Miracle-dz/UniFAR-FTUs huggingface dataset:
Decreased parvalbumin immunoreactivity in the cortex and striatum of mice lacking the CB1 receptor

Cortical and striatal regions of the brain contain high levels of the cannabinoid‐1 (CB1) receptor, the central neuronal mediator of activity‐dependent synaptic plasticity evoked by endocannabinoids. The expression levels of parvalbumin, a calcium‐binding protein found in fast‐spiking interneurons of both regions, may be controlled in part by synaptic activity during critical periods of development. However, there is currently no evidence that CB1 receptor expression affects parvalbumin levels in either cortical or striatal interneurons. To assess this possibility, we examined parvalbumin immunoreactivity in the dorsolateral striatum, primary motor cortex (M1), and prefrontal cortex (PFC) of CB1 knockout and wild‐type C57/BL6 mice. Quantitative densitometry showed a significant decrease in parvalbumin immunoreactivity within individual neurons in each of these regions of CB1 knockout mice relative to controls. A significantly lower density (number of cells per unit area) of parvalbumin‐labeled neurons was observed in the striatum, but not the cortical regions of CB1 knockout mice. These findings suggest that CB1 receptor deletion may elicit a compensatory mechanism for network homeostasis affecting parvalbumin‐containing cortical and striatal interneurons. Synapse, 2011. Published 2011 Wiley‐Liss, Inc.
Data loss prevention for cross-domain instant messaging

This paper proposes a cascading classifier for inspecting and validating the payload of chat messages in (military) instant messaging. The first step in the cascading classifier pipeline is an anomaly detection-based method whose purpose is to ensure that the message channel is not used to exfiltrate non-message data such as images, documents, binary files or encrypted content. Messages that pass the filtering phase then proceed to have their content analyzed for the presence of known sensitive information. This data loss prevention step is enhanced by incorporating an author profile signal that assesses the validity of the claimed authorship by capturing the stylometric signature embedded in each user's past message stream. The hypothesis being that the inference and subsequent inclusion of latent author traits such as gender, age and ethnicity will aid the data loss prevention solution by reducing the number of incorrect classifications. Experiments were conducted using message traffic that was generated during a field-training exercise conducted by members of the armed forces, as well as an internal repository of classified documents and a myriad of non-message based data sources. The results demonstrated that our proposed traffic-filtering classifier is successful in distinguishing between legitimate and illegitimate traffic. Further, the experiments showed that constructing authorship verification models, using sparse messages as a training set, is feasible and that including this signal in the data loss prevention solution leads to a significant increase in the predictive performance for the cross-domain messaging setting.
A Reliable Physical Layer Authentication Algorithm for Massive IoT Systems

As the request for network access and a variety of services, the Internet of Things (IoT) has evolved from special networks with simple functions to global information infrastructure, challenging the current authentication mechanism. Due to the dense population in the massive IoT scenario, the increasing connectivity requirements may aggravate the load of the authentication entity and increase the corresponding processing delay, which may further jeopardize the service and affect the quality of the experience. To solve this problem, especially for the case in which the authentication infrastructure is unavailable, a physical layer authentication method is proposed in this paper, which is based on modified physical authentication flow, a modified PDA algorithm based on soft information, and authentication key randomization based on hash mapping. Through Monte Carlo simulations, it is observed that the proposed physical layer authentication mechanism shows a favorable gain compared with that of the existing methods.
Cache conflict resolution through detection, analysis and dynamic remapping of active pages

