Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential Involvement of Kinase Activity of Ca2+/Calmodulin-Dependent Protein Kinase IIα in Hippocampus- and Amygdala-Dependent Memory Revealed by Kinase-Dead Knock-In Mouse

Abstract Ca2+/calmodulin-dependent protein kinase IIα (CaMKIIα) is a key mediator of activity-dependent neuronal modifications and has been implicated in the molecular mechanisms of learning and memory. Indeed, several types of CaMKIIα knock-in (KI) and knock-out (KO) mice revealed impairments in hippocampal synaptic plasticity and behavioral learning. On the other hand, a similar role for CaMKIIα has been implicated in amygdala-dependent memory, but detailed analyses have not much been performed yet. To better understand its involvement in amygdala-dependent memory as compared to hippocampus-dependent memory, here we performed biochemical analyses and behavioral memory tests using the kinase-dead CaMKIIα (K42R)-KI mouse. In the Morris water maze tasks, homozygous mutants performed well in the visible platform trials, while they failed to form spatial memory in the hippocampus-dependent hidden platform trials. In fear conditioning, these mice were impaired but showed a certain level of amygdala-dependent cued fear memory, which lasted four weeks, while they showed virtually no hippocampus-dependent context discrimination. Neither stronger stimulation nor repetitive stimulation compensated for their memory deficits. The differential outcome of hippocampus- and amygdala-dependent memory in the mutant mouse was not due to differential expression of CaMKIIα between the hippocampus and the amygdala, because biochemical analyses revealed that both kinase activity and protein levels of CaMKII were indistinguishable between the two brain regions. These results indicate that kinase activity of CaMKIIα is indispensable for hippocampus-dependent memory, but not necessarily for amygdala-dependent memory. There may be a secondary, CaMKIIα activity-independent pathway, in addition to the CaMKIIα activity-dependent pathway, in the acquisition of amygdala-dependent memory.
IT Equipment Monitoring and Analyzing System for Forecasting and Detecting Anomalies in Log Files Utilizing Machine Learning Techniques

The ability to detect anomalies in application log files has attracted the attention of researchers over the past decade as it has become a challenging issue. Intuitively, a noticeable variation in the performance can be as a result of some natural causes (e.g., CPU workload variations and memory leaks) or from internal anomalies or errors that may cause performance failure or application crash. In here, an account of prediction and detection of the performance anomalies together with their causes has been reported. A framework for the detection of anomalies was particularly targeted onto the application log files whereby some quantity of historical data was acquired and analyzed. From the data set, a correlation analysis was demonstrated which data was then submitted for Machine-Learning (ML) forecasting and anomaly detection algorithms. The best algorithms were chosen based on accuracy and precision. In the second phase, CPU usage and memory utilization for the data points collected previously were analyzed. From the obtained results it was evident that combining the parameters for approximation aided by time-series models with ML forecasting and anomaly detection techniques provided excellent results as regards to prediction of performance anomalies. And the framework is robust enough to identify the applications causing these anomalies and abnormal behaviors.
Tracking Cell Transplants in Femoral Osteonecrosis with Magnetic Resonance Imaging: A Proof-of-Concept Study in Patients

Purpose: Osteonecrosis is a devastating complication of high-dose corticosteroid therapy in patients with cancer. Core decompression for prevention of bone collapse has been recently combined with the delivery of autologous concentrated bone marrow aspirates. The purpose of our study was to develop an imaging test for the detection of transplanted bone marrow cells in osteonecrosis lesions. Experimental Design: In a prospective proof-of-concept clinical trial (NCT02893293), we performed serial MRI studies of nine hip joints of 7 patients with osteonecrosis before and after core decompression. Twenty-four to 48 hours prior to the surgery, we injected ferumoxytol nanoparticles intravenously to label cells in normal bone marrow with iron oxides. During the surgery, iron-labeled bone marrow cells were aspirated from the iliac crest, concentrated, and then injected into the decompression track. Following surgery, patients received follow-up MRI up to 6 months after bone marrow cell transplantation. Results: Iron-labeled cells could be detected in the access canal by a dark (negative) signal on T2-weighted MR images. T2* relaxation times of iron-labeled cell transplants were significantly lower compared with unlabeled cell transplants of control patients who were not injected with ferumoxytol (P = 0.02). Clinical outcomes of patients who received ferumoxytol-labeled or unlabeled cell transplants were not significantly different (P = 1), suggesting that the added ferumoxytol administration did not negatively affect bone repair. Conclusions: This immediately clinically applicable imaging test could become a powerful new tool to monitor the effect of therapeutic cells on bone repair outcomes after corticosteroid-induced osteonecrosis.
Non-smooth M-estimator for Maximum Consensus Estimation

