Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a novel immune infiltration-related diagnostic model for Alzheimer’s disease using bioinformatic strategies

Background The pathogenesis of Alzheimer’s disease (AD) is complex and multi-factorial. Increasing evidence has shown the important role of immune infiltration in AD. Thus the current study was designed to identify immune infiltration-related genes and to explore their diagnostic value in AD. Methods The expression data of AD patients were downloaded from the GEO database. The limma R package identified differentially expressed genes (DEGs) between AD and controls. The CIBERSORT algorithm identified differentially infiltrated immune cells (DIICs) between AD and controls. DIIC-correlated DEGs were obtained by Pearson correlation analysis. WGCNA was employed to identify DIIC-related modules. Next, LASSO, RFE, and RF machine learning methods were applied to screen robust DIIC-related gene signatures in AD, followed by the construction and validation of a diagnostic nomogram. Detection of the expression of related genes in the peripheral blood of Alzheimer’s disease and healthy volunteers by RT-PCR. In addition, the CTD database predicted chemicals targeting DIIC-related gene signatures in the treatment of AD. Results NK cells, M0 macrophages, activated myeloid dendritic cells, resting mast cells, CD8+ T cells, resting memory CD4+ T cells, gamma delta T cells, and M2 macrophages were differentially infiltrated between AD and controls. Pearson analysis identified a total of 277 DIIC-correlated DEGs between AD and controls. Thereafter, 177 DIIC-related genes were further obtained by WGCNA analysis. By LASSO, RFE and RF algorithms, CMTM2, DDIT4, LDHB, NDUFA1, NDUFB2, NDUFS5, RPL17, RPL21, RPL26 and NDUFAF2 were identified as robust gene signature in AD. The results of RT-PCR detection of peripheral blood samples from Alzheimer’s disease and healthy volunteers showed that the expression trend of ten genes screened was consistent with the detection results; among them, the expression levels of CMTM2, DDIT4, LDHB, NDUFS5, and RPL21 are significantly different among groups. Thus, a diagnostic nomogram based on a DIIC-related signature was constructed and validated. Moreover, candidate chemicals targeting those biomarkers in the treatment of AD, such as 4-hydroxy-2-nonenal, rosiglitazone, and resveratrol, were identified in the CTD database. Conclusion For the first time, we identified 10 immune infiltration-related biomarkers in AD, which may be helpful for the diagnosis of AD and provide guidance in the treatment of AD.
Macular thickness variations with sex, age, and axial length in healthy subjects: a spectral domain-optical coherence tomography study.

PURPOSE To assess the relationship between macular retinal thickness and volume and age, sex, and refractive error/axial length with spectral domain-optical coherence tomography (SD-OCT). METHODS One randomly selected eye of 198 consecutive ophthalmically normal subjects (104 men, 94 women) between July 2008 and January 2009, with corrected visual acuities better than 20/30 were included in this cross-sectional study. Complete ophthalmic examination, axial length measurement with a laser interferometer, and macular cube 512 x 128 scan by SD-OCT were performed. RESULTS The mean age was 55.6 +/- 16.4 years (range, 17-83), average refractive error was -2.17 +/- 4.82 (range, -23.50-3.75), and average axial length was 24.73 +/- 1.98 mm (range, 21.52-32.51). The central subfield thickness, average inner macular thickness, and overall macular volume were significantly lower in the female subjects (partial correlation: P = 0.009, P = 0.027, and P = 0.042, respectively). As age increased, average inner macular thickness, average outer macular thickness, overall average macular thickness, and macular volume decreased significantly (partial correlation: P = 0.002, P = 0.002, P = 0.002, and P = 0.000, respectively). Refractive error had no significant influence in partial correlation analysis. Axial length correlated negatively with average outer macular thickness, overall average macular thickness, and macular volume (partial correlation: P = 0.006, P = 0.044, and P = 0.003, respectively). CONCLUSIONS In normal subjects, SD-OCT showed that retinal thickness is related to age, sex, and axial length, with regional variations.
Graph Attention in Attention Network for Image Denoising

