Miracle-dz/UniFAR-FTUs huggingface dataset:
An effective and efficient model of the near-field hydrodynamic interactions for active suspensions of bacteria

Significance Active suspensions of microswimmers demonstrate novel emergent behaviors (self-organizations, active turbulence, etc.) on macroscopic length scales. For such systems with, minimally, thousands of microswimmers, direct numerical simulations of the hydrodynamic interactions are computationally infeasible, and reduced models are needed. We demonstrated that existing models are not satisfactory in describing the hydrodynamic interactions for microswimmers in close proximity with even qualitatively erroneous predictions, indicating a pressing need for an adequate model. We propose a model that is both physically effective and computationally efficient in describing such hydrodynamics. The main novelty of our model is the description of hydrodynamic interactions through a resistance tensor, as opposed to an effective steric interaction in existing models. Near-field hydrodynamic interactions in active fluids are essential to determine many important emergent behaviors observed, but have not been successfully modeled so far. In this work, we propose an effective model capturing the essence of the near-field hydrodynamic interactions through a tensorial coefficient of resistance, validated numerically by a pedagogic model system consisting of an Escherichia coli bacterium and a passive sphere. In a critical test case that studies the scattering angle of the bacterium–sphere pair dynamics, we prove that the near-field hydrodynamics can make a qualitative difference even for this simple two-body system: Calculations based on the proposed model reveal a region in parameter space where the bacterium is trapped by the passive sphere, a phenomenon that is regularly observed in experiments but cannot be explained by any existing model. In the end, we demonstrate that our model also leads to efficient simulation of active fluids with tens of thousands of bacteria, sufficiently large for investigations of many emergent behaviors.
Queuing to leave: A new approach to immigration

Abstract This paper uses queuing theory to examine the linkages between legal and illegal immigration. This approach is particularly appropriate for periods of mass migration and can be used to look at how the magnitude of people trying to migrate affects the choice between legal and illegal channels. An empirical illustration shows how origin-country conflict and past migration differently affect current legal and illegal flows. With data for Schengen countries from Eurostat for documented immigration and the European Agency for the Management of Operational Cooperation at the External Borders of the Member States of the European Union (Frontex) for illegal border crossings (IBCs), I implement a generalized method of moments (GMM) strategy using different estimates of conflict-related deaths and lagged flows of immigration as external and internal instruments, respectively. Violent conflict has a positive and significant effect on IBCs but not on documented migration flows. I find evidence of positive spillovers from the legal channel of immigration into the illegal channel but not vice versa.
The End of Serendipity: Will Artificial Intelligence Remove Chance and Choice in Everyday Life?

Software defines our everyday experiences! Communication in families as well as in the workplace is largely software mediated. The choices we make, from the news articles we read to the movies we watch and the people we date, are to a large extent software supported. Personalized news portals, navigation systems, social media platforms, shopping portals, music streaming services, and dating apps are only some examples of systems that affect what we experience, think, and do. Improvements in human computer interaction have led to a wide universal adoption of these systems in many areas. Artificial intelligence, learning about the users and their preferences, and striving for simplification in interaction, reduces the need to make active decisions and thereby removes chance and choice. Will this lead to highly optimized systems – that apparently work great for the user, but at the same time end the element of randomness and serendipity in our lives? Simplified content creating, recommender systems and augmented reality are drivers for this. Can interactive human centered artificial intelligence help to keep the user in control or is this just an illusion?
Virtual Assistants for Personalizing IoT Ecosystems: Challenges and Opportunities

End-user development (EUD) is widely used today for the personalization of the most diffused commercial virtual assistants (VAs), both for the configuration of VA-controlled smart environments and the definition of automatic behaviours. However, EUD is not yet exploited to its potential. In this paper, we present the results of a long-time use of VAs in our homes: we compare the most known VAs and discuss a set of challenges that we identified and that can guide the future directions of Human-Computer Interaction research in this field.
Algorithmic Fairness in Applied Machine Learning Contexts

Fairness is a common standard in machine learning principles, ethics declarations, and best practices statements. Fairness, though, does not have a singular definition in machine learning. One common theme among fairness concepts is equal treatment by a machine learning model across groups. In some cases, it might be more fair for the machine learning model to produce different predictions or classifications for each group in concordance with differences in their outcome rates. Equal treatment might be an appropriate standard of fairness in some circumstances but not in others. Codes of ethics and standards for machine learning offer many different suggestions about how machine learning ought to be fair. Not only is there a diversity of fairness concepts, but standards also often offer little or no guidance on how these fairness axioms should guide the real-world practice of developing and deploying machine learning models in applied settings. The context in which machine learning is applied may determine which aspects of fairness are expected or upheld. Machine learning to shape, for example, (a) consumer loan approval or rates, (b) job recommendations, (c) text translations, (d) credit decisions, and (e) justice decisions might all impel different conceptions of machine learning fairness. Going beyond an expectation of “equal treatment”, machine learning in each of these areas might think about fairness differently.
Joint BERT Model based Cybersecurity Named Entity Recognition

