Circular representative volume element for discrete model of concrete

Support of the project FAST-J-23-8323 is gratefully acknowledged. The first author is Brno Ph.D. Talent Scholarship Holder – Funded by the Brno City Municipality, Czech Republic

Tight bounds for Double Coverage against weak adversaries

We study the Double Coverage (DC) algorithm for the k-server problem in tree metrics in the (h,k)-setting, i.e., when DC with k servers is compared against an offline optimum algorithm with h ≤ k servers. It is well-known that in such metric spaces DC is k-competitive (and thus optimal) for h = k. We prove that even if k > h the competitive ratio of DC does not improve; in fact, it increases slightly as k grows, tending to h + 1. Specifically, we give matching upper and lower bounds of (k(h+1)) / (k+1) on the competitive ratio of DC on any tree metric

Differentially private correlation clustering

Correlation clustering is a widely used technique in unsupervised machine learning. Motivated by applications where individual privacy is a concern, we initiate the study of differentially private correlation clustering. We propose an algorithm that achieves subquadratic additive error compared to the optimal cost. In contrast, straightforward adaptations of existing non-private algorithms all lead to a trivial quadratic error. Finally, we give a lower bound showing that any pure differentially private algorithm for correlation clustering requires additive error of Ω(n)

Differentially private correlation clustering

Correlation clustering is a widely used technique in unsupervised machine learning. Motivated by applications where individual privacy is a concern, we initiate the study of differentially private correlation clustering. We propose an algorithm that achieves subquadratic additive error compared to the optimal cost. In contrast, straightforward adaptations of existing non-private algorithms all lead to a trivial quadratic error. Finally, we give a lower bound showing that any pure differentially private algorithm for correlation clustering requires additive error of $\Omega(n)$

Monodopsis and Vischeria genomes shed new light on the biology of eustigmatophyte algae

Acknowledgment This study was supported by the National Science Foundation Dimensions of Biodiversity grant (1831428) to F.-W.L., and the Czech Science Foundation grant 20-27648S to M.E. We thank the reviewers and editor for their thoughtful commentsPeer reviewedPublisher PD

Extensive molecular tinkering in the evolution of the membrane attachment mode of the Rheb GTPase

Rheb is a conserved and widespread Ras-like GTPase involved in cell growth regulation mediated by the (m)TORC1 kinase complex and implicated in tumourigenesis in humans. Rheb function depends on its association with membranes via prenylated C-terminus, a mechanism shared with many other eukaryotic GTPases. Strikingly, our analysis of a phylogenetically rich sample of Rheb sequences revealed that in multiple lineages this canonical and ancestral membrane attachment mode has been variously altered. The modifications include: (1) accretion to the N-terminus of two different phosphatidylinositol 3-phosphate-binding domains, PX in Cryptista (the fusion being the first proposed synapomorphy of this clade), and FYVE in Euglenozoa and the related undescribed flagellate SRT308; (2) acquisition of lipidic modifications of the N-terminal region, namely myristoylation and/or S-palmitoylation in seven different protist lineages; (3) acquisition of S-palmitoylation in the hypervariable C-terminal region of Rheb in apusomonads, convergently to some other Ras family proteins; (4) replacement of the C-terminal prenylation motif with four transmembrane segments in a novel Rheb paralog in the SAR clade; (5) loss of an evident C-terminal membrane attachment mechanism in Tremellomycetes and some Rheb paralogs of Euglenozoa. Rheb evolution is thus surprisingly dynamic and presents a spectacular example of molecular tinkering

Learning-augmented dynamic power management with multiple states via new ski rental bounds

We study the online problem of minimizing power consumption in systems with multiple power-saving states. During idle periods of unknown lengths, an algorithm has to choose between power-saving states of different energy consumption and wake-up costs. We develop a learning-augmented online algorithm that makes decisions based on (potentially inaccurate) predicted lengths of the idle periods. The algorithm's performance is near-optimal when predictions are accurate and degrades gracefully with increasing prediction error, with a worst-case guarantee almost identical to the optimal classical online algorithm for the problem. A key ingredient in our approach is a new algorithm for the online ski rental problem in the learning augmented setting with tight dependence on the prediction error. We support our theoretical findings with experiments

Study protocol – robot-assisted gait therapy using Lokomat Pro FreeD in patients in the subacute phase of ischemic stroke

Cíl: Roboticky asistovaný trénink chůze představuje moderní koncept neurorehabilitace u pacientů po CMP. Cílem naší randomizované intervenční studie je zhodnotit přídatný efekt robotické rehabilitace chůze u pacientů v subakutní fázi ischemické CMP a porovnání s kohortou pacientů absolvujících standardní (protokolem definovanou) ústavní rehabilitaci. Primárním sledovaným parametrem je úroveň funkční kategorizace chůze. Sekundárními sledovanými parametry jsou časové parametry chůze (10metrový test chůze, Timed Up and Go), změny tělesného složení, modifikovaná Rankinova škála, index Barthelové, balanční škála Bergové a dotazník Subjektivní hodnocení strachu z pádů. Radiologická substudie sleduje dynamiku vývoje strukturálních změn a atrofie mozkové tkáně pomocí MR. Metody: Prospektivní randomizovaná otevřená monocentrická studie zařazující pacienty do 6 týdnů od první ischemické CMP. Konvenční rehabilitací (fyzioterapie, ergoterapie a mechanoterapie) jsou léčeny obě skupiny po dobu 60 min 5× týdně, celkem 15× po dobu 3–4 týdnů (celkem 1 200 min). Intervenční skupina navíc absolvuje roboticky asistovaný trénink chůze pomocí přístroje Lokomat 20–50 min 5× týdně, celkem 15× po dobu 3–4 týdnů (celkem 1 800 min). Sběr dat probíhá ve čtyřech časových obdobích: před zahájením intervence (T0), v polovině intervence (T1; 8. den), hodnocení po ukončení rehabilitace (T2; 15. den) a 3 měsíce po ukončení (T3).Aim: Robot-assisted gait training represents a modern concept of neurorehabilitation in stroke patients. Our randomized interventional study aims to assess the additive effect of robot-assisted gait rehabilitation in subacute ischemic stroke patients and to compare its effect with patients undergoing standard institutional protocol-defined rehabilitation. The primary endpoint is the functional ambulation category. The secondary endpoints include gait time parameters (10 Meter Walk Test, Timed Up and Go), changes in body composition, modified Rankin scale, Barthel index, Berg balance scale, and a questionnaire Falls Efficacy Scale – International. Radiological sub-study evaluates the dynamics of brain structural changes and atrophy using MRI. Methods: This is a prospective randomized open monocentric study enrolling patients within 6 weeks from the onset of the firs ischemic stroke. Both groups are treated with conventional rehabilitation (physiotherapy, occupational therapy and mechanotherapy) for 60 min 5 times a week, a total of 15 times for 3 to 4 weeks (a total of 1,200 min). The Lokomat group undergoes robot-assisted gait training using the interventional exoskeleton for 20-50 minutes 5 times a week for a total of 15 times for 3 to 4 weeks (a total of 1,800 min). Data collection takes place over four time periods: pre-intervention (T0), mid-intervention (T1; day 8), post-rehabilitation assessment (T2; day 15), and 3 months post-intervention (T3).Web of Science84436636