Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Voltage Ride Through (LVRT) Constrained Transient Stability Assessment Using Lyapunov Functions Family Method

Growing penetration of renewable generation in power systems brings more challenges in transient stability assessment using direct methods. One of the reasons is the inability to assess the risk of instability brought by tripping of a large amount of RGs due to violation of their ride through curves. In this paper, a scalable approach is proposed based on the Lyapunov functions family to estimate the stability region under ride through constraints. An inner polytopic approximation to the feasibility region is proposed to convexify the overall problem. The acquired constrained stability region estimate helps capture trajectories that trigger undesirable tripping of renewable generation. A 2machine system is used to visualize its effectiveness.
Path Loss and Directional Gain Measurements at 28 GHz for Non-Line-of-Sight Coverage of Indoors With Corridors

Adequate coverage with high-gain antennas is a key to realizing the full promise of the bandwidth available at mm/cm wave bands. We report extensive indoor measurements at 28 GHz (1000 links, 9.9 million individual power measurements, 10 offices, 2 buildings), with/without line of sight (LOS) using a continuous wave channel sounder, with a 10° spinning horn, capable of capturing a full azimuth scan every 200 ms, in up to 171 dB path loss to characterize coverage with 90% confidence level. The environment had prominent corridors and rooms, as opposed to open/mixed offices in latest 3GPP standards. Guiding in corridors leads to much lower RMS azimuth spread (7° median in corridor non-LOS (NLOS) versus 42° in 3GPP) and higher penetration loss into rooms and around corners (30–32 dB, some 12 dB more loss than 3GPP at 20 m NLOS). Measured path gain in NLOS is predicted by a mode-diffusion model with 3.9 dB RMS error. Scattering degraded azimuth gain by up to 4 dB in the corridor and 7 dB in rooms with 90% probability. Link simulations in a canonical building indicate every corridor needs an access point to provide 1 Gb/s rate to adjoining rooms within 50 m using 400 MHz of bandwidth.
Robust subspace methods for outlier detection in genomic data circumvents the curse of dimensionality

The application of machine learning to inference problems in biology is dominated by supervised learning problems of regression and classification, and unsupervised learning problems of clustering and variants of low-dimensional projections for visualization. A class of problems that have not gained much attention is detecting outliers in datasets, arising from reasons such as gross experimental, reporting or labelling errors. These could also be small parts of a dataset that are functionally distinct from the majority of a population. Outlier data are often identified by considering the probability density of normal data and comparing data likelihoods against some threshold. This classical approach suffers from the curse of dimensionality, which is a serious problem with omics data which are often found in very high dimensions. We develop an outlier detection method based on structured low-rank approximation methods. The objective function includes a regularizer based on neighbourhood information captured in the graph Laplacian. Results on publicly available genomic data show that our method robustly detects outliers whereas a density-based method fails even at moderate dimensions. Moreover, we show that our method has better clustering and visualization performance on the recovered low-dimensional projection when compared with popular dimensionality reduction techniques.
Integration of shared-pathogen networks and machine learning reveals the key aspects of zoonoses and predicts mammalian reservoirs

Diseases that spread to humans from animals, zoonoses, pose major threats to human health. Identifying animal reservoirs of zoonoses and predicting future outbreaks are increasingly important to human health and well-being and economic stability, particularly where research and resources are limited. Here, we integrate complex networks and machine learning approaches to develop a new approach to identifying reservoirs. An exhaustive dataset of mammal–pathogen interactions was transformed into networks where hosts are linked via their shared pathogens. We present a methodology for identifying important and influential hosts in these networks. Ensemble models linking network characteristics with phylogeny and life-history traits are then employed to predict those key hosts and quantify the roles they undertake in pathogen transmission. Our models reveal drivers explaining host importance and demonstrate how these drivers vary by pathogen taxa. Host importance is further integrated into ensemble models to predict reservoirs of zoonoses of various pathogen taxa and quantify the extent of pathogen sharing between humans and mammals. We establish predictors of reservoirs of zoonoses, showcasing host influence to be a key factor in determining these reservoirs. Finally, we provide new insight into the determinants of zoonosis-sharing, and contrast these determinants across major pathogen taxa.
Development of medical imaging sensors

