Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Consistency of Extracellular Vesicle Production from Breast Cancer Subtypes Using CELLine Adherent Bioreactors

Extracellular vesicle (EV) research has grown rapidly in recent years, largely due to the potential use of EVs as liquid biopsy biomarkers or therapeutics. However, in-depth characterisation and validation of EVs produced using conventional in vitro cultures can be challenging due to the large area of cell monolayers and volumes of culture media required. To overcome this obstacle, multiple bioreactor designs have been tested for EV production with varying success, but the consistency of EVs produced over time in these systems has not been reported previously. In this study, we demonstrate that several breast cancer cell lines of different subtypes can be cultured simultaneously in space, resource, and time efficient manner using CELLine AD 1000 systems, allowing the consistent production of vast amounts of EVs for downstream experimentation. We report an improved workflow used for inoculating, maintaining, and monitoring the bioreactors, their EV production, and the characterisation of the EVs produced. Lastly, our proteomic analyses of the EVs produced throughout the lifetime of the bioreactors show that core EV-associated proteins are relatively consistent, with few minor variations over time, and that tracking the production of EVs may be a convenient method for indirectly monitoring the bioreactors’ health. These findings will aid future studies requiring the simultaneous production of large amounts of EVs from several cell lines of different subtypes of a disease and other EV biomanufacturing applications.
On the interaction between Janzen-Connell effects and habitat partitioning in spatially structured environments

Janzen-Connell Effects and Habitat Partitioning have both been proposed as potential stabilizing mechanisms in species-rich forest communities. Janzen-Connell Effects describe the process in which specialized predators are attracted to adult trees, which reduce the survivorship of nearby conspecific juveniles. This is thought to generate stabilizing negative frequency dependence. Habitation Partitioning describes when species exhibit different fitness responses to spatially heterogeneous environmental factors, typically causing species to aggregate into favorable habitat types. Despite substantial empirical evidence that both processes occur simultaneously in empirical systems, the theoretical implications of how their interactions shape species diversity remain underdeveloped. In this study, I examine a spatially explicit model that incorporates both processes. I show that Janzen-Connell Effects and Habitat Partitioning can act synergistically to promote coexistence such that their contribution to species coexistence is greater than the sum of their parts when environmental spatial heterogeneity is positively autocorrelated. The results of this study highlight the need to explicitly model the interaction between co-occurring spatially dependent coexistence mechanisms to understand the role they play in the maintenance of species diversity. Significance Statement A primary goal of ecology is to explain the maintenance of species diversity. However, the mechanisms that underlie the coexistence of highly diverse tropical rainforests remain contentious. Two leading hypotheses are Janzen-Connell Effects and Habitat Partitioning. Both hypotheses are based on space. Janzen-Connell Effects are based on the observation that natural enemies attracted to adult trees reduce the survival of their nearby seeds and seedlings. In contrast, Habitation Partitioning states that diversity can be maintained when species exhibit different responses to spatially varying environmental conditions such that species congregate into their preferred habitat types. In this paper, I demonstrate that these mechanisms can interact synergistiacally to promote high species diversity.
3D Point-cloud Segmentation System Based on AI Model

With the rapid development of technology, big data, Internet of Things, and deep learning technologies are gradually developing, among which the development of 2-D image recognition in deep learning is becoming more and more mature. For these reasons, the image recognition of 3D point-cloud is still under development. We combine projection and point-cloud algorithms for large scale semantic segmentation of 3D point-cloud images, and then combine the new neural network based on "Local Flattening for Point Convolution" and "Random Sampling Network" to lighten the model by using depth-separable convolution and quantization. Finally, we implement the convolutional layers in the neural network based on a digital integrated circuit design flow based on a standard cell library.
Face Classification Based on Multi-task Gaussian Process Regression and Chinese Medicine Five Element System

TCM(Traditional Chinese Medicine) physical classi-fication system based on “Five Elements” is the foundation and core material of physical study, and it is a classification system appropriate for a group's physical characteristics. Obtaining appropriate physical classification can aid in disease diagnosis efficiency. We believe that the original problem cannot be simply described as five separate tasks, like independent score inference using five different models for each element category. So we propose an approach based on the Insightface algorithm and Multi-Task Gaussian Process Regression (MTGPR) model to classify faces. MTGPR is a model that attempts to learn inter-task dependencies based solely on the task identities and the observed data for each task. It uses a parameterized covariance function over the input features x to develop a “free-form” task-similarity matrix. In MTGPR model, this is achieved by having a common covariance function over the features $x$ of the input observations. The experimental results show that our proposed method has improved results compared to the traditional Resnet-based classification method.
Causal Contributions of the Domain-General (Multiple Demand) and the Language-Selective Brain Networks to Perceptual and Semantic Challenges in Speech Comprehension

