The agglomeration and dispersion dichotomy of human settlements on Earth

Human settlements on Earth are scattered in a multitude of shapes, sizes and spatial arrangements. These patterns are often not random but a result of complex geographical, cultural, economic and historical processes that have profound human and ecological impacts. However, little is known about the global distribution of these patterns and the spatial forces that creates them. This study analyses human settlements from high-resolution satellite imagery and provides a global classification of spatial patterns. We find two emerging classes, namely agglomeration and dispersion. In the former, settlements are fewer than expected based on the predictions of scaling theory, while an unexpectedly high number of settlements characterizes the latter. To explain the observed spatial patterns, we propose a model that combines two agglomeration forces and simulates human settlements’ historical growth. Our results show that our model accurately matches the observed global classification (F1: 0.73), helps to understand and estimate the growth of human settlements and, in turn, the distribution and physical dynamics of all human settlements on Earth, from small villages to cities.

Path Planning for Autonomous Mobile Robots: A Review

Providing mobile robots with autonomous capabilities is advantageous. It allows one to dispense with the intervention of human operators, which may prove beneficial in economic and safety terms. Autonomy requires, in most cases, the use of path planners that enable the robot to deliberate about how to move from its location at one moment to another. Looking for the most appropriate path planning algorithm according to the requirements imposed by users can be challenging, given the overwhelming number of approaches that exist in the literature. Moreover, the past review works analyzed here cover only some of these approaches, missing important ones. For this reason, our paper aims to serve as a starting point for a clear and comprehensive overview of the research to date. It introduces a global classification of path planning algorithms, with a focus on those approaches used along with autonomous ground vehicles, but is also extendable to other robots moving on surfaces, such as autonomous boats. Moreover, the models used to represent the environment, together with the robot mobility and dynamics, are also addressed from the perspective of path planning. Each of the path planning categories presented in the classification is disclosed and analyzed, and a discussion about their applicability is added at the end.

Detection of Pancreatic Ductal Adenocarcinoma by Ex Vivo Magnetic Levitation of Plasma Protein-Coated Nanoparticles

Pancreatic Ductal Adeno Carcinoma (PDAC) is one of the most lethal malignancies worldwide, and the development of sensitive and specific technologies for its early diagnosis is vital to reduce morbidity and mortality rates. In this proof-of-concept study, we demonstrate the diagnostic ability of magnetic levitation (MagLev) to detect PDAC by using levitation of graphene oxide (GO) nanoparticles (NPs) decorated by a biomolecular corona of human plasma proteins collected from PDAC and non-oncological patients (NOP). Levitation profiles of corona-coated GO NPs injected in a MagLev device filled with a paramagnetic solution of dysprosium(III) nitrate hydrate in water enables to distinguish PDAC patients from NOP with 80% specificity, 100% sensitivity, and global classification accuracy of 90%. Our findings indicate that Maglev could be a robust and instrumental tool for the early detection of PDAC and other cancers.

Indeterminate thyroid cytology: detecting malignancy using analysis of nuclear images

Background: Thyroid nodules diagnosed as “Atypia of Undetermined Significance/Follicular Lesion of Undetermined Significance” (AUS/FLUS) or “Follicular Neoplasm/Suspected Follicular Neoplasm” (FN/SFN)”, according to Bethesda's classification, represent a challenge in clinical practice. Computerized analysis of nuclear images (CANI) could be a useful tool for these cases. Our aim was to evaluate the ability of CANI to correctly classify AUS/FLUS and FN/SFN thyroid nodules for malignancy. Methods: We studied 101 nodules cytologically classified as AUS/FLUS (n=68) or FN/SFN (n=33) from 97 thyroidectomy patients. Slides with cytological material were submitted to manual selection and analysis of the follicular cell nuclei for morphometric and texture parameters using ImageJ software. The histologically benign and malignant lesions were compared for such parameters which were then evaluated for the capacity to predict malignancy using the Classification and Regression Trees Gini model. The Intraclass Coefficient of Correlation was used to evaluate method reproducibility. Results: In AUS/FLUS nodule analysis, the benign and malignant nodules differed for Entropy (p<0.05), while the FN/SFN nodules differed for Fractal analysis, coefficient of variation (CV) of roughness, and CV-Entropy (p<0.05). Considering the AUS/FLUS and FN/SFN nodules separately, it correctly classified 90.0% and 100.0% malignant nodules, with a correct global classification of 94.1% and 97%, respectively. We observed that reproducibility was substantially or nearly complete (0.61-0.93) in 10 of the 12 nuclear parameters evaluated. Conclusion: CANI demonstrated an high capacity for correctly classifying AUS/FLUS and FN/SFN thyroid nodules for malignancy. This could be a useful method to help increase diagnostic accuracy in the indeterminate thyroid cytology.