This review describes sensor technologies for medical imaging. Medical imaging plays an extremely important role in medical imaging, and sensor technology affects the quality of imaging results. By analyzing the research progress of sensors used in various imaging technologies, the result that sensors applied to medical imaging will gradually achieve mutual coupling between various technologies was summed up, such as the coupling of flexible electrodes and dry electrodes, in ultrasound. Photoacoustic technology and thermoacoustic technology based on technology coupled with other technologies. In addition to the coupling between multiple technologies, sensor technology is also moving toward miniaturization, intelligence, and netting.
Buoyancy effects on nanoliquids film flow through a porous medium with gyrotactic microorganisms and cubic autocatalysis chemical reaction

This article is based on the mathematical model constructed to analyze the simultaneous flow and heat transfer of two nanoliquids (Casson and Williamson) in the presence of gyrotactic microorganisms and cubic autocatalysis chemical reaction through a porous medium under the potentiality of buoyancy forces. Heterogeneous reaction existing on the surface is described by isothermal cubic autocatalytic chemical reaction, whereas homogeneous reaction is taking place at far field described by first-order kinetics. Similarity transformations are used to get the different order differential equations from the governing equations which are solved via an efficient technique namely homotopy analysis method. The effects of all the non-dimensional parameters on velocity, temperature, concentration, and density of motile microorganisms are shown through graphs and elucidated. Velocity increases with the Weissenberg parameter and decreases with the Casson nanofluid parameter in the presence of magnetic field and porous medium. Temperature decreases with the high values of slip condition. The dual behavior of concentration profile for the strength of homogeneous reaction parameter is observed. Flow of microorganisms decreases based on the parameters of porous medium, magnetic field, and heterogeneous chemical reaction. There exists an excellent agreement between the present and published work.
A histopathological rarity: Primary signet ring cell carcinoma arising within a colonic villous adenoma

Signet ring cell carcinoma (SRCC) has been described most frequently in the stomach. It may also occur in other mucinous organs like the urinary bladder, prostate, pancreas, breast, lung etc. Within the gastrointestinal tract, up to 99% of SRCC arise in the stomach. SRCC is rare in the colorectal region as compared to the conventional adenocarcinoma, the incidence rates among all colorectal cancers ranging from 0.01% to 2.6%. [1] Even more unusual is the occurrence of a primary SRCC developing within colonic adenomatous polyps. There have been less than eight instances in literature documenting this rare phenomenon. [1‐6] This paper reports an additional case of primary SRCC arising in a colonic adenoma.
Use of smooth muscle markers is better than the endothelial cell markers for identification of tumor venous invasion and extramural tumor deposits in gastrointestinal tract tumors

Tumor invasion into the endothelial‐lined spaces (lymphatics and medium/ large veins) is a poor prognostic marker in various malignancies. As per the AJCC tumor‐node‐metastasis (TNM) staging system (AJCC Cancer Staging Manual, 8th edition), one has to look for and comment on the extramural venous invasion (VI) to predict distant tumor metastasis. Presence of VI also qualifies a patient to receive systemic adjuvant chemotherapy in TNM stage II and stage III colorectal carcinomas (CRCs). [1] Tumor invasion in the small vessels, comprising lymphatics, capillaries, and postcapillary venules (all considered under ‘L’ classification in current TNM staging system) is also a marker of higher lymph node (LN) metastasis. [2] Tumor deposits (TD) [tumor satellites] are the discrete macroscopic or microscopic tumor cell nodules, in the pericolorectal adipose tissue, discontinuous from the primary tumor without any histological evidence of residual LN, or vascular or neural structures. If a vessel wall can be demonstrated around a TD, the same should be classified as either a venous invasion (V1/2) or lymphatic invasion (L1), respectively. Like the VI, the presence of TDs also does not change the primary tumor ‘T’ category, but changes the LN status (N) to pN1c, in a regional LN‐negative CRC. In stage II CRC presence of pN1c further, upstage it to stage III. Though in previous editions of AJCC size of TDs (in TNM5) and their outer contour (in TNM6) were taken into account to differentiate them from pericolorectal metastatic LNs, neither size nor contour is part of the current definition of TD in AJCC 8th edition. [1,2]
Descriptive statistics: Measures of central tendency, dispersion, correlation and regression

