Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein complexes from mouse and chick brain that interact with phospho-KXGS motif tau/microtubule associated protein antibody

ABSTRACT Mouse monoclonal 12E8 antibody, which recognises conserved serine phosphorylated KXGS motifs in the microtubule binding domains of tau/tau-like microtubule associated proteins (MAPs), shows elevated binding in brain during normal embryonic development (mammals and birds) and at the early stages of human Alzheimer’s disease (AD). It also labels ADF/cofilin-actin rods that form in neurites during exposure to stressors. We aimed to identify direct and indirect 12E8 binding proteins in postnatal mouse brain and embryonic chick brain by immunoprecipitation (IP), mass spectrometry and immunofluorescence. Tau and/or MAP2 were major direct 12E8-binding proteins detected in all IPs, and actin and/or tubulin were co-immunoprecipitated in most samples. Additional proteins were different in mouse versus chick brain IP. In mouse brain IPs, FSD1l and intermediate filament proteins – vimentin, α-internexin, neurofilament polypeptides – were prominent. Immunofluorescence and immunoblot using recombinant intermediate filament subunits, suggests an indirect interaction of these proteins with the 12E8 antibody. In chick brain IPs, subunits of eukaryotic translation initiation factor 3 (EIF3) were found, but no direct interaction between 12E8 and recombinant Eif3e protein was detected. Fluorescence microscopy in primary cultured chick neurons showed evidence of co-localisation of Eif3e and tubulin labelling, consistent with previous data demonstrating cytoskeletal organisation of the translation apparatus. Neither total tau or MAP2 immunolabelling accumulated at ADF/cofilin-actin rods generated in primary cultured chick neurons, and we were unable to narrow down the major antigen recognised by 12E8 antibody on ADF/cofilin-actin rods.
DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG

Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects millions of people worldwide. Making early and accurate diagnosis of it is very meaningful. Recently, EEG, a non-invasive technique of recording spontaneous electrical activity of brains, has been widely used for MDD diagnosis. However, there are still some challenges in data quality and data size of EEG: (1) A large amount of noise is inevitable during EEG collection, making it difficult to extract discriminative features from raw EEG; (2) It is difficult to recruit a large number of subjects to collect sufficient and diverse data for model training. Both of the challenges cause the overfitting problem, especially for deep learning methods. In this paper, we propose DiffMDD, a diffusion-based deep learning framework for MDD diagnosis using EEG. Specifically, we extract more noise-irrelevant features to improve the model’s robustness by designing the Forward Diffusion Noisy Training Module. Then we increase the size and diversity of data to help the model learn more generalized features by designing the Reverse Diffusion Data Augmentation Module. Finally, we re-train the classifier on the augmented dataset for MDD diagnosis. We conducted comprehensive experiments to test the overall performance and each module’s effectiveness. The framework was validated on two public MDD diagnosis datasets, achieving the state-of-the-art performance.
Framework for implementing asylum seekers and refugees’ health into the undergraduate medical curriculum in the United Kingdom

Abstract The Ukrainian conflict and the increasing number of asylum seekers and refugees (AS&Rs) in the United Kingdom have highlighted the critical need for a dedicated AS&R health curriculum in medical undergraduate programmes. This study utilized a mixed-method approach to assess the current state of AS&R curricula worldwide, identify shortcomings in the UK’s medical undergraduate curriculum and determine the specific needs of AS&R. A scoping review of literature revealed an absence of comprehensive AS&R health curricula, with many programmes focusing on broader global health issues. Mapping the General Medical Council’s (GMC) Outcomes for Graduates to a UK medical school’s learning outcomes uncovered misalignments with AS&R health requirements and an online survey of AS&R charities exposed barriers related to accessibility and knowledge. The study’s findings emphasize the importance of introducing or reinforcing specific themes in the medical curriculum, such as clarifying terminology and promoting awareness of AS&R organizations. Employing various teaching methods and continuous assessment are vital to evaluate curriculum effectiveness. The development of an AS&R health curriculum is essential to equip future doctors with the necessary skills and knowledge to provide equitable healthcare to this vulnerable population. The study’s findings can serve as a basis for curriculum development and implementation in UK medical schools.
Remote sensing of wetland tree species in the iSimangaliso Wetland Park, KwaZulu-Natal, South Africa.

