Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved retrieval of aerosol size-resolved properties from moderate resolution imaging spectroradiometer over India: role of aerosol model and surface reflectance

[1] We have compared the total as well as fine mode aerosol optical depth (τ and τfine) retrieved by Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua (2001–2005) with the equivalent parameters derived by Aerosol Robotic Network (AERONET) at Kanpur (26.45°N, 80.35°E), northern India. MODIS Collection 005 (C005)–derived τ0. 55 was found to be in good agreement with the AERONET measurements. The τfine and η (τfine/τ) were, however, biased low significantly in most matched cases. A new set of retrieval with the use of absorbing aerosol model (SSA ∼ 0.87) with increased visible surface reflectance provided improved τ and τfine at Kanpur. The new derivation of η also compares well qualitatively with an independent set of in situ measurements of accumulation mass fraction over much of the southern India. This suggests that though MODIS land algorithm has limited information to derive size properties of aerosols over land, more accurate parameterization of aerosol and surface properties within the existing C005 algorithm may improve the accuracy of size-resolved aerosol optical properties. The results presented in this paper indicate that there is a need to reconsider the surface parameterization and assumed aerosol properties in MODIS C005 algorithm over the Indian region in order to retrieve more accurate aerosol optical and size properties, which are essential to quantify the impact of human-made aerosols on climate.
One-Year Survival for Developing Acute Kidney Injury in Adult Patients with AMI Cardiogenic Shock Receiving Venoarterial Extracorporeal Membrane Oxygenation

Objective The incidence of cardiogenic shock cases treated with veno-arterial extracorporeal membrane oxygenation (VA-ECMO) support has been on the rise. Acute kidney injury (AKI) is a significant complication of cardiogenic shock and a frequent serious complication in patients requiring ECMO-supported therapy. AKI is strongly associated with unfavorable patient prognosis. However, there is a paucity of data on the influence of AKI on the prognosis of patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS) who are receiving ECMO support, particularly with regard to long-term outcomes. Methods This retrospective observational study included 103 patients in the People’s Hospital of Guangxi Zhuang Autonomous Region from January 2017 and June 2022. AKI was defined according to Kidney Disease Improving Global Outcome (KDIGO) criteria. Cox regression and logistic regression were used to identify risk factors. Results In this study, the incidence of AKI was 63.11%, with AKI stage 1, 2, and 3 accounting for 21.36%, 12.62%, and 29.13%, respectively. Patients with severe AKI had significantly higher in-hospital mortality (43.33% vs 27.40%, P < 0.001), 30-day mortality (60.00% vs 31.51%, P = 0.001), and 1-year mortality (63.67% vs 34.25%, P<0.001) than those without severe AKI. Furthermore, severe AKI significantly increased the risk of one-year mortality (HR 10.816, CI 3.118–37.512, P<0.001). Baseline serum creatinine, baseline platelet, and active cardiopulmonary resuscitation were independent predictors of one-year mortality. In addition, baseline white blood cell count, baseline aspartate aminotransferase, baseline alanine aminotransferase (ALT), baseline serum creatinine, preoperative lactate, and postoperative mean arterial pressure were independent risk factors of severe AKI during hospitalization. Conclusion In patients with AMI-CS receiving ECMO support, AKI is highly prevalent. Development of severe AKI significantly increased the risk of one-year mortality.
Meet me at the local shop: designing community anchors for customer engagement

Design plays a vital role in facilitating community-building by triggering interpersonal social interactions, enhancing a sense of attachment, and connecting individuals. This study aims to identify how local creators are currently casting community anchors for their customers and explore design opportunities to further enhance these practices. First, a literature review is conducted to establish the significance and concepts of ‘local creators’, ‘community anchors’ and ‘level of customer engagement’. Subsequently, 40 practical cases are thematically analysed to identify which and how local creators cast community anchors to engage customers for community-building. Consequently, fourteen types of community anchors and five ways of anchoring the community anchors are identified: ‘Exploiting Locality’, ‘Village Well’, ‘Sparking Interest’, ‘Digging Interest’, and ‘Local Activism’. Based on these findings, this study discusses design implications and implementations to enhance local creators’ practices of creating community-anchored experiences so that they can have a greater impact on their regions beyond individual businesses. This study has significant implications in that it provides a foundation for customer experience design to create communities around local shops.
Impact of Microbiome–Brain Communication on Neuroinflammation and Neurodegeneration

