Life Sciences Research Articles

The life sciences and the history of analytic philosophy.

Comparative to the commonplace focus onto developments in mathematics and physics, the life sciences appear to have received relatively sparse attention within the early history of analytic philosophy. This paper addresses two related aspects of this phenomenon. On the one hand, it asks: to the extent that the significance of the life sciences was indeed downplayed by early analytic philosophers, why was this the case? An answer to this question may be found in Bertrand Russell's 1914 discussions of the relation between biology and philosophy. Contrary to received views of the history of analytic philosophy, Russell presented his own 'logical atomism' in opposition not only to British Idealism, but also to 'evolutionism'. On the other hand, I will question whether this purported neglect of the life sciences does indeed accurately characterise the history of analytic philosophy. In answering this, I turn first to Susan Stebbing's criticisms of Russell's overlooking of biology, her influence on J.H. Woodger, and her critical discussion of T.H. Huxley's and C.H. Waddington's application of evolutionary views to philosophical questions. I then discuss the case of Moritz Schlick, whose evolutionist philosophy has been overlooked within recent debates concerning Logical Empiricism's relation to the philosophy of biology.

Artistic mutilation of genitalia: an interpretive analysis of representations of genitalia in Indian school science textbooks

ABSTRACT Visual representations (and their censorship) are carriers of messages and are powerful tools to invoke discourses. Visuals are not just supplements to written text; rather, these can be read independently as the primary text and are crucial to scientific communication, particularly in life sciences. This paper aims to investigate various textbook depictions of genitalia and examine how ideologies are manifested through visuals in relation to the human body. Here we try to unpack the visual representations of genitalia in different discursive spaces within Indian science textbooks from 6th to 12th grade. We position our study within the hermeneutic methodological landscape and apply the socio-semiotic analytic framework developed by Serafini [Serafini, Frank. 2010. “Reading Multimodal Texts: Perceptual, Structural and Ideological Perspectives.” Children's Literature in Education 41 (2): 85–104. https://doi.org/10.1007/s10583-010-9100-5], consisting of three analytic perspectives: perceptual, structural, and ideological, to interpret the visual images of genitalia and the ideological matrix that produced these images. Our analysis of textbooks' portrayal reveals a tendency to censor or obscure genital depictions. The paper discusses the potential effects of these representations on students' health and relationships with their bodies and argues that they must engage in dialogue with discourses challenging normative understandings of the body and remain open to change.

Corrigendum to “Modeling PROTAC degradation activity with machine learning” [Artificial Intelligence in the Life Sciences 6 (2024) 100104]

Corrigendum to “Modeling PROTAC degradation activity with machine learning” [Artificial Intelligence in the Life Sciences 6 (2024) 100104]

Cover Picture: Engineering in Life Sciences 8'24

Cover Picture: Engineering in Life Sciences 8'24

Machine Learning-Driven Prediction of DLD Chip Throughput

Abstract The microfluidic chip technology, capable of manipulating fluids at the micrometer-scale, is increasingly being applied in the fields of cell biology, molecular biology, chemistry, and life sciences. The densely integrated microfluidic chip devices enable high-throughput parallel experiments and integration of various operational units. However, the development of densely integrated microfluidic chips also comes with high demands on driving equipment. Due to manufacturing processes and inherent design limitations, the driving capability of the equipment is restricted. To address potential challenges faced by microfluidic chips in the development towards integrated biological microsystems and to maximize their high-throughput performance, improvements are required not only in selecting appropriate driving equipment but also in design aspects. This study focuses on the DLD chip and delves into the complexity of microfluidic chip design. By combining Bézier curves to characterize arbitrarily shaped micropillars and conducting finite element analysis to compute the pressure field of DLD chips, we explore methods utilizing random forest, XGBoost, LightGBM, and ANN machine learning algorithms to predict the impedance of DLD chips. Our objective is to guide engineers in designing chips with smaller impedance (lower pressure drop) and larger throughput more quickly and efficiently during the design phase. Ultimately, through evaluating the predictive capabilities of the four models on new data, we select the ANN algorithm model to predict the pressure drop under different designs of DLD chips. This offers possibilities for enhancing the efficiency and integration of microfluidic technology in biomedical applications.

Pembelajaran Distribusi Gamma dalam Masalah Biomedis: Tinjauan pada Pengaruh Dosis Beracun pada Tikus

Purpose: The objective of this study is to examine the use of the Gamma function in the Gamma distribution and its application in analyzing the effect of toxic doses on rats. Methodology: The methodology employed in this study utilizes a non-standard Gamma distribution, with a continuous random variable X that has parameters α and β. The research methods employed in this study include a literature review and a case study. Findings: The results of the calculations indicated that the mean time to death for the rats was 50 weeks, with a standard deviation of 22.36 weeks. Significance: It was also found that the probability of rats surviving for more than 60 days was less than 60%. Based on these findings, the study offers recommendations for the teaching of mathematics, particularly in the context of understanding probability distributions and their applications in biological and toxicological contexts. This study underscores the significance of grasping the Gamma distribution in anticipating and interpreting data in the life sciences, as well as the capacity of mathematical concepts to be operationalized in genuine settings.