XI PREFACE. XIII DECLARATION 1 PLAGIARISM . XIV DECLARATION 2 PUBLICATION AND MANUSCRIPTS . XV ACKNOWLEDGEMENTS . XVI CHAPTER 1: GENERAL INTRODUCTION . 1 1.1. MONITORING THE IMPACT OF GLOBAL CHANGE ON FORESTED WETLANDS AT INDIVIDUAL TREE SPECIES LEVEL . 1 1.2. REMOTE SENSING OF SPECIES CLASSIFICATION ACROSS PHENOLOGICAL PHASES . 3 1.3. RESEARCH OBJECTIVE AND AIMS . 5 1.4. STUDY AREA . 6 1.5. THESIS OUTLINE . 9 CHAPTER 2: DO TREE SPECIES HAVE UNIQUE SEASONAL PROFILES? 11 2.
Co-production and the politics of usable knowledge for climate adaptation in Tanzania

The concept of ‘co-production’ is increasingly offered as an approach to enable more responsive and inclusive processes of knowledge production across a range of disciplines. While it is recognized that uneven power relations play a significant role in shaping co-production processes, how these dynamics affect, and are affected by, intentional efforts to ‘co-produce’ usable knowledge is not well understood. In this dissertation, I examine the multiple ways in which power is exercised within efforts to ‘co-produce knowledge’ for climate adaptation decision-making in Tanzania, and with what effect. I do so through a multi-scalar mixed methods case study. To begin, I conduct a systematic literature review to illustrate the lack of conceptual clarity around the term co-production and argue that there is a need to distinguish between ‘strong constitutive’, ‘strong interactional’, and ‘weak interactional’ interpretations of the term. Then, I present results from a survey examining the production, circulation, and use of climate knowledge across institutional scales in Tanzania. I find that existing landscapes of climate knowledge are complex, with both formal and informal pathways for knowledge production and circulation playing an important role. Next, I conduct a modified Actor-Network analysis to understand the ‘constitutive’ dimensions of co-production. I find that current efforts to co-produce climate knowledge in Tanzania rely on stable science-society configurations in which there is a clear demarcation between ‘producers’ and ‘users’ of knowledge. This may have the unintended effect of delimiting the full participation of some actors within instrumental co-production efforts. Finally, using a critical application of the Knowledge System Criteria (KSC) framework (credibility, salience, legitimacy), I examine the politics involved in the production of ‘usable’ knowledge. I find that how actors relate to and employ these criteria within instrumental co-production efforts represents a political move that can reinforce existing power differentials. In sum, I find that uncritical usages of the concept of co-production may contribute to the very problems they are intended to solve. These findings are offered as part of an effort toward developing more critical, yet integrative and productive, understandings of co-production.
A review of the Onychophora of South Africa, with discussion of their conservation

There are nine descriptionbed species of Onychophora (Peripatopsidae) in South Africa, two in Opisthopatus Purcell, 1899 and seven in Peripatopsis Pocock, 1894. These species are redescriptionbed, illustrated and keyed to facilitate future ecological and biodiversity studies. The nine species almost certainly form an incomplete list, as it appears that there are at least another five undescriptionbed species. Taxonomic investigations using molecular techniques and scanning electron microscopy will almost certainly also reveal many new species, these currently confused with descriptionbed taxa. Information on the biology, phylogeny and possible species diversity of South African Onychophora is also given. Conservation of these onychophorans is discussed, and recommendations made for the new IUCN Categorisation of the threatened species: Opisthopatus roseus Lawrence, 1947 (VU), Peripatopsis alba Lawrence, 1931 (VU), P. clavigera Purcell, 1899 (1c) and P. leonina Purcell, 1899 (CR). Suggestions for conservation management and legal protection are also given.
Observed and projected sea surface temperature seasonal changes in the Western English Channel from satellite data and CMIP5 multi‐model ensemble

