Miracle-dz/UniFAR-FTUs huggingface dataset:
Chemostratigraphic implications of spatial variation in the Paleocene‐Eocene Thermal Maximum carbon isotope excursion, SE Bighorn Basin, Wyoming

The Paleocene‐Eocene Thermal Maximum (PETM) is marked by a prominent negative carbon isotope excursion (CIE) of 3–5‰ that has a characteristic rapid onset, stable body, and recovery to near pre‐CIE isotopic composition. Although the CIE is the major criterion for global correlation of the Paleocene‐Eocene boundary, spatial variations in the position and shape of the CIE have not been systematically evaluated. We measured carbon isotope ratios of bulk organic matter (δ13Corg) and pedogenic carbonate (δ13Ccarb) at six PETM sections across a 16 km transect in the SE Bighorn Basin, Wyoming. Bed tracing and high‐resolution floral and faunal biostratigraphy allowed correlation of the sections independent of chemostratigraphy. The onset of the CIE in bulk organic matter at all six sections occurs within a single laterally extensive geosol. The magnitude of the CIE varies from 2.1 to 3.8‰. The absolute and relative stratigraphic thickness of the body of the CIE in bulk organic matter varies significantly across the field area and underrepresents the thickness of the PETM body by 30%–80%. The variations cannot be explained by basinal position and instead suggest that δ13Corg values were influenced by local factors such as reworking of older carbon. The stratigraphic thickness and shape of the CIE have been used to correlate sections, estimate timing of biotic and climatic changes relative to the presumed carbon isotope composition of the atmosphere, and calculate rates of environmental and biotic change. Localized controls on δ13Corg values place these inferences in question by influencing the apparent shape and duration of the CIE.
The bizarre Eustigmatacean alga, Pseudostaurastrum limneticum (Borge) Chodat, in a shallow, nutrient-enriched Scottish loch: new to the British Isles

1 Ecology Assessment Unit, Scottish Environment Protection Agency, Angus Smith Building, 6 Parklands Avenue, Eurocentral, Holytown, North Lanarkshire, ML1 4WQ, Scotland, U.K.; 2 Charles University, Faculty of Science, Department of Ecology, Vinicna 7, CZ-128 44 Prague, Czech Republic; 3 Freshwater Ecology Group, Centre for Ecology and Hydrology, Bush Estate, Penicuik, Midlothian, EH26 0QB, Scotland, U.K.; 4 Ecology Partnership Development Unit, Scottish Environment Protection Agency, Graesser House, Fodderty Way, Dingwall Business Park, Dingwall, IV15 9XB, Scotland, U.K.; 5 Broombank, Loch Flemington, Inverness, IV2 7QR, Scotland, U.K.
Direct measurements of cosmic rays and their possible interpretations

The last two decades have brought spectacular advances in astrophysics of cosmic rays (CRs) and space- and ground-based astronomy. Launches of missions that employ forefront detector technologies enabled measurements with large effective areas, wide fields of view, and precision that we recently could not even dream of. Meanwhile, interpretation of the individual slices of information about the internal working of the Milky Way provided by such experiments poses challenges to the traditional astrophysical models. New mysteries arise in the composition and spectra of CR species at low and high energies, in the energy range where we thought the main features were already understood fairly well. This accumulation of unsolved puzzles highlights the peculiarity of the current epoch and means that major breakthroughs are still ahead. In my talk, I review the current state of direct measurements of CRs and discuss their possible interpretations. Unfortunately, many important ideas and publications are not discussed here due to the space limitations.
Deciphering the Molecular Mechanism of Escin against Neuropathic Pain: A Network Pharmacology Study

