Dysfunction of contractile proteins in hypertrophic cardiomyopathy

The contractility of human heart samples from patients diagnosed with hypertrophic cardiomyopathy were studied using a quantitative in vitro motility assay. The aim of this work was to investigate the molecular phenotype of thin filament proteins in the HCM heart. Three biopsy samples with thin filament mutations were studied alongside samples acquired from a subset of HCM patients classified with hypertrophic obstructive cardiomyopathy. The primary effect of thin filament mutations was investigated by reconstituting Factin with ACTC E99K into thin filament with donor troponin. The E99K actin filaments had a higher Ca2+-sensitivity then filaments composed of donor F-actin (with no mutation) (EC50 E99K/donor 0.78±0.14, p=0.02). A similar higher Ca2+- sensitivity was found when recombinant TnT K273N was incorporated into donor troponin and compared to native donor troponin (EC50 K273N/donor 0.54±0.17, p=0.006). Troponin was also purified from HOCM heart samples. This troponin did not contain a causative mutation but behaved abnormally in the response of thin filament Ca2+- sensitivity to changes in TnI phosphorylation (EC50 PKA-HOCM/HOCM 1.08±0.25, p=0.3) as mean TnI phosphorylation of PKA-HOCM was 1.56 molsPi/molsTnI and HOCM was 0.29 molsPi/molsTnI. Thus, thin filament Ca2+-sensitivity was uncoupled from TnI phosphorylation in thin filaments with HOCM troponin. When the native TnT subunits were replaced with recombinant TnT this coupling was restored (EC50 HOCM rTnT/HOCM 0.63±0.26, p=0.03). It would appear that the result of HCM-causing mutations are two-fold. The primary effect of the HCM-causing mutations is to increase thin filament Ca2+-sensitivity. However, the contraction machinery appears to be the target of secondary modifications, that occur due to the pathology of the disease. Resulting in further changes, such as changes in protein composition and post-translational modification. One major consequence of these modifications may be to uncouple the relatively labile regulation of thin filament Ca2+-sensitivity by TnI phosphorylation

Intuitive scrolling for feed-based applications

This disclosure describes techniques to perform adaptive scrolling based on input gestures provided by a user. Input provided by the user is categorized as scroll, small fling, or big fling. The categorization is based on device-independent velocity thresholds. When the input is classified as scroll, e.g., a slow swipe gesture, a feed interface that scrolls over items is provided. When the input is classified as a small fling, the item list snaps to the item that is adjacent to a current item in the view, determined based on the direction of the swipe. When the input is classified as a big fling, the item list scrolls and skips items based on the gesture and snaps to the item that is nearest to the location where the scroll concludes, as determined based on the input. Such adaptive behavior that combines natural scrolling with snapping behavior improves recall and allows users to focus on each content item and provides a flexible browsing mechanism

Simheuristic and learnheuristic algorithms for the temporary-facility location and queuing problem during population treatment or testing events

Epidemic outbreaks, such as the one generated by the coronavirus disease, have raised the need for more efficient healthcare logistics. One of the challenges that many governments have to face in such scenarios is the deployment of temporary medical facilities across a region with the purpose of providing medical services to their citizens. This work tackles this temporary-facility location and queuing problem with the goals of minimizing costs, the expected completion time, population travel and waiting times. The completion time for a facility depends on the numbers assigned to those facilities as well as stochastic arrival times. This work proposes a learnheuristic algorithm to solve the facility location and population assignment problem. Firstly a machine learning algorithm is trained using data from a queuing model (simulation module). The learnheuristic then constructs solutions using the machine learning algorithm to rapidly evaluate decisions in terms of facility completion and population waiting times. The efficiency and quality of the algorithm is demonstrated by comparison with exact and simulation-only (simheuristic) methodologies. A series of experiments are performed which explore the trade offs between solution cost, completion time, population travel and waiting times.Peer ReviewedPostprint (author's final draft

Airline reserve crew scheduling under uncertainty

This thesis addresses the problem of airline reserve crew scheduling under crew absence and journey time uncertainty. This work is primarily concerned with the allocation of reserve crew to standby duty periods. The times at which reserve crew are on duty, determine which possible crew absence or delay disruptions they can be used to absorb. When scheduling reserve crew, the goal is to minimise the expected levels of delay and cancellation disruptions that occur on the day of operation. This work introduces detailed probabilistic models of the occurrence of crew absence and delay disruptions and how reserve crew are used to absorb such disruptions. Firstly, separate probabilistic models are developed for crew absence and delay disruptions. Then, an integrated probabilistic model of absence and delay disruptions is introduced, which accounts for: delays from all causes; delay propagation; cancellations resulting from excessive delays and crew absence; the use of reserve crew to cover such disruptions given a reserve policy; and the possibility of swap recovery actions as an alternative delay recovery action. The model yields delay and cancellation predictions that match those derived from simulation to a high level of accuracy and does so in a fraction of the time required by simulation. The various probabilistic models are used in various search methodologies to find disruption minimising reserve crew schedules. The results show that high quality reserve crew schedules can be derived using a probabilistic model. A scenario-based mixed integer programming approach to modelling operational uncertainty and reserve crew use is also developed in this thesis and applied to the problem of reserve crew scheduling. A scenario selection heuristic is introduced which improves reserve crew schedule quality using fewer input scenarios. The secondary objective of this thesis is to investigate the effect of the reserve policy used on the day of operation, that is, determining when and which reserve crew should be utilised. The questions of how reserve policies can be improved and how they should be taken into account when scheduling reserve crew are addressed. It was found that the approaches developed for reserve crew scheduling lend themselves well to an online application, that is, using them to evaluate alternative reserve decisions to ensure reserve crew are used as effectively as possible. In general it is shown that `day of operation' disruptions can be significantly reduced through both improved reserve crew schedules and/or reserve policies. This thesis also points the way towards future research based on the proposed approaches

