Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitochondrial genome phylogeny reveals the deep‐time origin of Gomphomastacinae (Orthoptera: Eumastacidae) and its alpine genera in China

Gomphomastacinae is a grasshopper subfamily in Eumastacidae, with a morphology and distribution distinct from other subfamilies. The alpine genera of Gomphomastacinae that inhabit the Qinghai–Tibet Plateau in China show unique characteristics adapted to high‐altitude life. However, their phylogenetic position and biogeographic history remain controversial. Thus, to determine the diversification history of these alpine genera and the origin of the subfamily, we obtained mitochondrial genome sequences from all seven Gomphomastacinae genera distributed in China. The reconstructed phylogeny was well supported and confirmed the phylogenetic position of Gomphomastacinae within Eumastacidae. Time calibration revealed a deep‐time origin of the subfamily dating back to the Cretaceous period, and the diversification among alpine genera was also an ancient pre‐Miocene event (30–50 Ma). Based on phylogeny and time estimates, the most likely biogeographic scenario is that Gomphomastacinae originated from an ancestral lineage that lived in East Gondwana and dispersed to Central and Western Asia through India. Subsequently, the alpine genera likely diverged along with the uplift of the Qinghai–Tibet Plateau and survived drastic climate change by in situ adaptation to high‐altitude dwellings.
The Influence of Capital Structure and Management Quality on Firm Value With Capital Expenditure as Moderating Variable in the Primary Consumer Goods Sector on the Indonesia Stock Exchange

The purpose of this study is to determine the effect of capital structure and management quality on firm value and the effect of capital expenditure as a moderating variable on the effect of capital structure and management quality on firm value. The population in this study is the Primary Consumer Goods Sector Companies on the Indonesia Stock Exchange (IDX) from 2016 to 2020, of which there are 92 companies. The sampling technique is purposive sampling, so the research sample is 59 companies. The analysis stages of this study include descriptive statistical analysis, classical assumption testing, multiple linear analysis, correlation coefficient analysis and determination, hypothesis testing, and moderating regression analysis. The results of the study show that capital structure and management quality have a positive effect on firm value, and capital expenditure has a moderating effect that weakens the effect of capital structure on firm value, while capital expenditure has a moderating effect that strengthens the influence of management quality on firm value.
Comparison of Antioxidant Properties and Metabolite Profiling of Acer pseudoplatanus Leaves of Different Colors

Acer pseudoplatanus (maple) is a widely grown ornamental plant. In addition to its ornamental and ecological value, it also has potentially high economic value. It is a rich source of polyphenols and exhibits antioxidant activity. However, the relationship between polyphenol content and antioxidant activity in maple leaves of different colors (green, yellow, and red) has not yet been investigated. In this study, the total polyphenol (TP), total flavonoid (TFlav), tannin (TET), chlorophyll a and b (Chl a and b), total anthocyanin (TAN), and total carotene (TAC) contents in maple leaves of different colors were evaluated. Their antioxidant activities were determined based on the inhibition of lipid oxidation, DPPH scavenging, ferric ion-reducing antioxidant power, and iron-chelating abilities. The concentrations of TP, TET, TFlav, TAN, and TAC in red maple leaves were higher than those in green and yellow maple leaves. In addition, red maple leaves showed a higher antioxidant effect than the leaves of the other two colors. We observed that antioxidant activity was positively correlated with TP, TFlav, and TAN and negatively correlated with Chl a and b. Finally, we analyzed the metabolites of the different colored (i.e., green, yellow, and red) maple leaves using gas chromatography/mass spectrometry (GC/MS) and found that the metabolite profile significantly varied between the different colors. These results suggest that red leaves are a good source of polyphenols and antioxidants and have potential use in the development of functional foods and medicinal applications.
Russell versus Steiner on Physics and Causality