This paper revisits the application of M-estimators for a spectrum of robust estimation problems in computer vision, particularly with the maximum consensus criterion. Current practice makes use of smooth robust loss functions, e.g. Huber loss, which enables M-estimators to be tackled by such well-known optimization techniques as Iteratively Re-weighted Least Square (IRLS). When consensus maximization is used as loss function for M-estimators, however, the optimization problem becomes non-smooth. Our paper proposes an approach to resolve this issue. Based on the Alternating Direction Method of Multiplier (ADMM) technique, we develop a deterministic algorithm that is provably convergent, which enables the maximum consensus problem to be solved in the context of M-estimator. We further show that our algorithm outperforms other differentiable robust loss functions that are currently used by many practitioners. Notably, the proposed method allows the sub-problems to be solved efficiently in parallel, thus entails it to be implemented in distributed settings.
Scalable Hierarchical Mixture of Gaussian Processes for Pattern Classification

This paper introduces a novel Gaussian process (GP) classification method that combines advantages of global and local GP approximators through a two-layer hierarchical model. The upper layer consists of a global sparse GP to coarsely model the entire dataset. The lower layer is a mixture of GP experts which uses local information to learn a fine-grained model. A variational inference algorithm is developed for simultaneous learning of the global GP, the experts and the gating network. Stochastic optimization can be employed for large-scale problems. Experiments on benchmark binary classification datasets demonstrate the advantages of the method in terms of scalability and classification accuracy.
Teaching Human Teachers to Teach Robot Learners

Using Programming by Demonstration to teach robot learners generalisable skills relies on having effective human teachers. This paper aims to address two problems commonly observed in demonstration data sets that arise due to poor teaching strategies; undemonstrated states and ambiguous demonstrations. Overcoming these issues through the use of visual feedback and simple heuristic rules is investigated as a potential way of guiding novice users to more effectively teach robot learners to generalise a task. The proposed method intends to offer the user a more transparent understanding of the robot learner's model state during the teaching phase, to create a more interactive and robust teaching process. Results from a single-factor, three-phase repeated measures study with $\mathbf{n}=\mathbf{30}$ participants, comparing the proposed feedback and heuristic rules set against an unguided condition, show a statistically significant $(\mathbf{F}(\mathbf{2},\mathbf{58})=\mathbf{7.952},\mathbf{p}=\mathbf{0.001})$ improvement of user teaching efficiency of approximately 180% when using the proposed feedback visualisation.
PIPA: A New Proximal Interior Point Algorithm for Large-Scale Convex Optimization

Interior point methods have been known for decades to be useful for the resolution of small to medium size constrained optimization problems. These approaches have the benefit of ensuring feasibility of the iterates through a logarithmic barrier. We propose to incorporate a proximal forward-backward step in the resolution of the barrier subproblem to account for non-necessarily differentiable terms arising in the objective function. The combination of this scheme with a novel line-search strategy gives rise to the so-called Proximal Interior Point Algorithm (PIPA) suitable for the minimization of the sum of a smooth convex function and a non-smooth convex one under general convex constraints. The convergence of PIPA is secured under mild assumptions. As demonstrated by numerical experiments carried out on a large-scale hyperspectral image unmixing application, the proposed method outperforms the state-of-the-art.
A comparison of mosquito densities, weather and infection rates of Aedes aegypti during the first epidemics of Chikungunya (2014) and Zika (2016) in areas with and without vector control in Puerto Rico

In Puerto Rico, the first records of the transmission of Chikungunya (CHIKV) and Zika (ZIKV) viruses were confirmed in May 2014 and December 2015, respectively. Transmission of CHIKV peaked in September 2014, whereas that of ZIKV peaked in August 2016. The emergence of these mosquito‐transmitted arboviruses in the context of a lack of human population immunity allowed observations of whether the outbreaks were associated with Aedes aegypti (Diptera: Culicidae) densities and weather. Mosquito density was monitored weekly in four communities using sentinel autocidal gravid ovitraps (AGO traps) during 2016 in order to provide data to be compared with the findings of a previous study carried out during the 2014 CHIKV epidemic. Findings in two communities protected against Ae. aegypti using mass AGO trapping (three traps per house in most houses) were compared with those in two nearby communities without vector control. Mosquito pools were collected to detect viral RNA of ZIKV, CHIKV and dengue virus. In areas without vector control, mosquito densities and rates of ZIKV detection in 2016 were significantly higher, similarly to those observed for CHIKV in 2014. The density of Ae. aegypti in treated sites was less than two females/trap/week, which is similar to the putative adult female threshold for CHIKV transmission. No significant differences in mosquito density or infection rates with ZIKV and CHIKV at the same sites between years were observed. Although 2016 was significantly wetter, mosquito densities were similar.
Local AM/FM Parameters Estimation: Application to Sinusoidal Modeling and Blind Audio Source Separation

