Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Approach Analysing the Dynamic Brain Functional Connectivity for Improved MCI Detection

Objective: Dynamic functional connectivity (dFC) of the brain has been explored for the detection of mild cognitive impairment (MCI), preventing potential development of Alzheimer's disease. Deep learning is widely used method for dFC analysis but is unfortunately computationally expensive and unexplainable. Root mean square value (RMS) of the pairwise Pearson's correlation of the dFC is also proposed but is insufficient for accurate MCI detection. The present study aims at exploring the feasibility of several novel features for dFC analysis, and thus, reliable MCI detection. Methods: A public resting-state functional magnetic resonance imaging dataset containing healthy controls (HC), early MCI (eMCI), and late MCI (lMCI) patients was used. In addition to RMS, nine features were extracted from the pairwise Pearson's correlation of the dFC, inducing amplitude-, spectral-, entropy-, and autocorrelation-related features, and time reversibility. A Student's $t$-test and a least absolute shrinkage and selection operator (LASSO) regression were employed for feature dimension reduction. A SVM was then adopted for two classification objectives: HC vs. lMCI and HC vs. eMCI. Accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve were calculated as performance metrics. Results: 6109 out of 66700 features are significantly different between HC and lMCI and 5905 between HC and eMCI. Besides, the proposed features produce excellent classification results for both tasks, outperforming most of the existing methods. Significance: This study proposes a novel and general framework for dFC analysis, providing a promising tool for the detection of many neurological brain diseases using different brain signals.
Urban landscape monitoring based on high-resolution spaceborne TerraSAR-X data: a case study of Nanjing City, China

ABSTRACT Recently, the performance of high-resolution spaceborne synthetic aperture radar (SAR) data for urban landscape monitoring has attracted increased interest, in particular for regions where rainy weather prevails throughout of year. In this study, the phase component of SAR data was exploited for landscape monitoring in the western area of Nanjing city, China. Twenty-six scenes of TerraSAR-X single-look complex (SLC) interferometric images were acquired for the period of May 2013 to February 2015 and applied for urban change detection as well as surface subsidence monitoring by means of interferometric SAR (InSAR) technologies. Firstly, a small baseline strategy in conjunction with a fast statistically homogeneous pixel selection (FaSHPS) approach were applied for the coherence imagery generation and for the coherent change detection, resulting in a correct probability of better than 90%. Second, the surface deformation was derived with millimetre accuracy using an enhanced multi-temporal InSAR (MT-InSAR) tool by synergistically analyzing the amplitudes and phases of the SAR acquisitions. This study indicates that high-resolution SAR data have the potential for urban landscape monitoring by taking advantage of the interferometric performance and the all-day and all-weather imaging capability.
Correntropy-Induced Wasserstein GCN: Learning Graph Embedding via Domain Adaptation

Graph embedding aims at learning vertex representations in a low-dimensional space by distilling information from a complex-structured graph. Recent efforts in graph embedding have been devoted to generalizing the representations from the trained graph in a source domain to the new graph in a different target domain based on information transfer. However, when the graphs are contaminated by unpredictable and complex noise in practice, this transfer problem is quite challenging because of the need to extract helpful knowledge from the source graph and to reliably transfer knowledge to the target graph. This paper puts forward a two-step correntropy-induced Wasserstein GCN (graph convolutional network, or CW-GCN for short) architecture to facilitate the robustness in cross-graph embedding. In the first step, CW-GCN originally investigates correntropy-induced loss in GCN, which places bounded and smooth losses on the noisy nodes with incorrect edges or attributes. Consequently, helpful information are extracted only from clean nodes in the source graph. In the second step, a novel Wasserstein distance is introduced to measure the difference in marginal distributions between graphs, avoiding the negative influence of noise. Afterwards, CW-GCN maps the target graph to the same embedding space as the source graph by minimizing the Wasserstein distance, and thus the knowledge preserved in the first step is expected to be reliably transferred to assist the target graph analysis tasks. Extensive experiments demonstrate the significant superiority of CW-GCN over state-of-the-art methods in different noisy environments.
Dispersive nanoSQUID magnetometry