In "Events and Causality" Mark Steiner argues that though Bertrand Russell was right to claim that the laws of physics do not express causal relations, nevertheless, Russell was wrong to suppose that therefore causality plays no role in physics. I argue that Steiner misses the point of Russell's argument for the first of these claims, and because of this Steiner's argument against the second fails to controvert it. Steiner fails to see that Russell's argument against causation, is in fact an argument against the existence of causal directionality or asymmetry. Steiner gives no reason to suppose physical theory requires this asymmetry after all.
Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations

The present memoir is the outcome of an attempt to obtain the conditions under which a given symmetric and continuous function k ( s, t ) is definite, in the sense of Hilbert. At an early stage, however, it was found that the class of definite functions was too restricted to allow the determination of necessary and sufficient conditions in terms of the determinants of § 10. The discovery that this could be done for functions of positive or negative type, and the fact that almost all the theorems which are true of definite functions are, with slight modification, true of these, led finally to the abandonment of the original plan in favour of a discussion of the properties of functions belonging to the wider classes. The first part of the memoir is devoted to the definition of various terms employed, and to the re-statement of the consequences which follow from Hilbert’s theorem.
Controlled catalytic growth and characterization of zinc oxide nanopillars on a-plane sapphire

Using the vapor-liquid-solid (VLS) technique, we have grown well-aligned nanopillars on [112¯0]-sapphire (a-plane) substrates at atmospheric pressure in a horizontal tube furnace employing gold catalyst seeds of different sizes and densities. It was the aim of the present work to find experimental conditions (source and template temperatures, temperature gradients, carrier gas flow, gold cluster size and density) under which controlled catalytic growth of nanopillars takes place. The VLS process is expected to result in a correlation of the zinc oxide (ZnO) pillar diameters with the gold catalyst cluster size. This is indeed found to hold true except for very small gold clusters. A minimum value of the pillar diameter of about 20 nm on a-plane sapphire is obtained in our experiments, which apparently represents a general limit. Structure characterization relies on high-resolution x-ray diffraction, atomic force microscopy, and high-resolution scanning electron microscopy. Electronic characterization is do.
Low-contact-resistance and smooth-surface Ti∕Al∕Nb∕Au ohmic electrode on AlGaN∕GaN heterostructure

We have achieved both low contact resistance and smooth surface morphology by using Ti∕Al∕Nb∕Au formed on an Al0.3Ga0. 7N∕GaN heterostructure. A low contact resistance of less than 0. 6Ω∕mm was reproducibly recorded after thermal treatment with a wide range of annealing temperature from 830 to 1000°C and annealing time from 10 to 300s. It was found that root-mean square surface roughness of under 35nm was obtained with all the annealing conditions investigated. In consideration of surface roughness and reproducibility, the most suitable annealing condition was 850°C for 100s, which leads to a contact resistance and a specific contact resistivity of 0. 48Ω∕mm and 5×10−6Ω∕cm2, respectively.
Event-Triggered Data-Driven Control for Nonlinear Systems Under Frequency-Duration-Constrained DoS Attacks

This paper addresses the event-triggered model free adaptive control (MFAC) problem for unknown nonlinear systems under denial-of-service (DoS) attacks, where the design and analysis are discussed under the data-driven framework. Firstly, by using the novel pseudo partial derivative, the nonlinear systems are converted into an equivalent data-relationship model. Then, the DoS attacks are described as limited by their frequency and duration, and without any more specific assumptions about the attack structure or strategy. Next, a novel event-triggered MFAC scheme is proposed. By employing the Lyapunov stability theory, the stability performance is analyzed. Furthermore, a compensation algorithm is designed to against the adverse impact brought by the DoS attacks. Finally, simulations including a numerical example and a load frequency control (LFC) example for multi-area power systems are given to demonstrate the validity and applicability of the proposed schemes.
Iron transportation proteins Hepcidin and Ferroportin and alterations in depressive and anxiety disorders

