Miracle-dz/UniFAR-FTUs huggingface dataset:
Uniformity in premating reproductive isolation along an intraspecific cline

Abstract Premating reproductive isolation (RI) may reduce gene flow across populations that have differentiated in traits important for mate choice. Examining RI across genetic and phenotypic clines can inform the fundamental evolutionary processes that underlie population and lineage differentiation. We conducted female mate-choice studies across an intraspecific red-eyed treefrog cline in Costa Rica and Panama with 2 specific aims: (1) to characterize RI across the cline and examine the relationship between premating RI and genetic and phenotypic distance and (2) to evaluate our results within a broader evolutionary and taxonomic perspective through examination of other RI studies. We found that female red-eyed treefrogs prefer local males relative to non-local males, indicating that some premating RI has evolved in this system, but that preference strength is not associated with phenotypic or geographic distance. Our analysis of 65 other studies revealed no clear pattern between the strength of RI and geographic distribution (allopatry, parapatry, cline) or phenotypic distance, but revealed extreme variation and overlap in levels of intra- and interspecific levels of RI. This work contributes to a growing body of literature that examines intraspecific RI across a cline to understand the selective processes that shape evolutionary patterns at the earliest stages of divergence.
Establishment and characterization of a cell line from tilapia brain for detection of tilapia lake virus.

Tilapia lake virus (TiLV) is an emerging disease threatening tilapia culture in many parts of the world. A cell line from the brain of tilapia, which was named TiB, was established, characterized and subcultured with more than 100 passages. The TiB cell line was optimally maintained at 27°C using medium 199 (M199) supplemented with 10% foetal bovine serum (FBS). Chromosome analysis revealed that 60% of TiB cells at passage 5 maintained the modal chromosome number 2n = 44, while at passage 60, there were 43% of TiB cells with the diploid chromosome number 2n = 50. A significant cytopathic effect was observed in TiB cells after infection with tilapia lake virus (TiLV-2017A), and the viral replication in the cells was confirmed by transmission electron microscopy, immunofluorescence assays and viral titres, indicating the susceptibility of TiB cells to TiLV-2017A. The viral titres of TiLV-2017A in TiB cells reached 107.43 TCID50 /ml within 10 days. The stable growth and susceptibility to fish viruses make TiB cells a useful tool for fish virus-host cell interaction and for immune response of fish.
Adaptive Codebook Optimization for Beam Training on Off-the-Shelf IEEE 802.11ad Devices

Beamforming is vital to overcome the high attenuation in wireless millimeter-wave networks. It enables nodes to steer their antennas in the direction of communication. To cope with complexity and overhead, the IEEE 802.11ad standard uses a sector codebook with distinct steering directions. In current off-the-shelf devices, we find codebooks with generic pre-defined beam patterns. While this approach is simple and robust, the antenna modules that are typically deployed in such devices are capable of generating much more precise antenna beams. In this paper, we adaptively adjust the sector codebook of IEEE 802.11ad devices to optimize the transmit beam patterns for the current channel. To achieve this, we propose a mechanism to extract full channel state information (CSI) regarding phase and magnitude from coarse signal strength readings on off-the-shelf IEEE 802.11ad devices. Since such devices do not expose the CSI directly, we generate a codebook with phase-shifted probing beams that enables us to obtain the CSI by combining strategically selected magnitude measurements. Using this CSI, transmitters dynamically compute a transmit beam pattern that maximizes the signal strength at the receiver. Thereby, we automatically exploit reflectors in the environment and improve the received signal quality. Our implementation of this mechanism on off-the-shelf devices demonstrates that adaptive codebook optimization achieves a significantly higher throughput of about a factor of two in typical real-world scenarios.
Improved Stochastic Gradient Matching Pursuit Algorithm Based on the Soft-Thresholds Selection

