Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrasonographic measurements of fascicle length overestimate adaptations in serial sarcomere number

Ultrasound-derived measurements of muscle fascicle length (FL) are often used to infer increases (chronic stretch or training) or decreases (muscle disuse or aging) in serial sarcomere number (SSN). Whether FL adaptations measured via ultrasound can truly approximate SSN adaptations has not been investigated. We casted the right hindlimb of 15 male Sprague-Dawley rats in a dorsiflexed position (i.e., stretched the plantar flexors) for 2 weeks, with the left hindlimb serving as a control. Ultrasound images of the soleus, lateral gastrocnemius (LG), and medial gastrocnemius (MG) were obtained with the ankle at 90° and full dorsiflexion for both hindlimbs pre and post-cast. Following post-cast ultrasound measurements, legs were fixed in formalin with the ankle at 90°, then muscles were dissected, and fascicles were teased out for measurement of sarcomere lengths via laser diffraction and calculation of SSN. Ultrasound detected an 11% increase in soleus FL, a 12% decrease in LG FL, and an 8-11% increase in MG FL for proximal fascicles and at full dorsiflexion. These adaptations were partly reflected by SSN adaptations, with a 6% greater soleus SSN in the casted leg than the un-casted leg, but no SSN differences for the gastrocnemii. Weak relationships were observed between ultrasonographic measurements of FL and measurements of FL and SSN from dissected fascicles. Our results showed that ultrasound-derived FL measurements can overestimate an increase in SSN by ∼5%. Future studies should be cautious when concluding a large magnitude of sarcomerogenesis from ultrasound-derived FL measurements, and may consider applying a correction factor. Key Points Summary Measurements of muscle fascicle length via ultrasound are often used to infer changes in serial sarcomere number, such as increases following chronic stretch or resistance training, and decreases with aging or muscle disuse The present study used a rat model of casting the plantar flexor muscles in a stretched position to investigate directly whether ultrasound-derived fascicle length can accurately detect adaptations in serial sarcomere number Ultrasound detected an ∼11% increase in soleus fascicle length, but measurements on dissected fascicles showed the actual increase in serial sarcomere number was only ∼6%; therefore, measurements of ultrasound-derived fascicle length can overestimate serial sarcomere number adaptations by as much as 5%
What fraction of cellular DNA turnover becomes cfDNA?

Cell-free DNA (cfDNA) tests use small amounts of DNA in the bloodstream as biomarkers. While it is thought that cfDNA is largely released by dying cells, the proportion of dying cells’ DNA that reaches the bloodstream is unknown. Here, we integrate estimates of cellular turnover rates to calculate the expected amount of cfDNA. By comparing this to the actual amount of cell type-specific cfDNA, we estimate the proportion of DNA reaching plasma as cfDNA. We demonstrate that <10% of the DNA from dying cells is detectable in plasma, and the ratios of measured to expected cfDNA levels vary a thousand-fold among cell types, often reaching well below 0.1%. The analysis suggests that local clearance, presumably via phagocytosis, takes up most of the dying cells’ DNA. Insights into the underlying mechanism may help to understand the physiological significance of cfDNA and improve the sensitivity of liquid biopsies.
Antibody Complementarity-Determining Region Sequence Design using AlphaFold2 and Binding Affinity Prediction Model

Affinity maturation in the immune response is limited in terms of affinity gain, and natural antibodies often do not have the binding affinity required for therapeutic applications. Therefore, improving the binding affinity of antibodies is essential for developing antibody-based therapeutics. Designing antibodies using experimental methods is expensive in terms of cost and time owing to the large range of complementarity-determining regions to be explored. Recently, computational methods have been developed as low-cost and fast means of designing and redesigning antibodies. This study evaluated the design performance of AlphaFold2 and the binder hallucination, which can predict protein 3D structures with high accuracy even without experimental antibodies, by redesigning antibody sequences to improve the binding affinity of existing antigen-antibody complexes. Therefore, antibody sequences with higher affinity can be designed for antigen-antibody complexes not included in the training data of AlphaFold2, indicating that the proposed method may be effective as an antibody design method.
Bifurcation in space: Emergence of function modularity in the neocortex

