Miracle-dz/UniFAR-FTUs huggingface dataset:
First-in-human study of SBRT and adenosine pathway blockade to potentiate the benefit of immunochemotherapy in early-stage luminal B breast cancer: results of the safety run-in phase of the Neo-CheckRay trial

Background Luminal B breast cancer (BC) presents a worse prognosis when compared with luminal A BC and exhibits a lower sensitivity to chemotherapy and a lower immunogenicity in contrast to non-luminal BC subtypes. The Neo-CheckRay clinical trial investigates the use of stereotactic body radiation therapy (SBRT) directed to the primary tumor in combination with the adenosine pathway inhibitor oleclumab to improve the response to neo-adjuvant immuno-chemotherapy in luminal B BC. The trial consists of a safety run-in followed by a randomized phase II trial. Here, we present the results of the first-in-human safety run-in. Methods The safety run-in was an open-label, single-arm trial in which six patients with early-stage luminal B BC received the following neo-adjuvant regimen: paclitaxel q1w×12 → doxorubicin/cyclophosphamide q2w×4; durvalumab (anti-programmed cell death receptor ligand 1 (PD-L1)) q4w×5; oleclumab (anti-CD73) q2w×4 → q4w×3 and 3×8 Gy SBRT to the primary tumor at week 5. Surgery must be performed 2–6 weeks after primary systemic treatment and adjuvant therapy was given per local guidelines, RT boost to the tumor bed was not allowed. Key inclusion criteria were: luminal BC, Ki67≥15% or histological grade 3, MammaPrint high risk, tumor size≥1.5 cm. Primary tumor tissue samples were collected at three timepoints: baseline, 1 week after SBRT and at surgery. Tumor-infiltrating lymphocytes, PD-L1 and CD73 were evaluated at each timepoint, and residual cancer burden (RCB) was calculated at surgery. Results Six patients were included between November 2019 and March 2020. Median age was 53 years, range 37–69. All patients received SBRT and underwent surgery 2–4 weeks after the last treatment. After a median follow-up time of 2 years after surgery, one grade 3 adverse event (AE) was reported: pericarditis with rapid resolution under corticosteroids. No grade 4–5 AE were documented. Overall cosmetical breast evaluation after surgery was ‘excellent’ in four patients and ‘good’ in two patients. RCB results were 2/6 RCB 0; 2/6 RCB 1; 1/6 RCB 2 and 1/6 RCB 3. Conclusions This novel treatment combination was considered safe and is worth further investigation in a randomized phase II trial. Trial registration number NCT03875573.
What Do We Reward in Reflection? Assessing Reflective Writing with the Index for Metacognitive Knowledge

Reflection is a staple of contemporary writing pedagogy and writing assessment. Although the power of reflective writing has long been understood in writing studies, the field has not made progress on articulating how to assess the reflective work. Developed at the crossroads of research in reflection and metacognition, the Index for Metacognitive Knowledge (IMK) is designed to help writing researchers, teachers, and students articulate what is being rewarded in the assessment of reflection and to articulate the role of metacognitive knowledge in critical reflective writing. The IMK was used to code final portfolio introductions from first-year writing courses in order to analyze the distribution of the three kinds of metacognitive knowledge (declarative, procedural, and conditional) and to explore the quality and complexity of students’ metacognitive knowledge. Inter-rater reliability testing on the IMK showed that it is highly reliable; the Fleiss’ kappa was 83% (K=.834). The IMK offers researchers, teachers, and students language with which to explore the unique work of reflective writing in order to develop more metacognitively rich observations. It provides a framework to explain the evolving complexity of students’ reflective writing and to assess and describe the impacts of other pedagogical interventions.
Deep Learning Hierarchical Methods for Insect Pest Recognition on Plants

