Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach

Monitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability from a 32-years global physical-biogeochemical simulation can generally be skillfully reproduced with a SVR using the model surface variables as input parameters. We then apply the SVR to reconstruct satellite Chl observations using the physical predictors from the above numerical model and show that the Chl reconstructed by this SVR more accurately reproduces some aspects of observed Chl variability and trends compared to the model simulation. This SVR is able to reproduce the main modes of interannual Chl variations depicted by satellite observations in most regions, including El Niño signature in the tropical Pacific and Indian Oceans. In stark contrast with the trends simulated by the biogeochemical model, it also accurately captures spatial patterns of Chl trends estimated by satellite data, with a Chl increase in most extratropical regions and a Chl decrease in the center of the subtropical gyres, although the amplitude of these trends are underestimated by half. Results from our SVR reconstruction over the entire period (1979–2010) also suggest that the Interdecadal Pacific Oscillation drives a significant part of decadal Chl variations in both the tropical Pacific and Indian Oceans. Overall, this study demonstrates that non-linear statistical reconstructions can be complementary tools to in situ and satellite observations as well as conventional physical-biogeochemical numerical simulations to reconstruct and investigate Chl decadal variability.
Developmental Differences of Structural Connectivity and Effective Connectivity in Semantic Judgments of Chinese Characters

Previous studies have investigated the developmental differences of semantic processing regarding brain activation between adults and children. However, little is known about whether the patterns of structural connectivity and effective connectivity differ between adults and children during semantic processing. Functional magnetic resonance imaging (fMRI), diffusion spectrum imaging (DSI), and dynamic causal modeling (DCM) were used to study the developmental differences of brain activation, structural connectivity, and effective connectivity during semantic judgments. Twenty-six children (8- to 12-year-olds) and 26 adults were asked to indicate if character pairs were related in meaning. Compared to children, adults showed greater activation in the left ventral inferior frontal gyrus (IFG) and left middle temporal gyrus (MTG). Also, adults had significantly greater structural connectivity in the left ventral pathway (inferior frontal occipital fasciculus, IFOF) than children. Moreover, adults showed significantly stronger bottom-up effects from left fusiform gyrus (FG) to ventral IFG than children in the related condition. In conclusion, our findings suggest that age-related increases in brain activation (ventral IFG and MTG), IFOF, and effective connectivity (from FG to ventral IFG) might be associated with the bottom-up influence of orthographic representations on retrieving semantic representations for processing Chinese characters.
Epigenetic regulations follow cell cycle progression during differentiation of human pluripotent stem cells

Most mammalian stem cells undergo cellular division during their differentiation to produce daughter cells with a new cellular identity. However, the cascade of epigenetic events and molecular mechanisms occurring between successive cell divisions upon differentiation have not yet been described in detail due to technical limitations. Here, we address this question by taking advantage of the Fluorescent Ubiquitination-based Cell Cycle Indicator (FUCCI) reporter to develop a culture system allowing the differentiation of human Embryonic Stem Cells (hESCs) synchronised for their cell cycle. Using this approach, we have assessed the epigenome and transcriptome dynamics during the first two divisions leading to definitive endoderm. We first observed that transcription of key markers of differentiation occurs before division suggesting that differentiation is initiated during the progression of cell cycle. Furthermore, ATAC-seq shows a major decrease in chromatin accessibility after pluripotency exit indicating that the first event of differentiation is the inhibition of alternative cell fate. In addition, using digital genomic footprinting we identified novel cell cycle-specific transcription factors with regulatory potential in endoderm specification. Of particular interest, Activator protein 1 (AP-1) controlled p38/MAPK signalling seems to be necessary for blocking endoderm shifting cell fate toward mesoderm lineage. Finally, histone modifications analyses suggest a temporal order between different marks. We can also conclude that enhancers are dynamically and rapidly established / decommissioned between different cell cycle upon differentiation. Overall, these data not only reveal key the successive interplays between epigenetic modifications during differentiation but also provide a valuable resource to investigate novel mechanisms in germ layer specification.
Understanding Cluster Headache Using Magnetic Resonance Imaging

Cluster headache is an excruciating pain syndrome characterized by unilateral head pain attacks, lasting between 15 and 180 min, accompanied by marked ipsilateral cranial autonomic symptoms, such as lacrimation and conjunctival injection. Despite important insights provided by neuroimaging studies and deep brain stimulation findings, the pathophysiology of cluster headache and its pathways of chronicization are still elusive. In this mini-review, we will provide an overview of the functional and structural neuroimaging studies in episodic and chronic cluster headache conditions conducted to clarify the underlying pathophysiology.
North-eastern range extension of the anemone Stichodactyla haddoni to the Marshall Islands represents a new record of host use by the endemic anemonefish Amphiprion tricinctus