Airline reserve crew scheduling under uncertainty

This thesis addresses the problem of airline reserve crew scheduling under crew absence and journey time uncertainty. This work is primarily concerned with the allocation of reserve crew to standby duty periods. The times at which reserve crew are on duty, determine which possible crew absence or delay disruptions they can be used to absorb. When scheduling reserve crew, the goal is to minimise the expected levels of delay and cancellation disruptions that occur on the day of operation. This work introduces detailed probabilistic models of the occurrence of crew absence and delay disruptions and how reserve crew are used to absorb such disruptions. Firstly, separate probabilistic models are developed for crew absence and delay disruptions. Then, an integrated probabilistic model of absence and delay disruptions is introduced, which accounts for: delays from all causes; delay propagation; cancellations resulting from excessive delays and crew absence; the use of reserve crew to cover such disruptions given a reserve policy; and the possibility of swap recovery actions as an alternative delay recovery action. The model yields delay and cancellation predictions that match those derived from simulation to a high level of accuracy and does so in a fraction of the time required by simulation. The various probabilistic models are used in various search methodologies to find disruption minimising reserve crew schedules. The results show that high quality reserve crew schedules can be derived using a probabilistic model. A scenario-based mixed integer programming approach to modelling operational uncertainty and reserve crew use is also developed in this thesis and applied to the problem of reserve crew scheduling. A scenario selection heuristic is introduced which improves reserve crew schedule quality using fewer input scenarios. The secondary objective of this thesis is to investigate the effect of the reserve policy used on the day of operation, that is, determining when and which reserve crew should be utilised. The questions of how reserve policies can be improved and how they should be taken into account when scheduling reserve crew are addressed. It was found that the approaches developed for reserve crew scheduling lend themselves well to an online application, that is, using them to evaluate alternative reserve decisions to ensure reserve crew are used as effectively as possible. In general it is shown that `day of operation' disruptions can be significantly reduced through both improved reserve crew schedules and/or reserve policies. This thesis also points the way towards future research based on the proposed approaches

Probabilistic Airline Reserve Crew Scheduling Model

This paper introduces a probabilistic model for airline reserve crew scheduling. The model can be applied to any schedules which consist of a stream of departures from a single airport. We assume that reserve crew demand can be captured by an independent probability of crew absence for each departure. The aim of our model is to assign some fixed number of available reserve crew in such a way that the overall probability of crew unavailability in an uncertain operating environment is minimised. A comparison of different probabilistic objective functions, in terms of the most desirable simulation results, is carried out, complete with an interpretation of the results. A sample of heuristic solution methods are then tested and compared to the optimal solutions on a set of problem instances, based on the best objective function found. The current model can be applied in the early planning phase of reserve crew scheduling, when very little information is known about crew absence related disruptions. The main conclusions include the finding that the probabilistic objective function approach gives solutions whose objective values correlate strongly with the results that these solutions will get on average in repeated simulations. Minimisation of the sum of the probabilities of crew unavailability was observed to be the best surrogate objective function for reserve crew schedules that perform well in simulation. A list of extensions that could be made to the model is then provided, followed by conclusions that summarise the findings and important results obtained

High allelic diversity in the methyltransferase gene of a phase variable type III restriction-modification system has implications for the fitness of Haemophilus influenzae

Phase variable restriction-modification (R-M) systems are widespread in Eubacteria. Haemophilus influenzae encodes a phase variable homolog of Type III R-M systems. Sequence analysis of this system in 22 non-typeable H.influenzae isolates revealed a hypervariable region in the central portion of the mod gene whereas the res gene was conserved. Maximum likelihood (ML) analysis indicated that most sites outside this hypervariable region experienced strong negative selection but evidence of positive selection for a few sites in adjacent regions. A phylogenetic analysis of 61 Type III mod genes revealed clustering of these H.influenzae mod alleles with mod genes from pathogenic Neisseriae and, based on sequence analysis, horizontal transfer of the mod–res complex between these species. Neisserial mod alleles also contained a hypervariable region and all mod alleles exhibited variability in the repeat tract. We propose that this hypervariable region encodes the target recognition domain (TRD) of the Mod protein and that variability results in alterations to the recognition sequence of this R-M system. We argue that the high allelic diversity and phase variable nature of this R-M system have arisen due to selective pressures exerted by diversity in bacteriophage populations but also have implications for other fitness attributes of these bacterial species