Insect pests have always been a global agricultural problem because the severity and extent of their occurrence threaten crop yield. Recognizing them early can help farmers have efficient measures to handle them, which can help mitigate negative impacts from insect pests. However, insect pest recognition still relies heavily on experts, which is expensive and time-consuming. With the power of Deep Learning, we propose two methods to solve this task in this paper. First, we proposed a method that uses models pre-trained on the ImageNet dataset, including ResNet-50, EfficientNet-B4, and VisionTransformer-B16, respectively. We also change the structure of these models by adding a Dropout layer before the output layer of these pre-trained models to avoid overfitting. Second, we apply hierarchical learning for this task. In the latter approach, we first use the baseline model to create a confusion matrix. Through this matrix, we cluster classes that the baseline model misses to each other because of the similar appearance across classes into bigger classes, and we consider them as sub-datasets. Then, we build each model for each sub-dataset using the identical backbones as the baseline methods with the hope that it helps the method classify better in these classes. We do experiments to evaluate the performance of methods on the IP102 dataset. From experiments, our proposed method, which uses VisionTransformer-B16 backbone combined with hierarchical learning, gets the best accuracy of 74.50% on the IP102 dataset. When ensemble 3 above models and combine with hierarchical learning, we get the best accuracy of 76.24% on this dataset.
An Effective, Efficient and Scalable Link Discovery (EESLD) Framework for Hybrid Multi-Controller SDN Networks

The emergence of Software-Defined Networking (SDN) has revolutionised network management, offering improved flexibility, programmability, and scalability through the introduction of centralised controllers. Such a controller, e.g. an SDN controller, typically uses an OpenFlow discovery protocol to establish and maintain a global viewof the topology in the underlying network. An accurate global view of the network topology is essential for effective routing, load balancing, and deployment of mobilitybased applications. However, in a hybrid multi-controller SDN network, the OpenFlow discovery protocol introduces repetitive operations, degrading effectiveness, efficiency, and scalability. This paper addresses this issue by presenting a novel link discovery framework for establishing and maintaining topology information in a hybrid multi-controller SDN network. The framework, named EESLD, uses an event-driven approach utilising the Bidirectional Forwarding Detection (BFD) protocol to detect direct and indirect SDN links in intra-domain and inter-domain networks. Additionally, EESLD uses the sFlow protocol to discover and monitor legacy links and employs a distributed messaging system to maintain a consistent network view across controllers. The EESLD framework has been implemented and evaluated on a Mininet emulator with an RYU controller and sFlow server. Performance evaluation results show that the EESLD framework can discover direct SDN links 10.3 times faster than OFDPv2, and indirect SDN links 12.9 times faster than BDDP in a network with 85 switches. Evaluation results also indicate that the sFlow-based link discovery outperforms the OSPF-based link discovery in legacy link discovery and removal times. These results show that the EESLD framework is a more effective, efficient and scalable solution for dynamic and large-scale hybrid multi-controller SDN networks.
Soil Physical Properties and Wheat Yield as Influenced by Varying Levels of Different Organic Materials

The quantity and quality of organic amendments play vital role in changing or amending the soil physical properties and crop yield. The improvement in soil physical properties and crop growth is well correlated with the organic carbon status of soil. The behavior of carbon supplied through different materials may vary in the soil. This study was designed with an objective to observe the variation in the response to the different manures applied in the soil with respect to differences in physical properties and crop yield. To accomplish this objective a field trial was executed in September, 2011 at two different sites i.e. Research Farm, PMAS Arid Agriculture University, Rawalpindi and Koont Farm, Chakwal, using wheat as a test crop for two years (2012 and 2013). Three organic amendments i.e. Municipal solid waste compost (MSWC), Farmyard manure (FYM) and Poultry litter (PL) each at four levels i.e. 0, 0.25, 0.50 and 1% of soil organic carbon were applied in a two factorial randomized complete block design with four replications. The manures Original Research Article Bashir et al.; IJPSS, 4(5): 445-454, 2015; Article no.IJPSS. 2015.044 446 were characterized on the basis of humic (Humic acid and Fulvic acid) and non humic (Total polysaccharides and Microbial biomass carbon). Soil samples were analyzed for field saturated hydraulic conductivity, total organic carbon, bulk density, moisture content and wheat crop yield. The results showed clear differences in physical properties with respect to the different applied materials. These results suggest that the variation among the organic amendments depends upon the humic composition of materials rather than total organic carbon.
Trichoderma harzianum Cellulase Gene thph2 Affects Trichoderma Root Colonization and Induces Resistance to Southern Leaf Blight in Maize