Seasonal Sea Surface Temperature (SST) changes in the Western English Channel have been estimated for the previous decades from high‐resolution satellite data. Coastal seas, well separated from offshore waters by intense frontal structures, show colder SST by 1–2 °C in summer. A significant warming trend is observed in the autumn season. This positive trend is stronger offshore, with an annual mean SST increase of 0.32 °C decade−1, but weaker in coastal waters (0.23 °C decade−1), where strong vertical mixing induced by tides and winds acts to reduce surface warming. The performance of an ensemble of CMIP5 climate model in simulating recent seasonal changes of SST in the region is estimated. The median of CMIP5 models very well reproduces the observed SST mean seasonal cycle in offshore waters but is less proficient in the coastal sector due to the coarse resolution of the models and the absence of tidal forcing and related processes. In the Iroise Sea, a region of intense biological activity located off the western tip of Brittany, the trend of the annual mean SST is relatively well simulated, albeit somewhat underestimated (0.20°C decade−1) and evenly distributed throughout the year. Here, the increase in annual mean SST in CMIP5 future scenarios simulations ranges from 0.5 °C (RCP2.6) to 2.5 °C (RCP8.5) by year 2100, with a seasonal modulation leading to a more intense warming in summer than that in winter. This increase in SST may strongly affect marine biology, particularly phytoplankton phenology, macro‐algae biomass and benthic fauna, including exploited shellfish, in the Western English Channel.
Revised European Association for Palliative Care (EAPC) recommended framework on palliative sedation: An international Delphi study

Background: The European Association for Palliative Care (EAPC) acknowledges palliative sedation as an important, broadly accepted intervention for patients with life-limiting disease experiencing refractory symptoms. The EAPC therefore developed 2009 a framework on palliative sedation. A revision was needed due to new evidence from literature, ongoing debate and criticism of methodology, terminology and applicability. Aim: To provide evidence- and consensus-based guidance on palliative sedation for healthcare professionals involved in end-of-life care, for medical associations and health policy decision-makers. Design: Revision between June 2020 and September 2022 of the 2009 framework using a literature update and a Delphi procedure. Setting: European. Participants: International experts on palliative sedation (identified through literature search and nomination by national palliative care associations) and a European patient organisation. Results: A framework with 42 statements for which high or very high level of consensus was reached. Terminology is defined more precisely with the terms suffering used to encompass distressing physical and psychological symptoms as well as existential suffering and refractory to describe the untreatable (healthcare professionals) and intolerable (patient) nature of the suffering. The principle of proportionality is introduced in the definition of palliative sedation. No specific period of remaining life expectancy is defined, based on the principles of refractoriness of suffering, proportionality and independent decision-making for hydration. Patient autonomy is emphasised. A stepwise pharmacological approach and a guidance on hydration decision-making are provided. Conclusions: This is the first framework on palliative sedation using a strict consensus methodology. It should serve as comprehensive and soundly developed information for healthcare professionals.
The extension of Pearson correlation coefficient, measuring noise, and selecting features

Not a matter of serious contention, Pearson's correlation coefficient is still the most important statistical association measure. Restricted to just two variables, this measure sometimes doesn't live up to users' needs and expectations. Specifically, a multivariable version of the correlation coefficient can greatly contribute to better assessment of the risk in a multi-asset investment portfolio. Needless to say, the correlation coefficient is derived from another concept: covariance. Even though covariance can be extended naturally by its mathematical formula, such an extension is to no use. Making matters worse, the correlation coefficient can never be extended based on its mathematical definition. In this article, we briefly explore random matrix theory to extend the notion of Pearson's correlation coefficient to an arbitrary number of variables. Then, we show that how useful this measure is at gauging noise, thereby selecting features particularly in classification.
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser

Multimodal Magnetic Resonance (MR) Imaging plays a crucial role in disease diagnosis due to its ability to provide complementary information by analyzing a relationship between multimodal images on the same subject. Acquiring all MR modalities, however, can be expensive, and, during a scanning session, certain MR images may be missed depending on the study protocol. The typical solution would be to synthesize the missing modalities from the acquired images such as using generative adversarial networks (GANs). Yet, GANs constructed with convolutional neural networks (CNNs) are likely to suffer from a lack of global relationships and mechanisms to condition the desired modality. To address this, in this work, we propose a transformer-based modality infuser designed to synthesize multimodal brain MR images. In our method, we extract modality-agnostic features from the encoder and then transform them into modality-specific features using the modality infuser. Furthermore, the modality infuser captures long-range relationships among all brain structures, leading to the generation of more realistic images. We carried out experiments on the BraTS 2018 dataset, translating between four MR modalities, and our experimental results demonstrate the superiority of our proposed method in terms of synthesis quality. In addition, we conducted experiments on a brain tumor segmentation task and different conditioning methods.
Exploring Felix Klein's contested modernism

An alleged opposition between David Hilbert and Felix Klein as modern vs countermodern has been pursued by marxist historian Herbert Mehrtens and others. Scholars such as Epple, Grattan-Guinness, Gray, Quinn, Rowe, and recently Siegmund-Schultze and Mazzotti have voiced a range of opinions concerning Mehrtens' dialectical methodology. We explore contrasting perspectives on Klein's contested modernism, as well as Hilbert's and Klein's views on intuition, logic, and physics. We analyze Jeremy Gray's comment on Klein's ethnographic speculations concerning Jewish mathematicians and find it to be untenable. We argue that Mehrtens was looking for countermoderns at the wrong address.
Alleviation of salinity stress by geothermal water in seed germination and seedling growth in wheat

The effects of geothermal waters (GW) on the seed germination and seedling growth of wheat (Triticum aestivum L. cv. Atay-85) at various salt (NaCl) concentrations were examined. It was found that 150 mM NaCl decreased significantly both seed germination and seedling growth of wheat. Salt stress of seed germination and seedling growth of wheat were alleviated by GWs of Sefakoy and Murat Dagi but it was not by GW Sarikiz. The highest alleviation effect was found with GW Murat Dagi and the effect was attributed to its calcium richness and lower level of toxic elements.
RadDQN: a Deep Q Learning-based Architecture for Finding Time-efficient Minimum Radiation Exposure Pathway

Recent advancements in deep reinforcement learning (DRL) techniques have sparked its multifaceted applications in the automation sector. Managing complex decision-making problems with DRL encourages its use in the nuclear industry for tasks such as optimizing radiation exposure to the personnel during normal operating conditions and potential accidental scenarios. However, the lack of efficient reward function and effective exploration strategy thwarted its implementation in the development of radiation-aware autonomous unmanned aerial vehicle (UAV) for achieving maximum radiation protection. Here, in this article, we address these intriguing issues and introduce a deep Q-learning based architecture (RadDQN) that operates on a radiation-aware reward function to provide time-efficient minimum radiation-exposure pathway in a radiation zone. We propose a set of unique exploration strategies that fine-tune the extent of exploration and exploitation based on the state-wise variation in radiation exposure during training. Further, we benchmark the predicted path with grid-based deterministic method. We demonstrate that the formulated reward function in conjugation with adequate exploration strategy is effective in handling several scenarios with drastically different radiation field distributions. When compared to vanilla DQN, our model achieves a superior convergence rate and higher training stability.
WayFASTER: a Self-Supervised Traversability Prediction for Increased Navigation Awareness

Accurate and robust navigation in unstructured environments requires fusing data from multiple sensors. Such fusion ensures that the robot is better aware of its surroundings, including areas of the environment that are not immediately visible but were visible at a different time. To solve this problem, we propose a method for traversability prediction in challenging outdoor environments using a sequence of RGB and depth images fused with pose estimations. Our method, termed WayFASTER (Waypoints-Free Autonomous System for Traversability with Enhanced Robustness), uses experience data recorded from a receding horizon estimator to train a self-supervised neural network for traversability prediction, eliminating the need for heuristics. Our experiments demonstrate that our method excels at avoiding obstacles, and correctly detects that traversable terrains, such as tall grass, can be navigable. By using a sequence of images, WayFASTER significantly enhances the robot's awareness of its surroundings, enabling it to predict the traversability of terrains that are not immediately visible. This enhanced awareness contributes to better navigation performance in environments where such predictive capabilities are essential.
Orientation-aware Incremental Potential Contact

