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4. Next-generation phenotyping integrated in a national framework for patients with ultrarare disorders improves genetic diagnostics and yields new molecular findings

Author

Schmidt, Axel, Danyel, Magdalena, Grundmann, Kathrin, Brunet, Theresa, Klinkhammer, Hannah, Hsieh, Tzung-Chien, Engels, Hartmut, Peters, Sophia, Knaus, Alexej, Moosa, Shahida, Averdunk, Luisa, Boschann, Felix, Sczakiel, Henrike Lisa, Schwartzmann, Sarina, Mensah, Martin Atta, Pantel, Jean Tori, Holtgrewe, Manuel, Bösch, Annemarie, Weiß, Claudia, Weinhold, Natalie, Suter, Aude-Annick, Stoltenburg, Corinna, Neugebauer, Julia, Kallinich, Tillmann, Kaindl, Angela M., Holzhauer, Susanne, Bührer, Christoph, Bufler, Philip, Kornak, Uwe, Ott, Claus-Eric, Schülke, Markus, Nguyen, Hoa Huu Phuc, Hoffjan, Sabine, Grasemann, Corinna, Rothoeft, Tobias, Brinkmann, Folke, Matar, Nora, Sivalingam, Sugirthan, Perne, Claudia, Mangold, Elisabeth, Kreiss, Martina, Cremer, Kirsten, Betz, Regina C., Mücke, Martin, Grigull, Lorenz, Klockgether, Thomas, Spier, Isabel, Heimbach, André, Bender, Tim, Brand, Fabian, Stieber, Christiane, Morawiec, Alexandra Marzena, Karakostas, Pantelis, Schäfer, Valentin S., Bernsen, Sarah, Weydt, Patrick, Castro-Gomez, Sergio, Aziz, Ahmad, Grobe-Einsler, Marcus, Kimmich, Okka, Kobeleva, Xenia, Önder, Demet, Lesmann, Hellen, Kumar, Sheetal, Tacik, Pawel, Basin, Meghna Ahuja, Incardona, Pietro, Lee-Kirsch, Min Ae, Berner, Reinhard, Schuetz, Catharina, Körholz, Julia, Kretschmer, Tanita, Di Donato, Nataliya, Schröck, Evelin, Heinen, André, Reuner, Ulrike, Hanßke, Amalia-Mihaela, Kaiser, Frank J., Manka, Eva, Munteanu, Martin, Kuechler, Alma, Cordula, Kiewert, Hirtz, Raphael, Schlapakow, Elena, Schlein, Christian, Lisfeld, Jasmin, Kubisch, Christian, Herget, Theresia, Hempel, Maja, Weiler-Normann, Christina, Ullrich, Kurt, Schramm, Christoph, Rudolph, Cornelia, Rillig, Franziska, Groffmann, Maximilian, Muntau, Ania, Tibelius, Alexandra, Schwaibold, Eva M. C., Schaaf, Christian P., Zawada, Michal, Kaufmann, Lilian, Hinderhofer, Katrin, Okun, Pamela M., Kotzaeridou, Urania, Hoffmann, Georg F., Choukair, Daniela, Bettendorf, Markus, Spielmann, Malte, Ripke, Annekatrin, Pauly, Martje, Münchau, Alexander, Lohmann, Katja, Hüning, Irina, Hanker, Britta, Bäumer, Tobias, Herzog, Rebecca, Hellenbroich, Yorck, Westphal, Dominik S., Strom, Tim, Kovacs, Reka, Riedhammer, Korbinian M., Mayerhanser, Katharina, Graf, Elisabeth, Brugger, Melanie, Hoefele, Julia, Oexle, Konrad, Mirza-Schreiber, Nazanin, Berutti, Riccardo, Schatz, Ulrich, Krenn, Martin, Makowski, Christine, Weigand, Heike, Schröder, Sebastian, Rohlfs, Meino, Vill, Katharina, Hauck, Fabian, Borggraefe, Ingo, Müller-Felber, Wolfgang, Kurth, Ingo, Elbracht, Miriam, Knopp, Cordula, Begemann, Matthias, Kraft, Florian, Lemke, Johannes R., Hentschel, Julia, Platzer, Konrad, Strehlow, Vincent, Abou Jamra, Rami, Kehrer, Martin, Demidov, German, Beck-Wödl, Stefanie, Graessner, Holm, Sturm, Marc, Zeltner, Lena, Schöls, Ludger J., Magg, Janine, Bevot, Andrea, Kehrer, Christiane, Kaiser, Nadja, Turro, Ernest, Horn, Denise, Grüters-Kieslich, Annette, Klein, Christoph, Mundlos, Stefan, Nöthen, Markus, Riess, Olaf, Meitinger, Thomas, Krude, Heiko, Krawitz, Peter M., Haack, Tobias, Ehmke, Nadja, and Wagner, Matias