Background Escin is the main active component in Aesculus hippocastanum. It has been demonstrated that escin has anti-inflammatory properties. This study combined the methods of network pharmacology, molecular docking, and molecular dynamics to explore the molecular mechanism of escin against neuropathic pain (NP). Methods The Swiss Target Prediction and the Pharm Mapper database were employed for predicting the targets of escin. Also, the candidate targets of NP were gathered via the databases including Therapeutic Targets, DisGeNet, GeneCards, DrugBank, and OMIM. Subsequently, the network of protein-protein interaction was screened for the key targets by the software Cytoscape 3.8. 0. Then, the intersection of these targets was analysed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Additionally, we further investigated the ligand-target interactions by molecular docking and molecular dynamics simulations. Results In total, 94 escin targets were predicted by network pharmacology. Among them, SRC, MMP9, PTGS2, and MAPK1 were the core candidate targets. Subsequently, the analysis of GO and KEGG enrichment revealed that escin affected NP by regulating protein kinase C, MAP kinase, TRP channels, the T-cell receptors signaling pathway, and the TNF signaling pathway. The results of molecular docking and molecular dynamics simulation confirmed that escin not only had a strong binding activity with the four core target proteins but also stably combined in 50 ns. Conclusions Our study revealed that escin acts on the core targets SRC, MMP9, PTGS2, MAPK1, and associated enrichment pathways to alleviate neuronal inflammation and regulate the immune response, thus exerting anti-NP efficacy. This study provided innovative ideas and methods for the promising treatment of escin in relieving NP.
Temperature Demodulation for an Interferometric Fiber-Optic Sensor Based on Artificial Bee Colony–Long Short-Term Memory

Demodulation methods play a critical role in achieving high-performance interferometric fiber-optic temperature sensors. However, the conventional passive 3 × 3 coupler demodulation method overlooks certain issues, such as the non-1:1:1 splitting ratio of the coupler, resulting in a non-ideal phase difference in the three output interference signals. These problems significantly impact the measurement results of interferometric temperature sensors. In this paper, we propose a novel arc-tangent method based on a 3 × 3 coupler and a demodulation algorithm combining long short-term memory (LSTM) with an artificial bee colony (ABC). The arc-tangent method is employed to enhance the input phase signal of the ABC-LSTM network model and establish a nonlinear mapping between the phase signal and temperature, effectively preventing the influence of the spectral ratio and phase difference of the 3 × 3 coupler on temperature demodulation. The proposed ABC-LSTM method achieves high-resolution measurements with an interval of 0.10 °C, and the absolute error is below 0.0040 °C within the temperature range of 25.00–25.50 °C. To demonstrate the stability and adaptability of the proposed method under long-term constant temperature conditions, we conducted measurements for approximately three hours in a controlled temperature environment set at 25.00 °C. Experimental results indicate that the maximum error of LSTM-ABC method remains around 0.0040 °C, outperforming the conventional algorithm (0.0095 °C). Furthermore, when comparing the average error values of the conventional passive 3 × 3 coupler method (0.0023 °C), LSTM model (0.0019 °C), and ABC-LSTM model (0.0014 °C), it is evident that the demodulation results of the ABC-LSTM method exhibit the highest level of stability. Therefore, the ABC-LSTM method enhances the accuracy and reliability of interferometric fiber-optic temperature-sensing systems.
Use of Stromal Intervention and Exogenous Neoantigen Vaccination to Boost Pancreatic Cancer Chemo-Immunotherapy by Nanocarriers

