scholarly journals Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus

BME Frontiers ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Angela Zhang ◽  
Amil Khan ◽  
Saisidharth Majeti ◽  
Judy Pham ◽  
Christopher Nguyen ◽  
...  
Keyword(s):  
Normal Pressure Hydrocephalus ◽  
State Of The Art ◽  
Normal Pressure ◽  
Regions Of Interest ◽  
Computational Method ◽  
Ct Scans ◽  
Convolutional Network ◽  
Automated Method ◽  
Current State ◽  
The Impact

Objective and Impact Statement. We propose an automated method of predicting Normal Pressure Hydrocephalus (NPH) from CT scans. A deep convolutional network segments regions of interest from the scans. These regions are then combined with MRI information to predict NPH. To our knowledge, this is the first method which automatically predicts NPH from CT scans and incorporates diffusion tractography information for prediction. Introduction. Due to their low cost and high versatility, CT scans are often used in NPH diagnosis. No well-defined and effective protocol currently exists for analysis of CT scans for NPH. Evans’ index, an approximation of the ventricle to brain volume using one 2D image slice, has been proposed but is not robust. The proposed approach is an effective way to quantify regions of interest and offers a computational method for predicting NPH. Methods. We propose a novel method to predict NPH by combining regions of interest segmented from CT scans with connectome data to compute features which capture the impact of enlarged ventricles by excluding fiber tracts passing through these regions. The segmentation and network features are used to train a model for NPH prediction. Results. Our method outperforms the current state-of-the-art by 9 precision points and 29 recall points. Our segmentation model outperforms the current state-of-the-art in segmenting the ventricle, gray-white matter, and subarachnoid space in CT scans. Conclusion. Our experimental results demonstrate that fast and accurate volumetric segmentation of CT brain scans can help improve the NPH diagnosis process, and network properties can increase NPH prediction accuracy.

scholarly journals Smoothing additive manufactured parts using ns-pulsed laser radiation

2021 ◽  
Author(s):  
Florian Kuisat ◽  
Fernando Lasagni ◽  
Andrés Fabián Lasagni
Keyword(s):  
Surface Roughness ◽  
Mechanical Performance ◽  
State Of The Art ◽  
Pulsed Laser ◽  
Industrial Applications ◽  
Laser Source ◽  
Pulsed Laser Radiation ◽  
Current State ◽  
The Impact

AbstractIt is well known that the surface topography of a part can affect its mechanical performance, which is typical in additive manufacturing. In this context, we report about the surface modification of additive manufactured components made of Titanium 64 (Ti64) and Scalmalloy®, using a pulsed laser, with the aim of reducing their surface roughness. In our experiments, a nanosecond-pulsed infrared laser source with variable pulse durations between 8 and 200 ns was applied. The impact of varying a large number of parameters on the surface quality of the smoothed areas was investigated. The results demonstrated a reduction of surface roughness Sa by more than 80% for Titanium 64 and by 65% for Scalmalloy® samples. This allows to extend the applicability of additive manufactured components beyond the current state of the art and break new ground for the application in various industrial applications such as in aerospace.

scholarly journals Climate-dependent propagation of precipitation uncertainty into the water cycle

2020 ◽  
Author(s):  
Ali Fallah ◽  
Sungmin O ◽  
Rene Orth
Keyword(s):  
Hydrological Model ◽  
Water Cycle ◽  
State Of The Art ◽  
Rain Gauge ◽  
Multiple Sources ◽  
Current State ◽  
Wide Range ◽  
The Impact ◽  
Conceptual Hydrological Model

