Large Pool Research Articles

Automated Learning Strategy to Reinforce Business Writing Mechanics for Accounting Students

ABSTRACT Given the importance of accountants’ writing skills, the written communication expectation-performance gap, and limited accounting faculty time and expertise, this Business Writing Mechanics (BWM) learning strategy provides students with a spelling, grammar, punctuation, and word choice refresher using minimal faculty effort. BWM is an online, self-graded resource that students complete outside of class and consists of four separate assignment modules covering (1) Effective Written Communication, (2) Basic Grammar, (3) Advanced Grammar, and (4) Business Grammar. For each module, students access a PowerPoint slide deck, watch lecture video(s), and demonstrate mastery of the material via a quiz. Large quiz pools allow students to take a quiz multiple times until mastery is achieved. Students’ feedback indicated the PowerPoint slide decks were a good reference tool, the videos were easy to follow, and students’ knowledge, confidence, and awareness of writing mechanics increased.

Quantitative single molecule analysis of podoplanin clustering in fibroblastic reticular cells uncovers CD44 function

Upon initial immune challenge, dendritic cells (DCs) migrate to lymph nodes and interact with fibroblastic reticular cells (FRCs) via C-type lectin-like receptor 2 (CLEC-2). CLEC-2 binds to the membrane glycoprotein podoplanin (PDPN) on FRCs, inhibiting actomyosin contractility through the FRC network and permitting lymph node expansion. The hyaluronic acid receptor CD44 is known to be required for FRCs to respond to DCs but the mechanism of action is not fully elucidated. Here, we use DNA-PAINT, a quantitative single molecule super-resolution technique, to visualize and quantify how PDPN clustering is regulated in the plasma membrane of FRCs. Our results indicate that CLEC-2 interaction leads to the formation of large PDPN clusters (i.e. more than 12 proteins per cluster) in a CD44-dependent manner. These results suggest that CD44 expression is required to stabilize large pools of PDPN at the membrane of FRCs upon CLEC-2 interaction, revealing the molecular mechanism through which CD44 facilitates cellular crosstalk between FRCs and DCs.

PO-05-085 ENHANCED PAUSE ALGORITHM FOR INSERTABLE CARDIAC MONITOR REDUCES CLINICAL REVIEW BURDEN

A critical feature of an Insertable Cardiac Monitor (ICM) is to identify clinically significant pauses/bradycardic events in patients (pts) with unexplained syncope. Reducing the number of false positive pause episodes will reduce the clinic review workload. To evaluate an enhanced pause detection algorithm and reduce the clinical burden of evaluating pause episodes while maintaining high relative sensitivity and increasing specificity. The development set consisted of 1033 real world stored LUX-Dx (Boston Scientific) ICM pause events (347 pts) which were selected to include more episodes with low signal to noise ratio (SNR). Another independent test set of 728 real world stored Pause events (93 pts) was randomly selected from a large pool of sequestered patient episode data and adjudicated by two experts until agreement as to specificity. The LUX-Dx pause algorithm uses a proprietary two-step detect/confirm process: step 1 detects when an R-R interval is longer than a programmed threshold, and step 2 confirms (or rejects) a suspected pause after analyzing for broadband myopotential noise and signal-to-noise ratios (SNR) (undersensing). Using the development set (1033 episodes), we have enhanced this algorithm to reject false positive pauses caused by noise and low amplitude signals. The false positive (FP) reduction was 62.5% in the Development set and 48.6% in the Test set. Further, the pt-level reduction in FP episodes ranged from 31.3 - 74.1%. No previously detected true positives were eliminated by the enhanced algorithm in either the development or test sets. This enhanced pause detection algorithm maintained high relative sensitivity, and exhibited a substantial reduction in false pauses, which would decrease the review burden in ICM device clinics while preserving sensitivity to detect clinically important events.Tabled 1DatasetNumber of PatientsNumber of episodesRelative Sensitivity (%)Reduction in FP episodes (%)Reduction in patients with FP events (%)Development347103310062.574.1Test9372810048.631.3 Open table in a new tab

Systematic Characterization of Multi-Rust Resistance Genes from a 'Parula × Thatcher' Population with a High-Density Genetic Map.

