Miracle-dz/UniFAR-FTUs huggingface dataset:
Honokiol Attenuates Choroidal Neovascularization by Inhibiting the Hypoxia-Inducible Factor-α/Vascular Endothelial Growth Factor Axis via Nuclear Transcription Factor-Kappa B Activation

Abstract Purpose Honokiol is a lignan isolated from Magnolia officinalis and exhibits anti-angiogenic properties. This study was conducted to investigate the role of honokiol in choroidal neovascularization. Methods C57BL/6 mice were treated with honokiol at 10–20 mg/kg by daily intraperitoneal injection from day 1 to 6 after laser photocoagulation. ARPE-19 cells were cultured under hypoxic conditions with or without the presence of honokiol. After laser photocoagulation and honokiol treatment, hematoxylin and eosin staining, immunofluorescence and fundus fluorescein angiography were used to analyze the effect of honokiol on choroidal neovascularization formation. Quantitative real-time PCR, western blot, enzyme-linked immunosorbent assay, immunofluorescence, luciferase assay, and chromatin immunoprecipitation were performed to explore the mechanism of honokiol in the pathological process of choroidal neovascularization. Finally, the role of honokiol on the human choroidal vascular endothelial cells was detected by using 5-ethynyl-20-deoxyuridine assay, Transwell and Tube formation assays. Results The results of hematoxylin and eosin staining and immunofluorescence suggested that honokiol reduced the thickness, length, and area of choroidal neovascularization lesions in laser-induced choroidal neovascularization mouse model. Fundus fluorescein angiography showed that choroidal neovascularization leakage was reduced in honokiol group and the concentration of 20 mg/kg showed better effects. Mechanism studies have shown that honokiol exerted inhibitory effects on choroidal neovascularization by inactivating hypoxia-inducible factor-1α/vascular endothelial growth factor axis through the nuclear transcription factor-kappa B signaling pathway. The same results were obtained in ARPE-19 cells under hypoxic conditions. Furthermore, the conditional medium of retinal pigmented epithelial cells promoted the proliferation, migration, and tube formation of human choroidal vascular endothelial cells, while honokiol reversed these. Conclusion We demonstrated that honokiol attenuated choroidal neovascularization formation by inactivating the hypoxia-inducible factor-1α/vascular endothelial growth factor axis through nuclear transcription factor-kappa B signaling pathway.
ChatGPT or LLM in next-generation drug discovery and development: pharmaceutical and biotechnology companies can make use of the artificial intelligence-based device for a faster way of drug discovery and development

Two recent correspondence articles published in this journal about arti ﬁ cial intelligence (AI) in drug discovery and development are timely. The ﬁ rst article by Chakraborty C. et al . discussed AI in drug discovery and development, and the second article by Chakraborty C. illustrated AI in clinical trials [1,2] . These two articles elucidate the readers about the recent trend of AI in drug discovery and development. Recently, the increased application of AI has been observed in various sectors of society, including the healthcare sector. AI has been used in healthcare, from disease diagnosis to therapeutic discovery. AI-based drug discovery has played a signi ﬁ cant role as a whole process of drug discovery and development. These two articles also provide signi ﬁ cant information on AI-enabled drug discovery and development. However, one of the signi ﬁ cant milestones of AI-based discovery was achieved by two companies, Exscientia and Sumitomo Dainippon Pharma, through the collaboration. Their discovered drug has entered into the clinical trial. Exscientia is a British start-up, and Sumitomo Dainippon Pharma is a Japanese pharmaceutical company. The drug molecule was developed using sophisticated AI methodologies, and the name of the molecule was DSP-1181 [3] . Recently, AI-enabled ChatGPT has shown its application in other areas of medicine and healthcare, including drug discovery and development.
HAPI Fit: An Exercise Intervention to Improve Peak Aerobic Capacity in Young Adults Born Very Preterm

