Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Low Frequency Ultrasound Enhances Bactericidal Activity of Vancomycin against Methicillin-Resistant Staphylococcus aureus Biofilms

Methicillin-resistant Staphylococcus aureus (MRSA) biofilm infections are difficult to treat due to the high antimicrobial resistance of biofilm. Therefore, new treatments are needed for more effective bacteria clearance. This study was to investigate whether low frequency ultrasound (LFU) can enhance the activity of antimicrobial agents against MRSA biofilm infection. Broth microdilution method was used to determine the minimum inhibitory concentration (MIC) of vancomycin (VAN), linezolid (LIN), and levofloxacin (LEV) against three clinical isolated strains, including one methicillin-susceptible Staphylococcus aureus (MSSA) strain and two MRSA strains. Effects of various influencing factors, such as antimicrobial agents, drug concentrations, ultrasonic intensity, and single (S-LFU, 5 or 15 min) or multiple ultrasound (M-LFU, 5 min every 8 h), on the inhibition of biofilms were investigated. The bactericidal effects of S-LFU or M-LFU on MRSA or MSSA biofilms were determined by colony counts. Right after ultrasound, synergistic effects were observed in groups of S-LFU combined with three antimicrobial agents against MSSA biofilm, but for MRSA biofilm, only S-LFU plus VAN had synergistic effect. At the time point of 24 h, M-LFU plus VAN treatment had synergistic bactericidal effect against MRSA and MSSA biofilms, and the synergy showed that VAN is concentration-dependent, but no synergistic effects were observed in all S-LFU combination groups. In conclusion, combination of M-LFU and antimicrobial agents had a better synergistic effect than S-LFU against MRSA or MSSA biofilm. LFU may be useful in treating biofilm infection in the future.
Frequency Specific Cortical Dynamics During Motor Imagery Are Influenced by Prior Physical Activity

Motor imagery is often used inducing changes in electroencephalographic (EEG) signals for imagery-based brain-computer interfacing (BCI). A BCI is a device translating brain signals into control signals providing severely motor-impaired persons with an additional, non-muscular channel for communication and control. In the last years, there is increasing interest using BCIs also for healthy people in terms of enhancement or gaming. Most studies focusing on improving signal processing feature extraction and classification methods, but the performance of a BCI can also be improved by optimizing the user’s control strategies, e.g., using more vivid and engaging mental tasks for control. We used multichannel EEG to investigate neural correlates of a sports imagery task (playing tennis) compared to a simple motor imagery task (squeezing a ball). To enhance the vividness of both tasks participants performed a short physical exercise between two imagery sessions. EEG was recorded from 60 closely spaced electrodes placed over frontal, central, and parietal areas of 30 healthy volunteers divided in two groups. Whereas Group 1 (EG) performed a physical exercise between the two imagery sessions, Group 2 (CG) watched a landscape movie without physical activity. Spatiotemporal event-related desynchronization (ERD) and event-related synchronization (ERS) patterns during motor imagery (MI) tasks were evaluated. The results of the EG showed significant stronger ERD patterns in the alpha frequency band (8–13 Hz) during MI of tennis after training. Our results are in evidence with previous findings that MI in combination with motor execution has beneficial effects. We conclude that sports MI combined with an interactive game environment could be a future promising task in motor learning and rehabilitation improving motor functions in late therapy processes or support neuroplasticity.
3ASCA: Application-Aware Autonomous SCM Capacity Adjustment for SCM and NAND Flash Pooled Storage

