Miracle-dz/UniFAR-FTUs huggingface dataset:
Intracellular Trafficking of Fluorescent Nanodiamonds and Regulation of Their Cellular Toxicity

In this paper, cellular management of fluorescent nanodiamonds (FNDs) has been studied for better understanding in the design for potential applications of FNDs in biomedicine. The FNDs have shown to be photostable probes for bioimaging and thus are well-suited, for example, long-term tracking purposes. The FNDs also exhibit good biocompatibility and, in general, low toxicity for cell labeling. To demonstrate the underlying mechanism of cells coping the low but potentially toxic effects by nondegradable FNDs, we have studied their temporal intracellular trafficking. The FNDs were observed to be localized as distinct populations inside cells in early endosomes, lysosomes, and in proximity to the plasma membrane. The localization of FNDs in early endosomes suggests the internalization of FNDs, and lysosomal localization, in turn, can be interpreted as a prestate for exocytosis via lysosomal degradation pathway. The endocytosis and exocytosis appear to be occurring simultaneously in our observations. The mechanism of continuous endocytosis and exocytosis of FNDs could be necessary for cells to maintain normal proliferation. Furthermore, 120 h cell growth assay was performed to verify the long-term biocompatibility of FNDs for cellular studies.
Stable, Environmental Specific and Novel QTL Identification as Well as Genetic Dissection of Fatty Acid Metabolism in Brassica napus

Fatty acid (FA) composition is the typical quantitative trait in oil seed crops, of which study is not only closely related to oil content, but is also more critical for the quality improvement of seed oil. The double haploid (DH) population named KN with a high density SNP linkage map was applied for quantitative trait loci (QTL) analysis of FA composition in this study. A total of 406 identified QTL were detected for eight FA components with an average confidence interval (CI) of 2.92 cM, the explained phenotypic variation (PV) value ranged from 1.49 to 45.05%. Totally, 204 consensus and 91 unique QTL were further obtained via meta-analysis method for the purpose of detecting multiple environment expressed and pleiotropic QTL, respectively. Of which, 74 stable expressed and 22 environmental specific QTL were also revealed, respectively. In order to make clear the genetic mechanism of FA metabolism at individual QTL level, conditional QTL analysis was also conducted and more than two thousand conditional QTL which could not be detected under the unconditional mapping were detected, which indicated the complex interrelationship of the QTL controlling FA content in rapeseed. Through comparative genomic analysis and homologous gene annotation, 61 candidates related to acyl lipid metabolism were identified underlying the CI of FA QTL. To further visualize the genetic mechanism of FA metabolism, an intuitive and meticulous network about acyl lipid metabolism was constructed and some closely related candidates were positioned. This study provided a more accurate localization for stable and pleiotropic QTL, and a deeper dissection of the molecular regulatory mechanism of FA metabolism in rapeseed.
Cryptochrome Interacts With Actin and Enhances Eye-Mediated Light Sensitivity of the Circadian Clock in Drosophila melanogaster

Cryptochromes (CRYs) are a class of flavoproteins that sense blue light. In animals, CRYs are expressed in the eyes and in the clock neurons that control sleep/wake cycles and are implied in the generation and/or entrainment of circadian rhythmicity. Moreover, CRYs are sensing magnetic fields in insects as well as in humans. Here, we show that in the fruit fly Drosophila melanogaster CRY plays a light-independent role as “assembling” protein in the rhabdomeres of the compound eyes. CRY interacts with actin and appears to increase light sensitivity of the eyes by keeping the “signalplex” of the phototransduction cascade close to the membrane. By this way, CRY also enhances light-responses of the circadian clock.
Predicting Visitors Using Location-Based Social Networks

Location-based social networks (LBSN) are social networks complemented with users' location data, such as geo-tagged activity data. Predicting such activities finds application in marketing, recommendation systems, and logistics management. In this paper, we exploit LBSN data to predict future visitors at given locations. We fetch the travel history of visitors by their check-ins in LBSNs and identify five features that significantly drive the mobility of a visitor towards a location: (i) historic visits, (ii) location category, (iii) time, (iv) distance, and (v) friends' activities. We provide a visitor prediction model, CMViP, based on collective matrix factorization and influence propagation. CMViP first utilizes collective matrix factorization to map the first four features to a common latent space to find visitors having a significant potential to visit a given location. Then, it utilizes an influence-mining approach to further incorporate friends of those visitors, who are influenced by the visitors' activities and likely to follow them. Our experiments on two real-world data-sets show that our methods outperform the state of art in terms of precision and accuracy.
Evaluation of Highly Available Cloud Streaming Systems for Performance and Price

This paper presents a systematic evaluation of Amazon Kinesis and Apache Kafka for meeting highly demanding application requirements. Results show that Kinesis and Kafka can provide high reliability, performance and scalability. Cost and performance trade-offs of Kinesis and Kafka are presented for a variety of application data rates, resource utilization, and resource configurations.
SLATS

