Miracle-dz/UniFAR-FTUs huggingface dataset:
Connected speech and language in mild cognitive impairment and Alzheimer’s disease: A review of picture description tasks

ABSTRACT Introduction: The neuropsychological profile of people with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) dementia includes a history of decline in memory and other cognitive domains, including language. While language impairments have been well described in AD dementia, language features of MCI are less well understood. Connected speech and language analysis is the study of an individual’s spoken discourse, usually elicited by a target stimulus, the results of which can facilitate understanding of how language deficits typical of MCI and AD dementia manifest in everyday communication. Among discourse genres, picture description is a constrained task that relies less on episodic memory and more on semantic knowledge and retrieval, within the cognitive demands of a communication context. Understanding the breadth of evidence across the continuum of cognitive decline will help to elucidate the areas of strength and need in terms of using this method as an evaluative tool for both cognitive changes and everyday functional communication. Method: We performed an extensive literature search of peer-reviewed journal articles that focused on the use of picture description tasks for evaluating language in persons with MCI or AD dementia. We selected articles based on inclusion and exclusion criteria and described the measures assessed, the psychometric properties that were reported, the findings, and the limitations of the included studies. Results: 36 studies were selected and reviewed. Across all 36 studies, there were 1, 127 patients with AD dementia and 274 with MCI or early cognitive decline. Multiple measures were examined, including those describing semantic content, syntactic complexity, speech fluency, vocal parameters, and pragmatic language. Discriminant validity widely reported and distinct differences in language were observable between adults with dementia and controls; fewer studies were able to distinguish language differences between typically aging adults and those with MCI. Discussion: Our review shows that picture description tasks are useful tools for detecting differences in a wide variety of language and communicative measures. Future research should expand knowledge about subtle changes to language in preclinical AD and Mild Cognitive Impairment (MCI) which may improve the utility of this method as a clinically meaningful screening tool.
A non-orthogonal SVD-based decomposition for phase invariant error-related potential estimation

The estimation of the Error Related Potential from a set of trials is a challenging problem. Indeed, the Error Related Potential is of low amplitude compared to the ongoing electroencephalographic activity. In addition, simple summing over the different trials is prone to errors, since the waveform does not appear at an exact latency with respect to the trigger. In this work, we propose a method to cope with the discrepancy of these latencies of the Error Related Potential waveform and offer a framework in which the estimation of the Error Related Potential waveform reduces to a simple Singular Value Decomposition of an analytic waveform representation of the observed signal. The followed approach is promising, since we are able to explain a higher portion of the variance of the observed signal with fewer components in the expansion.
ML frequency offset and carrier phase estimation in OFDM systems with noncircular transmissions

In this paper the problem of blind frequency offset and carrier phase estimation in orthogonal frequency-division multiplexing (OFDM) systems with noncircular transmissions is considered. In this case, if the number of subcarriers is sufficiently large, the OFDM signal becomes an improper complex Gaussian process. By exploiting the joint probability density function for improper (or noncircular) complex Gaussian random vectors maximum likelihood estimators for the parameters of interest are derived, and moreover, the Cramér-Rao lower bound is evaluated.
Predicting Invasive Fungal Pathogens Using Invasive Pest Assemblages: Testing Model Predictions in a Virtual World

Predicting future species invasions presents significant challenges to researchers and government agencies. Simply considering the vast number of potential species that could invade an area can be insurmountable. One method, recently suggested, which can analyse large datasets of invasive species simultaneously is that of a self organising map (SOM), a form of artificial neural network which can rank species by establishment likelihood. We used this method to analyse the worldwide distribution of 486 fungal pathogens and then validated the method by creating a virtual world of invasive species in which to test the SOM. This novel validation method allowed us to test SOM's ability to rank those species that can establish above those that can't. Overall, we found the SOM highly effective, having on average, a 96–98% success rate (depending on the virtual world parameters). We also found that regions with fewer species present (i.e. 1–10 species) were more difficult for the SOM to generate an accurately ranked list, with success rates varying from 100% correct down to 0% correct. However, we were able to combine the numbers of species present in a region with clustering patterns in the SOM, to further refine confidence in lists generated from these sparsely populated regions. We then used the results from the virtual world to determine confidences for lists generated from the fungal pathogen dataset. Specifically, for lists generated for Australia and its states and territories, the reliability scores were between 84–98%. We conclude that a SOM analysis is a reliable method for analysing a large dataset of potential invasive species and could be used by biosecurity agencies around the world resulting in a better overall assessment of invasion risk.
Investigation of Effective Parameters on Size of Paclitaxel Loaded PLGA Nanoparticles