With an increase in the volume of threats in Cyber threat defense, knowledge graph is a good solution for cyber threat intelligence (CTI) to rapidly analyze advanced cyber threat in threat situations. Cybersecurity named entity recognition (Cs-NER) is a critical task of knowledge graph to identify cybersecurity related terms in cybersecurity records and achieved significant success using deep neural network with word2vec method. This paper introduces BERT language representation model as pretrained model with Long Short-Term Memory (LSTM), Iterated Dilated Convolutional Neural Networks (ID-CNNs) and Conditional Random Field (CRF) on the top to extract character and text features and predict sequence labels. Aiming to tackle the low recognition accuracy of software entity, we add software dictionary features into the Model to improve the performance of software entity recognition. We compare several models based on BERT with models based on word2vec methods. The results showed that our joint BERT models significantly outperform the state-of-the-art methods.
Charlie: A chatbot to improve the elderly quality of life and to make them more active to fight their sense of loneliness

The development of a conversation agent in the healthcare domain presents several technical, design and linguistic challenges. In our paper, we describe a chatbot conversing with elderly people, with age-related problems. A chatbot, named Charlie, able to remember commitments and medicines, connect remotely with doctors, family, entertain and assist elders. The idea is to investigate solutions to increase their quality of life by providing companionship through innovative strategies based on gamification, active notifications, and promotion of self-compassion that can be explored for preventive mental healthcare. The hope of helping the elderly is more needed than ever today due to the Covid-19 pandemic since lockdowns and isolation have disrupted social lives, affecting their mental health due to loneliness. In this paper, we also describe a preliminary evaluation of Charlie's personality and its level di acceptability by older people.
Rethinking digital identity for post-COVID-19 societies: Data privacy and human rights considerations

Abstract The COVID-19 pandemic has exposed the need for more contactless interactions, leading to an acceleration in the design, development, and deployment of digital identity tools and contact-free solutions. A potentially positive outcome of the current crisis could be the development of a more data privacy and human rights compliant framework for digital identity. However, for such a framework to thrive, two essential conditions must be met: (1) respect for and protection of data privacy irrespective of the type of architecture or technology chosen and (2) consideration of the broader impacts that digital identity can have on individuals’ human rights. The article draws on legal, technology-facing, and policy-oriented academic literature to evaluate each of these conditions. It then proposes two ways to leverage the process of digitalization strengthened by the pandemic: a data privacy-centric and a human rights-based approach to digital identity solutions fit for post-COVID-19 societies.
Bacterial type 1A topoisomerases maintain the stability of the genome by preventing and dealing with R-loop-and nucleotide excision repair-dependent topological stress

E. coli type 1A topoisomerases (topos), topo I (topA) and topo III (topB) have both relaxation and decatenation activities. B. subtilis and E. coli topA topB null cells can survive owing to DNA amplifications allowing overproduction of topo IV, the main cellular decatenase that can also relax supercoiling. We show that overproducing human topo IB, a relaxase but not a decatenase, can substitute for topo IV in allowing E. coli topA null but not topA topB null cells to survive. Deleting topB exacerbates phenotypes of topA null mutants including hypernegative supercoiling, R-loop formation, and RNase HI-sensitive replication, phenotypes that are not corrected by topo IV overproduction. These phenotypes lead to Ter DNA amplification causing a chromosome segregation defect that is corrected by topo IV overproduction. Furthermore, topA topB null mutants not overproducing topo IV acquire uvrB or uvrC mutations, revealing a nucleotide excision repair (NER)-dependent problem with replication fork progression. Thus, type IA topos maintain the stability of the genome by providing essential relaxase and decatenase activities to prevent and solve topological stress related to R-loops and NER. Moreover, excess R-loop formation is well tolerated in cells that have enough topoisomerase activity to support the subsequent replication-related topological stress.
Stochastic comparison of synchronization in activator- and repressor-based coupled gene oscillators