This letter extends our recently introduced method which was designed to estimate instantaneous frequency and chirp rate of linearly modulated signals. Indeed, we derive several new estimators related to our previous ones which provide in the time-frequency plane all the signal parameters of the investigated model: amplitude, frequency, and their local modulations (AM/FM). Our estimators are first introduced and compared in terms of statistical efficiency with theoretical bounds and with other state-of-the-art estimators. Then, they are used to improve spectral analysis applied to audio sinusoidal modeling. Finally, they lead to a new source separation technique based on coherent amplitude and frequency modulation that is evaluated on real-world music signals.
An Entire- Domain Galerkin Method for the Analysis of Wave Scattering by All-Dielectric Metasurfaces

Plane-wave scattering by a planar all-dielectric metasurface is considered. The associated boundary-value problem is reformulated in an integral form involving the layered medium's Green's function. Then, a rigorous entire-domain integral-equation methodology is developed for the accurate determination of the reflected and transmitted fields. Certain numerical results are reported, exhibiting specific metasurfaces, which can operate efficiently as narrow-band reflection filters. Connections to applications of controlling the propagation direction of electromagnetic waves are pointed out.
[Semi-quantitative risk assessment of human infection with H7N9 avian influenza epidemic in Zhejiang province].

OBJECTIVE To assess the risk of local outbreaks of H7N9 avian influenza infection in Zhejiang province and to explore the semi-quantitative assessment method for public health risks in emergency. METHODS Risk index system of human infection with H7N9 avian influenza caused by local transmission were reviewed. The weights of indexes were calculated by analytic hierarchy process, which was combined with the TOPSIS method to calculate the risk comprehensive index. RESULTS Four primary indexes and 23 secondary indexes were identified for risk assessment in local outbreaks of H7N9 avian influenza infection. The weights ranked on the top five were:morbidity (0.0972), closure measures (0.0718), sterilization measures (0.0673), fatality rate (0.0651), and epidemic spread (0.0616). The comprehensive index of the risk of local outbreaks of H7N9 avian influenza ranged from high to low were Hangzhou (0.5910), Shaoxing (0.5711), Jiaxing (0.5199), Taizhou (0.5198), Huzhou (0.4662), Ningbo (0.3828), Wenzhou (0.3719), Jinhua (0.3392), Lishui (0.2727), Quzhou (0.2001) and Zhoushan (0.0508). CONCLUSIONS A semi-quantitative method has been established in this study, which provides scientific basis for prevention and control of H7N9 avian influenza epidemic in Zhejiang province.
Depth Super-Resolution Using Joint Adaptive Weighted Least Squares And Patching Gradient

This paper presents a flexible framework for the challenging task of color-guided depth upsampling. Some state-of-the-art approaches apply an aligned RGB image for depth recovery. Unfortunately, these kinds of methods may result in texture copying artifacts and edge blurring artifacts. To address these difficulties, we propose an adaptive weighted least squares framework of choosing different guidance weight for variant conditions flexibly. First of all, in the framework, we propose a joint adaptive color weighting scheme in which the depth maps and color images jointly choose a proper weight term for diverse cases. Then, a patch-based smoothness measuring approach called patching-gradient method (PGM) is proposed to distinguish the discontinuities and smooth areas. Our PGM is robust to dense noise and preserve weak edges effectively. Quantitative and qualitative experiments on noisy T of -like datasets demonstrate our frameworks effectiveness on suppressing both texture copying artifacts and edge blurring artifacts.
Personalized Point of Interest Recommendation Using Check-In History and Friend's Interest

With the popularity of location based social networks, check-in activity has increased tremendously and that has resulted in research in point of interest recommendation. Point of interest recommendation recommends new places to user when he checks in at a location. In the proposed system, we develop a personalized point of interest recommendation system by considering user preferences which are obtained from user's check in history. Also location popularity and other users' preference are considered when user visits new location. Friend's check-in history is used to give recommendations. Using hybrid approach along with tag and location correlation gives accurate point of interest recommendations.
A Generalized Uncorrelated Ridge Regression with Nonnegative Labels for Unsupervised Feature Selection

The ridge regression has been widely applied in multiple domains and gains the promising performance. However, due to the unavailability of labels, the ridge regression easily incurs the trivial solution towards unsupervised learning. In this paper, we investigate unsupervised feature selection by virtue of an uncorrelated and nonnegative ridge regression model (UN-RFS). To be specific, a generalized uncorrelated constraint on the projection matrix, and a nonnegative orthogonal constraint on the indicator matrix are imposed upon the proposed regression model. With the proposed method, the most uncorrelated features on the embedded Stiefel manifold is exploited for feature selection and trivial solutions of projection matrix are avoided as well. Besides, equipped with a generalized scatter matrix, the proposed uncorrelated constraint is superior to conventional uncorrelated constraint, since the closed form solution can be achieved directly. In addition, owing to the nonnegative of real labels, the nonnegative orthogonal constraint is employed to suppress the indicator matrix such that the learned labels confront to reality further.
Neural activity reveals effects of aging on inhibitory processes during word retrieval

