Counting Strategies Research Articles

Heterobinuclear Vanadium-Nickel Lantern-Type Complexes: Metal-Metal bonding and structure

Heterobinuclear complexes with metal–metal bonds have been increasingly studied over the past two decades. Binuclear vanadium-nickel lantern-type complexes are studied here by density functional theory using the MN15-L functional. Three series of digonal, trigonal, and tetragonal lantern-type complexes VNiLx (x = 2, 3, 4) are treated, where the ligand L is formamidinate (fad), guanidinate (gua) and formate (OCHO). A trigonal complex with phosphinoamide ligands is also treated. Each complex is treated in three low-lying spin states to predict the ground state spin multiplicity. The V-Ni bond lengths obtained are noted with reference to the metal–metal formal bond order (fBO) values derived from molecular orbital analysis. Electron-bookkeeping diagrams derived from an electron counting strategy also yield V-Ni formal bond order values consistent with the V-Ni bond lengths and with the fBO values from MO analysis. The four ligands studied are compared with regard to their effects upon metal–metal bonding. The V-Ni bond lengths in these complexes, along with the V-Ni bond stretching frequencies, Wiberg bond index and formal shortness ratio values, are all categorized into ranges according to the V-Ni bond orders from zero to two.

Interpreting young children’s multiplicative strategies through their drawn representations

The exploration of children’s drawings as mathematical representations is a current focus in early years mathematics education research. This paper presents a qualitative analysis of 72 kindergarten to Grade 3 (5 to 8 years old) children’s drawings produced during problem-solving tasks centred on multiplicative strategies. Existing frameworks for the developmental sequence of mathematical drawings and the progression of children’s strategies for multiplicative situations were an interpretive lens through which to analyse the drawings. Children used pictographic and iconic drawing types to represent the “story” in the problem and the multiplicative strategies employed to solve the tasks. Exploration of the children’s drawings suggested that as children’s drawings become more structural, schematic in nature, it may be easier for children to show their understanding of the structural elements of multiplicative relationships. Results revealed that structural elements of multiplicative relationships were more easily seen in iconic representations; however, both pictographic and iconic drawings were useful to observe counting, additive, and multiplicative strategies when mathematical elements of the problem were visible. Additional representations attached to the drawings (e.g. numerical) were needed to confirm children’s strategies when their drawings lacked structure. These findings have implications for how young children’s drawings are interpreted by classroom teachers. The interpretation of these drawings suggested that some children may not yet realise how their drawings in mathematics need to shift from illustrations of the problem’s story context to representing mathematical ideas and processes — which requires intentional teaching of the purpose of drawings for mathematical contexts.

Revealing the Statistics of Extreme Events Hidden in Short Weather Forecast Data

AbstractExtreme weather events have significant consequences, dominating the impact of climate on society. While high‐resolution weather models can forecast many types of extreme events on synoptic timescales, long‐term climatological risk assessment is an altogether different problem. A once‐in‐a‐century event takes, on average, 100 years of simulation time to appear just once, far beyond the typical integration length of a weather forecast model. Therefore, this task is left to cheaper, but less accurate, low‐resolution or statistical models. But there is untapped potential in weather model output: despite being short in duration, weather forecast ensembles are produced multiple times a week. Integrations are launched with independent perturbations, causing them to spread apart over time and broadly sample phase space. Collectively, these integrations add up to thousands of years of data. We establish methods to extract climatological information from these short weather simulations. Using ensemble hindcasts by the European Center for Medium‐range Weather Forecasting archived in the subseasonal‐to‐seasonal (S2S) database, we characterize sudden stratospheric warming (SSW) events with multi‐centennial return times. Consistent results are found between alternative methods, including basic counting strategies and Markov state modeling. By carefully combining trajectories together, we obtain estimates of SSW frequencies and their seasonal distributions that are consistent with reanalysis‐derived estimates for moderately rare events, but with much tighter uncertainty bounds, and which can be extended to events of unprecedented severity that have not yet been observed historically. These methods hold potential for assessing extreme events throughout the climate system, beyond this example of stratospheric extremes.