Published
2024
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16. ECN based Congestion Control for a Software Defined Network

Author

Moorthy, Mohana Prasad Sathya, Sure, Manoj Kumar, and Sivalingam, Krishna M.

Subjects
Computer Science - Networking and Internet Architecture
Abstract

This paper deals with congestion control in a software defined network (SDN) setting. Presently, explicit router schemes, such as Explicit Congestion Notification (ECN), work in conjunction with the TCP protocol to handle congestion in a distributed manner. With the emergence of SDN and centralized control, it is possible to leverage the global view of the network state to make better congestion control decisions. In this work, we explore the advantages of bringing in global information into distributed congestion control. We propose a framework where the controller with its global view of the network actively participates in the congestion control decisions of the end TCP hosts, by setting the ECN bits of IP packets appropriately. Our framework can be deployed very easily without any change to the end node TCPs or the SDN switches. We also show 30x improvement over the TCP Cubic variant and 1.7x improvement over TCP/RED in terms of flow completion times for one implementation of this framework, using the Mininet emulator.

Published
2023

17. Decomposition Based Interference Management Framework for Local 6G Networks

Author

Gunarathne, Samitha, Sivalingam, Thushan, Mahmood, Nurul Huda, Rajatheva, Nandana, and Latva-Aho, Matti

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Managing inter-cell interference is among the major challenges in a wireless network, more so when strict quality of service needs to be guaranteed such as in ultra-reliable low latency communications (URLLC) applications. This study introduces a novel intelligent interference management framework for a local 6G network that allocates resources based on interference prediction. The proposed algorithm involves an advanced signal pre-processing technique known as empirical mode decomposition followed by prediction of each decomposed component using the sequence-to-one transformer algorithm. The predicted interference power is then used to estimate future signal-to-interference plus noise ratio, and subsequently allocate resources to guarantee the high reliability required by URLLC applications. Finally, an interference cancellation scheme is explored based on the predicted interference signal with the transformer model. The proposed sequence-to-one transformer model exhibits its robustness for interference prediction. The proposed scheme is numerically evaluated against two baseline algorithms, and is found that the root mean squared error is reduced by up to 55% over a baseline scheme., Comment: Accepted for IEEE GLOBECOM 2023 Workshops, Kuala Lumpur, Malaysia

Published
2023

32. Can clinicians identify community-acquired pneumonia on ultralow-dose CT? A diagnostic accuracy study

Author

Anne Heltborg, Christian Backer Mogensen, Helene Skjøt-Arkil, Matthias Giebner, Ayham Al-Masri, Usha Bc Khatry, Sangam Khatry, Ina Isabell Kathleen Heinemeier, Jonas Jannick Andreasen, Sanne Sarmila Sivalingam Hariesh, Tenna Termansen, Anna Natalie Kolnes, Morten Hjarnø Lorentzen, Christian Borbjerg Laursen, Stefan Posth, Michael Brun Andersen, Bo Mussmann, Camilla Stræde Spile, and Ole Graumann