ABSTRACT Introduction Individuals born preterm have reduced aerobic capacity, which could be related to impaired organ development. Their capacity to improve aerobic capacity with exercise training could therefore be limited, but this remains unknown. We aimed to test an exercise intervention to improve cardiorespiratory fitness in adults born preterm. Methods Twenty-one very preterm and 37 full-term individuals aged 18–33 yr took part in a 14-wk supervised exercise intervention of cardiovascular, resistance, and flexibility training (two group and one individual sessions per week). Adherence and compliance to intervention were recorded. Primary outcome was change in peak oxygen consumption (peak V̇O2) measured before and after the intervention. Within- and between-group differences were estimated using nonparametric tests. Results Of 219 eligible individuals, 58 were enrolled and 14 participants dropped out over the course of the intervention. Among the 44 who completed the intervention, mean adherence was 82% and 66% for group and individual sessions, respectively. Compliance with training requirement varied between 71% and 100%. There was no difference in adherence between the preterm and full-term groups. Because only one preterm male met the adherence criteria, subsequent analyses were done exclusively on females. Both the term and preterm groups achieved higher peak V̇O2 after the intervention [term = +4.2 (SD 4.3) mL·min−1·kg−1, P < 0.01; preterm = +4.7 (2.9) mL·min−1·kg-1, P < 0.01]. There was no between-group difference in the response to the intervention (P = 0.729). Conclusions Recruitment and adherence to an exercise intervention are challenging. Results could indicate improvements in cardiorespiratory fitness in young women born preterm after current exercise program. Adaptation of this intervention is needed for wider uptake.
Periostin C-Terminal Is Intrinsically Disordered and Interacts with 143 Proteins in an In Vitro Epidermal Model of Atopic Dermatitis

Human periostin is a 78–91 kDa matricellular protein implicated in extracellular matrix remodeling, tumor development, metastasis, and inflammatory diseases like atopic dermatitis, psoriasis, and asthma. The protein consists of six domains, including an N-terminal Cys-rich CROPT domain, four fasciclin-1 domains, and a C-terminal domain. The exons encoding the C-terminal domain may be alternatively spliced by shuffling four exons, generating ten variants of unknown function. Here, we investigate the structure and interactome of the full-length variant of the C-terminal domain with no exons spliced out. The structural analysis showed that the C-terminal domain lacked a tertiary structure and was intrinsically disordered. In addition, we show that the motif responsible for heparin-binding is in the conserved very C-terminal part of periostin. Pull-down confirmed three known interaction partners and identified an additional 140 proteins, among which nine previously have been implicated in atopic dermatitis. Based on our findings, we suggest that the C-terminal domain of periostin facilitates interactions between connective tissue components in concert with the four fasciclin domains.
Relationship of Species and Site Index to Habitat in the White Mountains of New Hampshire

Eleven forest habitats, representing distinct differences in soil materials or substrate, were defined for areas of granitic drift in the White Mountains of New Hampshire. Beech/sugar maple/yellow birch characterize successional stands on the fine tills and the enriched or cove sites (where white ash also is common). Washed fine till and coarse till are dominated during succession by beech and birch with some red maple. Red maple is the most abundant species on sandy sediments, silty sediments, and dry compact till. Softwoods, especially red spruce and eastern hemlock, characterize successional stands on habitats representing poor drainage, shallow rock, outwash, and wet compact till. Comparative data from a previous study in old stands indicate that coarse till, fine till, and enriched sites are the only habitats where pure hardwoods are the characteristic vegetation in both successional and older stands; most of the other habitats exhibit a trend over time toward softwoods. Site index generally averages highest on habitats where hardwoods predominate and lowest on softwood habitats. Habitat classification should be used in conjunction with gradient analysis to define the relationships of forest vegetation to environmental conditions.
Beyond Home Bias: Foreign Portfolio Holdings and Information Heterogeneity

We provide new evidence that international portfolios reflect the underlying heterogeneity in investors’ beliefs. Using data on foreign sovereign debt holdings of European banks matched with their forecasts on future bond yields, we find that expecting higher returns and having more accurate forecasts are associated with larger bond holdings. Crucially, the elasticity of portfolio holdings to expected returns is increasing in the precision of the forecast, implying that investors optimally exploit comparative advantages in information production. We rationalize the results in a model where partial information specialization arises endogenously, which leads to further testable implications that we confirm in the data. JEL classification: F21, F36, G11, D82.
Detection of Advanced Persistent Threat based on Kill Chain Node Mapping and LSTM