A visualized automatic particle counting strategy for single‐cell level telomerase activity quantification

AbstractThe accurate evaluation of telomerase activity, a typical cancer biomarker, is vital for early cancer screening. In this study, we developed a dark‐field microscopy (DFM) visual single‐particle detection scheme to detect telomerase activity based on automatic counting gold nanoparticles (AuNPs). This method started with attaching the telomerase substrate (TS) primer to the magnetic beads (MBs) through streptavidin‐biotin interaction. In the presence of telomerase and dNTPs, the TS primer was expanded with (TTAGGG)n repeat units to form the telomerase extension product (MBs‐telomerase extension product), which could be hybridized with the complementary DNA (cDNA) modified with AuNPs through Au‐S bonds (AuNPs‐SH‐cDNA). After magnetic separation and DNA double‐strand unwinding, AuNPs were collected from the supernatant, and the telomerase activity was quantitatively measured by visually counting bright spots based on DFM. This strategy achieved a limit of detection as low as 1 HeLa cell and distinguished telomerase activity among different cell lines, thus verifying its excellent sensitivity and specificity. Further, two common telomerase inhibitors (BIBR1532 and curcumin) were screened with the consistent IC50 values with other methods, respectively. It is worth mentioning that this strategy can clearly identify bladder cancer among various urinary diseases. Consequently, the visualized automatic particle counting strategy is potential as a powerful tool in early and noninvasive diagnosis of bladder cancer.

Modernized Crowd Counting Strategies for Mass Gatherings-A Review.

Estimation of crowd size for large gatherings is an indispensable metric for event planners, local authorities, and emergency management. Currently, most crowd counting relies on dated methods such as people counters, entrance sensors, and ticket sales. Over the past decade, there has been rapid development in crowd counting techniques and related technology. Despite progress, theoretical advances in crowd counting technology have outpaced practical applications. The emergence of the vast array of crowd counting techniques has added to the challenge of determining those advances that can be most readily implemented. This article aims to provide an overview of promising crowd counting strategies and recent developments applied within the disaster medicine context along with the best use cases and limitations.

Engineered Cancer Cells as Signal Probes for Fluorescence-Assisted Digital Counting Analysis.

Fluorescence-assisted digital counting analysis allowed sensitive quantification of targets by measuring individual fluorescent labels. However, traditional fluorescent labels suffered from low brightness, small size, and sophisticated preparation procedures. Herein, engineering fluorescent dye-stained cancer cells with magnetic nanoparticles were proposed to construct single-cell probes for fluorescence-assisted digital counting analysis by quantifying the target-dependent binding or cleaving events. Various engineering strategies of cancer cells including biological recognition and chemical modification were developed for rationally designing single-cell probes. Introduction of suitable recognition elements into single-cell probes allowed digital quantification of each target-dependent event via counting the colored single-cell probes in the representative image taken using a confocal microscope. The reliability of the proposed digital counting strategy was corroborated by traditional optical microscopy- and flow cytometry-dependent counting technologies. The advantages of single-cell probes, including high brightness, big size, ease of preparation, and magnetic separation, contributed to the sensitive and selective analysis of targets of interest. As proof-to-concept assays, indirect analysis of exonuclease III (Exo III) activity, as well as direct quantitation of cancer cells, were investigated, and the potential in biological sample analysis was also assessed. This sensing strategy will open a new avenue for the development of biosensors.

Codex Sierra: A Nahuatl-Mixtec Book of Accounts from Colonial Mexico

Codex Sierra: A Nahuatl-Mixtec Book of Accounts from Colonial Mexico

Multiplexed Protein Detection and Parallel Binding Kinetics Analysis with Label-Free Digital Single-Molecule Counting.

Multiplexed protein detection is critical for improving the drug and biomarker screening efficiency. Here, we show that multiplexed protein detection and parallel protein interaction analysis can be realized by evanescent scattering microscopy (ESM). ESM enables binding kinetics measurement with label-free digital single-molecule counting. We implemented an automatic single-molecule counting strategy with high temporal resolution to precisely determine the binding time, which improves the counting efficiency and accuracy. We show that digital single-molecule counting can recognize proteins with different molecular weights, thus making it possible to monitor the protein binding processes in the solution by real-time tracking of the numbers of free and bound proteins landing on the sensor surface. Furthermore, we show that this strategy can simultaneously analyze the kinetics of two different protein interaction processes on the surface and in the solution. This work may pave a way to investigate complicated protein interactions, such as the competition of biomarker-antibody binding in biofluids with biomarker-protein binding on the cellular membrane.

Strategic adaptation to dual-task in verbal working memory: Potential routes for theory integration.