We describe the theory and implementation of a dispersive magnetometer based on an aluminum nanoSQUID. The nanoSQUID consists of a superconducting loop interrupted by two variable-thickness weak-link nanobridge Josephson junctions. When the nanoSQUID is placed in parallel with a lumped-element capacitor, it acts as the inductive element in a resonant tank circuit. By performing microwave reflectometry on the circuit, the SQUID inductance can be measured, providing a sensitive meter of the flux threading the SQUID loop. In this review we provide the theoretical basis for the device, describe design and operation considerations, and present characterization results on several devices.
Female reproductive fluid concentrations affect sperm performance of alternative male phenotypes in an external fertilizer

There is growing evidence that the female reproductive fluid (FRF) plays an important role in cryptic female choice through its differential effect on the performance of sperm from different males. In a natural spawning event, the male(s) may release ejaculate closer or further away from the spawning female. If the relative spatial proximity of competing males reflects the female pre‐mating preference towards those males, then favoured males will encounter higher concentrations of FRF than unpreferred males. Despite this being a common situation in many external fertilizers, whether different concentrations of FRF can differentially influence the sperm performance of distinct male phenotypes (favoured and unfavoured by the female) remains to be elucidated. Here, we tested this hypothesis using the grass goby (Zosterisessor ophiocephalus), a fish with distinct territorial‐sneaker reproductive tactics and female pre‐mating preference towards territorial males, that consequently mate in an advantaged position and whose sperm experience higher concentrations of FRF. Our findings revealed a differential concentration‐dependent effect of FRF over sneaker and territorial sperm motility only at low concentrations (i.e. at the distance where sneakers typically ejaculate), with increasing FRF concentrations (i.e. close to the eggs) similarly boosting the sperm performance of both sneaker and territorial males. The ability to release sperm close to the eggs is a prerogative of territorials, but FRF can likewise advantage the sperm of those sneakers that are able to get closer, allowing flexibility in the direction of female post‐mating choice.
The effect of nirmatrelvir‐ritonavir on the long‐term risk of neuropsychiatric sequelae following COVID‐19

The retrospective cohort was conducted to assess the effect of nirmatrelvir‐ritonavir (NMV‐r) on the long‐term risk of neuropsychiatric sequela following COVID‐19. TriNetX research network was used to identify nonhospitalized adult patients who tested positive for severe acute respiratory syndrome coronavirus 2 infection or were diagnosed with COVID‐19 between March 1, 2020 and July 1, 2022. Further propensity score matching method was used to create two matched cohorts with and without receiving NMV‐r. The primary outcome was the incidence of neuropsychiatric sequela within a 90‐day to 1‐year period following a diagnosis of COVID‐19. After screening 119 494 527 electronic health records, two matched cohorts of each 27 194 patients were identified. During the follow‐up period, the NMV‐r group demonstrated a reduced risk of any neuropsychiatric sequelae compared to the control group (odds ratio [OR], 0.634; 95% confidence interval [CI], 0.604−0.667). In comparison with the control group, the patient treated with NMV‐r exhibited a markedly diminished risk of developing neurocognitive sequela (OR, 0.377; 95% CI, 0.325−0.439) and psychiatric sequela (OR, 0.629; 95% CI, 0.593−0.666). In addition, patients treated with NMV‐r had a significantly reduced risk of developing dementia (OR, 0.365; 95% CI, 0.255−0.522), depression (OR, 0.555; 95% CI, 0.503−0.612), insomnia (OR, 0.582; 95% CI, 0.508−0.668) and anxiety disorder (OR, 0.645 95% CI, 0.600−0.692). Moreover, the beneficial effect of NMV‐r on the neuropsychiatric sequelae was observed across further subgroup analyses. Among nonhospitalized COVID‐19 patients, who at risk of disease progression, the use of NMV‐r is associated with a reduction in the long‐term risk of neuropsychiatric sequela, including dementia, depression, insomnia and anxiety disorder. It may be necessary to re‐evaluate the use of NMV‐r, as a preventive measure to reduce the risk of severe acute disease and post‐acute adverse mental health outcomes.
An immersed boundary solver for inviscid compressible flows

