Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing Entities in the Bitcoin Blockchain

Bitcoin has created a new exchange paradigm within which financial transactions can be trusted without an intermediary. This premise of a free decentralized transactional network however requires, in its current implementation, unrestricted access to the ledger for peer-based transaction verification. A number of studies have shown that, in this pseudonymous context, identities can be leaked based on transaction features or off-network information. In this work, we analyze the information revealed by the pattern of transactions in the neighborhood of a given entity transaction. By definition, these features which pertain to an extended network are not directly controllable by the entity, but might enable leakage of information about transacting entities. We define a number of new features relevant to entity characterization on the Bitcoin Blockchain and study their efficacy in practice. We show that even a weak attacker with shallow data mining knowledge is able to leverage these features to characterize the entity properties.
Analysis of KNN Information Estimators for Smooth Distributions

KSG mutual information estimator, which is based on the distances of each sample to its k-th nearest neighbor, is widely used to estimate mutual information between two continuous random variables. Existing work has analyzed the convergence speed of this estimator for random variables with bounded support. In practice, however, KSG estimator also performs well for a much broader class of distributions, including not only those with bounded support but also those with long tail distributions. In this paper, we analyze the convergence speed of the error of KSG estimator for smooth distributions, whose support can be both bounded and unbounded. As KSG mutual information estimator can be viewed as an adaptive combination of KL entropy estimators, in our analysis, we also provide convergence analysis of KL entropy estimator for a broad class of distributions.
Understanding Ductal Carcinoma In Situ Invasion using a Multiscale Agent-Based Model*

Ductal carcinoma in situ (DCIS) is commonly treated clinically through surgical resection. Although surgical options exist for resection, it is unclear which is optimal to reduce the likelihood of future invasive disease. This is further complicated by challenges in determining correct surgical margins from disease diagnostics, with mammographic imaging misidentifying surgical margins by as much as 2 cm vs. histological examination. We have implemented a threedimensional, hybrid multiscale model of DCIS to study disease initiation and progression. In order to shed new light on current biological questions and clinical challenges surrounding the disease, we present here an improved version of this model, with more biologically relevant molecular signaling pathways, cell phenotype hierarchies, and duct architecture variation. In particular, a cell necrosis, lysis and calcification pathway has been incorporated into the model to help better understand the relationship between diagnostic imaging and the true extent of disease invasion. We observe that deficiencies in availability of molecular signaling molecules that upregulate cell proliferation may be overcome by dynamic shifts in phenotypic distributions within the disease mass. Hypoxia, necrosis, and calcification together functioned as a hypoxia relief mechanism, and were observed to maintain a consistent distance between the DCIS leading edge and the site of necrosis onset, providing insights for improving surgical margins.
Pilose antler polypeptide protects against sevoflurane-mediated neurocyte injury

Pilose antler polypeptide (PAP) is an active substance isolated from the traditional Chinese medicine pilose antler, which possesses multiple biological activities. In the present study, the role and mechanism of PAP in sevoflurane (SEV)-induced neurocyte injury was explored. Cell viability was determined by Cell Counting kit-8 assay. Cell proliferation and apoptosis were analyzed by flow cytometry. Western blotting and reverse transcription-quantitative polymerase chain reaction analysis were used to evaluate the protein and mRNA expression levels, respectively. The results revealed that PAP enhanced the cell viability of SEV-treated nerve cells. In addition, through modulation of apoptosis-associated protein expression, PAP suppressed SEV-induced nerve cell apoptosis. Furthermore, PAP activated the p38 mitogen-activated protein kinase (p38)/c-Jun N-terminal kinase (JNK) pathway in the neurocyte injury model, whereas inhibition of the p38/JNK pathway reversed the beneficial effects produced by PAP. In conclusion, PAP protected against SEV-mediated neurocyte injury via upregulation of the p38/JNK pathway. The present findings suggested that PAP may be an effective agent for neurocyte injury.
An update of the pathogenesis of frontal fibrosing alopecia: What does the current evidence tell us?

Frontal fibrosing alopecia (FFA) is a primary patterned cicatricial alopecia with a complicated pathogenesis yet to be fully understood. FFA appears to be increasing in incidence worldwide, especially in the last decade. In order to consider current treatment options, we reviewed current evidence for its pathogenesis comprising immune‐mediated, genetic, hormonal and environmental factors. Th1‐mediated inflammation with collapse of hair follicle immune privilege and bulge epithelial stem cell destruction, peroxisome proliferator‐activated receptor gamma (PPAR‐γ) depletion and epithelial–mesenchymal transition are key events leading to permanent hair follicle destruction in FFA. Although the vast majority of cases are sporadic, familial reports of FFA implicate genetic or epigenetic mechanisms in its pathogenesis. The frequent onset of FFA in post‐menopausal women, similar patterning and co‐existence with female pattern hair loss, together with a reportedly good response to 5α‐reductase inhibitors suggest a role for sex steroid hormones. The reported increasing incidence invites speculation for, yet unproven, environmental triggers such as sun exposure and topical allergens. More robust research into this unique entity is required to help understand the complexity of the pathogenesis of FFA in order to find satisfactory therapeutic targets for this often distressing condition.
Deficient Wnt Signaling and Synaptic Vulnerability in Alzheimer’s Disease: Emerging Roles for the LRP6 Receptor