The gut microbiome plays a pivotal role in maintaining human health, with numerous studies demonstrating that alterations in microbial compositions can significantly affect the development and progression of various immune-mediated diseases affecting both the digestive tract and the central nervous system (CNS). This complex interplay between the microbiota, the gut, and the CNS is referred to as the gut–brain axis. The role of the gut microbiota in the pathogenesis of neurodegenerative diseases has gained increasing attention in recent years, and evidence suggests that gut dysbiosis may contribute to disease development and progression. Clinical studies have shown alterations in the composition of the gut microbiota in multiple sclerosis patients, with a decrease in beneficial bacteria and an increase in pro-inflammatory bacteria. Furthermore, changes within the microbial community have been linked to the pathogenesis of Parkinson’s disease and Alzheimer’s disease. Microbiota–gut–brain communication can impact neurodegenerative diseases through various mechanisms, including the regulation of immune function, the production of microbial metabolites, as well as modulation of host-derived soluble factors. This review describes the current literature on the gut–brain axis and highlights novel communication systems that allow cross-talk between the gut microbiota and the host that might influence the pathogenesis of neuroinflammation and neurodegeneration.
A Novel Deep Neural Network to Analyze and Monitoring the Physical Training Relation to Sports Activities

—In the research paper, authors meticulously detail the development, testing, and application of an innovative deep learning model aimed at monitoring physical activities of students in real-time. Drawing upon the advanced capabilities of convolutional neural networks (CNNs), the proposed system exhibits an exceptional ability to track, analyze, and evaluate the physical exercises performed by students, thereby providing an unprecedented scope for customization in physical education strategies. This piece of scholarly work bridges the gap between physical education and cutting-edge technology, highlighting the burgeoning role of artificial intelligence in health and fitness sector. With an expansive study spanning various cohorts of physical culture students, the paper provides compelling empirical evidence that underlines the superiority of the deep learning system over conventional methods in aspects of accuracy, speed, and efficiency of monitoring. The authors demonstrate the transformative potential of their system, capable of facilitating personalized and optimized physical training strategies based on real-time feedback. Moreover, the potential implications of the study extend beyond the realm of education and into wider public health applications, with the possibility of fostering improved health outcomes on a larger scale. This research paper makes a significant contribution to the burgeoning field of AI in physical education, embodying a paradigm shift in the approach towards physical fitness and health monitoring. It underscores the potential of AI-driven technology to revolutionize traditional methods in physical education, paving the way for more personalized and effective teaching and training regimes, and ultimately contributing to enhanced health and fitness outcomes among students.
The Ecology of Seashores