Amphiprion tricinctus is an anemonefish endemic to the Marshall Islands that was previously reported to inhabit four species of sea anemone. Underwater visual surveys in 2009 and 2010 located five individuals of the anemone Stichodactyla haddoni in the shallow lagoon of Majuro Atoll. This is the first record of S. haddoni in the Marshall Islands and a significant north-eastern range extension by 2200 km from its nearest record in Chuuk State, Federated States of Micronesia. Stichodactyla haddoni was inhabited by juvenile and adult A. tricinctus. This report is the first to describe a symbiotic relationship between A tricinctus and S. haddoni. The number of host anemone species inhabited by A. tricinctus now extends to five.
SEX PHEROMONE COMMUNICATION IN THE APPLE LEAFCURLING MIDGE (DASINEURA MALI)

The use of a pheromone for sexual communication by the apple leafcurling midge (ALCM) was demonstrated in field and laboratory tests. In field tests, traps baited with virgin ALCM females caught large numbers of ALCM males. Empty traps and traps baited with ALCM males caught no males. When flight responses of male ALCM to various odour sources were tested in a wind tunnel, males flew upwind and landed on cages that contained virgin ALCM females and on filter papers treated with a pentane extract of virgin ALCM females. Males did not fly upwind to mated ALCM females, odours released by apple foliage, filter papers treated with pentane, or pear leafcurling virgin females. Male ALCM that emerged between the hours of 7:00 and 9:00 a.m. responded to pentane extracts of virgin ALCM throughout the day of their emergence.
Comparison of 12 molecular detection assays for SARS-CoV-2

Molecular testing for SARS-CoV-2 is the mainstay for accurate diagnosis of the infection, but the diagnostic performances of available assays have not been defined. We compared 12 molecular diagnostic assays, including 8 commercial kits using 155 respiratory samples (65 nasopharyngeal swabs, 45 oropharyngeal swabs, and 45 sputum) collected at 2 Japanese hospitals. Sixty-eight samples were positive for more than one assay and one genetic locus and were defined as true positive samples. All the assays showed a specificity of 100% (95% confidence interval, 95.8 to 100). The N2 assay kit of the US Centers for Disease Control and Prevention (CDC) and the N2 assay of the Japanese National Institute of Infectious Disease (NIID) were the most sensitive assays with 100% sensitivity (95% confidence interval, 94.7 to 100), followed by the CDC N1 kit, E assay by Corman, and NIID N2 assay multiplex with internal control reactions. These assays are reliable as first-line molecular assays in laboratories when combined with appropriate internal control reactions.
Swarm Learning as a privacy-preserving machine learning approach for disease classification

Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis and COVID-19 is an important goal of precision medicine. We recently illustrated that leukemia patients are identified by machine learning (ML) based on their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, blockchain-based peer-to-peer networking and coordination as well as privacy protection without the need for a central coordinator thereby going beyond federated learning. Using more than 14,000 blood transcriptomes derived from over 100 individual studies with non-uniform distribution of cases and controls and significant study biases, we illustrate the feasibility of SL to develop disease classifiers based on distributed data for COVID-19, tuberculosis or leukemias that outperform those developed at individual sites. Still, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.
The Effects of Temperature on the Mosquito Pathogenic Fungus Culicinomyces.

The optimum temperatures for germination and growth in culture of an Australian isolate of Culicinomyces were 27.5 and 25oC respectively. At 30oC the fungus germinated but did not grow. Larvae of Anopheles amictus hilli and Culex fatigans in water containing conidia were killed when incubated at temperatures ranging from 15 to 27.5oC but not at 30oC. At 30oC conidia adhered to the normal infection sites of the foregut and the gut cuticle was penetrated, but hyphae did not invade the haemocoel and the infection was discarded at the next moult. The fungus was lethal to larvae maintained at 30oC for 8 h and at 25oC for 16 h each day, but not to those reared at 30oC for 16 h and at 25oC for 8 h per day. Temperature should not limit the utilization of Culicinomyces for mosquito control, provided that the temperatures of breeding sites are below 30oC. The fungus might not be effective against larvae living continuously at 30oC, but it might be effective if the temperature were at this level during only part of each day.
CARMA1 Is Required for Akt-Mediated NF-κB Activation in T Cells

