Prior Knowledge Research Articles

Probe set selection for targeted spatial transcriptomics

AbstractTargeted spatial transcriptomic methods capture the topology of cell types and states in tissues at single-cell and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing the spatial signals present in a tissue. This requires selecting the most informative, yet minimal, set of genes to profile (gene set selection) for which it is possible to build probes (probe design). However, current selections often rely on marker genes, precluding them from detecting continuous spatial signals or new states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both gene set specificity for cell type identification and within-cell type expression variation to resolve spatially distinct populations while considering prior knowledge as well as probe design and expression constraints. We evaluated Spapros and show that it outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a single-cell resolution in situ hybridization on tissues (SCRINSHOT) experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types.

Efficient Robot Manipulation via Reinforcement Learning with Dynamic Movement Primitives-Based Policy

Reinforcement learning (RL) that autonomously explores optimal control policies has become a crucial direction for developing intelligent robots while Dynamic Movement Primitives (DMPs) serve as a powerful tool for efficiently expressing robot trajectories. This article explores an efficient integration of RL and DMP to enhance the learning efficiency and control performance of reinforcement learning in robot manipulation tasks by focusing on the forms of control actions and their smoothness. A novel approach, DDPG-DMP, is proposed to address the efficiency and feasibility issues in the current RL approaches that employ DMP to generate control actions. The proposed method naturally integrates a DMP-based policy into the actor–critic framework of the traditional RL approach Deep Deterministic Policy Gradient (DDPG) and derives the corresponding update formulas to learn the networks that properly decide the parameters of DMPs. A novel inverse controller is further introduced to adaptively learn the translation from observed states into various robot control signals through DMPs, eliminating the requirement for human prior knowledge. Evaluated on five robot arm control benchmark tasks, DDPG-DMP demonstrates significant advantages in control performance, learning efficiency, and smoothness of robot actions compared to related baselines, highlighting its potential in complex robot control applications.

Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA.

Pathway analysis is a crucial analytical phase in disease research on single-cell RNA sequencing (scRNA-seq) data, offering biological interpretations based on prior knowledge. However, currently available tools for generating cell-level pathway activity scores (PAS) exhibit computational inefficacy in large-scale scRNA-seq datasets. Additionally, disease-related pathways are often identified through cross-condition comparisons within specific cell types, overlooking potential patterns that involve multiple cell types. Here, we present single-cell pathway activity factor analysis (scPAFA), a Python library designed for large-scale single-cell datasets allowing rapid PAS computation and uncovering biologically interpretable disease-related multicellular pathway modules, which are low-dimensional representations of disease-related PAS alterations in multiple cell types. Application on colorectal cancer (CRC) datasets and large-scale lupus atlas over 1.2 million cells demonstrated that scPAFA can achieve over 40-fold reductions in the runtime of PAS computation and further identified reliable and interpretable multicellular pathway modules that capture the heterogeneity of CRC and transcriptional abnormalities in lupus patients, respectively. Overall, scPAFA presents a valuable addition to existing research tools in disease research, with the potential to reveal complex disease mechanisms and support biomarker discovery at the pathway level.

Strategies, Factors and Challenges in Core Science Subject Delivery at Cuenca Senior High School

This study aims to identify the strategies, factors, and challenges in delivering core science subjects through quantitative non-experimental research. Using a random sampling technique, data were collected from 284 respondents via a Google Form questionnaire. The findings indicate that the most used teaching strategy among senior high school science teachers is the lecture method, primarily utilizing PowerPoint slides. This is followed by project-based learning through performance tasks and lectures that incorporate ICT tools, such as e-games and quizzes. Key factors influencing science literacy include students’ prior knowledge, focus, and perceptions of the topic. The goal of this study is to develop a project to enhance core science literacy instruction within the institution. To enhance student performance, a plan of action is proposed that employs a metacognitive strategy—specifically, a contextualized or personalized scientific dictionary—to develop scientific literacy.

It's Time for a "Brain Drain!"

In their classrooms, teachers create learning environments that foster knowledge acquisition through the strategies they choose to implement. This article highlights a strategy called Brain Drain, which teachers can set up as an activity that will help students access their prior knowledge, connect new learning, and have an opportunity to discuss and build upon this learning with peers. The Schema, Cognitive Load, and Sociocultural Learning Theories are mentioned, briefly highlighted, and connected to this strategy.