Listening to spoken language engages domain-general Multiple Demand (MD, fronto-parietal) regions of the human brain, in addition to domain-selective (fronto-temporal) language regions, particularly when comprehension is challenging. However, there is limited evidence that the MD network makes a functional contribution to core aspects of comprehension. In a behavioural study of volunteers (n=19) with chronic brain lesions, but without aphasia, we assessed the causal role of these networks in perceiving, comprehending and adapting to challenging spoken sentences. A first task measured word report for acoustically degraded (noise-vocoded) sentences before and after training. Participants with greater damage to MD but not language regions required more vocoder channels to achieve 50% word report indicating impaired perception. Perception improved following training, reflecting adaptation to acoustic degradation, but perceptual learning was unrelated to lesion location or extent. A second task used sentence coherence judgements to measure the speed and accuracy of comprehension of spoken sentences using lower-frequency meanings of semantically ambiguous words. Comprehension accuracy was high and unaffected by lesion location or extent. The availability of the lower-frequency meaning, as measured in a subsequent word association task, increased following comprehension (word-meaning priming). Word-meaning priming was reduced for participants with greater damage to language but not MD regions. We conclude that language and MD networks make dissociable contributions to challenging speech comprehension: using recent experience to update word meaning preferences depends on language specialised regions, whereas the domain-general MD network plays a causal role in reporting words from degraded speech.
Plant-host shift, spatial persistence, and the viability of an invasive insect population

Assessing the effects of a plant-host shift is important for monitoring insect populations over long time periods and for interventions in a conservation or pest management framework. In a heterogeneous environment, individuals may disperse between sources and sinks in order to persist. Here we propose a single-species two-patch model that aims to capture the generational movement of an insect that exhibits density-dependent dispersal, to see how shifting between hosts could alter its viability and asymptotic dynamics. We then analyse the stability and persistence properties of the model and further validate it using parameter estimates derived from laboratory experiments. In order to evaluate the potential of this model, we applied it to Drosophila suzukii (Diptera: Drosophilidae), which has become a harmful pest in several countries around the world. Although many studies have investigated the preference and attractiveness of potential hosts on this invasive drosophilid, no studies thus far have investigated whether a shift of fruit host could affect such a species’ ecological viability or spatiotemporal persistence. The model results show that a shift in host choice can significantly affect the growth potential and fecundity of a species such as D. suzukii, which ultimately could aid such invasive populations in their ability to persist within a changing environment.
Pattern recognition of concrete surface cracks and defects using integrated image processing algorithms

Pattern recognition of concrete surface crack defects is very important in determining stability of structure like building, roads or bridges. Surface crack is one of the subjects in inspection, diagnosis, and maintenance as well as life prediction for the safety of the structures. Traditionally determining defects and cracks on concrete surfaces are done manually by inspection. Moreover, any internal defects on the concrete would require destructive testing for detection. The researchers created an automated surface crack detection for concrete using image processing techniques including Hough transform, LoG weighted, Dilation, Grayscale, Canny Edge Detection and Haar Wavelet Transform. An automatic surface crack detection robot is designed to capture the concrete surface by sectoring method. Surface crack classification was done with the use of Haar trained cascade object detector that uses both positive samples and negative samples which proved that it is possible to effectively identify the surface crack defects.
Crossing Borders within the ABC Automation, Biomedical Engineering and Computer Science 55. IWK

Sailing vessels with wind as their main means of propulsion possess a unique property that the paths they take depend on the wind direction, which, in the literature, has attracted less attention than normal vehicles propelled by propellers or thrusters. This paper considers the problem of motion planning and controllability for sailing vehicles representing the no-sailing zone effect in sailing. Following our previous work, we present an extended algorithm for automatic path generation with a prescribed initial heading for a simple model of sailing vehicles, together with a feedforward controller guiding these vessels along desired trajectories of bounded curvatures. Further, this method immedi-ately adapts to varying wind conditions. Simulation results are hereby presented to illustrate the approach.
Disparities in telehealth utilization in patients with pain during COVID-19