Using storage class memories (SCMs) as non-volatile cache of NAND flash memory is a promising solution for the high performance storage. However, the problem is the high SCM cost per bit which is about 10 times higher than NAND flash and the optimal SCM capacity is application dependent. The optimum SCM capacity is conventionally determined manually for every application operating on the data centers' storages. To achieve high performance while reducing the overall storage cost, this paper proposes Application-Aware Autonomous SCM Capacity Adjustment (3ASCA) method. SCM capacity in hybrid memory pool of SCM and NAND flash is autonomously adjusted by observing the warm data in NAND flash. SCM-assisted eviction algorithm is also proposed to reduce the warm data and use SCM more efficiently. As a result, the total storage cost during storage operation is decreased by up to 42% while its IOPS performance is degraded by only 4.8%. If the access count in SCM is also considered by introducing SCM-assisted eviction algorithm, the total storage cost during one-week storage operation is further decreased by 6.4%
Design of Approximate FFT with Bit-width Selection Algorithms

This paper presents the approximate designs of Fast Fourier Transformation (FFT) circuit. The tradeoff between accuracy and hardware performance is achieved by using bit-width selection for each stage. The error rate can be tuned with bit-width selection. We proposed two algorithms for bit-width selection under certain error restriction. The first algorithm is targeting an approximate FFT design with low hardware cost. While the second algorithm is proposed to achieve high performance. Both of proposed algorithms allow the designer to tradeoff hardware performance and computation accuracy in each stage. The proposed two designs are implemented on FPGA. The results show that the approximate FFT design using the first algorithm can reduce hardware resource consumption up to 30.2%. The second algorithm can increases the performance of the approximate FFT deisgn up to 24.0%, while it also saves 25.2% resource consumption.
AUNet: Breast Mass Segmentation of Whole Mammograms

Deep learning based segmentation has seen rapid development lately in both natural and medical image processing. However, its application to mammographic mass segmentation is still a challenging task due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. In this study, we propose a new network, AUNet, for the breast mass segmentation. Different from most methods that need to extract mass-centered image patches, AUNet could directly process the whole mammograms. Furthermore, it introduces an asymmetrical structure to the traditional encoder-decoder segmentation architecture and proposes a new upsampling block, Attention Up (AU) Block. Especially, the AU block is designed to have three merits. Firstly, it compensates the information loss of bilinear upsampling by dense upsampling. Secondly, it designs a more effective method to fuse high- and low-level features. Thirdly, it includes a channel-attention function to highlight rich-information channels. We evaluated the proposed method on two publicly available datasets, CBIS-DDSM and INbreast. Compared to three existing fully convolutional networks, AUNet achieved the best performances with an average Dice similarity coefficient of 81.8% for CBIS-DDSM and 79.1% for INbreast.
Its About the Face Impostor Distribution

We studied the effects of factors on the false accept rate (FAR) for three modern video face recognition algorithms. We examined the effects of environment (location), video-(imagery-) based, and demographic factors. The study is performed on the handheld video in the Point and Shoot Face Recognition Challenge (PaSC), which consists of 1401 handheld videos of 265 subjects. The results of our analysis are consistent across the three algorithms. Our analysis shows that FAR can signiﬁcantly vary. Surprisingly, for environment and video-based factors, there was a clear relationship between veriﬁcation rate (VR) and FAR. An increase (resp. decrease) in the FAR results in an increase (resp. decrease) in the VR. We looked at the shape of the marginal impostor distributions for each level of a factor. In most cases these impostor distributions for a given algo-rithm moved according to a simple afﬁne transform, translation and scaling, when moving between factor levels.
Predicted impacts of climate warming on aerobic performance and upper thermal tolerance of six tropical freshwater fishes spanning three continents