Trichoderma, widely distributed all over the world, is commonly found in soil and root ecosystems. It is a group comprising beneficial fungi that improve plant disease resistance and promote plant growth. Studies have shown that Trichoderma cellulases can also improve plant disease resistance. Based on previous studies, we reported that a C6 zinc finger protein (Thc6) regulates two cellulase genes, thph1 and thph2, to induce ISR responses in plants. Therefore, in this study, we focused on the role of thph2 in the colonization of maize roots by T. harzianum and the induction of systemic resistance against southern leaf blight. The results showed that thph2 had a positive regulatory effect on the Trichoderma colonization of maize roots. After the root was treated with Trichoderma, the leaf defense genes AOS, LOX5, HPL, and OPR1 were expressed to resist the attack of Cochliobolus heterostrophus. The pure Thph2 protein also resulted in a similar induction activity of the AOS, LOX5, HPL, and OPR1 expression in maize roots, further demonstrating that thph2 can induce plant defense responses and that signal transduction occurs mainly through the JA signaling pathway.
Enhancing Aviation Software Development: An Experience Report on Conducting Audits

Software audits have paramount importance as they play a critical role in upholding the integrity, security, safety, and regulatory compliance of software systems. In the context of aviation, the RTCA DO-178C standard is specifically designed to guide the development and certification of safety-critical software used in aircraft systems. The RTCA DO-178C standard is a crucial framework for ensuring that safety-critical software in aviation systems adheres to rigorous guidelines and objectives throughout the development process. These guidelines are indispensable in ensuring that the software fulfills the stringent safety requirements necessary for airworthiness certification. The primary objective of this report is to share the author’s extensive experience in conducting software audits onboard aircraft. Over the course of 20 years working in the industry, the authors have amassed valuable data from numerous audits. The findings encompass 4 distinct scenarios and 7 key pitfalls identified in 93 Planning Audits, 110 Development Audits, and 98 Verification Audits.
Appendix for: ’Constrained deep neural network architecture search for IoT devices accounting hardware calibration’

The following Sections cover technical details of our work. Section 1 defines the search spaces 1 and manually designed sampling laws, Section 2 provides statistics over networks sampled from 2 the defined laws, Section 3 details data augmentation and hyper-parameters used for training and 3 Section 4 justifies the choice of the IoT device and explains the workflow to export a model from 4 the back-end framework and to deploy it on the IoT device. Section 5 elaborates how we scale our 5 algorithm to multiple datasets and provides further information of the datasets. 6
Bias-free Trading Algorithms with Momentum Scores for the Vietnamese Stock Market

This paper aims to assess the profitability of the Momentum Score trading strategy when applied to the Vietnamese stock market, while concurrently comparing its performance against the VN100 Index and addressing the potential effects of survivorship bias. Our findings reveal that the Momentum Score trading strategy has yielded impressive results, exhibiting a cumulative return of 498.90%, which is nearly 5 times greater than the 95.97% return achieved by the VN100 Index over the same period from February 2015 to June 2023. Moreover, when evaluating returns against the maximum drawdown, the Momentum Score strategy outperforms the VN100 Index by approximately 9.7 times. The robustness of the Momentum Score’s profitability is particularly noteworthy given that both strategies adhere to the same trading rules. However, the examination of these results also underscores the considerable influence of survivorship bias on strategy outcomes, emphasizing the importance of accounting for this bias in performance evaluations.
En train d'oublier: toward affective virtual environments