Influence maximization under imbalanced heterogeneous networks via lightweight reinforcement learning with prior knowledge

Influence maximization under imbalanced heterogeneous networks via lightweight reinforcement learning with prior knowledge

Enhanced performance and robustness in anti-lock brake systems using barrier function-based integral sliding mode control

Abstract In anti-lock brake systems (ABS), the primary goal of the controller is to maximize vehicle deceleration by maintaining the slip ratio at an optimal level. This work presents a fresh approach that enhances ABS performance by integrating a sliding mode controller with a barrier function. This method combines integral sliding mode control with adaptive laws informed by barrier functions, effectively managing external disturbances and uncertainties in inertia. A significant benefit of this approach is that it does not require prior knowledge of the upper limits of these uncertainties and disturbances, thanks to the barrier function-based sliding mode control. The system state is initially aligned with the switching manifold, ensuring robust compensation for any uncertainties and disturbances right from the start of braking. During the sliding mode phase, dynamic properties are finely tuned to ensure that the system’s performance remains consistent. The effectiveness and reliability of the proposed controller have been demonstrated through numerical simulations conducted in MATLAB/Simulink, proving its capability across a range of road conditions.

Depth-resolved imaging through scattering media using time-gated light field tomography.

We present a novel, to the best of our knowledge, approach to overcome the limitations imposed by scattering media using time-gated light field tomography. By integrating the time-gating technique with light field imaging, we demonstrate the ability to capture and reconstruct images with different depths through highly scattering environments. Our method exploits the temporal characteristics of light propagation to selectively isolate ballistic photons, enabling enhanced depth resolution and improved imaging quality. Through comprehensive experimental validation and analysis, we showcase the effectiveness of our technique in resolving depth information with high fidelity, even in the presence of significant scattering. The resultant system can simultaneously acquire multi-angled projections of the object without requiring prior knowledge of the media or the target. This advancement holds promise for a wide range of applications, including non-invasive medical imaging, environmental monitoring, and industrial inspection, where imaging through scattering media is critical for an accurate and reliable analysis.

The limits of curiosity? New evidence for the roles of metacognitive abilities and curiosity in learning

AbstractThe current study investigated how metacognitive abilities such as Knowledge Confidence (subjective prior knowledge estimate) and Correctness Confidence (appraisal of the likelihood of closing the gap) relate to curiosity, and examined the roles of these metacognitive measures and curiosity in learning. Using a blurred picture paradigm in which participants viewed blurred pictures and provided their metacognitive and curiosity estimates, the current study identified distinct connections between Knowledge Confidence, Correctness Confidence and curiosity. Our finding suggests that when Knowledge Confidence is at low or medium levels, curiosity is particularly heightened. In contrast, Correctness Confidence linearly influences curiosity such that the higher the Correctness Confidence, the greater the curiosity. We also find that learning is best predicted by a learner’s metacognitive appraisal of their knowledge gap, especially when they are on the verge of knowing, and this learning effect is independent of curiosity. These findings provide new evidence for the role of curiosity and metacognition in learning, highlighting limits of the effect of curiosity on learning.

THE VIEWS OF STUDENTS AT DIFFERENT SUCCESS LEVELS ON THE FACTORS AFFECTING MATHEMATICS ACHIEVEMENT

The aim of this study was to explore the opinions of secondary school students with different achievement levels about mathematics course. In this study, in which qualitative research design was used, interviews were conducted with 36 middle school students, divided into three groups based on their achievement levels: high, average, and low. The interviews focused on the factors influencing their success in mathematics. The findings were presented under three themes: attitudes and beliefs towards mathematics, self-efficacy, and teaching-learning processes. Results of this study indicated that the majority of students across high, average and low achievement levels agreed that mathematics was necessary in daily life under the attitudes and beliefs theme and that they had belief of being successful in mathematics under the self-efficacy theme. In addition, for the teaching learning process theme, the most of the students stated that making lessons in enjoyable ways facilitated learning whereas the noise in the classroom hindered it. Also, in this study, high-achieving students, unlike students at other success levels, stated that they were disciplined, had meta-cognitive skills, use of reinforcement, individualized instruction, and assignment of project facilitated learning mathematics, and that difficult formulas, too much emphasis on the same content, lack of prior knowledge, and teacher quality hindered learning mathematics. Besides, only students with average success stated that mathematics was necessary for school grade point average and was a course that required memorization, that explaining the importance of the subject facilitated learning mathematics, and that students' inability to focus on the lesson hindered learning more. In addition, only low-achieving students stated that they saw mathematics as torture, a necessary and demanding course for upper grades, that participation in class and the interest of teachers facilitated learning mathematics, but that poor physical conditions, the abstract nature of mathematics, and too much writing hindered mathematics.

