Miracle-dz/UniFAR-FTUs huggingface dataset:
Nature of the Yanbian suture zone and structure of Northeast Asia

Based on recent advances in geological work, this paper discusses the nature of the Yanbian suture zone. The authors think that this suture zone is an accretionary suture zone of the combined Late Jurassic Khanka block and Longgang-Guanmao block, belonging to the Northeast Asian Mesozoic circum-Pacific tectonic belt. As is the case with the whole of Northeast Asia, Mesozoic structure of Northeast China mainly formed in the Late Jurassic to Early Cretaceous. It composed of two belts: the inner belt resulted from accretion of fragments of continental crust(including the continental margin), and the outer belt mainly formed by accretion of island arc and oceanic crustal fragments with the characteristics of the Tethys Ocean.
A review of robot grasping tactile perception based on deep learning

For robots, grasping is a necessary skill, and agile grasping by robotic hands is currently a cutting-edge research hotspot. Tactile perception serves as the primary sensory channel for both humans and robots to perceive the surface properties of objects, playing a fundamental role in enabling robots to perform dexterous operations. Tactile perception allows a robotic arm to sense the geometric contour, roughness, hardness, and other material properties of an object during the grasping process. It provides the robotic arm with predictive information on force and angles, thereby enhancing the efficiency of grasping. In recent years, deep learning has achieved remarkable advancements in various industrial domains, including intelligent sorting, defect detection, textile manufacturing, and autonomous driving. These achievements have spurred researchers to shift their focus from machine learning to deep learning in the study of tactile perception for agile robotic manipulation. Despite the unprecedented progress made in deep learning-assisted robot tactile perception, there are still some unresolved challenges in this field. This article begins by discussing the implementation methods of robot tactile perception and then provides a comprehensive overview of the current research and application status of deep learning-based robot tactile perception. Firstly, it highlights the latest advancements in tactile perception for dexterous robot operations. Secondly, it presents an overview of the sources and data acquisition methods employed in existing research. Additionally, the article summarizes the applications of deep learning in robot tactile perception. Finally, it explores current trends and potential future research directions in the field of tactile perception during robot grasping.
Biomass and selected ecological factors of epiphytic bryophyte along altitudinal gradients in Southern Thailand

Biomass of epiphytic bryophytes was investigated along three altitudinal transects in southern Thailand: Tarutao National Park (25-700 m), Khao Nan National Park (400-1,300 m), and Khao Luang National Park (400-1,500 m). The dry weight of epiphytic bryophytes per surface area increased from 1.15 g/m 2 in the lowland to a maximum 199 g/m 2 at the lower montane forests. The estimation of dry weight per hectare increased along transect from 2.4 to 620 kg. The water storing capacity of epiphytic bryophytes was about 1.2 to 2.4 times as dry weight and was generally higher in the lower montane forest (up to 1,500 l/ha) than in the lowland forests. The bark pH of host trees range between 3.19 and 6.84, and show negative correlation with the altitude (r=-0.635, p<0.05). Air temperature gradually decreases with the increasing altitude ca 0.6°C per 100 m elevation.
Shear Strength of Fiber-Reinforced Sands

Soil reinforcement using discrete randomly distributed fibers has been widely investigated over the last 30 years. Several models were suggested to estimate the improvement brought by fibers to the shear strength of soils. The objectives of this paper are to (1) supplement the data available in the literature on the behavior of fiber-reinforced sands; (2) study the effect of several parameters which are known to affect the shear strength of fiber-reinforced sands; and (3) investigate the effectiveness of current models in predicting the improvement in shear strength of fiber-reinforced sand. An extensive direct shear testing program was implemented using coarse and fine sands tested with three types of fibers. Results indicate the existence of a fiber-grain scale effect which is not catered for in current prediction models. A comparison between measured and predicted shear strengths indicates that the energy dissipation model is effective in predicting the shear strength of fiber-reinforced specimens in reference to the tests conducted in this study. On the other hand, the effectiveness of the predictions of the discrete model is affected by the parameters of the model, which may depend on the test setup and the procedure used for mixing the fibers.
Hellenism in Byzantium: The Transformations of Greek Identity and the Reception of the Classical Tradition

