bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experiences from teaching graduate students how to design net Zero Emission Buildings in Norway
Authors: Bohne R A , Rasmussen F N , Kvande T 
Published: July 2024

Abstract
The ambition of the Paris agreement is “carbon neutrality” by 2050. But what is “carbon neutrality”? How do we design a “carbon neutral” building? And how to teach this concept to engineering students? TBA4171 is an advanced course in Building Technology, given as the last course before the final thesis for the master students in building technology at NTNU. This paper describes how we use flipped-classroom and problem-based-learning to teach these concepts, and presents the results from the student projects. TBA4171 is targeting students specializing in building materials, building physics or fire safety in buildings. But the course also attracts students specializing in building processes and real estate management. In the course, the project of the students is to redesign five different current state-of-art Norwegian single-family houses to net Zero Emission Buildings. The students are given full access to BIM drawings, bill of quantities, EPD of materials and an LCA of each building. The students engage in an iteratively optimized design process, investigating the influence of geometry layout, material selection, energy performance, technical installations, and implementation of renewable energy sources. Life cycle based GHGe accounting are done for each design iteration to investigate where the biggest emission savings are obtained, and how the final building design complies with the Norwegian approach to net zero GHGe performance. The results show that graduate students are capable of designing net Zero Emission Buildings. They achieve this, primarily by optimizing material use (e.g. reduced use of concrete and steel) and geometrical layout (solar heat gains during winter/shading during summer and space for PVs). The results show that is possible to design net Zero Emission Buildings if provided with data on material and energy mix. The results also show that problem-based-learning is a powerful method of teaching these concepts to graduate students.
Core skills and competencies for Danish construction graduates to contribute to the 2030 Agenda
Authors: Findinge H U , Gade A N , Selman A D 
Published: July 2024

Abstract
The United Nations’ 17 Sustainable Development Goals (SDGs) provide a global, ambitious framework for sustainable development. The construction sector holds great potential for contributing to achieving the goals towards 2030. Therefore, highly skilled professionals are required to solve the challenges related to sustainable construction. This study investigates which core skills construction graduates need to contribute to the SDGs efficiently and provides best-case examples of projects from the Danish construction industry contributing to the SDGs. A focus group workshop was conducted with 12 participants representing professional building owners, advisors, researchers, professional associations, demolition companies, and the United Nations Youth Association - all experts in sustainable construction. The qualitative data were collected through audio recordings and written notes and were analysed using the Gioia method. The results showed that the core skills needed from future construction graduates were 1) expert knowledge of construction, materials and sustainability, 2) skills in construction practice and sustainable adaption, and 3) competencies in social qualities and strategic processes. The best practice examples were divided into two categories: potential solutions to sustainable construction practices and business cases related to sustainable construction. The results provide insights into the knowledge, skills, and competencies needed from practitioners within the Danish construction industry and can support the future adaptation of construction education to achieve the 2030 Agenda.
Co-designing educational spaces: unveiling opportunities for empowered learnings in post-apartheid secondary school in South Africa
Authors: Dion-Barbin N , Hagy S , Thuvander L 
Published: July 2024

Abstract
In the context of a secondary school in a township in South Africa, this study employs a participatory action research approach to engage primarily the students into co-designing a self-managed space. This approach aims at utilising the local resources to reduce the gap between students from townships to other privileged secondary schools in the city. The applied concept is based on self-management, a novel approach in the South African context, draws inspiration from global educational reforms. The methodology is developed in collaboration with the Tsako Thabo Secondary School in Mamelodi, Pretoria, South Africa, with students aged 14 to 18 years acting as co-designers and space managers. In three on-site workshops, spanning from contextual understanding, over design exercises, to the base foundation of the self-management’s concept, the workshops yield significant outcomes. Two different designs: an architectural design of the co-created space, and a design of the system regulating the space between all its members, with a funding strategy, have been developed. The design proposal includes a self-managed space with four distinct areas, and three booklets to guide future initiatives. Challenges in the field, including cultural differences and logistical issues, were navigated with adaptability and effective communication. Despite these challenges, the students’ resilience underscores the transformative potential of participatory research in fostering an empowering educational environment. This research contributes a unique perspective to the self-management discourse, emphasising a macro-level impact on students and their autonomy. The co-designed space provides a platform for experimentation and addresses resource deficiencies in the community. Overall, the study unveils novel opportunities for collaborative educational initiatives, emphasising the importance of adaptability and meticulous planning in diverse contexts.
Skin-on-a-chip technologies towards clinical translation and commercialization
Authors: Ismayilzada Nilufar , Tarar Ceren , Dabbagh Sajjad Rahmani , Tokyay Begüm Kübra , Dilmani Sara Asghari , Sokullu Emel , Abaci Hasan Erbil , Tasoglu Savas 
Published: July 2024

