearing on its capability to withstand loads. Morphology has been studied at different scales and dimensions wherein parameters quantified as lengths, areas, volumes and curvatures in two and three dimensions contribute towards characterising strength. The challenge has been to isolate the regions that show response to habitual loads. This thesis seeks to build on the principles of computational anatomy and develop procedures to study the distribution of mechanically relevant parameters. Methods are presented that increase the spatial resolution of traditional cross-sectional studies and develop a conformal mapping procedure for proximal femur shape matching. In addition, prevalent methods in cross-sectional analyses and finite element simulations are employed to analyse the morphology of the unique dataset. The results present the spatial heterogeneity and a multi-scale understanding of the adaptive response in the proximal femur morphology to habitual exercise loading

Role of Drones in Characterizing Soil Water Content in Open Field Cultivation

Soil water content is a central topic in open field cultivation. In Finland’s boreal region with four thermal seasons, it has many roles which alter throughout the year. Climate change is changing the weather patterns, affecting all water-related processes and challenging the current farming practices. Better understanding of soils and their characteristics regarding response to water processes is called for, and data collection has a key role in this. Precision agriculture has been driving data intensification in farming. Unmanned aerial vehicles, or drones, have many applications and overall wide interest as an emerging technology in agriculture. Yet they lack an established role in day-to-day farming practices. Regarding data collection in open field cultivation, drones can be compared – or combined – with satellites, rovers, stationary devices, as well as plain old on-site observations by the farmer. In this study we give an overview of recent published literature, looking at data collection from the perspective of soil water information. We assess the opportunities and challenges of using drones in characterizing soil water content, mainly using soil and plant properties as proxies for it. Drones are useful in on-demand, nonintrusive, high-resolution spatial mapping of field properties. Soil moisture monitoring however requires frequent measurements, limiting the applicability of current drones.acceptedVersionPeer reviewe

INFLUENCE OF EXERCISE HISTORY ON FALL-INDUCED HIP FRACTURE RISK

Hip fracture is a major public health problem. Thin superolateral cortex of the femoral neck experiences unusually high stress in a sideway fall, contributing to hip fracture risk. The aim of this study is to examine how exercise based loading history, known to affect the femoral neck cortical structure, influences fall-induced fracture risk. For this purpose, finite element models were created from the proximal femur MRI of 91 young athletic and 20 control females. Fall-induced superolateral cortical safety factors (SF) were estimated in the distal volume of femoral neck. Significantly higher (p \u3c 0.05) SFs were observed from femoral necks with high impact (H-I), odd impact (O-I), and repetitive impact (R-I) exercise history, indicating lower fracture risk. The results indicate that it is advisable to include some impact exercise in a fracture preventive exercise progra

Assessment of Crop Yield Prediction Capabilities of CNN Using Multisource Data

The growing abundance of digitally available spatial, geological, and climatological data opens up new opportunities for agricultural data-based input–output modeling. In our study, we took a convolutional neural network model previously developed on Unmanned Aerial Vehicle (UAV) image data only and set out to see whether additional inputs from multiple sources would improve the performance of the model. Using the model developed in a preceding study, we fed field-specific data from the following sources: near-infrared data from UAV overflights, Sentinel-2 multispectral data, weather data from locally installed Vantage Pro weather stations, topographical maps from National Land Survey of Finland, soil samplings, and soil conductivity data gathered with a Veris MSP3 soil conductivity probe. Either directly added or encoded as additional layers to the input data, we concluded that additional data helps the spatial point-in-time model learn better features, producing better fit models in the task of yield prediction. With data of four fields, the most significant performance improvements came from using all input data sources. We point out, however, that combining data of various spatial or temporal resolution (i.e., weather data, soil data, and weekly acquired images, for example) might cause data leakage between the training and testing data sets when training the CNNs and, therefore, the improvement rate of adding additional data layers should be interpreted with caution.acceptedVersionPeer reviewe

Assessment of Cloud Cover in Sentinel-2 Data Using Random Forest Classifier

In this paper, a novel cloud coverage assessment method for the Sentinel-2 data is presented. The method is based on the Random Forest classifier and the target values used in the training process are obtained by comparing the NDVI indexes calculated from the satellite and the UAV data. The developed method is shown to outperform the Sentinel Cloud Probability Mask (CLDPRB) and Scene Classification (SCL) data layers in detecting cloudy areas.acceptedVersionPeer reviewe

Multiscale Geometric Methods for Isolating Exercise Induced Morphological Adaptations in the Proximal Femur

