Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Antibiotics on the Bacterial Community, Metabolic Functions and Antibiotic Resistance Genes in Mariculture Sediments during Enrichment Culturing

The effect of antibiotics on the diversity and functioning of indigenous microorganisms in the environment has attracted much attention. In this study, effects of exposure to six different antibiotics on the bacterial community, metabolic functions and antibiotic resistance genes (ARGs) in marine sediments during enrichment culturing were investigated. Classical culture-dependent method and high-throughput 16S rRNA gene sequencing method were both applied. In the culture-dependent analysis, the obtained 1549 isolates belonged to four phyla (Proteobacteria, Firmicutes, Bacteroidetes and Actinobacteria) and 155 genera. Proteobacteria and Firmicutes were the dominant phyla. The diversity and abundance of obtained bacteria after antibiotic processing exhibited different degrees of decrease. Enrichment culturing for different time could also affect the bacterial community composition. Some genera of bacteria were not isolated in the control group, but they could be isolated in the antibiotic-treated groups. In high-throughput 16S rRNA gene amplicon sequencing analyses, all the effective reads were clustered into 2822 OTUs at 97% similarity cutoff; they were annotated to 49 phyla, 103 class, 220 orders, 347 families, 624 genera and 1122 species. An alpha diversity analysis indicated that the community diversity and richness decreased under antibiotic exposure. The changes at the genus level were much more obvious. Only 48 genera of 129 genera were shared by all the samples. A total of 29 genera which were not detected in the initial control sample could be detected in at least one antibiotic-treated group. SIMPER analysis showed that OTU2543 and OTU1450 were the most common taxa to the dissimilarity of bacterial community between antibiotic-treated groups and the control group. OTU2034 and OUT2543 were the most contributive taxa to dissimilarity of groups incubating for different time. Metabolism was the predominant bacterial function. A total of 30 ARGs were detected in the samples. This study mainly focused on the changes of microbiota under the selective pressure of antibiotics for different time and the results demonstrated that the antibiotic could affect the bacterial diversity and richness in the marine ecosystem.
Designing and Validating a New Method the TUNEL Microwave (TUNEL-MW) for Rapid Quantification of Apoptosis in Islets Following Isolation and Post-Thaw

Background: Among the current quality control assays used in islet transplantation, there is an urgent need for more appropriate assays that measure cell damage via apoptosis that are accurate and rapid. Although the Terminal Uridine Nucleotide End Labeling (TUNEL) is a popular marker for apoptosis, the protocol takes 4 hours to complete. In this regard, microwave assisted histoprocessing, which shortens the time taken for processing, holds promise. Keeping this in mind, a new TUNEL Microwave (TUNEL-MW) method, for rapid quantification of apoptosis, was designed, developed and validated. Method: Two lots of post-thaw isolated human islets cultured for 24 hours, 3 days, 5 days and 7 days i.e. 8 samples, were used for the study. Dewaxed and rehydrated tissues were processed for routine histology, stained with haematoxylin and eosin (H&E) and the conventional TUNEL was carried out as per manufacturer’s instructions. For the TUNEL-MW, kit instructions were modified and microwave-assisted histoprocessing was done. The assessment of apoptotic index (AI%) by light microscopy (LM) was carried out by a pathologist who was completely blinded to the study. Results: The new TUNEL-Microwave (TUNEL-MW) developed by us reduced processing time from 4 hours to 30 minutes (saving 3½ hours). Results were validated by univariate linear regression (r2>0.990), coefficient of variation (<5% between all three methods) and the Bland Altman plot comparing AI% determined by the new TUNEL-MW with the conventional TUNEL and with LM (gold standard). Conclusion: TUNEL Microwave appears to be an ideal method. It is simple and takes just 30 minutes to perform and can therefore be used along with existing quality control measures to rule out or measure apoptosis prior to islet release for islet transplantation.
Do urban carbon reduction practices under China’s institutional arrangement go beyond “low-hanging fruits”? Empirical evidence from Guangzhou

There has been increasing interest in whether low carbon cities developed under Chinese institutional arrangements can facilitate a transformative change. Unlike their western counterparts, Chinese low carbon cities are mainly developed through a top-down approach characterized by strong regulatory influences and centrally-led pilots. Inspired by insights from institutional theory, this paper assessed the progress that has been made in Chinese low carbon cities through understanding urban actors’ willingness and capacity to pursue more radical changes. Based on semi-structured interviews and documentary analysis, it is found that due to the low recognition of the intrinsic value of carbon reduction, low carbon transition was largely represented as a practical discussion around national binding targets and local economic development. Following this, it is suggested that any attempt to speed up urban low carbon transition should consider strengthening incentive structures for cultural or paradigm change.
Management’s Attitude Towards Risk Management is the Moderating Variable Between Management’s Support and Risk Management Performance