How working memory supports dual-task performance is the focus of a long-standing debate. Most previous research on this topic has focused on participant performance data. In three experiments, we investigated whether changes in participant-reported strategies across single- and dual-task conditions might help resolve this debate by offering new insights that lead to fruitful integration of theories rather than perpetuating debate by attempting to identify which theory best fits the data. Results indicated that articulatory suppression was associated with reduced reports of the use of rehearsal and clustering strategies but to an increase of the reported use of a visual strategy. Elaboration and clustering strategies were reported less for memory under dual task compared with single task. Under both dual task and articulatory suppression, more participants reported attempting to remember fewer memory items than were presented (memory reduction strategy). For arithmetic verification, articulatory suppression and dual task resulted in a reduction in reports of a counting strategy and an increase in reports of a retrieval strategy for arithmetic knowledge. It is argued that experimenters should not assume that participants perform the same task in the same way under different experimental conditions and that carefulty investigation of how participants change their strategies in response to changes in experimental conditions has considerable potential for resolving theoretical challenges. It is argued further that this approach points toward the value of attempting to integrate rather than proliferate theories of working memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Stimulus Control for Making Math Verbal

In two experiments, we used multiple probe designs to first examine the correlation between the presence of transformation of stimulus function (TSF) across math operants and the presence of TSF across saying and writing for spelling, and then tested the effects of the establishment of TSF across saying and writing words on the establishment of TSF across math operants. Eight middle school students with learning disabilities participated in Experiment 1. Four participants demonstrated TSF across saying and writing for spelling. The dependent variables of Experiment 1 were the accuracy and rate of solving word problems after receiving fluency training on math facts, and the number of counting strategies used when solving word problems. Results showed that all participants who demonstrated TSF demonstrated significant increases in accuracy and rate when solving word problems whereas three participants without TSF did not. Three participants subsequently participated in Experiment 2, which tested the effects of establishment of TSF across saying and writing words on the establishment of TSF across math operants. All three participants demonstrated increased accuracy and rate for word problems and extinction of counting strategies. The results of the two experiments suggested the importance of teaching math as a verbal behavior, in particular, as a speaker-as-own-listener behavior.

Sustainable prime movers selection for biogas-based combined heat and power for a community microgrid: A hybrid fuzzy multi criteria decision-making approach with consolidated ranking strategies

Combined heat and power (CHP) based community microgrid using renewable resources such as biogas is gaining fast prominence in municipalities across the world as a strategy to decarbonise energy sector and brings power supply close to the consumers. But for profitable operation of a CHP system, the selection of suitable prime movers (PMs) is very germane. It has become a significant issue to assess PM alternatives in detail from the investor’s or policy maker’s view point. Due to varying characteristics of each PM, multi criteria decision making (MCDM) techniques could be efficient tools for selecting optimal PM and making a reasonable decision. In this work, four PMs such as internal combustion engine (ICE), gas turbine (GT), micro-gas turbine (MGT) and fuel cells (FCs) are selected as alternatives with twenty evaluation criteria. The weights of criteria for each PM are evaluated taking a cue from the integration of Fuzzy-best-worst (Fuzzy-BWM) and Fuzzy-Entropy methods using fuzzy preference variables. These PMs are ranked using Fuzzy-Combined Compromise Solution (Fuzzy-CoCoSo) on the basis of technical, economic, environmental and social specifications. Thereafter, results based on each weighting preference are merged by using the Borda count and Copeland ranking strategies. The results reveal that initial investment cost in the economic category and footprint in the social category are respectively the most and least significant criteria for PM assessment. It is also revealed that FCs are the most sustainable PM with a compromise score of 2.8053 followed by ICE with a score of 2.5767. The least ranked PM is the MGT with a compromise value of 2.5541. Furthermore, the robustness and reliability of the results are tested by applying a sensitivity analysis and comparative assessment with five existing Fuzzy MCDM methods. This research could be useful to investors and policy makers for the selection of sustainable PM at the planning stage of CHP based community microgrid.

Single Nanoparticle Counting-Based Liquid Biopsy for Cancer Diagnosis.

Liquid biopsy has become a sought-after technique for disease diagnosis because it provides real-time, dynamic, and comprehensive information that can alleviate the dilemmas faced by tissue biopsy. Multiplexed microRNAs (miRNAs) have been demonstrated to be promising biomarkers in liquid biopsy but fall short in providing simple and sensitive analytical methods. In this work, we established a novel nanoparticle counting strategy to accomplish simultaneous analysis of three breast cancer-associated miRNAs for breast cancer diagnosis. The frequency of nanoparticles not involved in the formation of sandwich structures was detected by single-particle inductively coupled plasma mass spectrometry (SP-ICPMS) to quantify the targets. The detection limits for miR-21, miR-155, and miR-16 were 49, 51, and 55 amol, respectively. The strategy was applied to clinical samples and successfully achieved the diagnosis between normal individuals and breast cancer patients. The area under the curve (AUC) for the combination of the relative expression of miR-21 and miR-155 was 0.818. The above results indicate that this strategy has potential and holds great promise for playing an essential role in cancer diagnosis.

