Trees as determinants of soil carbon stock sizes across geographically different treeline ecotones in Norway

Treelines in boreal regions migrate uphill as a consequence of global warming. The transition from previous open tundra to forest will likely influence alpine soil carbon stores, and there is considerable concern that increased carbon effluxes from the soil to the atmosphere will induce positive feedback on global warming. Thus, in order to predict what future climate change could mean for soil carbon stores in the forest-tundra ecotone, knowledge about the current carbon stores along the forest-tundra gradient is key. My objective was therefore to look at trees as determinants of soil carbon stock sizes across geographically different treelines. Specifically, I investigated whether there is a difference between carbon stocks in forest and tundra soils in treeline ecotones, and modeled the relationship between soil carbon stocks and tree biomass in treelines at different spatial scales. To achieve this, carbon stocks were calculated for surface soil samples, together constituting soil sample lines, collected in forest and tundra at 34 treelines along an extensive north-south gradient in Norway. Remote sensing data acquired by drone were supplemented by field collected tree data, and used to estimate tree biomass around soil sample lines. Statistical tests were performed, and mixed models incorporating soil carbon stocks and tree biomass as variables were developed for buffer zones of different sizes. The results showed that soil carbon stocks in forest and tundra were not significantly different. Moreover, there was not a clear relationship, positive or negative, between soil carbon stocks and tree biomass for any of the buffer zones. Tree biomass therefore seemed to be a weak determinant of soil carbon stock sizes irrespective of spatial scale. However, what study site soil sample lines originated from was important. A possible explanation for these findings is that increased litter inputs in forest soils are counterbalanced by increased microbial decomposition. In addition, potential implications of study design are discussed, as well as potential sources of error. Complex local interactions between various abiotic and biotic factors may explain the importance of study site in determining soil carbon stock sizes. This site-specificity makes it difficult to predict what consequences future changes in the treeline will have for the soil carbon stocks, but in general my results indicate that a transition from tundra to forest will not cause a net increase in soil carbon losses. More information is needed to get a deeper understanding of carbon dynamics at Norwegian forest-tundra ecotones, and to develop management strategies that maximize carbon storage at treelines and mitigate global warming

Reducing Internet Latency : A Survey of Techniques and their Merit

Bob Briscoe, Anna Brunstrom, Andreas Petlund, David Hayes, David Ros, Ing-Jyh Tsang, Stein Gjessing, Gorry Fairhurst, Carsten Griwodz, Michael WelzlPeer reviewedPreprin

Improving latency for interactive, thin-stream applications over reliable transport

A large number of network services use IP and reliable transport protocols. For applications with constant pressure of data, loss is handled satisfactorily, even if the application is latencysensitive. For applications with data streams consisting of intermittently sent small packets, users experience extreme latencies more frequently. Due to the fact that such thin-stream applications are commonly interactive and time-dependent, increased delay may severely reduce the experienced quality of the application. When TCP is used for thin-stream applications, events of highly increased latency are common, caused by the way retransmissions are handled. Other transport protocols that are deployed in the Internet, like SCTP, model their congestion control and reliability on TCP, as do many frameworks that provide reliability on top of unreliable transport. We have tested several application- and transport layer solutions, and based on our findings, we propose sender-side enhancements that reduce the application-layer latency in a manner that is compatible with unmodified receivers. We have implemented the mechanisms as modifications to the Linux kernel, both for TCP and SCTP. The mechanisms are dynamically triggered so that they are only active when the kernel identifies the stream as thin. To evaluate the performance of our modifications, we have conducted a wide range of experiments using replayed thin-stream traces captured from real applications as well as artificially generated thin-stream data patterns. From the experiments, effects on latency, redundancy and fairness were evaluated. The analysis of the performed experiments shows great improvements in latency for thin streams when applying the modifications. Surveys where users evaluate their experience of several applications’ quality using the modified transport mechanisms confirmed the improvements seen in the statistical analysis. The positive effects of our modifications were shown to be possible without notable effects on fairness for competing streams. We therefore conclude that it is advisable to handle thin streams separately, using our modifications, when transmitting over reliable protocols to reduce retransmission latency

Man vs. AI: An in silico study of polyp detection performance

AI-based colon polyp detection systems have received much attention, and several products and prototypes report good results. In silico verification is a crucial step when developing such systems, but very few compare human versus AI performance. This paper, therefore, describes methods and results for an in silico test of an AI model with two different versions for polyp detection in colonoscopy and compares them to the performance of endoscopist doctors who reviewed the same colonoscopy video clips. The two versions have different thresholds for false positive rate reduction. Our models perform polyp detection within the range of the endoscopists’ performance, although faster, showing a potential for use in a clinical setting. For the AI and the endoscopists alike, the results show a trade-off between high sensitivity and high specificity; to achieve perfect detection, one will also get abundance of false positives. This can cause alarm fatigue in a clinical setting.acceptedVersio

Vegetation change on mountaintops in northern Sweden: Stable vascular-plant but reordering of lichen and bryophyte communities

publishedVersio

Kvasir-Capsule, a video capsule endoscopy dataset

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology

Preoperative assessment enables the early diagnosis and successful treatment of lymphedema.

BACKGROUND The incidence of breast cancer (BC)-related lymphedema (LE) ranges from 7% to 47%. Successful management of LE relies on early diagnosis using sensitive measurement techniques. In the current study, the authors demonstrated the effectiveness of a surveillance program that included preoperative limb volume measurement and interval postoperative follow-up to detect and treat subclinical LE. METHODS LE was identified in 43 of 196 women who participated in a prospective BC morbidity trial. Limb volume was measured preoperatively and at 3-month intervals after surgery. If an increase >3% in upper limb (UL) volume developed compared with the preoperative volume, then a diagnosis of LE was made, and a compression garment intervention was prescribed for 4 weeks. Upon reduction of LE, garment wear was continued only during strenuous activity, with symptoms of heaviness, or with visible swelling. Women returned to the 3-month interval surveillance pathway. Statistical analysis was a repeated-measures analysis of variance by time and limb ( P ≤ .001) comparing the LE cohort with an age-matched control group. RESULTS The time to onset of LE averaged 6.9 months postoperatively. The mean (±standard deviation) affected limb volume increase was 83 mL (±119 mL; 6.5% ± 9.9%) at LE onset ( P = .005) compared with baseline. After the intervention, a statistically significant mean 48 mL (±103 mL; 4.1% ± 8.8%) volume decrease was realized ( P < .0001). The mean duration of the intervention was 4.4 weeks (±2.9 weeks). Volume reduction was maintained at an average follow-up of 4.8 months (±4.1 months) after the intervention. CONCLUSIONS A short trial of compression garments effectively treated subclinical LE. Cancer 2008. Published 2008 by the American Cancer Society.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/60215/1/23494_ftp.pd

A multi-centre polyp detection and segmentation dataset for generalisability assessment

Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp’s number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to different population datasets. To this extent, we have curated a dataset from six unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3762 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as PolypGen) curated by a team of computational scientists and expert gastroenterologists. The paper provides insight into data construction and annotation strategies, quality assurance, and technical validation