Miracle-dz/UniFAR-FTUs huggingface dataset:
Interferon system I e III protects patients with cystic fibrosis from Sars Cov2 infection

Cystic fibrosis (CF) is an autosomal recessive disease caused by a deficiency of expression of a chloride channel. Children with CF seem to be more protected than the general population from SARS-CoV2 infection. The interferon system (IFN) plays an important role in the susceptibility and resolution of infections. The aim of this study was to evaluate the interferon system response in patients with CF compared to a control group. The expression levels of genes coding for IFN types I and III and of some interferon-induced genes in the respiratory mucosa of CF patients compared to a control group were analyzed. 16 patients affected by CF and 15 healthy controls, aged between 2 and 18 years were enrolled. The expression of genes coding for IFN types I and III and of some interferon-induced genes ISG15 and ISG56 was studied in nasal swab cells. We found an increase expression of the following genes in cases compared to controls: IFN-β (p = 0.002), IFN-ε (p = 0.002) and ISG56 (p = 0.001). This study suggests that in patients with CF the innate immune response is more active than in the healthy population and it may explain why these patients are less exposed to SARS-CoV2 infection.
Flagship Attractions and Sustainable Rural Tourism Development: The Case of the Alnwick Garden, England

This paper proposes an alternative approach to rural tourism that returns to a more traditional model of development: large, flagship attractions that act as a ‘growth pole’ for the local economy and community. It questions some of the accepted beliefs about sustainable rural tourism development current in recent years. It is based on a case study of Alnwick Garden in Northumberland, England. It suggests that, under certain circumstances, flagship or mega-attractions can not only increase substantially the number of visitors to rural areas but also, through appropriate policies and processes, can underpin the longer-term, sustainable development of those areas.
Are Primary Outcomes Really Primary? An Exploratory Cross-Sectional Nationwide Web-Based Survey Study for Outcomes Reflecting Real Symptoms and Needs of Patients with Lumbar Disc Herniation

As primary outcomes differ among clinical lumbar disc herniation (LDH) studies, this study aimed to explore outcomes reflecting real-world patient experiences through an exploratory questionnaire survey. Those diagnosed with LDH having radiating leg pain in South Korea in November of 2022 (N = 500) were administered a questionnaire including basic characteristics, disease onset, symptoms and severity, priority symptoms for improvement, and important treatment factors. Outcome measures included the identification of priority symptoms and disabilities. Most common symptoms were numbness in the leg (N = 435, 87.0%) and back pain (N = 406, 81.2%); most common disabilities were discomfort in sitting (N = 323, 64.6%) and lifting (N = 318, 63.6%). The highest priority symptom was back pain (N = 242, 48.4%). A satisfactory degree of symptom improvement was a decrease of at least 3 points on the numeric rating scale. The majority of respondents preferred improvement in disability over pain (N = 270, 55.8%), a stable effect over a rapid effect (N = 391, 78.2%), and safety over treatment efficacy (N = 282, 56.4%). Safety (N = 129, 25.8%) and cost (N = 111, 22.2%) were the most important treatment factors. Improvements in back pain, leg pain, sitting, and sleeping were prioritized, and safety, stable treatment effect, and functional recovery were desired. Clinical trials for LDH should be designed to reflect this real-world patient need. Further study to examine the patients’ symptoms and needs in details is needed.
Vital Signs in Palliative Care: A Scoping Review

Simple Summary Vital signs (heart rate, blood pressure, respiratory rate, oxygen saturation, and temperature) are often used in healthcare settings as indicators of how unwell a patient is, and how they are responding to treatment. They are often not measured routinely for patients who are receiving palliative care. This review highlights that measuring vital signs for patients with cancer who are receiving palliative care may be of some benefit in determining prognosis, but further studies are needed to clarify the extent of association between a patient’s vital signs and prognosis. Abstract Vital signs are routinely measured in secondary healthcare settings and can be used to detect clinical problems, guide treatment, and monitor response to treatment. Vital signs are less frequently measured in palliative care settings. Reasons for this are unclear. This scoping review aimed to assess the generic use of vital signs in palliative care, and its role in prognostication for adult patients with cancer receiving palliative care. Medline, Embase, and CINAHL were searched for articles involving adult patients with advanced cancer receiving palliative care who had their vital signs measured. Twenty-six articles were identified in which one or a combination of vital signs, with or without other clinical parameters, was used to prognosticate for patients. An additional three articles investigated the generic use of vital signs in patients with advanced cancer. There was significant heterogeneity between identified studies, with some indication that changes in vital signs may indicate that a patient is close to death. However, other studies suggested that patients may maintain normal vital signs until the time of death. Further studies are needed to explore whether abnormal vital signs may be used as a prognostic indicator for patients with cancer receiving palliative care.
Skeleton-Based Gesture Recognition With Learnable Paths and Signature Features