Combined LOFAR and DEMON Spectrums for Simultaneous Underwater Acoustic Object Counting and F0 Estimation

In a typical underwater acoustic target detection mission, we have to estimate the target number (N), perform source separation when N>1, and consequently predict the motion parameters such as fundamental frequency (F0) from separated noises for each target. Although deep learning methods have been adopted in each task, their successes strongly depend on the feed-in features. In this paper, we evaluate several time-frequency features and propose a universal feature extraction strategy for object counting and F0 estimation simultaneously, with a convolutional recurrent neural network (CRNN) as the backbone. On one hand, LOFAR and DEMON are feasible for low-speed and high-speed analysis, respectively, and are combined (LOFAR + DEMON) to cope with full-condition estimation. On the other hand, a comb filter (COMB) is designed and applied to the combined spectrum for harmonicity enhancement, which will be further streamed into the CRNN for prediction. Experiments show that (1) in the F0 estimation task, feeding the filtered combined feature (LOFAR + DEMON + COMB) into the CRNN achieves an accuracy of 98% in the lake trial dataset, which is superior to LOFAR + COMB (83%) or DEMON + COMB (94%) alone, demonstrating that feature combination is plausible. (2) In a counting task, the prediction accuracy of the combined feature (LOFAR + DEMON, COMB included or excluded) is comparable to the state-of-the-art on simulation dataset and dominates the rest on the lake trial dataset, indicating that LOFAR + DEMON can be used as a common feature for both tasks. (3) The inclusion of COMB accelerates the convergence speed of the F0 estimation task, however, it penalizes the counting task by a depression of 13% on average, partly due to the merging effects brought in by the broadband filtering of COMB.

Counting strategies of students struggling in mathematics in a computer-based learning environment

Children with mathematics difficulties often show challenges in additive reasoning and problem solving and may experience challenges with more advanced mathematics. To address these challenges, we implemented a computer-based intervention to promote children’s conceptual understanding of numbers, counting, and additive strategies. The purpose of this exploratory case study was to provide an in-depth analysis of the progression of counting and composite unit development as well as strategies for solving addition and subtraction problems by two third-graders with mathematics difficulties in a computer-based learning environment. We analyzed video-recorded sessions, field notes, and pre- and post-assessments. Our findings show that participating students negotiated solving addition and subtraction problems through inventing more advanced and efficient additive double-counting strategies. In addition, it seems that the growth of their counting through the computer-based intervention was generalized to solving additive problems on a paper-and-pencil test.

D[Formula: see text]: Sensitivity at the optimal criterion location.

Signal detection theory (SDT) was developed to provide a measure of the discriminability of a signal against background noise, independently of response bias. However, equal discriminability over a range of bias is only achieved by the traditional signal detection measure d[Formula: see text] under a narrow set of conditions - i.e., binormal noise and signal distributions of equal variance and base rates. In response to observed departures from these conditions, more robust alternative measures of d[Formula: see text] have been developed, including da and, more recently, d[Formula: see text]. Each of these alternatives addresses some, but not all, of the difficulties that arise when the assumptions of SDT are violated. Moreover, none of these measures directly follow from a central idea of discriminability by an observer that adopts a minimize error count (MEC) strategy. I propose a new d[Formula: see text] alternative, d[Formula: see text], that is robust to violations of the standard signal detection assumptions, remains consistent with varying bias, and is grounded in the principle of discriminability following a MEC strategy. Simulations illustrate how d[Formula: see text] is similar to the recently developed d[Formula: see text] when the observer optimizes their criterion placement to minimize the number of errors but, unlike d[Formula: see text], remains consistent irrespective of the observer's criterion placement Moreover, unlike da, d[Formula: see text] reflects changes in discriminability related to base rates of signal vs. noise presentations. The use of d[Formula: see text] also has implications for the interpretation of bias metrics, such as β and c, which are examined at the optimal criterion under a variety of conditions.

Learning to count biological structures with raters' uncertainty.