The Incremental Potential Contact (IPC) method enables robust complex simulations of deformable objects with contact and friction. The key to IPC's robustness is its strict adherence to geometric constraints, avoiding intersections, which are a common cause of robustness issues in contact mechanics. A key element of the IPC approach to contact is a geometric barrier function, which is defined directly in the discrete setting. While IPC achieves its main goal of providing guarantees for contact constraints, its parameters need to be chosen carefully to avoid significant simulation artifacts and inaccuracies. We present a systematic derivation of an IPC-like continuum potential defined for smooth and piecewise smooth surfaces, starting from identifying a set of natural requirements for contact potentials, including the barrier property, locality, differentiable dependence of shape, and absence of forces in rest configurations, based on the idea of candidate sets. Our potential is formulated in a way independent of surface discretization. This new potential is suitable for piecewise-linear surfaces and its efficiency is similar to standard IPC. We demonstrate its behavior and compare it to IPC on a range of challenging contact examples.
Defect Cluster Morphology in W from Collision Cascades: Results Comparing Five Inter-atomic potentials

The size and morphology of defect clusters formed during primary damage play an important part in the subsequent evolution of the micro-structure of irradiated materials. Molecular dynamics (MD) simulations of collision cascades in W have been carried out using five interatomic potentials (IAP) including the quantum accurate machine learned (ML) spectral neighbor analysis potential (WSNAP), the ML based tabGAP potential and three embedded atom method (EAM) based potentials. A total of 3500 MD simulations with the primary knock-on atoms (PKA) having energys 5, 10, 20, 50, 75, 100 and 150 keV were carried out. The PKA are launched in hundred random directions at each of the PKA energies to obtain statistically valid results. Analysis using CSaransh, a web based tool to analyze a large collision cascade database, was carried out to obtain the number of defects (individual and in clusters), the defect cluster morphologies, the defect cluster size distributions and the number of sub-cascades formed. It is seen that<1 1 1>clusters dominate across all the inter-atomic potentials (IAP), except for the WSNAP potential. The WSNAP potential shows significantly higher number of C-15 like rings across all PKA energies. The WSNAP potential also shows a higher fraction of in-cluster vacancies. It is also seen that the stiffness and range of IAP do not affect the number of defects, defect clustering or vacancy clusters, but shows a tendency to form more sub-cascades for PKA energies less than 60 keV.
Rapid Integrator for a Class of Multi-Contact Systems

Many problems in robotics involve creating or breaking multiple contacts nearly simultaneously or in an indeterminate order. We present a novel general purpose numerical integrator based on the theory of Event Selected Systems (ESS). Many multicontact models are ESS, which has recently been shown to imply that despite a discontinuous vector field, the flow of these systems is continuous, piecewise smooth, and has a well defined orbital derivative for all trajectories, which can be rapidly computed. We provide an elementary proof that our integrator is first-order accurate and verify numerically that it is in fact second-order accurate as its construction anticipated. We also compare our integrator, implemented in NumPy, to a MuJoCo simulation on models with 2 to 100 contacts, and confirm that the increase in simulation time per contact is nearly identical. The results suggest that this novel integrator can be invaluable for modelling and control in many robotics applications.
Efficient Exploration for LLMs

We present evidence of substantial benefit from efficient exploration in gathering human feedback to improve large language models. In our experiments, an agent sequentially generates queries while fitting a reward model to the feedback received. Our best-performing agent generates queries using double Thompson sampling, with uncertainty represented by an epistemic neural network. Our results demonstrate that efficient exploration enables high levels of performance with far fewer queries. Further, both uncertainty estimation and the choice of exploration scheme play critical roles.