Miracle-dz/UniFAR-FTUs huggingface dataset:
Modifiable Risk Factor Possession Patterns of Dementia in Elderly with MCI: A 4-Year Repeated Measures Study

This study clarified the patterns of possessing modifiable risk factors of dementia that can be corrected by the elderly who were primarily determined to have mild cognitive impairment (MCI), and then determined the relationship between retention patterns and outcomes from MCI through a 4-year follow-up study. The participants were 789 community-dwelling elders who were ≥65 years old with MCI at baseline. After 4 years, participants were classified into reverters and nonreverters, according to their cognitive function. Repeated measures analysis was performed after imputing missing values due to dropout. Nine modifiable risk factors at baseline were classified by latent class analysis. Subsequently, we performed binomial logistic regression analysis. The reversion rate of 789 participants was 30.9%. The possession patterns of modifiable risk factors among the elderly with MCI were classified into five patterns: low risk, psychosocial, health behavior, educational, and smoking factors. According to logistic regression analysis, the low risk factors class was more likely to recover from MCI to normal cognitive than the other classes (p < 0.05). These results may provide useful information for designing interventions to prevent cognitive decline and dementia in individuals with MCI.
Carnitine palmitoyltransferase 1C contributes to progressive cellular senescence

Stable transfection manipulation with antibiotic selection and passaging induces progressive cellular senescence phenotypes. However, the underlying mechanisms remain poorly understood. This study demonstrated that stable transfection of the empty vector induced PANC-1 cells into cellular senescence. Metabolomics revealed several acylcarnitines and their upstream regulatory gene, carnitine palmitoyltransferase 1C (CPT1C) involved in fatty acid β-oxidation in mitochondria, were strikingly decreased in senescent PANC-1 cells. Low CPT1C expression triggered mitochondrial dysfunction, inhibited telomere elongation, impaired cell survival under metabolic stress, and hindered the malignance and tumorigenesis of senescent cells. On the contrary, mitochondrial activity was restored by CPT1C gain-of-function in senescent vector PANC-1 cells. PPARα and TP53/CDKN1A, crucial signaling components in cellular senescence, were downregulated in senescent PANC-1 cells. This study identifies CPT1C as a key regulator of stable transfection-induced progressive PANC-1 cell senescence that inhibits mitochondrial function-associated metabolic reprogramming. These findings confirm the need to identify cell culture alterations after stable transfection, particularly when cells are used for metabolomics and mitochondria-associated studies, and suggest inhibition of CPT1C could be a promising target to intervene pancreatic tumorigenesis.
Two Cases of Paediatric Essential Thrombocythaemia with Calreticulin Gene Mutation

Essential thrombocythaemia is a rare entity among the various paediatric haematological disorders. We report two symptomatic cases admitted into a regional hospital in Hong Kong. Both patients were found to have mutated Calreticulin gene at different sites. One patient has a positive family history. The patients were given anti-platelet and cytoreductive agents, as well as interferon-alpha with good clinical outcome. Our report highlights the clinical features and treatment strategies of this myeloproliferative disorder, emphasizing on the contribution of molecular investigations in the diagnostic pathway.
Automatic detection of speech disorders with the use of Hidden Markov Model

The most frequently used methods of automatic detection and classification of speech disordersare based on experimental determination of specific distinctive features for a given kind ofdisorder, and working out a suitable algorithm that finds such a disorder in the acoustic signal. Forexample, for detection of prolonged phonemes, analysis of the duration of articulation is used, andon the contrary, phoneme repetition can be detected with the spectrum correlation methods.Additionally, in the case of prolonged phonemes, classification based on their kind is required(nasal or whispered phonemes, vowels, consonants, etc). Therefore, for every kind of a disorder, aseparate algorithm needs to be worked out.Another, more flexible approach is the application of the Hidden Markov Models (HMM). Forthe needs of the presented work, the HMM procedures were implemented and some basic tests ofspeech disorder detection were conducted.
Using Quantum Probability for Word Embedding Problem

Over the past years, there has been being a contradiction between the growth rate of data that is available to humanity and the possibilities of their intellectual processing. Most of the knowledge that mankind operates is stored in the form of text documents in natural languages, which are not accompanied by additional markup tools for automated text processing tools. Thus, the exponential increase of the amount of information in the baggage of knowledge of mankind is faced with the inability to process it eﬀectively. To resolve this contradiction, there are systems for the automatic processing of natural language data. Most intelligent data processing algorithms operate with numerical data, so the basic task of any process of working with natural language texts is to represent text units in numerical form. In our research we propose to use framework of of the quantum theory of probabilities. In this case we can operating correctly with as clean as entangled states of words. For implementation of calculation of the matrix for generalized context we using the machine learning technique, named gradient descent, and apply some of restrictions ensuring for elimination extra degrees of freedom. Our approach provides a probabilistic interpretation of research results. And it allows easy to ﬁnd a probability that word’s context similar to another one. The proposed model of word will can be to integrate to diﬀerent text data analysis processes. This paper presents the results of a comparison of our proposed method with other similar algorithms.
Skeletal Maturity Indicators-Review Article