Despite the formidable treatment challenges of pancreatic ductal adenocarcinoma (PDAC), considerable progress has been made in improving drug delivery via pioneering nanocarriers. These innovations are geared towards overcoming the obstacles presented by dysplastic stroma and fostering anti-PDAC immune reactions. We are currently conducting research aimed at enhancing chemotherapy to stimulate anti-tumor immunity by inducing immunogenic cell death (ICD). This is accomplished using lipid bilayer-coated nanocarriers, which enable the attainment of synergistic results. Noteworthy examples include liposomes and lipid-coated mesoporous silica nanoparticles known as “silicasomes”. These nanocarriers facilitate remote chemotherapy loading, as well as the seamless integration of immunomodulators into the lipid bilayer. In this communication, we elucidate innovative ways for further improving chemo-immunotherapy. The first is the development of a liposome platform engineered by the remote loading of irinotecan while incorporating a pro-resolving lipoxin in the lipid bilayer. This carrier interfered in stromal collagen deposition, as well as boosting the irinotecan-induced ICD response. The second approach was to synthesize polymer nanoparticles for the delivery of mutated KRAS peptides in conjunction with a TLR7/8 agonist. The dual delivery vaccine particle boosted the generation of antigen-specific cytotoxic T-cells that are recruited to lymphoid structures at the cancer site, with a view to strengthening the endogenous vaccination response achieved by chemo-immunotherapy.
Toward predictive modelling of construction cost overruns using support vector machine techniques

Abstract The development of cost overrun prediction models using data mining techniques has considerably increased in recent years. Estimating the final cost of construction projects is essential during the contract award stage of the building process. Projects variables from archival data are important in developing prediction models. This research examines the effectiveness of support vector machines in predicting construction project cost overruns using data from archival records.The independent variables, like number of stories, gross floor area, change in scope, contract type, provisional sum, tendering type, and initial contract sum, were extracted from historical records. In this study, SVM models using linear, RBF, and polynomial kernel functions demonstrated that SVM using linear and polynomial kernel techniques were used in this research. This study looks at how well data mining tools forecast cost overruns in building projects using information from historical records.The results revealed that the linear kernel SVM model could produce accurate construction cost predictions with 0.99 R2, 0.099 RMSE, 0.05 MAE, 0.278 MAPE, and 0.01 MSE on the accuracy test data. When considered collectively, it is clear that gross floor space, story count, tendering method, and scope modification are reliable indicators of cost overruns in the construction sector.The created SVM model can be applied as a cost-estimating tool to predict potential cost overruns for Ghanaian construction projects.
Plant Image Disease Detection Using Deep Learning

Crop diseases present a significant challenge to food security, but the lack of infrastructure in many regions makes timely diagnosis difficult. However, the widespread adoption of smartphones and recent advancements in computer vision, powered by deep learning, have opened up new possibilities for disease detection with the help of smartphones. Through the utilization of a deep convolutional neural network, trained on a publicly available dataset of 54,306 images featuring damaged and healthy plant leaves obtained under controlled conditions, it becomes possible to recognize 14 crop species and identify 26 different illnesses or determine if the plant is healthy. This deep learning model, boasting an impressive accuracy rate of 98.59%, proves to be the most effective artificial neural network topology for detecting plant diseases. By following a strategy that involves developing deep learning models on progressively larger image datasets and making them accessible to the public, the path is paved for widespread smartphone-assisted crop disease diagnosis on a global scale. Deep Convolutional Neural Networks (CNNs) have shown remarkable performance in image recognition tasks, including plant disease detection. These models are trained on large datasets of labeled images to learn patterns and features indicative of different diseases.
Heuristic Data-Driven Adaptive Model Predictive Control Strategy

Model predictive control is a popular advanced control strategy, but its performance is largely limited by the accuracy of the predictive model, the learning ability of the model, and the capacity of solving the optimal control variables online. As it is often challenging to construct an error-free model, this paper proposes a heuristic data-driven adaptive model predictive control(A-MPC)framework to achieve precise control effects in uncertain environments. A - MPC combines the heuristic hill-climbing search strategy with a data-driven model thes conducting simple online optimisation for the prediction interval. After obtaining feedback from the environment, A-MPC uses gradient descent to update the prediction model synchronously to reduce the error between it and real environment and thus offers an adaptive ability. Experiments verify that the A-MPC algorithm offers a self-learning ability for unknown environments while achieving a better control effect.
Comparison of methodologies for flow prediction through teleconnection indices. Case study: Paute river basin