Reducing cache conflict misses by improving the placement of page frames in the cache has been shown to increase overall system performance. Research efforts have focused on improving the distribution of page frames across cache bins with the goal of obtaining an even distribution of physical page frames in each bin. The problem with attempting to increase performance through even distribution of pages into cache bins is that these approaches do not consider accesses to the pages in the bins. In this paper, we propose an approach that considers page activity together with page placement in tuning system performance. We design and implement a data collection tool to allow us to analyze page activity in cache bins. Our analysis indicates that page placement techniques that provide for an even distribution of pages to bins can result in a disproportionate number of long lived, active pages being mapped to the same bin. We refer to these active bins as hot bins. We then design and implement a technique using standard paging hardware and software techniques to reduce activity in hot bins by remapping long lived active pages to bins with less active pages. Our experiments indicate that the remapping of active, long lived pages can increase system performance.
Is human herpesvirus 8 infection more common in men than in women? Systematic review and meta‐analysis

All forms of Kaposi sarcoma (KS) are more common in men than in women. It is unknown if this is due to a higher prevalence of human herpesvirus 8 (HHV‐8), the underlying cause of KS, in men compared to women. We did a systematic review and meta‐analysis to examine the association between HHV‐8 seropositivity and gender in the general population. Studies in selected populations like for example, blood donors, hospital patients and men who have sex with men were excluded. We searched Medline and Embase from January 1994 to February 2015. We included observational studies that recruited participants from the general population and reported HHV‐8 seroprevalence for men and women or boys and girls. We used random‐effects meta‐analysis to pool odds ratios (OR) of the association between HHV‐8 and gender. We used meta‐regression to identify effect modifiers, including age, geographical region and type of HHV‐8 antibody test. We included 22 studies, with 36,175 participants. Men from sub‐Saharan Africa (SSA) [OR 1.21, 95% confidence interval (CI) 1.09–1.34], but not men from elsewhere (OR 0.94, 95% CI 0.83–1.06), were more likely to be HHV‐8 seropositive than women (p value for interaction = 0.010). There was no difference in HHV‐8 seroprevalence between boys and girls from SSA (OR 0.90, 95% CI 0.72–1.13). The type of HHV‐8 assay did not affect the overall results. A higher HHV‐8 seroprevalence in men than women in SSA may partially explain why men have a higher KS risk in this region.
Super Resolution of B-Mode Ultrasound Images With Deep Learning

Ultrasound offers a safe, non-invasive, and inexpensive way of imaging. However, due to its natural intrinsic imaging characteristics, it produces poor quality images with low resolution (LR) compared to other medical imaging modalities. Various image enhancement techniques have been extensively studied to overcome these shortcomings. Super-resolution (SR) is one of these methods, which endeavor to obtain high resolution (HR) images from LR images while enlarging them. Numerous studies have already utilized different SR techniques in various stages of ultrasound imaging (USI). Unlike other studies, which aimed at obtaining SR in the pre-processing phase or early stages of the post-processing phase of USI, we achieved SR on B-mode ultrasound images, which is the last stage of USI. We constructed a deep convolutional neural network (CNN) and trained it with a very large dataset of B-mode ultrasound images for the scale factors 2, 3, 4, and 8. We evaluated the performance of our proposed model quantitatively with eight image quality measures. The quantitative results revealed that our algorithm is much more successful than other methods at each magnification factor. Furthermore, we also verified that there is a statistically significant difference between our approach and others. Besides, qualitative analysis of the reconstructed images also confirms that it produces much better quality HR images than other methods in terms of the human visual system.
Vitellogenin-immunohistochemistry in the Liver and the Testis of the Medaka, Oryzias latipes, Exposed to 17β-estradiol and p-nonylphenol

Abstract Vitellogenin (VTG) produced in male fish has been used for a biomarker to study endocrine disrupters. However, the characteristics of VTG produced in male fish have not been studied well. In this study, we investigated the localization of VTG in the liver and the testis of male medaka (Oryzias latipes) treated with 17β-estradiol (E2) and p-nonylphenol (NP). The male fish were exposed to 1 μg/L E2 and 500 μg/L NP for 1–12 days. Control groups were kept in water including only vehicle. The frozen sections of the liver and the testis were stained with immunohistochemical methods using an antiserum against medaka VTG as the first antibody. In the E2 and NP treated liver, the hepatocytes showed immunoreactivity. In particular, the cytoplasm close to the cell membrane surrounding the sinusoids was strongly immunopositive. In the testis of both treatments, the interstitial tissues and the cells (spermatocytes) in the seminiferous tubules were immunopositive. The concentration of VTG became gradually higher in both tissues with longer treatments. These results suggest that germ cells in the testis treated with E2 and NP are able to incorporate and accumulate VTG.
Remote sensing panchromatic images classification using moment features and decision fusion