How does functional modularity emerge in a multiregional cortex made with repeats of a canonical local circuit architecture? We investigated this question by focusing on neural coding of working memory, a core cognitive function. Here we report a mechanism dubbed “bifurcation in space”, and show that its salient signature is spatially localized “critical slowing down” leading to an inverted V-shaped profile of neuronal time constants along the cortical hierarchy during working memory. The phenomenon is confirmed in connectome-based large-scale models of mouse and monkey cortices, offering an experimentally testable prediction to assess whether working memory representation is modular. Many bifurcations in space could explain the emergence of different activity patterns potentially deployed for distinct cognitive functions, This work demonstrates that a distributed mental representation is compatible with functional specificity as a consequence of macroscopic gradients of neurobiological properties across the cortex, suggesting a general principle for understanding brain’s modular organization.
Role of Mce proteins in Mycobacterium avium paratuberculosis infection

Mycobacterium avium subspecies paratuberculosis (MAP) is the causative agent of Johne’s Disease, a chronic granulomatous enteritis of ruminants. MAP establishes an infection in the host via the small intestine. This requires the bacteria to adhere to and be internalised by cells of the intestinal tract. The effector molecules expressed by MAP for this remain to be fully identified and understood. The mammalian cell entry (Mce) proteins play an essential role for other Mycobacterial species to facilitate the attachment and invasion of host epithelial cells. Here, we have expressed Mce1A, Mce1D, Mce3C and Mce4A proteins derived from MAP on the surface of a non-invasive E. coli host to characterise their role in the initial interaction between MAP and the host. To this end, mce1A was found to significantly increase the ability of the E. coli to attach and survive intracellularly in THP-1 cells, and mce1D was found to significantly increase attachment and invasion of MDBK cells. Both genes were implicated in the increased ability of E. coli to infect 3D bovine basal-out enteroids. Together, these results have identified two effector molecules used by MAP with a degree of cell-type specificity.
Vision rivals audition in alerting humans for fast action

Successful behaviour requires that humans act promptly upon the ubiquitous rapid changes in the environment. Prompt actions are supported by phasic alertness: the increased readiness for perception and action elicited by warning stimuli (alerting cues). Audition is assumed to induce phasic alertness for action faster and more strongly than other senses. Here, we show that vision can be equally effective as audition. We investigated the temporal evolution and the effectiveness of visual and auditory alerting for action in a speeded choice task, while controlling for basic sensitivity differences between the modalities that are unrelated to action control (by matching auditory and visual stimuli according to reaction times in a prior simple detection task). Results revealed that alerting sped up responses, but this happened equally fast and equally strong for visual and auditory alerting cues. Thus, these findings argue that vision rivals audition in phasic alerting for prompt actions, and suggest that the underlying mechanisms work across both modalities.
GDF3 is an obesity-induced regulator of TGFβ superfamily signaling

Growth differentiation factor 3 (GDF3) is a relatively understudied member of the TGFβ superfamily that is highly expressed during development. However, the function of GDF3 in adult biology is contentious. We use in vivo approaches to show that GDF3 loss-of-function in adipose tissue of obese adult mice causes reduced body weight and improved whole-body insulin sensitivity. These effects are accompanied by altered regulation of genes targeted by the TGFβ superfamily in vivo. Using in vitro approaches, we show that GDF3 can influence both arms of the TGFβ superfamily: GDF3 simultaneously inhibits BMP signaling and activates activin-like SMAD 2/3 signaling. We identify the type II receptors mediating this activity. GDF3 binds to the type II receptors BMPR2, ACTRIIA and ACTRIIB and achieves dose-dependent inhibition of multiple BMP proteins including BMP2, BMP7, BMP9, BMP10, and BMP15 in vitro. We also find that GDF3 activates TGFβ/activin-like SMAD2/3 signaling. Unbiased expression profiling confirms that GDF3 both attenuates BMP2-regulated gene expression and drives TGFβ/activin-like gene expression. Together these results provide much needed clarity to both the molecular pathways involved in GDF3 signaling and the physiological effects of GDF3 loss of function.
Using Multiple Solution Tasks for the Evaluation of Students’ Problem-Solving Performance in Geometry