For the skeleton-based gesture recognition, graph convolutional networks (GCNs) have achieved remarkable performance since the human skeleton is a natural graph. However, the biological structure might not be the crucial one for motion analysis. Also, spatial differential information like joint distance and angle between bones may be overlooked during the graph convolution. In this article, we focus on obtaining meaningful joint groups and extracting their discriminative features by the path signature (PS) theory. Firstly, to characterize the constraints and dependencies of various joints, we propose three types of paths, i.e., spatial, temporal, and learnable path. Especially, a learnable path generation mechanism can group joints together that are not directly connected or far away, according to their kinematic characteristic. Secondly, to obtain informative and compact features, a deep integration of PS with few parameters are introduced. All the computational process is packed into two modules, i.e., spatial-temporal path signature module (ST-PSM) and learnable path signature module (L-PSM) for the convenience of utilization. They are plug-and-play modules available for any neural network like CNNs and GCNs to enhance the feature extraction ability. Extensive experiments have conducted on three mainstream datasets (ChaLearn 2013, ChaLearn 2016, and AUTSL). We achieved the state-of-the-art results with simpler framework and much smaller model size. By inserting our two modules into the several GCN-based networks, we can observe clear improvements demonstrating the great effectiveness of our proposed method.
Sound production in sea turtle nests and hatchlings (Eretmochelys imbricata and Caretta caretta) in Northeast Brazil

ABSTRACT Sea turtles have been considered the least vocal of living reptiles, but recent studies are changing this perception. Here, we aimed to investigate the acoustics of hatchlings of, Eretmochelys imbricata and Caretta caretta in Northeast Brazil, to describe their acoustic signals and compare sound structure within and between species. We monitored 13 km in Ipojuca, southern coast of Pernambuco State. We recorded five nests of each species and the hatchlings’ walk to the sea in 2020/2021. We recorded 12 sonotypes in C. caretta and 5 in E. imbricata, increasing the number of described signals. Nevertheless, not all sonotypes were produced by all or in the different developmental stages. Some of the sounds for both species reached ultrasonic frequencies. The sound structure varied between C. caretta nests, suggesting a potential vocal signature, but the sound structure had no variation between E. imbricata nests. The sound structure varied between species. Despite their reputation for being silent, this study shows a diverse acoustic repertoire in the target species, further supporting speculations of acoustic signals mediating the potential synchronisation of hatchlings’ departure to the sea. Our signal descriptions could be used for future passive acoustic monitoring of the target species. Future studies should focus on understanding the functionality of acoustic signals in the different life stages.
Topic-Aware Fake News Detection Based on Heterogeneous Graph

In recent years, fake news has had a bad impact on individuals and society, which has aroused widespread concern about fake news detection. The existing heterogeneous graph-based fake news detection model (CompareNet) mainly focuses on the semantic consistency analysis between news content and external knowledge, which out-performs traditional content detection models in terms of its efficiency and scalability. However, we found that the framework ignores the fact that the node content of heterogeneous graphs is mostly in the form of short text, and such methods often have difficulty in extracting effective features due to the sparsity problem of short text data. In addition, previous studies have not considered the structural relationship between different writing styles of fake news. Aiming at the above problems this paper proposes a topic-aware fake news detection (FND) method based on heterogeneous graphs, the model investigates the effect of news topics on fake news detection and enhances the discriminative ability of fake news detection. Our model introduces semantically enhanced topic node information in the fake news detector, which fully utilizes three types of information: external knowledge (Wikipedia), news content, and news topics. Therefore, it can better enhance the fake news detection performance.
Beyond Explainability: Leveraging Interpretability for Improved Adversarial Learning

In this study, we propose the leveraging of interpretability for tasks beyond purely the purpose of explainability. In particular, this study puts forward a novel strategy for leveraging gradient-based interpretability in the realm of adversarial examples, where we use insights gained to aid adversarial learning. More specifically, we introduce the concept of spatially constrained one-pixel adversarial perturbations, where we guide the learning of such adversarial perturbations towards more susceptible areas identified via gradient-based interpretability. Experimental results using different benchmark datasets show that such a spatially constrained one-pixel adversarial perturbation strategy can noticeably improve the speed of convergence as well as produce successful attacks that were also visually difficult to perceive, thus illustrating an effective use of interpretability methods for tasks outside of the purpose of purely explainability.
The Historical Development of Large‐Scale Paleoclimate Field Reconstructions Over the Common Era