Introduction Part I. Greeks, Romans, and Christians in Late Antiquity: 1. 'We too are Greeks! ': the legacies of Hellenism 2. 'The world a city': Romans of the east 3. 'Nibbling on Greek learning': the Christian predicament Interlude. Hellenism in limbo: the middle years (400-1040) Part II. Hellenic Revivals in Byzantium: 4. Michael Psellos and the instauration of philosophy 5. The third sophistic: the performance of Hellenism under the Komnenoi 6. Imperial failure and the emergence of national Hellenism General conclusions.
A reappraisal of the default mode and frontoparietal networks in the common marmoset brain

In recent years the common marmoset homologue of the human default mode network (DMN) has been a hot topic of discussion in the marmoset research field. Previously, the posterior cingulate cortex regions (PGM, A19M) and posterior parietal cortex regions (LIP, MIP) were defined as the DMN, but some studies claim that these form the frontoparietal network (FPN). We restarted from a neuroanatomical point of view and identified two DMN candidates: Comp-A (which has been called both the DMN and FPN) and Comp-B. We performed GLM analysis on auditory task-fMRI and found Comp-B to be more appropriate as the DMN, and Comp-A as the FPN. Additionally, through fingerprint analysis, a DMN and FPN in the tasking human was closer to the resting common marmoset. The human DMN appears to have an advanced function that may be underdeveloped in the common marmoset brain.
42. MAGNETOSTRATIGRAPHY OF UPPER CRETACEOUS AND LOWER TERTIARY SEDIMENTS, SITES 761 AND 762, EXMOUTH PLATEAU, NORTHWEST AUSTRALIA1

Lower Campanian to middle Eocene chalks and oozes were recovered at Sites 761 and 762 of Ocean Drilling Program Leg 122 on the Exmouth Plateau, northwest Australia. Paleomagnetic analyses were made on 125 samples from Hole 761B and 367 samples from Hole 762C. Thermal cleaning, alternating field demagnetization, or mixed treatment reveals a stable remanent component of normal or reversed polarity. Correlation of the magnetic polarity sequences established for these holes with the standard magnetic polarity time scale was aided by nannofossil zonation. At Hole 761B, the sequence extends from Subchron C32-N (upper Campanian) through Subchron C17-R (middle Eocene), but given the low sedimentation rate, not all the subchrons of the standard magnetic polarity sequence were recognized. The sequence at Hole 762C extends from Subchron C13-R (middle Eocene) to the boundary between Chrons C33 and C34 (lower Campanian). The sedimentation rate is higher at Hole 762C, and all the magnetic polarity subchrons of the Campanian and Maestrichtian stages were identified. Thus, this hole could be a reference section to refine the Upper Cretaceous time scale.
DualNetGO: A Dual Network Model for Protein Function Prediction via Effective Feature Selection