Structured methods for parameter inference and uncertainty quantification for mechanistic models in the life sciences.

Parameter inference and uncertainty quantification are important steps when relating mathematical models to real-world observations and when estimating uncertainty in model predictions. However, methods for doing this can be computationally expensive, particularly when the number of unknown model parameters is large. The aim of this study is to develop and test an efficient profile likelihood-based method, which takes advantage of the structure of the mathematical model being used. We do this by identifying specific parameters that affect model output in a known way, such as a linear scaling. We illustrate the method by applying it to three toy models from different areas of the life sciences: (i) a predator-prey model from ecology; (ii) a compartment-based epidemic model from health sciences; and (iii) an advection-diffusion reaction model describing the transport of dissolved solutes from environmental science. We show that the new method produces results of comparable accuracy to existing profile likelihood methods but with substantially fewer evaluations of the forward model. We conclude that our method could provide a much more efficient approach to parameter inference for models where a structured approach is feasible. Computer code to apply the new method to user-supplied models and data is provided via a publicly accessible repository.

Design and Application of PBL Model for Life Science Teacher Training Curriculum with Enhanced Linkage to Education Field

Design and Application of PBL Model for Life Science Teacher Training Curriculum with Enhanced Linkage to Education Field

Life sciences teachers’ integration of indigenous knowledge: A vision for making science classrooms culturally responsive

This article presents findings from a qualitative study exploring factors influencing life sciences teachers’ integration of indigenous knowledge (IK) across various topics in grades 10-12, as mandated by the curriculum and assessment policy statement in South Africa. Despite this mandate aiming to enhance accessibility and comprehension, many teachers struggle to integrate IK meaningfully. Through in-depth interviews and lesson observations with five teachers in Gauteng Province, the study reveals challenges such as inadequate training, limited support structures, and negative attitudes towards IK. Observations highlight deficiencies in pedagogical content knowledge related to IK integration. The study suggests intensified teacher training and future research focusing on textbook content and leveraging information and communication technologies to digitize IK and make its integration more meaningful and interesting. This will enhance educational experiences for both the teachers and learners in life sciences classrooms.

Molecular Differences With Therapeutic Implications in Early-Onset Compared With Average-Onset Biliary Tract Cancers.

Early-onset biliary tract cancer (eoBTC) is among the fast-growing subset of early-onset cancers, yet little is known about its biology. We sought to identify novel molecular characteristics of eoBTC in relation to average-onset BTC (aoBTC) using a real-world multiomics data set. The study comprised patients with BTC whose tumors underwent molecular analyses at Caris Life Sciences and were categorized by age (<50 years for eoBTC, ≥50 years for aoBTC). P values were adjusted for multiple testing and considered significant at Q < 0.05 (molecular comparisons) or Q < 0.25 (Gene Set Enrichment Analysis [GSEA]). Insurance claims data were used for survival analysis. The study included 5,587 patients with BTC (453 eoBTC, median age = 44 years and 5,134 aoBTC, median age = 68 years). FGFR2 fusion (15.7% in eoBTC v 5.9% in aoBTC) and NIPBL fusion (1.1% v 0%) were significantly more prevalent in eoBTC (both Q < 0.001). The interferon gamma-IFG score (fold change [FC], 1.1; Q = 0.01) and T-cell inflammation score (FC, 17.3; Q = 0.03) were significantly higher in aoBTC. On GSEA, angiogenesis was enriched in eoBTC (normalized enrichment score [NES] = 1.51; Q = 0.16), whereas IFG (NES = -1.58; Q = 0.06) and inflammatory response (NES = -1.46; Q = 0.18) were enriched in aoBTC. The median overall survival (OS) was 16.5 (eoBTC) versus 13.3 months (aoBTC), hazard ratio = 0.86, P = .004. The median OS by FGFR2 fusion (with fusion v without) was 21.7 versus 15.0 months (P = .47) for eoBTC and 18.6 versus 12.2 months (P < .001) for aoBTC. We identified crucial differences including higher prevalence of FGFR2 fusions in eoBTC and variations in immunotherapy-related markers. Better outcomes in eoBTC were affected by the FGFR2 fusion status. Our findings underscore the need for ensuring access to next-generation sequencing testing, including prompt identification of actionable targets.