Image denoising aims to remove the noise from noisy images. With the increasing complexity of the noise within the noisy images, current denoising methods cannot satisfactorily address this issue. This article proposes a graph attention in attention network (GAiA-Net) for image denoising. First, we introduce a novel approach to graph construction for the GAiA-Net. In the process of such graph construction, the noisy images are divided into patches to formulate the nodes in a graph. The edges are initialized using $k $ -nearest neighbors. Hence, through iterative transformation and learning, both the pixel-level and structure-level features can be captured by different information exchanges and aggregation within (pixel-level) and outside (structure-level) of the nodes, respectively. Second, we propose the graph attention in attention (GAiA) in the GAiA-Net. The proposed GAiA produces the pixel-level attention within nodes to be further induced to the nodes with various distances to generate the final attention. Therefore, our GAiA-Net can capture the long dependencies on both the pixel-level and structure-level features, which can effectively reduce the complex noise in the denoising process. Comprehensive experiments demonstrate that the proposed GAiA-Net produces state-of-the-art performances on both synthetic noise image and real noise image datasets. Especially, when experimenting on complex noisy Nam datasets, our GAiA-Net achieves a PSNR of 40.40 dB and SSIM of 0.989. These results prove the satisfactory potential and effectiveness of our GAiA-Net.
Suppressive myeloid cells in SARS-CoV-2 and Mycobacterium tuberculosis co-infection

Epidemiologic data show that both current and previous tuberculosis (TB) increase the risk of in-hospital mortality from coronavirus disease-2019 (COVID-19), and there is a similar trend for poor outcomes from Mycobacterium tuberculosis (Mtb) infection after recent SARS-CoV-2. A shared dysregulation of immunity explains the dual risk posed by co-infection, but the specific mechanisms are being explored. While initial attention focused on T cell immunity, more comprehensive analyses revealed a dysfunctional innate immune response in COVID-19, characterized by reduced numbers of dendritic cells, NK cells and a redistribution of mononuclear phagocytes towards intermediate myeloid subsets. During hyper- or chronic inflammatory processes, activation signals from molecules such as growth factors and alarmins lead to the expansion of an immature population of myeloid cells called myeloid-deprived suppressor cells (MDSC). These cells enter a state of pathological activation, lose their ability to rapidly clear pathogens, and instead become broadly immunosuppressive. MDSC are enriched in the peripheral blood of patients with severe COVID-19; associated with mortality; and with higher levels of inflammatory cytokines. In TB, MDSC have been implicated in loss of control of Mtb in the granuloma and ineffective innate and T cell immunity to the pathogen. Considering that innate immune sensing serves as first line of both anti-bacterial and anti-viral defence mechanisms, we propose MDSC as a crucial mechanism for the adverse clinical trajectories of TB-COVID-19 coinfection.
NOTE DETECTION FOR SAZ WITH HARMONIC PRODUCT SPECTRUM METHOD

Abstract - This research is about automatic note detection for Turkish instrument “Saz”. The Harmonic Product Spectrum (HPS) method is used for detection. The reason of doing this research is that so many applications are created for usage of music industry. Most of the music that we listen today is usually created in digital studios. However there are only few research on Turkish traditional instruments. Listening to a melody and extracting its notes is not possible for everyone. Therefore the musician publishes the notes on a paper then others can play the same melody. Today, even if we don’t have the notes written on a paper we can extract the notes with a software. This is possible for many instruments, such as guitar, piano, clarinet etc. however there is no application created to detect notes of Saz yet. Therefore this research is important for increasing the usage of Saz by Musicians. In this research, Harmonic Product Spectrum (HPS) method is used as feature extraction method. HPS applies down sampling to the input data in spectrum format then multiplies the each result of the down sampled input. Saz Notes are recorded in Signal Laboratory, as Wav file. A Matlad based algorithm is used to simulate the system. Notes are fed to the system offline. Firstly. The input is preprocessed and converted to feasible form for HPS. Secondly, preprocessed data is fed to the HPS. HPS extracts the feature of the note. The feature is consist of two frequency value. Finally the features are compared with the training data with k-NN method to identify the note. In experiments we achieved 97% accuracy for noiseless notes and 89% accuracy for noisy notes. The software is able to extract the feature around 0.01 second. Overall computation time is around 0.0603 second. In a very similar research, done by Paul M. Brossier, the accuracy is 96% and the computation time of their software is 0.3 second. In another research, done by Chris Duxbury, the accuracy for onset detection was 92%. The common accuracy level for detection din researches about musical instruments varies from 92% to 98%. There are some rare researches achieved 100% accuracy level. Therefore the accuracy level of our research can be labeled as very satisfying. Keywords: Saz, Harmonic Product Spectrum, Pitch, Note Detection
Land–Atmosphere Interactions: The LoCo Perspective