This study explores the development of intelligent affective virtual environments generated by bimodal emotion recognition techniques and multimodal feedback. A semantic and acoustic analysis predicts emotions conveyed by spoken language, fostering an expressive and transparent control structure. Textual contents and emotional predictions are mapped to virtual environments in real locations as audiovisual feedback. To demonstrate the application of this system, we developed a case study titled "En train d'oublier," focusing on a train cemetery in Uyuni, Bolivia. The train cemetery holds historical significance as a site where abandoned trains symbolize the passage of time and the interaction between human activities and nature's reclamation. The space is transformed into an immersive and emotionally poetic experience through oral language and affective virtual environments that activate memories, as the system utilizes the transcribed text to synthesize images and modifies the musical output based on the predicted emotional states. The proposed bimodal emotion recognition techniques achieve 94% and 89% accuracy. The audiovisual mapping strategy allows for considering divergence in predictions generating an intended tension between the graphical and the musical representation. Using video and web art techniques, we experimented with the environments generated to create diverses poetic proposals.
Abusive Span Detection for Vietnamese Narrative Texts

Abuse in its various forms, including physical, psychological, verbal, sexual, financial, and cultural, has a negative impact on mental health. However, there are limited studies on applying natural language processing (NLP) in this field in Vietnam. Therefore, we aim to contribute by building a human-annotated Vietnamese dataset for detecting abusive content in Vietnamese narrative texts. We sourced these texts from VnExpress, Vietnam’s popular online newspaper, where readers often share stories containing abusive content. Identifying and categorizing abusive spans in these texts posed significant challenges during dataset creation, but it also motivated our research. We experimented with lightweight baseline models by freezing PhoBERT and XLM-RoBERTa and using their hidden states in a BiLSTM to assess the complexity of the dataset. According to our experimental results, PhoBERT outperforms other models in both labeled and unlabeled abusive span detection tasks. These results indicate that it has the potential for future improvements.
Fracture behavior of ultra‐high performance lightweight concrete: In situ investigations using μ‐CT

In various research projects at the University of Kassel, the concept of lightweight concrete has been combined with that of ultra‐high performance concrete (UHPC). Due to the high performance of the UHPC matrix, lightweight concretes (UHPLC) with strengths of 60‐130 MPa and bulk density of 1.5‐1.9 kg/m3 were developed. This material can be used in many applications because of its low thermal conductivity, low weight and thus easy to transport, recyclable and can be prefabricated. Thus, it meets today's multifunctional requirements for building materials. The brittle material failure of UHPC without fibers is not observed with UHPLC. Instead, the concretes fail in hourglass shape under compressive stress, as seen in standard concretes. This constellation is not only responsible for the reduction of strength and Y‐modulus, but also predicts a ductile failure. For a better interpretation of the fracture behavior and fracture pattern, images of the UHPLC were taken in a highresolution computed tomography (μ‐CT) scanner. Between images, the sample was loaded and the load was kept constant during the measurement. The data was evaluated on the basis of a damage parameter, as well as crack openings during stable crack growth and the crack pattern after the maximum load was exceeded.
Warm temperature and mild water stress cooperatively promote root elongation

Warm temperatures have a dramatic effect on plant development. In shoots, stems elongate, and leaves are raised in a developmental programme called thermomorphogenesis. This results in enhanced leaf cooling capacity1. Thermomorphogenesis is tightly intertwined with light signalling pathways. The level of integration is so high that it has been proposed that shoot temperature sensing may have evolved from the co-option of an existing light signalling pathway during the colonisation of land by plants2. Roots also undergo thermomorphogenesis, but the mechanism by which this occurs is less well understood. Main root elongation is enhanced at warm temperatures, and this response is independent of many of the light and temperature signalling components of the shoot3. Roots develop in darkness and so it is a reasonable assumption that root temperature signalling is not through modulation of light signalling. It was recently speculated that due to the close correlation between warm temperature and soil moisture content, root temperature signalling could feasibly be related to water availability signals2. In this study we tested the interaction between temperature and water availability signalling in plant roots. We found that these environmental factors co-operatively enhance main root elongation. This interaction effect was dependent on SUCROSE NON-FERMENTING RELATED KINASE 2.2 (SnRK2.2) and SnRK2.3 and the E3 ubiquitin ligase CONSTITUTIVELY PHOTOMORPHOGENIC 1 (COP1). We found that SnRK2.2 / 2.3 and COP1 have opposite effects on the stability of the transcription factor ELONGATED HYPOCOTYL 5 (HY5) in elongation zone hair cells. The stability of HY5 in these cell types generally corresponded to the degree of root elongation seen in each mutant background. Our study reveals several molecular components of root thermomorphogenesis and highlights the importance of an integrative approach to plant environmental signalling. Our results may have direct implications for agricultural land management, especially as global climates become more unpredictable.
Enhancing Intrusion Detection and Explanations for Imbalanced Vehicle CAN Network Data