1e preliminary atom set exits redundant atoms in the stochastic gradient matching pursuit algorithm, which affects the accuracy of the signal reconstruction and increases the computational complexity. To overcome the problem, an improved method is proposed. Firstly, a limited soft-threshold selection strategy is used to select the new atoms from the preliminary atom set, to reduce the redundancy of the preliminary atom set. Secondly, before finding the least squares solution of the residual, it is determined whether the number of columns of the measurement matrix is smaller than the number of rows. If the condition is satisfied, the least squares solution is calculated; otherwise, the loop is exited. Finally, if the length of the candidate atomic index set is less than the sparsity level, the current candidate atom index set is the support atom set. If the condition is not satisfied, the support atom index set is determined by the least squares solution. Simulation results indicate that the proposed method is better than other methods in terms of the reconstruction probability and shorter running time than the stochastic gradient matching pursuit algorithm.
Bayesian neural networks increasingly sparsify their units with depth

We investigate deep Bayesian neural networks with Gaussian priors on the weights and ReLU-like nonlinearities, shedding light on novel sparsity-inducing mechanisms at the level of the units of the network, both pre-and post-nonlinearities. The main thrust of the paper is to establish that the units prior distribution becomes increasingly heavy-tailed with depth. We show that first layer units are Gaussian, second layer units are sub-Exponential, and we introduce sub-Weibull distributions to characterize the deeper layers units. Bayesian neural networks with Gaussian priors are well known to induce the weight decay penalty on the weights. In contrast, our result indicates a more elaborate regularization scheme at the level of the units, ranging from convex penalties for the first two layers-weight decay for the first and Lasso for the second to non convex penalties for deeper layers. Thus, despite weight decay does not allow for the weights to be set exactly to zero, sparse solutions tend to be selected for the units from the second layer onward. This result provides new theoretical insight on deep Bayesian neural networks, underpinning their natural shrinkage properties and practical potential.
Introducing a Comprehensive Framework to Measure Spike-LFP Coupling

Measuring the coupling of single neuron's spiking activities to the local field potentials (LFPs) is a method to investigate neuronal synchronization. The most important synchronization measures are phase locking value (PLV), spike field coherence (SFC), pairwise phase consistency (PPC), and spike-triggered correlation matrix synchronization (SCMS). Synchronization is generally quantified using the PLV and SFC. PLV and SFC methods are either biased on the spike rates or the number of trials. To resolve these problems the PPC measure has been introduced. However, there are some shortcomings associated with the PPC measure which is unbiased only for very high spike rates. However evaluating spike-LFP phase coupling (SPC) for short trials or low number of spikes is a challenge in many studies. Lastly, SCMS measures the correlation in terms of phase in regions around the spikes inclusive of the non-spiking events which is the major difference between SCMS and SPC. This study proposes a new framework for predicting a more reliable SPC by modeling and introducing appropriate machine learning algorithms namely least squares, Lasso, and neural networks algorithms where through an initial trend of the spike rates, the ideal SPC is predicted for neurons with low spike rates. Furthermore, comparing the performance of these three algorithms shows that the least squares approach provided the best performance with a correlation of 0.99214 and R2 of 0.9563 in the training phase, and correlation of 0.95969 and R2 of 0.8842 in the test phase. Hence, the results show that the proposed framework significantly enhances the accuracy and provides a bias-free basis for small number of spikes for SPC as compared to the conventional methods such as PLV method. As such, it has the general ability to correct for the bias on the number of spike rates.
Kaleidoscope: An RDF-based Exploratory Data Analysis Tool for Ideation Outcomes

Evaluating and selecting ideas is a critical and time-consuming step in collaborative ideation, making computational support for this task a desired research goal. However, existing automatic approaches to idea selection might eliminate valuable ideas. In this work we combine automatic approaches with human sensemaking. Kaleidoscope is an exploratory data analytics tool based on semantic technologies. It supports users in exploring and annotating existing ideas interactively. In the following, we present key design principles of Kaleidoscope. Based on qualitative feedback collected on a prototype, we identify potential improvements and describe future work.
Effects of Image Degradations to CNN-based Image Classification