Growth biologically and histologically is a composite of morphogenetic and histogenetic changes occurring continuously over a period in response to genetic coding and environmental influence. It is one of the most myriad variations and plays an important role in the etiology of malocclusion and also in the evaluation of diagnosis, treatment planning retention and stability of any case. In this review, various methods currently used as skeletal maturity indicators have been discussed.
A 126 μW Readout Circuit in 65 nm CMOS With Successive Approximation-Based Thresholding for Domain Wall Magnet-Based Random Number Generator

We present a novel readout circuit for a ferro-magnetic Hall cross-bar based random number generator. The random orientation of magnetic domains are result of anomalous Hall-effect. These ferromagnetic Hall cross-bar structures can be integrated with the read out circuit to form a plug and play random number generator. The system can resolve up to 15–<inline-formula> <tex-math notation="LaTeX">$20~\mu \text{V}$ </tex-math></inline-formula> Hall-voltages from Hall probe. Application of current densities around <inline-formula> <tex-math notation="LaTeX">$10^{12} {A/m}^{2}$ </tex-math></inline-formula> through the Ferromagnetic Hall cross-bar produces random Hall-voltage on the output terminals. To amplify the weak Hall-voltages (10–<inline-formula> <tex-math notation="LaTeX">$100~\mu \text{V}$ </tex-math></inline-formula>) in the presence of DC offsets, a modulation scheme is used to up-convert the signal and a band-pass amplifier is used to amplify the modulated signal. The band-pass amplifier circuit, motivated by neural recording amplifier is designed in 65nm CMOS and consumes <inline-formula> <tex-math notation="LaTeX">$126~\mu \text{W}$ </tex-math></inline-formula> of power from a 1.2 V supply. Further, we present a successive approximation algorithm and its embedded implementation to set the desired threshold for digitizing the amplified Hall-voltage in presence of signal drift. Experimental results show that the resulting system can tolerate drifts in voltage up to <inline-formula> <tex-math notation="LaTeX">$440~\mu \text{V}$ </tex-math></inline-formula>.
Sketch Synthesis By Facial Attribute Description Using GAN

: In criminal investigation, face sketch is the main approach to search out suspects, especially in many cases when facial attribute descriptions of suspects by witnesses are available. The paper proposes a model to generate sketches from the given facial attribute text which is provided by the witness as an input source. It generates sketches by translating the text in the form of human described facial attribute text to the desired outcome. Problems arise in synthesizing high quality face sketches due to ambiguity and complexity of description of facial attributes. In this paper we propose a facial sketch synthesis approach using Generative Adversarial Network. Our contributions lie in two aspects: 1) we construct a text to face sketch database (T2S). The database, namely T2S dataset, is annotated with CUFSF dataset of 1194 sketches with originally labelled data;2) we synthesize detailed face sketches using GAN.
Devising an automated technology to organize the railroad transportation of containers for intermodal deliveries based on the theory of point processes

A significant number of problems and associated additional expenses emerge for operators due to imperfections in the existing technology of operational planning of the functioning of railroad transport as a part of the intermodal transportation system. The source of problems is not only the process of transportation of containers by rail but also the processes that occur immediately before and after it. These processes are uncertain because of their probabilistic nature. Their random nature provokes additional idle time of rolling stock, causes additional operator expenses, and reduces the quality of customer service. However, the direct influence on them is very difficult or economically inexpedient.The study shows that taking into account the probabilistic nature of these processes to reduce their negative impact is most effective precisely at the stage of operational planning of the functioning of railroad enterprises involved in the intermodal transportation process. One should note that it is necessary to take into account random factors of processes of formation and processing of container trains at stations, their movement along sections and transfer to a port simultaneously in order to improve the quality of such planning. However, the arrival of containers at terminal railroad stations requires special attention.It has been proven that the key to the solution to the problem of synchronization of the processes is the formation of automated technology for the organization of the transportation of containers by railroad.We have formalized the technological process of formation and movement of container trains to seaports in the form of a model of stochastic optimization using a mathematical apparatus from the theory of point processes for this purpose. The optimization criterion for this model represents the operating expenses of an operator for the organization of the railroad part of intermodal transportation. The stochastic nature of the model gives a possibility to find the optimal parameters of the operational plan for the organization of container transportation while controlling a level of certainty in the possibility of implementation of the plan taking into account the probabilistic nature of the constituent processes.Based on the developed model, software was created in the MATLAB programming environment and an automated technology for moving container trains was formed. The application of the proposed model in the formation of railroad container transportation technology could reduce operating expenses of a railroad part of intermodal container transportation by at least 10 %
A Numerical Study of Metachronal Propulsion at Low to Intermediate Reynolds Numbers