Panchromatic remote sensing images have useful information about textural classification in land-use and land-cover applications. Various methods model texture and extract features for classification tasks. In supervised classification, all of the feature extraction methods try to increase the accuracy of classification and simultaneously decrease the computationally load. At the present work, we use the moments to extract texture information. The moments are categorized into different kernel functions. We use three conventional moments in pattern recognition such as Geometric, Legendre and Zernike moments and we obtain strong performance from almost unknown Chebyshev moment in feature extraction. The challenging part of pixel-wised image classification is classifying pixels in the near of different classes margin. To overcome the problem, we use majority voting as decision fusion rule. The well-known support vector machine (SVM) is used for supervised classification. We compare our proposed method with gray-level co-occurrence matrix (GLCM), Gabor filters and attribute profiles (APs). Different criteria such as average accuracy, overall accuracy, κ statistic and computation time are used for assessment of classification performance.
Multiple Sclerosis Detection via Machine Learning Algorithm, Accurate Simulated Database 3D MRI to 2D Images, using value of Binary Pattern Classification - A Case Study

This paper is aiming to review some machine learning tools and to see if support vector machines (SVM) are accurate in 3D MRI images. We intend to show that 3D images can be converted into 2D and by considering machine learning techniques and SVM tools. According many research and database, 3D images are more informative than 2D ones, however working with 3D images is time consuming and requires specific programs and coding. In this research work and next which case study is under progress, we try to see the benefits of having a 3D database but to use 2D vectors only for comparison and more accurate results. This case study helps to gain better outcome. Support vector machines (SVM) can be a useful tool during the diagnosis process, however to be able to make better assumptions, more tests are needed. The technology can be viewed as detailed because it is usually applied in the discrimination of the blocks found in the areas of MS lesions and the regions which are not affected by the lesions. Primarily, to correctly segregate the different areas the textural background plays a crucial part in elevating the effectiveness of the imaging concept. In this case, the study of the slice blocks should be done comprehensively as it aims in ensuring that the type of results provided showcases the exact situation of the individual suffering from the ailment.
Effects of Man in the Middle (MITM) Attack on Bit Error Rate of Bluetooth System

The ad-hoc network formed by Bluetooth works on radio frequency links. The security aspect of Bluetooth has to be handled more carefully. The radio frequency waves have a characteristic that the waves can pierce the obstructions in the communication path, get rid of the requirement of line of sight between the communicating devices. We propose a software model of man-in-the-middle attack along with unauthorized and authorized transmitter and receiver. Advanced White Gaussian Noise channel is simulated in the designed architecture. The transmitter uses Gaussian Frequency Shift Keying (GFSK) modulation like in Bluetooth. The receiver uses GFSK demodulation. In order to validate the performance of the designed system, bit error rate (BER) measurements are taken with respect to different time intervals. We found that BER drops roughly 18% if hopping duration of 150 seconds is chosen. We propose that a Bluetooth system with hopping rate of 0.006 Hz is used instead of 10Hz.
Premium Calculation for Insurance Businesses Based on Cyber Risks in IP-Based Power Substations