Pyramiding multiple resistant genes has been proposed as the most effective way to control wheat rust diseases globally. Identifying the most effective pyramids is challenged by the large pool of rust resistance genes and limited information about their mechanisms of resistance and interactions. Here, using a high-density genetic map, a double haploid population, and multi-rust field testing, we aimed to systematically characterize the most effective gene pyramids for rust resistance from the durable multi-rust resistant CIMMYT cultivar Parula. We revealed that the Parula resistance gene pyramid contains Lr34/Yr18/Sr57 (Lr34), Lr46/Yr29/Sr58 (Lr46), Lr27/Yr30/Sr2 (Sr2), and Lr68. The efficacy, magnitude of effect, and interactions varied for the three rust diseases. A subpopulation mapping approach was applied to characterize the complex interactions of the resistance genes by controlling for the effect of Lr34. Using this approach, we found that Lr34 and Lr68 have a strong additive effect for leaf rust, whereas no additive effects were observed for any rusts between Lr34 and Lr46. Lr34 combined synergistically with Sr12 from Thatcher for stem rust, whereas the additive effect of Lr34 and Sr2 was dependent on the type of rust and environment. Two novel leaf rust quantitative trait loci (QTLs) from Parula were identified in this study, a stable QTL QLr-7BS and QLr-5AS, which showed Lr34 dependent expression. With these findings, we propose combining two to three high-value genes from Canadian wheat (e.g., Sr12 from Thatcher) with a foundational multi-adult plant resistance cassette for desirable and durable resistance to all three rusts in Canadian wheat.

Text Summarization Using Natural Language Processing

The availability of information today accessible in digital form has accelerated. Retrieving useful document from such large pool of information gets difficult. So, to summarize these text documents is very crucial. Text summarization is a process of minimizing the original source document to get essential information of that document. It eliminates the redundant, less important content and provides you with the vital information in a shorter version usually half a length of the original text. Creating a manual summary is a very time-consuming task. Automatic summarization helps in getting the gist of information present in a particular document in a very short period. In the comparison of all Indian regional languages, there is very less amount of work done for summarization of Hindi documents. This paper presents an effective way to summarize using a Text Rank algorithm. It focuses on summarizing single Hindi text document at a time based on natural language processing (NLP).

The Imperative for Population-based Cancer Registration of All Metastatic Cancers

Metastases are the main cause of morbidity and mortality from solid tumors. Surprisingly, population-based cancer registries in various countries, including the National Cancer Institute's Surveillance, Epidemiology, and End Results program in the United States, only capture data on individuals diagnosed with cancers that are metastatic at diagnosis (M1). Metastatic recurrences of previously diagnosed, initially nonmetastatic tumors are missed. Devasia and colleagues specify an illness-death model for chronic disease and estimate that in prostate cancer, which has a large pool of primary disease that may or may not progress to metastases, about half of all metastatic cancers arise as recurrences from initially nonmetastatic disease. Capturing all incident metastatic cancer cases across all tumor types in population-based cancer registries, not only based on initial stage at diagnosis, would be critical to better understand the disparities in metastatic disease burden and the effectiveness of primary prevention, screening, and therapies for primary and metastatic disease. See related article by Devasia et al., p. 659.