Synapse dysfunction and loss represent critical early events in the pathophysiology of Alzheimer’s disease (AD). While extensive research has elucidated the direct synaptotoxic effects of Amyloid-β (Aβ) oligomers, less is known about how signaling pathways at the synapse are affected by Aβ. A better understanding of the cellular and molecular mechanisms underlying synaptic vulnerability in AD is key to illuminating the determinants of AD susceptibility and will unveil novel therapeutic avenues. Canonical Wnt signaling through the Wnt co-receptor LRP6 has a critical role in maintaining the structural and functional integrity of synaptic connections in the adult brain. Accumulating evidence suggests that deficient Wnt signaling may contribute to AD pathology. In particular, LRP6 deficiency compromises synaptic function and stability, and contributes to Aß production and plaque formation. Here, we review the role of Wnt signaling for synaptic maintenance in the adult brain and the contribution of aberrant Wnt signaling to synaptic degeneration in AD. We place a focus on emerging evidence implicating the LRP6 receptor as an important modulator of AD risk and pathology.
Low Gamma Band Cortico-muscular Coherence Inter-Hemisphere Difference following Chronic Stroke

Brain oscillation and motor control process would change due to chronic stroke. Inter-hemisphere brain activation patterns may relate to motor related recovery. This study employed cortico-muscular coherence to explore cortical motor control process during wrist isometric contraction experiments of both affected and unaffected hands from chronic stroke subjects. Eleven chronic stroke subjects with moderate hand function involved in the experiments and each subject took three visits. Multitaper coherence estimation with bias-correction was performed to acquire cortico-muscular coherence, neuronal coherence source Localization was conducted to determine typical scalp motivation area during isometric contraction. Non-parametric permutation based multiple frequency bin statistics was utilized to compare the difference between two sides. The results demonstrated significant typical low gamma band inter-hemisphere disparity in cortico-muscular coherence between two sides after chronic stroke. The spatial topographical pattern and source Localization outcomes also supported these findings.
Rotavirus: the guard dies, but it does not surrender

Rotavirus: the guard dies, but it does not surrender A. Tilmanne, P. Lepage, O. Vandenberg, D. Martiny, M. Hallin & C. Quach To cite this article: A. Tilmanne, P. Lepage, O. Vandenberg, D. Martiny, M. Hallin & C. Quach (2018): Rotavirus: the guard dies, but it does not surrender, Infectious Diseases, DOI: 10.1080/23744235.2018. 1508885 To link to this article: https://doi.org/10.1080/23744235.2018. 1508885
A New Static-Based Framework for Ransomware Detection

Recently, ransomware attacks are on the rise hitting critical infrastructures and organizations globally. Ransomware uses advanced encryption techniques to encrypt important files on the targeted computer, then it requests payment to decrypt the encrypted files again. Therefore, the detection and prevention of ransomware attacks represent major challenges for security researchers. This research proposes a novel static-based rules ransomware detection framework. The decision rules of the proposed framework are based on static features extracted from the ransomware files. When scanned file reached rules threshold, the framework evaluates triggered rules through logical operations to assign a score for each file. Every score represents a confidence level whether this file is ransomware or not from critical to low. The proposed framework has proven that it can detect new families based on rules and logical operations with high accuracy and detection ratio
Hyperspectral Identification and Classification of Oilseed Rape Waterlogging Stress Levels Using Parallel Computing