The article demonstrates that multiple solution tasks (MSTs) in the context of geometry can serve as a research instrument for evaluating geometry knowledge and creativity. Geometry knowledge is evaluated based on the correctness and connectedness of solutions, whereas creativity is evaluated based on a combination of fluency, flexibility, and originality of solutions. In this article, the MST research instrument is introduced in connection with the theoretical analysis of the research literature and then explained and analyzed using geometry students’ performance results on one MST. The analysis shows that the research instrument differentiates between students belonging to high- and regular-level instruction groups and sheds light on the interrelations between components of geometry knowledge and creativity. RésuméL’article montre que les tâches à solutions multiples (TSM) dans le contexte de la géométrie peuvent servir comme outils de recherche pour évaluer les connaissances en géométrie et la créativité. Les connaissances en géométrie sont évaluées sur la base de la justesse des solutions, tandis que la créativité est évaluée sur la base d’une combinaison de facteurs tels que la facilité, la souplesse et l’originalité des solutions. Dans cet article, les TSM sont introduites comme outils de recherche en lien avec l’analyse théorique de la littérature dans le domaine, et elles sont ensuite expliquées et analysées au moyen des résultats obtenus par des étudiants de géométrie dans une TSM. L’analyse montre que cet outil de recherche permet de distinguer les étudiants qui appartiennent à des groupes de niveau régulier ou supérieur, et jette la lumière sur les interrelations qui existent entre les différents aspects des connaissances en géométrie et la créativité.
Range dimension super resolution by using fractional Fourier transform

Resolution of the range profile is essential for target identification of broad band radar.To process the moving target echo of the ultra wide band radar,Doppler difference is compensated by using the Fractional Fourier Transform(FrFT) algorithm.The time delay information is turned into the Doppler domain.The data processed by FrFT is reprocessed by the multiple singnal classification(MUSIC) algorithm.Super resolution of range dimension is realized by using our approach,and its performance is superior to the conventional pulse compression or Dechirp method.It is useful for target identification.The efficiency of our approach is proven by computer simulation.
Influence of a generalized Eddington bias on galaxy counts

We study the influence of the Eddington bias on measured distributions, in particular counts of galaxies when the accuracy of magnitude measurements is variable, e.g. when it changes towards fainter objects. Numerical experiments using different error laws illustrate the effect on the measured slope, helping one to decide if the variable Eddington bias is important, when the simple analytic correction is no longer valid. Common views on the origin and appearance of the Eddington bias are clarified and its relation to the classical Malmquist bias is briefly discussed. We illustrate the "Eddington shift" approach with the counts of bright galaxies in the LEDA database.
Associations Between Cognitive Impairment Severity and Barriers to Healthcare Engagement Among Older Adults

Objectives: To assess whether older adults with a cognitive impairment were more likely to report challenges interacting with medical providers, or to avoid needed medical care. Methods: Data for this exploratory, cross-sectional analysis were from older adults (N = 493) ages 60–82 participating in the “LitCog” cohort study. Multivariable generalized linear models compared cognitive impairment (none, mild, moderate, severe) with validated measures of healthcare engagement. Results: A moderate cognitive impairment was associated with delays in medical care due to embarrassment (RR 5.34.95% CI 1.30–22.0) and discomfort asking the doctor questions (RR 4.07, 95% CI 1.00–16.5). Conclusions: Intermediate cognitive deficits, such as with mild cognitive impairment (MCI) or mild dementias, may impact meaningful engagement with healthcare systems, potentially affecting timely detection and appropriate management of cognitive concerns and other chronic medical conditions. More research is needed to understand mechanisms underlying this relationship.
Anaphora Resolution from Social Media Text