Abstract
Skin is the largest organ of the human body which plays a critical role in thermoregulation, metabolism (e.g. synthesis of vitamin D), and protection of other organs from environmental threats, such as infections, microorganisms, ultraviolet radiation, and physical damage. Even though skin diseases are considered to be less fatal, the ubiquity of skin diseases and irritation caused by them highlights the importance of skin studies. Furthermore, skin is a promising means for transdermal drug delivery, which requires a thorough understanding of human skin structure. Current animal and in vitro two/three-dimensional skin models provide a platform for disease studies and drug testing, whereas they face challenges in the complete recapitulation of the dynamic and complex structure of actual skin tissue. One of the most effective methods for testing pharmaceuticals and modeling skin diseases are skin-on-a-chip (SoC) platforms. SoC technologies provide a non-invasive approach for examining 3D skin layers and artificially creating disease models in order to develop diagnostic or therapeutic methods. In addition, SoC models enable dynamic perfusion of culture medium with nutrients and facilitate the continuous removal of cellular waste to further mimic the in vivo condition. Here, the article reviews the most recent advances in the design and applications of SoC platforms for disease modeling as well as the analysis of drugs and cosmetics. By examining the contributions of different patents to the physiological relevance of skin models, the review underscores the significant shift towards more ethical and efficient alternatives to animal testing. Furthermore, it explores the market dynamics of in vitro skin models and organ-on-a-chip platforms, discussing the impact of legislative changes and market demand on the development and adoption of these advanced research tools. This article also identifies the existing obstacles that hinder the advancement of SoC platforms, proposing directions for future improvements, particularly focusing on the journey towards clinical adoption.
Community-Derived Recommendations for Improving Gender Affirmation of Black and Latine Transgender/Nonbinary Youth
Authors: Vance Stanley R , Venegas Luz , Johnson Jack , Sinha Anoushka , Chaphekar Anita V , Sevelius Jae 
Published: July 2024

Abstract

Background
Gender affirmation is a process by which gender-diverse individuals are supported in their gender identity. Parents are critical in how gender-diverse youth, including Black and Latine transgender/nonbinary youth (BLTY), access various forms of gender affirmation—for example, social and medical transition. Culturally relevant supports are needed to bolster how BLTY and their parents navigate gender affirmation.

Purpose
This study aimed to explore recommendations for aiding BLTY and parents in navigating the youth’s gender journey.

Methods
Semi-structured interviews were conducted with parents of BLTY, BLTY, and BLT young adults (BLTYAs) recruited from clinics, community organizations, and social media. Interviews focused on gender affirmation and recommendations to promote BLTY’s gender affirmation. Primary and secondary analysts coded transcripts using a priori and emergent codes. For this analysis, excerpts pertaining to recommended supports were analyzed to identify themes.

Results
Ten parents of BLTY, 10 BLTY (14–18 years), and 23 BLTYAs (18–30 years) participated. Participants provided recommendations at different socio-ecological levels. On the societal level, participants recommended improvements in media representation of racial and ethnic minority gender-diverse individuals. For organizations, participants recommended more clinicians who shared minoritized identities, clinicians knowledgeable in gender-affirming care, affordability of gender-affirming services, and school-based education regarding gender diversity. On interpersonal/individual levels, they suggested culturally informed peer support among BLTY and parents, including support groups, peer mentors, and camps with individuals who share their minoritized identities.