Inspired by the forward swimming of long-tailed crustaceans, we study an underwater propulsion mechanism for a swimming body with multiple rigid paddles attached underneath undergoing cycles of power and return strokes with a constant phase-difference between neighboring paddles, a phenomenon known as metachronal propulsion. To study how inter-paddle phase-difference affects flux production, we develop a computational fluid dynamics model and a numerical algorithm based on the immersed boundary method, which allows us to simulate metachronal propulsion at Reynolds numbers (RE) ranging from close to 0 to about 100. Our main finding is that the highest average flux is generated when nearest-neighbor paddles maintain an approximate 20%–25% phase-difference with the more posterior paddle leading the cycle; this result is independent of stroke frequency across the full range of RE considered here. We also find that the optimal paddle spacing and the number of paddles depend on RE; we see a qualitative transition in the dynamics of flow generated by metachronal propulsion as RE rises above 80. Roughly speaking, in terms of average flux generation, a tight paddle spacing is preferred when RE is less than 10, but a wider spacing becomes clearly favored when RE is close to or above 100. In terms of efficiency of flux generation, at RE 0.1 the maximum efficiency occurs at two paddles, and the efficiency decreases as the number of paddles increases. At RE 100 the efficiency increases as the number of paddles increases, and it appears to saturate by eight paddles, whereas using four paddles is a good tradeoff for both low and intermediate RE.
Image Abstraction through Overlapping Region Growth

We propose a region-based abstraction of a photograph, where the image plane is covered by overlapping irregularly shaped regions that approximate the image content. We segment regions using a novel region growth algorithm intended to produce highly irregular regions that still respect image edges, different from conventional segmentation methods that encourage compact regions. The final result has reduced detail, befitting abstraction, but still contains some small structures such as highlights; thin features and crooked boundaries are retained, while interior details are softened, yielding a painting-like abstraction effect.
Building a Feature Taxonomy of the Terms Extracted from a Text Collection

This position paper presents an approach for feature grouping and taxonomic relationship extraction with the further objective to build a feature taxonomy of a learned ontology. The approach needs to be developed as a part of the OntoElect methodology for domain ontologies refinement. The paper contributes a review of the related work in taxonomic relationships extraction from natural language texts. Within this review, the research gaps and remaining challenges are analyzed. The paper proceeds with outlining the envisioned solution. It presents the approach to this solution starting with the research questions, followed by the initial research hypotheses to be tested. Consequently, the plan of research is presented, including the potential research problems, the rationale to use and re-use existing components, and evaluation plan. Finally, the proposed solution, and the project, are placed in the broader context of the overall OntoElect work-flow.
4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation

Multiple sclerosis lesion activity segmentation is the task of detecting new and enlarging lesions that appeared between a baseline and a follow-up brain MRI scan. While deep learning methods for single-scan lesion segmentation are common, deep learning approaches for lesion activity have only been proposed recently. Here, a two-path architecture processes two 3D MRI volumes from two time points. In this work, we investigate whether extending this problem to full 4D deep learning using a history of MRI volumes and thus an extended baseline can improve performance. For this purpose, we design a recurrent multi-encoder-decoder architecture for processing 4D data. We find that adding more temporal information is beneficial and our proposed architecture outperforms previous approaches with a lesion-wise true positive rate of 0.84 at a lesion-wise false positive rate of 0.19.
Interactive Tool for Nuclei Instance Segmentation and Detection

Object segmentation and classiﬁcation in medical imaging are essential tasks for the diagnosis and understanding of diseases. Manual classiﬁcation of the anatomical structures and the annotation of their boundaries are laborious tasks that also require strong medical expertise. Deep neural networks can be used to automate these tasks, but because of the unavailability of large datasets with multiple structures annotated and labeled, their performance is not at par with manual annotations. We propose a semi-automated interactive tool based on deep learning to produce high-quality annotations quickly. The architecture uses two convolutional networks: the ﬁrst network produces multiple segmentations using a few clicks inside and outside the object, while the second classiﬁes the object and selects one segmentation. We use MonuSAC histopathology data with four classes of labeled and annotated nuclei annotated as a testbed. On held-out images, our method was signiﬁcantly more accurate in both segmentation and classiﬁcation as compared to fully automated methods, while it was also 5x faster than manual annotation.
Stumbling across the Same Phage: Comparative Genomics of Widespread Temperate Phages Infecting the Fish Pathogen Vibrio anguillarum