Abstract. Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in-situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over gauge-sparse regions. Nevertheless, uncertainties and corresponding differences across products can limit the applicability of these data. This study examines the usefulness of current state-of-the-art precipitation datasets in hydrological modelling. For this purpose, we force a conceptual hydrological model with multiple precipitation datasets in > 200 European catchments. We consider a wide range of precipitation products, which are generated via (1) interpolation of gauge measurements (E-OBS and GPCC V.2018), (2) combination of multiple sources (MSWEP V2) and (3) data assimilation into reanalysis models (ERA-Interim, ERA5, and CFSR). For each catchment, runoff and evapotranspiration simulations are obtained by forcing the model with the various precipitation products. Evaluation is done at the monthly time scale during the period of 1984–2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs, and thus show significant differences between the simulations. By contrast, simulated evapotranspiration is generally much less influenced. The results are further analysed with respect to different hydro-climatic regimes. We find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation datasets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the best agreement, while furthermore ERA5, GPCC V.2018 and MSWEP V2 show good performance. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions such as Central Europe, there are increasing implications on evapotranspiration towards drier regions.

Modelling Nitrogen Deposition on a Local Scale—A Review of the Current State of the Art

2006 ◽  
Vol 3 (5) ◽  
pp. 317 ◽  
Author(s):  
Ole Hertel ◽  
Carsten Ambelas Skjøth ◽  
Per Løfstrøm ◽  
Camilla Geels ◽  
Lise Marie Frohn ◽  
...  
Keyword(s):  
Nitrogen Deposition ◽  
Environmental Assessment ◽  
State Of The Art ◽  
N Deposition ◽  
Model Systems ◽  
Special Focus ◽  
Ammonia Emissions ◽  
High Nitrogen ◽  
Current State ◽  
The Impact

Abstract. Local ammonia emissions from agricultural activities are often associated with high nitrogen deposition in the close vicinity of the sources. High nitrogen (N) inputs may significantly affect the local ecosystems. Over a longer term, high loads may change the composition of the ecosystems, leading to a general decrease in local biodiversity. In Europe there is currently a significant focus on the impact of atmospheric N load on local ecosystems among environmental managers and policy makers. Model tools designed for application in N deposition assessment and aimed for use in the regulation of anthropogenic nitrogen emissions are, therefore, under development in many European countries. The aim of this paper is to present a review of the current understanding and modelling parameterizations of atmospheric N deposition. A special focus is on the development of operational tools for use in environmental assessment and regulation related to agricultural ammonia emissions. For the often large number of environmental impact assessments needed to be carried out by local environmental managers there is, furthermore, a need for simple and fast model systems. These systems must capture the most important aspects of dispersion and deposition of N in the nearby environment of farms with animal production. The paper includes a discussion on the demands on the models applied in environmental assessment and regulation and how these demands are fulfilled in current state-of-the-art models.

scholarly journals The Impact of Selected Cytokines in the Follow-Up of Normal Pressure Hydrocephalus

2015 ◽  
pp. S283-S290 ◽  
Author(s):  
L. SOSVOROVA ◽  
M. MOHAPL ◽  
J. VCELAK ◽  
M. HILL ◽  
J. VITKU ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Normal Pressure Hydrocephalus ◽  
Neurodegenerative Disorders ◽  
Time Course ◽  
Normal Pressure ◽  
Shunt Insertion ◽  
Mediators Of Inflammation ◽  
The Impact ◽  
Surgical Shunt

Cytokines are widely known mediators of inflammation accompanying many neurodegenerative disorders including normal pressure hydrocephalus (NPH). NPH is caused by impaired cerebrospinal fluid (CSF) reabsorption and treated by surgical shunt insertion. The diagnostics is still complicated and the shunt effect is not durable; after several years, dementia may develop. In the clinical practice, biomarkers support the diagnostics as well as the further time course of many neurodegenerative diseases. Until recently, no reliable biomarker for NPH was evaluated. The attempt of this review was to make a survey concerning cytokines as possible NPH markers. Among all reviewed cytokines, the most promising are CSF IL-10 and IL-33, enabling to follow-up the disease progression and monitoring the effectiveness of the shunt insertion.

scholarly journals A Comparative Study of Semantic Segmentation Models for Identification of Grape with Different Varieties