Climate field reconstructions (CFRs) combine modern observational data with paleoclimatic proxies to estimate climate variables over spatiotemporal grids during time periods when widespread observations of climatic conditions do not exist. The Common Era (CE) has been a period over which many seasonally‐ and annually‐resolved CFRs have been produced on regional to global scales. CFRs over the CE were first produced in the 1970s using dendroclimatic records and linear regression‐based approaches. Since that time, many new CFRs have been produced using a wide range of proxy data sets and reconstruction techniques. We assess the early history of research on CFRs for the CE, which provides context for our review of advances in CFR research over the last two decades. We review efforts to derive gridded hydroclimatic CFRs over continental regions using networks of tree‐ring proxies. We subsequently explore work to produce hemispheric‐ and global‐scale CFRs of surface temperature using multi‐proxy data sets, before specifically reviewing recently‐developed data assimilation techniques and how they have been used to produce simultaneous reconstructions of multiple climatic fields globally. We then review efforts to develop standardized and digitized databases of proxy networks for use in CFR research, before concluding with some thoughts on important next steps for CFR development.
Satellite ozone and UV measurements over Cyprus

Ultraviolet (UV) radiation is a small but significant part of solar radiation that reaches the Earth's surface. Overexposure to UV radiation can have harmful effects on human health and ecosystems, including sunburn, skin cancer, eye damage, and immune system weakening. The factors that affect UV levels at the surface include solar elevation angle, total ozone amount, optical properties of aerosols, clouds, altitude, and surface albedo. Ozone is an efficient absorber of UV radiation, and its depletion leads to increased ground-level UV radiation. The recovery of the ozone layer in response to the phase-out of ozone-depleting substances is also linked to climate change, which can also affect future UV levels. Therefore, it is crucial to continuously monitor UV radiation levels. The Ultraviolet Index (UVI) is a tool that increases public awareness of the need for sun protection and lowers the risk of skin cancer. Cyprus, as the European country with the highest sunshine duration, is an ideal location for studying UVI levels and ozone column amounts. In this study, we present a climatology of ozone and UVI over the four major cities in Cyprus (Nicosia, Limassol, Larnaca, and Paphos) based on satellite overpass measurements from the Ozone Monitoring Instrument for the period 2004–2022. We also investigate the sensitivity of UV radiation to ozone changes using the radiation amplification factor and also to aerosol and clouds. This study provides valuable insights into the climatology of ozone and UVI in Cyprus and the sensitivity of UV radiation to ozone changes, which can aid in the development of effective public health policies and sun protection measures.
Assessment of Community Dependence and Perceptions of Wetlands in the Upper Chindwin Basin, Myanmar

Understanding the relationship between communities and wetland ecosystems is imperative to facilitate the development of wetland management and conservation strategies that can effectively safeguard wetland health and sustain the flow of ecosystem services. To understand the knowledge, attitude, and perception of communities on wetland ecosystem services, a survey was conducted involving 133 households from 4 villages dependent on 5 wetlands within the Upper Chindwin Basin (UCB), northwestern Myanmar. Most of the respondents extracted wetland resources for subsistence and income. A total of 84% of the households depended on wetland fish for their primary protein consumption, while 70% (n = 94) collected fuelwood from wetlands for subsistence. The survey participants unanimously recognized the benefits of wetland ecosystem services (i.e., provisioning benefits), particularly for fish, food, fiber, fuel, natural medicines, ornamental resources, and minerals. A total of 97% of the participants lacked knowledge of any existing law or regulation that ensures wetland protection in Myanmar. Furthermore, 87% of the respondents concurred that the government has not adequately endeavored to promote awareness of wetland conservation in this remote area due to lack of capacity and resources. This study establishes a baseline for the region and recommends designing and implementing a community-centric wetland action plan. This action plan provides a self-sustaining and cost-effective approach to conserve wetlands and is crucial in enhancing the capacity of dependent communities to participate and eventually lead wetland management of UCB.
Emerging Immunotherapy Approaches for Treating Prostate Cancer