Inside living cells, proteins or mRNA can show oscillations even without a periodic driving force. Such genetic oscillations are precise timekeepers for cell-cycle regulations, pattern formation during embryonic development in higher animals, and daily cycle maintenance in most organisms. The synchronization between oscillations in adjacent cells happens via intercellular coupling, which is essential for periodic segmentation formation in vertebrates and other biological processes. While molecular mechanisms of generating sustained oscillations are quite well understood, how do simple intercellular coupling produces robust synchronizations are still poorly understood? To address this question, we investigate two models of coupled gene oscillators - activator-based coupled oscillators (ACO) and repressor-based coupled oscillators (RCO) models. In our study, a single autonomous oscillator (that operates in a single cell) is based on a negative feedback, which is delayed by intracellular dynamics of an intermediate species. For the ACO model (RCO), the repressor protein of one cell activates (represses) the production of another protein in the neighbouring cell after a intercellular time delay. We investigate the coupled models in the presence of intrinsic noise due to the inherent stochasticity of the biochemical reactions. We analyze the collective oscillations from stochastic trajectories in the presence and absence of explicit coupling delay and make careful comparison between two models. Our results show no clear synchronizations in the ACO model when the coupling time delay is zero. However, a non-zero coupling delay can lead to anti-phase synchronizations in ACO. Interestingly, the RCO model shows robust in-phase synchronizations in the presence and absence of the coupling time delay. Our results suggest that the naturally occurring intercellular couplings might be based on repression rather than activation where in-phase synchronization is crucial.
Enhanced chemotaxis through spatially-regulated absolute concentration robustness

Chemotaxis, the directional motility of cells in response to spatial gradients of chemical cues, is a fundamental process behind a wide range of biological events, including the innate immune response and cancer metastasis. Recent advances in cell biology have shown that the protrusions that enable amoeboid cells to move are driven by the stochastic threshold crossings of an underlying excitable system. As a cell encounters a chemoattractant gradient, the size of this threshold is regulated spatially so that the crossings are biased towards the front of the cell. For efficient directional migration, cells must limit undesirable lateral and rear-directed protrusions. The inclusion of a control mechanism to suppress these unwanted firings would enhance chemotactic efficiency. It is known that absolute concentration robustness (ACR) exerts tight control over the mean and variance of species concentration. Here, we demonstrate how the coupling of the ACR mechanism to the cellular signaling machinery reduces the likelihood of threshold crossings in the excitable system. Moreover, we show that using the cell’s innate gradient sensing apparatus to direct the action of ACR to the rear, suppresses the lateral movement of the cells and that this results in improved chemotactic performance.
Unconventional tonicity-regulated nuclear trafficking of NFAT5 mediated by KPNB1, XPOT and RUVBL2

NFAT5 is the only known mammalian tonicity-responsive transcription factor functionally implicated in diverse physiological and pathological processes. NFAT5 activity is tightly regulated by extracellular tonicity but the underlying mechanisms remain elusive. We demonstrated that NFAT5 enters the nucleus via the nuclear pore complex. We also found that NFAT5 utilizes a non-canonical nuclear localization signal (NFAT5-NLS) for nuclear imports. siRNA screening revealed that karyopherin β1 (KPNB1) drives nuclear import of NFAT5 via directly interacting with NFAT5-NLS. Proteomics analysis and siRNA screening further revealed that nuclear export of NFAT5 under hypotonicity is mediated by Exportin-T, and that it requires RuvB-Like AAA type ATPase 2 (RUVBL2) as an indispensable chaperone. Our findings have identified KPNB1 and RUVBL2 as key molecules responsible for the unconventional tonicity-regulated nucleocytoplasmic shuttling of NFAT5. These findings offer an opportunity for developing novel NFAT5 targeting strategies that are potentially useful for the treatment of diseases associated with NFAT5 dysregulation.
Brain-engrafted macrophages provide protection against therapeutic irradiation and secondary concussive injury

Brain resident microglia have a distinct origin compared to macrophages in other organs. Under physiological conditions, microglia are maintained by self-renewal from the local pool, independent of hematopoietic progenitors. Pharmacological depletion of microglia during therapeutic whole-brain irradiation prevents synaptic loss and long-term recognition memory deficits but the mechanisms behind these protective effects are unknown. Here we demonstrate that after a combination of therapeutic whole-brain irradiation and microglia depletion, macrophages originating from circulating monocytes engraft into the brain and replace the microglia pool. Comparisons of transcriptomes reveal that brain-engrafted macrophages have an intermediate phenotype that resembles both monocytes and embryonic microglia. Brain-engrafted macrophages display reduced phagocytic activity for synaptic compartments compared to microglia from normal brains in response to a secondary concussive brain injury. In addition to sparing mice from brain radiotherapy-induced long-term cognitive deficits, replacement of microglia by brain-engrafted macrophages can prevent concussive injury-induced memory loss. These results demonstrate the long-term functional role of brain-engrafted macrophages as a possible therapeutic tool against radiation-induced cognitive deficits.
Effect of Hot Aqueous Extract of Citrullus colocynthis L. Fruit on some Biochemical and Haematological Parameters in Alloxan –Induced Diabetic Rats