As the density of wireless, resource-constrained sensors grows, so does the need to choreograph their actions across both time and space. Recent advances in ultra-wideband RF communication have enabled accurate packet timestamping, which can be used to precisely synchronize time. Location may be further estimated by timing signal propagation, but this requires additional communication overhead to mitigate the effect of relative clock drift. This additional communication lowers overall channel efficiency and increases energy consumption. This article describes a novel approach to simultaneously localizing and time synchronizing networked mobile devices. An Extended Kalman Filter is used to estimate all devices’ positions and clock errors, and packet timestamps serve as measurements that constrain time and overall network geometry. By inspection of the uncertainty in our state estimate, we can adapt the number of messages sent in each communication round to balance accuracy with communication cost. This reduces communication overhead, which decreases channel congestion and power consumption compared to traditional time of arrival and time difference of arrival localization techniques. We demonstrate the performance and efficiency of our approach using a real network of custom RF devices and mobile quadrotors.
Joint Optimization of Channel Selection and Frame Scheduling for Coexistence of LTE and WLAN

Recently, the interest in operating long term evolution (LTE) in unlicensed bands (i.e., LTE-U) has increased. However, since LTE does not sense for channel vacancy prior to transmissions, whereas a wireless local area network (WLAN) is based on carrier sensing multiple access, LTE interference severely degrades the performance of WLAN. To maximize the LTE throughput while maintaining fairness between LTE and WLAN, we formulate a joint optimization problem of channel selection and frame scheduling in LTE. As a practical solution of this problem, we propose a low-complexity heuristic algorithm called the sequential operation algorithm (SOA). Evaluation results demonstrate that SOA can achieve an LTE throughput comparable to that of the optimal solution, while maintaining fairness between LTE and WLAN by selecting appropriate channels and conducting effective frame scheduling.
Partial discharge measurement at DC voltage — Evaluation and characterization by NoDi∗ pattern

In this paper the requirements on PD measurement systems for DC voltage are summarized and the experience gained about the PD behavior of some typical defects in HVDC GIS and GIL are discussed. Some physical effects at different voltage levels are shown and a method for the representation and characterization of the PD pulse sequences is introduced. Special test cells with different types of defects such as a protrusion, a floating electrode, a free moving particle and a void are used for the investigations. The procedure for PD measurement at DC voltage is improved and PD pulse sequences at different gas pressures and voltage levels were recorded. In a further step the PD behavior up to the breakdown voltage was determined. The NoDi∗ pattern was developed for the visualization and interpretation of the measured PD pulse sequences. This pattern supports the visualization and the physical interpretation of the pulse sequences and may allow the characterization of the typical defects in HVDC GIS and GIL by a human expert.
Smartphone-Based User Positioning in a Multiple-User Context with Wi-Fi and Bluetooth

In a multi-user context, the Bluetooth data from the smartphone could give an approximation of the distance between users. Meanwhile, the Wi-Fi data can be used to calculate the user's position directly. However, both the Wi-Fi-based position outputs and Bluetooth-based distances are affected by some degree of noise. In our work, we propose several approaches to combine the two types of outputs for improving the tracking accuracy in the context of collaborative positioning. The two proposed approaches attempt to build a model for measuring the errors of the Bluetooth output and Wi-Fi output. In a non-temporal approach, the model establishes the relationship in a specific interval of the Bluetooth output and Wi-Fi output. In a temporal approach, the error measurement model is expanded to include the time component between users' movement. To evaluate the performance of the two approaches, we collected the data from several multi-user scenarios in indoor environment. The results show that the proposed approaches could reach a distance error around 3.0m for 75 percent of time, which outperforms the positioning results of the standard Wi-Fi fingerprinting model.
Approaching Miscorrection-Free Performance of Product Codes With Anchor Decoding

Product codes (PCs) protect a 2-D array of bits using short component codes. Assuming transmission over the binary symmetric channel, the decoding is commonly performed by iteratively applying bounded-distance decoding to the component codes. For this coding scheme, undetected errors in the component decoding—also known as miscorrections—significantly degrade the performance. In this paper, we propose a novel iterative decoding algorithm for PCs which can detect and avoid most miscorrections. The algorithm can also be used to decode many recently proposed classes of generalized PCs, such as staircase, braided, and half-product codes. Depending on the component code parameters, our algorithm significantly outperforms the conventional iterative decoding method. As an example, for double-error-correcting Bose–Chaudhuri–Hocquenghem component codes, the net coding gain can be increased by up to 0.4 dB. Moreover, the error floor can be lowered by orders of magnitude, up to the point where the decoder performs virtually identical to a genie-aided decoder that avoids all miscorrections. We also discuss post-processing techniques that can be used to reduce the error floor even further.
Deployment-related trauma and post-traumatic stress disorder: does gender matter?