In modern automobiles, the reliance on vehicle Controller Area Network (CAN) networks has surged, underlining the paramount significance of safeguarding these networks against intrusions. In this work, we unveil an innovative approach for intrusion detection and explanation within in-vehicle CAN networks, employing the formidable synergy of Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). Our method is tailored to address network imbalance challenges, offering prowess in binary and multiclass classification tasks. Integral to our approach is the seamless integration of SHAP values, serving as illuminating guides that unravel the intricacies of detected intrusions. This fusion elevates the system’s interpretability, equipping stakeholders with deeper insights. Our contribution is underpinned by a rigorous evaluation of our approach, featuring a comprehensive analysis of a published dataset alongside comparisons with established literature. The results underscore the exceptional efficacy of our method, showcasing its remarkable accuracy in detecting intrusions. However, the essence of our methodology transcends mere detection precision. The explanatory capabilities of SHAP values come to the forefront. This augments both understanding and decision-making into the contributing factors behind the detected intrusion classification model.
Distance-AF: Modifying Predicted Protein Structure Models by Alphafold2 with User-Specified Distance Constraints

The three-dimensional structure of a protein plays a fundamental role in determining its function and has an essential impact on understanding biological processes. Despite significant progress in protein structure prediction, such as AlphaFold2, challenges remain on those hard targets that Alphafold2 does not often perform well due to the complex folding of protein and a large number of possible conformations. Here we present a modified version of the AlphaFold2, called Distance-AF, which aims to improve the performance of AlphaFold2 by including distance constraints as input information. Distance-AF uses AlphaFold2’s predicted structure as a starting point and incorporates distance constraints between amino acids to adjust folding of the protein structure until it meets the constraints. Distance-AF can correct the domain orientation on challenging targets, leading to more accurate structures with a lower root mean square deviation (RMSD). The ability of Distance-AF is also useful in fitting protein structures into cryo-electron microscopy maps.
A Case Study on Data Science Processes in an Academia-Industry Collaboration

Data Science (DS) is emerging in major software development projects and often needs to follow software development practices. Therefore, DS processes will likely continue to attract Software Engineering (SE) practices and vice-versa. This case study aims to map and describe a software development process for Machine Learning(ML)-enabled applications and associated practices used in a real DS project at the Recod.ai laboratory in collaboration with an industrial partner. The focus was to analyze the process and identify the strengths and primary challenges, considering their expertise in robust ML practices and how they can contribute to general software quality. To achieve this, we conducted semi-structured interviews and analyzed them using procedures from the Straussian Grounded Theory. The results showed that the DS development process is iterative, with feedback between activities, which differs from the processes in the literature. Additionally, this process presents a greater involvement of domain experts. Besides, the team prioritizes software quality characteristics (attributes) in these DS projects to ensure some aspects of the final product’s quality, i.e., functional correctness and robustness. To achieve those, they use regular accuracy metrics and include explainability and data leakage as quality metrics during training. Finally, the software engineer’s role and its responsibilities differ from those of a traditional industry software engineer, as s/he is involved in most of the process steps. These characteristics can contribute to high-quality models achieving the partner needs and, consequently, relevant contributions to the intersection between SE and DS.