Abstract Equatorial fishes, and the critically important fisheries based on them, are thought to be at-risk from climate warming because the fishes have evolved in a relatively aseasonal environment and possess narrow thermal tolerance windows that are close to upper thermal limits. We assessed survival, growth, aerobic performance and critical thermal maxima (CTmax) following acute and 21 d exposures to temperatures up to 4°C higher than current maxima for six species of freshwater fishes indigenous to tropical countries and of importance for human consumption. All six species showed 1.3–1.7°C increases in CTmax with a 4°C rise in acclimation temperature, values which match up well with fishes from other climatic regions, and five species had survival >87% at all temperatures over the treatment period. Specific growth rates varied among and within each species in response to temperature treatments. For all species, the response of resting metabolic rate (RMR) was consistently more dynamic than for maximum metabolic rate, but in general both acute temperature exposure and thermal acclimation had only modest effects on aerobic scope (AS). However, RMR increased after warm acclimation in 5 of 6 species, suggesting incomplete metabolic compensation. Taken in total, our results show that each species had some ability to perform at temperatures up to 4°C above current maxima, yet also displayed certain areas of concern for their long-term welfare. We therefore suggest caution against the overly broad generalization that all tropical freshwater fish species will face severe challenges from warming temperatures in the coming decades and that future vulnerability assessments should integrate multiple performance metrics as opposed to relying on a single response metric. Given the societal significance of inland fisheries in many parts of the tropics, our results clearly demonstrate the need for more species-specific studies of adaptive capacity to climate change-related challenges.
Tremor Types in Parkinson Disease: A Descriptive Study Using a New Classification

Background The current classification of tremor types in Parkinson disease (PD) is potentially confusing, particularly for mixed tremor, and there is no label for pure resting tremor. With a view to better defining the clinical phenomenological classification of these tremors, our group relabeled the different types as follows: pure resting tremor (type I); mixed resting and action tremor with similar frequencies (type II) divided, according to action tremor presentation, into II-R when there is a time lag and II-C otherwise; pure action tremor (type III); and mixed resting and action tremor with differing frequencies (type IV). We performed a descriptive study to determine prevalence and clinical correlates for this new tremor classification. Patient/Methods A total of 315 consecutively recruited patients with PD and tremor were clinically evaluated. X2 tests were used to assess tremor type associations with categorical variables, namely, sex, family history of PD, motor fluctuations, and anticholinergic and beta-blocker use. With tremor type as the independent variable, ANOVA was performed to study the relationship between dependent quantitative variables, namely, age, age at PD diagnosis, disease duration, and UPDRS scores for rigidity. Results The studied patients had tremor types as follows: type I, 30%; type II, 50% (II-R, 25% and II-C, 25%); type III, 19%; and type IV, 1%. No significant association was found between the studied clinical variables and tremor types. Conclusions Mixed tremor was the most common tremor type in our series of patients with PD according to our proposed classification, which we hope will enhance understanding of the broad clinical phenomenology of PD.
Local Homology of Word Embeddings

Topological data analysis (TDA) has been widely used to make progress on a number of problems. However, it seems that TDA application in natural language processing (NLP) is at its infancy. In this paper we try to bridge the gap by arguing why TDA tools are a natural choice when it comes to analysing word embedding data. We describe a parallelisable unsupervised learning algorithm based on local homology of datapoints and show some experimental results on word embedding data. We see that local homology of datapoints in word embedding data contains some information that can potentially be used to solve the word sense disambiguation problem.
Contrast Detection in Event-Streams from Dynamic Vision Sensors with Fixational Eye Movements

The purpose of fixational eye movements of the human eye has long been — and still is — an intensively discussed topic in the research community. Such micro-movements seem to prevent fading, may sharpen the visual image by increasing high-frequency signal components and help to counteract the eye drifting away from its target. We investigate the impact of these movements for event-based visual sensing with a DVS128 camera. Events are only generated whenever the luminance at a pixel changes and, therefore, static form cannot be resolved in a stationary camera. We present a specially designed mirror system that creates virtual fixational eye movements for an event-based camera. In addition, we investigate the shape of the Fourier spectrum of random miniature motions of the sensory recordings for stationary as well as moving contrast features that give rise to ON/OFF event responses. From this we draw conlusions concerning the optimal shape of filters to detect local motion.
Stochastic Functional Verification of DNN Design through Progressive Virtual Dataset Generation