The purpose of this study is to know whether management attitudes can moderate the relationship between management support and risk management performance. Respondents from 112 companies were taken. They were the top rank risk management personal in those companies. Questionnaire was designed in collecting the data. The validity and reliability of the questionnaire has been tested. Multiple regression analysis techniques were used to test the null hypothesis that attitude is the moderating variable between management support and risk management performance. The null hypothesis was accepted indicating that attitude toward risk management was not a moderating variable in the relationship between management support and risk management performance. However further analysis with the use of path analysis found that management support instead was the mediating variable between attitudes and performance of risk management. Therefore, when top management has positive attitude toward risk management will increase the risk management performance of the organization. Keywords—risk management, attitude, management support, risk management performance
Seasonal Forecasts of the Exceptional Northern Hemisphere Winter of 2020

The winter of 2019–2020 was dominated by an extremely strong stratospheric polar vortex and positive tropospheric Arctic Oscillation (AO). Here, we analyze forecasts from six different prediction systems contributing to the C3S seasonal forecast database. Most performed very strongly, with consistently high skill for January–March 2020 from forecasts launched through October–December 2019. Although the magnitude of the anomalies was underestimated, the performance of most prediction systems was extremely high for a positive AO winter relative to the common hindcast climate. Ensemble members which better predicted the extremely strong stratospheric vortex better predicted the extreme tropospheric state. We find a significant relationship between forecasts of the anomalous midlatitude tropospheric wave pattern in early winter, which destructively interfered with the climatological stationary waves and the strength of the stratospheric vortex later in the winter. Our results demonstrate a strong interdependence between the accuracy of stratospheric vortex and AO forecasts.
Catalytic Properties of Hydrothermally Synthesized Flower-like NiO@Fe3O4

In this study, flower-like NiO structures were synthesized by hydrothermal method. Then, Fe3O4 was doped on NiO that NiO structures gain magnetic properties. TEM (Transmission Electron Microscopy) and SEM (Scanning Electron Microscopy) were used to assess the morphology of the NiO structures. XRD (X-ray Diffraction) was used to evaluate the crystal structures of the NiO structures. Magnetic properties of the NiO structures were investigated using VSM (Vibrating Sample Magnetometry). Catalysis properties of the produced structures were assessed use of products as catalyst in the reduction of 4-nitrophenol (4-NP) was examined. Especially NiO and Fe3O4 were found to exhibit excellent catalytic performance for nitrophenol reduction, while NiO@Fe3O4 magnetic microflowers were found to exhibit very low catalytic activity.
On the sensitivity of food webs to multiple stressors.

Evaluating the effects of multiple stressors on ecosystems is becoming increasingly vital with global changes. The role of species interactions in propagating the effects of stressors, although widely acknowledged, has yet to be formally explored. Here, we conceptualise how stressors propagate through food webs and explore how they affect simulated three-species motifs and food webs of the Canadian St. Lawrence System. We find that overlooking species interactions invariably underestimate the effects of stressors, and that synergistic and antagonistic effects through food webs are prevalent. We also find that interaction type influences a species' susceptibility to stressors; species in omnivory and tri-trophic food chain interactions in particular are sensitive and prone to synergistic and antagonistic effects. Finally, we find that apex predators were negatively affected and mesopredators benefited from the effects of stressors due to their trophic position in the St. Lawrence System, but that species sensitivity is dependent on food web structure. In conceptualising the effects of multiple stressors on food webs, we bring theory closer to practice and show that considering the intricacies of ecological communities is key to assess the net effects of stressors on species.
Uplift, climate and biotic changes at the Eocene–Oligocene transition in south-eastern Tibet

Abstract The uplift history of south-eastern Tibet is crucial to understanding processes driving the tectonic evolution of the Tibetan Plateau and surrounding areas. Underpinning existing palaeoaltimetric studies has been regional mapping based in large part on biostratigraphy that assumes a Neogene modernization of the highly diverse, but threatened, Asian biota. Here, with new radiometric dating and newly collected plant-fossil archives, we quantify the surface height of part of the south-eastern margin of Tibet in the latest Eocene (∼34 Ma) to be ∼3 km and rising, possibly attaining its present elevation (3.9 km) in the early Oligocene. We also find that the Eocene–Oligocene transition in south-eastern Tibet witnessed leaf-size diminution and a floral composition change from sub-tropical/warm temperate to cool temperate, likely reflective of both uplift and secular climate change, and that, by the latest Eocene, floral modernization on Tibet had already taken place, implying modernization was deeply rooted in the Palaeogene.
Absolute measurements of the double differential electronic emission cross-sections of isolated pyrene molecule (C16H10) in interaction with keV protons

Electronic emission of an isolated pyrene molecule induced by 50 to 125 keV protons has been investigated. The corresponding absolute double differential cross-sections ∂2σ∂Ω∂E have been measured at an angle of 35° with respect to the incident proton beam axis. These measurements were compared to the simulations of emitted electrons’ spectra using a semi-classical trajectory Monte-Carlo (SCTMC) approach. The simulated SCTMC cross-sections show reasonably good agreement with the measured ones.
Error detection and classification in patient-specific IMRT QA with dual neural networks.