This paper aims to evaluate the feasibility and performance of two related applications in agricultural big data: hyperspectral imaging and parallel computing. After selecting two oilseed rape varieties (NingYou 22 and NingZa 19) as the objects of study, we captured hyperspectral images of these siliques following their exposure to three different waterlogging stress levels (0, 3, and 6 days). The machine learning library for Spark was used to realize both artificial neural network (ANN) and support vector machine (SVM) classification algorithms, and to conduct hyperspectral classification analysis of the oilseed rape siliques under the various levels of waterlogging stress on the parallel computing platform. From the classification data sets, 70% of the data were randomly selected for training, and the remaining 30% were used for prediction. The experimental results indicate that, when the hyperspectral image of a region of interest (400–1000 nm) was extracted and combined with the spectrum image data, the oilseed rape waterlogging detection model based on the Spark parallel computing framework was feasible and efficient. For the multi-class classification problem, the accuracy of the ANN algorithm was superior to that of the SVM, but its convergence time exceeded that of the SVM algorithm. Using the ANN and SVM algorithms for binary classification of the samples from both varieties, the results indicate that the performance of the SVM algorithm was superior in terms of the binary classification problem. Meanwhile, of the two oilseed rape varieties, the NZ 19 waterlogging samples yielded better classification results. Five optimal wavebands (512, 621, 689, 953, and 961 nm) were selected as the inputs for the classification algorithm. The results show that the classification accuracy of full-waveband hyperspectral imaging was slightly higher than that of optimal waveband imaging, while the ANN algorithm was more accurate than the SVM algorithm. Finally, the three indices of speedup, scaleup and sizeup were used to evaluate the operation performance of the hyperspectral data set algorithm based on the Spark parallel computing platform.
A 5-ms Error, 22-μA Photoplethysmography Sensor using Current Integration Circuit and Correlated Double Sampling

This paper presents a low-power Photoplethysmography (PPG) sensing method. The PPG is commonly used in recent wearable devices to detect cardiovascular information including heartbeat. The heartbeat is useful for physical activity and stress monitoring. However, the PPG circuit consumes large power because it consists of LED and photodiode. To reduce its power consumption without accuracy degradation, a cooperative design of circuits and algorithms is proposed in this work. A straightforward way to reduce the power is intermittent driving of LED, but there is a disadvantage that the signal is contaminated by a noise while circuit switching. To overcome this problem, we introduce correlated double sampling (CDS) method, which samples an integration circuit output twice with short intervals after the LED turns on and uses the difference of these voltage. Furthermore, an up-conversion method using linear interpolation, and an error correction using autocorrelation are introduced. The proposed PPG sensor, which consists of the LED, the photodiode, the current integration circuit, a CMOS switch, an A/D converter, and an MCU, is prototyped. It is evaluated by actual measurement with 22-year-old subject. The measurement results show that 22-μA total current consumption is achieved with 5-ms mean absolute error.
Existing Climate Change Will Lead to Pronounced Shifts in the Diversity of Soil Prokaryotes

There have been many studies highlighting how plant and animal communities lag behind climate change, causing extinction and diversity debts that will slowly be paid as communities equilibrate. By virtue of their short generation times and dispersal abilities, soil bacteria might be expected to respond to climate change quickly and to be effectively in equilibrium with current climatic conditions. We found strong evidence to the contrary in Tibet and North America. These findings could significantly improve understanding of climate impacts on soil microbial communities. ABSTRACT Soil bacteria are key to ecosystem function and maintenance of soil fertility. Leveraging associations of current geographic distributions of bacteria with historic climate, we predict that soil bacterial diversity will increase across the majority (∼75%) of the Tibetan Plateau and northern North America if bacterial communities equilibrate with existing climatic conditions. This prediction is possible because the current distributions of soil bacteria have stronger correlations with climate from ∼50 years ago than with current climate. This lag is likely associated with the time it takes for soil properties to adjust to changes in climate. The predicted changes are location specific and differ across bacterial taxa, including some bacteria that are predicted to have reductions in their distributions. These findings illuminate the widespread potential of climate change to influence belowground diversity and the importance of considering bacterial communities when assessing climate impacts on terrestrial ecosystems. IMPORTANCE There have been many studies highlighting how plant and animal communities lag behind climate change, causing extinction and diversity debts that will slowly be paid as communities equilibrate. By virtue of their short generation times and dispersal abilities, soil bacteria might be expected to respond to climate change quickly and to be effectively in equilibrium with current climatic conditions. We found strong evidence to the contrary in Tibet and North America. These findings could significantly improve understanding of climate impacts on soil microbial communities.
Association between changes in the intrathoracic impedance and ventricular arrhythmias in patients with heart failure

Ventricular arrhythmias (VAs) are independently related to mortality risk in patients with heart failure (HF). The wide availability of implantable cardioverter defibrillators and cardiac resynchronization therapy devices now offers an opportunity to clinically correlate the two disease processes. We hypothesized that there is an association between changes in the intrathoracic impedance and episodes of VA.
Confusion State Induction and EEG-based Detection in Learning