Purpose: The size of polymeric nanoparticles is considered as an effective factor in cancer therapy due to enterance into tumor tissue via the EPR effect. The purpose of this work was to investigate the effective parameters on poly(lactic-co-glycolic acid)-paclitaxel (PLGA –PTX) nanoparticles size. Methods: We prepared PLGA-PTX nanoparticles via single emulsion and precipitation methods with variable paremeters including drug concentration, aqueous to organic phase volume ratio, polymer concentration, sonication time and PVA concentration. Results: PLGA-PTX nanoparticles were characterized by dynamic light scattering (DLS) and scanning electron microscopy (SEM). The results exhibited that the diameter of nanoparticles enhanced with increasing drug, polymer and PVA concentrations whereas organic to aqueous phase volume ratio and sonication time required to the optimization for a given size. Conclusion: The precipitation method provides smaller nanoparticles compared to emulsion one. Variable parameters including drug concentration, aqueous to organic phase volume ratio, polymer concentration, sonication time and PVA concentration affect diameter of nanoparticles.
Improving Recommendation via Inference of User Popularity Preference in Sparse Data Environment

SUMMARY The Collaborative Filtering (CF) algorithms work fairly well in personalized recommendation except in sparse data environment. To deal with the sparsity problem, researchers either take into account auxiliary information extracted from additional data resources, or set the missing ratings with default values, e.g., video popularity. Nevertheless, the former often costs high and incurs di ﬃ culty in knowledge transference whereas the latter degrades the accuracy and coverage of recommendation results. To our best knowledge, few literatures take advantage of users’ preference on video popularity to tackle this problem. In this paper, we intend to enhance the performance of recommendation algorithm via the inference of the users’ popularity preferences ( PPs ), especially in a sparse data environment. We propose a scheme to aggregate users’ PPs and a Collaborative Filtering based algorithm to make the inference of PP feasible and e ﬀ ective from a small number of watching records. We modify a k-Nearest-Neighbor recommendation algorithm and a Matrix Factorization algorithm via introducing the inferred PP. Experiments on a large-scale commercial dataset show that the modiﬁed algorithm outperforms the original CF algorithms on both the recommendation accuracy and coverage. The signiﬁcance of improvement is signiﬁcant especially with the data sparsity.
Long-Term Culture of Organotypic Hippocampal Slice from Old 3xTg-AD Mouse: An ex vivo Model of Alzheimer’s Disease

Objective Conventional methods for organotypic hippocampal tissue slice culture (OHSC) have shown several disadvantages or limitations regarding age of animals used, duration of culture and difficulty using neurodegenerative models. Therefore, we tried to establish OHSC from old 3xTg-Alzheimer’s disease (AD) mice for longer period (over 4 weeks) and to validate utility of this system as a valid platform for translational neuroscience of AD. Methods OHSC was performed with old 3xTg-AD mice (12–14 months), old wild type mice (12–14 months) and young 3xTg-AD mice (2–4 months) using serum-free medium for 4 weeks. Hippocampal structure was evaluated by 4’, 6-diamidino-2-phenylindole (DAPI) intensity and neuronal metabolism was measured by Alamarblue assay. Pathologic characteristics of AD were also investigated; β-amyloid levels by ELISA, amyloid plaque deposition by Thioflavin-S staining, and glial activation by immunohistochemistry. Results Following 4-week culture in serum-free media, hippocampal cells and layers were well preserved in cultured slices from old AD mice as was in those from young AD and old wild type mice. On the contrary, excessive regression of total visible cells was observed in conventional serum-containing medium regardless of genotype of mice. In parallel with this well preserved structure, major pathologic characteristics of AD were also well manifested in hippocampal slices from old AD mice. Conclusion Our findings suggest that long-term OHSC from old 3xTg-AD mouse can serve as a promising ex vivo system for studies on pathophysiology of AD, especially with the minimum number of sacrifice of experimental animals.
Understanding Sociomateriality through the Lens of Assemblage Theory: Examples from Police Body-Worn Cameras

IS researchers have used several theoretical lenses, singly or in combination, to shed light on IS phenomena. Such development is encouraged as a necessary ingredient in the intellectual endeavor to build a cumulative tradition. We continue in that endeavor by introducing assemblage theory to provide a lucid elucidation of organizations as social machine assemblages. We provide examples involving the use of police Body-Worn Cameras to illustrate the potential of assemblage theory as a philosophical foundation with the facilities and flexibility to advance understandings along a continuum of configurations of social and material interactions in organizations. Our research contributes to the IS knowledge base by demonstrating how assemblage theory accommodates the oppositional swings in the agency-structure debate, and add clarity to the notion of sociomateriality, a new and evolving area of IS scholarship. Additionally, we augment DeLanda’s presentation of assemblage theory to improve its appeal and amenability for IS research.
Molecular identification of leaf litter fungi potential for cellulose degradation