Just like many other topics in computer vision, image classification has achieved significant progress recently by using deep-learning neural networks, especially the Convolutional Neural Networks (CNN). Most of the existing works are focused on classifying very clear natural images, evidenced by the widely used image databases such as Caltech-256, PASCAL VOCs and ImageNet. However, in many real applications, the acquired images may contain certain degradations that lead to various kinds of blurring, noise, and distortions. One important and interesting problem is the effect of such degradations to the performance of CNN-based image classification. More specifically, we wonder whether image-classification performance drops with each kind of degradation, whether this drop can be avoided by including degraded images into training, and whether existing computer vision algorithms that attempt to remove such degradations can help improve the image-classification performance. In this paper, we empirically study this problem for four kinds of degraded images -- hazy images, underwater images, motion-blurred images and fish-eye images. For this study, we synthesize a large number of such degraded images by applying respective physical models to the clear natural images and collect a new hazy image dataset from the Internet. We expect this work can draw more interests from the community to study the classification of degraded images.
Study on the Aging Dynamics of the Periorbital Region: From Observation to Knowledge of Physiopathology.

PURPOSE Several anatomical and physio-pathologic studies of eyelid region have allowed the creation of theories on facial tissues aging dynamics, which have not been clarified yet. We assessed the signs of aging in the region over the time by observing the characteristics in the same person at different times of his/her life. METHODS We compared the position of the main anatomical landmarks of the eyelid region of 80 patients by overlaying their photographs when they were 20, 40, and 60 years old. Then we made comparisons in the group of men (40 people) and in the group of women (40 people) and between men and women. RESULTS The medial portion of the eyebrow was higher in the photograph taken at 60 years of age than in the one taken at 20 years old in 56.2% of cases; it was higher in 47.5% of cases when comparing the images at 20 and 40. The lateral portion of the eyebrow was seen higher in women in the 20 to 40 group, and it was seen lower in men at 60 years in statistical significance. In more than half of the patients observed, there was not a real descent of the eyebrow and the presence of a more or less accentuated form of upper eyelid's ptosis in the photograph at 60 (globally 47.5%, 55.0% of women and 40.0% of men). This condition was observed in 27.5% of the photographs at 40. There were changes in the horizontal dimension of the palpebral fissure (shortening in 53.7%, preserved in 33.7%, and increased in 12.5% of cases at 60 years old). The position of lateral canthus appeared lower in 40.0% of patients photographed at 60, but it was stable in those photographed at 40. The herniation of the upper eyelid bags was observed in 31.2% of the patients photographed at 60 years old but only in 13.7% at 40. Dermatochalasis of the upper eyelid was present in 67.5% of the people at 60 years old and in 55.0% of those seen when they were 40. Comparing men and women groups 20 to 40, dermatochalasis is more present in men than women at 40 years old with statistical significance. The eyelid-cheek junction was seen to be lower in 75.0% of cases at 60 and in 48.7% of cases at 40, and it is more represented in men than in women. CONCLUSION We have highlighted some interesting elements, partly agreeing with the data already recorded by other authors', and our data suggest an important role of the eyelid structures senescence and its impact on the surrounding structures.
Voltage-gated calcium channels: Their discovery, function and importance as drug targets