Anaphora resolution for social media texts is essential yet difficult task for text understanding. An important characteristic of anaphora is that it creates a connection between the antecedent and the anaphor buried in the anaphoric sentence. This paper outlines the methods used to locate anaphora and their antecedents in a particular text. The text is a social media tweet for the SocAnaRes-IL 2022 challenge that was part of FIRE 2022. The proposed model uses a Neural Co-reference Network for the anaphora resolution.
Photopolymer based VPHGs: from materials to sky results

Volume Phase Holographic Gratings cover a relevant position as transmission dispersing elements in astronomical spectrographs and each astronomical observation could take advantage of specific dispersive elements with features tailored for achieving the best performances. The design and manufacturing of high efficiency and reliable VPHGs require photosensitive materials where it is possible to control both the refractive index modulation and the film thickness. By means of Bayfol® HX photopolymers, we designed and manufactured six VPHGs for astronomical instrumentation in a GRISM configuration. We demonstrated how photopolymers are reliable holographic materials for making astronomical VPHGs with performances comparable to those provided by VPHGs based on Dichromated Gelatins (DCGs), but with a much simpler production process.
A Fuzzy Logic–Based Model for the Classification of Faults in the Pump Seals of the Primary Heat Transport System of a CANDU 6 Reactor

Abstract Many efforts are currently devoted to the development of soft-computing diagnostic tools. Within these efforts, the present work illustrates the application of a fuzzy logic system approach to fault classification in the case of single and multiple failures of different intensity. The approach bears the great advantage that the identification task does not rely on an explicit mathematical model of the behavior of the monitored plant, the if-then rules of the fuzzy classification model being inferred from available preclassified signal data. A case study is presented regarding the classification of faults in the gland seal system of a pump of the primary heat transport system in a pressurized heavy water reactor, Canada deuterium uranium 6.
Patterns of the Satisfaction and Frustration of Psychological Needs and Their Associations with Adolescent Students’ School Affect, Burnout, and Achievement

Based on self-determination theory and adopting a person-oriented approach, we aimed to investigate the latent profiles of adolescent students’ basic psychological needs and their associations with personal characteristics (gender, socioeconomic status) as well as school functioning (school affect, burnout, academic achievement). Latent profile analyses based on a group of 1521 Chinese high school students identified four need profiles: low satisfaction/moderate frustration, high satisfaction/low frustration, average all, and moderate satisfaction/high frustration. Furthermore, there were significant differences in students’ school functioning among the four latent profiles. Specifically, students with moderate to high levels of need frustration were most likely to experience maladaptive school functioning, regardless of their need satisfaction level. Additionally, gender and socioeconomic status were significant predictors of profile membership. The findings of this study can assist educators in gaining a better understanding of the diverse patterns of psychological needs among students and help them to implement targeted interventions.
Human Skin Disease Detection using MLXG Model

Human skin disease detection deals with the recognition of skin type and the skin disease in a given image. Edges, textures, and colour pixels are often used to detect skin diseases. These features are invariant and are fast to process. A variant Human Skin Disease Detection model (MLXG) is put forward in this paper. The three main parameters which help to detect skin diseases are the edges, the RGB (Red, Green, Blue) colour model of the image, and the texture of the skin. The goal of the MLXG model is to improve the current human skin disease detection models in terms of accuracy and to work on different skin diseases. The model not only considers the above parameters but also uses Machine Learning to improve the accuracy.
Artificial Intelligence Classification Model for Modern Chinese Poetry in Educatio

