Happens before-based dynamic analysis is the go-to technique for detecting data races in large scale software projects due to the absence of false positive reports. However, such analyses are expensive since they employ expensive vector clock updates at each event, rendering them usable only for in-house testing. In this paper, we present a sampling-based, randomized race detector that processes only
This content will become publicly available on May 18, 2024
Runners competing in races are looking to optimize their performance. In this paper, a runner's performance in a race, such as a marathon, is formulated as an optimal control problem where the controls are: the nutrition intake throughout the race and the propulsion force of the runner. As nutrition is an integral part of successfully running long distance races, it needs to be included in models of running strategies.
We formulate a system of ordinary differential equations to represent the velocity, fat energy, glycogen energy, and nutrition for a runner competing in a long-distance race. The energy compartments represent the energy sources available in the runner's body. We allocate the energy source from which the runner draws, based on how fast the runner is moving. The food consumed during the race is a source term for the nutrition differential equation. With our model, we are investigating strategies to manage the nutrition and propulsion force in order to minimize the running time in a fixed distance race. This requires the solution of a nontrivial singular control problem.
As the goal of an optimal control model is to determine the optimal strategy, comparing our results against real data presents a challenge; however, in comparing our results to the world record for the marathon, our results differed by 0.4%, 31 seconds. Per each additional gel consumed, the runner is able to run 0.5 to 0.7 kilometers further in the same amount of time, resulting in a 7.75% increase in taking five 100 calorie gels vs no nutrition.
Our results confirm the belief that the most effective way to run a race is to run approximately the same pace the entire race without letting one's energies hit zero, by consuming in-race nutrition. While this model does not take all factors into account, we consider it a building block for future models, considering our novel energy representation, and in-race nutrition.
- Award ID(s):
- 2031213
- NSF-PAR ID:
- 10481504
- Publisher / Repository:
- frontiersin.org
- Date Published:
- Journal Name:
- Frontiers in Nutrition
- Volume:
- 10
- ISSN:
- 2296-861X
- Subject(s) / Keyword(s):
- ["optimization","differential equation models","running","nutrition","metabolism","bioenergetics"]
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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constantly many events of the input trace even in the worst case. This is the firstsub-linear time (i.e., running ino (n ) time wheren is the length of the trace) dynamic race detection algorithm; previous sampling based approaches like run in linear time (i.e.,O (n )). Our algorithm is a property tester for -race detection — it is sound in that it never reports any false positive, and on traces that are far, with respect to hamming distance, from any race-free trace, the algorithm detects an -race with high probability. Our experimental evaluation of the algorithm and its comparison with state-of-the-art deterministic and sampling based race detectors shows that the algorithm does indeed have significantly low running time, and detects races quite often. -
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Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022.
Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance.
Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models.
Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks.
Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (
https://www.nfdi4health.de/task-force-covid-19-2 ) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z). -
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Abstract Probiotics, whether taken as capsules or consumed in foods, have been regarded as safe for human use by regulatory agencies. Being living cells, they serve as “tunable” factories for the synthesis of a vast array of beneficial molecules. The idea of reprogramming probiotics to act as controllable factories, producing potential therapeutic molecules under user‐specified conditions, represents a new and powerful concept in drug synthesis and delivery. Probiotics that serve as drug delivery vehicles pose several challenges, one being targeting (as seen with nanoparticle approaches). Here, we employ synthetic biology to control swimming directionality in a process referred to as “pseudotaxis.”