Subjects
Community-acquired pneumonia, Community-acquired infections, Ultralow-dose CT, Diagnostic imaging, Antimicrobial stewardship, Emergency medicine, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

Abstract Background Without increasing radiation exposure, ultralow-dose computed tomography (CT) of the chest provides improved diagnostic accuracy of radiological pneumonia diagnosis compared to a chest radiograph. Yet, radiologist resources to rapidly report the chest CTs are limited. This study aimed to assess the diagnostic accuracy of emergency clinicians’ assessments of chest ultralow-dose CTs for community-acquired pneumonia using a radiologist’s assessments as reference standard. Methods This was a cross-sectional diagnostic accuracy study. Ten emergency department clinicians (five junior clinicians, five consultants) assessed chest ultralow-dose CTs from acutely hospitalised patients suspected of having community-acquired pneumonia. Before assessments, the clinicians attended a focused training course on assessing ultralow-dose CTs for pneumonia. The reference standard was the assessment by an experienced emergency department radiologist. Primary outcome was the presence or absence of pulmonary opacities consistent with community-acquired pneumonia. Sensitivity, specificity, and predictive values were calculated using generalised estimating equations. Results All clinicians assessed 128 ultralow-dose CTs. The prevalence of findings consistent with community-acquired pneumonia was 56%. Seventy-eight percent of the clinicians’ CT assessments matched the reference assessment. Diagnostic accuracy estimates were: sensitivity = 83% (95%CI: 77–88), specificity = 70% (95%CI: 59–81), positive predictive value = 80% (95%CI: 74–84), negative predictive value = 78% (95%CI: 73–82). Conclusion This study found that clinicians could assess chest ultralow-dose CTs for community-acquired pneumonia with high diagnostic accuracy. A higher level of clinical experience was not associated with better diagnostic accuracy.

Published
2024
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33. Bacteriological quality and physicochemical analysis of the Kalte River at Wolaita Sodo Town, southern Ethiopia

Author

Krishna Moorthy Sivalingam, Israel Solomon Choramo, and Eyob Chukalo Chutulo

Subjects
Bacteriological analysis, Kalte River, Physicochemical analysis, Water quality, Coliform, Wolaita Sodo Town, Medicine, Biology (General), QH301-705.5, Science (General), Q1-390
Abstract

Abstract Objective The current research aimed to investigate the physicochemical and bacteriological quality status of the Kalte River in Wolaita Sodo Town, southern Ethiopia. Methods A total of 42 water samples were collected using sterile glass bottles from three different river sites: Damota (upstream), Kera (midstream), and Gututo (downstream). All the water samples were examined for the presence of heterotrophic bacteria, total coliform and fecal coliform using direct plate count method and membrane filtration method. Standard methods suggested by American water works association were used to analysis the physicochemical parameters of the water samples. Results The results revealed that the total heterotrophic bacteria, total coliform, and fecal coliform count ranged from 8.9 to 12.6 × 104 cfu/ml, 7.5–11.3 × 102 cfu/ml and 5.7–9.7 × 104 cfu/ml, respectively. The bacterial count results indicated that the river water crossed the WHO-recommended limit of potable water. Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Salmonella, and Shigella species were the common bacterial pathogens isolated from river water samples. The results of the physicochemical analysis revealed that some of the parameters Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and turbidity exceeded the maximum permissible limits of WHO and other parameters were below the WHO permissible limits. Conclusion Therefore, the presence of bacterial pathogens, fecal coliform indicators, and some physicochemical parameters of the Kalte River exceeding the recommended limits may expose users of the river water to the risk of infection.