Motivation Protein-protein Interaction (PPI) networks are crucial for automatically annotating protein functions. As there are different types of evidence to define PPI networks, multiple PPI networks exist for the same set of proteins to capture their properties from different aspects, creating challenges in effectively utilizing these heterogeneous graphs for protein function prediction. Recently, several deep learning models have combined PPI networks from all evidence, or concatenated all graph embeddings. However, the lack of a delicate selection procedure prevents the effective harness of information from different PPI networks as they vary in densities, structures and noise levels. Consequently, combining protein features indiscriminately could increase the noise level, leading to decreased model performance. Results We develop DualNetGO, a dual network model comprised of a classifier and a selector, to predict protein functions by effectively selecting features from different sources including graph embeddings of PPI networks, protein domain and subcellular location information. Evaluation of DualNetGO on human and mouse datasets in comparison with other network-based models show at least 4.5%, 6.2% and 14.2% improvement on Fmax in BP, MF and CC Gene Ontology categories respectively for human, and 3.3%, 10.6% and 7.7% improvement on Fmax for mouse. We further show that our model is insensitive to the choice of graph embedding method and is time- and memory-saving. These results demonstrate that combining a subset of features including PPI networks and protein attributes selected by our model is more effective in utilizing PPI network information than only using one kind of or concatenating graph embeddings from all kinds of PPI networks. Availability and implementation The source code of DualNetGO and some of the experiment data are available at: https://github.com/georgedashen/DualNetGO.
APPLICATION OF A LUE MODEL TO ESTIMATE GPP 8-d USING THE FPAR MODIS PRODUCT IN AN AGRICULTURAL ECOSYSTEM IN THE UPPER SPANISH PLATEAU

This paper presents the results of the GPP 8-d derived from the FPAR MODIS product, processed with version V005, using a LUE model in an agricultural ecosystem located in the geographical centre of the Spanish plateau over selected periods between 2003 and 2006, especially during the growing season. The LUE model is based on ground PAR measurements, FPAR and a scalar f varying between 0 and 1 to take account of water stress, which was assumed to be equal to the evaporative fraction, EF. The fourth parameter, optimal light use efficiency, emax, of the dominant land use was fitted through a linear fit between the calculated and observed GPP 8-d values measured at the available eddy covariance tower. The LUE model fitted the observed GPP 8-d (R 2 =90.6%) properly, providing an emax of 2.371 g C m -2 MJ -1 , a substantially higher value than typically reported in the literature for crops.
Human mutations in high-confidence Tourette disorder genes affect sensorimotor behavior, reward learning, and striatal dopamine in mice

Tourette disorder (TD) is poorly understood, despite affecting 1/160 children. A lack of animal models possessing construct, face, and predictive validity hinders progress in the field. We used CRISPR/Cas9 genome editing to generate mice with mutations orthologous to human de novo variants in two high-confidence Tourette genes, CELSR3 and WWC1. Mice with human mutations in Celsr3 and Wwc1 exhibit cognitive and/or sensorimotor behavioral phenotypes consistent with TD. Sensorimotor gating deficits, as measured by acoustic prepulse inhibition, occur in both male and female Celsr3 TD models. Wwc1 mice show reduced prepulse inhibition only in females. Repetitive motor behaviors, common to Celsr3 mice and more pronounced in females, include vertical rearing and grooming. Sensorimotor gating deficits and rearing are attenuated by aripiprazole, a partial agonist at dopamine type II receptors. Unsupervised machine learning reveals numerous changes to spontaneous motor behavior and less predictable patterns of movement. Continuous fixed-ratio reinforcement shows Celsr3 TD mice have enhanced motor responding and reward learning. Electrically evoked striatal dopamine release, tested in one model, is greater. Brain development is otherwise grossly normal without signs of striatal interneuron loss. Altogether, mice expressing human mutations in high-confidence TD genes exhibit face and predictive validity. Reduced prepulse inhibition and repetitive motor behaviors are core behavioral phenotypes and are responsive to aripiprazole. Enhanced reward learning and motor responding occurs alongside greater evoked dopamine release. Phenotypes can also vary by sex and show stronger affection in females, an unexpected finding considering males are more frequently affected in TD. Significance Statement We generated mouse models that express mutations in high-confidence genes linked to Tourette disorder (TD). These models show sensorimotor and cognitive behavioral phenotypes resembling TD-like behaviors. Sensorimotor gating deficits and repetitive motor behaviors are attenuated by drugs that act on dopamine. Reward learning and striatal dopamine is enhanced. Brain development is grossly normal, including cortical layering and patterning of major axon tracts. Further, no signs of striatal interneuron loss are detected. Interestingly, behavioral phenotypes in affected females can be more pronounced than in males, despite male sex bias in the diagnosis of TD. These novel mouse models with construct, face, and predictive validity provide a new resource to study neural substrates that cause tics and related behavioral phenotypes in TD.
A fluorogenic complementation tool kit for interrogating lipid droplet-organelle interaction

