Miracle-dz/UniFAR-FTUs huggingface dataset:
Mental time travel in mild cognitive impairment

ABSTRACT Introduction: Mental Time Travel (MTT) is the people’s ability to remember themselves in the past and to imagine themselves in the future, and influence important life domains such as making decisions and planning future actions. It is widely recognized that patients with aMCI have deficits in episodic memory, but they also show impairments in semantic memory. It has been controversial whether MTT tasks are disturbed in aMCI mainly in relation to internal details related to episodic information, or external details, representing semantic and other extraneous information. The present study assessed whether patients with aMCI are affected in MTT regarding generation of internal details and external details, in past and future dimensions. Furthermore, it analyzed production in individual detail categories (internal: event details, thought/emotion, place, time, perceptual; external: extraneous events, semantic, other, repetitions). Method: Twenty-nine patients with aMCI and 29 healthy controls underwent a MTT task based on an Autobiographical Interview, where they had to generate past and future events in response to cue words. Transcriptions were segmented and classified into internal detail categories and external detail categories, and composite scores were obtained. Results: Patients with aMCI could globally produce significantly less details than controls. Similar to controls, patients with aMCI produced more internal details than external details, had more difficulty in generating details regarding the future as compared to the past, and scored higher in the detail categories event details and thought/emotion which represent internal detail types. Conclusions: Patients with aMCI showed widespread deficits in MTT, presumably reflecting deficiencies in the complex and multiple cognitive abilities required for MTT tasks.
Post-traumatic growth in mental health recovery: qualitative study of narratives

Objectives Post-traumatic growth, defined as positive psychological change experienced as a result of the struggle with challenging life circumstances, is under-researched in people with mental health problems. The aim of this study was to develop a conceptual framework for post-traumatic growth in the context of recovery for people with psychosis and other severe mental health problems. Design Qualitative thematic analysis of cross-sectional semi-structured interviews about personal experiences of mental health recovery. Setting England. Participants Participants were adults aged over 18 and: (1) living with psychosis and not using mental health services (n=21); (2) using mental health services and from black and minority ethnic communities (n=21); (3) underserved, operationalised as lesbian, gay, bisexual and transgender community or complex needs or rural community (n=19); or (4) employed in peer roles using their lived experience with others (n=16). The 77 participants comprised 42 (55%) female and 44 (57%) white British. Results Components of post-traumatic growth were present in 64 (83%) of recovery narratives. Six superordinate categories were identified, consistent with a view that post-traumatic growth involves learning about oneself (self-discovery) leading to a new sense of who one is (sense of self) and appreciation of life (life perspective). Observable positively valued changes comprise a greater focus on self-management (well-being) and more importance being attached to relationships (relationships) and spiritual or religious engagement (spirituality). Categories are non-ordered and individuals may start from any point in this process. Conclusions Post-traumatic growth is often part of mental health recovery. Changes are compatible with research about growth following trauma, but with more emphasis on self-discovery, integration of illness-related experiences and active self-management of well-being. Trauma-related growth may be a preferable term for participants who identify as having experienced trauma. Trauma-informed mental healthcare could use the six identified categories as a basis for new approaches to supporting recovery. Trial registration number ISRCTN11152837
Performance of selected noisy covert channels and their countermeasures in IP networks

Encryption alone secures communication by preventing adversaries from easily decoding one’s transmissions. Covert channels go one step further by attempting to hide the very existence of communication. They hide inside legitimate overt network traffic. Huge amounts of traffic make the Internet an ideal vehicle for covert communications. Most existing covert channels are simple and in principle easy to detect or eliminate. The more complex channels are usually harder to detect and eliminate, but typically suffer from channel noise. Previous work has only partly analysed the performance of noisy channels and their countermeasures and has not compared different types of channels. We characterise the trade-offs between channel simplicity, capacity and ease of detection and elimination by investigating the performance of selected noisy covert channels and their countermeasures. Not all chosen channels are entirely new, but we propose novel improved encoding schemes. We also develop techniques for reliable data transmission. We analyse the theoretical channel capacities as well as empirically measure achievable throughputs. We show that the Internet’s potential to support more sophisticated covert channels is considerably greater than suggested by most existing simple channels. First, we analyse a channel in the IP Time-to-live (TTL) header field. We develop new stealthier encoding schemes that also provide a slightly increased capacity. The channel has a comparatively high capacity of up to a few hundred bits per second depending on the overt traffic, but is easy to detect and eliminate. Next, we analyse an inter-packet gap timing channel. We develop novel stealthy encoding schemes because previous schemes are easy to detect. The channel only has up to 70–80% of the TTL channel’s capacity, but is harder to detect. However, it can still be eliminated. Then we propose and analyse a novel indirect channel in multiplayer game traffic. The channel is impractical to eliminate, but is still detectable. The capacity is up to 10–20 bits per second – lower than that of direct channels. Next, we analyse an indirect timing channel that transmits bits via temperature changes. We develop an improved version of the channel that has increased capacity. Still the capacity is only 10–20 bits per hour, but the channel is potentially hard to detect and eliminate. Finally, we develop techniques to detect and eliminate the covert channels and evaluate their effectiveness. While the proposed elimination methods are effective but channelspecific, we demonstrate that machine-learning techniques detect different covert channels with over 95% accuracy.
CYTOP Fibre Bragg Grating Sensors for Harsh Radiation Environments