The Environment Introduction Shore Environments and Stresses on the Shore Salient Features of the Shore Environment Patterns of Zonation on the Shore Overview Hard Shores Zonation Patters on the Hard Shores Zonation Patterns on Representative Shores The Causes of Zonation Hard Shore Microalgae Hard Shore Micro- and Meiofauna Rocky Shore Lichens Hard Shore Macroalgae Key Faunal Components Special Habitats Overview Soft Shores Soft Shores as a Habitat The Physio-Chemical Environment Soft Shore Types Biologic Modification of the Sediments Estuaries Soft Shore Primary Producers Soft Shore Fauna Biological Modification of the Sediments Microbial Ecology and Organic Detritus Nutrient Cycling Estuarine Shelf Interactions Overview Adaptations to Shore Life Introduction Ecological Niches on the Shore The Establishment of Zonation Patterns The Maintenance of Zonation Patterns Overview Control of Community Structure Introduction Hard Shores Soft Shores Synthesis of Factors Involved in Determining Community Structure Overview Energy Flow, Food Webs, and Material Cycling Introduction Food Sources Energy Budgets for Individual Species Optimal. Secondary Production P:B Rations and Production Efficiency Relative Contribution of Soft Shore Benthic Infauna to Secondary Production Community Metabolism Trophic Structure and Food Webs Carbon Flow Models Stable Isotopes and Food Web Analysis Top-Down and Bottom-Up Control of Community Structure Overview Ecosystem Models Introduction Hard Shores Sand Beaches Intertidal Mud Flat in the Lynher Estuary, Cornwall, UK Salt Marshes Seagrass Ecosystem Model Mangrove Ecosystems Estuaries Network Analysis Potential Applications of the Ascendency Concept Emergy Analysis Appendix Index
Differential optical transfer function wavefront sensing and pupil calibration with the HiCAT testbed

The High-contrast imager for Complex Aperture Telescopes (HiCAT) testbed is a segmented aperture coronagraph simulator designed to be a systems level prototype demonstration for the proposed Habitable Worlds Observatory recommended by the 2020 Astronomical Decadal Survey. An important technical component of operating HiCAT is a method of measuring the Wavefront Error (WFE) on the testbed. Here we present our implementation of the differential Optical Transfer Function (dOTF) phase retrieval technique on HiCAT and the WFE calibration experiments we have developed using the technique. We use dOTF to improve the wavefront calibration map of one of HiCAT’s Deformable Mirrors (DMs) as well as to measure low-order Non-Common Path Aberrations (NCPA) between the focal and imaging planes of the coronagraph.
Clearance of variant Creutzfeldt–Jakob disease prions in vivo by the Hsp70 disaggregase system

The metazoan Hsp70 disaggregase protects neurons from proteotoxicity that arises from the accumulation of misfolded protein aggregates. Hsp70 and its co-chaperones disassemble and extract polypeptides from protein aggregates for refolding or degradation. The effectiveness of the chaperone system decreases with age and leads to accumulation rather than removal of neurotoxic protein aggregates. Therapeutic enhancement of the Hsp70 protein disassembly machinery is proposed to counter late-onset protein misfolding neurodegenerative disease that may arise. In the context of prion disease, it is not known whether stimulation of protein aggregate disassembly paradoxically leads to enhanced formation of seeding competent species of disease-specific proteins and acceleration of neurodegenerative disease. Here we have tested the hypothesis that modulation of Hsp70 disaggregase activity perturbs mammalian prion-induced neurotoxicity and prion seeding activity. To do so we used PrP transgenic Drosophila that authentically replicate mammalian prions. RNASeq identified that Hsp70, DnaJ-1 and Hsp110 gene expression was down-regulated in prion-exposed PrP Drosophila. We demonstrated that RNAi knockdown of Hsp110 or DnaJ-1 gene expression in variant CJD prion-exposed human PrP Drosophila enhanced neurotoxicity, whereas over-expression mitigated toxicity. Strikingly, prion seeding activity in variant CJD prion-exposed human PrP Drosophila was ablated or reduced by Hsp110 or DnaJ-1 over-expression, respectively. Similar effects were seen in scrapie prion-exposed ovine PrP Drosophila with modified Hsp110 or DnaJ-1 gene expression. These unique observations show that the metazoan Hsp70 disaggregase facilitates the clearance of mammalian prions and that its enhanced activity is a potential therapeutic strategy for human prion disease.
Backdoor Attack on Deep Learning-Based Medical Image Encryption and Decryption Network