Thermo-fluid dynamic analysis of HLM pool. Circe experiments

This work reports a summary of the experimental and numerical activities performed to investigate thermal hydraulic phenomena occurring in response to hypothetical accidental scenarios of Protected Loss of Heat Sink (PLOHS), and Protected Loss of Flow (PLOFA) and the heat transfer in the lead–bismuth (LBE) cooled fuel pin bundle simulator (FPS) of the CIRCE facility.A series of four experiments (PLOHS-PLOFA) were carried out simulating the total loss of the secondary circuit and the coolant pump trip with the subsequent simulation of the reactor scram (reduction of the electric power supplied to the fuel pin simulator) and activation of DHR system to remove the decay heat power (∼5% of the nominal value).Tests differ from each other by the applied boundary conditions such as the electrical power supplied to the Fuel Pin Simulator, the duration of the test, the power removed by the HX etc., while test #4 also differs for the forced circulation maintained after the simulation of the accidental transient.In parallel experimental test were carried out to investigate the heat transfer in a wrapped, grid spaced FPS. The FPS is composed of 37 electrical pins placed on a hexagonal lattice with a pitch to diameter ratio of 1.8. The bundle is hosted in the CIRCE large pool facility and represents the hot source of the Integral Circulation Experiment (ICE) test section.Several thermocouples (0.5 mm O.D.) are installed in the FPS to measure both clad and subchannel LBE temperatures at different ranks and two different sections. Experimental data are used to evaluate the Nu number in the Pe range 500–3000 and obtained data are compared with Ushakov and Mikityuk correlations having a validity range containing the experimental p/d ratio and for the selected Pe range.Experimental data relating to the obtained Nu number and the thermal hydraulic phenomena such as the modification of the thermal stratification in “pool type” configuration, coolant mass flow rate adaptation in the test section occurring during the simulation of the designed accidental conditions and the capability to cool the FPS under natural circulation conditions are described in this paper. Finally, the experimental database was used for validation of the geometrical and numerical model of the FPS adopted for 3D CFD calculations. In the performed simulations a sensitivity analysis was carried out for the turbulent Prandtl number in the range between 1 and 3 and a comparison between the first order turbulence model k-ω SST and the higher order Reynolds Stress Model was accomplished.

New Short Scale to Measure Workers’ Attitudes Toward the Implementation of Cooperative Robots in Industrial Work Settings: Instrument Development and Exploration of Attitude Structure

The implementation of new robotic technology at workplaces is oftentimes accompanied by social and organizational change processes. A new context-specific questionnaire was developed as a diagnostic tool to measure workers’ attitudes toward mobile cooperative manufacturing robots to provide a basis for managerial decisions and interventions—the “Attitudes toward Cooperative Industrial Robots Questionnaire” (ACIR-Q). Two samples, an online sample of 355 German manufacturing workers and a field sample of 201 workers from 4 local manufacturing companies were collected. For a large item pool, exploratory and confirmatory factor analysis was used to identify the attitudinal factor structure. Data showed a combined affective-behavioral factor and two cognitive factors on task-related and social-related beliefs. Based on this, the 12-item short scale ACIR-Q was derived using ant colony optimization. As attitudes can also be interpreted as networks of evaluative responses, network analysis was used for further insights. The small-world network structure (high clustering and connectivity) allows to hold complex attitudes and centrality measures indicate the most influential evaluative responses. Additionally, we explored relationships between workers’ attitudes and interpersonal variables (perceived competence, perceived control, and general self-efficacy), as well as social/organizational variables (trust in management, support climate, job insecurity and job characteristics). Based on the results, practical implications are suggested to improve workers’ attitudes.

Network analysis of toxin production in Clostridioides difficile identifies key metabolic dependencies.

Clostridioides difficile pathogenesis is mediated through its two toxin proteins, TcdA and TcdB, which induce intestinal epithelial cell death and inflammation. It is possible to alter C. difficile toxin production by changing various metabolite concentrations within the extracellular environment. However, it is unknown which intracellular metabolic pathways are involved and how they regulate toxin production. To investigate the response of intracellular metabolic pathways to diverse nutritional environments and toxin production states, we use previously published genome-scale metabolic models of C. difficile strains CD630 and CDR20291 (iCdG709 and iCdR703). We integrated publicly available transcriptomic data with the models using the RIPTiDe algorithm to create 16 unique contextualized C. difficile models representing a range of nutritional environments and toxin states. We used Random Forest with flux sampling and shadow pricing analyses to identify metabolic patterns correlated with toxin states and environment. Specifically, we found that arginine and ornithine uptake is particularly active in low toxin states. Additionally, uptake of arginine and ornithine is highly dependent on intracellular fatty acid and large polymer metabolite pools. We also applied the metabolic transformation algorithm (MTA) to identify model perturbations that shift metabolism from a high toxin state to a low toxin state. This analysis expands our understanding of toxin production in C. difficile and identifies metabolic dependencies that could be leveraged to mitigate disease severity.