Objective: Iron element has critical roles such as myelin synthesis and neurotransmitter synthesis. Critical enzymes and proteins strictly control iron metabolism. Alterations in the enzyme activities could modify iron metabolism. Metabolic and endocrine changes may influence iron turnover in patients with major depression and anxiety disorders. Materials and Methods: 30 patients with major depressive disorder (MDD), 30 with anxiety disorders (ADs) according to the DSM 5 criteria, and 30 healthy controls were included. Hamilton Depression and Anxiety Scales were the clinical evaluation tools. Blood samples were collected 12 hours of fasting. Hepcidin and Ferroportin levels were measured with ELISA method. Results: Both Hepcidin and Ferroportin levels were lower in the MDD group compared to the ADs group, Hepcidin levels were found to be statistically significantly lower (p= 0.014). In addition, an inverse correlation was observed between the Hamilton Depression Scale score and Ferroportin levels (r= -0.214, p<0.05). Conclusion: Decreased Hepcidin and Ferroportin levels indicate metabolic effects in patients with MDD and disruption of the feedback mechanism between the two proteins. Considering the long duration of the disease in the MDD group in our study, the treatment period was also thought to be prolonged and the use of antidepressants might affect negative feedback.
Machine Learning Approach for Rapid Estimation of Five-Day Biochemical Oxygen Demand in Wastewater

Improperly managed wastewater effluent poses environmental and public health risks. BOD evaluation is complicated by wastewater treatment. Using key parameters to estimate BOD in wastewater can improve wastewater management and environmental monitoring. This study proposes a BOD determination method based on the Artificial Neural Networks (ANN) model to combine Chemical Oxygen Demand (COD), Suspended Solids (SS), Total Nitrogen (T-N), Ammonia Nitrogen (NH4-N), and Total Phosphorous (T-P) concentrations in wastewater. Twelve different transfer functions are investigated, including the common Hyperbolic Tangent Sigmoid (HTS), Log-sigmoid (LS), and Linear (Li) functions. This research evaluated 576,000 ANN models while considering the variable random number generator due to the ten alternative ANN configuration parameters. This study proposes a new approach to assessing water resources and wastewater facility performance. It also demonstrates ANN’s environmental and educational applications. Based on their RMSE index over the testing datasets and their configuration parameters, twenty ANN architectures are ranked. A BOD prediction equation written in Excel makes testing and applying in real-world applications easier. The developed and proposed ANN-LM 5-8-1 model depicting almost ideal performance metrics proved to be a reliable and helpful tool for scientists, researchers, engineers, and practitioners in water system monitoring and the design phase of wastewater treatment plants.
TRANSIENT TWO-DIMENSIONAL COMPRESSIBLE ANALYSIS FOR HIGH-TEMPERATURE HEAT PIPES WITH PULSED HEAT INPUT

A complete mathematical model for transient two-dimensional heat pipes is presented. The numerical results for both simulated compressible vapor flow with high Mach numbers and the vapor flow of a high-temperature heat pipe are compared with the experimental data in the literature. The transient responses of heat pipes to a pulsed heat input are also investigated. It is very important to include the porous wick and the wall in the numerical calculations for the transient analysis of heat pipes and to treat the entire heat pipe as a single system rather than to analyze the vapor flow alone.
Multitarget Angle of Arrival Estimation Using Rotating mmWave FMCW Radar and Yolov3

It is still challenging to accurately localize unmanned aerial vehicles (UAVs) from a ground control station (GCS) using various sensors. The mmWave frequency-modulated continuous wave (FMCW) radars offer excellent performance for target detection and localization in harsh environments and low lighting conditions. However, the estimated angle of arrival (AoA) of targets in the captured scene is quite poor. This article focuses on improving AoA estimation by combining the cutting-edge machine learning (ML) algorithms with a mechanical radar rotor setup. An mmWave FMCW radar system is mounted on a programmable rotor to capture range–angle maps of targets at various locations. The range–angle images are then labeled and trained further with the Yolov3 algorithm. Subsequent testing reveals that for detected target objects, the centroid of the bounding boxes from the detected objects provides accurate AoA estimation with very low root mean square error (RMSE). The results show that the proposed approach outperforms traditional methods in terms of performance and estimation accuracy.
Hydromorphological Structure Assessment of Urban Streams after Close-to-Nature Stream Restoration Using LAWA