SCORE: Skill-Conditioned Online Reinforcement Learning

Solving complex long-horizon tasks through Reinforcement Learning (RL) from scratch presents challenges related to efficient exploration. Two common approaches to reduce complexity and enhance exploration efficiency are (i) integrating learning-from-demonstration techniques with online RL, where the prior knowledge acquired from demonstrations is used to guide exploration, refine representations, or tailor reward functions, and (ii) using representation learning to facilitate state abstraction. In this study, we present Skill-Conditioned Online REinforcement Learning (SCORE), a novel approach that leverages these two strategies and utilizes skills acquired from an unstructured demonstrations dataset in a policy gradient RL algorithm. This integration enriches the algorithm with informative input representations, improving downstream task learning and exploration efficiency. We evaluate our method on long-horizon robotic and navigation tasks and game environments, demonstrating enhancements in online RL performance compared to the baselines. Furthermore, we show our approach’s generalization capabilities and analyze its effectiveness through an ablation study.

Application of metagenomic next-generation sequencing in the diagnosis of infectious diseases

Metagenomic next-generation sequencing (mNGS) is a transformative approach in the diagnosis of infectious diseases, utilizing unbiased high-throughput sequencing to directly detect and characterize microbial genomes from clinical samples. This review comprehensively outlines the fundamental principles, sequencing workflow, and platforms utilized in mNGS technology. The methodological backbone involves shotgun sequencing of total nucleic acids extracted from diverse sample types, enabling simultaneous detection of bacteria, viruses, fungi, and parasites without prior knowledge of the infectious agent. Key advantages of mNGS include its capability to identify rare, novel, or unculturable pathogens, providing a more comprehensive view of microbial communities compared to traditional culture-based methods. Despite these strengths, challenges such as data analysis complexity, high cost, and the need for optimized sample preparation protocols remain significant hurdles. The application of mNGS across various systemic infections highlights its clinical utility. Case studies discussed in this review illustrate its efficacy in diagnosing respiratory tract infections, bloodstream infections, central nervous system infections, gastrointestinal infections, and others. By rapidly identifying pathogens and their genomic characteristics, mNGS facilitates timely and targeted therapeutic interventions, thereby improving patient outcomes and infection control measures. Looking ahead, the future of mNGS in infectious disease diagnostics appears promising. Advances in bioinformatics tools and sequencing technologies are anticipated to streamline data analysis, enhance sensitivity and specificity, and reduce turnaround times. Integration with clinical decision support systems promises to further optimize mNGS utilization in routine clinical practice. In conclusion, mNGS represents a paradigm shift in the field of infectious disease diagnostics, offering unparalleled insights into microbial diversity and pathogenesis. While challenges persist, ongoing technological advancements hold immense potential to consolidate mNGS as a pivotal tool in the armamentarium of modern medicine, empowering clinicians with precise, rapid, and comprehensive pathogen detection capabilities.

Estimating the vertical profile of water quality variables in reservoirs: Application of remotely sensed data and machine learning techniques.

Water quality assessment and management of reservoirs depend on accurate, large-scale, and continuous monitoring of the vertical profile of Water Quality Variables (WQVs). Remote sensing data have been widely used to retrieve high spatiotemporal water quality data; however, their application has practically been limited to evaluating surface WQVs. In this paper, a novel and efficient approach is introduced for assessing the profile of WQVs in reservoirs that depend on stratification, by taking into account the shape of profile as prior knowledge. First, an appropriate function is fitted to the WQVs vertical profile, and second, the parameters of that function as representative of the WQVs vertical profile are estimated using machine learning techniques. The model's inputs are day, maximum depth of point, and remote sensing data. Finally, PAWN sensitivity analysis is applied to show the extent to which each input influences different parts of the vertical profile. This method is applied in the Wadi Dayqah Reservoir, the largest dam in Oman, to evaluate water temperature, dissolved oxygen, pH, and chlorophyll-a profile. The results show that the predicted profiles are properly representative of in situ measurements, with a mean absolute error of 0.28 °C, 0.25 mg/L, 0.052, and 0.33 μg/L on test data sets of water temperature, dissolved oxygen, pH, and chlorophyll-a, respectively. Finally, PAWN sensitivity analysis illustrates that satellite data not only influence the parameters representing surface WQVs but also contribute to the estimation of other curve parameters.

