Performance-based funding mechanisms as a tool for quality management of research in higher education in Poland

Rising Star: Rewriting the Code of Life for the Future of Food.

Efficient DNA extraction and sequencing protocol for keratinised materials of animals.

Bangladesh has diverse and vibrant cultural sports, some of which have gained international recognition in recent years. However, there is a lack of standardized datasets for deep learning and computer vision tasks. To address this gap, BD Sports-10 was developed as a comprehensive dataset for Bangladeshi sports. It consists of ten unique sports categories, with a total of 3000 videos, 300 per class, with a resolution of 1920×1080 pixels and 30 frames per second (FPS). Each sport in the dataset features distinct rules, viewing angles, playground setups, and actions, which also depend on players' skills. The dataset captures a diverse range of actions, including jumping, running, tagging, throwing, attempting to hit a clay pot, and capturing an opponent before they cross a designated line. BD Sports-10 includes ten traditional and culturally significant sports: Kabaddi, Nouka Baich, Lathi Khela, Kho Kho, Kanamachi, Toilakto Kolagach Arohon (Kolagach), Hari Vanga, Morog Lorai, Lathim, and Joldanga. This standardized and balanced dataset is not only suitable for classification tasks but also for object detection, player tracking, and automated scoring systems. The dataset supports research in deep learning, machine learning, and computer vision by providing ready-to-use scripts, datasets, and preprocessing pipelines that facilitate diverse AI-based experimental workflows.

This study introduces a novel multilingual dataset designed to distinguish auto-tuned musical compositions from authentic recordings, addressing a significant gap in existing resources. The dataset encompasses songs in English, Mandarin, and Japanese, ensuring a diverse representation of linguistic contexts. The data collection process began with aggregating diverse datasets from the Music Information Retrieval domain, incorporating tracks from the three specified languages to capture a wide range of musical styles and recording qualities. Each audio file was subsequently standardized into 10-second intervals with the sample rate of 16 kHz to facilitate manageable analysis. For the creation of auto-tuned samples, pitch correction was implemented using the probabilistic YIN (PYIN) algorithm for accurate pitch detection, followed by transposition via the pitch-synchronized overlap and add (PSOLA) technique. To emulate realistic auto-tuning scenarios, pitch correction was randomly applied to portions of each 10-second segment, ensuring variability and realism in the dataset, which makes it suitable for training robust detection models. Additionally, time-domain labels indicating the exact locations of pitch correction within each segment were generated, providing precise annotations crucial for developing accurate detection algorithms. The resulting multilingual dataset comprises a comprehensive collection of both auto-tuned and authentic musical segments across English, Mandarin, and Japanese languages, each annotated with detailed information about pitch correction applications. This rich annotation allows for nuanced analysis and supports various research applications, while the dataset's structure and thorough documentation of its creation process make it a valuable resource for researchers in music analysis, machine learning, and audio signal processing.

Overexpression of MERTK and FLT3 plays a crucial role in activating signal transduction pathways in various human hematological malignancies. These signaling pathways have been extensively studied and have shown significant potential as a promising therapeutic target for the treatment of acute myeloid leukemia (AML). In this study, we employed a modern medicinal chemistry approach, hybridizing machine learning (ML) with a bioisosterism strategy, to design and synthesize a new series of pyrrolo[2,3-d] pyridine derivatives as potent dual inhibitors of MERTK and FLT3. Through successive structure-activity relationship (SAR) studies, we successfully identified the lead compound 31l as a highly potent and selective MERTK/FLT3 dual inhibitor. Compound 31l exhibited remarkable kinase inhibitory activity against MERTK and FLT3 with IC50 values of 2.58 and 0.86 nM, respectively, and potential anti-proliferative activity against MOLM-13 cell lines (IC50 value of 7.50 nM). Furthermore, compound 31l displayed a favorable metabolic stability profile in both human and mouse liver microsome screens and an oral bioavailability of 56%. This finding suggests that lead compound 31l is a promising tool for further optimization and development as a potential MERTK/FLT3 dual inhibitor anti-AML drug candidate.

Considerations and practical recommendations for identifying perimenopause in longitudinal research.

ABSTRACT Decades after Brown V. Board of Education, Black undergraduates remain at the margins of campus life at historically white institutions (HWIs). While higher education research has consistently documented de facto segregation, few studies, if any, have interrogated the role of antiblackness — the paradigmatic position of blackness as slave — in reproducing racial division in modernity. This intrinsic case study centers the lived experiences of 8 Black undergraduates to examine Black/white relations at a Midwestern HWI. More specifically, I employ the Afterlife of School Segregation as an analytical lens to elaborate how equal participation in mainstream campus life is foreclosed to Black students, reifying the structural antagonism between blackness and humanity. The findings are articulated through the following themes: (1) blackness as abjection, (2) blackness as isolate, and (3) blackness as refugee. This study offers meaningful implications for select higher education stakeholders invested in supporting Black students in the Afterlife of School Segregation.