This review will first describe the importance of Ca2+ entry for function of excitable cells, and the subsequent discovery of voltage-activated calcium conductances in these cells. This finding was rapidly followed by the identification of multiple subtypes of calcium conductance in different tissues. These were initially termed low- and high-voltage activated currents, but were then further subdivided into L-, N-, PQ-, R- and T-type calcium currents on the basis of differing pharmacology, voltage-dependent and kinetic properties, and single channel conductance. Purification of skeletal muscle calcium channels allowed the molecular identification of the pore-forming and auxiliary α2δ, β and ϒ subunits present in these calcium channel complexes. These advances then led to the cloning of the different subunits, which permitted molecular characterisation, to match the cloned channels with physiological function. Studies with knockout and other mutant mice then allowed further investigation of physiological and pathophysiological roles of calcium channels. In terms of pharmacology, cardiovascular L-type channels are targets for the widely used antihypertensive 1,4-dihydropyridines and other calcium channel blockers, N-type channels are a drug target in pain, and α2δ-1 is the therapeutic target of the gabapentinoid drugs, used in neuropathic pain. Recent structural advances have allowed a deeper understanding of Ca2+ permeation through the channel pore and the structure of both the pore-forming and auxiliary subunits. Voltage-gated calcium channels are subject to multiple pathways of modulation by G-protein and second messenger regulation. Furthermore, their trafficking pathways, subcellular localisation and functional specificity are the subjects of active investigation.
fastMitoCalc: an ultra‐fast program to estimate mitochondrial DNA copy number from whole‐genome sequences

&NA; Mitochondrial DNA (mtDNA) copy number is tightly regulated in tissues, and is both a critical determinant of mitochondrial function and a potential biomarker for disease. We and other groups have shown that the mtDNA copy number per cell can be directly estimated from whole‐genome sequencing. The computation is based on the rationale that sequencing coverage should be proportional to the underlying DNA copy number for autosomal and mitochondrial DNA, and most computing time is spent calculating the average autosomal DNA coverage across ˜3 billion bases. That makes analyzing tens of thousands of available samples very slow. Here we present fastMitoCalc, which takes advantage of the indexing of sequencing alignment files and uses a randomly selected small subset (0.1%) of the nuclear genome to estimate autosomal DNA coverage accurately. It is more than 100 times faster than current programs. fastMitoCalc also provides an option to estimate copy number using a single autosomal chromosome, which could also achieve high accuracy but is slower. Using fastMitoCalc, it becomes much more feasible now to conduct analyses on large‐scale consortium data to test for association of mtDNA copy number with quantitative traits or nuclear variants. Availability and Implementation: fastMitoCalc is available at https://lgsun.irp. nia.nih.gov/hsgu/software/mitoAnalyzer/index.html Contact: jun.ding@nih.gov Supplementary information: Supplementary data are available at Bioinformatics online.
Aliasing-free micro-Doppler analysis based on short-time compressed sensing

Time-frequency distribution (TFD) has been widely used for micro-Doppler analysis in radar signal processing. However, the spectrogram will suffer from aliasing if the maximum Doppler frequency exceeds half of the pulse repetition frequency, which may lead to false estimation of the targets' kinematic properties. In this study, by transmitting a series of random pulse repetition interval (RPRI) pulses, a concise TFD approach named short-time compressed sensing (STCS) is proposed for aliasing-free micro-Doppler analysis. In STCS, precise analysis and synthesis of the random sampling time series can be achieved by exploiting the signal's sparsity in the frequency domain. Furthermore, adaptive to the data, the widths of the particular rectangle windows are determined by sequential processing with a proper optimisation rule. To speed up the STCS procedure, the smoothed L0 algorithm is chosen for sparse recovery, where the pseudoinverse of the dictionaries can be calculated iteratively. The simulation results indicate that the proposed STCS approach can achieve both preferable TFD and acceptable computational cost. The effectiveness of the STCS is finally verified by the application for micro-Doppler estimating in RPRI radar.
Differences in Small Molecule Neurotransmitter Profiles From the Crown-of-Thorns Seastar Radial Nerve Revealed Between Sexes and Following Food-Deprivation