EXPRESS: The Effect of Lexical Semantic Activation on Reasoning About Evolution: A Cross-linguistic Study.

We hypothesized that people of different language backgrounds (English vs. Mandarin Chinese) might think about evolutionary relationships among living things differently. In particular, some reasoning heuristics may come from how living things are named. Our research examined if sub-word and sub-lexical elements in written Chinese influence people's inferences. Some taxon names in Chinese are conjunctive concepts that include another taxon: e.g., panda is called bear cat in Chinese, and the skunk character has a semantic radical (semantic component of a character) that means mouse. These conjunctions might influence Chinese readers to infer that conjunctive concepts share biological characteristics with their constituents (e.g., that skunks share biological properties with mice). Readers in a language (English) without lexical activation from constituents of conjunctive concepts would not be expected to show such effects. This research provided insights into how differences in prior knowledge due to different language backgrounds affect thinking and reasoning.

Technological Interface Components That Support Accelerated Learning in the Acquisition of Foreign Language Vocabulary

There is a need to find innovative learning methods that enable accelerated learning of a foreign language. This study examined the effect of computer-assisted language learning (CALL) in acquiring a foreign language, which combines cognitive and emotional stimuli in the background. The study explored two factors related to the acquisition of a foreign language: the duration and scope of the learning process and the depth of internalization of the newly acquired language. Another objective was to assess the learning method in two learning environments, 2D and VR, to determine if the learning environment affects the learning results and leads to better vocabulary retention. One hundred native French speakers, with an average age of 47.5, participated in the study and had no prior knowledge of the newly acquired language. We randomly divided the participants into two groups (2D and VR). They studied 550 words in a new language for five days: 30 min each evening and 15 min in the morning. The post-learning test pointed out that both groups improved their vocabulary scores significantly. Approximately one month after the learning experience, we administered a knowledge retention test to 32 participants and found that the level of knowledge had been retained. Finally, background variables (e.g., gender, age, and previous knowledge of the newly acquired language) did not affect the learning results. The findings indicate that CALL, which integrates background cognitive and emotional stimuli in both learning environments, significantly accelerates learning pace, broadens the scope of newly acquired words, and ensures retention. The level of improvement observed in our study is notably higher than that reported in the literature for studies that had previously evaluated CALL and in-class language acquisition.

Nonperturbative phase boundaries in the Gross-Neveu model from a stability analysis

Two out of three phase boundaries of the 1+1-dimensional Gross-Neveu model in the chiral limit can be obtained from a standard, perturbative stability analysis of the homogeneous phases. The third one separating the massive homogeneous phase from the kink crystal is nonperturbative and could so far only be inferred from the full solution of the model. We show that this phase boundary can also be obtained via a modified stability analysis, based on the thermodynamic potential of a single kink or baryon. The same method works for the massive Gross-Neveu model, so that all phase boundaries of the Gross-Neveu model could have been predicted quantitatively without prior knowledge of the full crystal solution. Published by the American Physical Society 2024

Cross-Cultural Biology Teaching Using Next-Generation Science Standards

This study explored Next Generation Science Standards (NGSS) in cross-cultural biology teaching through collaborative lesson studies involving educators from the USA and the Philippines. We employed grounded theory and examined iterative feedback processes during lesson development to refine learning exemplars. Learning exemplars validation affirmed their alignment with both NGSS and the Philippine science education frameworks, ensuring cultural relevance and educational rigor. Five key themes were identified as pivotal: retrieval of prior knowledge, fostering meaningful learning experiences, enhancing memory and retention, fostering active engagement, and cultivating critical thinking skills—integral for developing culturally responsive curricula. Moreover, students became independent learners, responsible for their learning, reflective and critical thinkers, problem solvers, inquiry-oriented, creative, collaborative communicators, modelers, data analysts, persistent, adaptable, and self-directed. Implications include enhancing educational policies to support cultural diversity and integrating cross-cultural learning exemplars to enhance global teaching practices. This study underscored the transformative potential of cross-cultural collaboration in advancing science education, fostering engaging learning environments, and preparing students for global citizenship.