Published
2024
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34. Comparison of weight loss outcomes 1 year after sleeve gastrectomy and Roux-en-Y gastric bypass in patients aged above 50 years

Author

Palanivelu Praveenraj, Rachel M Gomes, Saravana Kumar, Sivalingam Perumal, Palanisamy Senthilnathan, Ramakrishnan Parthasarathi, Subbiah Rajapandian, and Chinnusamy Palanivelu

Subjects
Bariatric surgery, elderly, Roux-en-Y gastric bypass, sleeve gastrectomy, weight loss, Surgery, RD1-811, Diseases of the digestive system. Gastroenterology, RC799-869
Abstract

Introduction: Safe, effective weight loss with resolution of comorbidities has been convincingly demonstrated with bariatric surgery in the aged obese. They, however, lose less weight than younger individuals. It is not known if degree of weight loss is influenced by the choice of bariatric procedure. The aim of this study was to compare the degree of weight loss between laparoscopic sleeve gastrectomy (LSG) and laparoscopic Roux-en-Y gastric bypass (LRYGB) in patients above the age of 50 years at 1 year after surgery. Materials and Methods: A retrospective analysis was performed of all patients more than 50 years of age who underwent LSG or LRYGB between February 2012 and July 2013 with at least 1 year of follow-up. Data evaluated at 1 year included age, sex, weight, body mass index (BMI), mean operative time, percentage of weight loss and excess weight loss, resolution/remission of diabetes, morbidity and mortality. Results: Of a total of 86 patients, 54 underwent LSG and 32 underwent LRYGB. The mean percentage of excess weight loss at the end of 1 year was 60.19 ± 17.45 % after LSG and 82.76 ± 34.26 % after LRYGB (P = 0.021). One patient developed a sleeve leak after LSG, and 2 developed iron deficiency anaemia after LRYGB. The remission/improvement in diabetes mellitus and biochemistry was similar. Conclusion: LRYGB may offer better results than LSG in terms of weight loss in patients over 50 years of age.

Published
2016
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35. DECICE: Device-Edge-Cloud Intelligent Collaboration Framework

Author

Kunkel, Julian, Boehme, Christian, Decker, Jonathan, Magugliani, Fabrizio, Pleiter, Dirk, Koller, Bastian, Sivalingam, Karthee, Pllana, Sabri, Nikolov, Alexander, Soyturk, Mujdat, Racca, Christian, Bartolini, Andrea, Tate, Adrian, and Yaman, Berkay

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence
Abstract

DECICE is a Horizon Europe project that is developing an AI-enabled open and portable management framework for automatic and adaptive optimization and deployment of applications in computing continuum encompassing from IoT sensors on the Edge to large-scale Cloud / HPC computing infrastructures. In this paper, we describe the DECICE framework and architecture. Furthermore, we highlight use-cases for framework evaluation: intelligent traffic intersection, magnetic resonance imaging, and emergency response.

Published
2023

36. Automatic and Flexible Transmission of Semantic Map Images using Polar Codes for End-to-End Semantic-based Communication Systems

Author

Rezaei, Hossein, Sivalingam, Thushan, and Rajatheva, Nandana

Subjects
Computer Science - Information Theory, Computer Science - Databases
Abstract

Semantic communication represents a promising roadmap toward achieving end-to-end communication with reduced communication overhead and an enhanced user experience. The integration of semantic concepts with wireless communications presents novel challenges. This paper proposes a flexible simulation software that automatically transmits semantic segmentation map images over a communication channel. An additive white Gaussian noise (AWGN) channel using binary phase-shift keying (BPSK) modulation is considered as the channel setup. The well-known polar codes are chosen as the channel coding scheme. The popular COCO-Stuff dataset is used as an example to generate semantic map images corresponding to different signal-to-noise ratios (SNRs). To evaluate the proposed software, we have generated four small datasets, each containing a thousand semantic map samples, accompanied by comprehensive information corresponding to each image, including the polar code specifications, detailed image attributes, bit error rate (BER), and frame error rate (FER). The capacity to generate an unlimited number of semantic maps utilizing desired channel coding parameters and preferred SNR, in conjunction with the flexibility of using alternative datasets, renders our simulation software highly adaptable and transferable to a broad range of use cases.