Land–atmosphere (L-A) interactions are a main driver of Earth’s surface water and energy budgets; as such, they modulate near-surface climate, including clouds and precipitation, and can influence the persistence of extremes such as drought. Despite their importance, the representation of L-A interactions in weather and climate models remains poorly constrained, as they involve a complex set of processes that are difficult to observe in nature. In addition, a complete understanding of L-A processes requires interdisciplinary expertise and approaches that transcend traditional research paradigms and communities. To address these issues, the international Global Energy and Water Exchanges project (GEWEX) Global Land–Atmosphere System Study (GLASS) panel has supported “L-A coupling” as one of its core themes for well over a decade. Under this initiative, several successful land surface and global climate modeling projects have identified hot spots of L-A coupling and helped quantify the role of land surface states in weather and climate predictability. GLASS formed the Local Land–Atmosphere Coupling (LoCo) project and working group to examine L-A interactions at the process level, focusing on understanding and quantifying these processes in nature and evaluating them in models. LoCo has produced an array of L-A coupling metrics for different applications and scales and has motivated a growing number of young scientists from around the world. This article provides an overview of the LoCo effort, including metric and model applications, along with scientific and programmatic developments and challenges.
Age is a determinant factor in the susceptibility of domestic ducks to H5 clade 2.3.2.1c and 2.3.4.4e high pathogenicity avian influenza viruses

High pathogenicity avian influenza (HPAI) is a viral disease with devastating consequences for the poultry industry worldwide. Domestic ducks are a major source of HPAI viruses in many Eurasian countries. The infectivity and pathogenicity of HPAI viruses in ducks vary depending on host and viral factors. To assess the factors influencing the infectivity and pathogenicity of HPAI viruses in ducks, we compared the pathobiology of two HPAI viruses (H5N1 clade 2.3.2.1c and H5N6 clade 2.3.4.4e) in 5- and 25-week-old ducks. Both HPAI viruses caused mortality in a dose-dependent manner (104, 106, and 108 EID50) in young ducks. By contrast, adult ducks were infected but exhibited no mortality due to either virus. Viral excretion was higher in young ducks than in adults, regardless of the HPAI strain. These findings demonstrate the age-dependent mortality of clade 2.3. 2.1c and clade 2.3. 4.4e H5 HPAI viruses in ducks.
A search for neutrino-induced single photons and measurement of oscillation analysis systematic errors with electron and anti-electron neutrino selections, using the off-axis near detector of the Tokai to Kamioka experiment

