Miracle-dz/UniFAR-FTUs huggingface dataset:
AdipoRon Affects Cell Cycle Progression and Inhibits Proliferation in Human Osteosarcoma Cells

AdipoRon (AdipoR) is the first synthetic molecule acting as a selective and potent adiponectin receptor agonist. Recently, the possible pharmacological use of AdipoR in different pathological conditions has been addressed. Interestingly, initial evidence suggests that AdipoR may have anticancer properties in different preclinical models, such as pancreatic and ovarian cancer. To our knowledge, so far no research has been directed at determining the impact of AdipoR on osteosarcoma, the most aggressive and metastatic bone malignancy occurring in childhood and adolescence age. Here, we investigate the possible antitumor effects of AdipoR in osteosarcoma cell lines. MTT and cell growth curve assays clearly indicate that AdipoR inhibits, at different extents, proliferation in both U2OS and Saos-2 osteosarcoma cell lines, the latter being more sensitive. Moreover, flow cytometry-based assays point out a significant G0/G1 phase accumulation and a contemporary S phase decrease in response to AdipoR. Consistent with the different sensitivity, a strong subG1 appearance in Saos-2 after 48 and 72 hours of treatment is also observed. The investigation of the molecular mechanisms highlights a common and initial ERK1/2 activation in response to AdipoR in both Saos-2 and U2OS cells. Interestingly, a simultaneous and dramatic downregulation of p70S6K phosphorylation, one of the main targets of mTORC1 pathway, has also been observed in AdipoR-treated Saos-2, but not in U2OS cells. Importantly, a strengthening of AdipoR-induced effects was reported upon everolimus-mediated mTORC1 perturbation in U2OS cells. In conclusion, our findings provide initial evidence of AdipoR as an anticancer molecule differently affecting various signaling pathways involved in cell cycle and cell death in osteosarcoma cells and encourage the design of future studies to further understand its pattern of activities.
Distorted Pacific–North American teleconnection at the Last Glacial Maximum

Abstract. The Pacific–North American (PNA) teleconnection is one of the most important climate modes in the present climate condition, and it enables climate variations in the tropical Pacific to exert a significant influence on North America. Here, we show climate simulations in which the PNA teleconnection was largely distorted or broken at the Last Glacial Maximum (LGM). The distorted PNA is caused by a split in the westerly jet stream, which is ultimately forced by the large, thick Laurentide ice sheet that was present at the LGM. Changes in the jet stream greatly alter the extratropical waveguide, distorting wave propagation from the North Pacific to North America. The distorted PNA suggests that climate variability in the tropical Pacific, notably El Niño–Southern Oscillation (ENSO), would have little direct impact on North American climate at the LGM.
Ramification of gum arabic microencapsulation on the physicochemical and microbiological properties of butterfly pea (Clitoria ternatea) flowers using ultrasonic spray dryer

Butterfly Pea (Clitoria ternatea) flower is commonly used to treat various diseases and has long been part of the science of phytomedicines. Ultrasonic assisted extraction technique is a simple, cheap and efficient alternative to traditional extraction methods, producing a much smaller particle size than conventional spray drier. This study was aimed to evaluate the physicochemical and microbiological properties of microencapsulated butterfly pea flower (MEBP) powder prepared using ultrasonic spraying technique. The samples were microencapsulated with different amount of Gum Arabic (GA) (0, 2, 4, 6, 8 and 10%). Surface morphology of the MEBP powder was examined using SEM showed different molecular structures in different MEBP powder. FTIR analysis identified phenolic hydroxyl group (1061.69 cm-1 and 3385.36 cm-1 ), sulfite group (1062.01 cm-1 , 1065.92 cm-1 , 1077.15 cm-1 , 1234.72 cm-1 and 1261.74 cm-1 ) and amide group (1635.99 cm-1 , 1636.74 cm-1 , 1654.14 cm-1 and 3417.2 cm-1 ). 2,2-diphenyl-1 -picrylhydrazyl (DPPH), thiobarbituric acid (TBA), total phenolic content (TPC), total flavonoid content (TFC) found to be ranging from 35.85±0.57 to 130.85±3.00 mg GAE/g dried sample, 4.32±0.04 to 6.90±0.11 mg QE/g dried sample, respectively. It was found that Bacillus cereus was sensitive to MEBP powder as the results showed a prominent inhibition zone (5.67±0.6 mm to 11.67±2.3 mm). MEBP produced with GA 6% ultrasonic spray drier was found to be effective in sustaining its antioxidative activities and the microbial activities were found to be active. This result provided useful information data on the best technique to use ultrasonic spray dryer to retain antioxidant compounds.
CTRP3 induces an intermediate switch of CD14++CD16+ monocyte subset with anti-inflammatory phenotype