ABSTRACT Word retrieval difficulties are one of the most frustrating problems for older adults. Poorer access to phonological representation of the target word has been postulated as the underlying deficit, supported by findings of improved word retrieval after phonological priming. To better understand aging effects in the underlying neurophysiology associated with word retrieval, this study examined electrophysiological correlates of phonological priming and word retrieval in adults. Young, middle-aged, and older adults viewed pictures that were preceded by pseudo-word primes that either shared initial phonemes with or were unrelated to the picture’s name. Participants made phonological judgments regarding the prime and picture prior to naming the picture. Behavioral and event-related potential correlates of phonological priming and word retrieval were recorded. All groups benefitted similarly from phonological priming, evidenced by faster phonological judgment response times and increased ease of word retrieval for primed pictures, indexed by the N400 priming effect. The peak latency of the N2, however, showed an incremental delay with age. High correlation between N2 peak latency and clinical measures of inhibition suggested an age-related delay in the inhibition of primed lexical competitors. Taken together, our results indicated intact activation of phonological representation of the picture’s name but age-related delays in inhibition of primed competitors. Interestingly, our findings revealed that delays in inhibiting lexical competitors may begin as early as middle age, highlighting the importance of including multiple age groups to better represent the aging trajectory.
Hybrid Beamforming for Multi-User Massive MIMO Systems

The large scale multiple-input multiple-output (MIMO) system with hybrid beamforming (HBF) is a promising communications technology due to its excellent tradeoff between hardware complexity and system performance. Assuming perfect channel state information is acquired, we consider a single cell downlink multi-user massive MIMO system working in a generic channel model with a hybrid structure that supports multiple streams per UE. We aim to find an analog and digital precoder/combiner that maximizes the sum-rate of the communication system. Unlike the traditional two-stage design criterion, which separately designs the analog and digital stages, our proposed criterion jointly designs two stages by trying to avoid the loss of information at each stage. When double the least number of radio frequency (RF) chains are available, we provide an asymptotically optimal solution in a massive MIMO regimen, i.e., the sum-rate of such an HBF solution could approach the channel capacity under large base station (BS) antenna arrays. A corresponding solution using the fewest RF chains is then derived. Finally, the simulation results are shown to validate the proposed schemes. Specifically, the solution with the fewest RF chains is shown to outperform the state of the art for HBF systems, even when the number of BS antennas is not very large. It should be noted that the schemes proposed in this paper have low complexity owing to their closed-form solutions.
Breast cancer is associated with methylation and expression of the a disintegrin and metalloproteinase domain 33 (ADAM33) gene affected by endocrine‑disrupting chemicals.

A disintegrin and metalloproteinase domain 33 (ADAM33) gene is a transmembrane glycoprotein that mediates changes in cell adhesion and plays an important role in cancer progression. Since bisphenol A (BPA) and phthalates are epigenetically toxic, the purpose of this study was to examine whether BPA and phthalate metabolites, including monoethyl phthalate (MEP), mono‑n‑butyl phthalate (MBP), mono‑isobutyl phthalate (MIBP), mono(2‑ethylhexyl) phthalate (MEHP), mono(2‑ethyl‑5‑hydroxyhexyl) phthalate (MEHHP), mono(2‑ethyl‑5‑carboxypentyl) phthalate (MECPP), and mono(2‑ethyl‑5‑oxohexyl) phthalate (MEOHP), have an epigenetic impact on ADAM33 and the incidence of breast cancer. CpG islands of breast cancer microarray datasets obtained from the Gene Expression Omnibus (GEO) were used to assess the ADAM33 methylation profile. We designed a case‑control study including 44 cases and 22 age‑matched controls to detect the methylation status of intron 1 in ADAM33 from peripheral blood mononuclear cells (PBMCs) in blood, using BSP, nested PCR, and bisulfite sequencing, and measured the in vivo gene expression of ADAM33 and the urinary concentrations of endocrine‑disrupting chemicals (EDCs), using real‑time PCR, high‑performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC‑MS). Only one dataset, GSE32393, reached significance (P=0.016). ADAM33 expression and methylation frequencies at CpG site 3 in intron 1 were higher in the control group. We found a positive association between intron 1 methylation level and ADAM33 expression as well as urinary concentrations of MEHHP, MECPP, MEOHP and Σ4MEHP (the sum of MEHP, MECPP, MEHHP, and MEOHP) in the cases. This study suggests that metabolites of phthalate such as MEHHP, MECPP, MEOHP and Σ4MEHP may increase the intron 1 methylation level to elevate ADAM33 gene expression and have a protective effect on reducing the risk of breast cancer.