In this paper, a simple and efficient immersed boundary (IB) method is developed for the numerical simulation of inviscid compressible Euler equations. We propose a method based on coordinate transformation to calculate the unknowns of ghost points. In the present study, the body‐grid intercept points are used to build a complete bilinear (2‐D)/trilinear (3‐D) interpolation. A third‐order weighted essentially nonoscillation scheme with a new reference smoothness indicator is proposed to improve the accuracy at the extrema and discontinuity region. The dynamic blocked structured adaptive mesh is used to enhance the computational efficiency. The parallel computation with loading balance is applied to save the computational cost for 3‐D problems. Numerical tests show that the present method has second‐order overall spatial accuracy. The double Mach reflection test indicates that the present IB method gives almost identical solution as that of the boundary‐fitted method. The accuracy of the solver is further validated by subsonic and transonic flow past NACA2012 airfoil. Finally, the present IB method with adaptive mesh is validated by simulation of transonic flow past 3‐D ONERA M6 Wing. Global agreement with experimental and other numerical results are obtained.
Nonlinear Mapping From EMG to Prosthesis Closing Velocity Improves Force Control With EMG Biofeedback

When using EMG biofeedback to control the grasping force of a myoelectric prosthesis, subjects need to activate their muscles and maintain the myoelectric signal within an appropriate interval. However, their performance decreases for higher forces, because the myoelectric signal is more variable for stronger contractions. Therefore, the present study proposes to implement EMG biofeedback using nonlinear mapping, in which EMG intervals of increasing size are mapped to equal-sized intervals of the prosthesis velocity. To validate this approach, 20 non-disabled subjects performed force-matching tasks using Michelangelo prosthesis with and without EMG biofeedback with linear and nonlinear mapping. Additionally, four transradial amputees performed a functional task in the same feedback and mapping conditions. The success rate in producing desired force was significantly higher with feedback (65.4±15.9%) compared to no feedback (46.2±14.9%) as well as when using nonlinear (62.4±16.8%) versus linear mapping (49.2±17.2%). Overall, in non-disabled subjects, the highest success rate was obtained when EMG biofeedback was combined with nonlinear mapping (72%), and the opposite for linear mapping with no feedback (39.6%). The same trend was registered also in four amputee subjects. Therefore, EMG biofeedback improved prosthesis force control, especially when combined with nonlinear mapping, which showed to be an effective approach to counteract increasing variability of myoelectric signal for stronger contractions.
Pregnancy outcome in patients with a medical history of immunoglobulin A vasculitis: a case–control study

Objective Data on obstetric outcomes in patients with a history of immunoglobulin A vasculitis (IgA-V) are lacking. The aim of this study was to assess maternal, neonatal, and vasculitis outcomes during pregnancy. Method We conducted a French retrospective case–control study. Pregnancies of patients with a history of IgA-V (cases) were retrospectively studied and compared to pregnancies in women who developed IgA-V after their pregnancies and to pregnancies in healthy women (controls). Results Twenty-six pregnancies in patients with a history of IgA-V were included and compared to 15 pregnancies in women who later developed IgA-V and 52 pregnancies in healthy women. Both gestational hypertension and pre-eclampsia were more frequent in the case group than in the other groups (23% vs 0% vs 0%, p < 0.01; 12% vs 7% vs 0%, p = 0.04). Hypertensive disorder of pregnancy occurred more frequently in patients with pre-existing kidney disease (78% vs 12%, p < 0.01). Caesarean section was more often performed in the case group than in the other groups (27% vs 0% vs 10%, p = 0.04). No foetal loss or maternal deaths occurred. There were no differences in delivery term or birth weight. No vasculitis flares were observed during pregnancy. Conclusion Women with a history of IgA-V appear to be at higher risk for gestational hypertension and pre-eclampsia, especially in cases with renal involvement; however, both mother and newborn outcomes appear to be favourable.
Rhéoépaississement des suspensions denses : mise en évidence de la transition frictionnelle

