sequential sampling
Recently Published Documents

‘Candidatus Phytoplasma pruni’ infection in cherries causes small, misshapen fruit with poor color and taste, rendering the fruit unmarketable. However, this is a disease with a long development cycle and a scattered, non-uniform symptom distribution in the early stages. To better understand the biology as well as the relationship between pathogen titer and disease expression, we carried out seasonal, spatial, and temporal examinations of ‘Ca. P. pruni’ titer and distribution in infected orchard-grown trees. Sequential sampling of heavily infected trees revealed marked seasonal patterns, with differential accumulation in woody stem and leaf tissues, and most notably within fruit in the early stages of development from bloom to pit hardening. Furthermore, mapping phytoplasma distribution and titer in trees at different stages of infection indicated that infection proceeds through a series of stages. Initially, infection spreads basipetally and accumulates in the roots before populating aerial parts of the trees from the trunk upwards, with infection of specific tissues and limbs followed by an increasing phytoplasma titer. Finally, we observed a correlation between phytoplasma titer and symptom severity, with severe symptom onset associated with 3-4 orders of magnitude more phytoplasma than mild symptoms. Cumulatively, these data aid in accurate sampling and management decision making and furthers our understanding of disease development.

Exploring novel environments through sequential sampling is essential for efficient decision-making under uncertainty. In the laboratory, human exploration has been studied in situations where exploration is traded against reward maximisation. By design, these ‘explore-exploit’ dilemmas confound the behavioural characteristics of exploration with those of the trade-off itself. Here we designed a sequential sampling task where exploration can be studied and compared in the presence and absence of trade-off with exploitation. Detailed model-based analyses of choice behaviour revealed specific exploration patterns arising in situations where information seeking is not traded against reward seeking. Human choices are directed toward the most uncertain option available, but only after an initial sampling phase consisting of choice streaks from each novel option. These findings outline competing cognitive pressures on information seeking: the repeated sampling of the current option (for hypothesis testing), and the directed sampling of the most uncertain option available (for structure mapping).

The effects of effluent discharged from Kaduna Refining and Petrochemical Corporations (KRPC) on the Phytoplankton species distribution and abundance was studied along river Rido for a period of twelve months from February 2013 to January 2014. A total of Ninety-three (93) species of phytoplankton distributed in five divisions were recorded from four different study stations (A, B, C and D) along the river. Random sequential sampling was employed for selecting a sampling station. Physicochemical parameters were determined in accordance with the standard procedure for wastewater assessment guidelines of American Public Health Association (APHA). Phytoplankton identification was done using appropriate reference materials. The phytoplankton community was dominated by Bacillariophyta9 constituting 78.98% with 41 species, followed by Chlorophyta (6.67%) having 25 species, and then Cyanophyta (6.46%) having 12 species, while 10 species of Dinophyta and 5 species of Euglenophyta constituting 5.42% and 2.49% respectively were observed. The results from the study revealed low density of phytoplankton in station B, while high density of phytoplankton was observed in stations A and D. The physicochemical parameters showed both seasonal and spatial variations. Values of some parameters studied were observed to be above Federal Environmental Protection Agency (FEPA) limits at station B. Thus, effluent from KRPC is a source of contamination of water quality and adversely affects phytoplankton community along river Rido. Therefore, effluent retention facility should be established for long term treatment of effluent before discharge into the river

The proportion of young platelets, also known as newly formed or reticulated, within the overall platelet population has been clinically correlated with adverse cardiovascular outcomes. Our understanding of this is incomplete, however, because of limitations in the technical approaches available to study platelets of different ages. In this study we have developed and validated an in vivo ′temporal labelling′ approach using injectable fluorescent anti-platelet antibodies to sub-divide platelets by age and assess differences in functional and molecular characteristics. With this approach we found that young platelets (<24h old) in comparison to older platelets, respond to stimuli with greater calcium flux and degranulation, and contribute more to the formation of thrombi in vitro and in vivo. Sequential sampling confirmed this altered functionality to be independent of platelet size with no size differences or changes relative to the global population seen at any age. The age associated decrease in thrombotic function was accompanied by significant decreases in the surface expression of GPVI and CD31 (PECAM-1) and an increase in CD9. Platelet mRNA content also decreased with age but at different rates for individual mRNAs indicating apparent conservation of those encoding granule proteins. Our pulse-chase type approach to define circulating platelet age has allowed timely re-examination of commonly held beliefs regarding size and reactivity of young platelets whilst providing novel insights into the temporal regulation of receptor and protein expression. Overall, future application of this validated tool will inform on age-based platelet heterogeneity in physiology and disease.

Background: Patients with severe COVID-19 evolve to acute respiratory distress syndrome (ARDS) and require management in Intensive Care Units (ICU) where they are exposed to immobilization, immunosuppression, malnutrition, nosocomial infections; may develop ICU Acquired Weakness (ICUAW), which increases with the stay and use of mechanical ventilation (MV).There is evidence of the use of different modalities in rehabilitation to mitigate these effects. Goal: To determine the efficacy of a Multimodal Rehabilitation Program (MRP) in reducing the number of days of mechanical ventilation and stay in patients hospitalized for COVID-19 in ICU, as well as to describe its clinical and hospital characteristics. Material and Methods: An quasi-experimental study was designed, with sequential sampling and without blinding. A control and intervention group was formed, with 32 participants each. A Multimodal Rehabilitation Program (MRP) based on four therapeutic modalities was applied and the intervention was quantified through the use of proposed indicators. Results: The variation in days of ICU stay and days of MV were similar in both groups. The Multimodal Rehabilitation Index (iMR) ranged from 0.1 to 2.7 (mean = 1.2, SD = 0.7) and had significance for cut-off points ≤ 0.81 and ≤ 0.94 in mortality (p = 0.02) and Ventilator-free days at 28 days (VFDs-28) (p = 0.01). Conclusions: No statistically significant difference was found in favor of the intervention in terms of days of stay in the ICU and days of MV. Explanatorily, it was reported that iMR was related to (VFDs-28) and mortality in patients with severe COVID-19.

Previous work has established that rank ordered single-day activity sequences from various study areas exhibit a universal power law distribution called Zipf’s law. By analyzing datasets from across the world, evidence was provided that it is in fact a universal distribution. This study focuses on a potential mechanism that leads to the power law distribution that was previously discovered. It makes use of 15 household travel survey (HTS) datasets from study areas all over the world to demonstrate that reasonably accurate sets of activity sequences (or “schedules”) can be generated with extremely little information required; the model requires no input data and contains few tunable parameters. The activity sequence generation mechanism is based on sequential sampling from two universal distributions: (i) the distributions of the number of activities (trips) and (ii) the activity types (trip purposes). This paper also attempts to demonstrate the universal nature of these distributions by fitting several equations to the 15 HTS datasets. The lightweight activity sequence generation model can be implemented in any (lightweight) transportation model to create a basic set of activity sequences, saving effort and cost in data collection and in model development and calibration.