We present a polymer fibre Bragg grating sensor and its sensitivity to gamma radiation by observing the reflected spectral profile. The Bragg grating is femtosecond inscribed within a perfluorinated CYTOP fibre and the alteration of the Bragg wavelength corresponds to the total radiation dose received. Over a total dose of 41 kGy, the fibre demonstrates a sensitivity of −26.2 pm/kGy and a resolution of 40 Gy. Under active consideration for the instrumentation of nuclear waste repositories, this study gives a better understanding of the effects of gamma radiation upon Bragg gratings in CYTOP fibres.
Recent Progress in Self-Powered Skin Sensors

Self-powered skin sensors have attracted significant attention in recent years due to their great potential in medical care, robotics, prosthetics, and sports. More importantly, self-powered skin sensors do not need any energy-supply components like batteries, which allows them to work sustainably and saves them the trouble of replacement of batteries. The self-powered skin sensors are mainly based on energy harvesters, with the device itself generating electrical signals when triggered by the detected stimulus or analyte, such as body motion, touch/pressure, acoustic sound, and chemicals in sweat. Herein, the recent research achievements of self-powered skin sensors are comprehensively and systematically reviewed. According to the different monitoring signals, the self-powered skin sensors are summarized and discussed with a focus on the working mechanism, device structure, and the sensing principle. Based on the recent progress, the key challenges that exist and the opportunities that lie ahead are also discussed.
Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach

The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, we present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows us to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, we apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, we count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that our proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average—dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure.
Comparison Between Multiline Transmission and Diverging Wave Imaging: Assessment of Image Quality and Motion Estimation Accuracy

High frame rate imaging is particularly important in echocardiography for better assessment of the cardiac function. Several studies showed that diverging wave imaging (DWI) and multiline transmission (MLT) are promising methods for achieving a high temporal resolution. The aim of this study was to compare MLT and compounded motion compensation (MoCo) DWI for the same transmitted power, same frame rates [image quality and speckle tracking echocardiography (STE) assessment], and same packet size [tissue Doppler imaging (TDI) assessment]. Our results on static images showed that MLT outperforms DW in terms of resolution (by 30% on average). However, in terms of contrast, MLT outperforms DW only for the depth of 11 cm (by 40% on average), the result being reversed at a depth of 4 cm (by 27% on average). In vitro results on a spinning phantom at nine different velocities showed that similar STE axial errors (up to 2.3% difference in median errors and up to 2.1% difference in the interquartile ranges) are obtained with both ultrafast methods. On the other hand, the median lateral STE estimates were up to 13% more accurate with DW than with MLT. On the contrary, the accuracy of TDI was only up to ~3% better with MLT, but the achievable DW Doppler frame rate was up to 20 times higher. However, our overall results showed that the choice of one method relative to the other is therefore dependent on the application. More precisely, in terms of image quality, DW is more suitable for imaging structures at low depths, while MLT can provide an improved image quality at the focal point that can be placed at higher depths. In terms of motion estimation, DW is more suitable for color Doppler-related applications, while MLT could be used to estimate velocities along selected lines of the image.
Bovine leptospirosis in abattoirs in Uganda: Molecular detection and risk of exposure among workers

