Toward fitness through adjustment over time : people, genius loci and climate

The city and its architecture are the largest artifact that demonstrates the interaction of human beings, human culture and nature. Harmonious architecture is epitomized by fitness of the built environment, the natural environment and people who inhabit the built and natural environment. Fitness is the optimum relationship among the members in a system. It puts the members into a beneficial relationship with each other, which we call harmony. In today\u27s architectural theory and practice, there are many different approaches that address fitness through the consideration of the relationship between the built environment, the natural environment and people. This thesis asserts that the fitness can only be gained through the integration of all three by the adjustment of their relationships over time. A holistic decoding, transmitting and encoding of the patterns of people\u27s activities, the place\u27s genius loci, and the local climate into a seedmap helps the consensus to occur among interdependent architects, who coherently adjust these relationships over time. Through the adjustment cycles, an architecture of fitness unfolds, placing human beings into a harmonious relationship with nature and culture

SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis

Data-driven methods have shown tremendous progress in medical image analysis. In this context, deep learning-based supervised methods are widely popular. However, they require a large amount of training data and face issues in generalisability to unseen datasets that hinder clinical translation. Endoscopic imaging data incorporates large inter- and intra-patient variability that makes these models more challenging to learn representative features for downstream tasks. Thus, despite the publicly available datasets and datasets that can be generated within hospitals, most supervised models still underperform. While self-supervised learning has addressed this problem to some extent in natural scene data, there is a considerable performance gap in the medical image domain. In this paper, we propose to explore patch-level instance-group discrimination and penalisation of inter-class variation using additive angular margin within the cosine similarity metrics. Our novel approach enables models to learn to cluster similar representative patches, thereby improving their ability to provide better separation between different classes. Our results demonstrate significant improvement on all metrics over the state-of-the-art (SOTA) methods on the test set from the same and diverse datasets. We evaluated our approach for classification, detection, and segmentation. SSL-CPCD achieves 79.77% on Top 1 accuracy for ulcerative colitis classification, 88.62% on mAP for polyp detection, and 82.32% on dice similarity coefficient for segmentation tasks are nearly over 4%, 2%, and 3%, respectively, compared to the baseline architectures. We also demonstrate that our method generalises better than all SOTA methods to unseen datasets, reporting nearly 7% improvement in our generalisability assessment.Comment: 1

Dynamical Analysis of a Parasite-Host Model within Fluctuating Environment

A parasite-host model within fluctuating environment is proposed. Firstly, the positivity and boundedness of solutions of the model within deterministic environment are discussed, and, then, the asymptotical stability and global stability of equilibria of deterministic model are investigated. Secondly, we show that the stochastic model has a unique global positive solution; furthermore, we show that the stochastic model has a stationary distribution under certain conditions. Finally, we give some numerical simulations to illustrate our analytical results

Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring

Inflammatory bowel disease (IBD), in particular ulcerative colitis (UC), is graded by endoscopists and this assessment is the basis for risk stratification and therapy monitoring. Presently, endoscopic characterisation is largely operator dependant leading to sometimes undesirable clinical outcomes for patients with IBD. We focus on the Mayo Endoscopic Scoring (MES) system which is widely used but requires the reliable identification of subtle changes in mucosal inflammation. Most existing deep learning classification methods cannot detect these fine-grained changes which make UC grading such a challenging task. In this work, we introduce a novel patch-level instance-group discrimination with pretext-invariant representation learning (PLD-PIRL) for self-supervised learning (SSL). Our experiments demonstrate both improved accuracy and robustness compared to the baseline supervised network and several state-of-the-art SSL methods. Compared to the baseline (ResNet50) supervised classification our proposed PLD-PIRL obtained an improvement of 4.75% on hold-out test data and 6.64% on unseen center test data for top-1 accuracy.Comment: 1

Apple Quality Evaluation Based on Entropy Weight Method, Grey Relational Degree Method and Low-field Nuclear Magnetic Resonance Detection

To study the quality characteristics of different apple varieties and establish a comprehensive evaluation model of apple quality, taking five varieties of apples (Tianshui Huaniu, Aksu Tangxin, Marshal Huang, Cream Fuji, and Luochuan Red Fuji) as the research object, the four texture characteristics, including hardness, adhesion, chewability, cohesion, and four physical and chemical indicators, including water content, titratable acid (TA), soluble sugar (SS), and soluble solid content (SSC) were tested. Combining the low-field nuclear magnetic resonance detection technology, the correlation between the water distribution and the physicochemical and texture characteristics of apple was explored, and the main indicators for evaluating apple quality were established by principal component analysis. Based on the entropy weight method, each core index was given weight, and a grey correlation degree evaluation model was established. The results showed that there were significant differences in various indexes of different varieties of apples (P<0.05), and there was a high correlation between their water distribution and texture characteristics and physical and chemical indexes. The spin-spin relaxation time T22 (immobilized water), T21 (bound water) and TA, SS, SSC were established as the core indexes. The weight calculated by entropy weight method showed that the sum of T22 and T21 was 35.31%, accounting for the largest proportion, indicating that the water distribution had the greatest impact on apple quality. The grey correlation analysis showed that the quality of Tianshui Huaniu and Aksu Tangxin was better. The method adopted in this study could quickly and accurately establish the quality evaluation model of apples, and provide a new method for the quality evaluation of fruits and vegetables including apples

