Miracle-dz/UniFAR-FTUs huggingface dataset:
Daily Maximum Temperatures Induce Lagged Effects on Leaf Unfolding in Temperate Woody Species Across Large Elevational Gradients

The timing of leaf unfolding in temperate woody species is predominantly controlled by the seasonal course of temperature in late winter and early spring. However, quantifying lagged temperature effects on spring phenology is still challenging. Here, we aimed at investigating lagged and potentially non-linear effects of daily maximum temperatures on the probability of leaf unfolding in temperate woody species growing across large elevational gradients. We analyzed 5280 observations of leaf-out time of four tree species (European beech, horse chestnut, European larch, Norway spruce) and one shrub species (common hazel) that were recorded by volunteers over 40 years at 42 locations in Switzerland. We used a case-crossover sampling design to match leaf-out dates with control dates (i.e., dates before or after leaf-out), and analyzed these data with conditional logistic regression accounting for lagged temperature effects over 60 days. Multivariate meta-analyses were used to synthesize lagged temperature and elevational effects on leaf unfolding across multiple phenological stations. Temperature effects on the probability of leaf unfolding were largest at relatively short lags (i.e., within ca. 10 days) and decreased with increasing lags. Short- to mid-term effects (i.e., within ca. 10 to 20 days) were larger for late-leafing species known to be photoperiod-sensitive (beech, Norway spruce). Temperature effects increased for the broadleaved species (horse chestnut, hazel, beech) with decreasing elevation, particularly within ca. 10 to 40 days, i.e., leaf unfolding occurs more rapidly at low elevations for a given daily maximum temperature. Our novel findings provide evidence of cumulative and long-term temperature effects on leaf unfolding, whereby the efficiency of relatively high temperatures to trigger leaf-out becomes higher shortly before bud burst. These lagged associations between temperature and leaf unfolding improve our understanding of phenological responses across temperate woody species with differing ecological requirements that occur along elevational gradients.
Experiences of aspiring school principals receiving coaching as part of a leadership development programme

PurposeThis paper adds depth to our understanding of how coaching works by exploring the experiences of 14 aspiring school principals who received one-to-one leadership coaching as part of a leadership development programme.Design/methodology/approachThis study adopts a phenomenological approach. Individual semi-structured interviews were conducted with the participants. Thematic analysis was used to code the data and identify themes.FindingsThis paper reports on four themes based on the experiences of the participants: having time to reflect, feeling safe to explore, focussing on what's important for me and experiencing positive emotions.Research limitations/implicationsThe findings are unique to the participants who volunteered to take part in this study and therefore not representative of a general population of aspiring educational leaders. Further research is needed into the possible benefits of coaching to support educators undergoing leadership training.Practical implicationsThe findings raise a potential dilemma within the teaching profession about the use of educators' time; while they need to give time and attention to multiple stakeholders, they also need to protect time for their own development and self-reflection. Based on the reported experiences of the participants in this study, it is recommended that coaching be considered a component of professional development for educational leaders.Originality/valueThis paper adds to the growing research base for coaching in education, providing a unique insight into the experiences of aspiring school principals who received one-to-one leadership coaching as part of a leadership development programme.
Analysis of the impacts of Motor Vehicle Exhaust Emissions at Pancasila University on health in order to create A Green Campus

The current carbon monoxide (CO) emissions are the main component of diseases, such as cough, dizziness, nausea, and shortness of breath for those who inhale them. Pancasila University is one of the colleges in the city of Jakarta that cares about the poor quality of air in the city of Jakarta; therefore, Pancasila University wants to apply the concept of green campus as a form of concern for the environment. This research was conducted at Pancasila University to discover the relationship between air quality in the campus, especially carbon monoxide (CO) substance, and its impact on the health of the university’s academic community members. The methods used in this study are simple linear regression, HIRARC, and hierarchy of control. The results of this study show a relationship between CO substance variables and complaints about diseases suffered by members of the university’s academic community due to the substance, with an R count value of 0.9519. There are substantial risks that the students can experience, including student activities that cause nausea and outdoor employee activities that cause dizziness and nausea. These big risks can be prevented by issuing a decision letter to reduce air pollution at Pancasila University.
COVID-19 immunity certificates: science, ethics, policy, and law

Abstract There is much discussion of adopting COVID-19 immunity certificates to allow those proven to have antibodies to the SARS-CoV-2 virus that causes COVID-19 to resume normal life and help restart the economy. This article points out issues that must be considered before adopting any such program. These issues fall into six categories: the uncertain science of COVID-19 immunity; the questionable quality of COVID-19 antibody tests; practical problems with issuing such certificates; deciding how the certificates might be used; ethical and social issues they would raise, especially fairness and self-infection; and potential legal barriers. It does not ultimately take a position on whether some narrow COVID-19 immunity plans should be adopted, concluding that the answer depends on too many currently unknown conditions. But its seventh part makes recommendations to decision-makers who might consider implementing such programs.
Analysis of big data processing technologies