Developing commercial awareness and digital video interview skills in second-year life sciences students

Employers are increasingly using video interviews in the application process, highlighting the need for graduates to adapt to digital technologies. To ensure graduates are adequately prepared, a Year 2 skills module was redeveloped, replacing face-to-face interviews with asynchronous video interviews using ShortlistMe. Aligning the assessment to a careers event allowed students to engage with employers and develop commercial awareness. Out of 430 students, 395 completed the video interview, and 329 connected with employers during the careers event (a 63% increase compared to the previous year). A survey explored student perceptions of the video interview (n= 39). Student confidence in completing a video interview increased by 44.6%, and in demonstrating commercial awareness by 30%. This model effectively connected employers with the curriculum at scale, fostering digital fluency, commercial awareness, and enhancing employability skills. This adaptable approach could benefit any discipline, equipping students for success in a dynamic job market. This article was published open access under a CC BY-NC-ND licence: https://creativecommons.org/licenses/by-nc-nd/4.0/ .

EC number prediction of protein sequences based on combination of hierarchical and global features.

The identification of enzyme functions plays a crucial role in understanding the mechanisms of biological activities and advancing the development of life sciences. However, existing enzyme EC number prediction methods did not fully utilize protein sequence information and still had shortcomings in identification accuracy. To address this issue, we proposed an EC number prediction network using hierarchical features and global features (ECPN-HFGF). This method first utilized residual networks to extract generic features from protein sequences, and then employed hierarchical feature extraction modules and global feature extraction modules to further extract hierarchical and global features of protein sequences. Subsequently, the prediction results of both feature types were combined, and a multitask learning framework was utilized to achieve accurate prediction of enzyme EC numbers. Experimental results indicated that the ECPN-HFGF method performed best in the task of predicting EC numbers for protein sequences, achieving macro F1 and micro F1 scores of 95.5% and 99.0%, respectively. The ECPN-HFGF method effectively combined hierarchical and global features of protein sequences, allowing for rapid and accurate EC number prediction. Compared to current commonly used methods, this method offers significantly higher prediction accuracy, providing an efficient approach for the advancement of enzymology research and enzyme engineering applications.

Enhancing Life Science Learning in Middle School

Enhancing Life Science Learning in Middle School

Mathematics in medical and life sciences: special issue on applications of AI in computational modelling in medical and life sciences

Mathematics in medical and life sciences: special issue on applications of AI in computational modelling in medical and life sciences

Ultrafast Metaphotonic PCR Chip with Near-Perfect Absorber.

Polymerase chain reaction (PCR) is the gold standard for nucleic acid amplification and quantification in diverse fields such as life sciences, global health, medicine, agricultural science, forensic science, and environmental science for global sustainability. However, implementing a cost-effective PCR remains challenging for rapid preventive medical action to the widespread pandemic diseases due to the absence of highly efficient and low-cost PCR chip-based POC molecular diagnostics. Here, this work reports an ultrafast metaphotonic PCR chip as a solution of a cost-effective and low-power-consumption POC device for the emerging global challenge of sustainable healthcare. This work designs a near-perfect photonic meta-absorber using ring-shaped titanium nitride to maximize the photothermal effect and realize rapid heating and cooling cycles during the PCR process. This work fabricates a large-area photonic meta-absorber on a 6-inch wafer cost-effectively using simple colloidal lithography. In addition, this work demonstrates 30 thermocycles from 65 (annealing temperature) to 95°C (denaturation temperature) within 3min 15s, achieving an average 16.66°Cs-1 heating rate and 7.77°Cs-1 cooling rate during thermocycling, succeeding rapid metaphotonic PCR. This work believes a metaphotonic PCR chip can be used to create a low-cost, ultrafast molecular diagnostic chip with a meta-absorber.

Machine Learning in Complex Organic Mixtures: Applying Domain Knowledge Allows for Meaningful Performance with Small Data Sets.

The ability to quantify individual components of complex mixtures is a challenge found throughout the life and physical sciences. An improved capacity to generate large data sets along with the uptake of machine-learning (ML)-based analysis tools has allowed for various "omics" disciplines to realize exceptional advances. Other areas of chemistry that deal with complex mixtures often do not leverage these advances. Environmental samples, for example, can be more difficult to access, and the resulting small data sets are less appropriate for unconstrained ML approaches. Herein, we present an approach to address this latter issue. Using a very small environmental data set─35 high-resolution mass spectra gathered from various solvent extractions of Canadian petroleum fractions─we show that the application of specific domain knowledge can lead to ML models with notable performance.

Project Based Learning (PLO) with a Bio-Entrepreneurship (BEP) Approach: Can it Improve Students' Science Literacy and Life Skills?

