Habitat structure versus food abundance: the importance of sparse vegetation for the common redstart Phoenicurus phoenicurus

As many other birds breeding in agricultural areas, the common redstart declined strongly in many Central European countries over the last 60years. The destruction of traditionally managed orchards, an important breeding habitat in Central Europe, is a relevant cause. An additional factor for the decline of this species could be the intensified management of the ground vegetation in orchards through reducing food availability and lowering prey detectability and accessibility. In this study we examined the importance of surfaces with sparse vegetation for the location of redstart territories and for foraging. To validate the results of these field studies we made habitat-choice experiments in aviaries with captive birds. Territories occupied by redstarts in orchards of northwestern Switzerland contained a significantly higher proportion of surfaces with sparse vegetation than unoccupied control sites. Redstarts made almost five times more hunting flights into experimentally established ruderal vegetation strips than into adjacent unmown meadows. No difference was observed when the meadow was freshly mown. Vegetation height and the proportion of open ground surface correctly predicted the vegetation type for hunting in 77% of the cases. Experiments in aviaries offering two types of sparse vegetation and a dense meadow supported the results of the field experiments. Even a four-fold increase of the food abundance in the meadow did not lead to a noticeable change in preference for the sparse vegetation types. For the conservation of the common redstart, not only traditionally managed orchards with tall trees with cavities should be preserved but also areas with sparse vegetation should be favore

Convolutional neural network for automated segmentation of the liver and its vessels on non-contrast T1 vibe Dixon acquisitions

We evaluated the effectiveness of automated segmentation of the liver and its vessels with a convolutional neural network on non-contrast T1 vibe Dixon acquisitions. A dataset of non-contrast T1 vibe Dixon liver magnetic resonance images was labelled slice-by-slice for the outer liver border, portal, and hepatic veins by an expert. A 3D U-Net convolutional neural network was trained with different combinations of Dixon in-phase, opposed-phase, water, and fat reconstructions. The neural network trained with the single-modal in-phase reconstructions achieved a high performance for liver parenchyma (Dice 0.936 ± 0.02), portal veins (0.634 ± 0.09), and hepatic veins (0.532 ± 0.12) segmentation. No benefit of using multi -modal input was observed (p=1.0 for all experiments), combining in-phase, opposed-phase, fat, and water reconstruction. Accuracy for differentiation between portal and hepatic veins was 99% for portal veins and 97% for hepatic veins in the central region and slightly lower in the peripheral region (91% for portal veins, 80% for hepatic veins). In conclusion, deep learning-based automated segmentation of the liver and its vessels on non-contrast T1 vibe Dixon was highly effective. The single-modal in-phase input achieved the best performance in segmentation and differentiation between portal and hepatic veins

Stochastic Segmentation with Conditional Categorical Diffusion Models

Semantic segmentation has made significant progress in recent years thanks to deep neural networks, but the common objective of generating a single segmentation output that accurately matches the image's content may not be suitable for safety-critical domains such as medical diagnostics and autonomous driving. Instead, multiple possible correct segmentation maps may be required to reflect the true distribution of annotation maps. In this context, stochastic semantic segmentation methods must learn to predict conditional distributions of labels given the image, but this is challenging due to the typically multimodal distributions, high-dimensional output spaces, and limited annotation data. To address these challenges, we propose a conditional categorical diffusion model (CCDM) for semantic segmentation based on Denoising Diffusion Probabilistic Models. Our model is conditioned to the input image, enabling it to generate multiple segmentation label maps that account for the aleatoric uncertainty arising from divergent ground truth annotations. Our experimental results show that CCDM achieves state-of-the-art performance on LIDC, a stochastic semantic segmentation dataset, and outperforms established baselines on the classical segmentation dataset Cityscapes.Comment: Code available at https://github.com/LarsDoorenbos/ccdm-stochastic-segmentatio

Automated liver segmental volume ratio quantification on non-contrast T1-Vibe Dixon liver MRI using deep learning.

PURPOSE To evaluate the effectiveness of automated liver segmental volume quantification and calculation of the liver segmental volume ratio (LSVR) on a non-contrast T1-vibe Dixon liver MRI sequence using a deep learning segmentation pipeline. METHOD A dataset of 200 liver MRI with a non-contrast 3 mm T1-vibe Dixon sequence was manually labeledslice-by-sliceby an expert for Couinaud liver segments, while portal and hepatic veins were labeled separately. A convolutional neural networkwas trainedusing 170 liver MRI for training and 30 for evaluation. Liver segmental volumes without liver vessels were retrieved and LSVR was calculated as the liver segmental volumes I-III divided by the liver segmental volumes IV-VIII. LSVR was compared with the expert manual LSVR calculation and the LSVR calculated on CT scans in 30 patients with CT and MRI within 6 months. RESULTS Theconvolutional neural networkclassified the Couinaud segments I-VIII with an average Dice score of 0.770 ± 0.03, ranging between 0.726 ± 0.13 (segment IVb) and 0.810 ± 0.09 (segment V). The calculated mean LSVR with liver MRI unseen by the model was 0.32 ± 0.14, as compared with manually quantified LSVR of 0.33 ± 0.15, resulting in a mean absolute error (MAE) of 0.02. A comparable LSVR of 0.35 ± 0.14 with a MAE of 0.04 resulted with the LSRV retrieved from the CT scans. The automated LSVR showed significant correlation with the manual MRI LSVR (Spearman r = 0.97, p < 0.001) and CT LSVR (Spearman r = 0.95, p < 0.001). CONCLUSIONS A convolutional neural network allowed for accurate automated liver segmental volume quantification and calculation of LSVR based on a non-contrast T1-vibe Dixon sequence