In plant litter decomposition in forest ecosystem, fungi play a central role through nutrient cycling and humus formation in soil because they colonize the lignocellulose matrix in litter that other organisms are unable to decompose. It has been described that cellulase is an adaptive enzyme in most fungi and had the most common carbohydrate on earth. For this reason, the study provided information about the fungal species isolated in leaf litters contributing to its biodiversity database. In this study, there are five species of fungi found to have cellulose degrading ability. The five species were identified using molecular approach and identified as Aspergillus eucalypticola, Aspergillus fumigatus, Colletotrichum gloeosporioides, Fusarium oxysporum and Penicilliun echinulatum. BLAST analysis showed the species of fungi ITS sequence from leaf litters, supported by 100% (A. fumigatus), 74% (A. eucalypticola), 100% (C. gloeosporioides), 100% (F. oxysporum) and 100% (P. echinolatum) sequence similarity.
Adverse Childhood Experiences and Sexually Transmitted Diseases in Men and Women: A Retrospective Study

Objective. Adverse childhood experiences (ACEs) may have long-term consequences on at-risk behaviors that lead to an increased risk of sexually transmitted diseases (STDs) during adulthood. Therefore, we examined the relationship between ACEs and subsequent STDs for both men and women. Methods. A total of 9323 (4263 men and 5060 women) adults ≥18 years of age participated in a retrospective cohort study evaluating the association between ACEs and self-reported STDs. Participants were adult members of a managed care organization who underwent routine medical evaluations and completed standardized questionnaires about 7 categories of ACEs, including emotional, physical, or sexual abuse; living with a battered mother; and living with a substance-abusing, mentally ill, or criminal household member. Logistic regression was used to model the association between the cumulative categories of ACEs (range: 0–7) and a history of STDs. Results. We found that 59% (2986) of women and 57% (2464) of men reported 1 or more categories of adverse experiences during childhood. Among those with 0, 1, 2, 3, 4 to 5, and 6 to 7 ACEs, the proportion with STDs was 4.1%, 6.9%, 8.0%, 11.6%, 13.5%, and 20.7% for women and 7.3%, 10.9%, 12.9%, 17.1%, 17.1%, and 39.1% for men. After adjustment for age and race, all odds ratios for reporting an STD had confidence intervals that excluded 1. Among those with 1, 2, 3, 4 to 5, and 6 to 7 ACEs, the odds ratios were 1.45, 1.54, 2.22, 2.48, and 3.40 for women and 1.46, 1.67, 2.16, 2.07, and 5.3 for men. Conclusions. We observed a strong graded relationship between ACEs and a self-reported history of STDs among adults.
GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs

Detecting fraudulent users in online social networks is a fundamental and urgent research problem as adversaries can use them to perform various malicious activities. Global social structure based methods, which are known as guilt-by-association, have been shown to be promising at detecting fraudulent users. However, existing guilt-by-association methods either assume symmetric (i.e., undirected) social links, which oversimplifies the asymmetric (i.e., directed) social structure of real-world online social networks, or only leverage labeled fraudulent users or labeled normal users (but not both) in the training dataset, which limits detection accuracies. In this work, we propose GANG, a guilt-by-association method on directed graphs, to detect fraudulent users in OSNs. GANG is based on a novel pairwise Markov Random Field that we design to capture the unique characteristics of the fraudulent-user-detection problem in directed OSNs. In the basic version of GANG, given a training dataset, we leverage Loopy Belief Propagation (LBP) to estimate the posterior probability distribution for each user and uses it to predict a user's label. However, the basic version is not scalable enough and not guaranteed to converge because it relies on LBP. Therefore, we further optimize GANG and our optimized version can be represented as a concise matrix form, with which we are able to derive conditions for convergence. We compare GANG with various existing guilt-by-association methods on a large-scale Twitter dataset and a large-scale Sina Weibo dataset with labeled fraudulent and normal users. Our results demonstrate that GANG substantially outperforms existing methods, and that the optimized version of GANG is significantly more efficient than the basic version.
A Three-Dimensional Node Security Localization Method for WSN Based on Improved RSSI-LSSVR Algorithm