Exploiting well-labeled training sets has led deep learning models to astonishing results for counting biological structures in microscopy images. However, dealing with weak multi-rater annotations, i.e., when multiple human raters disagree due to non-trivial patterns, remains a relatively unexplored problem. More reliable labels can be obtained by aggregating and averaging the decisions given by several raters to the same data. Still, the scale of the counting task and the limited budget for labeling prohibit this. As a result, making the most with small quantities of multi-rater data is crucial. To this end, we propose a two-stage counting strategy in a weakly labeled data scenario. First, we detect and count the biological structures; then, in the second step, we refine the predictions, increasing the correlation between the scores assigned to the samples and the raters' agreement on the annotations. We assess our methodology on a novel dataset comprising fluorescence microscopy images of mice brains containing extracellular matrix aggregates named perineuronal nets. We demonstrate that we significantly enhance counting performance, improving confidence calibration by taking advantage of the redundant information characterizing the small sets of available multi-rater data.

Investigating volume estimation performance and strategies of 6th-Grade children and adults

The present study aimed to examine children’s and adults’ performance and strategies in volume estimations. Three tasks were designed and presented to 40 adults and 40 6th grade children who were asked to estimate: (a) the number of unit measures (bricks/rice spoons) that may be put in a container, (b) the quantity of bricks/rice, between two different quantities of bricks/rice, that could fit in a container, and (c) the container, between two containers, that may hold a quantity given. Participants were – randomly and in equal numbers from each age group – assigned to four groups differing in their opportunity for trial condition and the presence of the half reference point in the containers. Although the majority of participants showed quite low performance rates in estimating volume, their estimations were more successful when the trial condition was available to them as well as when the trials referred to small containers and discrete quantities. The mental counting strategy was mainly used by both age groups. Adults, however, often used the algorithm and processed precise volume measurement. These results are further discussed in relation to educational implications as well as to the perspective of early supporting children’s abilities for estimations at the primary school.

Nucleic Acids Detection for Mycobacterium tuberculosis Based on Gold Nanoparticles Counting and Rolling-Circle Amplification.

Tuberculosis (TB) is a common infectious disease caused by Mycobacterium tuberculosis, which usually disturbs the lungs, and remains the second leading cause of death from an infectious disease worldwide after the human immunodeficiency virus. Herein, we constructed a simple and sensitive method for Mycobacterium tuberculosis-specific DNA detection with the dark-field microscopic imaging of gold nanoparticles (AuNPs) counting strategy and rolling-circle amplification (RCA). Taking advantage of RCA amplification, one target molecule produced hundreds of general oligonucleotides, which could form the sandwich structure with capture-strand-modified magnetic beads and AuNPs. After magnetic separation, AuNPs were released and detected by dark-field imaging; about 10 fM Mycobacterium tuberculosis-specific DNA target can still be differentiated from the blank. No significant change of the absorbance signals was observed when the target DNA to genomic DNA ratio (in mass) was from 1:0 to 1:106. The spike recovery results in genomic DNA from human and Klebsiella pneumoniae suggested that the proposed method has the feasibility for application with biological samples. This proposed method is performed on an entry-level dark-field microscope setup with only a 6 μL detection volume, which creates a new, simple, sensitive, and valuable tool for pathogen detection.

Reducing the tendency for chronometric counting in duration discrimination tasks

Chronometric counting is a prevalent issue in the study of human time perception as it reduces the construct validity of tasks and can conceal existing timing deficits. Several methods have been proposed to prevent counting strategies, but the factors promoting those strategies in specific tasks are largely uninvestigated. Here, we modified a classical two-interval duration discrimination task in two aspects that could affect the tendency to apply counting strategies. We removed the pause between the two intervals and changed the task instructions: Participants decided whether a short event occurred in the first or in the second half of a reference duration. In Experiment 1, both classical and modified task versions were performed under timing conditions, in which participants were asked not to count, and counting conditions, in which counting was explicitly instructed. The task modifications led to (i) a general decrease in judgment precision, (ii) a shift of the point of subjective equality, and (iii) a counting-related increase in reaction times, suggesting enhanced cognitive effort of counting during the modified task version. Precision in the two task versions was not differently affected by instructed counting. Experiment 2 demonstrates that—in the absence of any counting-related instructions—participants are less likely to engage in spontaneous counting in the modified task version. These results enhance our understanding of the two-interval duration discrimination task and demonstrate that the modifications tested here—although they do not significantly reduce the effectiveness of instructed counting—can diminish the spontaneous tendency to adopt counting strategies.

Effect of a novel-app based strategy for carbohydrate counting on glucose control in type 1 diabetes

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)