The aerosol pathway is crucial for observationally constraining climate sensitivity and anthropogenic forcing

Abstract. Climate sensitivity and aerosol forcing are two of the most central, but uncertain, quantities in climate science that are crucial for assessing historical climate as well as future climate projections. Here, we use a Bayesian approach to estimate inferred climate sensitivity and aerosol forcing using observations of temperature and global ocean heat content as well as prior knowledge of effective radiative forcing (ERF) over the industrial period. Due to limited information on uncertainties related to the time evolution of aerosol forcing, we perform a range of sensitivity analyses with idealized aerosol time evolution. The estimates are sensitive to the aerosol forcing pathway, with the mean estimate of inferred effective climate sensitivity ranging from 2.0 to 2.4 K, present-day (2019 relative to 1750) aerosol ERF ranging from −0.7 to −1.1 W m−2, and anthropogenic ERF ranging from 2.6 to 3.1 W m−2. Using observations and forcing up to and including 2022, the inferred effective climate sensitivity is 2.2 K with a 1.6 to 3.0 K 90 % uncertainty range. Analysis with more freely evolving aerosol forcing between 1950 and 2014 shows that a strong negative aerosol forcing trend in the latter part of the 20th century is not consistent with observations. Although we test our estimation method with strongly idealized aerosol ERF pathways, our posteriori estimates of the climate sensitivity consistently end up in the weaker end of the range assessed in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). As our method only includes climate feedbacks that have occurred over the historical period, it does not include the pattern effect, i.e., where climate feedbacks are dependent on the pattern of warming which will likely change into the future. Adding the best estimate of the pattern effect from IPCC AR6, our climate sensitivity estimate is almost identical to the IPCC AR6 best estimate and very likely range.

Leadership canvas: towards a leadership meta-theory

Purpose The voluminous research on leadership, along with multiple theories, makes it challenging for organizational leaders, managers, and trainers to keep track of and reconcile the different theories of leadership to derive benefits from them. We propose a leadership canvas as a meta-theory of leadership theories, integrating the major existing theories by highlighting the unifying principles that underpin these theories. Design/methodology/approach This paper has been triggered by students’ and executives’ questions regarding leadership theories and how they choose one over the other. In this paper, we have coded the leadership theories and plotted them on the two-dimensional framework using two dimensions: (1) the collaboration between leader and followers engrained in the theories, and (2) the leader focus, as implied by the different theorists. Findings Our proposed two-dimensional leadership canvas reveals that concepts in theories lower on the canvas are necessary conditions for theories higher on the leadership canvas. Thus, our framework illustrates how different leadership theories are part of a continuum rather than distinct from each other. Originality/value The proposed framework ensures a bird’s eye view of the theories, facilitating rapid comprehension of existing leadership theories regardless of readers’ prior knowledge. It also incorporates environmental complexity and leader focus in a single framework.

Knowledge and awareness of undergraduate medical students regarding palliative care in Pakistan: a cross-sectional study.

Our study assesses the current knowledge and prior awareness of undergraduate medical students in Pakistan regarding palliative care. This descriptive Cross-sectional online survey was distributed among undergraduate medical students across Pakistan, with a sample size of 246 participants. The questionnaire, adapted from the PaCKS questionnaire by Kozlov et al. (JAMA 15(5):524-34, 2017), was designed to assess the students' prior knowledge about palliative care. The mean PaCKS score was 9.7 out of 13, with a standard deviation of 2.76. Having heard of palliative care before participation in the study was significantly associated with higher PaCKS scores. Key factors associated with a higher incidence of prior awareness of palliative care included attending private institutions, being in a more advanced year of study, and having a higher mean monthly family income (P < 0.001 for each factor). This study explores medical students' understanding of palliative care in Pakistan, finding that while many have a basic grasp of the concept, there are notable misconceptions, particularly in differentiating palliative care from hospice care and understanding its broader role. Interestingly, students' knowledge did not significantly improve as they advanced through medical school, suggesting potential shortcomings in the curriculum. The findings highlight the need for more precise education and targeted training to better equip future healthcare providers for delivering patient-centered care.