Examining Visitor Collected Species Data from Denali National Park and Preserve

In citizen science programs, the concept of “participant” is often used as a catch-all without considering how different participants (e.g., young, old, wealthy, poor, differently-abled, local inhabitants, and visitors) affect scientific outcomes of citizen science programs (e.g., the data collection, data analysis, publications, etc.). This research advances the understanding of tourist participants’ ability to produce data comparable to participants who live near the study area. To examine data collected by tourist participants, we performed a case study on wildlife observation data collected through the Map of Life-Denali program in Denali National Park and Preserve in Alaska, USA. The species observation data collected by tourists and Alaska Residents were compared visually using heatmaps and statistically with the Ripley’s K function variation, the L function. Results from the analysis show that the tourist and resident data have similar point patterns for the three species we compared, <em>Ursus arctos</em> (Grizzly Bear), <em>Rangifer tarandus</em> (Caribou), and <em>Alces alces</em> (Moose). Our results indicate that tourists can be effective citizen science participants. And show the potential for leveraging this large pool of untapped participants in popular tourist destinations such as U.S. National Parks.

Heterotrophic aquatic metabolism and sustained carbon dioxide emissions in a mineral-soil wetland restored with treated effluent

Wetlands are economically valuable ecosystems, in part because they purify wastewater by retaining and processing nutrients, organic matter (OM), and other pollutants. While natural wetlands are highly productive and sequester large pools of carbon (C), it is unclear whether the C cycle of restored treatment wetlands is functionally consistent with natural systems. This knowledge gap limits our appreciation for the role that wetland restoration can play as a natural solution to climate change. Here, we quantified metabolic and C cycling patterns of a restored, multi-basin wetland (Frank Lake, Alberta, Canada) receiving municipal and beef processing plant effluents rich in nutrients and OM. We conducted metabolic measurements in all three basins using dissolved oxygen sensors deployed under ice and in open water. Extreme production and respiration indicated that effluent was largely mineralized and replaced with wetland OM in transit. The heterotrophic status of all basins aligned with a published mass budget demonstrating the aquatic habitat of the wetland was an OM sink under current drought conditions that lengthen effluent processing time. Floating chamber measurements in open water zones confirmed that the wetland was a source of CO2 to the atmosphere. From input to outflow, sustained emissions led to declining pCO2 and a decline in the ratio of dissolved inorganic to organic C. Over 30 years post-restoration, the open water habitats in Frank Lake remain heterotrophic and a net source of CO2, suggesting that the trajectory of aquatic C cycling may be distinct from wetlands restored with non-effluent water sources.

Expert Consensus on the Use of Human Serum Albumin in Adult Cardiac Surgery.

Expert Consensus on the Use of Human Serum Albumin in Adult Cardiac Surgery.

High cycle fatigue behaviour of Invar 36 alloy fabricated by laser powder bed fusion

ABSTRACT The Invar 36 alloy was additively manufactured by laser powder bed fusion (PBF-LB), and systematical observations and experiments for microstructure, defects, metallography, especially high cycle fatigue behaviour and fractography were conducted. Inadequate laser energy density results in hardly overlapping melting traces, generating numerous defects. Accordingly, the fabricated Invar 36 alloy presents an inferior high cycle fatigue life, as it failures from the rapid aggregation of the defects. In contrast, an adequate laser energy density remarkably enlarges the overlapping between adjacent melting traces. The large molten pools with steady boundaries are beneficially to generate favourable microstructures and low porosity. Consequently, the Invar 36 alloy shows superior high cycle fatigue life, completely generated from small crack propagation, long crack propagation and final fracture stages. Above experimental results and analysis primarily link up the PBF-LB process, microstructures (defects) and high cycle fatigue performance for PBF-LB Invar 36 alloy.

Sideritis scardica Extracts Demonstrate Neuroprotective Activity against Aβ25-35 Toxicity.