This thesis describes the search for neutrino-production of single photons using the offaxis near detector at 280 metres (ND280) of the T2K experiment. A photon selection is used to perform the searches using the first Fine Grained Detector (FGD1) of the ND280. The thesis also highlights the importance of systematic uncertainties in the analysis, since the selection is background dominated. After careful characterisation of the systematic uncertainties and estimation of the efficiency, it is concluded that, with the selected 39 data events and the expected background of 45 events, the limit for neutrino-induced single photons, at T2K energies, is 0.0903 × 10−38cm2/nucleon. This result can be compared with the expected limit of 0.1068 × 10−38cm2/nucleon. Using ND280’s neutrino energy distribution (peaked at 600 MeV), NEUT predicts a flux-averaged cross section of 0.000239× 10−38cm2/nucleon. A fit to the muon and electron (anti-) neutrinos selections in the ND280 was performed. The aim of this analysis is to use a data-driven method to constrain the electron (anti-) neutrinos background events at SK, the far detector and electron neutrino cross section parameters for oscillation analyses. These are fundamental inputs in the context of the searches for Charge-Parity (CP) violation in the neutrino sector. After a fit to the nominal Monte Carlo was realised, the electron neutrino and anti-neutrino cross section normalisation uncertainties are found to be 7.6% and 19.3%, repectively. Although these numbers are much higher than the assumed 3% uncertainty of all the CP violation searches performed at T2K up to now, the difference in the δCP log-likelihood is found to be acceptable as the one sigma contours are not very different and the exclusion of the δCP = 0 is roughly the same.
Granularity-Dependent Roughness Metric for Tactile Sensing Assessment

It is challenging to have a unified roughness metric representing the tactile sensation given the complexity of surface properties such as hardness, viscosity, and texture. In this article, a metric is proposed to evaluate tactile roughness across materials based on the granularity commonly used for sandpapers and implemented through a combination of sensing hardware, operating paradigm, reference object, and assessment method. A laboratory-customized FingerVision imitating the properties of human skin and mechanoreceptors is used as the core component of hardware. A wedge contact method is introduced as the operating paradigm to make full use of the array sensor and validated by a recognition task of 13 sandpapers. Moreover, the LSTM network is applied to a more challenging task and outperforms the conventional methods, indicating its superiority for temporally correlated samples. So that with the sandpapers’ set being reference objects, an S2SR LSTM network is used as the assessment method and tested on sandpapers’ set itself with the standardized RMSE of 0.0158. It is further tested on the fabrics with the correlation coefficient between the metrics and their linear mass densities being over −0.9740. Finally, it is applied to unidentified objects across materials, and the result is consistent with human tactile perception. The experimental results demonstrated that the proposed granularity-dependent roughness metric for a certain object is stable and repeatable, indicating that it is credible, valid, necessary, and valuable for tactile sensing assessment, paving the way to a unified tactile roughness representation for robot dexterous manipulation.
New biomarkers: prospect for diagnosis and monitoring of thyroid disease

After the metabolic syndrome and its components, thyroid disorders represent the most common endocrine disorders, with increasing prevalence in the last two decades. Thyroid dysfunctions are distinguished by hyperthyroidism, hypothyroidism, or inflammation (thyroiditis) of the thyroid gland, in addition to the presence of thyroid nodules that can be benign or malignant. Thyroid cancer is typically detected via an ultrasound (US)-guided fine-needle aspiration biopsy (FNAB) and cytological examination of the specimen. This approach has significant limitations due to the small sample size and inability to characterize follicular lesions adequately. Due to the rapid advancement of high-throughput molecular biology techniques, it is now possible to identify new biomarkers for thyroid neoplasms that can supplement traditional imaging modalities in postoperative surveillance and aid in the preoperative cytology examination of indeterminate or follicular lesions. Here, we review current knowledge regarding biomarkers that have been reliable in detecting thyroid neoplasms, making them valuable tools for assessing the efficacy of surgical procedures or adjunctive treatment after surgery. We are particularly interested in providing an up-to-date and systematic review of emerging biomarkers, such as mRNA and non-coding RNAs, that can potentially detect thyroid neoplasms in clinical settings. We discuss evidence for miRNA, lncRNA and circRNA dysregulation in several thyroid neoplasms and assess their potential for use as diagnostic and prognostic biomarkers.
Oral paclitaxel and encequidar in patients with breast cancer: a pharmacokinetic, safety, and antitumor activity study