Deep Neural Networks have emerged as state-of-the-art solutions for complex intelligence problems. DNNs derive their predictive power by learning from millions of training examples in either a supervised or semi-supervised fashion. As such, a critical aspect of the DNN system design procedure is the collection of large annotated training datasets that exhibit high coverage of the problem space. While data synthesis and annotation techniques have been proposed to mitigate the burden of acquiring large datasets, these methods do not quantify the usefulness of each generated dataset and its subsequent impact on training effort. In this work we establish parallels between the autonomous design of DNNs for machine vision applications and the task of functionally verifying a hardware design. Similar to automatic test vector generation, we propose a technique that progressively generates training datasets using virtual synthetic models. Furthermore, we propose an automated DNN design framework that jointly tries to stochastically maximize training coverage while minimizing the number of training and validation cycles utilizing insights from functional verification.
Vehicle classification using ResNets, localisation and spatially-weighted pooling

We investigate whether ResNet architectures can outperform more traditional Convolutional Neural Networks on the task of fine-grained vehicle classification. We train and test ResNet-18, ResNet-34 and ResNet-50 on the Comprehensive Cars dataset without pre-training on other datasets. We then modify the networks to use Spatially Weighted Pooling. Finally, we add a localisation step before the classification process, using a network based on ResNet-50. We find that using Spatially Weighted Pooling and localisation both improve classification accuracy of ResNet50. Spatially Weighted Pooling increases accuracy by 1.5 percent points and localisation increases accuracy by 3.4 percent points. Using both increases accuracy by 3.7 percent points giving a top-1 accuracy of 96.351\% on the Comprehensive Cars dataset. Our method achieves higher accuracy than a range of methods including those that use traditional CNNs. However, our method does not perform quite as well as pre-trained networks that use Spatially Weighted Pooling.
A review and update of vertebral fractures due to metastatic tumors of various sites to the spine: Percutaneous vertebroplasty

Background Vertebral fractures (VFs) are the most usual convolution of metastatic tumors and the vertebral column is the third most ordinary site for painful bone metastases and remains a chief factor of morbidity in cancer patients. Methods In this paper, we investigated the previous literature on the status of clinical and prospects for the use of percutaneous vertebroplasty (PVP) with polymethylmethacrylate as a remedial alternative for the therapy of refractory pain resulting from malignant vertebral compression and pathologic fractures associated with metastatic tumors of various sites in numerous studies. The scientific document for this remedy, containing safety, immediate and long-term efficacy, and outcome measures, and also the risks of complications, was analyzed in detail. Results PVP is a safe, feasible, reliable, effective, and useful procedure, a minimally invasive treatment, and a significant tool for reduction of pain and the relief of pain symptoms. Conclusions This method can be employed as a further or narcotic remedy in elected patients. The techniques of PVP present a novel alternative therapy for diverse metastases with potentially large application.
Characterization and Genomic Analyses of Pseudomonas aeruginosa Podovirus TC6: Establishment of Genus Pa11virus

Phages have attracted a renewed interest as alternative to chemical antibiotics. Although the number of phages is 10-fold higher than that of bacteria, the number of genomically characterized phages is far less than that of bacteria. In this study, phage TC6, a novel lytic virus of Pseudomonas aeruginosa, was isolated and characterized. TC6 consists of an icosahedral head with a diameter of approximately 54 nm and a short tail with a length of about 17 nm, which are characteristics of the family Podoviridae. TC6 can lyse 86 out of 233 clinically isolated P. aeruginosa strains, thus showing application potentials for phage therapy. The linear double-stranded genomic DNA of TC6 consisted of 49796 base pairs and was predicted to contain 71 protein-coding genes. A total of 11 TC6 structural proteins were identified by mass spectrometry. Comparative analysis revealed that the P. aeruginosa phages TC6, O4, PA11, and IME180 shared high similarity at DNA sequence and proteome levels, among which PA11 was the first phage discovered and published. Meanwhile, these phages contain 54 core genes and have very close phylogenetic relationships, which distinguish them from other known phage genera. We therefore proposed that these four phages can be classified as Pa11virus, comprising a new phage genus of Podoviridae that infects Pseudomonas spp. The results of this work promoted our understanding of phage biology, classification, and diversity.
Modeling and Linearization of Multi-Antenna Transmitters Using Over-the-Air Measurements

