Improved Adaptive Group Testing Algorithms with Applications to Multiple Access Channels and Dead Sensor Diagnosis

We study group-testing algorithms for resolving broadcast conflicts on a multiple access channel (MAC) and for identifying the dead sensors in a mobile ad hoc wireless network. In group-testing algorithms, we are asked to identify all the defective items in a set of items when we can test arbitrary subsets of items. In the standard group-testing problem, the result of a test is binary--the tested subset either contains defective items or not. In the more generalized versions we study in this paper, the result of each test is non-binary. For example, it may indicate whether the number of defective items contained in the tested subset is zero, one, or at least two. We give adaptive algorithms that are provably more efficient than previous group testing algorithms. We also show how our algorithms can be applied to solve conflict resolution on a MAC and dead sensor diagnosis. Dead sensor diagnosis poses an interesting challenge compared to MAC resolution, because dead sensors are not locally detectable, nor are they themselves active participants.Comment: Expanded version of a paper appearing in ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), and preliminary version of paper appearing in Journal of Combinatorial Optimizatio

Can local application of Tranexamic acid reduce post-coronary bypass surgery blood loss? A randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Diffuse microvascular bleeding remains a common problem after cardiac procedures.</p> <p>Systemic use of antifibrinolytic reduces the postoperative blood loss.</p> <p>The purpose of this study was to examine the effectiveness of local application of tranexamic acid to reduce blood loss after coronary artery bypass grafting (CABG).</p> <p>Methods</p> <p>Thirty eight patients scheduled for primary isolated coronary artery bypass grafting were included in this double blind, prospective, randomized, placebo controlled study.</p> <p>Tranexamic acid (TA) group (19 patients) received 1 gram of TA diluted in 100 ml normal saline. Placebo group (19 patients) received 100 ml of normal saline only. The solution was purred in the pericardial and mediastinal cavities.</p> <p>Results</p> <p>Both groups were comparable in their baseline demographic and surgical characteristics. During the first 24 hours post-operatively, cumulative blood loss was significantly less in TA group (median of 626 ml) compared to Placebo group (median of 1040 ml) (P = 0.04). There was no significant difference in the post-op Packed RBCs transfusion between both groups (median of one unit in each) (P = 0.82). Significant less platelets transfusion required in TA group (median zero unit) than in placebo group (median 2 units) (P = 0.03). Apart from re-exploration for excessive surgical bleeding in one patient in TA group, no difference was found in morbidity or mortality between both groups.</p> <p>Conclusion</p> <p>Topical application of tranexamic acid in patients undergoing primary coronary artery bypass grafting led to a significant reduction in postoperative blood loss without adding extra risk to the patient.</p

Insertable cardiac monitor with a long sensing vector: Impact of obesity on sensing quality and safety

BackgroundFat layers in obese patients can impair R-wave detection and diagnostic performance of a subcutaneous insertable cardiac monitor (ICM). We compared safety and ICM sensing quality between obese patients [body mass index (BMI) ≥ 30 kg/m2] and normal-weight controls (BMI &lt;30 kg/m2) in terms of R-wave amplitude and time in noise mode (noise burden) detected by a long-sensing-vector ICM.Materials and methodsPatients from two multicentre, non-randomized clinical registries are included in the present analysis on January 31, 2022 (data freeze), if the follow-up period was at least 90 days after ICM insertion, including daily remote monitoring. The R-wave amplitudes and daily noise burden averaged intraindividually for days 61–90 and days 1–90, respectively, were compared between obese patients (n = 104) and unmatched (n = 268) and a nearest-neighbour propensity score (PS) matched (n = 69) normal-weight controls.ResultsThe average R-wave amplitude was significantly lower in obese (median 0.46 mV) than in normal-weight unmatched (0.70 mV, P &lt; 0.0001) or PS-matched (0.60 mV, P = 0.003) patients. The median noise burden was 1.0% in obese patients, which was not significantly higher than in unmatched (0.7%; P = 0.056) or PS-matched (0.8%; P = 0.133) controls. The rate of adverse device effects during the first 90 days did not differ significantly between groups.ConclusionAlthough increased BMI was associated with reduced signal amplitude, also in obese patients the median R-wave amplitude was &gt;0.3 mV, a value which is generally accepted as the minimum level for adequate R-wave detection. The noise burden and adverse event rates did not differ significantly between obese and normal-weight patients.Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04075084 and NCT04198220

SIRT1 contributes to telomere maintenance and augments global homologous recombination

SIRT1 is a positive regulator of telomere length and attenuates age-associated telomere shortening

Construction, assembly and tests of the ATLAS electromagnetic barrel calorimeter

The construction and assembly of the two half barrels of the ATLAS central electromagnetic calorimeter and their insertion into the barrel cryostat are described. The results of the qualification tests of the calorimeter before installation in the LHC ATLAS pit are given

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License