Various modern Chinese poetry styles have influenced the development of new Chinese poetry; therefore, the classification of poetry style is very important for understanding these poems and promoting education regarding new Chinese poetry. For poetry learners, due to a lack of experience, it is difficult to accurately judge the style of poetry, which makes it difficult for learners to understand poetry. For poetry researchers, classification of poetry styles in modern poetry is mainly carried out by experts, and there are some disputes between them, which leads to the incorrect and subjective classification of modern poetry. To solve these problems in the classification of modern Chinese poetry, the eXtreme Gradient Boosting (XGBoost) algorithm is used in this paper to build an automatic classification model of modern Chinese poetry, which can automatically and objectively classify poetry. First, modern Chinese poetry is divided into words, and stopwords are removed. Then, Doc2Vec is used to obtain the vector of each poem. The classification model for modern Chinese poetry was iteratively trained using XGBoost, and each iteration promotes the optimization of the next generation of the model until the automatic classification model of modern Chinese poetry is obtained, which is named Modern Chinese Poetry based on XGBoost (XGBoost-MCP). Finally, the XGBoost-MCP model built in this paper was used in experiments on real datasets and compared with Support Vector Machine (SVM), Deep Neural Network (DNN), and Decision Tree (DT) models. The experimental results show that the XGBoost-MCP model performs above 90% in all data evaluations, is obviously superior to the other three algorithms, and has high accuracy and objectivity. Applying this to education can help learners and researchers better understand and study poetry.
NLP and Insurance - Workshop Results at SwissText 2022

Natural Language Processing, briefly NLP, will lead in the next years the revolution of the Artificial Intelligence for the Insurance industry. There are several opportunities to employ NLP in the insurance activities, from claims processing to fraud detection and chatbots. In the marketing field, NLP can be used to monitor the sentiment analysis of feedback that people publish on different social networks to better consider insured needs or extract insight risks. Textual analysis of claims and classification can simplify the claims processing to reduce time treatment, operational errors or provide help in fraud detection. Underwriting process can be improved by a better textual assessment. The workshop had the goal to show NLP techniques on fraud detection by the disaster Tweets data set from Kaggle classification competition.
Brain Anomalies of MRI Segmentation

For several decades, MRI is used widely in neurological research and in clinical examinations. The most dominant step in preparing MRI brain images is removing unwanted brain regions. The identification of brain lesions through Magnetic Resonance Imaging (MRI) remains challenging. The discovery of tumor in the brain using diverse steps of brain image segmentation is challenging. The use of automated brain tumor detection has just recently been proposed through a variety of methods. Methods based on Unsupervised Anomaly Detection are initiated to detect anatomical variability as the deviation of a healthy model well informed by using a VAE. This paper provides detail about unsupervised anomaly detection with autoencoders. We discussed the dataset that contains healthy and unhealthy images of the brain. In our future work, we planned for anomaly detection and classification using the variational autoencoders by training the model.
Information Retrieval in Software Engineering utilizing a pre-trained BERT model

The task is to detect whether a source code comment is useful or not for a given comment, and the surrounding code is paired together as input. IRSE (Information Retrieval in Software Engineering) shared task organized by FIRE 2022 (Forum for Information Retrieval Evaluation), gives a binary classification task where a system classifies Comments and Surrounding Code Context pairs into two classes: (a) USEFUL or (b) NOT_USEFUL. To do the task, we experimented with the roberta-base model, and the result was 0.9047 in F1 Marco. Our submission gets the second position out of all submissions.
Intelligent Hepatocellular Carcinoma Detection using Hybrid Radial basis Function Networks

Patients with hepatocellular carcinoma may have a higher chance of survival with earlier detection and treatment. It takes a lot of time and effort to manually identify malignant cells. In order to make decisions that result in an efficient treatment, diagnosis methods based on computational techniques is essential. In this work, deep learning approaches are employed to automatically determine feature representation from sample images using radial basis function networks, as opposed to traditional image-dependent “contextual” feature evaluation by professionals. The primary goal of the proposed model is to detect hepatocellular carcinoma in diagnostic imaging. The suggested model accomplishes this by using pre-processing in two steps, namely image denoising using a Wiener filter and segmentation using region-based segmentation technique. The Radial Basis Function Networks model’s hyper parameters are tweaked using the Salp Swarm optimization Algorithm technique, which is then used to perform the classification of hepatocellular carcinoma. The proposed model showed superior performance compared to the existing methods.