The Paute river basin (southern Ecuador) suffers hydrological changes due to climate change and human activities. Hydrological changes cause extreme events and affect ecosystems, hydroelectric plants, and quality of life. It highlights the importance of understanding hydrological behavior to make appropriate decisions in extreme environments. This study seeks to predict discharges in the Paute river basin through global teleconnection indices. Multiple Linear Regression (MLR) was obtained using three different methodologies: multicollinearity analysis, Principal Component Analysis (PCA), and correlation with monthly delays. It was shown that the principal component analysis scenario obtained the best predictive fits, specifically by including 41 indices and 20 components. For the scenario using monthly delays, the best delay occurs within a single month for most seasons. Finally, with the multicollinearity analysis scenario, better results were obtained using 41 indices, although essentially the performance corresponds to the number and indices of each model. Teleconnection indices are not sufficient when used as the only input variable for download modeling and prediction, giving mostly unsatisfactory results. However, a clear trend links the behavior of flows and indices, and it is possible to improve the models based on more climatic variables or with other predictive methods.
A Vision-Based Target Localization Method for Robot-Assisted Sonography

This article proposes a vision-based ultrasound scanning localization method that solves the issue of human pose constraints. Specifically, by proposing an improved AlphaPose network, the neck positioning for individuals with different body types is addressed. The interference caused by other moving human subjects in the scene is resolved through an improved DeepSORT tracking algorithm. The impact of environmental noise interference is reduced through depth information processing techniques. Additionally, a robotic platform for neck ultrasound scanning is constructed. This system is capable of automatically identifying and localizing moving human necks, planning trajectories for the ultrasound probe approach, and achieving real-time interaction.
Profile and mental health characterization of childhood overprotection/overcontrol experiences among Chinese university students: a nationwide survey

Introduction The childhood experiences of being overprotected and overcontrolled by family members have been suggested to be potentially traumatic. However, the possible associated factors of these experiences among young people are still not well studied. This study aimed to partly fill such gaps by a relatively large, nationwide survey of Chinese university students. Methods A total of 5,823 university students across nine different provinces in China were included by the convenience sampling method in the data analyses. All participants completed the overprotection/overcontrol (OP/OC) subscale in a recently developed 33-item childhood trauma questionnaire (CTQ- 33). Data were also collected on all participants’ socio-demographic profiles and characterization of mental health. Binary logistic regression was conducted to investigate the associated socio-demographic and psychological factors of OP/ OC. Results The prevalence of childhood OP/OC was estimated as 15.63% (910/5,823) based on a cutoff OP/OC subscale score of ≥ 13. Binary logistic regression suggested that being male, being a single child, having depression, having psychotic-like experiences, lower family functioning, and lower psychological resilience were independently associated with childhood OP/OC experiences (all corrected-p < 0.05). The OP/OC was also positively associated with all the other trauma subtypes (abuses and neglects) in the CTQ-33, while there are both shared and unique associated factors between the OP/OC and other trauma subtypes. Post-hoc analyses suggested that OP/OC experiences were associated with depression in only females and associated with anxiety in only males. Discussion Our results may provide initial evidence that childhood OP/OC experiences would have negative effects on young people’s mental health which merits further investigations, especially in clinical populations.
How to Estimate the Probability of Tolerance Long-Term in Liver Transplant Recipients

Background: Operational tolerance as the ability to accept the liver transplant without pharmacological immunosuppression is a common phenomenon in the long-term course. However, it is currently underutilized due to a lack of simple diagnostic support and fear of rejection despite its recognized benefits. In the present work, we present a simple score based on clinical parameters to estimate the probability of tolerance. Patients and methods: In order to estimate the probability of tolerance, clinical parameters from 82 patients after LT who underwent weaning from the IS for various reasons at our transplant center were extracted from a prospectively organized database and analyzed retrospectively. Univariate testing as well as multivariable logistic regression analysis were performed to assess the association of clinical variables with tolerance in the real-world setting. Results: The most important factors associated with tolerance after multivariable logistic regression were IS monotherapy, male sex, history of hepatocellular carcinoma pretransplant, time since LT, and lack of rejection. These five predictors were retained in an approximate model that could be presented as a simple scoring system to estimate the clinical probability of tolerance or IS dispensability with good predictive performance (AUC = 0.89). Conclusion: In parallel with the existence of a tremendous need for further research on tolerance mechanisms, the presented score, after validation in a larger collective preferably in a multicenter setting, could be easily and safely applied in the real world and already now address all three levels of prevention in LT patients over the long-term course.
Use of 16s rRNA Gene Sequencing for the Identification of Viridans Group Streptococci