The hydromorphological structure assessment of the urban streams of Anyang stream, Yangjae stream, Osan stream, Jangsu stream, and Hakui stream 5 years the after close to nature stream restoration work was performed using LAWA (Laenderarbeitsgemeinschaft Wasser) for the investigation of the ecology of streams in urban areas. Based on the results, the effectiveness of stream restoration in urban areas was least in spite that existing various types of stream improvements were applied. Significantly, the grade of the longitudinal, the waterfront, and the use of land nearby waterfront was poorly evaluated. It would be estimated that this trend was due to the discontinuity of the longitudinal, the lack of diversity in waterfront, the installation of facilities nearby waterfront, and was due to the use of hard materials as like stones, blocks, frames, and mats in waterfront. The further studies on the implementation of structural diversity in waterfront should be performed for the improvement of the ecology of urban streams.
Technical versus biological variability in a synthetic human gut community

ABSTRACT Synthetic communities grown in well-controlled conditions are an important tool to decipher the mechanisms driving community dynamics. However, replicate time series of synthetic human gut communities in chemostats are rare, and it is thus still an open question to what extent stochasticity impacts gut community dynamics. Here, we address this question with a synthetic human gut bacterial community using an automated fermentation system that allows for a larger number of biological replicates. We collected six biological replicates for a community initially consisting of five common gut bacterial species that fill different metabolic niches. After an initial 12 hours in batch mode, we switched to chemostat mode and observed the community to stabilize after 2–3 days. Community profiling with 16S rRNA resulted in high variability across replicate vessels and high technical variability, while the variability across replicates was significantly lower for flow cytometric data. Both techniques agree on the decrease in the abundance of Bacteroides thetaiotaomicron, accompanied by an initial increase in Blautia hydrogenotrophica. These changes occurred together with reproducible metabolic shifts, namely a fast depletion of glucose and trehalose concentration in batch followed by a decrease in formic acid and pyruvic acid concentrations within the first 12 hours after the switch to chemostat mode. In conclusion, the observed variability in the synthetic bacterial human gut community, as assessed with 16S rRNA gene sequencing, is largely due to technical variability. The low variability seen in HPLC and flow cytometry data suggests a highly deterministic system.
Optical collimation and compression of a thermal atomic beam

We have performed an experiment to optically cool and compress a thermal beam of sodium atoms by employing a two‐dimensional version of a magneto‐optical trap. The beam compressor employs a tapered transverse magnetic field gradient formed by four pairs of permanent magnets. We measure the efficiency of collimation and compression as function of longitudinal velocity group.
Computational and Structural Biotechnology

Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field. © 2023 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creative-commons.org/licenses/by/4.0/).
Secondary Metabolites of Various Indonesian Medicinal Plants as SARS-CoV-2 Inhibitors: In Silico Study