Insurance is a promising risk transfer tool against switching cyberattacks on power grids that can disrupt operation and potentially lead to widespread outages. This paper emphasizes on a framework of premium calculation for cyber insurance businesses by modeling potential electronic intrusion with steady-state simulation results and its direct hypothesized impacts. The proposed actuarial framework models the operational monetary losses with the hypothesized power outages based on the cyber-reliability assessment and its expected mean time to restore power (MTTRP). The ruin probability is employed as the fundamental formula to determine the feasible insurance premium pool. Hypothesized spatial correlation studies on multiple substation contingencies associated with switching attacks have shown estimated operational losses and expected duration of power restoration, which is translated into the calculation of insurance premium. The proposed actuarial framework is validated using generalized IEEE test cases with modified parameters on substations. The potential implications of grid’s hypothetical scenarios are discussed.
Detection of Abandoned and Stolen Objects Based on Dual Background Model and Mask R-CNN

Dual background models have been widely used for detecting stationary objects in video surveillance systems. However, there is a problem that both abandoned and stolen objects are equally detected as stationary objects, making it difficult to distinguish them. Another problem is the ghost region created by shadow shift or light changes, which makes the discrimination issue more complicated. In this paper, we present an efficient method to distinguish abandoned objects, stolen objects, and ghost regions in the surveillance video. This method contains two main strategies: the first one is the dual background model for extracting candidate stationary objects, the second one is object segmentation based on mask regions with CNN features (Mask R-CNN) for providing the object mask information. The basic idea is: given a candidate stationary object from the background model, it is checked whether a corresponding segmented object exists in the current video frame or the previous background frame to take into account the current and past situations. And the final state of the candidate stationary object is determined by considering various situations through the comparative analysis technique presented in this paper. The proposed algorithm has qualitatively experimented with our own dataset focusing on the discrimination issue, which generated satisfactory results. Therefore, it is expected to be widely applied to automatic detection of stolen objects as well as abandoned objects in open environments such as exhibition halls and public parks where existing intrusion detection-based security services are difficult to be deployed.
Candidate driver genes in microsatellite‐unstable colorectal cancer

Defects in the mismatch repair system lead to microsatellite instability (MSI), a feature observed in ∼ 15% of all colorectal cancers (CRCs). Microsatellite mutations that drive tumourigenesis, typically inactivation of tumour suppressors, are selected for and are frequently detected in MSI cancers. Here, we evaluated somatic mutations in microsatellite repeats of 790 genes chosen based on reduced expression in MSI CRC and existence of a coding mononucleotide repeat of 6–10 bp in length. All the repeats were initially sequenced in 30 primary MSI CRC samples and whenever frameshift mutations were identified in >20%, additional 70 samples were sequenced. To distinguish driver mutations from passengers, we similarly analyzed the occurrence of frameshift mutations in 121 intronic control repeats and utilized a statistical regression model to determine cut‐off mutation frequencies for repeats of all types (A/T and C/G, 6–10 bp). Along with several know target genes, including TGFBR2, ACVR2, and MSH3, six novel candidate driver genes emerged that harbored significantly more mutations than identical control repeats. The mutation frequencies in 100 MSI CRC samples were 51% in G8 of GLYR1, 47% in T9 of ABCC5, 43% in G8 of WDTC1, 33% in A8 of ROCK1, 30% in T8 of OR51E2, and 28% in A8 of TCEB3. Immunohistochemical staining of GLYR1 revealed defective protein expression in tumors carrying biallelic mutations, supporting a loss of function hypothesis. This is a large scale, unbiased effort to identify genes that when mutated are likely to contribute to MSI CRC development.
Quantitation and localization of blood‐to‐brain influx by magnetic resonance imaging and quantitative autoradiography in a model of transient focal ischemia