Acute myocardial infarction (AMI) evokes a temporally coordinated immune response, in which monocytes are critically involved in the clearance of cell debris; however, excessive inflammation induced by the classical sub-population of monocytes frequently limits the endogenous reparative process. In the present study, the potential of the anti-inflammatory adipokine complement C1q tumor necrosis factor (TNF)-related protein-3 (CTRP3) to induce intermediate switch of monocytes to an anti-inflammatory phenotype was explored. Circulating monocytes were isolated from patients with AMI at various time-points (3–5 h, 3 days and 7 days) and categorized by flow cytometry/immunostaining into three sub-divisions based on the expression of CD14 and CD16 epitopes: Classical (CD14++/CD16−), non-classical (CD14+/CD16++) and intermediate populations (CD14++/CD16+). The phagocytic activity was evaluated by the ingestion of FITC-Zymosan and 19F-nanoemulsion and the migratory activity using Thin Cert™ Transwell assay. Monocytes were cultured using autologous serum in the presence of CTRP3 (1 µg/ml) for 24 h and the expression of interleukin 6 (IL-6) and TNF-α was quantified by reverse-transcription quantitative PCR. In addition, SB203580, a p38 mitogen-activated protein kinase (MAPK)/ERK inhibitor, was used to examine the downstream pathways of CTRP3. AMI evoked a transient increase in monocyte counts of the classical subset after onset of the ischemic insult, while the non-classical and intermediate subsets persistently expanded (P<0.01). The monocytes from patients at 3 days after AMI displayed enhanced phagocytic and migratory activities in comparison with those from healthy volunteers (P<0.01). Of note, addition of CTRP3 induced an intermediate switch of monocyte subsets and antagonized the enhanced expression of cytokines, particularly IL-6, in monocytes stressed by lipopolysaccharides, likely by blunting the ERK1/2 and P38 MAPK signaling pathway. In conclusion, the present study demonstrated a dynamic fluctuation of monocyte subsets and enhanced phagocytic and migratory activities in patients with AMI. Furthermore, the ‘proof-of-concept’ evidence pinpoints CTRP3 as an alternative candidate to modulate the ‘uncontrolled’ inflammatory response and thus to augment cardiac reparative processes in patients with AMI.
Pop Music Transformer: Generating Music with Rhythm and Harmony

The task automatic music composition entails generative modeling of music in symbolic formats such as the musical scores. By serializing a score as a sequence of MIDI-like events, recent work has demonstrated that state-of-the-art sequence models with self-attention work nicely for this task, especially for composing music with long-range coherence. In this paper, we show that sequence models can do even better when we improve the way a musical score is converted into events. The new event set, dubbed "REMI" (REvamped MIDI-derived events), provides sequence models a metric context for modeling the rhythmic patterns of music, while allowing for local tempo changes. Moreover, it explicitly sets up a harmonic structure and makes chord progression controllable. It also facilitates coordinating different tracks of a musical piece, such as the piano, bass and drums. With this new approach, we build a Pop Music Transformer that composes Pop piano music with a more plausible rhythmic structure than prior arts do. The code, data and pre-trained model are publicly available. \footnote{\url{this https URL}}
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.
An infinite square-well potential as a limiting case of a square-well potential in a minimal-length scenario

One of the most widely problem studied in quantum mechanics is of an infinite square-well potential. In a minimal-length scenario its study requires additional care because the boundary conditions at the walls of the well are not well fixed. In order to avoid this we solve the finite square-well potential whose the boundary conditions are well fixed, even in a minimal-length scenario, and then we take the limit of the potential going to infinity to find the eigenfunctions and the energy equation for the infinite square-well potential. Although the first correction for the energy eigenvalues is the same one has been found in the literature, our result shows that the eigenfunctions have the first derivative continuous at the square-well walls what is in disagreement those previous work. That is because in the literature the authors have neglected the hyperbolic solutions and have assumed the discontinuity of the first derivative of the eigenfunctions at the walls of the infinite square-well which is not correct. As we show, the continuity of the first derivative of the eigenfunctions at the square-well walls guarantees the continuity of the probability current density and the unitarity of the time evolution
DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data