Supplemental Digital Content is Available in the Text. For patients living with pain in New York City during the first wave of COVID-19, the relationship between sociodemographic factors and telehealth utilization evolved with time. Introduction: The shift from in-person visits to telehealth visits during the COVID-19 pandemic presented unique challenges for patients with pain. Disparities in health care access already existed, and the impact of telehealth on these inequities has not been studied. Objectives: To identify sociodemographic characteristics of patients with pain obtaining care through video, telephone, and in-person visits as social distancing restrictions evolved during the COVID-19 pandemic. Methods: Using our institutional clinical data warehouse, we identified 3314 patients with pain receiving care at a large academic institution in New York City during a baseline period (September 23, 2019–March 22, 2020) and counted telephone, video, and in-person visits during the following conditions: a shutdown period (March 23, 2020–May 23, 2020), when nonessential in-person visits were strictly limited, and a reopening period (May 23, 2020–September 23, 2020), when restrictions were relaxed and in-person visits were available. Patients were categorized into 4 groups based on the technology used to complete a visit: (1) video, (2) telephone, (3) in-person, and (4) no visit. Results: Patients who were older, publicly insured, and identified as Black or Hispanic were overrepresented in the telephone visit group during shutdown and the in-person group during reopening. A video visit during shutdown increased the likelihood of continued video visit use during reopening despite the return of in-person visits. Conclusions: Results show differences in how patients with pain accessed clinical care in a socially distanced world and that flexibility in method of health care delivery may reduce barriers to access. Future research will identify factors (eg, Internet access, digital literacy, provider–patient relationships) driving heterogeneity in telehealth use in patients with pain.
Foster Inclusive Entrepreneurship in BOP Areas: The Role of Asset Specificity and Supply Chain Interconnection

Inclusive entrepreneurship has been proven to be an elixir for the inclusive growth in the ‘Base of the Pyramid’ (BOP) regions. Consequently, governors are trying various ways to direct resources into local region to support inclusive entrepreneurship. Some of these policies worked, while others don’t. Why? We argue that, although more resources enhance the chances to start business, it also leads to fierce competition among entrepreneurs, and finally impair the benefits of resource supports. So where to direct the resources to and how to arrange these resources should be carefully thought. Analysis based on data collected from inclusive entrepreneurs in Zhejiang province in China shows that, when the resources are directed to sectors with highly specific assets, the competition will be intensified. On the other hand, if the resources are carefully allocated to different links of an intact supply chain, direct competition among entrepreneurs will be mitigated, so the entrepreneurs will benefit more from the resource supporting plans. This study extends literatures on inclusiveness by offering an operational measurement of inclusiveness, and identifying two boundary conditions that will affect the efficacy of resource supporting plans.
Non-linear dynamics in mammalian voice production

Animal vocalizations range from tonal sounds to irregular atonal sounds and are generated from non-linear oscillations of the vocal folds as well as from turbulent noise in the glottis. Comprehensive study on bioacoustic signals indicates the existence of a diversity of non-linear phenomena, such as limit cycles, subharmonics, biphonation, chaos, and bifurcations in animal vocalizations, which may provide keys to understanding animal communications. In this paper, we review the concept of nonlinear dynamics and its methodology as applicable to bioacoustics. Acoustical analysis of recorded sounds, simulation of a biomechanical model of the voice production system, and physical experiment of the vocal tract and vocal folds are presented to demonstrate non-linear features inherent in animal vocalizations. We focus on source–filter interaction as one of the main regulators of the non-linear prop erty that can lead either to efficient vocalization or to voice instability in animal sounds. A combination of different approaches is suggested to be of great use for extracting the essential features of non-linear dynamics in animal vocalizations.
Analysis and Correction of Through-bolt End-region Overheating and Breakdown Failure in a Large Tubular Hydro-generator

– A field-circuit coupling model of a typical faulty generator is established to correct through-bolt end-region overheating and breakdown failure in a tubular hydro-generator. Using the model, eddy current loss and electromagnetic forces on through bolts under normal and failure conditions are analyzed and compared and the natural frequency of a through bolt is determined. Based on the analysis results, the causative mechanism of failure is revealed and targeted improvement design measures are proposed. The numerical results are found to be consistent with the actual fault characteristics, validating the design measure improvements. The results are useful in improving the design and manufacturing standards and enhancing the operational reliability of large tubular hydro-generators.
Design and control of inductive power transfer system for electric vehicle charging

During the last decades, public awareness of the environmental, economic and social consequences of using fossil fuels has considerably grown. Moreover, not only the supply of fossil resources is limited, but also the environmental impact represents a relevant issue, so leading to an increased consideration of clean and renewable alternatives to traditional technologies. During recent years, the automotive industry has shown a growing interest in electric and hybrid electric vehicles. However, the transition to all-electric transportation is now limited by the high cost of the vehicles, the limited range and the long recharging time. Distributed IPT (inductive power transfer) systems can be the solution to the range restrictions of EVs by charging the vehicle while driving thanks to, a set of loosely coupled coils, so also reducing required battery size as well as overall cost of the vehicle. The concept of wireless power transfer via magnetic induction was introduced two decades ago. Nowadays, this technology is becoming more efficient and more suitable for new applications. This dissertation made an effort to address the requirements of IPT EV battery charging system with high efficiency and good tolerance to misalignment. A survey of a typical IPT for EV application has been reported, while a concentrated DD-BP solution has been proposed in order to enhance the IPT charging system capability of transferring power to a stationary EV with good efficiency and good tolerance to movement. The current trend in EV battery charging application is represented by the lamped coil system, whose different structures have been reviewed. Moreover, this thesis presented the design of a charging pad magnetic structure, called Double D pad combined with a Bipolar secondary pad, in order to enhance coupling performance. A finite element magnetic analysis has been performed in order to obtain the electric parameters of the proposed magnetic coupler. Furthermore, a mathematical model has been developed by considering the different sides of the system. The mathematical model allows to accurately predict the behavior of inductive coils and coreless transformer. A set of simulation has been carried out in order to compare the analytical and simulated results. The proposed EV IPT system has shown the feasibility of using fixed frequency, single pick up system to transfer power efficiently across a large air gap, with variable coupling. This result has been reached by means of proper design of the charging pad magnetics, of tuning network and of a pick-control based on a buck boost converter topology.
Energy Efficient Cluster-Head Selection for Heterogeneous Wireless Sensor Networks

Aimed at the heterogeneousness in wireless sensor networks,an energy-efficient double cluster-heads selection algorithm(DCHsS) is proposed to reduce the burden of single cluster-head and balance the network's energy consumption.DCHsS makes an improvement on the double-CHs-one-cluster scheme,within every cluster defining a "transfer" cluster-head and a "communication" cluster-head to respectively performing the task of data fusion and data communication.In order to better solve the load balancing for energy heterogeneous networks,DCHsS also puts forward a new cluster-head selection parameter.Based on the parameter,one node with higher residual energy,lower communication cost and more strong data processing capacity,will be prior to become the cluster-head.Simulation results show that DCHsS can utilize nodes' energies more effectively and make less energy consumption of the cluster-head than the current one-CH-one-cluster scheme in heterogeneous environments.
Big Data Mining Method of Marketing Management Based on Deep Trust Network Model

Through big data mining, enterprises can deeply understand the consumer preferences, behavior characteristics, market demand and other derived data of customers, so as to provide the basis for formulating accurate marketing strategies. Therefore, this paper proposes a marketing management big date mining method based on deep trust network model. This method first preprocesses the big data of marketing management, including data cleaning, data integration, data transformation and data reduction, and then establishes a big data mining model by using deep trust network to realize the research on the classification of marketing management data. Experimental results show that the proposed method has 99.08% accuracy, the capture rate reaches 88.11%, and the harmonic average between the accuracy and the recall rate is 89.27%, allowing for accurate marketing strategies.
Outcomes of Selective Dorsal Rhizotomy In Non-Walking Children With Spastic Cerebral Palsy

Introduction: There are divergent opinions on the use of selective dorsal rhizotomy (SDR) to treat spasticity in non-walking children. Objective: to investigate the neurological outcomes and perioperative complications after lumbar SDR in non-walking children with spastic cerebral palsy (CP). Methods: A total of 59 non-walking children with spastic CP, between 3 and 9 year-old, and submitted to lumbar SDR with surgical access to the medullary cone were submitted. The patients were followed-up by a multidisciplinary team and underwent intensive physical rehabilitation after surgery. Functional results were measured using the modified Ashworth and GMFM-88 scales 10 months after surgery. Results: The muscle relaxation documented in the lower limbs by the modified Ashworth scale resulted in significant functional improvement in dimensions A and B of the GMFM-88 scale. Peri-operative complications were present in 19 patients (32.2%), and consisted of urinary retention (n = 5), delayed wound healing (n = 2), fever of unknown cause (n = 1), pain severe (n = 11), and dysesthesias and spasms (n = 9). Conclusions: Lumbar SDR in non-walking children with spastic CP promoted muscle relaxation in the lower limbs with significant improvement in gross motor functions, and mild and temporary perioperative complications.