Confusion, as an affective state, has been proved beneficial for learning, although this emotion is always mentioned as negative affect. Confusion causes the learner to solve the problem and overcome difficulties in order to restore the cognitive equilibrium. Once the confusion is successfully resolved, a deeper learning is generated. Therefore, quantifying and visualizing the confusion that occurs in learning as well as intervening has gained great interest by researchers. Among these researches, triggering confusion precisely and detecting it is the critical step and underlies other studies. In this paper, we explored the induction of confusion states and the feasibility of detecting confusion using EEG as a first step towards an EEG-based Brain Computer Interface for monitoring the confusion and intervening in the learning. 16 participants EEG data were recorded and used. Our experiment design to induce confusion was based on tests of Raven’s Standard Progressive Matrices. Each confusing and not-confusing test item was presented during 15 seconds and the raw EEG data was collected via Emotiv headset. To detect the confusion emotion in learning, we propose an end-to-end EEG analysis method. End-to-end classification of Deep Learning in Machine Learning has revolutionized computer vision, which has gained interest to adopt this method to EEG analysis. The result of this preliminary study was promising, which showed a 71.36% accuracy in classifying users’ confused and unconfused states when they are inferring the rules in the tests.
Salivary Gland Extract from Aedes aegypti Improves Survival in Murine Polymicrobial Sepsis through Oxidative Mechanisms

Sepsis is a systemic disease with life-threatening potential and is characterized by a dysregulated immune response from the host to an infection. The organic dysfunction in sepsis is associated with the production of inflammatory cascades and oxidative stress. Previous studies showed that Aedes aegypti saliva has anti-inflammatory, immunomodulatory, and antioxidant properties. Considering inflammation and the role of oxidative stress in sepsis, we investigated the effect of pretreatment with salivary gland extract (SGE) from Ae. aegypti in the induction of inflammatory and oxidative processes in a murine cecum ligation and puncture (CLP) model. Here, we evaluated animal survival for 16 days, as well as bacterial load, leukocyte migration, and oxidative parameters. We found that the SGE pretreatment improved the survival of septic mice, reduced bacterial load and neutrophil influx, and increased nitric oxide (NO) production in the peritoneal cavity. With regard to oxidative status, SGE increased antioxidant defenses as measured by Trolox equivalent antioxidant capacity (TEAC) and glutathione (GSH), while reducing levels of the oxidative stress marker malondialdehyde (MDA). Altogether, these data suggest that SGE plays a protective role in septic animals, contributing to oxidative and inflammatory balance during sepsis. Therefore, Ae. aegypti SGE is a potential source for new therapeutic molecule(s) in polymicrobial sepsis, and this effect seems to be mediated by the control of inflammation and oxidative damage.
Structural Brain Correlates of Attention Dysfunction in Lewy Body Dementias and Alzheimer’s Disease

Lewy body dementia (LBD) and Alzheimer’s disease (AD) are common forms of dementia that have different clinical profiles but are both commonly associated with attentional deficits. The aim of this study was to investigate efficiency of different attentional systems in LBD and AD and its association with brain structural abnormalities. We studied reaction time (RT) data from 45 LBD, 31 AD patients and 22 healthy controls (HCs) using the Attention Network Test (ANT) to assess the efficiency of three different attentional systems: alerting, orienting and executive conflict. Voxel-based morphometry (VBM) was used to investigate relations between different attention components and cortical volume. Both dementia groups showed slower overall RTs than controls, with additional slowing in LBD relative to AD. There was a significant alerting effect in controls which was absent in the dementia groups, the executive conflict effect was greater in both dementia groups compared to controls, but the orienting effect did not differ between groups. Mean RT in AD was negatively correlated with occipital gray matter (GM) volume and in LBD orienting efficiency was negatively related to occipital white matter (WM) volume. Given that previous studies in less impaired patients suggest a maintenance of the alerting effect, the absent alerting effect in our study suggests a loss of alerting efficiency with dementia progression. While orienting was largely preserved, it might be related to occipital structural abnormalities in LBD. Executive function was markedly impaired in both dementia groups, however, the absence of relations to brain volume suggests that it might be more related to functional rather than macrostructural pathophysiological changes.
Reliving, Replaying Lived Experiences Through Auditory Verbal Hallucinations: Implications on Theories and Management

Objective: This study aims to understand the impact of negative life experience (NLE) in auditory hallucinations (AHs) and explain the heterogeneity in phenomenology of auditory verbal hallucinations (AVHs). Method: In depth interviews were conducted with 21 individuals (7 males and 14 females) experiencing AHs and accessing mental health treatment services at a not-for-profit organization. Maximum variation purposive sampling technique was used to select the sample to ensure variegation is accounted for and was collected until saturation of themes data was obtained. Results: Various different forms and functions of hallucinations are obtained with an evident pattern that links voices back to the NLE of the individual. Implications for therapeutic methods focusing on distress arising from said NLE is emphasized. Conclusions: The results obtained from this study implicate NLEs as a contributing factor in the development and maintenance of hallucinations. Sociocultural factors act as a catalyst with psychological factors creating distress and contributing to the voice-hearing experience. Treatment strategies must thus focus on content of voices and past experiences of the individual to promote recovery. A model toward conceptualization of the diversity in phenomenology is put forth.