We present a method for depth estimation with monocular images, which can predict high-quality depth on diverse scenes up to an affine transformation, thus preserving accurate shapes of a scene. Previous methods that predict metric depth often work well only for a specific scene. In contrast, learning relative depth (information of being closer or further) can enjoy better generalization, with the price of failing to recover the accurate geometric shape of the scene. In this work, we propose a dataset and methods to tackle this dilemma, aiming to predict accurate depth up to an affine transformation with good generalization to diverse scenes. First we construct a large-scale and diverse dataset, termed Diverse Scene Depth dataset (DiverseDepth), which has a broad range of scenes and foreground contents. Compared with previous learning objectives, i.e., learning metric depth or relative depth, we propose to learn the affine-invariant depth using our diverse dataset to ensure both generalization and high-quality geometric shapes of scenes. Furthermore, in order to train the model on the complex dataset effectively, we propose a multi-curriculum learning method. Experiments show that our method outperforms previous methods on 8 datasets by a large margin with the zero-shot test setting, demonstrating the excellent generalization capacity of the learned model to diverse scenes. The reconstructed point clouds with the predicted depth show that our method can recover high-quality 3D shapes. Code and dataset are available at: this https URL
Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension

Abstract Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD—only marginally lower than when trained on data collected using RoBERTa itself (41.0F1).
Citation Text Generation

We introduce the task of citation text generation: given a pair of scientific documents, explain their relationship in natural language text in the manner of a citation from one text to the other. This task encourages systems to learn rich relationships between scientific texts and to express them concretely in natural language. Models for citation text generation will require robust document understanding including the capacity to quickly adapt to new vocabulary and to reason about document content. We believe this challenging direction of research will benefit high-impact applications such as automatic literature review or scientific writing assistance systems. In this paper we establish the task of citation text generation with a standard evaluation corpus and develop several strong baseline models. We provide extensive automatic and human evaluations to illustrate the successes and shortcomings of current text generation techniques for this task.
Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository

Nowadays, Internet is a primary source of attaining health information. Massive fake health news which is spreading over the Internet, has become a severe threat to public health. Numerous studies and research works have been done in fake news detection domain, however, few of them are designed to cope with the challenges in health news. For instance, the development of explainable is required for fake health news detection. To mitigate these problems, we construct a comprehensive repository, FakeHealth, which includes news contents with rich features, news reviews with detailed explanations, social engagements and a user-user social network. Moreover, exploratory analyses are conducted to understand the characteristics of the datasets, analyze useful patterns and validate the quality of the datasets for health fake news detection. We also discuss the novel and potential future research directions for the health fake news detection.
Computing quantum dynamics in the semiclassical regime

The semiclassically scaled time-dependent multi-particle Schrödinger equation describes, inter alia, quantum dynamics of nuclei in a molecule. It poses the combined computational challenges of high oscillations and high dimensions. This paper reviews and studies numerical approaches that are robust to the small semiclassical parameter. We present and analyse variationally evolving Gaussian wave packets, Hagedorn’s semiclassical wave packets, continuous superpositions of both thawed and frozen Gaussians, and Wigner function approaches to the direct computation of expectation values of observables. Making good use of classical mechanics is essential for all these approaches. The arising aspects of time integration and high-dimensional quadrature are also discussed.
Topological Bose-Mott insulators in one-dimensional non-Hermitian superlattices

We study the topological properties of Bose-Mott insulators in one-dimensional non-Hermitian superlattices, which may serve as effective Hamiltonians for cold atomic optical systems with either two-body loss or one-body loss. We find that in the strongly repulsive limit, the Mott insulator states of the Bose-Hubbard model with a finite two-body loss under integer fillings are topological insulators characterized by a finite charge gap, nonzero integer Chern numbers, and nontrivial edge modes in a low-energy excitation spectrum under an open boundary condition. The two-body loss suppressed by the strong repulsion results in a stable topological Bose-Mott insulator which has shares features similar to the Hermitian case. However, for the non-Hermitian model related to the one-body loss, we find the non-Hermitian topological Mott insulators are unstable with a finite imaginary excitation gap. Finally, we also discuss the stability of the Mott phase in the presence of two-body loss by solving the Lindblad master equation.
Sound Event Detection with Depthwise Separable and Dilated Convolutions