Le rheoepaississement est un phenomene spectaculaire apparaissant dans certaines suspensions concentrees en particules. Il se manifeste par l’augmentation brutale de la viscosite de la suspension au dela d’une contrainte critique. L’exemple emblematique de suspension presentant ce type de comportement est le melange d’amidon de mais et d’eau. Le rheoepaississement est longtemps reste une enigme, jusqu’a des travaux theoriques et numeriques recents proposant un modele microscopique coherent. Selon celui-ci, le rheoepaississement provient d’une transition frictionnelle due a la presence d’une force repulsive entre les grains. Au cours de ma these, j’ai realise une des premieres demonstrations experimentales directe de ce mecanisme. En m'inspirant de travaux venant du domaine des milieux granulaires, j'ai montre qu'en accord avec le modele de transition frictionnelle, une suspension rheoepaississante possede a faible pression granulaire un etat non frottant. J'ai ensuite mis en evidence la transition en elle-meme dans des suspensions controlees de billes de silice dans des solutions salines. Pour cela, j'ai du developper de nouvelles methodes de rheologie a pression imposee. En effet, les rheometres standard ne permettent pas d'acceder aux proprietes de frottement des suspensions. Le seul rheometre qui le permet n'est pas adapte aux suspensions etudiees ici, qui sont constituees de particules colloidales. Ce travail ouvre donc la voie au developpement d’une nouvelle generation de rheometres permettant de mesurer le frottement dans les suspensions colloidales, un enjeu majeur pour la rheologie des fluides complexes.
Gas distribution and clumpiness in the galaxy group NGC 2563

We present a Chandra study of the hot intragroup medium (hIGM) of the galaxy group NCG2563. The Chandra mosaic observations, with a total exposure time of ∼ 430 ks, allow the gas density to be detected beyond R200 and the gas temperature out to 0.75 R200. This represents the first observational measurement of the physical properties of a poor groups beyond R500. By capitalizing on the exquisite spatial resolution of Chandra that is capable to remove unrelated emission from point sources and substructures, we are able to radially constrain the inhomogeneities of gas (“clumpiness”), gas fraction, temperature and entropy distribution. Although there is some uncertainty in the measurements, we find evidences of gas clumping in the virialization region, with clumping factor of about 2 3 at R200. The gas clumping-corrected gas fraction is significantly lower than the cosmological baryon budget. These results may indicate a larger impact of the gas inhomogeneities with respect to the prediction from hydrodynamic numerical simulations, and we discuss possible explanations for our findings.
Prospective evaluation of an enhanced recovery after surgery (ERAS) pathway in a Norwegian cohort of patients with suspected or advanced ovarian cancer

Objective This prospective cohort study evaluated the introduction of an enhanced recovery after surgery (ERAS) pathway in a tertiary gynecologic oncology referral center. Compliance and clinical outcomes were studied in two separate surgical cohorts. Methods Patients undergoing laparotomy for suspected or verified advanced ovarian cancer at Oslo University Hospital were prospectively included in a pre- and post-implementation cohort. A priori, patients were stratified into: cohort 1, patients planned for surgery of advanced disease; and cohort 2, patients undergoing surgery for suspicious pelvic tumor. Baseline characteristics, adherence to the pathway, and clinical outcomes were assessed. Results Of the 439 included patients, 235 (54%) underwent surgery for advanced ovarian cancer in cohort 1 and 204 (46%) in cohort 2. In cohort 1, 53% of the patients underwent surgery with an intermediate/high Aletti complexity score. Post-ERAS, median fasting times for solids (13.1 hours post-ERAS vs 16.0 hours pre-ERAS, p<0.001) and fluids (3.7 hours post-ERAS vs 11.0 hours pre-ERAS, p<0.001) were significantly reduced. Peri-operative fluid management varied less and was reduced from median 15.8 mL/kg/hour (IQR 10.8–22.5) to 11.5 mL/kg/hour (IQR 9.0–15.4) (p<0.001). In cohort 2 only there was a statistically significant reduction in length of stay (mean (SD) 4.3±1.5 post-ERAS vs 4.6±1.2 pre-ERAS, p=0.026). Despite stable readmission rates, there were significantly more serious complications reported in cohort 1 post-ERAS. Conclusions ERAS increased adherence to current standards in peri-operative management with significant reduction in fasting times for both solids and fluids, and peri-operative fluid administration. Length of stay was reduced in patients with suspicious pelvic tumor. Despite serious complications being common in patients with advanced disease undergoing debulking surgery, a causal relationship with the ERAS protocol could not be established. Implementing ERAS and continuous performance auditing are crucial to advancing peri-operative care of patients with ovarian cancer.
Un-Paradoxing Privacy: Considering Hopeful Trust