In this paper, we present a technique to model and linearize a multi-antenna transmitter using only a small set of observation receivers that perform over-the-air measurements. We assume that the transmitter suffers from distortion due to power amplifier (PA) nonlinearities but not from crosstalk. By avoiding the use of an observation receiver in every transmitter branch, the hardware complexity and cost of multi-antenna transmitters can be reduced. First, equations are developed to extract PA models from observation receivers. Based on the extracted PA models, predistorters can then be identified for every transmit branch. We present simulation results that demonstrate that it is indeed possible to model and linearize a set of PAs using only one single observation receiver.
Interactive effects of pain catastrophizing and mindfulness on pain intensity in women with fibromyalgia

The objective of this study was to examine the association between facets of trait mindfulness, pain catastrophizing, and pain severity in a sample of patients with fibromyalgia. Patients with fibromyalgia completed validated baseline and diary assessments of clinical pain, mindfulness, and pain catastrophizing. Multilevel modeling analyses indicated that the daily association between catastrophizing and pain intensity was moderated by certain mindfulness facets. Our findings suggest that various aspects of mindfulness may interact differently with pain and catastrophizing, which may have implications for the design and testing of interventions targeting mindfulness and catastrophizing in fibromyalgia patients.
Fixed- Versus Adjustable-Loop Femoral Cortical Suspension Devices for Anterior Cruciate Ligament Reconstruction: A Systematic Review and Meta-analysis of Biomechanical Studies

Background: Fixed- and adjustable-loop femoral cortical suspension devices are commonly used for femoral graft fixation during anterior cruciate ligament reconstruction (ACLR). Purpose: To compare the biomechanical results of fixed- versus adjustable-loop femoral cortical suspension devices in studies simulating ACLR with an isolated device and/or specimen setup using porcine femora and bovine flexor tendons. Study Design: Systematic review. Methods: Two independent reviewers searched PubMed, Embase, and the Cochrane Library databases to find studies comparing the biomechanical strength of fixed- and adjustable-loop cortical suspension devices for ACLR with isolated device and/or specimen setups using porcine femora and bovine flexor tendons. Studies that compared both devices with similar biomechanical methods were included. Data extracted included displacement during cyclic loading, ultimate load to failure, and mode of failure of the different cortical suspension devices for ACLR. Results: Six studies were identified that met the inclusion criteria, including a total of 76 fixed-loop devices and 120 adjustable-loop devices. Load to failure was significantly different (P < .0001), with the strongest fixation device being the ToggleLoc with ZipLoop adjustable-loop device (1443.9 ± 512.3 N), compared with the Endobutton CL fixed-loop device (1312.9 ± 258.1 N; P = .04) and the TightRope RT adjustable-loop device (863.8 ± 64.7 N; P = .01). Cyclic displacement was significantly different, with Endobutton CL (3.7 ± 3.9 mm) showing the least displacement, followed by ToggleLoc with ZipLoop (4.9 ± 2.3 mm) and TightRope RT (7.7 ± 11.1 mm) (P < .0001). Mode of failure was statistically different between the 3 groups (P = .01), with suture failure accounting for 83.8% of TightRope RT devices, 69.4% of ToggleLoc with ZipLoop devices, and 60.3% of Endobutton CL devices. Conclusion: Current biomechanical data suggest that the ToggleLoc with ZipLoop device is the strongest fixation device at “time zero” in terms of ultimate load to mechanical failure. However, the Endobutton CL device demonstrated the least cyclic displacement, which may be a more clinically applicable measure of device superiority.