This article describes large amounts of information. Examples of large volumes are provided and some of their processing technologies have been analyzed. The advantages and disadvantages of the methods of processing parallel processing technologies of large volumes of data processing, the methods of selection of newly selected methods of selection and models are considered. These methods are compared with the distributed technology of data processing. This article discusses a few of the large volumes of data processing principles. They are: the principle of horizontal measurements, the principle of inverse stability, and the principles of data positioning. On the basis of horizontal measurements, any data processing system is expanding, with data doubling. In contrast to the principle of horizontal stability, the number of machines should not be increased. In the location of data, the data is distributed to a large number of machines. If the data is located on a server and processed in another, the cost of data transmission may exceed the cost of processing. Therefore, the most effective principle of data processing is the principle of information location processing. It is desirable to recycle data in a machine that will save them if possible. All models of large volumes of data processing are available in these three methods. This article analyzes a larger model of data that works with these principles. The MapReduce model also operates on the basis of the above three principles. This is also a model for distributed data processing to process large amounts of data in the computer cluster. In this article, several methods and models are analyzed and their advantages and disadvantages are discussed.
AGR3 promotes estrogen receptor-positive breast cancer cell proliferation in an estrogen-dependent manner

Breast cancer is one of the most common malignancies and the leading cause of cancer-associated death among women. Anterior gradient 3 (AGR3) is a cancer-associated gene and is similar to its homologous oncogene AGR2. However, whether AGR3 participates in breast cancer progression remains unclear. The present study aimed to investigate the function of AGR3 in ER-positive breast cancer. In the present study, reverse transcription-quantitative PCR was used to detect AGR3 mRNA expression in breast cancer tissues and cell lines; linear correlation analysis was used to investigate the correlation between AGR3 and estrogen receptor 1 (ESR1) expression in breast cancer via GEO dataset analysis; western blotting was used to assess the levels of AGR3, ER and GAPDH; small interfering (si)RNA transfection was used to knock down AGR3 and ESR1 expression; and finally the Cell Counting Kit-8 assay was used to evaluate cell viability. In the present study, AGR3 expression was markedly increased in estrogen receptor (ER)-positive breast cancer tissues and cell lines compared with that in ER-negative breast cancer. AGR3 expression was upregulated in estrogen-treated T47D cells, whereas 4-hydroxytamoxifen, an inhibitor of estrogen-ER activity in breast cancer cells, downregulated AGR3 expression in T47D cells. Functional assays demonstrated that knockdown of AGR3 using siRNAs inhibited T47D cell proliferation compared with that of the negative control group. Additionally, AGR3 expression was decreased after knocking down ESR1. The present results suggested that AGR3 may serve an important role in estrogen-mediated cell proliferation in breast cancer and that AGR3 knockdown may be a potential therapeutic strategy for ER-positive breast cancer.
A negative binomial thinning‐based bivariate INAR(1) process

This article considers a bivariate INAR(1) process based on an extension of the negative binomial thinning operator by prespecifying the distribution of the innovations. The dependence is introduced through the innovation components. The existence, uniqueness, strict stationarity, ergodicity, and some probabilistic properties of the process are derived. The estimation methods of conditional least squares and conditional maximum likelihood are considered. Some numerical results of the estimates are presented by simulation study. An application to crime data set is provided.
Implementation of Convolutional Neural Networks to Recognize Images of Common Indonesian Food

One of the applications of computer vision in the popular culture is food recognition which is popularized in the internet with a “hotdog and not hotdog” problem. Food recognition is also useful in many popular lifestyle apps such as calorie counter app or any diet related app. In this paper is proposed a CNN aided technique for recognizing food that is common in Indonesia. The technique is consist of 3 main phases, one is pre-processing normalize the data one is model formation and training which is known as the common binary classifier template, boosted with pooling and evaluated by cross-entropy technique in this paper the model used is the pre-trained model to test the testing data afterward, which is show a promising result with a relatively short training time. The experiments focused on how CNN can be used as a component to recognize food so that in the future it can be used to develop better calorie counter applications. In this experiment 10,000 data were used for training and 50 tests for each food category with a total of 500 food image data used for testing with the best accuracy reaching 88% for one of the categories.
Cranial anatomy of Microsyops annectens (Microsyopidae, Euarchonta, Mammalia) from the middle Eocene of Northwestern Wyoming