PURPOSE Despite being the standard metric in patient-specific quality assurance (QA) for intensity-modulated radiotherapy (IMRT), gamma analysis has two shortcomings: (a) it lacks sensitivity to small but clinically relevant errors (b) it does not provide efficient means to classify the error sources. The purpose of this work is to propose a dual neural network method to achieve simultaneous error detection and classification in patient-specific IMRT QA. METHODS For a pair of dose distributions, we extracted the dose difference histogram (DDH) for the low dose gradient region and two signed distance-to-agreement (sDTA) maps (one in x direction and one in y direction) for the high dose gradient region. An artificial neural network (ANN) and a convolutional neural network (CNN) were designed to analyze the DDH and the two sDTA maps, respectively. The ANN was trained to detect and classify six classes of dosimetric errors: incorrect multileaf collimator (MLC) transmission (±1%) and four types of monitor unit (MU) scaling errors (±1% and ±2%). The CNN was trained to detect and classify seven classes of spatial errors: incorrect effective source size, 1 mm MLC leaf bank overtravel or undertravel, 2 mm single MLC leaf overtravel or undertravel, and device misalignment errors (1 mm in x- or y direction). An in-house planar dose calculation software was used to simulate measurements with errors and noise introduced. Both networks were trained and validated with 13 IMRT plans (totaling 88 fields). A fivefold cross-validation technique was used to evaluate their accuracy. RESULTS Distinct features were found in the DDH and the sDTA maps. The ANN perfectly identified all four types of MU scaling errors and the specific accuracies for the classes of no error, MLC transmission increase, MLC transmission decrease were 98.9%, 96.6%, and 94.3%, respectively. For the CNN, the largest confusion occurred between the 1-mm-MLC bank overtravel class and the 1-mm-device alignment error in x-direction class, which brought the specific accuracies down to 90.9% and 92.0%, respectively. The specific accuracy for the 2-mm-single MLC leaf undertravel class was 93.2% as it misclassified 5.7% of the class as being error free (false negative). Otherwise, the specific accuracy was above 95%. The overall accuracies across the fivefold were 98.3 ± 0.7% and 95.6% ± 1.5% for the ANN and the CNN, respectively. CONCLUSIONS Both the DDH and the sDTA maps are suitable features for error classification in IMRT QA. The proposed dual neural network method achieved simultaneous error detection and classification with excellent accuracy. It could be used in complement with the gamma analysis to potentially shift the IMRT QA paradigm from passive pass/fail analysis to active error detection and root cause identification.
Noise-driven multistability versus deterministic chaos in phenomenological semi-empirical models of whole-brain activity

An outstanding open problem in neuroscience is to understand how neural systems are capable of producing and sustaining complex spatiotemporal dynamics. Computational models that combine local dynamics with in vivo measurements of anatomical and functional connectivity can be used to test potential mechanisms underlying this complexity. We compared two conceptually different mechanisms: noise-driven switching between equilibrium solutions (modeled by coupled Stuart-Landau oscillators) and deterministic chaos (modeled by coupled Rossler oscillators). We found that both models struggled to simultaneously reproduce multiple observables computed from the empirical data. This issue was especially manifest in the case of noise-driven dynamics close to a bifurcation, which imposed overly strong constraints on the optimal model parameters. In contrast, the chaotic model could produce complex behavior over an ampler range of parameters, thus being capable of capturing multiple observables at the same time with good performance. Our observations support the view of the brain as a non-equilibrium system able to produce endogenous variability. We presented a simple model capable of jointly reproducing functional connectivity computed at different temporal scales. Besides adding to our conceptual understanding of brain complexity, our results inform and constraint the future development of biophysically realistic large-scale models. The quote “What I cannot create, I do not understand” was found written in the blackboard of celebrated physicist Richard Feynman at the time of his death. This sentence suggests a way forward for neuroscientists interested in unravelling the principles behind the richness and complexity of spontaneous brain dynamics. Over the last decades, tremendous advances in neuroimaging enabled the construction of whole-brain activity models with real predictive power in the statistical sense. It is now possible to create realistic complex dynamics, instead of passively screening for their presence in neuroimaging data. We contrasted two different types of building blocks (i.e. two choices of local dynamics) and tested their capacity to reproduce the empirical data, with the purpose of increasing our conceptual understanding of the mechanisms behind large-scale spontaneous activity in the human brain.
Code Comment Assessment Development for Basic Programming Subject using Online Judge