The present study deals with the effect of the aqueous extract of Citrullus colocynthis L. fruit on some biochemical and haematological values in alloxan-induced diabetic rats, including: serum glucose, total cholesterol, triglycerides, total protein, total urea, serum aspartate aminotransferase (AST), serum alanine aminotransferase (ALT), alkaline phosphatase (ALP), haemoglobin (Hb), packed cell volume (PCV) and white blood cell (WBC) were determined after oral administration of the extract to diabetic rats in a dose of 300mg/Kg body weight for 3 weeks. A total of 12 rats were involved in this study and were divided into three experimental groups: control, diabetic and C.colocynthis -treated diabetic. It was found that C.colocynthis treatment decrease the elevated serum glucose, cholesterol, triglycerides, ALP and ALT in diabetic rats. Other parameters including: total protein, urea, AST, Hb, PCV and WBC were found to be unchanged. It is concluded that oral C. colocynthis treatment might normalize some abnormal biochemical parameters in diabetic rats but it has no effect on the haematological parameters.
A Pansharpening Generative Adversarial Network with Multilevel Structure Enhancement and a Multistream Fusion Architecture

Deep learning has been widely used in various computer vision tasks. As a result, researchers have begun to explore the application of deep learning for pansharpening and have achieved remarkable results. However, most current pansharpening methods focus only on the mapping relationship between images and the lack overall structure enhancement, and do not fully and completely research optimization goals and fusion rules. Therefore, for these problems, we propose a pansharpening generative adversarial network with multilevel structure enhancement and a multistream fusion architecture. This method first uses multilevel gradient operators to obtain the structural information of the high-resolution panchromatic image. Then, it combines the spectral features with multilevel gradient information and inputs them into two subnetworks of the generator for fusion training. We design a comprehensive optimization goal for the generator, which not only minimizes the gap between the fused image and the real image but also considers the adversarial loss between the generator and the discriminator and the multilevel structure loss between the fused image and the panchromatic image. It is worth mentioning that we comprehensively consider the spectral information and the multilevel structure as the input of the discriminator, which makes it easier for the discriminator to distinguish real and fake images. Experiments show that our proposed method is superior to state-of-the-art methods in both the subjective visual and objective assessments of fused images, especially in road and building areas.
Improving Parcel-Level Mapping of Smallholder Crops from VHSR Imagery: An Ensemble Machine-Learning-Based Framework

Explicit spatial information about crop types on smallholder farms is important for the development of local precision agriculture. However, due to highly fragmented and heterogeneous cropland landscapes, fine-scale mapping of smallholder crops, based on low- and medium-resolution satellite images and relying on a single machine learning (ML) classifier, generally fails to achieve satisfactory performance. This paper develops an ensemble ML-based framework to improve the accuracy of parcel-level smallholder crop mapping from very high spatial resolution (VHSR) images. A typical smallholder agricultural area in central China covered by WorldView-2 images is selected to demonstrate our approach. This approach involves the task of distinguishing eight crop-level agricultural land use types. To this end, six widely used individual ML classifiers are evaluated. We further improved their performance by independently implementing bagging and stacking ensemble learning (EL) techniques. The results show that the bagging models improved the performance of unstable classifiers, but these improvements are limited. In contrast, the stacking models perform better, and the Stacking #2 model (overall accuracy = 83.91%, kappa = 0.812), which integrates the three best-performing individual classifiers, performs the best of all of the built models and improves the classwise accuracy of almost all of the land use types. Since classification performance can be significantly improved without adding costly data collection, stacking-ensemble mapping approaches are valuable for the spatial management of complex agricultural areas. We also demonstrate that using geometric and textural features extracted from VHSR images can improve the accuracy of parcel-level smallholder crop mapping. The proposed framework shows the great potential of combining EL technology with VHSR imagery for accurate mapping of smallholder crops, which could facilitate the development of parcel-level crop identification systems in countries dominated by smallholder agriculture.
Voxel Grid-Based Fast Registration of Terrestrial Point Cloud

: Terrestrial laser scanning (TLS) is an important part of urban reconstruction and terrain surveying. In TLS applications, 4-point congruent set (4PCS) technology is widely used for the global registration of point clouds. However, TLS point clouds usually enjoy enormous data and uneven density. Obtaining the congruent set of tuples in a large point cloud scene can be challenging. To address this concern, we propose a registration method based on the voxel grid of the point cloud in this paper. First, we establish a voxel grid structure and index structure for the point cloud and eliminate uneven point cloud density. Then, based on the point cloud distribution in the voxel grid, keypoints are calculated to represent the entire point cloud. Fast query of voxel grids is used to restrict the selection of calculation points and ﬁlter out 4-point tuples on the same surface to reduce ambiguity in building registration. Finally, the voxel grid is used in our proposed approach to perform random queries of the array. Using different indoor and outdoor data to compare our proposed approach with other 4-point congruent set methods, according to the experimental results, in terms of registration efﬁciency, the proposed method is more than 50% higher than K4PCS and 78% higher than Super4PCS.