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 997
Author(s):  
Yun Peng ◽  
Aichen Wang ◽  
Jizhan Liu ◽  
Muhammad Faheem
Keyword(s):  
Image Enhancement ◽  
Precision Agriculture ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Histogram Equalization ◽  
Performance Difference ◽  
Convolutional Network ◽  
Enhancement Method ◽  
The Impact ◽  
Segmentation Models

Accurate fruit segmentation in images is the prerequisite and key step for precision agriculture. In this article, aiming at the segmentation of grape cluster with different varieties, 3 state-of-the-art semantic segmentation networks, i.e., Fully Convolutional Network (FCN), U-Net, and DeepLabv3+ applied on six different datasets were studied. We investigated: (1) the segmentation performance difference of the 3 studied networks; (2) The impact of different input representations on segmentation performance; (3) The effect of image enhancement method to improve the poor illumination of images and further improve the segmentation performance; (4) The impact of the distance between grape clusters and camera on segmentation performance. The experiment results show that compared with FCN and U-Net the DeepLabv3+ combined with transfer learning is more suitable for the task with an intersection over union (IoU) of 84.26%. Five different input representations, namely RGB, HSV, L*a*b, HHH, and YCrCb obtained different IoU, ranging from 81.5% to 88.44%. Among them, the L*a*b got the highest IoU. Besides, the adopted Histogram Equalization (HE) image enhancement method could improve the model’s robustness against poor illumination conditions. Through the HE preprocessing, the IoU of the enhanced dataset increased by 3.88%, from 84.26% to 88.14%. The distance between the target and camera also affects the segmentation performance, no matter in which dataset, the closer the distance, the better the segmentation performance was. In a word, the conclusion of this research provides some meaningful suggestions for the study of grape or other fruit segmentation.

scholarly journals Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extraction with Rich Syntactic Knowledge

2021 ◽  
Author(s):  
Shengqiong Wu ◽  
Hao Fei ◽  
Yafeng Ren ◽  
Donghong Ji ◽  
Jingye Li
Keyword(s):  
State Of The Art ◽  
Boundary Detection ◽  
High Order ◽  
Experimental Results ◽  
Convolutional Network ◽  
Syntactic Knowledge ◽  
Current State ◽  
Syntactic Features ◽  
Benchmark Datasets

In this paper, we propose to enhance the pair-wise aspect and opinion terms extraction (PAOTE) task by incorporating rich syntactic knowledge. We first build a syntax fusion encoder for encoding syntactic features, including a label-aware graph convolutional network (LAGCN) for modeling the dependency edges and labels, as well as the POS tags unifiedly, and a local-attention module encoding POS tags for better term boundary detection. During pairing, we then adopt Biaffine and Triaffine scoring for high-order aspect-opinion term pairing, in the meantime re-harnessing the syntax-enriched representations in LAGCN for syntactic-aware scoring. Experimental results on four benchmark datasets demonstrate that our model outperforms current state-of-the-art baselines, meanwhile yielding explainable predictions with syntactic knowledge.

Online Social Exergames for Seniors

2020 ◽  
pp. 1599-1631
Author(s):  
Stathis Th. Konstantinidis ◽  
Ellen Brox ◽  
Per Egil Kummervold ◽  
Josef Hallberg ◽  
Gunn Evertsen ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Physical Training ◽  
State Of The Art ◽  
Social Contact ◽  
High Quality ◽  
Current State ◽  
The Future ◽  
The Impact

The population is getting older, and the resources for care will be even more limited in the future than they are now. There is thus an aim for the society that the seniors can manage themselves as long as possible, while at the same time keeping a high quality of life. Physical activity is important to stay fit, and social contact is important for the quality of life. The aim of this chapter is to provide a state-of-the-art of online social exergames for seniors, providing glimpses of senior users' opinions and games limitations. The importance of the motivational techniques is emphasized, as well as the impact that the exergames have to seniors. It contributes to the book objectives focusing on current state and practice in health games for physical training and rehabilitation and the use of gamification, exploring future opportunities and uses of gamification in eHealth and discussing the respective challenges and limitations.