The advanced persistent threat (APT) has caused serious damage to the core information infrastructure of many governments and organizations. How to correlatively analyze the massive logs generated by various security devices become a key point to effectively detect such new type of attack. This paper analyzes features of the APT attack and proposes a new APT detection method based on the extended cyber kill chain and the long and short-term memory network which can extensively correlate network behaviors. Furtherly, the idea of the proposed method provides a new direction for the application and practice of detecting complex cyber attacks.
Low‐coverage whole genome sequencing for highly accurate population assignment: Mapping migratory connectivity in the American Redstart (Setophaga ruticilla)

Understanding the geographic linkages among populations across the annual cycle is an essential component for understanding the ecology and evolution of migratory species and for facilitating their effective conservation. While genetic markers have been widely applied to describe migratory connections, the rapid development of new sequencing methods, such as low‐coverage whole genome sequencing (lcWGS), provides new opportunities for improved estimates of migratory connectivity. Here, we use lcWGS to identify fine‐scale population structure in a widespread songbird, the American Redstart (Setophaga ruticilla), and accurately assign individuals to genetically distinct breeding populations. Assignment of individuals from the nonbreeding range reveals population‐specific patterns of varying migratory connectivity. By combining migratory connectivity results with demographic analysis of population abundance and trends, we consider full annual cycle conservation strategies for preserving numbers of individuals and genetic diversity. Notably, we highlight the importance of the Northern Temperate‐Greater Antilles migratory population as containing the largest proportion of individuals in the species. Finally, we highlight valuable considerations for other population assignment studies aimed at using lcWGS. Our results have broad implications for improving our understanding of the ecology and evolution of migratory species through conservation genomics approaches.
Title: Estimating the Probability of Cellular Arrhythmias with Simplified Statistical Models that Account for Experimentally Observed Uncertainty

Early after-depolarizations (EADs) are action potential (AP) repolarization abnormalities that can trigger lethal arrhythmias in, for example, Long QT Syndrome and heart failure. Simulations using biophysically-detailed cardiac myocyte models can reveal how model parameters influence the probability of these cellular arrhythmias, however such analyses often pose a huge computational burden. Here, we develop a simplified approach in which logistic regression models (LRMs) are used to define a mapping between the parameters of complex cell models and the probability of EADs. Specifically, we develop an LRM for predicting the probability of EADs (P(EAD)) as a function of slow-activating delayed rectifier current (I Ks ) parameters, and for identifying those parameters with greatest influence on P(EAD). This LRM, which requires negligible computational resources, is also used to demonstrate how uncertainties in experimentally measured values of I Ks model parameters influence P(EAD). We refer to this as arrhythmia sensitivity analysis. In the investigation of five different I Ks parameters associated with Long QT syndrome 1 (LQTS1) mutations, the predicted P(EAD) when rank ordered for 6 LQTS1 mutations matches the trend in risk from patients with the same mutations as measured by clinical cardiac event rates. We also demonstrate the degree to which parameter uncertainties map to uncertainty of P(EAD), with I Ks conductance having the greatest impact. These results demonstrate the potential for arrhythmia risk prediction using model-based approaches for estimation of P(EAD).
Size-resolved source apportionment of ambient particles by positive matrix factorization at Gosan background site in East Asia