Diarrhea remains a leading cause of child mortality in Sub-Saharan Africa, necessitating advanced predictive tools for early intervention. Despite the growing adoption of machine learning in healthcare, gaps persist in deploying models as scalable, real-world solutions. This study developed an end-to-end machine learning framework to predict diarrhea among children under five in SSA, integrating rigorous model development with Flask-based deployment for practical use. Using nationally representative Demographic and Health Surveys (DHS) data from 27 SSA countries (2016-2024), we preprocessed data (handling missing values, feature selection, and SMOTE for class imbalance), trained a Random Forest classifier (optimized via RandomizedSearchCV), and deployed the model as a RESTful API with Flask. The final model demonstrated strong predictive power, with 79.6% accuracy and a particularly high recall of 84.1%, meaning it is exceptionally effective at identifying true diarrhea cases. Most importantly, the model is no longer just a research output; it is a deployed, interactive system ready for practical application. This work successfully demonstrates a complete pipeline from data to deployment, offering a tangible solution that can aid public health decision-making. We have proven that it is possible to close the gap between machine learning research and real-world implementation. To build on this foundation, future work should focus on enhancing the model's interpretability for health workers, adopting more scalable deployment technologies like FastAPI and Docker, and conducting rigorous field validation with community stakeholders to ensure these tools truly meet the needs of those they are designed to serve.

ABSTRACT Creative thinking is a primary driver of innovation in science, technology, engineering, and math (STEM), allowing students and practitioners to generate novel hypotheses, flexibly connect information from diverse sources, and solve ill-defined problems. To foster creativity in STEM education, there is a crucial need for assessment tools for measuring STEM creativity that educators and researchers can apply to test how different teaching approaches impact scientific creativity in undergraduate education. In this work, we introduce the Scientific Creative Thinking Test (SCTT). The SCTT includes three subtests that assess cognitive skills important for STEM creativity: generating hypotheses, research questions, and experimental designs. In five studies with young adults, we demonstrate the reliability and validity of the SCTT – including test-retest reliability and convergent validity with measures of creativity and academic achievement – as well as measurement invariance across race/ethnicity and gender. In addition, we present a method for automatically scoring SCTT responses, training the large language model Llama 2 to produce originality scores that closely align with human ratings – demonstrating STEM-specific, automated creativity assessment for the first time. The full SCTT, along with the code to automatically score it, are available on a repository in the Open Science Framework.

Bioprinting with stem cells is an emerging technique for creating human tissues from scratch, transforming our understanding of biology and its biomedical applications. While significant attention has been paid to biochemical cues, mechanobiology is emerging as an equally important regulator in stem cell-based bioprinting, yet it has long been unexplored. Recent advances in elucidating mechanotransduction pathways underscore the need to comprehend bioink mechanics to bridge printability and stem cell fate regulation. This review emphasises the central role of mechanobiology in stem cell-based bioprinting: ensuring adequate printability while maintaining and programming stem cell functionality through biomechanical signals. We discuss how the mechanical properties of bioinks influence stem cell behaviour, with a focus on mechanosensitive stem cells, including pluripotent, mesenchymal, neural, hepatic and lung stem cells. Special attention is given to stem cell-based organoids and their associated mechanotransduction signalling pathways. We further identify four key mechanobiological requirements that define the relationship between print fidelity and the mechanical cues governing stem cell mechanosensing. We propose integrative strategies drawing from innovations in materials science and bioprinting to reframe mechanics as a tunable parameter rather than a constraint. Our roadmap aims to leverage bioink mechanics not only to facilitate biofabrication but also to guide stem cell fate and functional remodelling of engineered tissues for potential clinical applications.

