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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Multi-Graph Convolution Network for Pose Forecasting
Recently, there has been a growing interest in predicting human motion, which
involves forecasting future body poses based on observed pose sequences. This
task is complex due to modeling spatial and temporal relationships. The most
commonly used models for this task are autoregressive models, such as recurrent
neural networks (RNNs) or variants, and Transformer Networks. However, RNNs
have several drawbacks, such as vanishing or exploding gradients. Other
researchers have attempted to solve the communication problem in the spatial
dimension by integrating Graph Convolutional Networks (GCN) and Long Short-Term
Memory (LSTM) models. These works deal with temporal and spatial information
separately, which limits the effectiveness. To fix this problem, we propose a
novel approach called the multi-graph convolution network (MGCN) for 3D human
pose forecasting. This model simultaneously captures spatial and temporal
information by introducing an augmented graph for pose sequences. Multiple
frames give multiple parts, joined together in a single graph instance.
Furthermore, we also explore the influence of natural structure and
sequence-aware attention to our model. In our experimental evaluation of the
large-scale benchmark datasets, Human3.6M, AMSS and 3DPW, MGCN outperforms the
state-of-the-art in pose prediction.Comment: arXiv admin note: text overlap with arXiv:2110.04573 by other author
Antenna Arrangement in UWB Helmet Brain Applicators for Deep Microwave Hyperthermia
Deep microwave hyperthermia applicators are typically designed as narrow-band conformal antenna arrays with equally spaced elements, arranged in one or more rings. This solution, while adequate for most body regions, might be sub-optimal for brain treatments. The introduction of ultra-wide-band semi-spherical applicators, with elements arranged around the head and not necessarily aligned, has the potential to enhance the selective thermal dose delivery in this challenging anatomical region. However, the additional degrees of freedom in this design make the problem non-trivial. We address this by treating the antenna arrangement as a global SAR-based optimization process aiming at maximizing target coverage and hot-spot suppression in a given patient. To enable the quick evaluation of a certain arrangement, we propose a novel E-field interpolation technique which calculates the field generated by an antenna at any location around the scalp from a limited number of initial simulations. We evaluate the approximation error against full array simulations. We demonstrate the design technique in the optimization of a helmet applicator for the treatment of a medulloblastoma in a paediatric patient. The optimized applicator achieves 0.3\ua0 (Formula presented.) C higher T90 than a conventional ring applicator with the same number of elements
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries
Complement mediated synapse elimination in schizophrenia
Schizophrenia (SCZ) is a devastating psychiatric disorder with a typically age of onset in late adolescence. The heritability is estimated to be in between 60-80% and large-scale genome-wide studies have revealed a prominent polygenic component to SCZ risk and identified more than three-hundred common risk variants. Despite a better understanding of which genetic risk variants that increases SCZ risk, it has been challenging to map out the pathophysiology of the disorder. This has stalled the development of target drugs and current treatment options display moderate efficacy and are prone to produce side-effects. SCZ is generally considered a neurodevelopmental disorder and it was proposed more than forty years ago that physiological removal of less active synapses in adolescence, i.e., synaptic pruning, is increased in SCZ and hereby causes the core symptoms of the disorder.
This theory has then been supported by post-mortem brain tissue and imaging studies displaying decreased synapse density in SCZ. More recently, it was then shown that the most strongly associated risk loci can largely be explained by copy numbers of a gene coding for the complement factor 4A (C4A). As microglia prune synapses with the help of complement signalling, we therefore decided to use a recently developed human 2D in vitro assay to assess microglial uptake of synaptic structures in models based on cells from individuals with SCZ and healthy controls (study I). We observed excessive uptake of synaptic structures in SCZ models and by mixing synapses from healthy controls with microglia from SCZ patients, and vice versa, we showed the contribution of microglial and neuronal factors contributing to this excessive uptake of synaptic structures.
We then developed an in vitro assay to study neuronal complement deposition dependent on copy numbers of C4A in the neuronal lines. Complement 3 (C3) deposition increased by C4A copy numbers but was independent of C4B copy numbers (also unrelated to SCZ risk). Similar C4A copy numbers correlated with the extent of microglial uptake of synapses. Microglial uptake of synaptic structures could also be inhibited by the tetracycline minocycline that also decreased risk of developing SCZ in an electronic health record cohort.
In study II, we cerebrospinal fluid (CSF) from first-episode psychosis patients to measure protein levels of C4A. In two independent cohorts, we observed elevated C4A levels (although not C4B levels) in first-episode patients that later were to develop SCZ and could show correlations with markers of synapse density. However, elevated C4A levels could not fully be explained by more copy numbers of C4A in individuals with SCZ and in vitro experiments revealed that SCZ-associated cytokines can induce C4A mRNA expression while also correlating with C4A in patient-derived CSF.
In study III, we set-up a 3D brain organoid models to more fully comprehensively capture processes in the developing human brain and then also included innately developing microglia. We display synaptic pruning within these models and use single cell RNA sequencing to validate them.