What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery

Training control policies in simulation is more appealing than on real robots directly, as it allows for exploring diverse states in a safe and efficient manner. Yet, robot simulators inevitably exhibit disparities from the real world, yielding inaccuracies that manifest as the simulation-to-real gap. Existing literature has proposed to close this gap by actively modifying specific simulator parameters to align the simulated data with real-world observations. However, the set of tunable parameters is usually manually selected to reduce the search space in a case-by-case manner, which is hard to scale up for complex systems and requires extensive domain knowledge. To address the scalability issue and automate the parameter-tuning process, we introduce an approach that aligns the simulator with the real world by discovering the causal relationship between the environment parameters and the sim-to-real gap. Concretely, our method learns a differentiable mapping from the environment parameters to the differences between simulated and real-world robot-object trajectories. This mapping is governed by a simultaneously-learned causal graph to help prune the search space of parameters, provide better interpretability, and improve generalization. We perform experiments to achieve both sim-to-sim and sim-to-real transfer, and show that our method has significant improvements in trajectory alignment and task success rate over strong baselines in a challenging manipulation task

Evaluate the effects of low-intensity pulsed ultrasound on dental implant osseointegration under type II diabetes

Objective: The objective of this study is to assess the impact of low-intensity pulsed ultrasound (LIPUS) therapy on the peri-implant osteogenesis in a Type II diabetes mellitus (T2DM) rat model.Methods: A total of twenty male Sprague-Dawley (SD) rats were randomly allocated into four groups: Control group, T2DM group, Control-LIPUS group, and T2DM-LIPUS group. Implants were placed at the rats’ bilateral maxillary first molar sites. The LIPUS treatment was carried out on the rats in Control-LIPUS group and T2DM-LIPUS group, immediately after the placement of the implants, over three consecutive weeks. Three weeks after implantation, the rats’ maxillae were extracted for micro-CT, removal torque value (RTV), and histologic analysis.Results: Micro-CT analysis showed that T2DM rats experienced more bone loss around implant cervical margins compared with the non-T2DM rats, while the LIPUS treated T2DM rats showed similar bone heights to the non-T2DM rats. Bone-implant contact ratio (BIC) were lower in T2DM rats but significantly improved in the LIPUS treated T2DM rats. Bone formation parameters including bone volume fraction (BV/TV), trabecular thickness (Tb.Th), bone mineral density (BMD) and RTV were all positively influenced by LIPUS treatment. Histological staining further confirmed LIPUS’s positive effects on peri-implant new bone formation in T2DM rats.Conclusion: As an effective and safe treatment in promoting osteogenesis, LIPUS has a great potential for T2DM patients to attain improved peri-implant osteogenesis. To confirm its clinical efficacy and to explore the underlying mechanism, further prospective cohort studies or randomized controlled trials are needed in the future

Epigenome-wide gene–age interaction study reveals reversed effects of MORN1 DNA methylation on survival between young and elderly oral squamous cell carcinoma patients

DNA methylation serves as a reversible and prognostic biomarker for oral squamous cell carcinoma (OSCC) patients. It is unclear whether the effect of DNA methylation on OSCC overall survival varies with age. As a result, we performed a two-phase gene–age interaction study of OSCC prognosis on an epigenome-wide scale using the Cox proportional hazards model. We identified one CpG probe, cg11676291MORN1, whose effect was significantly modified by age (HRdiscovery = 1.018, p = 4.07 × 10−07, FDR-q = 3.67 × 10−02; HRvalidation = 1.058, p = 8.09 × 10−03; HRcombined = 1.019, p = 7.36 × 10−10). Moreover, there was an antagonistic interaction between hypomethylation of cg11676291MORN1 and age (HRinteraction = 0.284; 95% CI, 0.135–0.597; p = 9.04 × 10−04). The prognosis of OSCC patients was well discriminated by the prognostic score incorporating cg11676291MORN1–age interaction (HRhigh vs. low = 3.66, 95% CI: 2.40–5.60, p = 1.93 × 10−09). By adding 24 significant gene–age interactions using a looser criterion, we significantly improved the area under the receiver operating characteristic curve (AUC) of the model at 3- and 5-year prognostic prediction (AUC3-year = 0.80, AUC5-year = 0.79, C-index = 0.75). Our study identified a significant interaction between cg11676291MORN1 and age on OSCC survival, providing a potential therapeutic target for OSCC patients