Leptospirosis is a zoonotic bacterial disease reported worldwide. In Uganda, seropositivity has been reported in both humans and domesticated animals, including cattle. However, it remains unknown whether cattle are shedding leptospires and thus acting as potential source for human leptospirosis. We conducted this cross‐sectional study in two cattle abattoirs in Kampala, Uganda between June and July 2017. Kidney and urine samples from 500 cattle sourced from across the country were analysed by real‐time PCR to establish the prevalence of Leptospira‐positive cattle and risk of exposure to abattoir workers. The species of infecting Leptospira was determined by amplification of secY gene and compared to reference sequences published in GenBank. Of 500 cattle tested, 36 (7.2%) had Leptospira DNA in their kidneys (carriers), 29 (5.8%) in their urine (shedders); with an overall prevalence (kidney and/or urine) of 8.8%. Leptospira borgpetersenii was confirmed as the infecting species in three cattle and Leptospira kirschneri in one animal. Male versus female cattle (OR = 3, p‐value 0.003), exotic versus local breeds (OR = 21.3, p‐value 0.002) or cattle from Western Uganda (OR = 4.4, p‐value 0.001) and from regions across the border (OR = 3.3, p‐value 0.032) versus from the central region were more likely to be Leptospira‐positive. The daily risk of exposure of abattoir workers to ≥1 (kidney and/or urine) positive carcass ranged from 27% (95% credibility interval 18.6–52.3) to 100% (95% CI 91.0–100.0), with halal butchers and pluck inspectors being at highest risk. In conclusion, cattle slaughtered at abattoirs in Uganda carry and shed pathogenic Leptospira species; and this may pose occupation‐related risk of exposure among workers in these abattoirs, with workers who handle larger numbers of animals being at higher risk.
Draft Genome Sequences of Flavobacterium columnare Strains ARS1 and BGFS27, Isolated from Channel Catfish (Ictalurus punctatus)

Flavobacterium columnare strain BGFS27 was isolated from an apparently healthy wild channel catfish (Ictalurus punctatus) collected from the Mobile River in 2005. F. columnare strain ARS1 was isolated from a channel catfish suffering from columnaris disease in a commercial farm in 1996. BGFS27 belongs to genomovar II (genetic group 2), while ARS1 belongs to genomovar III (genetic group 3). Here, we report the draft genome sequences of F. columnare BGFS27 and ARS1, obtained by PacBio sequencing. ABSTRACT Flavobacterium columnare strain BGFS27 was isolated from an apparently healthy wild channel catfish (Ictalurus punctatus) collected from the Mobile River in 2005. F. columnare strain ARS1 was isolated from a channel catfish suffering from columnaris disease in a commercial farm in 1996. BGFS27 belongs to genomovar II (genetic group 2), while ARS1 belongs to genomovar III (genetic group 3). Here, we report the draft genome sequences of F. columnare BGFS27 and ARS1, obtained by PacBio sequencing.
Zinc deficiency and advanced liver fibrosis among HIV and hepatitis C co-infected anti-retroviral naïve persons with alcohol use in Russia

Background and aims Liver disease in people living with HIV co-infected with hepatitis C virus is a source of morbidity and mortality in Russia. HIV accelerates liver fibrosis in the setting of HCV co-infection and alcohol use. Zinc deficiency is common among people living with HIV and may be a factor that facilitates the underlying mechanisms of liver fibrosis. We investigated the association between zinc deficiency and advanced liver fibrosis in a cohort of HIV/HCV co-infected persons reporting heavy drinking in Russia. Methods This is a secondary data analysis of baseline data from 204 anti-retroviral treatment naïve HIV/HCV co-infected Russians with heavy drinking that were recruited into a clinical trial of zinc supplementation. The primary outcome of interest in this cross-sectional study was advanced liver fibrosis. Zinc deficiency, the main independent variable, was defined as plasma zinc <0.75 mg/L. Exploratory analyses were performed examining continuous zinc levels and fibrosis scores. Analyses were conducted using multivariable regression models adjusted for potential confounders. Results The prevalence of advanced liver fibrosis was similar for those with zinc deficiency compared to those with normal zinc levels, (27.7% vs. 23.0%, respectively). We did not detect an association between zinc deficiency and advanced liver fibrosis in the adjusted regression model (aOR: 1.28, 95% CI: 0.62–2.61, p = 0.51) nor in exploratory analyses. Conclusions In this cohort of Russians with HIV/HCV co-infection, who are anti-retroviral treatment naïve and have heavy alcohol use, we did not detect an association between zinc deficiency or zinc levels and advanced liver fibrosis.
Nonlinear Reconstruction Methods for Parallel Magnetic Resonance Imaging