ABSTRACT Objective: Military research has attempted to identify whether women have an increased vulnerability to mental health issues following deployment-related trauma, but findings have been mixed. Most studies have controlled for childhood abuse, but not other non-deployment trauma (e.g. life-threatening illness), which may partly explain previous mixed results. This study assessed gender differences in the association between deployment-related trauma and post-traumatic stress disorder (PTSD) while controlling for non-deployment trauma. Methods: Data came from the 2013 Canadian Forces Mental Health Survey. Regular or reserve personnel who had been deployed at least once were included in this study (n = 5980). Logistic regression was used to examine the interaction between gender and deployment-related trauma in predicting lifetime PTSD. Results: After controlling for non-deployment trauma, the association of gender with PTSD went from being significant to being marginally significant. The interaction between gender and deployment-related trauma was not significant. Conclusion: Though controlling for non-deployment trauma did not completely dissipate gender differences in PTSD, such differences were greatly reduced, indicating that these may be partly related to traumatic experiences outside deployment. As gender did not moderate the link between deployment-related trauma and PTSD, the findings suggest that trauma experienced while on deployment does not disproportionately affect women compared to their male counterparts.
Phenanthrene Antibiotic Targets Bacterial Membranes and Kills Staphylococcus aureus With a Low Propensity for Resistance Development

New classes of antibiotics with different mechanisms of action are urgently required for combating antimicrobial resistance. Blestriacin, a dihydro-biphenanthrene with significant antibacterial activity, was recently isolated from the fibrous roots of Bletilla striata. Here, we report the further characterization of the antimicrobial potential and mode of action of blestriacin. The phenanthrene compound inhibited the growth of all tested clinical isolates of Staphylococcus aureus including methicillin-resistant S. aureus (MRSA). The minimum inhibitory concentrations (MICs) of blestriacin against these pathogens ranged from 2 to 8 μg/mL. Minimum bactericidal concentration (MBC) tests were conducted, and the results demonstrated that blestriacin was bactericidal against S. aureus. This effect was confirmed by the time-kill assays. At bactericidal concentrations, blestriacin caused loss of membrane potential in B. subtilis and S. aureus and disrupted the bacterial membrane integrity of the two strains. The spontaneous mutation frequency of S. aureus to blestriacin was determined to be lower than 10-9. The selection and whole genome sequencing of the blestriacin –resistant mutants of S. aureus indicated that the development of blestriacin resistance in S. aureus involves mutations in multi-genes. All these observations can be rationalized by the suggestion that membrane is a biological target of blestriacin.
Fine-Grained Multi-Attribute Adversarial Learning for Face Generation of Age, Gender and Ethnicity

Since the Generative Adversarial Network (GAN) was proposed, facial image generation used for face recognition has been studied in recent two years. However, there are few GAN-based methods applied for fine-grained facial attribute analysis, such as face generation with precise age. In this paper, fine-grained multi-attribute GAN (FM-GAN) is presented, which can generate fine-grained face image under specific multiply attributes, such as 30-year-old white man. It shows that the proposed FM-GAN with fine-grained multi-label conditions is better than conditional GAN (cGAN) in terms of image visual fidelity. Besides, synthetic images generated by FM-GAN are used for data augmentation for face attribute analysis. Experiments also demonstrate that synthetic images can assist the CNN training and relieve the problem of insufficient data.
Inverse Matrix Auto-Search Technique for the RTS MIMO OTA Test—Part II: Validations

Uncertainty analysis for the multi-input multi-output (MIMO) over-the-air (OTA) measurement is very important for the test standardizations. As one of the only two standard MIMO OTA test methods, the radiated two-stage (RTS) method is known for its simple test setup and cost-effective solution. The RTS measurement uncertainties have been researched and discussed. Especially the inverse matrix auto-search uncertainty is discussed and validated in this paper. The RTS method is based on three steps: the antenna pattern measurement, the inverse matrix solving, and the throughput test. With the inverse matrix applied, the throughput test could be conducted OTA or called virtual cables. However, the isolation of the virtual cable may cause great desensitization. This paper details the RTS MIMO OTA measurement uncertainties caused by the isolation. That is helpful for avoiding or reducing the test errors as much as possible in the process of RTS implementations.
Low Energy Subsurface Environments as Extraterrestrial Analogs