Medical images often contain sensitive information, and one typical security measure is to encrypt medical images prior to storage and analysis. A number of solutions, such as those utilizing deep learning, have been proposed for medical image encryption and decryption. However, our research shows that deep learning-based encryption models can potentially be vulnerable to backdoor attacks. In this paper, a backdoor attack paradigm for encryption and decryption network is proposed and corresponding attacks are respectively designed for encryption and decryption scenarios. For attacking the encryption model, a backdoor discriminator is adopted, which is randomly trained with the normal discriminator to confuse the encryption process. In the decryption scenario, a number of subnetwork parameters are replaced and the subnetwork can be activated when detecting the trigger embedded into the input (encrypted image) to degrade the decryption performance. Considering the model performance degradation due to parameter replacement, the model pruning is also adopted to further strengthen the attacking performance. Furthermore, the image steganography is adopted to generate invisible triggers for each image; subsequently, improving the stealthiness of backdoor attacks. Our research on designing backdoor attacks for encryption and decryption network can serve as an attacking mode for such networks, and provides another research direction for improving the security of such models. This research is also one of the earliest works to realize the backdoor attack on the deep learning based medical encryption and decryption network to evaluate the security performance of these networks. Extensive experimental results show that the proposed method can effectively threaten the security performance both for the encryption and decryption network.
A radar transparent layer in a temperate valley glacier: Bench Glacier, Alaska

Radar surveys of Bench Glacier, Alaska, collected over five field seasons between 2002 and 2006 reveal a surface layer of radar transparent ice in this temperate valley glacier. The transparent layer covers the up‐glacier half of the ablation zone and is defined by a distinct lack of the radar scattering events considered typical of temperate ice. Radar scattering ice underlies the transparent zone, and extends to the surface elsewhere on the glacier. We observed the layering in constant offset radar surveys conducted with characteristic frequencies ranging from 5 MHz to 100 MHz. The radar transparent layer extends from the surface to 20 m depth on average, but up to 50 m in some places. Bench Glacier's transparent layer appears similar to the cold surface layer of polythermal glaciers, however, observations in over 50 boreholes on Bench Glacier suggest there is no cold ice corresponding to the radar transparent layer. We conclude that spatially extensive radar‐transparent layers normally used to identify cold ice in polythermal glaciers are present in some temperate glaciers. Copyright © 2009 John Wiley & Sons, Ltd.
Reconstruction of eruption column dynamics on the basis of grain size of tephra fall deposits: 1. Methods

Two inversion methods are developed to reconstruct eruption column dynamics from granulometric data of Plinian deposits. They are based on the steady tephra dispersal model from umbrella clouds which spread as gravity currents. The methods provide (1) expansion rate of an umbrella cloud, (2) grain-size distribution at the top of the eruption column, and (3) total amount of ejecta including very fine particles which are extensively dispersed in the atmosphere. The first quantity is correlated to the magma discharge rate. The second quantity provides information about sorting processes during column rise, and hence, about deceleration in the “gas thrust region” of the eruption column and the timing of column collapse. The third quantity, together with the estimated magma discharge rate, provides the effective duration of the eruption. The two methods are based on the same dispersal model but use different averaging. Combining the two methods can provide additional information about eruption dynamics, such as minor flow structures of the umbrella cloud and fluctuations of eruption rate. The methods are applicable to large-scale eruptions where the radial expansion velocity of the umbrella cloud exceeds wind velocity. The applicability of the methods is tested from the viewpoint of comparison with real-time observations in the companion paper (paper 2).
Evaluation of the Drip Loss of 30 Cultivars and 9 Advanced Selections from Agriculture and Agri-Food Canada National Strawberry Breeding Program

Abstract Fruits of thirty-nine strawberry genotypes were evaluated for their freezing performance based on their drip loss percentage. The amount juice lost was evaluated for each genotype after four months of storage (at — 20°C) upon thawing at 20°C for 20 hr. A preliminary selection based on the drip loss method or exudation enabled us to eliminate genotypes that are the least interesting from a freezing standpoint and to focus our efforts on those with a high processing potential. ‘NY1529’, ‘Scott’, ‘Arking’, ‘SJ8317-5’ and ‘SJ83145-1’ with less than 30% juice loss seems suitable for jam, yogurt and frozen food production. On the other hand, with more than 60% juice loss, ‘Tenira’, ‘Primela’ and ‘Splendida’ seem less desirable for processing.
Text Mining Identifies Key Pattern Analysis and Process in Prostate Cancer