The ability of gadolinium‐diethylenetriaminepentaacetic acid (Gd‐DTPA) enhanced MRI to localize and quantitate blood–brain barrier (BBB) opening was evaluated against quantitative autoradiographic (QAR) imaging of 14C‐α‐aminoisobutyric acid (AIB) distribution. The blood‐to‐brain transfer constant (Ki) for Gd‐DTPA was determined by MRI in rats after 3 h of focal cerebral ischemia plus 2.5 h of reperfusion (n = 9), and that of AIB was determined by QAR shortly thereafter. Tissue regions of interest (ROIs) for Gd‐DTPA leakage were identified by ISODATA segmentation of pre‐ and post‐Gd‐DTPA Look–Locker (L‐L) T1 maps. Patlak plots were constructed using time course of blood and tissue T1 changes induced by Gd for estimating Ki. Among the nine rats, 14 sizable regions of AIB uptake were found; 13 were also identified by ISODATA segmentation. Although the 13 MRI‐ROIs spatially approximated those of AIB uptake, the segmentation sometimes missed small areas of lesser AIB uptake that did not extend through more than 60% of the 2.0‐mm‐thick slice. Mean Ki's of AIB were highly correlated with those of Gd‐DTPA across the 13 regions; the group means (±SD) were similar for the two tracers (7.1 ± 3.3 × 10−3 and 6.8 ± 3.5 × 10−3 ml.g−1 · min−1, respectively). In most instances, Gd‐DTPA MRI accurately localized areas of BBB opening. Magn Reson Med, 2005. © 2005 Wiley‐Liss, Inc.
EEG source localization and imaging using multiple signal classification approaches.

Equivalent current dipoles are a powerful tool for modeling focal sources. The dipole is often sufficient to adequately represent sources of measured scalp potentials, even when the area of activation exceeds 1 cm2 of cortex. Traditional least-squares fitting techniques involve minimization of an error function with respect to the location and orientation of the dipoles. The existence of multiple local minima in this error function can result in gross errors in the computed source locations. The problem is further compounded by the requirement that the model order, i.e. the number of dipoles, be determined before error minimization can be performed. An incorrect model order can produce additional errors in the estimated source parameters. Both of these problems can be avoided using alternative search strategies based on the MUSIC (multiple signal classification) algorithm. Here the authors review the MUSIC approach and demonstrate its application to the localization of multiple current dipoles from EEG data. The authors also show that the number of detectable sources can be determined in a recursive manner from the data. Also, in contrast to least-squares, the method can find dipolar sources in the presence of additional non-dipolar sources. Finally, extensions of the MUSIC approach to allow the modeling of distributed sources are discussed.
Cesarean section: analysis of the experience before and after the National Consensus Conference on Aspects of Cesarean Birth.

OBJECTIVE To examine the effect of recommendations to reduce the cesarean section rate issued by the National Consensus Conference on Aspects of Cesarean Birth in 1986 on obstetric practices and to identify current patient factors that predict cesarean section. DESIGN Descriptive retrospective cross-sectional study. SETTING A tertiary care perinatal referral centre and a general teaching hospital with a level 2 nursery in Hamilton, Ont. PATIENTS All patients who gave birth at the two hospitals in 1982 (4121 women) and 1990 (4431). MAIN OUTCOME MEASURES Cesarean section rates and indications and predictors of cesarean section. RESULTS Although a trial of vaginal delivery after cesarean section was offered 93% more often in 1990 than in 1982 (p = 0.0002), the rate of vaginal delivery increased only 2.6%, for a reduction of 8.7% in the total cesarean section rate and of 15% in the repeat cesarean section rate. The incidence rate and treatment of dystocia did not change. The rate of cesarean section for breech presentation remained unchanged, and fetal distress was rarely confirmed with pH measurement in scalp blood before cesarean section. The most important predictors of cesarean section in 1990 were previous cesarean section and labour induction. For the nulliparous women and the multiparous women with no previous cesarean section labour induction was the most important predictor. CONCLUSIONS The rate at which patients with previous cesarean section are offered a trial of vaginal delivery has increased significantly since 1982; however, the total and repeat cesarean section rates have not decreased proportionally. Induction of labour is currently the most important correctable predictor of cesarean section. The active management of dystocia, efforts to increase the rate of vaginal breech delivery and appropriate methods to diagnose fetal distress need to be improved; such improvements should reduce the cesarean section rate further.