Neurotransmitters serve as chemical mediators of cell communication, and are known to have important roles in regulating numerous physiological and metabolic events in eumetazoans. The Crown-of-Thorns Seastar (COTS) is an asteroid echinoderm that has been the focus of numerous ecological studies due to its negative impact on coral reefs when in large numbers. Research devoted to its neural signaling, from basic anatomy to the key small neurotransmitters, would expand our current understanding of neural-driven biological processes, such as growth and reproduction, and offers a new approach to exploring the propensity for COTS population explosions and subsequent collapse. In this study we investigated the metabolomic profiles of small molecule neurotransmitters in the COTS radial nerve cord. Multivariate analysis shows differential abundance of small molecule neurotransmitters in male and female COTS, and in food-deprived individuals with significant differences between sexes in gamma-aminobutyric acid (GABA), histamine and serotonin, and significant differences in histamine and serotonin between satiation states. Annotation established that the majority of biosynthesis enzyme genes are present in the COTS genome. The spatial distribution of GABA, histamine and serotonin in the radial nerve cord was subsequently confirmed by immunolocalization; serotonin is most prominent within the ectoneural regions, including unique neural bulbs, while GABA and histamine localize primarily within neuropil fibers. Glutamic acid, which was also found in high relative abundance and is a precursor of GABA, is known as a spawning inhibitor in seastars, and as such was tested for inhibition of ovulation ex-vivo which resulted in complete inhibition of oocyte maturation and ovulation induced by 1-Methyladenine. These findings not only advance our knowledge of echinoderm neural signaling processes but also identify potential targets for developing novel approaches for COTS biocontrol.
Stability of Mina v2 for Robot-Assisted Balance and Locomotion

The assessment of the risk of falling during robot-assisted locomotion is critical for gait control and operator safety, but has not yet been addressed through a systematic and quantitative approach. In this study, the balance stability of Mina v2, a recently developed powered lower-limb robotic exoskeleton, is evaluated using an algorithmic framework based on center of mass (COM)- and joint-space dynamics. The equivalent mechanical model of the combined human-exoskeleton system in the sagittal plane is established and used for balance stability analysis. The properties of the Linear Linkage Actuator, which is custom-designed for Mina v2, are analyzed to obtain mathematical models of torque-velocity limits, and are implemented as constraint functions in the optimization formulation. For given feet configurations of the robotic exoskeleton during flat ground walking, the algorithm evaluates the maximum allowable COM velocity perturbations along the fore-aft directions at each COM position of the system. The resulting velocity extrema form the contact-specific balance stability boundaries (BSBs) of the combined system in the COM state space, which represent the thresholds between balanced and unbalanced states for given contact configurations. The BSBs are obtained for the operation of Mina v2 without crutches, thus quantifying Mina v2's capability of maintaining balance through the support of the leg(s). Stability boundaries in single and double leg supports are used to analyze the robot's stability performance during flat ground walking experiments, and provide design and control implications for future development of crutch-less robotic exoskeletons.
A Quantitative Analysis on Relationship between an Early-Closed Bug and Its Amount of Clues: A Case Study of Apache Ant

Once a bug is reported, it is a major concern whether or not the bug is resolved (closed) soon. This paper examines seven metrics quantifying the amount of clues to the early close of reported bugs through a case study. The results show that one of the metrics, the similarity to already-closed bug reports, is strongly related to early-closed bugs. key words: bug report, early close, amount of clues, metrics, POS tagger
Dynamic Channel Pruning: Feature Boosting and Suppression

Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources. In this paper, we reduce this cost by exploiting the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and suppression (FBS), a new method to predictively amplify salient convolutional channels and skip unimportant ones at run-time. FBS introduces small auxiliary connections to existing convolutional layers. In contrast to channel pruning methods which permanently remove channels, it preserves the full network structures and accelerates convolution by dynamically skipping unimportant input and output channels. FBS-augmented networks are trained with conventional stochastic gradient descent, making it readily available for many state-of-the-art CNNs. We compare FBS to a range of existing channel pruning and dynamic execution schemes and demonstrate large improvements on ImageNet classification. Experiments show that FBS can respectively provide $5\times$ and $2\times$ savings in compute on VGG-16 and ResNet-18, both with less than $0.6\%$ top-5 accuracy loss.