Miracle-dz/UniFAR-FTUs huggingface dataset:
Oncostatin M expression induced by bacterial triggers drives airway inflammatory and mucus secretion in severe asthma

Description Bacterial-associated LPS drives oncostatin M–dependent airway inflammation and mucus hypersecretion in severe asthma. Addressing asthma Severe asthma is thought to be driven by bacterial dysbiosis, but the mechanism linking the two remains unclear. Headland et al. sought to understand this mechanism, identifying oncostatin M as a mediator of severe asthma. Blocking oncostatin M with an antibody reduced severe asthma-related symptoms in mice after exposure to bacterial stimuli. Together, these findings support further development of antibodies and other drugs targeting oncostatin M as a treatment for severe asthma. Exacerbations of symptoms represent an unmet need for people with asthma. Bacterial dysbiosis and opportunistic bacterial infections have been observed in, and may contribute to, more severe asthma. However, the molecular mechanisms driving these exacerbations remain unclear. We show here that bacterial lipopolysaccharide (LPS) induces oncostatin M (OSM) and that airway biopsies from patients with severe asthma present with an OSM-driven transcriptional profile. This profile correlates with activation of inflammatory and mucus-producing pathways. Using primary human lung tissue or human epithelial and mesenchymal cells, we demonstrate that OSM is necessary and sufficient to drive pathophysiological features observed in severe asthma after exposure to LPS or Klebsiella pneumoniae. These findings were further supported through blockade of OSM with an OSM-specific antibody. Single-cell RNA sequencing from human lung biopsies identified macrophages as a source of OSM. Additional studies using Osm-deficient murine macrophages demonstrated that macrophage-derived OSM translates LPS signals into asthma-associated pathologies. Together, these data provide rationale for inhibiting OSM to prevent bacterial-associated progression and exacerbation of severe asthma.
Cultural Beliefs and Stakeholder Affiliation Influence Attitudes Towards Responsible Research and Innovation Among United States Stakeholders Involved in Biotechnology and Gene Editing

Biotech developers are concerned about the future of gene editing having experienced the contentious history of first-generation GM foods. They have also expressed desires to do better with public engagement in gene-editing innovation. The framework of Responsible Research and Innovation (RRI) may provide a way forward to act on their desires for greater public legitimacy. However, in the United States, -there has also been reluctance to incorporate RRI into biotechnology innovation systems like gene editing in food and agriculture. In this article, we investigate individual- and group-level factors, including demographic, sociographic, and cultural factors, that influence attitudes towards RRI among biotechnology United States stakeholders. Using the Advocacy Coalition Framework’s (ACF) hierarchy of beliefs as a theoretical guide, biotechnology stakeholders (n = 110) were surveyed about their cultural (deep-core) beliefs and then about their attitudes towards principles (policy-core beliefs) and practices (secondary beliefs) of RRI applied to biotechnology innovation. Through statistical analysis of the results, we found significant relationships between stronger egalitarian cultural-beliefs and positive attitudes towards both the principles and practices of RRI. We also found that participants with higher levels of experience held more positive attitudes towards principles of RRI. In contrast, we found a significant inverse relationship between professional affiliation with industry or trade organizations and attitudes towards RRI practices. With these results, we present a model of factors that influence RRI attitudes for future testing. In closing, we interpret the results in the context of ACF to examine the potential for building cross-sector coalitions for practicing RRI within United States gene-editing innovation systems.
Object Detection and Classification Using Machine Learning Techniques: A Comparison of Haar Cascades and Neural Networks

Object recognition and object detection are sub fields of computer vision, the task of giving computers the ability to perceive and respond to the world around them. This is a very useful technology and has many different applications in many different disciplines. Examples of applications include but are not limited to: Use in security by using facial recognition; use in the medical field for classification of cancer types (malignant or benign) or detecting sickle cells in the blood; or it can be used as a research tool to automatically record data (e.g. count the number of trucks that use a particular highway); or used in agriculture or industrialization for quality control; or for entertainment purposes in games that can detect and track a users' movement to control a character in a game. And there are many more examples, in which it can be used. However, just as they are many applications, there are also many methods of implementing these systems, such as Convolutional neural networks, Haar cascades, Scale-Invariant Feature Transformations (SIFT), Histogram of Oriented Gradients (HOG) and several others including combinations of these. Each technique may have different setup procedures and different applications where one may work better than the other. This paper aims to measure and give a comparison of some of these techniques for object detection and tracking, by looking at their training and testing time as well as accuracy. The methods used in this paper was Haar Cascades and Neural Networks. The purpose of this paper is to learn more about these machine learning techniques by comparing the way they work and the way they were implemented, by doing so one can also understand contexts in which to use these systems. The artifacts used in this paper included a robot that can track an object and an application that could classify an image based on a set of objects it was trained on, that is the application can determine what the picture is of based on a set of images it was trained on.
Parameter identification and optimization of process for bio-dissimilation of glycerol to 1,3-propanediol in batch culture

Using the process of forming 1,3-propanediol by transforming glycerol using Klebsiella pneumoniae as study system,a simplified mathematical model of the nonlinear dynamic system is developed according to the cell growth kinetics of multiple-inhibitions,the metabolic overflow kinetics of substrate consumption and product formation.The identification model for this system is also established.The existence of the solution for the nonlinear dynamic system is discussed and the identifiability of the parameters is proved.Finally,the optimal control problem of batch fermentations is obtained by using the volumetric productivity of 1,3-propanediol as the objective function and the initial concentrations of glycerol and biomass and fermentation time as the control variables.The results can provide the theoretical instruction for production design of 1,3-propanediol.
Chart allocation and control techniques for multistage and run -to -run processes

Nowadays, quality is one of the very basic and crucial factors for any products or services in business world. It determines the success of an organization or product to a large extent. Large variability is believed to be the biggest enemy of excellent quality, especially, the variability caused by certain assignable causes. In practice, Statistical Process Control (SPC) techniques have been widely applied in industries to detect assignable causes and reduce variability. Most SPC charts are designed to detect assignable causes in single stage processes encountered in industrial practice. Due the complexity of modern techniques, more and more tasks and products require more than one stages/operations to accomplish. This is what we call multistage manufacturing processes (MMP). However, applications of conventional charts to such multistage processes usually result in an unsatisfactory performance due to two main obstacles. 1. Limited resources, such as measurement systems and manpower, it is usually too costly to place SPC charts at every stage, especially for the processes which have a large number of stages. 2. Incomplete information. Even if we have the capability to set up control charts for all the stages, it is still possible that the whole monitoring performance (in terms of Average Run Length and Average Time to Signal) is
The Role of the Digital Economy in Rebuilding and Maintaining Social Governance Mechanisms

The digital transformation has impacted society at different levels, mainly on the economic and governance levels. This paper investigates the impact of the digital economy on social governance mechanisms. Additionally, it captures the indirect effects or mediating forces such as social reforms and a sustainable digital economy. The study followed a positivism philosophy, and it is survey research influencing cross-sectional study. The unit of analysis in the current paper was employees from four different professions as economists, financial analysts, managers, and teachers. The random sampling technique was used as a sampling type, and a questionnaire was used for data collection. Structural equation modeling (SEM) was carried out as a data analysis technique. The research findings revealed that the digital economy has a favorable impact on the social governance mechanism. Likewise, the digital economy positively affects social reforms and a sustainable digital economy. Social reforms also proved to link with a sustainable digital economy positively. The output of the indirect effects and structural model confirmed that social reform played a partial mediation role between the digital economy and sustainable digital economy. Moreover, a sustainable digital economy confirmed a partial mediation between the digital economy and the social governance mechanism. Finally, analysis confirmed a serial mediation among digital economy, social reforms, sustainable digital economy, and social governance mechanism. Therefore, policymakers and government agents should improve the digital economy to have a strong social governance mechanism.
Relevant Physiological Indicators for Assessing Workload in Conditionally Automated Driving, Through Three-Class Classification and Regression

In future conditionally automated driving, drivers may be asked to take over control of the car while it is driving autonomously. Performing a non-driving-related task could degrade their takeover performance, which could be detected by continuous assessment of drivers' mental load. In this regard, three physiological signals from 80 subjects were collected during 1 h of conditionally automated driving in a simulator. Participants were asked to perform a non-driving cognitive task (N-back) for 90 s, 15 times during driving. The modality and difficulty of the task were experimentally manipulated. The experiment yielded a dataset of drivers' physiological indicators during the task sequences, which was used to predict drivers' workload. This was done by classifying task difficulty (three classes) and regressing participants' reported level of subjective workload after each task (on a 0–20 scale). Classification of task modality was also studied. For each task, the effect of sensor fusion and task performance were studied. The implemented pipeline consisted of a repeated cross validation approach with grid search applied to three machine learning algorithms. The results showed that three different levels of mental load could be classified with a f1-score of 0.713 using the skin conductance and respiration signals as inputs of a random forest classifier. The best regression model predicted the subjective level of workload with a mean absolute error of 3.195 using the three signals. The accuracy of the model increased with participants' task performance. However, classification of task modality (visual or auditory) was not successful. Some physiological indicators such as estimates of respiratory sinus arrhythmia, respiratory amplitude, and temporal indices of heart rate variability were found to be relevant measures of mental workload. Their use should be preferred for ongoing assessment of driver workload in automated driving.
Exercise Cardio-Oncology: Exercise as a Potential Therapeutic Modality in the Management of Anthracycline-Induced Cardiotoxicity

Anthracyclines are one of the most effective chemotherapy agents and have revolutionized cancer therapy. However, anthracyclines can induce cardiac injuries through ‘multiple-hits', a series of cardiovascular insults coupled with lifestyle risk factors, which increase the risk of developing short- and long-term cardiac dysfunction and cardiovascular disease that potentially lead to premature mortality following cancer remission. Therefore, the management of anthracycline-induced cardiotoxicity is a serious unmet clinical need. Exercise therapy, as a non-pharmacological intervention, stimulates numerous biochemical and physiologic adaptations, including cardioprotective effects, through the cardiovascular system and cardiac muscles, where exercise has been proposed to be an effective clinical approach that can protect or reverse the cardiotoxicity from anthracyclines. Many preclinical and clinical trials demonstrate the potential impacts of exercise on cardiotoxicity; however, the underlying mechanisms as well as how to implement exercise in clinical settings to improve or protect against long-term cardiovascular disease outcomes are not clearly defined. In this review, we summarize the current evidence in the field of “exercise cardio-oncology” and emphasize the utilization of exercise to prevent and manage anthracycline-induced cardiotoxicities across high-risk and vulnerable populations diagnosed with cancer.
Video Calls for Older Adults: A Narrative Review of Experiments Involving Older Adults in Elderly Care Institutions

Social isolation in geriatric institutions is a real threat to older adults' (OAs) well-being. Visits from family members, when they are not impacted by geographical distance or illness, sometimes fail to provide sufficient opportunities for social connectedness and interaction to prevent and/or combat OAs' loneliness and social isolation. Information and Communication Technologies (ICTs) offer promising solutions to this problem. Video calls provide a quick and convenient way for remote communication between OAs and their families, and a complement to face-to-face visits in geriatric settings. Over the last months, during the several confinements imposed to stop the transmission of COVID-19 over the world, several care homes and long-care facilities have equipped themselves with laptops, tablets and video call applications to help OAs remain in contact with their relatives. However, numerous technical and human-related factors may hinder the use of video calls in these settings. The complexity of technological devices, as well as OAs limited digital skills, low confidence and experience in the use of technology are some examples. Furthermore, the specific context of use and the required implication of multiple actors (care professionals, family members) should also be considered when examining the use and implementation of video calls in geriatric institutions. We conducted a narrative review of literature describing the use of video calls in geriatric institutions between 2000 and 2021, especially because of the little information related to OAs' use of video calls in geriatric settings. One thousand one hundred ninety-seven references were screened and 15 studies focusing on the usability, acceptability and effectiveness of video calls were included. A qualitative, deductive thematic analysis inspired by a Health Technology Assessment (HTA) multidimensional model was used to identify barriers, enablers and solutions to video calls implementation in geriatric institutions. The results from the HTA-based analysis provide encouraging evidence for the feasibility of video call use in geriatric settings, and its efficacy on reducing social isolation among residents. However, numerous technical, human-related, ethical and organizational barriers persist and should be addressed in future works. The present analysis has also allowed the identification of potential solutions to overcome these barriers, which are discussed in this publication.
A Plant Disease Recognition Method Based on Fusion of Images and Graph Structure Text

The disease image recognition models based on deep learning have achieved relative success under limited and restricted conditions, but such models are generally subjected to the shortcoming of weak robustness. The model accuracy would decrease obviously when recognizing disease images with complex backgrounds under field conditions. Moreover, most of the models based on deep learning only involve characterization learning on visual information in the image form, while the expression of other modal information rather than the image form is often ignored. The present study targeted the main invasive diseases in tomato and cucumber as the research object. Firstly, in response to the problem of weak robustness, a feature decomposition and recombination method was proposed to allow the model to learn image features at different granularities so as to accurately recognize different test images. Secondly, by extracting the disease feature words from the disease text description information composed of continuous vectors and recombining them into the disease graph structure text, the graph convolutional neural network (GCN) was then applied for feature learning. Finally, a vegetable disease recognition model based on the fusion of images and graph structure text was constructed. The results show that the recognition accuracy, precision, sensitivity, and specificity of the proposed model were 97.62, 92.81, 98.54, and 93.57%, respectively. This study improved the model robustness to a certain extent, and provides ideas and references for the research on the fusion method of image information and graph structure information in disease recognition.
Tumor Reduction in Multiple Myeloma: New Concepts for New Therapeutics

The development of new resources for a more accurate diagnosis and response assessment in multiple myeloma has been a long process for decades, mainly since the middle of the 20th century. During this time, the succession of technical advances has run parallel to the better knowledge of disease biology and the availability of novel therapeutic strategies. The cornerstone of standardized criteria to uniformly evaluate the disease response in myeloma dates back to the 1990s when the key role of complete remission was established. Since then, different updates have been implemented according to available scientific evidences not always without certain controversies. The progressive improvements in survival results of myeloma patients and the growing quality of responses due to the novel therapies have led to the need of developing new tools for better monitoring of tumor burden. In this way, the concept of minimal residual disease and its key value based on the prognostic significance and the clinical relevance has been consolidated during the last years, overcoming the value of conventional response criteria or classical adverse prognosis markers. Nevertheless, its precise role in the clinical management of myeloma patients to detect early treatment failure and trigger early rescue strategies is still pending to be defined. In this review, we revisit the major milestones in the understanding of tumor reduction in multiple myeloma until the most recent imaging techniques or liquid biopsy approaches, including a critical view of conventional response criteria, whose backbone has remained unchanged during the last 20 years.
Hemispheric Lateralization of Visuospatial Attention Is Independent of Language Production on Right-Handers: Evidence From Functional Near-Infrared Spectroscopy

It is well-established that visuospatial attention is mainly lateralized to the right hemisphere, whereas language production is mainly left-lateralized. However, there is a significant controversy regarding how these two kinds of lateralization interact with each other. The present research used functional near-infrared spectroscopy (fNIRS) to examine whether visuospatial attention is indeed right-lateralized, whereas language production is left-lateralized, and more importantly, whether the extent of lateralization in the visuospatial task is correlated with that in the task involving language. Specifically, fifty-two healthy right-handed participants participated in this study. Multiple-channel fNIRS technique was utilized to record the cerebral hemodynamic changes when participants were engaged in naming objects depicted in pictures (the picture naming task) or judging whether a presented line was bisected correctly (the landmark task). The degree of hemispheric lateralization was quantified according to the activation difference between the left and right hemispheres. We found that the picture-naming task predominantly activated the inferior frontal gyrus (IFG) of the left hemisphere. In contrast, the landmark task predominantly activated the inferior parietal sulcus (IPS) and superior parietal lobule (SPL) of the right hemisphere. The quantitative calculation of the laterality index also showed a left-lateralized distribution for the picture-naming task and a right-lateralized distribution for the landmark task. Intriguingly, the correlation analysis revealed no significant correlation between the laterality indices of these two tasks. Our findings support the independent hypothesis, suggesting that different cognitive tasks may engender lateralized processing in the brain, but these lateralized activities may be independent of each other. Meanwhile, we stress the importance of handedness in understanding the relationship between functional asymmetries. Methodologically, we demonstrated the effectiveness of using the multichannel fNIRS technique to investigate the hemispheric specialization of different cognitive tasks and their lateralization relations between different tasks. Our findings and methods may have important implications for future research to explore lateralization-related issues in individuals with neural pathologies.