Size- and time-resolved aerosol samples were collected using an eight-stage Davis rotating unit for monitoring (DRUM) sampler from 29 March to 29 May in 2002 at Gosan, Jeju Island, Korea, which is one of the representative background sites in East Asia. These samples were analyzed using synchrotron X-ray fluorescence for 3-h average concentrations of 19 elements consisting of S, Si, Al, Fe, Ca, Cl, Cu, Zn, Ti, K, Mn, Pb, Ni, V, Se, As, Rb, Cr, Br. The size-resolved data sets were then analyzed using the positive matrix factorization (PMF) technique in order to identify possible sources and estimate their contribution to particulate matter mass. PMF analysis uses the uncertainty of the measured data to provide an optimal weighting. Fifteen sources were resolved in eight size ranges (0.07~12 μm) and included continental soil, local soil, sea salt, biomass/biofuel burning, coal combustion, oil heating furnace, residual oil fired boiler, municipal incineration, nonferrous metal source, ferrous metal source, gasoline vehicle, diesel vehicle, copper smelter and volcanic emission. PMF analysis of size-resolved source contributions showed that natural sources represented by local soil, sea salt and continental soil contributed about 79% to the predicted primary particulate matter (PM) mass in the coarse size range (1.15~12 μm). On the other hand, anthropogenic sources such as coal combustion and biomass/biofuel burning contributed about 60% in the fine size range (0.56~2.5 μm). The diesel vehicle source contributed the most in the ultra-fine size range (0.07~0.56 μm) and was responsible for about 52% of the primary PM mass.
UVicSPACE: Research & Learning Repository

: A novel thermophilic, chemolithoautotrophic bacterium, designated as NE1206 T, was isolated from a Juan de Fuca Ridge hydrothermal vent sample (tubes of the annelid polychaete Paralvinella sulfincola attached to small pieces of hydrothermal chimney). The cells were rod-shaped (1.2–3.5×0.4–0.7 µm), occurring as single motile rods or forming macroscopic aggregates visible as pinkish to brownish streamers. The new isolate was anaerobic. It grew between 50 and 70 °C (optimum 60–65 °C; doubling time approximately 1 h 15 min at 60 °C), between pH 5.0 and 7.5 (optimum pH around 6.0–6.5) and at sea salts concentrations between 20 and 40 g l -1(optimum 30 g l -1). Cells grew chemolithoautotrophically in an H 2/CO 2 atmosphere (80/20, v/v; 200 kPa). Molecular hydrogen was the sole electron donor used by the strain. Nitrate and elemental sulfur served as electron acceptors, yielding ammonia and hydrogen sulfide, respectively (nitrate reduction supported higher growth rates than sulfur reduction). The G+C content of the genomic DNA was 36.7±0.8 mol%. Phylogenetic analyses of the 16S rRNA gene located the strain within the genus Desulfurobacterium. However, the novel isolate possesses physiological and biochemical characteristics that differ from the previously described species of this genus. We propose that the isolate represents a novel species, Desulfurobacterium crinifex sp. nov. The type strain is NE1206 T (DSM 15218 T, CIP 107649 T). An amendment of the genus Desulfurobacterium description is proposed, based on the phenotypic characteristics of the novel
Machine learning prediction of the total duration of invasive and non-invasive ventilation During ICU Stay

Predicting the duration of ventilation in the ICU helps in assessing the risk of ventilator-induced lung injury, ensuring sufficient oxygenation, and optimizing resource allocation. Prior models provided a prediction of total duration without distinguishing between invasive and non-invasive ventilation. This work proposes two independent gradient boosting regression models for predicting the duration of invasive and non-invasive ventilation based on commonly available ICU features. These models are trained on 2.6 million patient stays across 350 US hospitals between 2010 to 2019. The mean absolute error (MAE) for the prediction of duration was 2.08 days for invasive ventilation and 0.36 days for non-invasive ventilation. The total ventilation duration predicted by our model had MAE of 2.38 days, which outperformed the gold standard (APACHE) with MAE of 3.02 days. The feature importance analysis of the trained models showed that, for invasive ventilation, high average heart rate, diagnosis of respiratory infection and admissions from locations other than the operating room were associated with longer ventilation durations. For non-invasive ventilation, higher respiratory rates and having any GCS measurement were associated with longer durations.
Disorder-based Electromagnetic Devices