The computer program source code must have good documentation on it to be more understandable by others. One of the documentation is code comments. Therefore, university students need to learn and able to write comments. Some university students are familiar with an online judge as a tool for learning programming. An online judge usually does not check how the source code written instead of the output only. This research is aimed at implementing the code comment assessment module for online judge in the order it can check the comments in the source code which is uploaded by students. The comments on this module are categorized into two parts. There are header and process comments. The comments dataset have taken from the online judge which is used before as a learning tool for basic programming subject.
Research on the Preprocessing of Tai Le Character Recognition

Tai Le character is a kind of alphabetic writing with a long history, and its character recognition research plays an important role in inheriting and protecting Tai Le. Preprocessing is an key part of the Tai Le character recognition system, the quality of the preprocessing directly affects the recognition rate in the recognition system. Image preprocessing is the premise of feature extraction and recognition, since the directly input image usually contains noise, it affects the recognition effect. Therefore, it is of great application value and far-reaching historical significance to study the preprocessing technology of Tai Le character recognition. This paper proposes the image preprocessing method of Tai Le character, including graying, binarization, de-noise, and edge detection, and achieves good preprocessing results, which lays a foundation for feature extraction and recognition of Tai Le character.
Reliability of genetic evaluation using purebred and crossbred data from different pig farms

Impact of connectedness between contemporary groups on reliability of genetic evaluation was analyzed for Landrace (L) and Large-White (LW) pigs and their crossbreds on 23 245 data records from three large scale pig farms and 70 family farms in period 2002-2012. Gene flow and connectedness rating methods were used to assess information about the level of connectedness. Average reliability of estimated breeding values (EBV’s) was calculated for all contemporary groups in single and joint analysis for different combination of data. Focal trait for the analysis was backfat thickness Average gene flow in ten year period was low and was in range from 0% genes originating from other farms on several contemporary groups and 7% genes on farm B originating from farm C in Landrace and from 0% to 6% genes originating from farm A on group of family farms in Large White. Average connectedness rating was less than 0.03 between all contemporary groups. Thus, across-herd validation would be unreliable due to low connectedness. After inclusion of data of crossbred animals in data set, reliability increased from 0.08 to 0.27 on group of family farms in L and from 0.03 to 0.35 on group of farm C in LW. Results illustrate necessity to increase connectedness in order to increase individual of genetic evaluation and reliability of across-herd comparison. Adequate tool for increasing connectedness would be well organized artificial insemination.
Recent Advances in EBSD Characterization of Metals

Electron backscatter diffraction (EBSD) has been attracting enormous interest in the microstructural characterization of metals in recent years. This characterization technique has several advantages over conventional ones, since it allows obtaining a wide range of characterization possibilities in a single method, which is not possible in others. The grain size, crystallographic orientation, texture, and grain boundary character distribution can be obtained by EBSD analysis. Despite the limited resolution of this technique (20–50 nm), EBSD is powerful, even for nanostructured materials. Through this technique, the microstructure can be characterized at different scales and levels with a high number of microstructural characteristics. It is known that the mechanical properties are strongly related to several microstructural aspects such as the size, shape, and distribution of grains, the presence of texture, grain boundaries character, and also the grain boundary plane distribution. In this context, this work aims to describe and discuss the possibilities of microstructural characterization, recent advances, the challenges in sample preparation, and the application of the EBSD in the characterization of metals.
Genetic diversity and structure analysis of the endangered plant species Horsfieldia hainanensis Merr. in China

ABSTRACT The genetic diversity and structure of nine natural populations of Horsfieldia hainanensis Merr. , an endangered plant endemic to China, were studied using inter-simple sequence repeat markers. Nine primers were selected from 100 primers to evaluate 126 individual plants, from which a total of 136 bands were amplified and 108 bands were polymorphic. Our results demonstrated that the genetic diversity level of H. hainanensis was high with a percentage of polymorphic bands, Shannon's diversity index and Nei's genetic diversity index at the species level of 79.4%, 0.4787 and 0.3314, respectively, and correspondingly, averages of 40.4%, 0.2615 and 0.1843 at the population level. Significant genetic differentiation was observed among populations, showing that the coefficient of genetic differentiation among populations calculated using Nei's genetic diversity was 0.4509. The ranges of Nei's genetic identity and genetic distance among populations were 0.7387–0.8637 and 0.1466–0.3029, respectively. The unweighted pair group method with arithmetic mean clustering based on Nei's genetic distance indicated that nine natural H. hainanensis populations could be classified into two lineages. Collectively, we speculated that habitat fragmentation and disturbance from human activities could be considered the main reasons for the endangerment of H. hainanensis, and we propose in situ conservation for the existing natural H. hainanensis populations, especially at Mengtun, Niandou and Tongbiguan, where the genetic diversity is relatively high.