scholarly journals Enterosorption in the Treatment of Heavy Metal Poisoning

2021 ◽  
Author(s):  
Sergey Mikhalovsky ◽  
Oleksandr Voytko ◽  
Violetta Demchenko ◽  
Pavlo Demchenko
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Chronic Exposure ◽  
State Of The Art ◽  
Cost Effective ◽  
Heavy Metal Poisoning ◽  
Industrial Activities ◽  
Current State ◽  
Metal Poisoning ◽  
The Impact

Enterosorption is a cost-effective and efficient approach to reducing the impact of chronic exposure to heavy metals and radionuclides. As an auxiliary method to medical treatment, it can protect population chronically exposed to the intake of heavy metals or radioactivity due to industrial activities or in the aftermath of technogenic or natural accidents. This paper assesses the current state of the art in the treatment of acute and chronic heavy metal poisoning.

scholarly journals State-of-the-art surgery for pancreatic cancer

2021 ◽  
Author(s):  
Anna Nießen ◽  
Thilo Hackert
Keyword(s):  
Pancreatic Cancer ◽  
Pancreatic Surgery ◽  
Best Practice ◽  
New Technologies ◽  
State Of The Art ◽  
Surgical Techniques ◽  
Complication Management ◽  
Oncological Treatment ◽  
Current State ◽  
The Impact

Abstract Background The d evelopment of surgical techniques and specialization and specifically complication management in pancreatic surgery have improved surgical outcomes as well as oncological results in pancreatic surgery in recent decades. Historical morbidity and especially mortality rates of up to 80% have decreased to below 5% today. This review summarizes the current state of the art in pancreatic cancer surgery. Methods The present literature and clinical experience are summarized to give an overview of the present best practice in pancreatic surgery as one of the most advanced surgical disciplines today. Results Based on the available literature, three important aspects contribute to best patient care in pancreatic surgery, namely, surgical progress, interdisciplinary complication management, and multimodal oncological treatment in case of pancreatic cancer. In addition, minimally invasive and robotic procedures are currently fields of development and specific topics of research. Conclusion In experienced hands, pancreatic surgery—despite being one of the most challenging fields of surgery—is a safe domain today. The impact of multimodal, especially adjuvant, therapy for oncological indications is well established and evidence-based. New technologies are evolving and will be evaluated with high-evidence studies in the near future.

scholarly journals Rare Human Diseases: Model Organisms in Deciphering the Molecular Basis of Primary Ciliary Dyskinesia

Cells ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1614 ◽  
Author(s):  
Martyna Poprzeczko ◽  
Marta Bicka ◽  
Hanan Farahat ◽  
Rafal Bazan ◽  
Anna Osinka ◽  
...  
Keyword(s):  
Molecular Basis ◽  
Primary Ciliary Dyskinesia ◽  
State Of The Art ◽  
Model Organisms ◽  
Phenotypic Changes ◽  
Current State ◽  
Evolutionarily Conserved ◽  
Motile Cilia ◽  
The Impact ◽  
Ciliary Dyskinesia

Primary ciliary dyskinesia (PCD) is a recessive heterogeneous disorder of motile cilia, affecting one per 15,000–30,000 individuals; however, the frequency of this disorder is likely underestimated. Even though more than 40 genes are currently associated with PCD, in the case of approximately 30% of patients, the genetic cause of the manifested PCD symptoms remains unknown. Because motile cilia are highly evolutionarily conserved organelles at both the proteomic and ultrastructural levels, analyses in the unicellular and multicellular model organisms can help not only to identify new proteins essential for cilia motility (and thus identify new putative PCD-causative genes), but also to elucidate the function of the proteins encoded by known PCD-causative genes. Consequently, studies involving model organisms can help us to understand the molecular mechanism(s) behind the phenotypic changes observed in the motile cilia of PCD affected patients. Here, we summarize the current state of the art in the genetics and biology of PCD and emphasize the impact of the studies conducted using model organisms on existing knowledge.

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