Background: Paclitaxel is widely used for the treatment of metastatic breast cancer (MBC). However, it has a low oral bioavailability due to gut extrusion caused by P-glycoprotein (P-gp). Oral paclitaxel (oPAC) may be more convenient, less resource-intensive, and more tolerable than its intravenous form. Encequidar (E) is a first-in-class, minimally absorbed, gut-specific oral P-gp inhibitor that facilitates the oral absorption of paclitaxel. Objectives: To investigate the pharmacokinetics (PK), overall response rate (ORR), and safety of weekly oral paclitaxel with encequidar (oPAC + E) in patients with advanced breast cancer. Design: This is a multicenter, single-arm, open-label study in six medical centers in Taiwan. Methods: Patients with advanced breast cancer were administered 205 mg/m2 oPAC and 12.9 mg E for 3 consecutive days weekly for up to 16 weeks. Plasma samples were collected at weeks 1 and 4. PK, ORR, and safety were evaluated. Results: In all, 28 patients were enrolled; 27 had MBC; 23 had prior chemotherapy; and 14 had ⩾2 lines of prior chemotherapy. PK were evaluable in 25 patients. Plasma paclitaxel area under the curve (AUC)(0–52 h) at week 1 (3419 ± 1475 ng h/ml) and week 4 (3224 ± 1150 ng h/ml) were equivalent. Best overall response in 28 evaluable patients was partial response (PR) in 11 (39.3%), 13 (46.4%) stable disease (SD), and 1 (3.6%) with progressive disease (PD). No patient achieved complete response (CR). The clinical benefit rate (CR + PR + SD) was 85.7%. Major adverse events among the 28 treated patients were grade 3 neutropenia (25%), grade 4 neutropenia (18%), with febrile neutropenia in 4%, and grade 3 diarrhea (4%). No treatment-related deaths occurred. Grade 2 peripheral neuropathy occurred in 1 (4%) patient and grade 3 peripheral neuropathy in 1 (4%) patient. Conclusions: oPAC + E produced a consistent therapeutic plasma paclitaxel exposure during treatment. There was a high rate of radiologically assessed clinical benefit, and a low rate of neurotoxicity which may provide advantages over IV paclitaxel. Registration: ClinicalTrials.gov Identifier: NCT03165955
A Parallelized Iterative Closest Point Algorithm for Attitude Estimation

In recent decades, low-earth orbital debris has become a major concern. If the density of objects in orbit is too high, and nothing is done to remove any debris, there could be a cascade of collisions, each generating more debris, increasing the frequency of collisions even further. This would render future space missions very difficult and dangerous, if not impossible. Something must be done to remove space debris from orbit. This thesis attempts to solve one piece of the orbital debris problem that is, tracking a piece of debris and determining its attitude and position relative to a capture vehicle. A LIDAR camera is used to acquire images of the body to be tracked. To achieve a fast and practical solution, a parallelized Iterative Closest Point (ICP) algorithm and a Kalman filter are implemented to track the attitude and position of a model rocket booster. Additionally, this thesis presents a method to increase ICP’s accuracy and reliability by artificially increasing image resolution by algorithmically increasing image size. This work also explores the performance of different variations of ICP and the dependency of their performance on image size.
Potential therapeutic targets of macrophages in inhibiting immune damage and fibrotic processes in musculoskeletal diseases

Macrophages are a heterogeneous cell type with high plasticity, exhibiting unique activation characteristics that modulate the progression and resolution of diseases, serving as a key mediator in maintaining tissue homeostasis. Macrophages display a variety of activation states in response to stimuli in the local environment, with their subpopulations and biological functions being dependent on the local microenvironment. Resident tissue macrophages exhibit distinct transcriptional profiles and functions, all of which are essential for maintaining internal homeostasis. Dysfunctional macrophage subpopulations, or an imbalance in the M1/M2 subpopulation ratio, contribute to the pathogenesis of diseases. In skeletal muscle disorders, immune and inflammatory damage, as well as fibrosis induced by macrophages, are prominent pathological features. Therefore, targeting macrophages is of great significance for maintaining tissue homeostasis and treating skeletal muscle disorders. In this review, we discuss the receptor-ligand interactions regulating macrophages and identify potential targets for inhibiting collateral damage and fibrosis in skeletal muscle disorders. Furthermore, we explore strategies for modulating macrophages to maintain tissue homeostasis.