Cultural Issues Related to ICD-11 Mental, Behavioural and Neurodevelopmental Disorders

The challenge of producing a classificatory system that is truly representative of different regions and cultural variations is difficult. This can be conceptualized as an ongoing process, achievable by constant commitment in this regard from various stakeholders over successive generations of the classificatory systems. The objective of this article is to conduct a qualitative review of the process and outcome of the efforts that resulted in the ICD-11 classification of mental, behavioural and neurodevelopmental disorders becoming a global classification. The ICD-11 represents an important, albeit iterative, advance in the classification of mental, behavioural and neurodevelopmental disorders. Significant changes have been incorporated in this regard, such as the introduction of new, culturally-relevant categories, modifications of the diagnostic guidelines, based on culturally informed data and the incorporation of culture-related features for specific disorders. Notwithstanding, there are still certain significant shortcomings and areas for further improvement and research. Some of the key limitations of ICD-11 relate to the paucity of research on the role of culture in the pathogenesis of illnesses. To ensure a classificatory system that is fair, reliable and culturally useful, there is a need to generate empirical evidence on diversity in the form of illnesses, as well as mechanisms that explain these in all the regions of the world. In this review, we try to delineate the various cultural challenges and their influences in the formulation of ICD-11, along with potential shortcomings and areas in need of more improvement and research in this regard.

Future Land Use for Insect Meat Production Among Countries: A Global Classification

A potentially suitable alternative to reduce land use by livestock production is insect meat production. However, land use predictions for insect meat production, which are important in the planning of food production strategies in each country, have not been well-performed. To consider the strategy of insect meat production with regard to land use, the categorical perspectives of countries would be highly useful. Here, using previous simulation results, we used random forest machine learning to classify the potential land use of 157 countries for insect meat production under future climate change. From the categorical maps, we showed the global distribution of five different country categories and found that the land area of the countries may be an important factor in considering the future increase in insect meat production. Our classification could be used to help formulate future food policies by considering the increase in insect meat production in each country, as well as regionally and/or globally.

Evolution of combinatorial diversity in trans-acyltransferase polyketide synthase assembly lines across bacteria

Trans-acyltransferase polyketide synthases (trans-AT PKSs) are bacterial multimodular enzymes that biosynthesize diverse pharmaceutically and ecologically important polyketides. A notable feature of this natural product class is the existence of chemical hybrids that combine core moieties from different polyketide structures. To understand the prevalence, biosynthetic basis, and evolutionary patterns of this phenomenon, we developed transPACT, a phylogenomic algorithm to automate global classification of trans-AT PKS modules across bacteria and applied it to 1782 trans-AT PKS gene clusters. These analyses reveal widespread exchange patterns suggesting recombination of extended PKS module series as an important mechanism for metabolic diversification in this natural product class. For three plant-associated bacteria, i.e., the root colonizer Gynuella sunshinyii and the pathogens Xanthomonas cannabis and Pseudomonas syringae, we demonstrate the utility of this computational approach for uncovering cryptic relationships between polyketides, accelerating polyketide mining from fragmented genome sequences, and discovering polyketide variants with conserved moieties of interest. As natural combinatorial hybrids are rare among the more commonly studied cis-AT PKSs, this study paves the way towards evolutionarily informed, rational PKS engineering to produce chimeric trans-AT PKS-derived polyketides.

Patient Emotion Recognition in Human Computer Interaction System Based on Machine Learning Method and Interactive Design Theory

There are more and more human computer interaction systems (HCIS) in the medical field. Improving the service quality of HCIS and making them more intelligent is an inevitable trend in the future. Emotion recognition is of great significance for patients using HCIS. Some excellent HCIS not only satisfies the needs of patients, but also judges the emotional state of patients based on the results of emotional recognition, thereby providing more intimate medical services. Therefore, emotion recognition is crucial for HCIS. To effectively optimize the correct rate of emotion recognition, a novel emotion recognition framework based on machine learning is proposed. The core of the framework is to select the optimal classifier for different emotional data, and fuse the classification results of each classifier to get the global classification result. Experiments demonstrate that the proposed framework not only improves the accuracy of emotion recognition, but also improves the stability and reliability of the recognition results. The emotion recognition function based on the framework is applied to the HCIS design, so that the HCIS of the medical institution can better serve the patient during use, keep the patient happy, and improve the patient's happiness index and rehabilitation rate.