Nineteen Vibrio anguillarum-specific temperate bacteriophages isolated across Europe and Chile from aquaculture and environmental sites were genome sequenced and analyzed for host range, morphology and life cycle characteristics. The phages were classified as Siphoviridae with genome sizes between 46,006 and 54,201 bp. All 19 phages showed high genetic similarity, and 13 phages were genetically identical. Apart from sporadically distributed single nucleotide polymorphisms (SNPs), genetic diversifications were located in three variable regions (VR1, VR2 and VR3) in six of the phage genomes. Identification of specific genes, such as N6-adenine methyltransferase and lambda like repressor, as well as the presence of a tRNAArg, suggested a both mutualistic and parasitic interaction between phages and hosts. During short term phage exposure experiments, 28% of a V. anguillarum host population was lysogenized by the temperate phages and a genomic analysis of a collection of 31 virulent V. anguillarum showed that the isolated phages were present as prophages in >50% of the strains covering large geographical distances. Further, phage sequences were widely distributed among CRISPR-Cas arrays of publicly available sequenced Vibrios. The observed distribution of these specific temperate Vibriophages across large geographical scales may be explained by efficient dispersal of phages and bacteria in the marine environment combined with a mutualistic interaction between temperate phages and their hosts which selects for co-existence rather than arms race dynamics.
CS 230 Final Report: Deepfake Video Detection

In this paper, we investigate several approaches towards detecting deepfake videos. Misinformation is rampant in today’s world, and one method of spreading misinformation gaining in popularity is deepfakes, or facial manipulation in images and videos. We experiment with the usage of combining a CNN to generate an embedding from facial features with an RNN for temporal structure between frames within a video. Additionally, we make use of facial detection systems to focus solely on faces within frames of video, and ignore the rest of the image. Our model managed to achieve a 72.5% accuracy on the validation set.
Brain Activation in Primary Motor and Somatosensory Cortices during Motor Imagery Correlates with Motor Imagery Ability in Stroke Patients

Aims. While studies on healthy subjects have shown a partial overlap between the motor execution and motor imagery neural circuits, few have investigated brain activity during motor imagery in stroke patients with hemiparesis. This work is aimed at examining similarities between motor imagery and execution in a group of stroke patients. Materials and Methods. Eleven patients were asked to perform a visuomotor tracking task by either physically or mentally tracking a sine wave force target using their thumb and index finger during fMRI scanning. MIQ-RS questionnaire has been administered. Results and Conclusion. Whole-brain analyses confirmed shared neural substrates between motor imagery and motor execution in bilateral premotor cortex, SMA, and in the contralesional inferior parietal lobule. Additional region of interest-based analyses revealed a negative correlation between kinaesthetic imagery ability and percentage BOLD change in areas 4p and 3a; higher imagery ability was associated with negative and lower percentage BOLD change in primary sensorimotor areas during motor imagery.
Humanoid feet trajectory generation for the reduction of the dynamical effects

In this paper we present a different strategy for generating the trajectory of the swinging leg for a walking humanoid robot which takes into account the effects due to acceleration and velocities of the joints onto the center of mass of the robot. The trajectory of the leg is chosen to be constituted by two forth order polynomials interlaced by a via-point which satisfies the optimality criterium. This approach is validated on a humanoid robot HRP-2.
Language Proficiency and Students Behavior in the Classroom: A Comparative Study

Background: The impact of teacher competence and their level of proficiency on the language have long been debated among scholars. A number of researches advocate that teacher’s competence in ESL (English Second Language) instruction is directly proportional to the student’s acquisition of knowledge. The methods of instruction and activities have a direct influence on the classroom behavior of students in second language classes.
Fast C1 proximity queries using support mapping of sphere-torus-patches bounding volumes

STP-BV is a bounding volume made of patches of spheres and toruses. These patches are assembled so that a convex polyhedral hull is bulged, in a tunable way, into a strictly convex form. Strict convexity ensures at least C1 property of the distance function -and hence, its gradient continuity. STP-BV were introduced in our previous work [1], but proximity distance queries were limited to pairs of STP-BV covered objects. In this work we present an alternative to achieve fast proximity distance queries between a STP-BV object and any other convex shape. This is simply made by proposing a support mapping for STP-BV to be used with GJK algorithm [2] and its EPA extension to penetration cases [3]. Implementation and experiments of the proposed method and its performance are demonstrated with potential applications to robotics and computer graphics.