Alzheimer's disease (AD) is the most prevalent neurodegenerative condition, primarily affecting seniors. Despite the significant time and money spent over the past few decades, no therapy has been developed yet. In recent years, the research has focused on ameliorating the cytotoxic amyloid beta (Aβ) peptide aggregates and the increased elevated oxidative stress, two interconnected main AD hallmarks. Medicinal plants constitute a large pool for identifying bioactive compounds or mixtures with a therapeutic effect. Sideritis scardica (SS) has been previously characterized as neuroprotective toward AD. We investigated this ability of SS by generating eight distinct solvent fractions, which were chemically characterized and assessed for their antioxidant and neuroprotective potential. The majority of the fractions were rich in phenolics and flavonoids, and all except one showed significant antioxidant activity. Additionally, four SS extracts partly rescued the viability in Aβ25-35-treated SH-SY5Y human neuroblastoma cells, with the initial aqueous extract being the most potent and demonstrating similar activity in retinoic-acid-differentiated cells as well. These extracts were rich in neuroprotective substances, such as apigenin, myricetin-3-galactoside, and ellagic acid. Our findings indicate that specific SS mixtures can benefit the pharmaceutical industry to develop herbal drugs and functional food products that may alleviate AD.

Unique safety features and licensing requirements of the NuScale small modular reactor

Small modular reactors (SMR) offer a novel approach to the construction and operation of nuclear power plants. The NuScale VOYGR™ plant uses a simplified SMR design that is based on proven light-water reactor technology with substantial improvements in nuclear safety. It consists of a 250 MWt reactor core housed with other primary system components in an integral reactor pressure vessel surrounded by a steel containment vessel, all of which is immersed in a large pool of water that also serves as the ultimate heat sink. At the core of the NuScale safety case are three primary safety systems: the decay heat removal system, the emergency core cooling system, and the containment. The ability of the NuScale Power Module (NPM) passive safety systems to remove core decay heat for an unlimited duration is demonstrated through analysis of a beyond-design-basis extended loss of AC power with no replenishment of water to the ultimate heat sink or operator actions. The NuScale methodology to evaluate an indefinite loss of AC power uses the proprietary NRELAP5 systems analysis computer code. Analysis results show that the reactor coolant system liquid level above the core is maintained and that containment pressure remains below the vessel design pressure. Once full passive air cooling is established, containment pressure and temperature will decrease over time with decreasing core decay heat. NuScale received standard design approval in September 2020 and design certification in January 2023 for its 50 MWe NPM configured as a 12 module plant. NuScale is currently seeking standard design approval to increase its core power to 250 MWt, nominally 77 MWe per module, in a 6-module plant (VOYGR™-6) configuration. The high-level safety of NuScale’s SMR technology is foundational to a new standard of nuclear power plant resilience.

Stabilization of mineral-associated organic carbon in Pleistocene permafrost

Ice-rich Pleistocene-age permafrost is particularly vulnerable to rapid thaw, which may quickly expose a large pool of sedimentary organic matter (OM) to microbial degradation and lead to emissions of climate-sensitive greenhouse gases. Protective physico-chemical mechanisms may, however, restrict microbial accessibility and reduce OM decomposition; mechanisms that may be influenced by changing environmental conditions during sediment deposition. Here we study different OM fractions in Siberian permafrost deposited during colder and warmer periods of the past 55,000 years. Among known stabilization mechanisms, the occlusion of OM in aggregates is of minor importance, while 33-74% of the organic carbon is associated with small, <6.3 µm mineral particles. Preservation of carbon in mineral-associated OM is enhanced by reactive iron minerals particularly during cold and dry climate, reflected by low microbial CO2 production in incubation experiments. Warmer and wetter conditions reduce OM stabilization, shown by more decomposed mineral-associated OM and up to 30% higher CO2 production. This shows that considering the stability and bioavailability of Pleistocene-age permafrost carbon is important for predicting future climate-carbon feedback.

A Point Cloud-Based Deep Learning Model for Protein Docking Decoys Evaluation

Protein-protein docking reveals the process and product in protein interactions. Typically, a protein docking works with a docking model sampling, and then an evaluation method is used to rank the near-native models out from a large pool of generated decoys. In practice, the evaluation stage is the bottleneck to perform accurate protein docking. In this paper, PointNet, a deep learning algorithm based on point cloud, is applied to evaluate protein docking models. The proposed architecture is able to directly learn deep representations carrying the geometrical properties and atomic attributes from the 3D structural data of protein decoys. The experimental results show that the informative representations can benefit our proposed method to outperform other algorithms.