Abstract. The Microsyopidae are extinct mammals from the late Paleocene–late Eocene of North America and the late Paleocene of Europe. While results from phylogenetic analyses support euarchontan affinities, specific relationships of microsyopids to other plesiadapiforms (plausible stem primates), Euprimates (crown primates), Scandentia (treeshrews), and Dermoptera (colugos) are unresolved. An exceptionally well-preserved cranium of Microsyops annectens includes a basicranium that is generally primitive relative to that of other extinct and extant euarchontans in having: (1) a transpromontorial groove for an unreduced internal carotid artery (ICA) entering the middle ear posteromedially; (2) grooves (not tubes) on the promontorium, marking the course for both stapedial and promontorial branches of the ICA; (3) a foramen faciale that opens into the middle ear cavity, with the facial nerve exiting through a stylomastoid foramen primitivum; and (4) unexpanded caudal and rostral tympanic processes of the petrosal. The absence of any preserved bullar elements in the middle ear contrasts with that of other plesiadapiforms for which the region has been recovered, all of which have evidence of an ossified bulla. Microsyops lacks many of the specialized cranial characteristics of crown scandentians and dermopterans. The basicranial anatomy of microsyopids does not provide evidence in support of a clear link to any of the extant euarchontans, and suggests that the primitive morphology of this region in Euarchonta was little differentiated from that observed in the primitive placental mammals.
Method validation to assess in vivo cellular and subcellular changes in buccal mucosa cells and saliva following CBCT examinations.

OBJECTIVES: Cone-beam CT (CBCT) is a medical imaging technique used in dental medicine. However, there are no conclusive data available indicating that exposure to X-ray doses used by CBCT are harmless. We aim, for the first time, to characterize the potential age-dependent cellular and subcellular effects related to exposure to CBCT imaging. Current objective is to describe and validate the protocol for characterization of cellular and subcellular changes after diagnostic CBCT. METHODS: Development and validation of a dedicated two-part protocol: 1) assessing DNA double strand breaks (DSBs) in buccal mucosal (BM) cells and 2) oxidative stress measurements in saliva samples. BM cells and saliva samples are collected prior to and 0.5 h after CBCT examination. BM cells are also collected 24 h after CBCT examination. DNA DSBs are monitored in BM cells via immunocytochemical staining for γH2AX and 53BP1. 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxo-dG) and total antioxidant capacity are measured in saliva to assess oxidative damage. RESULTS: Validation experiments show that sufficient BM cells are collected (97.1 ± 1.4 %) and that γH2AX/53BP1 foci can be detected before and after CBCT examination. Collection and analysis of saliva samples, either sham exposed or exposed to IR, show that changes in 8-oxo-dG and total antioxidant capacity can be detected in saliva samples after CBCT examination. CONCLUSION: The DIMITRA Research Group presents a two-part protocol to analyze potential age-related biological differences following CBCT examinations. This protocol was validated for collecting BM cells and saliva and for analyzing these samples for DNA DSBs and oxidative stress markers, respectively.
Large Magellanic Cloud Cepheid Standards Provide a 1% Foundation for the Determination of the Hubble Constant and Stronger Evidence for Physics beyond ΛCDM

We present an improved determination of the Hubble constant from Hubble Space Telescope (HST) observations of 70 long-period Cepheids in the Large Magellanic Cloud (LMC). These were obtained with the same WFC3 photometric system used to measure extragalactic Cepheids in the hosts of SNe Ia. Gyroscopic control of HST was employed to reduce overheads while collecting a large sample of widely separated Cepheids. The Cepheid period–luminosity relation provides a zero-point-independent link with 0.4% precision between the new 1.2% geometric distance to the LMC from detached eclipsing binaries (DEBs) measured by Pietrzyński et al. and the luminosity of SNe Ia. Measurements and analysis of the LMC Cepheids were completed prior to knowledge of the new DEB LMC distance. Combined with a refined calibration of the count-rate linearity of WFC3-IR with 0.1% precision, these three improved elements together reduce the overall uncertainty in the geometric calibration of the Cepheid distance ladder based on the LMC from 2.5% to 1.3%. Using only the LMC DEBs to calibrate the ladder, we find H0 = 74.22 ± 1.82 km s−1 Mpc−1 including systematic uncertainties, 3% higher than before for this particular anchor. Combining the LMC DEBs, masers in NGC 4258, and Milky Way parallaxes yields our best estimate: H0 = 74.03 ± 1.42 km s−1 Mpc−1, including systematics, an uncertainty of 1.91%–15% lower than our best previous result. Removing any one of these anchors changes H0 by less than 0.7%. The difference between H0 measured locally and the value inferred from Planck CMB and ΛCDM is 6.6 ± 1.5 km s−1 Mpc−1 or 4. 4σ (P = 99.999% for Gaussian errors) in significance, raising the discrepancy beyond a plausible level of chance. We summarize independent tests showing that this discrepancy is not attributable to an error in any one source or measurement, increasing the odds that it results from a cosmological feature beyond ΛCDM.
Nanofibrous MgO composites: structures, properties, and applications

