Fluctuation Analysis in a Queue with (L,N)-Policy and Secondary Maintenance. Continuous Time Parameter Process

Investigation results on a queueing system with an auxiliary maintenance process launched during server’s vacancy period and initiated by the exhausted queue are presented. The server operation strategy at different queue conditions is proposed. Time-sensitive analysis to investigate the queueing process at arbitrary periods of time is used. The results are obtained in explicit forms for several related models. Computational examples illustrate their analytical tractability. Various performance measures (the buffer load, switchover rate, and the number of jobs processed per unit time) are introduced and optimization problems are discussed.Представлены результаты исследований систем очередей с дополнительным процессом обслуживания, который запускается в периоды простоя сервера и инициируется заполненной очередью. Предложена стратегия работы сервера при различных состояниях очереди. Использован времязависимый анализ для исследования процессов в системах очередей в произвольные периоды времени. Результаты получены в явной форме для нескольких моделей. Приведены примеры расчета, свидетельствующие о возможности их аналитической трактовки. Введены различные критерии производительности (загрузка буферов, скорость переключения и число заданий, обрабатываемых в единицу времени) и рассмотрены проблемы оптимизации.Наведено результати досліджень систем черг з доповняльним процесом обслуговування, який запускається в періоди простоїв сервера та ініціюється заповненою чергою. Запропоновано стратегію роботи сервера при різних станах черги. Використано часозалежний аналіз для дослідження процесів у системах черг в довільні періоди часу. Результати отримано в явній формі для декількох моделей. Наведено приклади розрахунків, які свідчать про можливість їхньої аналітичної трактовки. Введено різні критерії продуктивності (загрузка буферів, швидкість переключання та число завдань, оброблювальних за одиницею часу) і розглянуто проблеми оптимізації

Fluctuation Analysis in a Queue with (L,N)-Policy and Secondary Maintenance. Discrete Time Parameter Process

This paper generalizes numerous classes of queues with vacationing servers. In our model, a server not only leaves the system, but he services packets of jobs at a secondary facility up until the total number of single jobs exceeds a specific threshold. The strategy of server processes is represented for different state of queue.We use various techniques (including fluctuation analysis) to deliver explicit formulas for the queueing processwith discrete time parameters.We also utilize some game-theoretic principles (namely sequential games) to efficiently construct our model.Обобщены многочисленные классы очередей с простаивающими серверами. В предлагаемой модели сервер не просто выходит из системы, а обрабатывает пакеты задач в фоновом режиме до тех пор, пока общее число одиночных заданий не превысит определенный порог. Представлена стратегия работы сервера при различных состояниях очереди. Использованы различные методики получения явных формул (включая флуктуационный анализ) для процесса массового обслуживания с дискретным временем, а также некоторые подходы теории игр (последовательные игры) для эффективного конструирования модели.Узагальнено багатoчисельні класи черг серверів що простоюють. В запропонованій моделі сервер не просто виходить з системи, а обробляє пакети задач у фоновому режимі до тих пір, поки загальне число поодиноких завдань не перевищить означений поріг. Представлено стратегію роботи сервера в умовах різного стану черги. Використано різні методики отримання явних формул (включаючи флуктуаційний аналіз) для процесу масового обслуговування з дискретним часом, а також деякі підходи теорії гри (послідовні ігри) для ефективного конструювання моделі

Functional and genetic evidence that nucleoside transport is highly conserved in Leishmania species: Implications for pyrimidine-based chemotherapy

Leishmania pyrimidine salvage is replete with opportunities for therapeutic intervention with enzyme inhibitors or antimetabolites. Their uptake into cells depends upon specific transporters; therefore it is essential to establish whether various Leishmania species possess similar pyrimidine transporters capable of drug uptake. Here, we report a comprehensive characterization of pyrimidine transport in L. major and L. mexicana. In both species, two transporters for uridine/adenosine were detected, one of which also transported uracil and the antimetabolites 5-fluoruracil (5-FU) and 5F,2′deoxyuridine (5F,2′dUrd), and was designated uridine-uracil transporter 1 (UUT1); the other transporter mediated uptake of adenosine, uridine, 5F,2′dUrd and thymidine and was designated Nucleoside Transporter 1 (NT1). To verify the reported L. donovani model of two NT1-like genes encoding uridine/adenosine transporters, and an NT2 gene encoding an inosine transporter, we cloned the corresponding L. major and L. mexicana genes, expressing each in T. brucei. Consistent with the L. donovani reports, the NT1-like genes of either species mediated the adenosine-sensitive uptake of [3H]-uridine but not of [3H]-inosine. Conversely, the NT2-like genes mediated uptake of [3H]-inosine but not [3H]-uridine. Among pyrimidine antimetabolites tested, 5-FU and 5F,2′dUrd were the most effective antileishmanials; resistance to both analogs was induced in L. major and L. mexicana. In each case it was found that the resistant cells had lost the transport capacity for the inducing drug. Metabolomics analysis found that the mechanism of action of 5-FU and 5F-2′dUrd was similar in both Leishmania species, with major changes in deoxynucleotide metabolism. We conclude that the pyrimidine salvage system is highly conserved in Leishmania species - essential information for the development of pyrimidine-based chemotherapy

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Whole-genome sequencing reveals host factors underlying critical COVID-19

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease