Miracle-dz/UniFAR-FTUs huggingface dataset:
Human serum albumin-based nanoparticles alter raloxifene administration and improve bioavailability

Abstract Osteoporosis is a disease that reduces bone mass and microarchitecture, which makes bones fragile. Postmenopausal osteoporosis occurs due to estrogen deficiency. Raloxifene is a selective estrogen receptor modulator used to treat postmenopausal osteoporosis. However, it has a low bioavailability, which requires long-term, high-dose raloxifene administration to be effective and causes several side effects. Herein, raloxifene was encapsulated in human serum albumin (HSA)-based nanoparticles (Ral/HSA/PSS NPs) as an intravenous-injection pharmaceutical formulation to increase its bioavailability and reduce the treatment dosage and time. In vitro results indicated that raloxifene molecules were well distributed in HSA-based nanoparticles as an amorphous state, and the resulting raloxifene formulation was stabile during long-term storage duration. The Ral/HSA/PSS NPs were both biocompatible and hemocompatible with a decreased cytotoxicity of high-dose raloxifene. Moreover, the intravenous administration of the prepared Ral/HSA/PSS NPs to rats improved raloxifene bioavailability and improved its half-life in plasma. These raloxifene-loaded nanoparticles may be a potential nanomedicine candidate for treating postmenopausal osteoporosis with lower raloxifene dosages.
Increased glucose influx and glycogenesis in lung cancer cells surviving after irradiation

Abstract Purpose Lung cancer is considered as one of the most frequent malignancies worldwide. Radiotherapy is the main treatment modality applied for locally advanced disease, but remnant surviving cancer tissue results in disease progression in the majority of irradiated lung carcinomas. Metabolic reprogramming is regarded as a cancer hallmark and is associated with resistance to radiation therapy. Here, we explored metabolic alterations possibly related to cancer cell radioresistance. Materials and methods We compared the expression of metabolism-related enzymes in the parental A549 lung cancer cell line along with two new cell lines derived from A549 cells after recovery from three (A549-IR3) and six (A549-IR6) irradiation doses with 4 Gy. Differential GLUT1 and GYS1 expression on proliferation and radioresistance were also comparatively investigated. Results A549-IR cells displayed increased extracellular glucose absorption, and enhanced mRNA and protein levels of the GLUT1 glucose transporter. GLUT1 inhibition with BAY-876, suppressed cell proliferation and the effect was significantly more profound on A549-IR3 cells. Protein levels of molecules associated with aerobic or anaerobic glycolysis, or the phosphate pentose pathway were similar in all three cell lines. However, glycogen synthase 1 (GYS1) was upregulated, especially in the A549-IR3 cell line, suggestive of glycogen accumulation in cells surviving post irradiation. GYS1-gene silencing repressed the proliferation capacity of A549, but this increased their radioresistance. The radio-protective effect of the suppression of proliferative activity induced by GYS1 silencing did not protect A549-IR3 cells against further irradiation. Conclusions These findings indicate that GYS1 activity is a critical component of the metabolism of lung cancer cells surviving after fractionated radiotherapy. Targeting the glycogen metabolic reprogramming after irradiation may be a valuable approach to pursue eradication of the post-radiotherapy remnant of disease.
Bisabolane-type sesquiterpenoids with potential anti-inflammatory and anti-HIV activities from the stems and leaves of Morinda citrifolia

Abstract The phytochemical study on the stems and leaves of Morinda citrifolia L. resulted in the isolation of a new naturally occurring bisabolane-type sesquiterpenoid, morincitrinoid A (1), together with five known analogues (2–6). The chemical structure of 1 was elucidated by comprehensive spectral analyses. The known compounds 2–6 were identified by comparing their spectral data with those reported in the literature, which were isolated from M. citrifolia for the first time. In addition, the anti-inflammatory and anti-HIV activities of compounds 1–6 were evaluated in vitro. Compounds 1–6 displayed significant inhibitory activities on NO (nitric oxide) production induced by lipopolysaccharide in mouse macrophage RAW 264.7 cells with IC50 values ranging from 0.98 ± 0.07 to 6.32 ± 0.11 μM, which was comparable to hydrocortisone. Meanwhile, compounds 1–6 showed remarkable anti-HIV-1 reverse transcriptase (RT) effects with the EC50 values ranging from 0.16 to 6.29 μM. Graphical Abstract
Complete Genome Sequence of Methylococcus capsulatus MIR, a Methanotroph Capable of Growth on Methanol

Methylococcus capsulatus MIR is an aerobic methanotroph that was isolated from an activated sludge sample and is capable of growth on methanol. The finished genome of strain MIR is 3.2 Mb in size. It encodes both MxaFI and XoxF methanol dehydrogenases, as well as three different isozymes of formate dehydrogenase. ABSTRACT Methylococcus capsulatus MIR is an aerobic methanotroph that was isolated from an activated sludge sample and is capable of growth on methanol. The finished genome of strain MIR is 3.2 Mb in size. It encodes both MxaFI and XoxF methanol dehydrogenases, as well as three different isozymes of formate dehydrogenase.
Adverse outcome pathways (AOPs) for radiation-induced reproductive effects in environmental species: state of science and identification of a consensus AOP network

Abstract Background Reproductive effects of ionizing radiation in organisms have been observed under laboratory and field conditions. Such assessments often rely on associations between exposure and effects, and thus lacking a detailed mechanistic understanding of causality between effects occurring at different levels of biological organization. The Adverse Outcome Pathway (AOP), a conceptual knowledge framework to capture, organize, evaluate and visualize the scientific knowledge of relevant toxicological effects, has the potential to evaluate the causal relationships between molecular, cellular, individual, and population effects. This paper presents the first development of a set of consensus AOPs for reproductive effects of ionizing radiation in wildlife. This work was performed by a group of experts formed during a workshop organized jointly by the Multidisciplinary European Low Dose Initiative (MELODI) and the European Radioecology Alliance (ALLIANCE) associations to present the AOP approach and tools. The work presents a series of taxon-specific case studies that were used to identify relevant empirical evidence, identify common AOP components and propose a set of consensus AOPs that could be organized into an AOP network with broader taxonomic applicability. Conclusion Expert consultation led to the identification of key biological events and description of causal linkages between ionizing radiation, reproductive impairment and reduction in population fitness. The study characterized the knowledge domain of taxon-specific AOPs, identified knowledge gaps pertinent to reproductive-relevant AOP development and reflected on how AOPs could assist applications in radiation (radioecological) research, environmental health assessment, and radiological protection. Future advancement and consolidation of the AOPs is planned to include structured weight of evidence considerations, formalized review and critical assessment of the empirical evidence prior to formal submission and review by the OECD sponsored AOP development program.
Application of radiation omics in the development of adverse outcome pathway networks: an example of radiation-induced cardiovascular disease

Abstract Background Epidemiological studies have indicated that exposure of the heart to doses of ionizing radiation as low as 0.5 Gy increases the risk of cardiac morbidity and mortality with a latency period of decades. The damaging effects of radiation to myocardial and endothelial structures and functions have been confirmed radiobiologically at high dose, but much less are known at low dose. Integration of radiation biology and epidemiology data is a recommended approach to improve the radiation risk assessment process. The adverse outcome pathway (AOP) framework offers a comprehensive tool to compile and translate mechanistic information into pathological endpoints which may be relevant for risk assessment at the different levels of a biological system. Omics technologies enable the generation of large volumes of biological data at various levels of complexity, from molecular pathways to functional organisms. Given the quality and quantity of available data across levels of biology, omics data can be attractive sources of information for use within the AOP framework. It is anticipated that radiation omics studies could improve our understanding of the molecular mechanisms behind the adverse effects of radiation on the cardiovascular system. In this review, we explored the available omics studies on radiation-induced cardiovascular disease (CVD) and their applicability to the proposed AOP for CVD. Results The results of 80 omics studies published on radiation-induced CVD over the past 20 years have been discussed in the context of the AOP of CVD proposed by Chauhan et al. Most of the available omics data on radiation-induced CVD are from proteomics, transcriptomics, and metabolomics, whereas few datasets were available from epigenomics and multi-omics. The omics data presented here show great promise in providing information for several key events (KEs) of the proposed AOP of CVD, particularly oxidative stress, alterations of energy metabolism, extracellular matrix (ECM), and vascular remodeling. Conclusions The omics data presented here shows promise to inform the various levels of the proposed AOP of CVD. However, the data highlight the urgent need of designing omics studies to address the knowledge gap concerning different radiation scenarios, time after exposure, and experimental models. This review presents the evidence to build a qualitative omics-informed AOP and provides views on the potential benefits and challenges in using omics data to assess risk-related outcomes.
The impact of COVID-19 on individuals with ASD in the US: Parent perspectives on social and support concerns

The COVID-19 pandemic’s disruptions to daily routines and services have proven especially challenging for children with autism spectrum disorder (ASD) and their families. The current retrospective study aimed to determine the impact of the COVID-19 pandemic’s social environmental changes on parental ratings of personal and child concerns about family conflict, opportunities for social interaction, and loss of institutional support (school and therapy services). Analyses of responses from families with ASD in the US determined differences in concerns across three time points which were measured simultaneously: prior to COVID-19, at the start of COVID-19, and at the time of survey completion. From our sample of 246 school-aged children, parents retrospectively reported significantly increasing levels of concern for both themselves and their children over time, with parents’ personal concern levels rated consistently higher than their ratings of their child’s level of concern. Concerns about loss of institutional support were higher for parents of children reported as having co-occurring intellectual disability. Further, parents of younger children also reported more concerns about loss of services, as well as more social concerns. For parent ratings of child concerns, children who were reportedly aware of COVID-19 were determined to have higher levels of social concerns and concerns about loss of institutional support. Meanwhile, the child’s age and gender did not impact their parent ratings of child concerns. The increased level of parental and child-perceived concerns over the course of the pandemic suggests a need for improved service delivery and support for these families. The high levels of concerns observed in the current study provide support for the need to assess families’ priorities and tailor services to best meet families’ needs. This will potentially increase the quality of life of family members, and improve ASD services across the lifespan, and improve outcomes.
Artificial Intelligence in Allergy and Immunology: Comparing Risk Prediction Models to Help Screen Inborn Errors of Immunity

Background: Inborn errors of immunity (IEI) are underdiagnosed disorders, leading to increased morbimortality and expenses for healthcare system. Objectives: The study aimed to develop and compare risk prediction model to measure the individual chance of a confirmed diagnosis of IEI in children at risk for this disorder. Method: Clinical and laboratory data of 128 individuals were used to derive machine learning (ML) and logistic regression risk prediction models, to measure the individual chance of a confirmed diagnosis of IEI in children with suspected disorder, according to previous general pediatrician/clinician judgement. Their performances were compared. Results: Statistically significant variables were mainly leucopenia, neutropenia, lymphopenia, and low levels of immunoglobulins A/G/M. ML models performed better. Conclusion: The enhanced predictive power provided by ML models could be a resource to track IEI, providing better healthcare outcomes.
Electrographic seizures and brain hyperoxia may be key etiological factors for postconcussive deficits

Repetitive mild traumatic brain injuries (RmTBIs) are increasingly recognized to have long-term neurological sequelae in a significant proportion of patients. Individuals that have had RmTBIs exhibit a variety of sensory, cognitive, or behavioral consequences that can negatively impact quality of life. Brain tissue oxygen levels (pO2) are normally maintained through exquisite regulation of blood supply to stay within the normoxic zone (18 to 30 mmHg in the rat hippocampus). However, during neurological events in which brain tissue oxygen levels leave the normoxic zone, neuronal dysfunction, and behavioral deficits have been observed, and are frequently related to poorer prognoses. The oxygenation response in the brain after RmTBIs/repeated concussions has been poorly characterized, with most preliminary research limited to the neocortex. Furthermore, the mechanisms by which RmTBIs impact changes to brain oxygenation and vice versa remains to be determined. In the current study we demonstrate that upon receiving RmTBIs, rats exhibit post-traumatic, electrographic seizures in the hippocampus, without behavioral (clinical) seizures, that are accompanied by a long-lasting period of hyperoxygenation. These electrographic seizures and the ensuing hyperoxic episodes are associated with deficits in working memory and motor coordination that were reversible through attenuation of the post-traumatic and postictal (post-seizure) hyperoxia, via administration of a vasoconstricting agent, the calcium channel agonist Bay K8644. We propose that the post-traumatic period characterized by brain oxygenation levels well above the normoxic zone, may be the basis for some of the common symptoms associated with RmTBIs.
Meaningful work, pleasure in working, and the moderating effects of deep acting and COVID‐19 on nurses' work

Abstract Aims This study aims to verify the association between nurses' perception of the meaningfulness of their work and their pleasure in working, and whether this relationship may change based on the level of deep acting performed to cope with emotional regulation demands and the influence of the COVID‐19 pandemic on the healthcare work. Methods Nurses from both private and public Italian institutions (N = 239) completed an online questionnaire between June 2021 and January 2022. A moderated moderation model was tested through SPSS Process macro. The design is cross‐sectional. Results The results show that the perception of meaningfulness of work is positively associated with pleasure in working, especially in conditions of high deep acting. This relationship is further moderated by the COVID‐19 influence so that the association between meaningful work and pleasure in working is stronger in conditions of high COVID‐19 influence and at higher levels of deep acting performed. Conclusion Perceiving one's work as meaningful can be a job resource that protects nurses from the negative effects of emotional regulation demands and even from the stress of dealing with COVID‐19. Impact The study addresses the problem of nurses' emotional regulation demands at work and evaluates the protective role of meaningful work. The findings could be useful for planning prevention interventions (through training in adaptive emotional regulation strategies) or protection interventions (through the promotion of effective coping strategies and the stimulation of one's work engagement).
Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations

Partial differential equations (PDEs) that fit scientific data can represent physical laws with explainable mechanisms for various mathematically-oriented subjects, such as physics and finance. The data-driven discovery of PDEs from scientific data thrives as a new attempt to model complex phenomena in nature, but the effectiveness of current practice is typically limited by the scarcity of data and the complexity of phenomena. Especially, the discovery of PDEs with highly nonlinear coefficients from low-quality data remains largely under-addressed. To deal with this challenge, we propose a novel physics-guided learning method, which can not only encode observation knowledge such as initial and boundary conditions but also incorporate the basic physical principles and laws to guide the model optimization. We theoretically show that our proposed method strictly reduces the coefficient estimation error of existing baselines, and is also robust against noise. Extensive experiments show that the proposed method is more robust against data noise, and can reduce the estimation error by a large margin. Moreover, all the PDEs in the experiments are correctly discovered, and for the first time we are able to discover three-dimensional PDEs with highly nonlinear coefficients.
Tuberculosis Diagnosis Using Deep Transferred EfficientNet

Tuberculosis is a very deadly disease, with more than half of all tuberculosis cases dead in countries and regions with relatively poor health care resources. Fortunately, the disease is curable, and early diagnosis and medication can go a long way toward curing TB patients. Unfortunately, traditional methods of TB diagnosis rely on specialist doctors, which is lacking in areas with high TB mortality rates. Diagnostic methods based on artificial intelligence technology are one of the solutions to this problem. We propose a Deep Transferred EfficientNet with SVM (DTE-SVM), which replaces the pre-trained EfficientNet classification layer with an SVM classifier and achieves auspicious performance on a small dataset. After ten runs of 10-fold Cross-Validation, the DTE-SVM has a sensitivity of 93.89±1.96, a specificity of 95.35±1.31, a precision of 95.30±1.24, an accuracy of 94.62±1.00, and an F1-score of 94.62±1.00. In addition, our study conducted ablation studies on the effect of the SVM classifier on model performance and briefly discussed the results.
Recent Advances of Blockchain and its Applications

Blockchain is an emerging decentralized data collection, sharing and storage technology, which have provided abundant transparent, secure, tamper-proof, secure and robust ledger services for various real-world use cases. Recent years have witnessed notable developments of blockchain technology itself as well as blockchain-adopting applications. Most existing surveys limit the scopes on several particular issues of blockchain or applications, which are hard to depict the general picture of current giant blockchain ecosystem. In this paper, we investigate recent advances of both blockchain technology and its most active research topics in real-world applications. We first review the recent developments of consensus mechanisms and storage mechanisms in general blockchain systems. Then extensive literature is conducted on blockchain enabled IoT, edge computing, federated learning and several emerging applications including healthcare, COVID-19 pandemic, social network and supply chain, where detailed specific research topics are discussed in each. Finally, we discuss the future directions, challenges and opportunities in both academia and industry.
FOLD-SE: Scalable Explainable AI

FOLD-R++ is a highly efﬁcient and explainable rule-based machine learning algorithm for binary classiﬁcation tasks. It generates a stratiﬁed normal logic program as an (explain-able) trained model. We present an improvement over the FOLD-R++ algorithm, termed FOLD-SE, that provides scalable explainability (SE) while inheriting all the merits of FOLD-R++. Scalable explainability means that regardless of the size of the dataset, the number of learned rules and learned literals stay small and, hence, understandable by human beings, while maintaining good performance in classiﬁcation. FOLD-SE is competitive in performance with state-of-the-art algorithms such as XGBoost and Multi-Layer Perceptrons (MLP). However, unlike XGBoost and MLP, the FOLD-SE algorithm generates a model with scalable explainability. The FOLD-SE algorithm outperforms FOLD-R++ and RIPPER algorithms in efﬁciency, performance, and explainability, especially for large datasets. The FOLD-RM algorithm is an extension of FOLD-R++ for multi-class classiﬁcation tasks. An improved FOLD-RM algorithm built upon FOLD-SE is also presented.
Streaming Adaptive Submodular Maximization

Many sequential decision making problems can be formulated as an adaptive submodular maximization problem. However, most of existing studies in this field focus on pool-based setting, where one can pick items in any order, and there have been few studies for the stream-based setting where items arrive in an arbitrary order and one must immediately decide whether to select an item or not upon its arrival. In this paper, we introduce a new class of utility functions, semi-policywise submodular functions. We develop a series of effective algorithms to maximize a semi-policywise submodular function under the stream-based setting.
DeeperDive: The Unreasonable Effectiveness of Weak Supervision in Document Understanding A Case Study in Collaboration with UiPath Inc

Weak supervision has been applied to various Natural Language Understanding tasks in recent years. Due to technical challenges with scaling weak supervision to work on long-form documents, spanning up to hundreds of pages, applications in the document understanding space have been limited. At Lexion, we built a weak supervision-based system tailored for long-form (10-200 pages long) PDF documents. We use this platform for building dozens of language understanding models and have applied it successfully to various domains, from commercial agreements to corporate forma-tion documents. In this paper, we demonstrate the effectiveness of supervised learning with weak supervision in a situation with limited time, workforce, and training data. We built 8 high quality machine learning models in the span of one week, with the help of a small team of just 3 annotators working with a dataset of under 300 documents. We share some details about our overall architecture, how we utilize weak supervision, and what results we are able to achieve. We also include the dataset for researchers who would like to experiment with alternate approaches or refine ours. Furthermore, we shed some light on the additional complexities that arise when working with poorly scanned long-form documents in PDF format, and some of the techniques that help us achieve state-of-the-art performance on such data.