Miracle-dz/UniFAR-FTUs huggingface dataset:
ATF3 Suppresses Growth and Metastasis of Clear Cell Renal Cell Carcinoma by Deactivating EGFR/AKT/GSK3β/β-Catenin Signaling Pathway

Background Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant cancers in East Asia, with high incidence and mortality. Accumulating evidence has shown that ATF3 is associated with tumor progression. Methods Using qPCR, the expression of ATF3 was detected in 93 patients with ccRCC, including 24 paired normal and tumor tissues, which were used to further compare ATF3 expression through western blotting and immunohistochemistry. Lentivirus was used for the overexpression or knockdown of ATF3, and the consequent alteration in function was analyzed through CCK8 assay, colony formation assay, wound healing assay, invasion assay, and flow cytometry. The potential mechanism affected by ATF3 was analyzed through gene set enrichment analysis (GSEA) and verified using western blotting, invasion assay, or immunofluorescence staining. Furthermore, a xenograft mouse model was used to assess the function of ATF3 in vivo. Results ATF3 expression was significantly decreased in ccRCC compared to that in adjacent normal tissues. Through gain- and loss-of-function experiments performed in an in vitro assay, we found that ATF3 could regulate ccRCC cell proliferation, cycle progression, migration, and invasion. In the in vivo study, the xenograft mouse model revealed that ATF3 overexpression can inhibit the growth of ccRCC. Moreover, the mechanism analysis showed that suppression of ATF3 could lead to an increase the expression of β-catenin and promote β-catenin transfer to the nucleus, and might be affected by EGFR/AKT/GSK3β signaling. Conclusion ATF3 could be utilized as an independent protective factor to inhibit the progression of ccRCC. Potential treatment strategies for ccRCC include targeting the ATF3/EGFR/AKT/GSK3β/β−catenin signaling pathway.
Multi-Scale Flexible Fitting of Proteins to Cryo-EM Density Maps at Medium Resolution

Structure determination using cryo-electron microscopy (cryo-EM) medium-resolution density maps is often facilitated by flexible fitting. Avoiding overfitting, adjusting force constants driving the structure to the density map, and emulating complex conformational transitions are major concerns in the fitting. To address them, we develop a new method based on a three-step multi-scale protocol. First, flexible fitting molecular dynamics (MD) simulations with coarse-grained structure-based force field and replica-exchange scheme between different force constants replicas are performed. Second, fitted Cα atom positions guide the all-atom structure in targeted MD. Finally, the all-atom flexible fitting refinement in implicit solvent adjusts the positions of the side chains in the density map. Final models obtained via the multi-scale protocol are significantly better resolved and more reliable in comparison with long all-atom flexible fitting simulations. The protocol is useful for multi-domain systems with intricate structural transitions as it preserves the secondary structure of single domains.
Autonomic Function in Patients With Parkinson’s Disease: From Rest to Exercise

Parkinson’s disease (PD) is a common neurodegenerative disorder classically characterized by symptoms of motor impairment (e.g., tremor and rigidity), but also presenting with important non-motor impairments. There is evidence for the reduced activity of both the parasympathetic and sympathetic limbs of the autonomic nervous system at rest in PD. Moreover, inappropriate autonomic adjustments accompany exercise, which can lead to inadequate hemodynamic responses, the failure to match the metabolic demands of working skeletal muscle and exercise intolerance. The underlying mechanisms remain unclear, but relevant alterations in several discrete central regions (e.g., dorsal motor nucleus of the vagus nerve, intermediolateral cell column) have been identified. Herein, we critically evaluate the clinically significant and complex associations between the autonomic dysfunction, fatigue and exercise capacity in PD.
Study on Flame Retardancy and Mechanism of Talc Composite Foams

Under high temperature, aqueous film forming foam extinguishing agent has poor flame retardancy and low fire efficiency. In order to solve this problem, talc was introduced into foam to form composite foam. The fire resistance and fire extinguishing properties of the composite foam were studied. The results showed that talc composite foam had good flame retardant resistance. when the concentration of talc reached 40 g/100 ml, the 50% liquid separation time of the composite foam was 21.1 min. The fuel burning in the anti burning tank did not ignite the gasoline in the oil pan, and burned out at 51.5 min. It was related to the structure of composite foam and the properties of talc. Due to the introduction of talc, the viscosity of the composite foam increased. The network structure of composite foam was important to the improved stability of foam. Talc powder formed a dense layer covering the oil surface, which effectively isolated the oil from the air.
Adaptation of Microbial Communities to Environmental Arsenic and Selection of Arsenite-Oxidizing Bacteria From Contaminated Groundwaters