Engineered complex media with unusual and exotic electrical properties, realized through subwavelength building blocks, are leading to the development of new applications and improved functionalities in a variety of fields. These range from electromagneticssuch as frequency selective surfaces, photonic crystals and various forms of the so-called metamaterialsto optical microscopy, nanolithography, quantum optics, and medicine. The potential of this area has spurred growing investment by industry and a huge amount of interest from the research community, as they strive to develop novel structures that reduce the cost and improve the performance of existing and emerging systems. At the heart of this development is the engineering of electromagnetic wave interactions with complex structures composed of subwavelength pieces. Thus far, extensive efforts have been made to exploit the advantages offered by the periodicity and combination of different materials with desirable electromagnetic properties at the intended frequency bands. Despite the simplicity provided by periodic structures in terms of design, fundamental limitations are imposed by current fabrication techniques and material losses and imperfections. In response, designs based on disordered structures with exotic properties have attracted wide attention. It has been shown that disorder and aperiodicity can lead to novel applications that are either impossible or too expensive to implement through periodic structures. Examples include omnidirectional anti-reflection coatings, imaging through complex media, localization of light far beyond the diffraction limit, and cloaking devices, to name a few. The extension and development of novel applications based on innovative approaches is clearly of great value. The contributions of the present work, from an analysis and optimization point of view, include a new computationally efficient method to analyze the interaction of electromagnetic waves with complex structures composed of subwavelength scatterers in multilayer media, and the development of a novel design optimization algorithm for fast and efficient design of disorder-based electromagnetic devices. A complex images Green’s function technique is employed for the fast and accurate calculation of matrix elements in the method-of-moments used to model the field interaction with optical nanoantennas. A new scheme, for the analysis of multiple scattering in complex systems, and a novel optimization algorithm, called the “near optimal freeform inverse design” (NOFID) approach, are employed to design structures with new functionalities and to improve the efficiency in existing/emerging electromagnetic devices. In terms of design and application contributions, the devised analysis method and algorithm are used to design new structures based on engineering the multiple scattering
Genetic Algorithm and Its Implementation of Automatic Generation of Chinese SONGCI

Automatic generation of poetry has always been considered a hard nut in natural language generation.This paper reports some pioneering research on a possible generic algorithm and its automatic generation of SONGCI.In light of the characteristics of Chinese ancient poetry,this paper designed the level and oblique tones-based coding method,the syntactic and semantic weighted function of fitness,the elitism and roulette-combined selection operator,and the partially mapped crossover operator and the heuristic mutation operator.As shown by tests,the system constructed on the basis of the computing model designed in this paper is basically capable of generating Chinese SONGCI with some aesthetic merit.This work represents progress in the field of Chinese poetry automatic generation.
Observation of the resonant character of the Z(4430)- state

Resonant structures in B 0 → ψ 0 π − K þ decays are analyzed by performing a four-dimensional fit of the decay amplitude, using pp collision data corresponding to 3 fb − 1 collected with the LHCb detector. The data cannot be described with K þ π − resonances alone, which is confirmed with a model-independent approach. A highly significant Z ð 4430 Þ − → ψ 0 π − component is required, thus confirming the existence of this state. The observed evolution of the Z ð 4430 Þ − amplitude with the ψ 0 π − mass establishes the resonant nature of this particle. The mass and width measurements are substantially improved. The spin parity is determined unambiguously to be 1 þ .
A new thermal model for Northern and Central Asia

Central and Northern Asia is a key natural laboratory for the study of active intra-continental deformation in response to the ongoing far-field collision of India and Eurasia. The induced tectonic processes strongly depend on the thermo-mechanical and compositional (density and thickness) structure of the lithosphere. Density heterogeneities within the crust and upper mantle are important factors in the control of the dynamics of Earth deformation at shallow and deep levels. The main aim of this research project is to construct new high-resolution 3D models of the compositional, thermal and rheological structure of the intra-continental lithosphere of the study area. The 3D models will be constructed by combining and jointly analysing satellite gravity data with terrestrial data (seismic velocity distributions, seismic tomography, GPS derived surface deformations, heat flow measurements and terrestrial gravity).