ABSTRACT Many details of the generic pathway for induction of NF-κB have been delineated, but it is still not clear how multiple, diverse receptor systems are able to converge on this evolutionarily conserved family of transcription factors. Recent studies have shown that the CARMA1, Bcl10, and MALT1 proteins are critical for coupling the common elements of the NF-κB pathway to the T-cell receptor (TCR) and CD28. We previously demonstrated a role for the serine/threonine kinase Akt in CD28-mediated NF-κB induction. Using a CARMA1-deficient T-cell line, we have now found that the CARMA complex is required for induction of NF-κB by Akt, in cooperation with protein kinase C activation. Furthermore, using a novel selective inhibitor of Akt, we confirm that Akt plays a modulatory role in NF-κB induction by the TCR and CD28. Finally, we provide evidence for a physical and functional interaction between Akt and CARMA and for Akt-dependent phosphorylation of Bcl10. Therefore, in T cells, Akt impinges upon NF-κB signaling through at least two separate mechanisms.
Asymmetric U-shape Network for Contour Detection on Nonwoven Images

COVID-19 becomes a pandemic all over the world in 2020. It is important to supply high-quality protective equipments to medical staff. Nonwoven is a widely used material for medical protective clothing. In this paper, we present an asymmetric U-shape network (AUN) for high quality contour detections of nonwoven images. AUN utilizes multiscale features through an encoder path and rebuilds high resolution side-outputs through a decoder path, feature extraction path introduced the gradient features into the CNN. The performance of the proposed network was evaluated on two datasets: the proposed nonwoven image dataset and PASCAL VOC2012. It was found that AUN can obtain high accuracy object contours on both datasets.
Molecular investigations applied to nontuberculous mycobacteria identification

Objective. Aim of this study was molecular identification in clinical specimens of NonTuberculous Mycobacteria (NTM) with commercial methods and automated sequencing. Materials and methods. Three thousand clinical specimens were analyzed for the isolation of Mycobacteria. Forty strains of NTM were previously analyzed with GenoType Mycobacteria CM/AS kit and then with partial 16S rDNA sequencing. Results. 38/40 NMT strains were identified with GenoType Mycobacteria CM/AS kit as: M. gordonae (15), M. intracellulare (8), M. xenopi (8), M. mucogenicum (2), M. kansasii (2), M. chelonae (1), M. avium (1), M. lentiflavum (1). Two unidentified strains were subjected to 16S rDNA sequencing and were identified as M. kumamotonense. Conclusions. Partial 16SrDNA sequencing is a valid assay to study NTM strains unidentified with commercial assays. This new approach, applied to clinical diagnostic, also permits the recognition of unusual strains or new species.
Boosting toponym interlinking by paying attention to both machine and deep learning

Toponym interlinking is the problem of identifying same spatio-textual entities within two or more different data sources, based exclusively on their names. It comprises a significant task in geospatial data management and integration with application in fields such as geomarketing, cadastration, navigation, etc. Previous works have assessed the effectiveness of unsupervised string similarity functions, while more recent ones have deployed similarity-based Machine Learning techniques and language model-based Deep Learning techniques, achieving significantly higher interlinking accuracy. In this paper, we demonstrate the suitability of Attention-based neural networks on the problem, as well as the fact that all different approaches provide merit to the problem, proposing a hybrid scheme that achieves the highest accuracy reported on toponym interlinking on the widely used Geonames dataset.
Using IoT middleware solutions in interdisciplinary research projects in the context of AAL

Research projects in the field of Active and Assisted Living (AAL) are dedicated to enhance quality of life and independent living of the elderly by providing assistive technical and non-technical solutions. With the progress of the Age of Internet of Things (IoT) and digitalization, the implemented applications are also often focused on the integration of IoT technologies. As the field of AAL is characterized by a high amount of interdisciplinarity, there arises the need of integrating various proprietary solutions to one comprehensive application. This can be achieved by using dedicated IoT middleware solutions which provide the possibility to integrate information from different systems, users and devices into one comprehensive application. The aim of this paper is to show an overview on the process of interdisciplinary research in the field of AAL and to demonstrate the arising requirements for IoT middleware solutions in the given context. Furthermore, selected middleware frameworks and platforms are presented and the implementation of one selected solution is described for a given use case from the Smart VitAALity project.
Zooplankton communities are good indicators of potential impacts of Athabasca oil sands operations on downwind boreal lakes