Conclusions
Participants provided salient insights to supporting gender affirmation of BLTY, which can inform intervention development for BLTY and their families.
Epic Theatre as a Form of Platonic Drama
Authors: Gürsoy İhsan 
Published: July 2024

Abstract
Given Aristotle’s response to Plato’s views by positing a cathartic function for tragedy, it is understandable that an author opposing him through the development of a non-Aristotelian theatrical theory would spontaneously draw closer to Platonic thought. However, Brecht’s stance goes beyond this spontaneous proximity in this debate. This article challenges those critics who have overlooked the direct relationship between Plato and Brecht, and it offers a reasoned decision on Walter Benjamin’s verdict that epic theatre is a form of Platonic drama. Moreover, it argues that Brecht’s endeavour aimed to create a theatre that remained impervious to Plato’s critiques. The article analyzes Brecht’s achievements in epic theatre as an expression of disenchantment, demonstrating how, through the techniques he developed, he radically dismantled the magical effect that was essential to Aristotelian theatre.
AttABseq: an attention-based deep learning prediction method for antigen–antibody binding affinity changes based on protein sequences
Authors: Jin Ruofan , Ye Qing , Wang Jike , Cao Zheng , Jiang Dejun , Wang Tianyue , Kang Yu , Xu Wanting , Hsieh Chang-Yu , Hou Tingjun 
Published: July 2024

Abstract
The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods have been actively developed to accelerate and improve the development of therapeutic antibodies. In this study, we developed an end-to-end sequence-based deep learning model, termed AttABseq, for the predictions of the antigen–antibody binding affinity changes connected with antibody mutations. AttABseq is a highly efficient and generic attention-based model by utilizing diverse antigen–antibody complex sequences as the input to predict the binding affinity changes of residue mutations. The assessment on the three benchmark datasets illustrates that AttABseq is 120% more accurate than other sequence-based models in terms of the Pearson correlation coefficient between the predicted and experimental binding affinity changes. Moreover, AttABseq also either outperforms or competes favorably with the structure-based approaches. Furthermore, AttABseq consistently demonstrates robust predictive capabilities across a diverse array of conditions, underscoring its remarkable capacity for generalization across a wide spectrum of antigen-antibody complexes. It imposes no constraints on the quantity of altered residues, rendering it particularly applicable in scenarios where crystallographic structures remain unavailable. The attention-based interpretability analysis indicates that the causal effects of point mutations on antibody–antigen binding affinity changes can be visualized at the residue level, which might assist automated antibody sequence optimization. We believe that AttABseq provides a fiercely competitive answer to therapeutic antibody optimization.
Detecting tipping points of complex diseases by network information entropy
Authors: Lyu Chengshang , Chen Lingxi , Liu Xiaoping 
Published: July 2024

AbstractThe progression of complex diseases often involves abrupt and non-linear changes characterized by sudden shifts that trigger critical transformations. Identifying these critical states or tipping points is crucial for understanding disease progression and developing effective interventions. To address this challenge, we have developed a model-free method named Network Information Entropy of Edges (NIEE). Leveraging dynamic network biomarkers, sample-specific networks, and information entropy theories, NIEE can detect critical states or tipping points in diverse data types, including bulk, single-sample expression data. By applying NIEE to real disease datasets, we successfully identified critical predisease stages and tipping points before disease onset. Our findings underscore NIEE’s potential to enhance comprehension of complex disease development.
Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge
Authors: Chen Hegang , Lu Yuyin , Dai Zhiming , Yang Yuedong , Li Qing , Rao Yanghui 
Published: July 2024

Abstract
Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analysis of transcriptomic data. Nevertheless, relying on the potential space of these generative models alone is insufficient to generate biological explanations. In addition, most of the previous work based on generative models is limited to shallow neural networks with one to three layers of latent variables, which may limit the capabilities of the models. Here, we propose a deep interpretable generative model called d-scIGM for single-cell data analysis. d-scIGM combines sawtooth connectivity techniques and residual networks, thereby constructing a deep generative framework. In addition, d-scIGM incorporates hierarchical prior knowledge of biological domains to enhance the interpretability of the model. We show that d-scIGM achieves excellent performance in a variety of fundamental tasks, including clustering, visualization, and pseudo-temporal inference. Through topic pathway studies, we found that d-scIGM-learned topics are better enriched for biologically meaningful pathways compared to the baseline models. Furthermore, the analysis of drug response data shows that d-scIGM can capture drug response patterns in large-scale experiments, which provides a promising way to elucidate the underlying biological mechanisms. Lastly, in the melanoma dataset, d-scIGM accurately identified different cell types and revealed multiple melanin-related driver genes and key pathways, which are critical for understanding disease mechanisms and drug development.
Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks
Authors: Baul Sudipto , Tanvir Ahmed Khandakar , Jiang Qibing , Wang Guangyu , Li Qian , Yong Jeongsik , Zhang Wei 
Published: July 2024

AbstractSpatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT.
Design patterns for the construction of computational biological models
Authors: Herajy Mostafa , Liu Fei , Heiner Monika 
Published: July 2024

Abstract
Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models.
Feasibility and flexibility of a novel multi-dose level avoidance reirradiation technical methodology in recurrent head and neck cancer
Authors: Hague Thomas , Lad Rikki , Chiu Kevin 
Published: July 2024

Abstract
Reirradiation in recurrent head and neck cancer presents a considerable clinical challenge in radiation oncology. Though technically feasible due to advanced treatment delivery and planning techniques, confidence in delivering such treatments is not universal and patient selection is critical. Radiotherapy planning in reirradiation cases presents a complex technical challenge owing to the often-considerable overlap of dose from a patient’s first treatment plan. This technical note describes three clinical case studies of recurrent head and neck cancer and the technical details of how their multidose level reirradiation was planned. Each patient had confirmed recurrence of squamous cell carcinoma and was referred for reirradiation to a previously irradiated area. The clinical details for each patient are provided before a detailed description of the treatment planning methodology is presented, which specifies how to approach such complex overlapping treatment volumes. The patient outcomes are described and a discussion is presented outlining the clinical challenges associated with these cases and the variables that must be accounted for when considering patients for potential reirradiation.
Multicentre randomized clinical trial on robot-assisted versus video-assisted thoracoscopic oesophagectomy (REVATE trial)
Authors: Chao Yin-Kai , Li Zhigang , Jiang Hongjing , Wen Yu-Wen , Chiu Chen-Hung , Li Bin , Shang Xiaobin , Fang Tuan-Jen , Yang Yang , Yue Jie , Zhang Xiaobin , Zhang Chen , Liu Yun-Hen 
Published: July 2024

Abstract

Background
Surgery for oesophageal squamous cell carcinoma involves dissecting lymph nodes along the recurrent laryngeal nerve. This is technically challenging and injury to the recurrent laryngeal nerve may lead to vocal cord palsy, which increases the risk of pulmonary complications. The aim of this study was to compare the efficacy and safety of robot-assisted oesophagectomy (RAO) versus video-assisted thoracoscopic oesophagectomy (VAO) for dissection of lymph nodes along the left RLN.

Methods
Patients with oesophageal squamous cell carcinoma who were scheduled for minimally invasive McKeown oesophagectomy were allocated randomly to RAO or VAO, stratified by centre. The primary endpoint was the success rate of left recurrent laryngeal nerve lymph node dissection. Success was defined as the removal of at least one lymph node without causing nerve damage lasting longer than 6 months. Secondary endpoints were perioperative and oncological outcomes.

Results
From June 2018 to March 2022, 212 patients from 3 centres in Asia were randomized, and 203 were included in the analysis (RAO group 103; VAO group 100). Successful left recurrent laryngeal nerve lymph node dissection was achieved in 88.3% of the RAO group and 69% of the VAO group (P &lt; 0.001). The rate of removal of at least one lymph node according to pathology was 94.2% for the RAO and 86% for the VAO group (P = 0.051). At 1 week after surgery, the RAO group had a lower incidence of left recurrent laryngeal nerve palsy than the VAO group (20.4 versus 34%; P = 0.029); permanent recurrent laryngeal nerve palsy rates at 6 months were 5.8 and 20% respectively (P = 0.003). More mediastinal lymph nodes were dissected in the RAO group (median 16 (i.q.r. 12–22) versus 14 (10–20); P = 0.035). Postoperative complication rates were comparable between the two groups and there were no in-hospital deaths.

Conclusion
In patients with oesophageal squamous cell carcinoma, RAO leads to more successful left recurrent laryngeal nerve lymph node dissection than VAO, including a lower rate of short- and long-term recurrent laryngeal nerve injury. Registration number: NCT03713749 (http://www.clinicaltrials.gov).