The importance of skeletal bone in the functioning of the human body is well-established and acknowledged. Less pervasive among the populace, is the understanding of bone as an adaptive tissue which modulates itself to achieve the most construction sufficient for the role it is habituated to. These mechanisms are more pronounced in the long load bearing bones such as the femur. The proximal femur especially, functions under significant loads and does so with high degree of articulation, making it critical to mobility. Thus, exercising to buttress health and reinforce tissue quality is just as applicable to bone as it is to muscles. However, the efficiency of the adaptive (modelling/remodelling) processes is subdued after maturity, which makes the understanding of its potential even more important. Classically, studies have translated the evaluation of strength in terms of its material and morphology. While the morphology of the femur is constrained within a particular phenotype, minor variations can have a significant bearing on its capability to withstand loads. Morphology has been studied at different scales and dimensions wherein parameters quantified as lengths, areas, volumes and curvatures in two and three dimensions contribute towards characterising strength. The challenge has been to isolate the regions that show response to habitual loads. This thesis seeks to build on the principles of computational anatomy and develop procedures to study the distribution of mechanically relevant parameters. Methods are presented that increase the spatial resolution of traditional cross-sectional studies and develop a conformal mapping procedure for proximal femur shape matching. In addition, prevalent methods in cross-sectional analyses and finite element simulations are employed to analyse the morphology of the unique dataset. The results present the spatial heterogeneity and a multi-scale understanding of the adaptive response in the proximal femur morphology to habitual exercise loading

Crop Yield Prediction Using Multitemporal UAV Data and Spatio-Temporal Deep Learning Models

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Biomechanical performance of cranial implants with different thicknesses and material properties: A finite element study

This study investigated the effect of implant thickness and material on deformation and stress distribution within different components of cranial implant assemblies. Using the finite element method, two cranial implants, differing in size and shape, and thicknesses (1, 2, 3 and 4 mm, respectively), were simulated under three loading scenarios. The implant assembly model included the detailed geometries of the mini-plates and micro-screws and was simulated using a sub-modeling approach. Statistical assessments based on the Design of Experiment methodology and on multiple regression analysis revealed that peak stresses in the components are influenced primarily by implant thickness, while the effect of implant material is secondary. On the contrary, the implant deflection is influenced predominantly by implant material followed by implant thickness. The highest values of deformation under a 50 N load were observed in the thinnest (1 mm) Polymethyl Methacrylate implant (Small defect: 0.296 mm; Large defect: 0.390 mm). The thinnest Polymethyl Methacrylate and Polyether Ether Ketone implants also generated stresses in the implants that can potentially breach the materials' yield limit. In terms of stress distribution, the change of implant thickness had a more significant impact on the implant performance than the change of Young's modulus of the implant material. The results indicated that the stresses are concentrated in the locations of fixation; therefore, the detailed models of mini-plates and micro-screws implemented in the finite element simulation provided a better insight into the mechanical performance of the implant-skull system

Conceptual design of an autonomous rover with ground penetrating radar : Application in characterizing soils using deep learning

In the pursuit to make agricultural production efficient, the earliest farmers used data in the form of notes of observations. In the current age of data,it has become easier to collect data over a wide spectrum of parameters. There are numerous sensing technologies for measuring processes and parameters over the field surface, typically mounted on satellites, aerial (drone), ground vehicle and static platforms. In the latest understanding, soil is gaining increasing attention and recognition for its significance in not only increasing productivity but also stabilizing the environment. However, characterizing soil in a field is not trivial, especially when required toaccess the deeper layers and quantifying the essential contents –water, nutrients and organic matter. This paper presents a short review of applications of ground penetrating radars (GPR) in measuring soil content and structure. The focusis ondeeplearning constructs that have been used for interpreting and establishing correlations. The review serves to inform design considerations for a planned autonomous rover that will be used for surveying field soils in the Satakunta region of FinlandacceptedVersionPeer reviewe

Functional Outcome of Human Adipose Stem Cell Injections in Rat Anal Sphincter Acute Injury Model

Anal incontinence is a devastating condition that significantly reduces the quality of life. Our aim was to evaluate the effect of human adipose stem cell (hASC) injections in a rat model for anal sphincter injury, which is the main cause of anal incontinence in humans. Furthermore, we tested if the efficacy of hASCs could be improved by combining them with polyacrylamide hydrogel carrier, Bulkamid. Human ASCs derived from a female donor were culture expanded in DMEM/F12 supplemented with human platelet lysate. Female virgin Sprague-Dawley rats were randomized into four groups (n = 14–15/group): hASCs in saline or Bulkamid (3 × 105/60 μl) and saline or Bulkamid without cells. Anorectal manometry (ARM) was performed before anal sphincter injury, at two (n = 58) and at four weeks after (n = 33). Additionally, the anal sphincter tissue was examined by micro-computed tomography (μCT) and the histological parameters were compared between the groups. The median resting and peak pressure during spontaneous contraction measured by ARM were significantly higher in hASC treatment groups compared with the control groups without hASCs. There was no statistical difference in functional results between the hASC-carrier groups (saline vs. Bulkamid). No difference was detected in the sphincter muscle continuation between the groups in the histology and μCT analysis. More inflammation was discovered in the group receiving saline with hASC. The hASC injection therapy with both saline and Bulkamid is a promising nonsurgical treatment for acute anal sphincter injury. Traditional histology combined with the 3D μCT image data lends greater confidence in assessing muscle healing and continuity. Stem Cells Translational Medicine 2018:7:295–304.publishedVersionPeer reviewe