Extant literature has proposed an important role for trust in moderating people’s willingness to disclose personal information, but there is scant HCI literature that deeply explores the relationship between privacy and trust in apparent privacy paradox circumstances. Attending to this gap, this article reports a qualitative study examining how people account for continuing to use services that conflict with their stated privacy preferences, and how trust features in these accounts. Our findings undermine the notion that individuals engage in strategic thinking about privacy, raising important questions regarding the explanatory power of the well-known privacy calculus model and its proposed relationship between privacy and trust. Finding evidence of hopeful trust in participants’ accounts, we argue that trust allows people to morally account for their “paradoxical” information disclosure behavior. We propose that effecting greater alignment between people’s privacy attitudes and privacy behavior—or “un-paradoxing privacy”—will require greater regulatory assurances of privacy.
An Empirical Study on the Effects of Obfuscation on Static Machine Learning-Based Malicious JavaScript Detectors

Machine learning is increasingly being applied to malicious JavaScript detection in response to the growing number of Web attacks and the attendant costly manual identification. In practice, to hide their malicious behaviors or protect intellectual copyrights, both malicious and benign scripts tend to obfuscate their own code before uploading. While obfuscation is beneficial, it also introduces some additional code features (e.g., dead code) into the code. When machine learning is employed to learn a malicious JavaScript detector, these additional features can affect the model to make it less effective. However, there is still a lack of clear understanding of how robust existing machine learning-based detectors are on different obfuscators. In this paper, we conduct the first empirical study to figure out how obfuscation affects machine learning detectors based on static features. Through the results, we observe several findings: 1) Obfuscation has a significant impact on the effectiveness of detectors, causing an increase both in false negative rate (FNR) and false positive rate (FPR), and the bias of obfuscation in the training set induces detectors to detect obfuscation rather than malicious behaviors. 2) The common measures such as improving the quality of the training set by adding relevant obfuscated samples and leveraging state-of-the-art deep learning models can not work well.3) The root cause of obfuscation effects on these detectors is that feature spaces they use can only reflect shallow differences in code, not about the nature of benign and malicious, which can be easily affected by the differences brought by obfuscation. 4) Obfuscation has a similar effect on realistic detectors in VirusTotal, indicating that this is a common real-world problem.
DeUEDroid: Detecting Underground Economy Apps Based on UTG Similarity

In recent years, the underground economy is proliferating in the mobile system. These underground economy apps (UEware for short) make profits from providing non-compliant services, especially in sensitive areas (e.g., gambling, porn, loan). Unlike traditional malware, most of them (over 80%) do not have malicious payloads. Due to their unique characteristics, existing detection approaches cannot effectively and efficiently mitigate this emerging threat. To address this problem, we propose a novel approach to effectively and efficiently detect UEware by considering their UI transition graphs (UTGs). Based on the proposed approach, we design and implement a system, named DeUEDroid, to perform the detection. To evaluate DeUEDroid, we collect 25, 717 apps and build up the first large-scale ground-truth dataset (1, 700 apps) of UEware. The evaluation result based on the ground-truth dataset shows that DeUEDroid can cover new UI features and statically construct precise UTG. It achieves 98.22% detection F1-score and 98.97% classification accuracy, a significantly better performance than the traditional approaches. The evaluation result involving 24, 017 apps demonstrates the effectiveness and efficiency of UEware detection in real-world scenarios. Furthermore, the result also reveals that UEware are prevalent, i.e., 54% apps in the wild and 11% apps in the app stores are UEware. Our work sheds light on the future work of analyzing and detecting UEware. To engage the community, we have made our prototype system and the dataset available online.