Immunotherapy has emerged as an important approach for cancer treatment, but its clinical efficacy has been limited in prostate cancer compared to other malignancies. This review summarizes key immunotherapy strategies under evaluation for prostate cancer, including immune checkpoint inhibitors, bispecific T cell-engaging antibodies, chimeric antigen receptor (CAR) T cells, therapeutic vaccines, and cytokines. For each modality, the rationale stemming from preclinical studies is discussed along with outcomes from completed clinical trials and strategies to improve clinical efficacy that are being tested in ongoing clinical trials. Imperative endeavors include biomarker discovery for patient selection, deciphering resistance mechanisms, refining cellular therapies such as CAR T cells, and early-stage intervention were reviewed. These ongoing efforts instill optimism that immunotherapy may eventually deliver significant clinical benefits and expand treatment options for patients with advanced prostate cancer.
Artificial Intelligence Model Integrated with BIM Model for Core Construction of Transportation Hub

BIM technology comprises the digital representation of the physical and functional characteristics of the building or structures. It provides the architectural model, engineering, and professional construction with the collaboration of construction projects in an efficient and effective manner. BIM technology can be used to create 3D models of transportation hubs, which can help visualize and simulate different scenarios, optimize space utilization, and improve safety. With detailed 3D models and simulating different scenarios, BIM technology can help optimize space utilization, improve safety, and enhance the overall travel experience for passengers. Core architectural design refers to the fundamental design principles and elements that form the foundation of a building or structure. Hence, this research designed Artificial Intelligence (AI) integrated fuzzy set (AIF-BIM) model for the transportation hub construction. The design of the AIF-BIM model uses the associative rule-based model for the design of the transportation hub. With a designed AI model BIM technology is integrated for the examination of design, construction, and operation. Through the AIF-BIM model, the transportation hub engineering architects are evaluated for the different phases such as the design phase and construction phase. Simulation analysis stated that the application of BIM technology with AIF – BIM in transportation hubs can improve their design, construction, and operation.
Modeling Both Intra- and Inter-Modality Uncertainty for Multimodal Fake News Detection

Multimodal fake news detection has obtained increasing attention recently. Existing works generally encode multimodal contents into a deterministic point in semantic subspaces, and then fuse multimodal features by simple concatenation or attention mechanisms. However, most methods suffer from adapting to noisy multimodal contents since they neglect the robustness of modality-specific features. Besides, as different modalities usually have varying confidence levels, previous attention-based fusion models that learn modality-independent weights based on the input data feature, would limit the optimal integration of multimodal contents. To alleviate the above issues, we propose novel Multimodal Uncertainty Learning Network (MM-ULN) to enhance multimodal fake news detection by modeling both intra- and inter-modality uncertainty. Specifically, we incorporate a novel intra-modality uncertainty learning (EUL) module to better understand noisy multimodal contents. EULs provide feature regularization in a variational way, successfully alleviating the effects of data uncertainty within modalities. We design a new variational attention fusion (VAF) module to adaptively fuse multimodal contents with modality-dependent weights. The VAF module consider the relative confidence between modalities and enables to explore complementary properties for detection. Extensive experiments on two benchmark datasets demonstrate the effectiveness and superiority of MM-ULN on multimodal fake news detection.
Metabolic control from the endolysosome: lysosome-resident amino acid transporters open novel therapeutic possibilities

Amino acid transporters are generally recognized as machinery that transport amino acids from the extracellular environment into the cytoplasm. Although their primary function is the uptake of amino acids to supply the cell with nutrients and energy, endolysosome-resident amino acid (EL-aa) transporters possess several unique functions in accordance with their localization in intracellular vesicular membranes. They play pivotal roles in the maintenance of metabolic homeostasis via direct involvement in the amino acid sensing pathway, which regulates the activity of mechanistic target of rapamycin complex 1 (mTORC1), a master regulator of cellular metabolism. Additionally, some EL-aa transporters contribute to the maintenance of dynamic homeostasis of endolysosomes, including the regulation of endolysosomal acidity, by carrying amino acids out of endolysosomes. In addition, EL-aa transporters act as a scaffold to gather signaling molecules and multiple enzymes to control cellular metabolism on the endolysosomal membrane. Among EL-aa transporters, solute carrier family 15 member 4 (SLC15A4) is preferentially expressed in immune cells, including macrophages, dendritic cells, and B cells, and plays a key role in the integration of metabolic and inflammatory signals. In this review, we summarize our recent findings on EL-aa transporter contributions to inflammatory and metabolic signaling in the endolysosomes of immune cells by focusing on the SLC15 family, including SLC15A4 and SLC15A3, and discuss their uniqueness and universality. We also discuss the potential of targeting these EL-aa transporters in immune cells for the development of novel therapeutic strategies for inflammatory diseases. Because these transporters are highly expressed in immune cells and significantly alter the functions of immune cells, targeting them would provide a great advantage in ensuring a wide safety margin.