Earth’s subsurface is often isolated from phototrophic energy sources and characterized by chemotrophic modes of life. These environments are often oligotrophic and limited in electron donors or electron acceptors, and include continental crust, subseafloor oceanic crust, and marine sediment as well as subglacial lakes and the subsurface of polar desert soils. These low energy subsurface environments are therefore uniquely positioned for examining minimum energetic requirements and adaptations for chemotrophic life. Current targets for astrobiology investigations of extant life are planetary bodies with largely inhospitable surfaces, such as Mars, Europa, and Enceladus. Subsurface environments on Earth thus serve as analogs to explore possibilities of subsurface life on extraterrestrial bodies. The purpose of this review is to provide an overview of subsurface environments as potential analogs, and the features of microbial communities existing in these low energy environments, with particular emphasis on how they inform the study of energetic limits required for life. The thermodynamic energetic calculations presented here suggest that free energy yields of reactions and energy density of some metabolic redox reactions on Mars, Europa, Enceladus, and Titan could be comparable to analog environments in Earth’s low energy subsurface habitats.
Catalytic Fast Pyrolysis of Kraft Lignin With Conventional, Mesoporous and Nanosized ZSM-5 Zeolite for the Production of Alkyl-Phenols and Aromatics

The valorization of lignin that derives as by product in various biomass conversion processes has become a major research and technological objective. The potential of the production of valuable mono-aromatics (BTX and others) and (alkyl)phenols by catalytic fast pyrolysis of lignin is investigated in this work by the use of ZSM-5 zeolites with different acidic and porosity characteristics. More specifically, conventional microporous ZSM-5 (Si/Al = 11.5, 25, 40), nano-sized (≤20 nm, by direct synthesis) and mesoporous (9 nm, by mild alkaline treatment) ZSM-5 zeolites were tested in the fast pyrolysis of a softwood kraft lignin at 400–600°C on a Py/GC-MS system and a fixed-bed reactor unit. The composition of lignin (FT-IR, 2D HSQC NMR) was correlated with the composition of the thermal (non-catalytic) pyrolysis oil, while the effect of pyrolysis temperature and catalyst-to-lignin (C/L) ratio, as well as of the Si/Al ratio, acidity, micro/mesoporosity and nano-size of ZSM-5, on bio-oil composition was thoroughly investigated. It was shown that the conventional microporous ZSM-5 zeolites are more selective toward mono-aromatics while the nano-sized and mesoporous ZSM-5 exhibited also high selectivity for (alkyl)phenols. However, the nano-sized ZSM-5 zeolite exhibited the lowest yield of organic bio-oil and highest production of water, coke and non-condensable gases compared to the conventional microporous and mesoporous ZSM-5 zeolites.
Learning Transferable Deep Models for Land-Use Classification with High-Resolution Remote Sensing Images

In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-use mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-use classification with heterogeneous and high-resolution remote sensing images. In this paper, we propose a scheme to learn transferable deep models for land-use classification with HRRS images. The main idea is to rely on deep neural networks for presenting the semantic information contained in different types of land-uses and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models. More precisely, a deep Convolutional Neural Networks (CNNs) is first pre-trained with a well-annotated land-use dataset, referred to as the source data. Then, given a target image with no labels, the pre-trained CNN model is utilized to classify the image in a patch-wise manner. The patches with high classification probability are assigned with pseudo-labels and employed as the queries to retrieve related samples from the source data. The pseudo-labels confirmed with the retrieved results are regarded as supervised information for fine-tuning the pre-trained deep model. In order to obtain a pixel-wise land-use classification with the target image, we rely on the fine-tuned CNN and develop a hybrid classification by combining patch-wise classification and hierarchical segmentation. In addition, we create a large-scale land-use dataset containing $150$ Gaofen-2 satellite images for CNN pre-training. Experiments on multi-source HRRS images, including Gaofen-2, Gaofen-1, Jilin-1, Ziyuan-3, and Google Earth images, show encouraging results and demonstrate the efficiency of the proposed scheme.
Adaptive Exponential Sliding Mode Control for a Bearingless Induction Motor Based on a Disturbance Observer

In the interest of solving the unsatisfactory control capability of a bearingless induction motor (BIM) under parameter variations, external disturbance, and load mutation, an adaptive exponential sliding mode controller and an extended sliding mode disturbance observer for on-line identification of system disturbance variables are designed. The first-order norm and switching function law is integrated into traditional reaching law, and an adaptive exponential reaching law is presented. The convergence rate can be adjusted adaptively according to the change of the sliding mode switch surface and the system state, so that the control system has the characteristics of low buffeting, excellent convergence performance, and good operation quality. In addition, the disturbance sliding mode observer takes the speed and the external disturbance of the BIM as the observation object. Meanwhile, the output of the disturbance sliding mode observer uses as the feed forward compensation for the system speed. The feedback speed can adjust operation condition adaptively with the speed error and reduce the influence of the disturbance. The simulation and experiment results indicate that the system has strong robustness to uncertainties as well as achieves the accurate identification of uncertain disturbance, the anti-disturbance ability, and the quality of operation for BIM control system can be improved, the chattering can also be inhibited simultaneously.