Contact sites between lipid droplets and other organelles are essential for cellular lipid and energy homeostasis. Detection of these contact sites at nanometer scale over time in living cells is challenging. Here, we developed a tool kit for detecting contact sites based on Fluorogen- Activated Bimolecular complementation at CONtact sites, FABCON, using a reversible, low affinity split fluorescent protein, splitFAST. FABCON labels contact sites with minimal perturbation to organelle interaction. Via FABCON, we quantitatively demonstrated that endoplasmic reticulum (ER)- and mitochondria (mito)-lipid droplet contact sites are dynamic foci in distinct metabolic conditions, such as during lipid droplet biogenesis and consumption. An automated analysis pipeline further classified individual contact sites into distinct subgroups based on size, likely reflecting differential regulation and function. Moreover, FABCON is generalizable to visualize a repertoire of organelle contact sites including ER-mito. Altogether, FABCON reveals insights into the dynamic regulation of lipid droplet-organelle contact sites and generates new hypotheses for further mechanistical interrogation during metabolic switch.
Muscle contractile properties directly influence shared synaptic inputs to spinal motor neurons

Alpha band oscillations in shared synaptic inputs to the alpha motor neuron pool can be considered an involuntary source of noise that hinders precise voluntary force production. This study investigated the impact of altering muscle length on the shared synaptic oscillations to spinal motor neurons, particularly in the physiological tremor band. Fourteen healthy individuals performed low-level dorsiflexion contractions at ankle joint angles of 90° and 130°, while high-density surface electromyography (HD-sEMG) was recorded from the tibialis anterior (TA). We decomposed the HDsEMG into motor units spike trains and calculated the motor units’ coherence within the delta (1-5 Hz), alpha (5-15 Hz) and beta (15-35 Hz) bands. Additionally, torque steadiness and torque spectral power within the tremor band was quantified. Results showed no significant differences in torque steadiness between 90° and 130°. In contrast, alpha band oscillations in both synaptic inputs and force output decreased as the length of the TA was moved from shorter (90°) to longer (130°), with no changes in delta and beta bands. In a second set of experiments, evoked twitches were recorded with the ankle joint at 70° and 130°, revealing longer twitch durations in the longer muscle lengthen condition compared to the shorter. These experimental results, supported by a simple computational simulation, suggest that increasing muscle length enhances the muscle’s low-pass filtering properties, influencing the oscillations generated by the Ia afferent feedback loop. Therefore, this study provides valuable insights into the interplay between muscle biomechanics and neural oscillations.
Diagnosis of planktonic trophic network dynamics with sharp qualitative changes

Trophic interaction networks are notoriously difficult to understand and to diagnose (i.e., to identify contrasted network functioning regimes). Such ecological networks have many direct and indirect connections between species, and these connections are not static but often vary over time. These topological changes, as opposed to a dynamic on a static (frozen) network, can be triggered by natural forcings (e.g., seasons) and/or by human influences (e.g., nutrient or pollution inputs). Aquatic trophic networks are especially dynamic and versatile, thus suggesting new approaches for identifying network structures and functioning in a comprehensive manner. In this study, a qualitative model was devised for this purpose. Applying discrete-event models from theoretical computer science, a mechanistic and qualitative model was developed that allowed computation of the exhaustive dynamics of a given trophic network and its environment. Once the model definition is assumed, it provides all possible trajectories of the network from a chosen initial state. In a rigorous and analytical approach, for the first time, we validated the model on one theoretical and two observed trajectories recorded at freshwater stations in the La Rochelle region (Western France). The model appears to be easy to build and intuitive, and it provides additional relevant trajectories to the expert community. We hope this formal approach will open a new avenue in identifying and predicting trophic (and non-trophic) ecological networks.
A group of adolescents' experiences of care in relationships with older people in a resource-constrained environment