Escherichia coli , absent the motility regulatorcheZ , swim sporadically, missing the traditional “run” in the run:tumble swimming paradigm. Upon introduction ofcheZ in trans and its signal‐generated upregulation, engineered bacteria can be “programmed” to swim toward the source of the chemical cue. Here, engineered cells that encounter sufficient levels of the small signal molecule pyocyanin, produce an engineered CheZ and swim with programmed directionality. By incorporating a degradation tag at the C‐terminus of CheZ, the cells stop running when they exit spaces containing pyocyanin. That is, the engineered CheZ modified with a C‐terminal extension derived from the putative DNA‐binding transcriptional regulator YbaQ (RREERAAKKVA) is consumed by the ClpXP protease machine at a rate sufficient to “brake” the cells when pyocyanin levels are too low. Through this process, we demonstrate that over time, these engineeredE. coli accumulate in pyocyanin‐rich locales. We suggest that such approaches may find utility in engineering probiotics so that their beneficial functions can be focused in areas of principal benefit. -
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Abstract Motivation Proteoform identification is an important problem in proteomics. The main task is to find a modified protein that best fits the input spectrum. To overcome the combinatorial explosion of possible proteoforms, the proteoform mass graph and spectrum mass graph are used to represent the protein database and the spectrum, respectively. The problem becomes finding an optimal alignment between the proteoform mass graph and the spectrum mass graph. Peak error correction is an important issue for computing an optimal alignment between the two input mass graphs.
Results We propose a faster algorithm for the error correction alignment of spectrum mass graph and proteoform mass graph problem and produce a program package TopMGFast. The newly designed algorithms require less space and running time so that we are able to compute global optimal alignments for the two input mass graphs in a reasonable time. For the local alignment version, experiments show that the running time of the new algorithm is reduced by 2.5 times. For the global alignment version, experiments show that the maximum mass errors between any pair of matched nodes in the alignments obtained by our method are within a small range as designed, while the alignments produced by the state-of-the-art method, TopMG, have very large maximum mass errors for many cases. The obtained alignment sizes are roughly the same for both TopMG and TopMGFast. Of course, TopMGFast needs more running time than TopMG. Therefore, our new algorithm can obtain more reliable global alignments within a reasonable time. This is the first time that global optimal error correction alignments can be obtained using real datasets.
Availability and implementation The source code of the algorithm is available at https://github.com/Zeirdo/TopMGFast.
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Key points Our tibial fracture orthopaedic injury model in mice recapitulates the major manifestations of complex trauma, including nociceptive sensitization, bone fracture, muscle fibrosis and muscle fibre loss.
Delayed exercise after complex orthopaedic trauma results in decreased muscle fibrosis and improved pain
Losartan, an angiotensin‐receptor blocker with anti‐fibrotic abilities, recapitulates the effect of exercise on post‐injury recovery and may provide an enhanced recovery option for those who are unable to exercise after injury
Abstract Chronic pain and disability after limb injury are major public health problems. Early mobilization after injury improves functional outcomes for patients, although when and how to implement rehabilitation strategies remains a clinical challenge. Additionally, whether the beneficial effects of exercise can be reproduced using pharmacological tools remains unknown and may benefit patients who are unable to exercise as a result of immobilization. We developed a murine model of orthopaedic trauma combining tibia fracture and pin fixation with muscle damage. Behavioural measures included mechanical nociceptive thresholds and distances run on exercise wheels. Bone healing was quantified using microcomputed tomagraphic scanning, and muscle fibre size distribution and fibrosis were followed using immunohistochemistry. We found that the model provided robust mechanical allodynia, fibrosis and a shift to smaller average muscle fibre size lasting up to 5 weeks from injury. We also observed that allowing ‘late’ (weeks 1–2) rather than ‘early’ (weeks 0–1) exercise after injury resulted in greater overall running activity and greater reversal of allodynia. In parallel, the late running paradigm was associated with reduced muscle fibrosis, earlier increase in muscle fibre diameter and a short‐term benefit in reducing callus volume. Providing the anti‐fibrotic angiotensin receptor blocker losartan to mice in drinking water reduced both allodynia and muscle fibrosis. Combining losartan and late exercise provided no additional benefit. We conclude that early healing after orthopaedic trauma must be allowed prior to the initiation of exercise to achieve optimal pain, functional and physiological outcomes and that losartan is a viable candidate for translational studies.