Foundation models—large AI systems pretrained on broad, heterogeneous data—are transforming scientific discovery. These models (e.g., GPT, GenCast, AlphaFold) excel at learning generalizable representations and adapting to new tasks with limited data. Yet, epidemic modeling has not experienced a comparable transformation. Traditional models remain pathogen-specific and often struggle to generate rapid insights during emerging outbreaks, as starkly illustrated by the SARS-CoV-2 pandemic. This Perspective asks whether the foundation model paradigm can extend to epidemic science: Can we build a single, pretrained model that captures the shared principles of infectious disease dynamics across pathogens, populations, and settings? Such a model could be fine-tuned to new contexts with minimal data, enabling faster forecasting, inference, and response, especially valuable in resource-limited settings. We argue that the growing convergence of epidemiological insight and modern AI makes this goal both urgent and increasingly plausible. We outline the main challenges in building foundation models for epidemics—nonstationarity, fragmented surveillance data, presence of diverse dynamical regimes, and the need for interpretability. We then propose a roadmap toward epidemic foundation models, emphasizing both algorithmic innovations to address these challenges and progress beyond algorithms, including investments in open datasets and cross-disciplinary training and collaboration. Developing epidemic foundation models offers a potentially transformative opportunity to strengthen global health security, particularly by improving preparedness in underresourced settings. If successful, they will serve as powerful, generalizable tools that complement existing efforts. The process of building these models will itself be valuable, exposing critical data gaps and guiding investments in global surveillance.

ABSTRACT Nigeria's escalating banditry crisis highlights the limitations of its formal security institutions and the resurgence of traditional security systems. Rooted in indigenous norms, these traditional mechanisms comprising rulers, vigilantes, and spiritual institutions have historically maintained social order, especially in rural communities. However, the rise of transnational, technologically enabled threats like banditry has outpaced the perceived effectiveness of these traditional approaches necessitating a paradigm shift in security strategies in Nigeria. Using the northwest region as a case study, this paper explores the evolving interplay between traditional and modern security approaches, assessing the strengths, limitations and potential integration of indigenous systems into national security architecture. The study adopts a historical research methodology comprising the use of primary and secondary sources through a blend of published works and oral interviews. It argues for a hybrid strategy that harmonises cultural legitimacy with modern oversight, training, and coordination. By bridging the gap between local agency and state authority, Nigeria can construct a more inclusive and resilient framework to address contemporary security challenges effectively.

The diversity of emerging tick-borne viruses globally: From discoveries to zoonotic risk assessment.

Multiple roads between the nucleus and the cytoplasm: classes of linear NLSs and NESs and their receptors.

Qualitative research is invaluable in internal medicine research as it allows for the amplification of patient, care partner, and provider voices in diverse and understudied regions and clinical contexts. The increasing interest and application of qualitative research methods in clinical and health services research points to a need for guidance to ensure methodological rigor and quality. This article offers practical direction for internal medicine investigators on how to design, implement, and report qualitative research. We summarize key considerations across study design, sampling, data collection, coding, analysis, and reporting, and highlight applied examples drawn from the Group Medical Visits (GMV) model. We selected the GMV model as an illustrative case given its growing use in primary and specialty care and its relevance to complex, team-based, patient-centered care in internal medicine. By integrating methodological principles with real-world applications, this paper demonstrates how qualitative research can enhance the evidence base in internal medicine and inform improvements in care delivery and health system implementation.

The digital age has stimulated innovation momentum and brought about new types of digital risks. Legislative guarantee for scientific and technological innovation is the key to driving the new productive forces and supporting the high-quality development of the digital economy. At present, the national-level legislation on scientific and technological innovation in China presents a pattern of "strategic guidance - legislative strengthening - reform guarantee". However, in legislative practice, Anhui is faced with three dilemmas: the lack of digital justice in the legislative mechanism, the ineffective connection between legislative content and the development needs of new productive forces, and the insufficient integration and absorption of digital governance practice in the legislative process. To this end, with the allocation of rights and obligations as the basic category and liability regulation clauses as the normative carrier, an institutional framework should be constructed from three dimensions: comprehensive value guidance, innovation-oriented encouragement, and prudent and inclusive supervision. This will realize the in-depth integration of legal norms for scientific and technological innovation and local governance practice, and provide a solid legal guarantee for Anhui to build a source of scientific and technological innovation.

This conceptual understanding article is part of a series where we analyze the recognition and conversion of representations of the electric field concept; in this article, we present the case of algebraic notation. We conducted a study with introductory and upper-division physics students taking electricity and magnetism courses in a large private Mexican university to learn how students recognize the electric field’s main characteristics in the algebraic notation of the field and how they convert to and from different representations. We refer to the theory of registers of semiotic representations as a theoretical framework and use a phenomenographic approach to analyze data. We explored students’ recognition and conversion abilities through interpretation and construction tasks for the electric field’s algebraic notation. We found that the main difficulties of interpreting and constructing the algebraic notation are related to separating the mathematical expression from the situation’s physical meaning. Sometimes, students referred only to the physical meaning without using algebraic notation. In other cases, they construct algebraic notation without explicitly describing the physical meaning. Another source of difficulty is the treatment process because some students make mistakes or misinterpretations that they carry throughout. We recommend that introductory and upper-division electricity and magnetism instructors and physics education researchers in higher education be aware of the difficulties that some interpretation and construction tasks may present to students learning the electric field concept.