Corona virus disease 2019 caused by SARS-CoV-2 infection emerged in late 2019 and still become a worldwide pandemic up to this point with the drug remain unavailable. Meanwhile, Indonesia has an abundance variety of medicinal plants that are potential to be developed as inhibitors. By using the key role proteins as drug targets, namely spike glycoprotein and RNA-dependent RNA polymerase (RdRp) of delta variant of SARS-CoV-2 (which is known as strongly transmitted and highly virulent), we can develop inhibitors for the target proteins from potential Indonesian medicinal plants to prevent the protein interactions for viral entry and proliferation that leading to organ disfunction and death. This study aimed to identify the secondary metabolites of various Indonesian medicinal plants as SARS-CoV-2 inhibitors. The 184 ligands from nine plants were collected from IJAH webserver and their SMILES notation were collected from PubChem. Meanwhile 3D structures of spike glycoprotein (PDB ID: 6VXX) and RdRp (PDB ID: 6M71) were obtained from protein data bank (PDB). Molecular docking was conducted between ligands and the two SARS-CoV-2 proteins using Autodock Vina in PyRx with hesperidin and remdesivir as control compounds. Several potential compounds were selected for drug-likeness analysis and toxicity analysis. Results showed that lantanolic acid has the same amino acid interaction with RdRp as the control compound. It formed a hydrogen bond with Ser784 and hydrophobic bonds with Tyr32 and Ser7709. It had lower binding affinity than the control compounds, eligible as oral drug, and had LD50 of 2589 mg/kg.
Community Detection in Multilayer Networks Via Semi-Supervised Joint Symmetric Nonnegative Matrix Factorization

Community detection plays an important role in network analysis and has attracted considerable interest from researchers. In the past few decades, various community detection algorithms have been developed for single networks. However, in the real world, relationships between nodes are often of multiple natures, such as friendship, kinship and common interests among people in social networks. These relationships can be modeled by a multilayer network. Thus, identifying communities in multilayer networks has become a challenging problem. The existing algorithms for multilayer networks only utilize the topological structure and ignore the prior information, thereby resulting in low accuracy. In this article, by combining the graph regularization with the prior information, we propose a semi-supervised joint symmetric nonnegative matrix factorization(SSJSNMF) algorithm for community detection in multilayer networks. We use graph regularization term to penalize the latent space dissimilarity of some nodes when prior information shows that these nodes belong to same community. Then, by fusing graph regularization into a joint symmetric nonnegative matrix factorization(NMF) model, the proposed model can utilize the topological structure information and prior information simultaneously. Furthermore, we develop effective multiplicative updating rules to solve the proposed model. Finally, numerical experiments demonstrate that SSSNMF outperforms some existing algorithms.
Progress of 3D Organoid Technology for Preclinical Investigations: Towards Human In Vitro Models

Review Progress of 3D Organoid Technology for Preclinical Investigations: Towards Human In Vitro Models Yingjuan Liu *, Honglin Xu, Sabu Abraham, Xin Wang, and Bernard D. Keavney* Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, University of Manchester, M13 9PT, UK. * Correspondence: yingjuan.liu@manchester.ac. uk (Yingjuan Liu); bernard.keavney@manchester.ac. uk (Bernard D. Keavney) Received: 1 November 2022 Accepted: 24 November 2022 Published: 21 December 2022 Abstract: Currently, with an increased requirement for new therapeutic strategies, preclinical drug testing or screening platforms have rapidly evolved in recent years. In comparison to traditional 2D cell cultures, 3D organoids or spheroids with or without scaffolds improve the microenvironment of in vitro cultures, advancing the in vitro biological observation and enabling mechanistic studies of drug reactions in the human tissue-like environment. 3D organoids and spheroids are straightforward to produce, and relatively uniform in size and shape. This helps to facilitate high throughput screening requirements. Spheroids and organoids have been applied in anti-cancer drug testing, toxicity evaluations, as well as mechanism studies for variable organ systems, including the intestine, liver, pancreas, brain, and heart. Among 3D cultures of spheroids and organoids, ‘tumour spheroids’ formed by dissociated tumour tissues or cancer cell lines are relatively simple in composition and commonly applied to anticancer drug screening. The ‘healthy organoids’ differentiated from hiPSCs/hESCs are more complex in cell composition, distribution, structure and function with higher similarity to in vivo organs, and have found applications in toxicity tests, personalised medicine, and therapeutic and mechanistic studies. In most cases, the multicellular 3D organoids are more resistant and stable in reaction to stimulations or chemicals in vitro , suggesting more accurate modelling of in vivo responses. Here, we review recent progress in human-origin organoid/spheroid systems and their applications in preclinical studies.