Arsenic mobilization in groundwater systems is driven by a variety of functionally diverse microorganisms and complex interconnections between different physicochemical factors. In order to unravel this great ecosystem complexity, groundwaters with varying background concentrations and speciation of arsenic were considered in the Po Plain (Northern Italy), one of the most populated areas in Europe affected by metalloid contamination. High-throughput Illumina 16S rRNA gene sequencing, CARD-FISH and enrichment of arsenic-transforming consortia showed that among the analyzed groundwaters, diverse microbial communities were present, both in terms of diversity and functionality. Oxidized inorganic arsenic [arsenite, As(III)] was the main driver that shaped each community. Several uncharacterized members of the genus Pseudomonas, putatively involved in metalloid transformation, were revealed in situ in the most contaminated samples. With a cultivation approach, arsenic metabolisms potentially active at the site were evidenced. In chemolithoautotrophic conditions, As(III) oxidation rate linearly correlated to As(III) concentration measured at the parental sites, suggesting that local As(III) concentration was a relevant factor that selected for As(III)-oxidizing bacterial populations. In view of the exploitation of these As(III)-oxidizing consortia in biotechnology-based arsenic bioremediation actions, these results suggest that contaminated aquifers in Northern Italy host unexplored microbial populations that provide essential ecosystem services.
Automated detection of multiple structural changes of diabetic macular oedema in SDOCT retinal images through transfer learning in CNNs

: Diabetic Maculopathy, the major cause of vision loss, occurs due to uncontrolled diabetes. This affects the retinal layers of the eye causing bleeding of vessels, which results in Diabetic Macular Edema (DME), macular detachment etc., Basically three structural changes are involved in DME viz., Cystoid Macular Edema (CME), Serous Macular Detachment (SMD) and Intra Retinal Fluid (IRF). These changes may also coexist with each other such as CME with SMD or CME with IRF etc. In this work, the retinal images acquired through Spectral Domain Optical Coherence Tomography (SDOCT) imaging modality, which would provide high level of precision and resolution of the retinal layers are utilized. An automated algorithm to detect and differentiate seven types of DME based on deep learning is implemented using transfer learning on three Convolutional Neural Networks (CNNs), ResNet 50, VGGNet and AlexNet. In detection of DME, ResNet 50 performs excellently well, when compared with Alexnet and VGGNet, because of its depth and skip connection features. The average values of the statistical parameters such as accuracy (0.993), F1 score (0.975), Mathews Correlation Coefficient(MCC)(0.972) of ResNet are high when compared to that of AlexNet and VGGNet
Target object detection using chicken social-based deep belief network with hyperspectral imagery

: Target object detection is an important research direction in the area of hyperspectral imaging (HSI) as it aims to detect the anomalies or objects in HSI. Some of the existing target object detection methods are merely suitable for HSI with low resolution as they failed to apply directly in the high-resolution HSI. Therefore, an effective target detection method named chicken social-based deep belief network (CS-based DBN) is proposed to achieve an automatic target object detection framework in the high-resolution HSI. The proposed CS-based DBN is developed by integrating the chicken swarm optimisation with the social ski-driver algorithm. The optimal solution for detecting the target object is revealed through the fitness function, which accepts the minimal error value as the best solution. Moreover, the weights of the DBN classifier are optimally trained based on the proposed algorithm to render an accurate and optimal solution in detecting the target objects. The proposed CS-based DBN obtained better performance through the facility of stochastic exploration in search space. Moreover, the results achieved using the proposed model in terms of accuracy, specificity, and sensitivity are 0.8950, 0.8940, and 0.9, respectively.
Survey on mixed impulse and Gaussian denoising filters

: This study presents a comprehensive survey on mixed impulse and Gaussian denoising filters which are applied to an image in order to gauge the effects of this type of noise combination and to then determine optimal ways that can overcome such effects. The random noise model considered in this survey is the combined effect of impulse (salt and pepper) and Gaussian noise. After describing the noise models, the denoising filters which are applied to the images are classified and explained according to their design structure, the type of filters they use, the noise level they could overcome, and the limitations they face. This survey covers all related denoising methods and provides an assessment of the strengths and practical limitations of the different classes of denoising filters.
Multiobjective Model and Improved Artificial Raindrop Algorithm for Virtual Network Mapping

Network virtualization (NV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of 5G communication. Resource scheduling in virtual network mapping is an important problem faced by NV technology. To enhance the performance of the network, optimal resource scheduling schemes should be determined. In general, maximum index of frequency slots used, energy consumption, and the ratio of used frequency slots are three very important indicators for the network. Based on the previous research studies, we first take these three indicators into the objective of the mathematical model and define a novel multiobjective optimization model. Then, the three objectives are integrated into one objective to be minimized by using the sum weighted strategy. Finally, an efficient algorithm, which integrates the advantages of artificial raindrop algorithm (ARA), particle swarm optimization, and differential evolution, is proposed and denoted as IARA. Moreover, simulation experiments have been conducted in several experimental scenes with other compared algorithms. The experimental results show that the proposed algorithm IARA can obtain better scheduling schemes than the compared algorithms.
Image compact-resolution and reconstruction using reversible network