State-of-the-art sound event detection (SED) methods usually employ a series of convolutional neural networks (CNNs) to extract useful features from the input audio signal, and then recurrent neural networks (RNNs) to model longer temporal context in the extracted features. The number of the channels of the CNNs and size of the weight matrices of the RNNs have a direct effect on the total amount of parameters of the SED method, which is to a couple of millions. Additionally, the usually long sequences that are used as an input to an SED method along with the employment of an RNN, introduce implications like increased training time, difficulty at gradient flow, and impeding the parallelization of the SED method. To tackle all these problems, we propose the replacement of the CNNs with depthwise separable convolutions and the replacement of the RNNs with dilated convolutions. We compare the proposed method to a baseline convolutional neural network on a SED task, and achieve a reduction of the amount of parameters by 85% and average training time per epoch by 78%, and an increase the average frame-wise F1 score and reduction of the average error rate by 4.6% and 3.8%, respectively.
CMB and BBN constraints on evaporating primordial black holes revisited

We derive new CMB anisotropy power spectrum and BBN constraints for evaporating primordial black holes with mass between 1011 g and 1016 g by explicitly solving the electromagnetic particle cascades of emitted particles and the deposition of this emitted energy to the background baryon-photon plasma. We show that the CMB anisotropies can provide stronger constraints compared to BBN and CMB spectral distortions on black holes with masses as small as MBH=1.1× 1013 g, a slightly smaller mass than what has been considered in literature until now. We also show that, with more up-to-date data on abundances of deuterium and helium-3, BBN constraints are strengthened significantly. The abundance of these primordial black holes constrains the epoch of inflation 0∼ 4 e-folds after the epoch constrained by the CMB observations.
Evolving Neural Networks through a Reverse Encoding Tree

NeuroEvolution is one of the most competitive evolutionary learning strategies for designing novel neural networks for use in specific tasks, such as logic circuit design and digital gaming. However, the application of benchmark methods such as the NeuroEvolution of Augmenting Topologies (NEAT) remains a challenge, in terms of their computational cost and search time inefficiency. This paper advances a method which incorporates a type of topological edge coding, named Reverse Encoding Tree (RET), for evolving scalable neural networks efficiently. Using RET, two types of approaches – NEAT with Binary search encoding (Bi-NEAT) and NEAT with Golden-Section search encoding (GS-NEAT) – have been designed to solve problems in benchmark continuous learning environments such as logic gates, Cartpole, and Lunar Lander, and tested against classical NEAT and FS-NEAT as baselines. Additionally, we conduct a robustness test to evaluate the resilience of the proposed NEAT approaches. The results show that the two proposed approaches deliver improved performance, characterized by (1) a higher accumulated reward within a finite number of time steps; (2) using fewer episodes to solve problems in targeted environments, and (3) maintaining adaptive robustness under noisy perturbations, which outperform the baselines in all tested cases. Our analysis also demonstrates that RET expends potential future research directions in dynamic environments. Code is available from https://github.com/HaolingZHANG/ReverseEncodingTree.
Property of Innovation Under Different Group Size in an Open-ended Fitness Landscape: Demonstration Through Evolutionary Logical Circuits

Open-ended fitness landscape or architectural innovation is a key characteristic of combinatorial technological evolution. Though many have argued that this feature is important, many models were created in a closed fitness landscape. In this article, we modified a simulation by Arthur and Polak (2006) that used a computer simulation that builds logical circuits with circuits that were built in earlier trials. We used this simulation to investigate (1) whether the speed of innovation is increased by increasing group size and (2) if the nature of innovation would differ when innovation was separated by invention (new innovation that serves a purpose that never was used before) and improvement (already made innovation but is more efficient). The results indicated that indeed group size increases the speed of innovations but is limited than expected. Also, when innovation was separated with invention and improvement, the nature of the two differed. In improvement, the trajectory followed a convex function with productivity of one agent decreasing as group size increased. In invention, the trajectory showed a continuous pattern of rapid increase followed by a plateau. In sum, the results indicated that group size does increase both innovation and improvement, but the effect diminishes as group size increase in an open-ended fitness landscape and when innovation was split, invention resembled a technology s-curve.