ABSTRACT Nanomaterials have become an established area of academic research and have gained commercial importance due to their valuable physicochemical properties, such as large surface area, high mechanical strength as well as unique optical and electrical features. Numerous nanosize architectures have been researched and reported to date including quantum dots, fullerenes, nanorods, nanowires, nanofibers, nanosheets, nanowalls, nanocoils, and nanoballs, etc. Among these materials, nanofibers are extremely valuable morphologies used across many industries spanning from textile to medical applications and beyond. This review article focuses on the various works and reports of MgO-based applications and its overlap with nanofiber-based structures. Comprehensive review of the growing number of reports to date provides a unified resource for researchers going forward. As the acceptance and broader set of applications continues its exponential growth in commercial and academic output, it is envisaged that MgO-based composites will play a central part in many future reports of nanofibrous composites and other composite architectures, e.g., particle-reinforced composites, metal matrix composites, ceramic matrix composites, etc., due to the wide variety of applications to which MgO is suited, as well as other peripheral properties such as cost, availability, ease-of-handling, and use, etc. Indeed, despite the many preliminary reports to date, there remains a great deal yet to discover and optimize for such systems.
Using Artificial Intelligence to Recapture Norms: Did #metoo change gender norms in Sweden?

Norms are challenging to define and measure, but this paper takes advantage of text data and the recent development in machine learning to create an encompassing measure of norms. An LSTM neural network is trained to detect gendered language. The network functions as a tool to create a measure on how gender norms changes in relation to the Metoo movement on Swedish Twitter. This paper shows that gender norms on average are less salient half a year after the date of the first appearance of the hashtag #Metoo. Previous literature suggests that gender norms change over generations, but the current result suggests that norms can change in the short run.
Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction

High-strength concrete (HSC) is highly applicable to the construction of heavy structures. However, shear strength (Ss) determination of HSC is a crucial concern for structure designers and decision makers. The current research proposes the novel models based on the combination of adaptive neuro-fuzzy inference system (ANFIS) with several meta-heuristic optimization algorithms, including ant colony optimizer (ACO), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO), to predict the Ss of HSC slender beam. The proposed models were constructed using several input combinations incorporating several related dimensional parameters such as effective depth of beam (d), shear span (a), maximum size of aggregate (ag), compressive strength of concrete (fc), and percentage of tension reinforcement (ρ). To assess the impact of the non-homogeneity of the dataset on the prediction result accuracy, two possible modeling scenarios, (i) non-processed (initial) dataset (NP) and (ii) pre-processed dataset (PP), are inspected by several performance indices. The modeling results demonstrated that ANFIS-PSO hybrid model attained the best prediction accuracy over the other models and for the pre-processed input parameters. Several uncertainty analyses were examined (i.e., model, variables, and data), and results indicated predicting the HSC shear strength was more sensitive to the model structure uncertainty than the input parameters.
Graph Convolution for Multimodal Information Extraction from Visually Rich Documents

Visually rich documents (VRDs) are ubiquitous in daily business and life. Examples are purchase receipts, insurance policy documents, custom declaration forms and so on. In VRDs, visual and layout information is critical for document understanding, and texts in such documents cannot be serialized into the one-dimensional sequence without losing information. Classic information extraction models such as BiLSTM-CRF typically operate on text sequences and do not incorporate visual features. In this paper, we introduce a graph convolution based model to combine textual and visual information presented in VRDs. Graph embeddings are trained to summarize the context of a text segment in the document, and further combined with text embeddings for entity extraction. Extensive experiments have been conducted to show that our method outperforms BiLSTM-CRF baselines by significant margins, on two real-world datasets. Additionally, ablation studies are also performed to evaluate the effectiveness of each component of our model.
Linear double‐stranded DNAs as innovative biological parts to implement genetic circuits in mammalian cells

Synthetic biology employs engineering principles to redesign biological systems for biomedical or industrial purposes. Innovation and application of original biological parts for genetic circuit construction will significantly facilitate and expedite the development of synthetic biology. Here, we built two‐ or three‐input linear double‐stranded DNA (ldsDNA)‐based Boolean AND gate genetic circuits in mammalian cells. Bioluminescence imaging revealed the feasibility of ldsDNA‐based Boolean AND gate circuits in vivo. Inhibition of DNA‐PKcs, a pivotal enzyme in nonhomologous end joining, significantly attenuated the output signals from ldsDNA‐based Boolean AND gate circuits. We further showed that ldsDNA with additional terminal random nucleotide(s) could undergo end nucleotide deletion and generate in‐frame proteins via the Boolean AND gate response. Additionally, ldsDNAs or plasmids with identical overlapping sequences could also serve as input signals for Boolean AND gate genetic circuits. Our work establishes ldsDNAs as innovative biological parts for building low noise‐signal ratio Boolean AND gate circuits with application potential in biomedical engineering fields.