In conclusion, this thesis uses patient-derived cellular modelling to uncover a disease mechanism in SCZ that link genetic risk variants with bona fide protein changes in living patients
DeFeeNet: Consecutive 3D Human Motion Prediction with Deviation Feedback
Let us rethink the real-world scenarios that require human motion prediction
techniques, such as human-robot collaboration. Current works simplify the task
of predicting human motions into a one-off process of forecasting a short
future sequence (usually no longer than 1 second) based on a historical
observed one. However, such simplification may fail to meet practical needs due
to the neglect of the fact that motion prediction in real applications is not
an isolated ``observe then predict'' unit, but a consecutive process composed
of many rounds of such unit, semi-overlapped along the entire sequence. As time
goes on, the predicted part of previous round has its corresponding ground
truth observable in the new round, but their deviation in-between is neither
exploited nor able to be captured by existing isolated learning fashion. In
this paper, we propose DeFeeNet, a simple yet effective network that can be
added on existing one-off prediction models to realize deviation perception and
feedback when applied to consecutive motion prediction task. At each prediction
round, the deviation generated by previous unit is first encoded by our
DeFeeNet, and then incorporated into the existing predictor to enable a
deviation-aware prediction manner, which, for the first time, allows for
information transmit across adjacent prediction units. We design two versions
of DeFeeNet as MLP-based and GRU-based, respectively. On Human3.6M and more
complicated BABEL, experimental results indicate that our proposed network
improves consecutive human motion prediction performance regardless of the
basic model.Comment: accepted by CVPR202
Integrative multi-omics analysis for the effect of genetic alterations in cancer xenograft and organoid models
Department of Biomedical EngineeringDNA damage is a well-recognized factor in the development and progression of cancer. Numerous studies on genetic changes associated with cancer or the DNA repair pathway have been conducted, however, there is still a need for additional research on their function. The establishment of patient-derived xenografts or organoids for the purpose of testing functional genomic approaches is the subject of ongoing research. According to model-specific characteristics, it is not fully understood how these attempts to simulate patient cancer differ from original cancer. To comprehend the distinction between genuine patient cancer and these patient-derived disease models in more depth, multi-omics analysis is required to comprehend the overall genotypes, phenotypes, and environmental variables. Depending on the characteristics of each disease model, distinct omics analysis approaches and factors must be considered. In addition, care must be taken to avoid technical errors when integrating omics data generated by different sequencing equipment. There is currently no golden rule for data integration, but several approaches are being developed.
It is crucial to determine the function of genes linked with the DNA repair pathway because these genes contribute to the induction or prevention of cancer. In chapter 1, I identified the interaction between MRE11 and TRIP13 through proximity labeling combined with the SILAC method which is quantitative proteomics using metabolic labeling. TRIP13 depletion doesn???t affect the nuclease activity and conformation of the MRN complex but directly inhibits the interaction of MDC1 with MRN complex and MDC1 recruitment on the DNA damage site. TRIP13 degradation with mirin treatment shows additive effects on ATM signaling activation. In conclusion, TRIP13 regulates immediate-early DNA damage sensing through MRE11 and ATM signaling independently of mirin.
When assessing the functional genomic approach using patient-derived disease models, it is essential to determine which aspects of the models' correlation to actual cancer should be properly considered. In chapter 2, I found there are a few overlapped deleterious somatic mutations of the PDX model and their original tumor. I suspected novel mutagen exposure during PDX establishment or sample contamination. However, germline mutations of PDX models are well conserved from original tumors, and their mutational signatures of PDX also mimic that of their tumor. Though the number of overlapped mutations between the PDX model and their tumor was few, brain tumor-specific mutations are found in PDX samples. Especially, histone methylation- and cilia-related gene mutations are enriched in PDX samples. While it suggested these mutated genes are needed for maintaining the stemness of brain tumor PDX model or PDX model would be more appropriate for the samples with high heterogeneity, I have presented precautions and considerations in PDX model genome analysis.
Multi-omics analysis that takes into consideration genetic, expressive, and clinical aspects can provide important information for the study of diseases with complicated etiologies, such as cancer, and can contribute to the development of diagnosis and treatment. To utilize colorectal cancer organoids for Companion Diagnostics (CDx), in chapter 3, I characterized patient-derived colorectal cancer (CRC) organoids through well-known genomic markers such as Tumor mutation burden (TMB), Microsatellite instability (MSI) and propose a novel grouping method using sharing same mutation site. The classification of CRC patients was more detailed combined with consensus molecular subtype (CMS) classifications. Additionally, I extract the expression features of the patients who experience recurrence or metastasis after first-line chemotherapy treatment with reference to clinical data. Drug response of CRC organoids by patient group and knockdown of the extracted features in the selected organoids would be validated in further study.
In summary, with this dissertation, I conducted functional research on the DNA repair pathway of cancer-related genes, as well as the genetic analysis between patient-derived xenograft and original tumors, and introduced a novel perspective on the diagnosis and treatment of colorectal cancer patients using patient-derived organoids through multi-omics analysis.ope
Management of valvular heart disease in patients with cancer: Multidisciplinary team, cancer-therapy related cardiotoxicity, diagnosis, transcatheter intervention, and cardiac surgery. Expert opinion of the Association on Valvular Heart Disease, Association of Cardiovascular Interventions, and Working Group on Cardiac Surgery of the Polish Cardiac Society
The Association on Valvular Heart Disease, Association of Cardiovascular Interventions, and the Working Group on CardiacSurgery of the Polish Cardiac Society have released a position statement on risk factors, diagnosis, and management of patients with cancer and valvular heart disease (VHD). VHD can occur in patients with cancer in several ways, for example, it can exist or be diagnosed before cancer treatment, after cancer treatment, be an incidental finding during imaging tests, endocarditis related to immunosuppression, prolonged intravenous catheter use, or combination treatment, and nonbacterial thrombotic endocarditis. It is recommended to employ close cardiac surveillance for patients at high risk of complications during and after cancer treatment and for cancer treatments that may be cardiotoxic to be discussed by a multidisciplinary team. Patients with cancer and pre-existing severe VHD should be managed according to the 2021 European Society of Cardiology (ESC) and European Association for Cardio-Thoracic Surgery (EACTS) guidelines for VHD management, taking into consideration cancer prognosis and patient preferences
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