Published
2023

37. Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge

Author

Luo, Gongning, Wang, Kuanquan, Liu, Jun, Li, Shuo, Liang, Xinjie, Li, Xiangyu, Gan, Shaowei, Wang, Wei, Dong, Suyu, Wang, Wenyi, Yu, Pengxin, Liu, Enyou, Wei, Hongrong, Wang, Na, Guo, Jia, Li, Huiqi, Zhang, Zhao, Zhao, Ziwei, Gao, Na, An, Nan, Pakzad, Ashkan, Rangelov, Bojidar, Dou, Jiaqi, Tian, Song, Liu, Zeyu, Wang, Yi, Sivalingam, Ampatishan, Punithakumar, Kumaradevan, Qiu, Zhaowen, and Gao, Xin

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Efficient automatic segmentation of multi-level (i.e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications. However, most existing methods concentrate only on main PA or branch PA segmentation separately and ignore segmentation efficiency. Besides, there is no public large-scale dataset focused on PA segmentation, which makes it highly challenging to compare the different methods. To benchmark multi-level PA segmentation algorithms, we organized the first \textbf{P}ulmonary \textbf{AR}tery \textbf{SE}gmentation (PARSE) challenge. On the one hand, we focus on both the main PA and the branch PA segmentation. On the other hand, for better clinical application, we assign the same score weight to segmentation efficiency (mainly running time and GPU memory consumption during inference) while ensuring PA segmentation accuracy. We present a summary of the top algorithms and offer some suggestions for efficient and accurate multi-level PA automatic segmentation. We provide the PARSE challenge as open-access for the community to benchmark future algorithm developments at \url{https://parse2022.grand-challenge.org/Parse2022/}.

Published
2023

38. Predictive Resource Allocation for URLLC using Empirical Mode Decomposition

Author

Jayawardhana, Chandu, Sivalingam, Thushan, Mahmood, Nurul Huda, Rajatheva, Nandana, and Latva-Aho, Matti

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Effective resource allocation is a crucial requirement to achieve the stringent performance targets of ultra-reliable low-latency communication (URLLC) services. Predicting future interference and utilizing it to design efficient interference management algorithms is one way to allocate resources for URLLC services effectively. This paper proposes an empirical mode decomposition (EMD) based hybrid prediction method to predict the interference and allocate resources for downlink based on the prediction results. EMD is used to decompose the past interference values faced by the user equipment. Long short-term memory and auto-regressive integrated moving average methods are used to predict the decomposed components. The final predicted interference value is reconstructed using individual predicted values of decomposed components. It is found that such a decomposition-based prediction method reduces the root mean squared error of the prediction by $20 - 25\%$. The proposed resource allocation algorithm utilizing the EMD-based interference prediction was found to meet near-optimal allocation of resources and correspondingly results in $2-3$ orders of magnitude lower outage compared to state-of-the-art baseline prediction algorithm-based resource allocation., Comment: Accepted for European Conference on Networks and Communications (EUCNC) and 6G Summit-2023, Gothenburg, Sweden

Published
2023

42. Can clinicians identify community-acquired pneumonia on ultralow-dose CT? A diagnostic accuracy study

Author

Heltborg, Anne, Mogensen, Christian Backer, Skjøt-Arkil, Helene, Giebner, Matthias, Al-Masri, Ayham, Khatry, Usha Bc, Khatry, Sangam, Heinemeier, Ina Isabell Kathleen, Andreasen, Jonas Jannick, Hariesh, Sanne Sarmila Sivalingam, Termansen, Tenna, Kolnes, Anna Natalie, Lorentzen, Morten Hjarnø, Laursen, Christian Borbjerg, Posth, Stefan, Andersen, Michael Brun, Mussmann, Bo, Spile, Camilla Stræde, and Graumann, Ole

Published
2024
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