: The dual problem of image super-resolution (SR), which is referred to as compact-resolution (CR), and the corresponding image reconstruction are studied. These two problems have been studied independently by the researchers. In this study, a novel model for image CR and the corresponding reconstruction using the reversible network has been proposed. The reversible network has two properties, the first property, lossless information forwarding, which makes the compact-resolved image retain more information from the original HR image. The second property, bidirectional mapping, by which the forward and reverse propagation of a reversible network can be utilised to implement image CR and reconstruction, respectively, i.e. using the reverse process of image CR to guide the reconstruction. In addition, the utilisation of a reversible network may reduce the size of the model. The superiority of the proposed model was demonstrated by comparing its performance with the state-of-the-art methods on four well-known benchmark datasets.
Enhancing delivery of information technology projects through stakeholder sense-making

The aim of this paper was to examine the applicability of sense-making concepts within the context of stakeholder management in ICT projects in Kenya. The researchers adopted a qualitative approach that triangulates action research design, case-study and grounded theory. A total of 12 senior project management practitioners were identified through non-probability sampling. Conversational semi-structured interviews were carried out, responses were collated and analysed through the NVivo qualitative data analysis framework. It was found that existing stakeholder management and sense-making concepts in ICT project delivery contributes to the accurate identification of stakeholders in ICT projects. It was further found that conceptualisation encourages stakeholder engagement in ICT projects, and enhances the understanding of stakeholder roles in the projects and improves public commitment to stances that are aligned with proffered stakeholders.
Image dehazing method via a cycle generative adversarial network

: Aiming at the problems of colour distortion and residual dehazing in the existing image dehazing methods when processing outdoor images, a method of image dehazing based on the cycle generative adversarial network is proposed. Taking the cycle generative adversarial network as the overall framework of the model, firstly, the neural network is trained to obtain the mapping relationship between haze images and haze-free images. Secondly, to accelerate the convergence speed of network training, using residual structure to improve network stability and reduce parameters. Then, a new loss function is proposed, fusing the Wasserstein distance into adversarial loss and cycle consistency loss, which reduces the deviation between the generated dehaze image and the real haze-free image, and alleviating the problems of colour distortion and haze removal residue. Finally, use the optimised bounded ReLU (BReLU) activation function instead of the original activation function to improve the transmittance reflecting the haze depth information. The experimental results demonstrated that, compared with the comparison method, the proposed method improves the peak signal-to-noise ratio, structure similarity, information entropy, and average gradient, and has an achieved better performance in dehazing.
Synthetic medical image generator for data augmentation and anonymisation based on generative adversarial network for glioblastoma tumors growth prediction

: Prediction methods of glioblastoma tumours growth constitute a hard task due to the lack of medical data, which is mostly related to the patients' privacy, the cost of collecting a large medical data set, and the availability of related notations by experts.In this study, the authors propose a synthetic medical image generator (SMIG) with the purpose of generating synthetic data based on the generative adversarial network in order to provide anonymised data. In addition, to predict the glioblastoma multiform tumour growth the authors developed a tumour growth predictor based on end to end convolution neural network architecture that allows training on a public data set from the cancer imaging archive (TCIA), combined with the generated synthetic data. The authors also highlighted the impact of implicating a synthetic data generated using SMIG as a data augmentation tool. Despite small data size provided by TCIA data set, the obtained results demonstrate valuable tumour growth prediction accuracy.
Hybridisation of single-image super-resolution with edge-aware multi-focus image fusion for edge enrichment

: To break the curtailment of digital imaging and retrieve appropriate information with different focused images, a novel edge-aware multi-focus image fusion is proposed by integrating the single-image super-resolution (SISR) method along with the edge-preserving filters. Initially, the multi-focus images are converted to high resolution images by estimating missing high frequency details from its blurred versions. With acquired high resolution images, smoothing by median and the anisotropic diffusion filters are performed to extract focused regions. An initial weight map is constructed by using maximum selection of pixel intensities of the difference images obtained with filtering. The precision of estimated weight map is further improved by exhibiting morphological operations and guided filter. Finally, the images are fused based on the optimised decision map. Simulation results of proposed fusion work are evaluated with seven metrics and the values are compared with different state-of-the-art methods. Both the quantitative and qualitative analyses showed the excellence of proposed work over other fusion methods.
Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI

The use of black box algorithms in medicine has raised scholarly concerns due to their opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and responsibility, patient autonomy and compromised trust transpire with black box algorithms. These worries connect epistemic concerns with normative issues. In this paper, we outline that black box algorithms are less problematic for epistemic reasons than many scholars seem to believe. By outlining that more transparency in algorithms is not always necessary, and by explaining that computational processes are indeed methodologically opaque to humans, we argue that the reliability of algorithms provides reasons for trusting the outcomes of medical artificial intelligence (AI). To this end, we explain how computational reliabilism, which does not require transparency and supports the reliability of algorithms, justifies the belief that results of medical AI are to be trusted. We also argue that several ethical concerns remain with black box algorithms, even when the results are trustworthy. Having justified knowledge from reliable indicators is, therefore, necessary but not sufficient for normatively justifying physicians to act. This means that deliberation about the results of reliable algorithms is required to find out what is a desirable action. Thus understood, we argue that such challenges should not dismiss the use of black box algorithms altogether but should inform the way in which these algorithms are designed and implemented. When physicians are trained to acquire the necessary skills and expertise, and collaborate with medical informatics and data scientists, black box algorithms can contribute to improving medical care.
Conspiracist cognition: chaos, convenience, and cause for concern

ABSTRACT There has been much concern with the abundance of misinformation in public discourse. Although misinformation has always played a role in political debate, its character has shifted from support for a specific position to a ‘shock and chaos’ stream of misinformation and conspiracy theories. Exposure to conspiracy theories can have considerable adverse impact on society. I argue that scholars therefore have a responsibility to combat conspiracy theories and misinformation generally. Exercising this responsibility requires an understanding of the varied rhetorical roles of conspiracy theories. Here I focus on instances in which people reject unequivocal scientific evidence and invoke conspiracy theories, or radical anti-institutional positions, based on ideological imperatives. I argue that those positions do not always reflect true attitudes. Instead, people may deploy extreme rhetoric as a pragmatic tool of political expression. I investigate this possibility by focusing on the role of conspiracy theories in the rejection of science. Conspiracist cognition and rhetoric violate the epistemic standards that underpin science. Ironically, this violation of epistemic standards renders conspiracy theories useful as a rationally deployed tool that serves political purposes. I present a study that confirms that conspiracy theories can be deployed to support worldview-motivated denial of science. I provide suggestions how scholars can debunk or defang conspiratorial rhetoric.
Chitosan-glutaraldehyde activated carrageenan-alginate beads for β-D-galactosidase covalent immobilisation

Abstract Chitosan (CS) and glutaraldehyde (GA) were exploited to activate carrageenan-alginate (Car-Alg) beads and enable their utilisation as covalent immobilisation matrices. The GA/CS activated Car-Alg beads were utilised to immobilise the Aspergillus oryzae β-D-galactosidase (β-gal). Initially, the beads gelling solution was optimised, and it was shown that the upmost amount of immobilised β-gal (iβ-gal) was acquired upon utilising a 1% CaCl2–2% KCl gelling solution. Afterwards, the GA/CS activation protocol was honed via the Box Behnken design (BBD). The BBD revealed that soaking the Car-Alg beads with a 1.62% CS solution of pH 6 followed by a 3% GA solution was the optimum activation protocol that would provide 13.94 U/g beads iβ-gal. Noteworthy, a 72.64% immobilisation yield was accomplished via the optimally activated Car-Alg beads. The free and the iβ-gals were compared with respect to their Km and V max values, their temperature profiles, and their pH profiles. The reusability of the iβ-gal was also assessed, and 79.74% of its first round activity was retained throughout the 10th reusability round.
Batch Reinforcement Learning Through Continuation Method

Many real-world applications of reinforcement learning (RL) require the agent to learn from a ﬁxed set of trajectories, without collecting new interactions. Policy optimization under this setting is extremely challenging as: 1) the geometry of the objective function is hard to optimize efﬁciently; 2) the shift of data distributions causes high noise in the value estimation. In this work, we propose a simple yet effective policy iteration approach to batch RL using global optimization techniques known as continuation. By constraining the difference between the learned policy and the behavior policy that generates the ﬁxed trajectories
Effect of Secondary Electron Emission on the Sheath in SPT Chamber

A one-dimensional slab model of the plasma sheath in the stationary plasma thruster (SPT) chamber is developed in this study. It is considered that secondary electrons emitted from ceramic walls are partially trapped by the bulk plasma in the SPT chamber; some secondary electrons drift across the sheath where they are generated and the bulk and move towards the opposite sheath. Thus both the secondary electron emission (SEE) from one sheath and the partially trapped secondary electrons from the opposite sheath contribute to this sheath. The results indicate that both the SEE coefficient and trapping coefficient have a significant impact not only on the distributions of both electrons and ions of the SPT sheath but also on the energy flux loss to the SPT wall. When the trapping coefficient increases, the energy flux of electrons deposited to the walls will increase whereas that of ions will decrease. Besides, the critical electron temperature will decrease greatly with the increase of the trapping coefficient.