Streptococci belonging to the viridans group are gram-positive bacteria residing as commensals in the upper respiratory, gastrointestinal, and genital tracts in humans. Though they are largely known to be commensals, they may also cause life-threatening infections like infective endocarditis, septicemia, pyogenic infections, pneumonia, and meningitis. The viridans group streptococci (VGS) are usually identified by biotyping; however, species discrimination is not always possible by phenotypic characterization. We identified 53 isolates from blood cultures of patients with infective endocarditis and compared the results of biotyping with 16s rRNA gene sequencing for species identification. Organisms belonging to the mitis group were the most common. 16s rRNA gene polymerase chain reaction and sequencing were useful in identifying the etiological agents at the species level. S.oralis was the most common etiological agent.
Effect of dilution gas on the distribution characteristics of capacitively coupled plasma by comparing SiH4/He and SiH4/Ar

In this study, we focus on the difference in the spatial distribution of the plasma parameters between SiH4/He capacitively coupled plasma (CCP) and SiH4/Ar CCP. The SiH4/He mixture is modeled using the chemical reactions that were successfully derived in our previous studies. The chemical reaction model of the SiH4/Ar mixture built in this study is based on the detailed set of chemical reactions in Ar. The spatial distribution of the plasma parameters is examined with the aid of a 2D fluid model. The electron and radical densities of SiH4/Ar CCP are higher than those of SiH4/He CCP. In addition, dilution with Ar results in more uniform reaction rates, which leads to a more uniform deposition profile. Because helium requires higher threshold energies for excitation and ionization, dilution with He had little effect on the precursor production. As a result, the concentration of Si2H6 observed in the inter-electrode region when using Ar for dilution was observed to be about ten times higher than the concentration of Si2H6 observed for He. This high concentration played a large role in influencing the formation of important radicals that determine the deposition rate as well as the difference in the deposition rate profile between Ar and He as diluents. The higher concentration of Si2H6 when using Ar means that the production rate of Si2H5 is higher in Ar. An examination of the effect of the dilution gas on the deposition rate profile indicated that the deposition rate profile with Ar is 100% more uniform and the deposition rate nearly 87% higher than for dilution with He.
Understanding the Effects of Market Volatility on Profitability Perceptions of Housing Market Developers

Drastic shifts in prices and housing market trends in recent years, representing shocks to the housing system, have led many residential developers to pause or cancel their projects. In the already heated housing markets of the Greater Toronto Area (GTA), these supply frictions can have ramifications for affordability. Our study formulates a standardized “proforma” model of the profitability of a hypothetical condominium project in the city of Toronto, Canada, scheduled between 2019 to 2023, to explore the combined effect of developers’ price expectations and market volatility on developers’ decisions. Using the proposed proforma, we first identify the key drivers of development decisions. We then evaluate the impact of the expectation formation of key factors influencing perceived development profitability, including construction costs, sales prices, and interest rates, on the financial feasibility of potential developments. The results highlight that boundedly rational expectations can cause variations in profitability perceptions and potentially reverse development decisions in volatile market conditions. Our results highlight the sources of risk and uncertainty in development decisions, facilitating the recognition of possible solutions to mitigate these risks and increase affordable housing supplies. The proposed model can also enhance the realism of decision models in agent-based representations of land and housing markets.