One of the most typical malignancies in males is prostate cancer, and its global burden is increasing. Using text-mining technology, this study seeks to pinpoint important genes and biochemical processes related to prostate cancer. Using certain terms related to gene expression, PUBMED abstracts of interest were found. The extracted abstracts included gene pairings and functional connections. On the genes identified from the function interactions, biological procedures enrichment, network analysis, and gene prioritizing utilizing edge centrality of betweenness were carried out. For the modules containing at least five genes, which were retrieved from the network analysis, gene clustering and pathway enrichment analyses were built. The biological functions of the newly identified genes showed that they were involved in positive transcriptional regulation from the RNA polymerase II promoter, positive regulation of cell proliferation, and drug responsiveness. The prostate cancer enrichment analysis processes revealed that the NF signalling pathway, PI3k-Art signalling pathway, thyroid hormone signalling, and ErbB signalling pathways were enriched. According to the network analysis results, which were further sorted by their values for degree of between-ness, it was discovered that AKT1, AR, and KDM3A were the important genes. In conclusion, by concentrating on the discovered hub genes, prostate cancer can be medically treated.
Discovery of an Insect Neuroactive Helix Ring Peptide from Ant Venom

Ants are among the most abundant terrestrial invertebrate predators on Earth. To overwhelm their prey, they employ several remarkable behavioral, physiological, and biochemical innovations, including an effective paralytic venom. Ant venoms are thus cocktails of toxins finely tuned to disrupt the physiological systems of insect prey. They have received little attention yet hold great promise for the discovery of novel insecticidal molecules. To identify insect-neurotoxins from ant venoms, we screened the paralytic activity on blowflies of nine synthetic peptides previously characterized in the venom of Tetramorium bicarinatum. We selected peptide U11, a 34-amino acid peptide, for further insecticidal, structural, and pharmacological experiments. Insecticidal assays revealed that U11 is one of the most paralytic peptides ever reported from ant venoms against blowflies and is also capable of paralyzing honeybees. An NMR spectroscopy of U11 uncovered a unique scaffold, featuring a compact triangular ring helix structure stabilized by a single disulfide bond. Pharmacological assays using Drosophila S2 cells demonstrated that U11 is not cytotoxic, but suggest that it may modulate potassium conductance, which structural data seem to corroborate and will be confirmed in a future extended pharmacological investigation. The results described in this paper demonstrate that ant venom is a promising reservoir for the discovery of neuroactive insecticidal peptides.
Effects of sea ice dynamics on the Antarctic sea ice distribution in a coupled ocean atmosphere model

[1] Impact of sea ice dynamics on the Southern Ocean sea ice distribution is investigated using a coupled ocean-sea ice-atmosphere general circulation model (OAGCM) and a separate offline sea ice model driven by monthly mean climatological boundary conditions. Sea ice dynamics considerably affects sea ice distribution of the OAGCM as well as of the offline model. When sea ice dynamics (advection) is turned on, summer ice extent and winter ice thickness decrease in both models. Results of the OAGCM indicate that variation of clouds plays an important role when sea surface energy budget responds to the sea ice dynamics in summer. Surface wind field in summer responds to the sea ice dynamics so that northerly wind anomaly is directed to where sea ice retreats and air temperature is kept higher, leading to a positive feedback. In addition, meridional ocean circulation and upward ocean heat flux to the sea surface appear to be sensitive to the sea ice dynamics, which contributes to the response of winter sea ice thickness. Results obtained in the present study underline the importance of atmosphere-sea ice-ocean interaction when discussing the impact of sea ice dynamics on sea ice distribution.