This research aims to analyze the PjBL model with the Bio-Entrepreneurship (BEP) approach to scientific literacy abilities and life skills. The method used was quasi-experimental with a pretest posttest non-equivalent control group research design. The sample for this research consisted of 71 students consisting of experimental classes and control classes, using a purposive sampling technique. The results of research on scientific literacy abilities show that the average N-Gain in the experimental class is 0.67 with medium criteria, while the control class average N-Gain is 0.28 with low criteria. The results of life skill abilities after being given treatment in the experimental class increased higher with an average of 78.63 compared to those in the control class with an average of 51.74. Thus, this research shows significant implications for students' abilities in developing real projects by combining aspects of entrepreneurship and biological concepts in everyday life.ABSTRAK: This study aims to analyze the PjBL model with a Bio-Entrepreneurship (BEP) approach to science literacy and life skills. The method used was quasi experiment with pretest posttest nonequivalent control group design. The sample of this study amounted to 71 students consisting of experimental and control classes, with purposive sampling technique. The results of the research on science literacy skills showed that the average N-Gain in the experimental class was 0.67 with moderate criteria, while the control class had an average N-Gain of 0.28 with low criteria. The results of life skills ability after being treated in the experimental class increased higher with an average of 78.63 than in the control class with an average of 51.74. Thus, this research shows significant implications for the ability of students to develop real projects by combining entrepreneurial aspects and biological concepts in everyday life.

Reviewer Acknowledgements for Journal of Food Research, Vol. 13 No. 2

Journal of Food Research wishes to acknowledge the following individuals for their assistance with peer review of manuscripts for this issue. Their help and contributions in maintaining the quality of the journal are greatly appreciated. Journal of Food Research is recruiting reviewers for the journal. If you are interested in becoming a reviewer, we welcome you to join us. Please contact us for the application form at: jfr@ccsenet.org Reviewers for Volume 13, Number 2 Diego A. Moreno-Fern&amp;aacute;ndez, CEBAS-CSIC, Spain Elke Rauscher-Gabernig, Austrian Agency for Health and Food Safety, Austria Lenka Kourimska, Czech University of Life Sciences Prague, Czech Republic Liana Claudia Salanta, University of Agricultural Sciences and Veterinary Medicine, Romania Mehana E. E. Hamouda, Alexandria university, Egypt Rania I.M. Almoselhy, Agricultural Research Center, Egypt Rozilaine A. P. G. Faria, Federal Institute of Science, Education and Technology of Mato Grosso, Brazil

Teachers’ Views on the Interdependence of Humanity and Technology in Life Sciences Teaching and Learning within the Context of the 5IR

The advent of the 5th Industrial Revolution (5IR) has brought a transformative shift to the educational landscape. The primary goal of 5IR is to address the inconsistencies observed in the preceding 4th Industrial Revolution (4IR). With a heightened emphasis on digitalization, Artificial Intelligence, automation, and the Internet of Things, there is a renewed focus on the role of humans in an era of rapid technological innovation. The impact of 5IR on Life Sciences education is particularly intriguing, prompting an exploration of the interplay between technology and humanity from the perspective of Life Sciences teachers. This research employs a mixed-method approach, incorporating both qualitative and quantitative methods. A questionnaire was used to assess the baseline knowledge related to 5IR, followed by interviews with two participants immersed in technology-rich environments, which are essential for a 5IR-aligned classroom. The findings suggest that a Life Sciences classroom integrating digitalization and humanistic approaches aligns with 5IR standards. In the context of 5IR pedagogy, the focus is on the teacher and learner, with technology serving as a complementary tool to enhance the learning experience. The study reveals varying degrees of understanding and application among participants regarding the coexistence of human and technological elements in their teaching practices. These findings highlight the challenges and opportunities teachers face as they navigate the evolving educational landscape. Grounded in connectivism as its theoretical framework, the research has implications for educational policymakers, curriculum developers, and teacher training programs, emphasizing the need for targeted interventions to align teaching practices with the demands of the 5IR. Ultimately, this study contributes to the ongoing discourse on the interdependence of humanity and technology in Life Sciences education within the framework of the 5IR.

Correlated multimodal imaging in life sciences: lessons learnt

Correlated Multimodal Imaging (CMI) gathers information about the same specimen with two or more modalities that–combined–create a composite and complementary view of the sample (including insights into structure, function, dynamics and molecular composition). CMI allows one to reach beyond what is possible with a single modality and describe biomedical processes within their overall spatio-temporal context and gain a mechanistic understanding of cells, tissues, and organisms in health and disease by untangling their molecular mechanisms within their native environment. The field of CMI has grown substantially over the last decade and previously unanswerable biological questions have been solved by applying novel CMI workflows. To disseminate these workflows and comprehensively share the scattered knowledge present within the CMI community, an initiative was started to bring together imaging, image analysis, and biomedical scientists and work towards an open community that promotes and disseminates the field of CMI. This community project was funded for the last 4 years by an EU COST Action called COMULIS (COrrelated MUltimodal imaging in the LIfe Sciences). In this review we share some of the showcases and lessons learnt from the action. We also briefly look ahead at how we anticipate building on this initial initiative.