A CURRENT RETENTION BASED PROTECTION ALGORITHM FOR DC MICROGRIDS

Microgrids have been identified as a step towards goals of global green energy generation as they offer attractive options of renewable resource inclusion in decentralized energy networks, thus providing incentive towards meeting a booming energy demand sustainably. They are however impaired by the characteristic nature of Distributed Renewable Energy Resources (DRERs). While DRERs and microgrids offer the advantage of sustainable energy generation and autonomous operation with respect to the traditional grid, their intermittency and unconventional characteristics due to deviation from the traditional power grid structures causes trepidation while opting for them. A cause of concern while employing microgrids in daily use is the peril to personnel and equipment during the occurrence of a fault. To mitigate severe loss of life and property, it is important to develop and design protection algorithms for microgrids. While there is a comparatively large pool of knowledge on AC microgrid protection, DC microgrid protection is challenging and is being focused on by researchers around the world. The unorthodox nature of these networks cause conventional protection algorithms to be unsuitable and make the protection of these microgrids tasking. The work in this paper aims to contribute to efforts in the protection of hybrid microgrids. While the work in this paper is limited to the DC side of the grid, the proposed algorithm is able to detect and identify the location of various types of DC faults. The algorithm is verified on a secondary radial hybrid microgrid and is further compared with existing DC protection algorithms on various performance parameters.

The orthographic/phonological neighbourhood size effect and set size.

A growing number of studies have shown that on serial recall tests, words with more orthographic/phonological neighbours are better recalled than otherwise comparable words with fewer neighbours, the so-called neighbourhood size effect. Greeno et al. replicated this result when using a large stimulus pool but found a reverse neighbourhood size effect-better recall of words with fewer rather than more neighbours-when using a small stimulus pool. We report three registered experiments that further examine the role of set size in the neighbourhood size effect. Experiment 1 used the large pool from Greeno et al. and replicated their finding of a large-neighbourhood advantage. Experiment 2 used the small pool from Greeno et al. but found no difference in recall between the large and small neighbourhood conditions. Experiment 3 also used a small pool but the small pool was randomly generated for each subject from the large pool used in Experiment 1. This resulted in a typical large neighbourhood advantage. We suggest that set size is not critical to the direction of the neighbourhood size effect, with a large neighbourhood advantage appearing with both small and large pools.

Hippos alter their aggregations to mitigate density‐dependent drought effects

AbstractMegaherbivores play a critical role in the ecology of African savannas and grasslands. In addition, these systems are forecast to experience more frequent and severe droughts as a product of changes in the global climate. Thus, the continued conservation of megaherbivores and their associated ecosystems will require a better understanding of how megaherbivores respond to drought by shifting their movement, diet and social behaviour. We address this need by investigating the factors affecting changes in the abundance of common hippopotamus (Hippopotamus amphibius; hereafter: ‘hippos’) throughout the six major rivers of Kruger National Park, South Africa, during and following the severe drought of 2015/2016. Specifically, we aimed to understand the role of two environmental characteristics that have relevance to hippos and that changed in response to drought: vegetation condition and the extent of pooled surface water. In addition, we investigated the extent to which pre‐drought density affected changes in hippo abundance. Although vegetation and daytime refugia both appeared to influence pre‐drought hippo abundance, these factors were less important to the change in hippo abundance related to the drought. Instead, the response to drought was most strongly related to the pre‐drought abundance of hippos, where river segments supporting more than 50 individuals prior to the drought in 2015 decreased by more than half on average. Furthermore, we show that the degree of aggregation decreased from 2015 to 2016 because of the drought, but then began to increase again as the rains returned in 2017. Our results suggest that in addition to the large pools that support large aggregations of hippos in typical years, additional smaller pools are likely important for accommodating this drought‐induced dispersion. However, maintaining this distribution of pools will likely become more challenging as southern Africa's population and water demands increase.