In this paper, a three-dimensional node security localization method for WSN based on improved RSSI-LSSVR algorithm was proposed, which aims to improve the accuracy and security of the traditional wireless sensor network localization algorithm in three-dimensional environment. The RSSI method is first improved is improved by using the median weighting method so as to improve the range precision of the nodes. To further improve the localization accuracy of the nodes, a three-dimensional node localization model is established based on the least square support vector regression method. Lastly, a detection mechanism against sybil attacks is presented to improve the security of localization against sybil attacks. The simulation results show that the accuracy and safety of the improved localization algorithm are better than the traditional localization algorithm.
Robust Visual Localization for Hopping Rovers on Small Bodies

We present a collaborative visual localization method for rovers designed to hop and tumble across the surface of small Solar System bodies, such as comets and asteroids. In a two-phase approach, an orbiting primary spacecraft first maps the surface of a body by capturing images from various poses and illumination angles; these images are processed to create a prior map of 3D landmarks. In the second phase, a hopping rover is deployed to the surface where it uses a camera to relocalize to the prior map and to perform onboard visual simultaneous localization and mapping (SLAM). Small bodies present several unique challenges to existing visual SLAM algorithms, such as high-contrast shadows that move quickly over the surface due to the short (e.g. 1–12 hour) rotational periods, and large changes in visual appearance between orbit and the surface, where image scale varies by many orders of magnitude (kilometers to centimeters). In this work, we describe how to augment ORB-SLAM2—a state of the art visual SLAM implementation—to handle large variations in illumination by fusing prior images with varying illumination angles. We demonstrate how a hopping rover can use a wide field of view (FOV) camera to relocalize to prior maps captured by an orbiting spacecraft with a narrow FOV camera, and how the growth of pose and scale errors can be bounded by periodic loop closures during large hops. The proposed method is evaluated with sequences of images captured around a mock asteroid; it is shown to be robust to varying illumination angles, scene scale changes, and off-nadir camera pointing angles.
Celastrol reverses palmitic acid (PA)‐caused TLR4‐MD2 activation‐dependent insulin resistance via disrupting MD2‐related cellular binding to PA

Elevated plasma statured fatty acids (FFAs) cause TLR4/MD2 activation‐dependent inflammation and insulin tolerance, which account for the occurrence and development of obesity. It has been confirmed that statured palmitic acid (PA) (the most abundant FFA) could bind MD2 to cause cellular inflammation. The natural compound celastrol could improve obesity, which is suggested via inhibiting inflammation, yet the detailed mechanism for celastrol is still unclear. As celastrol is reported to directly target MD2, we thought disrupting the binding between FFAs and MD2 might be one of the ways for celastrol to inhibit FFAs‐caused inflammation and insulin resistance. In this study, we found evidence to support our hypothesis: celastrol could reverse PA‐caused TLR4/MD2 activation‐dependent insulin resistance, as determined by glucose‐lowering ability, cellular glucose uptake, insulin action‐related proteins and TLR4/MD2/NF‐κB activation. Bioinformatics and cellular experiments showed that both celastrol and PA could bind MD2, and that celastrol could expel PA from cells. Finally, celastrol could reverse high fat diet caused hyperglycemia and obesity, and liver NF‐kB activations. Taking together, we proved that celastrol could reverses PA‐caused TLR4‐MD2 activation‐dependent insulin resistance via disrupting PA binding to MD2.
Competencies necessary for becoming a leader in the field of community medicine: a Japanese qualitative interview study

Objectives To clarify competencies for inclusion in our curriculum that focuses on developing leaders in community medicine. Design Qualitative interview study. Setting All six regions of Japan, including urban and rural areas. Participants Nineteen doctors (male: 18, female: 1) who play an important leadership role in their communities participated in semistructured interviews (mean age 48.3 years, range 34–59; mean years of clinical experience 23.1 years, range 9–31). Method Semistructured interviews were held and transcripts were independently analysed and coded by the first two authors. The third and fourth authors discussed and agreed or disagreed with the results to give a consensus agreement. Doctors were recruited by maximum variation sampling until thematic saturation was achieved. Results Six themes emerged: (1)‘Medical ability’: includes psychological issues and difficult cases in addition to basic medical problems. High medical ability gives confidence to other medical professionals. (2)‘Long term perspective’: the ability to develop a long-term, comprehensive vision and to continuously work to achieve the vision. Cultivation of future generations of doctors is included. (3) ‘Team building’:the ability to drive forward programmes that include residents and local government workers, to elucidate a vision, to communicate and to accept other medical professionals. (4)‘Ability to negotiate’: the ability to negotiate with others to ensure that programmes and visions progress smoothly (5) ‘Management ability’: the ability to run a clinic, medical unit or medical association. (6) ‘Enjoying oneself’: doctors need to feel an attraction to community medicine, that it be fun and challenging for them. Conclusions We found six competencies that are needed by leaders in the field of community medicine. The results of this study will contribute to designing a curriculum that develops such leaders.