23 Introduction . 24 Research Methodology . 28 Research Method and Design . 28 Research Context and Participants . 28 Data Gathering and Procedure . 29 Data Analysis . 30 Trustworthiness . 31 Ethical Considerations . 32 Findings . 33 Main Themes and Sub-themes of Care Experienced by Adolescents . 33 Upward care . 34 Practical care: . 34 Emotional care: . 35 Respectful care: . 36 Caring by attaining an education: . 37 Downward care . 37 Practical care: . 38 Care is reciprocal . 39 Discussion . 39 Limitations and Recommendation . 43 Conclusion . 44 References . 45 CRITICAL REFLECTION . 51 Methodology . 53 Conclusion . 53 References . 55
Reflections on Identity Exploration in the Time of the COVID-19 Pandemic from Queer Students of Color

Abstract:The context of the COVID-19 global pandemic has shaped all aspects of modern life, creating a unique circumstance for minoritized groups as they have explored important questions related to the self. Specifically, scholars, practitioners, and policymakers have expressed interest in understanding how the COVID-19 pandemic affected minoritized groups, including queer people and queer people of color, specifically. This study used data from a longitudinal constructivist grounded theory project following queer students of color within and beyond their time in college to examine how the COVID-19 pandemic influenced the process of identity exploration for these individuals. Findings revealed that queer students of color engaged in self-reflection about identities because of the isolation, reexamined navigating relationships at home and within educational institutions, and sought connections and guidance through online spaces. Informed by the study findings, the manuscript provides implications for future research and practice in higher education and student affairs.
Foliar chemical composition of two oak species grown in a free-air enrichment system with elevated O3 and CO2

Ground level ozone ( O 3 ) is rapidly increasing in Asia and penetrates plants via stomata. Atmospheric carbon dioxide ( CO 2 ) has also been increasing in global and in short term may promote plant growth via photosynthetic enhancement. Oaks are representative deciduous broadleaved trees native to northeastern Asia. In this study, we focused on the effects of elevated O 3 and/or CO 2 on leaf nutrients status ( phosphorous, nitrogen, potassium, magnesium, etc. ) of two oak species: Konara oak ( Quercus serrata ) and Mizunara oak ( Quercus mongolica var. crispula ) to assess their function. We investigated foliar starch and sugar contents and nutrients composition of leaves of 2-year-old oak seedlings grown under elevated O 3 and/or CO 2 in a free air enrichment system. From elements concentration, nitrogen and magnesium may become the major indices in assessing the O 3 effects on these species, and investigation of both of them would be of use in field studies to discriminate O 3 and CO 2 effects, especially under the projected elevated CO 2 levels.
Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative Feedback

The advancements in generative modeling, particularly the advent of diffusion models, have sparked a fundamental question: how can these models be effectively used for discriminative tasks? In this work, we find that generative models can be great test-time adapters for discriminative models. Our method, Diffusion-TTA, adapts pre-trained discriminative models such as image classifiers, segmenters and depth predictors, to each unlabelled example in the test set using generative feedback from a diffusion model. We achieve this by modulating the conditioning of the diffusion model using the output of the discriminative model. We then maximize the image likelihood objective by backpropagating the gradients to discriminative model's parameters. We show Diffusion-TTA significantly enhances the accuracy of various large-scale pre-trained discriminative models, such as, ImageNet classifiers, CLIP models, image pixel labellers and image depth predictors. Diffusion-TTA outperforms existing test-time adaptation methods, including TTT-MAE and TENT, and particularly shines in online adaptation setups, where the discriminative model is continually adapted to each example in the test set. We provide access to code, results, and visualizations on our website: https://diffusion-tta.github. io/.