ConspectusFor centuries, the reductionist view that "the whole equals the sum of its parts" has guided scientific study, particularly materials design. Nature, however, often defies this logic: an aggregate (whole) can display emergent properties that are totally absent in its individual parts. Aggregation-induced emission (AIE) exemplifies this "anomaly": nonluminescent molecules become emissive upon aggregation, achieving a qualitative "0-to-1" leap that challenges the reductionist tenet and provides a unique lens through which to view the emergence of new properties.Since it was proposed as a concept in 2001, AIE has been mechanistically understood as arising from the restriction of molecular motion (RMM) in the excited state. In dilute solutions, molecular rotors and vibrators dissipate exciton energy through active motions, leading to nonradiative decay. Upon aggregation, these motions are physically restricted by molecular packing and noncovalent interactions, impeding nonradiative channels and opening radiative pathways. This mechanistic understanding has motivated extensive research into AIE and expanded the field into a diverse platform of aggregation-enabled luminescent systems, including clusteroluminescence (CL), room-temperature phosphorescence (RTP), and circularly polarized luminescence (CPL)─all absent in the isolated molecular constituents and emerging through aggregation.With accumulated knowledge in AIE, the attention has broadened toward the exploration of aggregation-generated function (AGF). From this perspective, molecular motions─previously viewed as energy "wasted" that reduced emission─can be harnessed to convert excited-state energy into heat through rotations and vibrations. By channeling the same exciton energy that underlies luminescence into nonradiative decay pathways, we can engineer aggregates to exhibit emergent photothermal (PT), photoacoustic (PA), and photocatalytic (PC) activities. These functions open new application avenues, including solar energy conversion, high-resolution deep-tissue imaging, and "intelligent" actuation.From the serendipitous encounter with AIE to the systematic study of AGF, advances in the field have shifted scientific attention from isolated molecules to complex aggregates. With the elucidation of principles governing emergent properties, it is becoming clear that a paradigm shift is needed─from molecularism to aggregatism or from molecular science to aggregate science (AS). Guided by emergentism, AS studies how molecules, through noncovalent interactions and hierarchical organization, give rise to macroscopic functions absent in their individual constituents. Notably, the materials we use and the life we see around us are all aggregates. This aggregate-level perspective enables the development of new systems with complex functionalities (e.g., advanced multimodal theranostics) and deepens our understanding of life─an archetypal multiary system in which the aggregation of nonliving biomolecular constituents yields a living organism.In this Account, we detail the intellectual trajectory from AIE to AGF and finally to AS. We distill the guiding principles and outline future directions, including transitions from unary to multiary systems, static structures to dynamic processes, and descriptive aggregate science to prescriptive aggregate engineering. A deeper understanding of AS will enable new scientific discoveries and technological innovations, inviting us to imagine a future designed not merely with matter but with the sophisticated organizational logic that endows it with "life-like" functions.

ABSTRACT With the cross‐integration of flexible electronics and artificial intelligence technologies, high‐sensitivity wearable sensors have shown great potential in fields such as medical rehabilitation, human‐computer interaction, and sports science. To meet the dual requirements of high sensitivity and flexibility for wearable applications, this study proposes a novel sandwich‐structured P(VDF‐TrFE)/BTO‐OH/P(VDF‐TrFE) piezoelectric sensor (SP‐sensor) using a fused deposition modeling (FDM) process. This structure effectively combines the high piezoelectricity of BTO nanoparticles with the excellent flexibility of the P(VDF‐TrFE) polymer, overcoming the limitations of traditional single‐layer piezoelectric sensors. Experimental results demonstrate that the piezoelectric response voltage and current of the SP‐sensor are enhanced by 51.1% and 546%, respectively, compared with those of single‐layer P(VDF‐TrFE) films. With improved piezoelectric performance, the SP‐sensor achieves approximately 50% higher sensitivity than the traditional designs. It also exhibits quick response and recovery capabilities, with a response time of 8.1 ms and a recovery time of 64 ms. Additionally, it exhibits excellent fatigue resistance, with no noticeable voltage decay observed after 18,000 cycles of testing. An intelligent wrist motion recognition system, integrating a deep learning algorithm (CNN model), was developed based on this SP‐sensor, enabling real‐time classification of three types of wrist movement patterns with an identification accuracy of 97.85%. This study, through the innovation in material structure and the integration of AI algorithms, paves the way for the application of next‐generation wearable devices in human‐machine interaction and medical diagnosis.