Parallel acquisition techniques have been proposed to reduce the measurement times for magnetic resonance imaging. These techniques rely on the simultaneous acquisition of an undersampled data set with multiple receive coils. The aim of the thesis is to improve the quality of the reconstructed images and to achieve higher acceleration factors. The analysis of existing approaches identifies a miscalibration of the receive coil sensitivities and the bad conditioning of the reconstruction problem as the limiting determinants. The problem of inaccurate coil sensitivities is addressed with a new non-linear algorithm for auto-calibrated parallel imaging, which allows for a reconstruction of artefact-free images with higher acceleration factors and less reference data than previous linear approaches. It is further shown that the general limitation of parallel imaging, which is caused by the bad conditioning of the reconstruction problem, can be relieved by better regularization techniques.
An Improved Method for Handling and Extracting useful Information from Big Data

Objectives: The main objective of this method is to extract the meaningful information from the large amount of data and provide the aggregated form of output to the users. This work tends to mine the large volume of data which are gathered from the multiple sites in order to provide the useful information to the sites for improving their performance. Method: Data aggregation plays a most concerned role in the big data environment where it is very complex to extract useful information from large volume of data. In the existing work, computation based partitioning and aggregation (CP-A) is used to divide the big data into multiple partitions in which aggregation would be done. However existing works do not focus on the content similarity present between the set of data’s which might degrades the accuracy of aggregation result. To overcome this problem in the proposed research methodology, hybridized content and computation aware partitioning and aggregation (HCCP-A) method is introduced. Initially, this work would partition the big data into multiple partitions with the concern content and computation properties. After partitioning, data de-duplication technique would be applied to eliminate the repeated data’s that are present in every partition. This partitioning and data de-duplication process would be done in the mapper stage. The output from the mapper node would be parsed into aggregator node which will perform aggregation. Finally, aggregation result from the aggregator node would be fused together in the reducer node. Results: Hybridized content and computation aware partitioning and aggregation method is introduced in this work for extracting useful information from the large volume of data’s in the summarized format. This methodology is used to aggregate the large volume of data and after aggregation, result produced were compared with the existing methodology called CP-A in terms of performance metrics called the error rate, execution time and CPU utilization. The experimental tests were conducted and the performance has been made against the different number of data size. From this experimental testing it has been proved that the proposed methodology provides a better result than the existing methodology in terms of all performance measures. Conclusion: The finding demonstrates that the data aggregation using Hybridized content and computation aware partitioning and aggregation method is presented and this method has high accuracy and less error rate than the previous methodologies.
Changes of Elbow Joint Load from Two-handed to One-handed Push-up Exercise

The purpose of this study was to compare the differences of the elbow joint load between two-handed and one-handed pushup exercises. Elbow joint loading was evaluated during two-handed and one-handed pushup exercises using a motion analysis system and a force plate. Results show that in one-handed pushup, large pronation/supination angles were found (p＜0.05) and posterial shear force and flexion moment were increased 5 times and 2.5 times, respectively, compared to two-handed pushup. It was noted that greater posterior shear forces and flexion moments of the elbow are encountered not only from loading increasing but also from the muscle coordination for the constraint of joint. Therefore, people with elbow ligament injury are suggested to pay more attention to heavy posterial shear force and flexion moment to avoid injury from two-handed pushup to one-handed pushup.
Receptor-gamma agonists exhibit anti-inflammatory and antiviral effects in an EcoHIV mouse model

The widespread use of combination antiretroviral therapy (cART) has resulted in significantly reduced deaths from HIV-1 associated complications and opportunistic infections. However, it is estimated that up to 50% of HIV-1 infected individuals still develop HIV-1 associated neurocognitive disorders (HAND). With no treatment currently available for patients, there is a critical need to identify therapeutic approaches that can treat this disorder. Evidence suggests that targeting Peroxisome ProliferatorActivated Receptor-gamma (PPARγ) can be anti-inflammatory in neurological disorders. Here we show that treatment with ppARγ agonists (rosiglitazone or pioglitazone) in primary cultures of mouse glial cells reversed EcoHIV-induced inflammatory genes (TNFα, IL-1β, CCL2, CCL3, CXCL10) and indicator of oxidative stress (iNOS). Furthermore, in vivo, mice administered with EcoHIV through intracranial injection resulted in upregulation of inflammatory genes (TNFα, IL-1β, IFNγ, CCL2, CCL3, CXCL10) and oxidative stress marker (iNOS) in the brain which was reversed through intraperitoneal administration of ppARγ agonists (rosiglitazone or pioglitazone). Finally, we demonstrated that treatment with these compounds in vivo reduced EcoHIV p24 protein burden in the brain. Our results suggest that treatment with ppARγ agonists are anti-inflammatory and antiviral in an in vivo model of EcoHIV infection. These drugs hold promise as potential candidates for HAND treatment in the future.