Patches of Bare Ground as a Staple Commodity for Declining Ground-Foraging Insectivorous Farmland Birds

Conceived to combat widescale biodiversity erosion in farmland, agri-environment schemes have largely failed to deliver their promises despite massive financial support. While several common species have shown to react positively to existing measures, rare species have continued to decline in most European countries. Of particular concern is the status of insectivorous farmland birds that forage on the ground. We modelled the foraging habitat preferences of four declining insectivorous bird species (hoopoe, wryneck, woodlark, common redstart) inhabiting fruit tree plantations, orchards and vineyards. All species preferred foraging in habitat mosaics consisting of patches of grass and bare ground, with an optimal, species-specific bare ground coverage of 30–70% at the foraging patch scale. In the study areas, birds thrived in intensively cultivated farmland where such ground vegetation mosaics existed. Not promoted by conventional agri-environment schemes until now, patches of bare ground should be implemented throughout grassland in order to prevent further decline of insectivorous farmland birds

Validation of the German version of the needs assessment tool: progressive disease-heart failure.

BACKGROUND The Needs Assessment Tool: Progressive Disease-Heart Failure (NAT: PD-HF) is a tool created to assess the needs of people living with heart failure and their informal caregivers to assist delivering care in a more comprehensive way that addresses actual needs that are unmet, and to improve quality of life. In this study, we aimed to (1) Translate the tool into German and culturally adapt it. (2) Assess internal consistency, inter-rater reliability, and test-retest reliability of the German NAT: PD-HF. (3) Evaluate whether and how patients and health care personnel understand the tool and its utility. (4) Assess the tool's face validity, applicability, relevance, and acceptability among health care personnel. METHODS Single-center validation study. The tool was translated from English into German using a forward-backward translation. To assess internal consistency, we used Cronbach´s alpha. To assess inter-rater reliability and test-retest reliability, we used Cohen´s kappa, and to assess validity we used face validity. RESULTS The translated tool showed good internal consistency. Raters were in substantial agreement on a majority of the questions, and agreement was almost perfect for all the questions in the test-retest analysis. Face validity was rated high by health care personnel. CONCLUSION The German NAT: PD-HF is a reliable, valid, and internally consistent tool that is well accepted by both patients and health care personnel. However, it is important to keep in mind that effective use of the tool requires training of health care personnel

An FPGA-based 7-ENOB 600 msample/s adc without any external components

Analog to digital converters (ADCs) are indispensable nowadays. Analog signals are digitized earlier and earlier in the processing chain to reduce the need for complex analog signal processing. For this reason, ADCs are often integrated directly into field-programmable gate arrays (FPGA) or microprocessors. However, such ADCs are designed for a specific set of requirements with limited flexibility. In this paper, a new structure of an FPGA-based ADC is proposed. The ADC is based on the slope ADC, where a time-to-digital converter (TDC) measures the time from the beginning of a reference slope until the slope reaches the voltage-to-be-measured. Only FPGA-internal elements are used to build the ADC. It is fully reconfigurable and does not require any external components. This innovation offers the flexibility to convert almost any digital input/output (I/O) into an ADC. Considering the very high number of digital I/O ports available in today\u27s FPGA systems, this enables the construction of a massive and powerful ADC array directly on a standard FPGA. The proposed ADC has a resolution of 9.3 bit and achieves an effective number of bits (ENOB) of 7 at a sample rate of 600 MSample/s. The differential nonlinearity (DNL) ranges from-0.9 to 0.9 bit, and the integral nonlinearity (INL) is in the range between-1.1 and 0.9 bit. An alternative version of the ADC operates at 1.2 GSample/s and achieves an ENOB of 5.3

Habitat structure versus food abundance : the importance of sparse vegetation for the common redstart Phoenicurus phoenicurus

As many other birds breeding in agricultural areas, the common redstart declined strongly in many Central European countries over the last 60 years. The destruction of traditionally managed orchards, an important breeding habitat in Central Europe, is a relevant cause. An additional factor for the decline of this species could be the intensified management of the ground vegetation in orchards through reducing food availability and lowering prey detectability and accessibility. In this study we examined the importance of surfaces with sparse vegetation for the location of redstart territories and for foraging. To validate the results of these field studies we made habitat-choice experiments in aviaries with captive birds. Territories occupied by redstarts in orchards of northwestern Switzerland contained a significantly higher proportion of surfaces with sparse vegetation than unoccupied control sites. Redstarts made almost five times more hunting flights into experimentally established ruderal vegetation strips than into adjacent unmown meadows. No difference was observed when the meadow was freshly mown. Vegetation height and the proportion of open ground surface correctly predicted the vegetation type for hunting in 77% of the cases. Experiments in aviaries offering two types of sparse vegetation and a dense meadow supported the results of the field experiments. Even a four-fold increase of the food abundance in the meadow did not lead to a noticeable change in preference for the sparse vegetation types. For the conservation of the common redstart, not only traditionally managed orchards with tall trees with cavities should be preserved but also areas with sparse vegetation should be favore