We used zooplankton communities as indicators to evaluate the potential influence of acidifying–eutrophying emissions from the Athabasca oil sands region (AOSR) on 244 downwind lakes in northwest Saskatchewan. The impacts of regional environmental change on zooplankton communities are determined by responses of resident species to altered local environmental conditions as well as changes in composition due to dispersal processes. To test and quantify the relative importance of these individual processes, we conducted ordination analyses, spatial modeling, and variation partitioning. Local environmental factors were the dominant determinants of community structure, including two major environmental gradients susceptible to atmospheric emissions (i.e., acid–base status and productivity). Spatial structuring of these factors induced similar spatial structures in zooplankton distribution across the region. However, disentangling any impacts of the AOSR on these environment–spatial–species relationships from t.
A sound-based crowd activity recognition with neural network based regression models

Activities performed by humans can be recognized by the sound they emit while being performed, hence, researchers have proposed methods that use sound to recognize human activities, by detecting the presence of sound events in short time frames. However, in crowded environments, many sound events overlap making it impossible to distinguish the individual events and methods of detection to fail. To address this issue and make the sound-based model suitable for crowd activities, this paper proposes to predict the proportion of activities happening in a specific place, by designing two neural network-based regression models: a CNN-model and a concatenate model. The CNN-model takes the Mel-bands as the input and is very popular in single activity recognition problems. Based on the CNN-model, we also designed a concatenate model which additionally inputting the global FFT feature to further improve the performance. The evaluation of this approach is performed over 3 generated groups of audio samples, where each group has a different crowded-level. Both RMSE and coefficient of determination (R2 score), are used as evaluation metrics. The experiments show that the concatenate model works statistically better throughout the dataset, with a R2 score of 0.7377. Results show that using the concatenate model with both short-frame and holistic features provides a better result than any single-feature based model.
S-IRFindeR: stable and accurate measurement of intron retention

Accurate quantification of intron retention levels is currently the crux for detecting and interpreting the function of retained introns. Using both simulated and real RNA-seq datasets, we show that current methods suffer from several biases and artefacts, which impair the analysis of intron retention. We designed a new approach to measure intron retention levels called the Stable Intron Retention ratio that we have implemented in a novel algorithm to detect and measure intron retention called S-IRFindeR. We demonstrate that it provides a significant improvement in accuracy, higher consistency between replicates and agreement with IR-levels computed from long-read sequencing data. S-IRFindeR is freely available at: https://github.com/lbroseus/SIRFindeR/.
The phosphatidylinositol 3-phosphate binding protein SNX4 controls ATG9A recycling and autophagy

Late endosomes and lysosomes (endolysosomes) receive proteins and cargo from the secretory, endocytic and autophagic pathways. Whereas these pathways and the degradative processes of endolysosomes are well characterized, less is understood about protein traffic from these organelles. In this study, we demonstrate the direct involvement of the phosphatidylinositol 3-phosphate (PI3P) binding SNX4 protein in membrane protein recycling from endolysosomes, and show that SNX4 is required for proper autophagic flux. We show that SNX4 mediates recycling of the transmembrane autophagy machinery protein ATG9A from endolysosomes to early endosomes, from where ATG9A is recycled to the trans-Golgi network in a retromer-dependent manner. Upon siRNA-mediated depletion of SNX4 or the retromer component VPS35, we observed accumulation of ATG9A on endolysosomes and early endosomes, respectively. Moreover, starvation-induced autophagosome biogenesis and autophagic flux were inhibited when SNX4 was downregulated. Altogether, we propose that proper ATG9A recycling by SNX4 sustains autophagy by preventing exhaustion of the available ATG9A pool.
Patchiness and long-term change in early Eocene insect feeding damage

Abstract Many studies have examined temporal changes in insect feeding on angiosperm leaves, but none have considered variability within a single stratigraphic level. If spatial variability within a level is high, a single sample will not adequately represent the level and may either mask true temporal changes or create spurious ones. In order to measure the spatial variability in fossil insect feeding damage, I collected 12 replicate samples from two laterally extensive carbonaceous shale beds (55.2 and 52.6 Ma) from the early Eocene of the Bighorn Basin, Wyoming. Over 2800 fossil angiosperm leaves were scored for presence or absence of 50 insect damage morphotypes. Damage frequency, diversity, and composition were computed for both the bulk flora and individual plant species in each sample, and variation within a bed was compared with differences between the two beds. Differences in diversity and composition between beds were significantly greater than variations within a bed, and intra-bed variation was primarily due to differing floral composition. Damage frequency within a bed, however, was more variable than diversity. Damage diversity and composition reflect the number of insect species present, whereas damage frequency also depends on the number of insects present, which may be much more variable over small distances.