Large data obtained from research are subjected to statistical analysis so that outcomes can be extrapolated to the larger population. Towards this end, such large data have to be consolidated into smaller, simpler expressions of measures, representing the outcomes of the whole sample. These form the descriptive statistics, which will later on help in inferential statistics, involving the different variables within one group and more than one group. Their distribution features are analysed and are described as sums, averages, relationships and differences. These measures are classified as those of central location and those of dispersion. Mean, Median and Mode are the three main measures of central tendency and Range. Percentile, variance, standard deviation, standard error and confidence interval are measures of dispersion. Correlation and regression can be used to describe the relationship between two numerical variables. Correlation is a measure of association and regression is used for prediction. Regression analysis helps to assess 'influential' relationships between the data. Changes among one or more variables might affect other variables.
Airway-focused dentistry – Hype versus truth

Dental sleep medicine (DSM) is an emerging subspecialty in dentistry. Many dentists are adopting it in clinical practice to screen for and treat obstructive sleep apnea (OSA). The value of evidence-based medicine for OSA, oral appliance treatment, and good collaboration with sleep medicine physicians in sleep disorder care is important for all dentists who practice DSM. This article aims to assess the “hype” and “truth” in the arena of DSM from contemporary literature. DSM is a science with rapidly expanding body of information that is dynamically evolving and can challenge previously accepted concepts.
Importance of MACC1 expression in breast cancer and its relationship with pathological prognostic markers

Background: Metastasis associated colon cancer gene 1 (MACC1) is a gene that was first described as a c-Met transcription regulator causing the progression of colon cancer. In this study, protein and messenger RNA (mRNA) expression of MACC1 in breast cancer and its relationship with clinicopathological prognostic parameters were investigated. Methods: Sixty-six cases with tumors underwent radical mastectomy for invasive ductal carcinoma and 25 control cases operated for mammoplasty were included in the study. In paraffin blocks of tumor and control tissues, MACC1 expression was investigated by the immunohistochemical method and Real-time polymerase chain reaction (Real-Time PCR). In addition, vascular endothelial growth factor (VEGF) expression was examined immunohistochemically in tumor tissues. The relationship between MACC1 expression in tumor tissues, clinicopathological prognostic parameters, and VEGF was investigated. Results: In this study, protein and mRNA expressions of MACC1 were found to be higher in tumor tissues compared with normal breast tissues. MACC1 protein expression was also associated with significant poor prognostic markers, such as high histologic grade, ER negativity, and HER2 positivity. However, there was no correlation between MACC1 expression and VEGF. Conclusion: According to these results, MACC1 expression may be a marker of breast carcinoma as well as an independent predictor of poor prognosis. In addition, MACC1 may not affect angiogenesis in breast cancer or even if it has an effect, it may not be associated with VEGF. However, it would be appropriate to support these results in a larger series by investigating in vivo and in vitro studies.
Ki 67: Are we counting it right?

Ki67 is a nuclear protein that is expressed in all phases of cell cycle. This nuclear protein is best detected during the interphase of the cell cycle within the nucleus of the actively dividing cells. During mitosis, Ki67 migrates to the surface of the chromosome and the cellular content peaks during the synthetic phase. The analysis of the IHC stained cells involves detection of the cells with high Ki67 content as they are positively stained (dark brown) than the rest (blue). [1] In breast carcinoma, particularly the Ki67 is an established independent risk factor determining the disease‐free survival. Study by Inwald et al. showed that the 5‐year disease‐free survival (DFS) rate was 86.7% in patients with a Ki‐67 value ≤15% compared to 75.8% in patients with a Ki‐67 value >45%. Based on the data from a large cohort of a clinical cancer registry, it was demonstrated that Ki‐67 is frequently determined in routine clinical work. [2] Similarly in neuroendocrine tumors, the grading of tumor is based categorically on ki67 count. The Ki67 IHC is immensely altered by preanalytical variables such as type of biopsy, cold ischemia time, type of fixative, and fixation time. To standardize the same ideally, IHC should be carried out on whole section instead of trucut biopsy; cold ischemia time should be <30 min, formaline is the best fixative (avoid Ki67 IHC on frozen tissue), and ideally the fixation should last between 6 and 72 h. The analytical variables include clone of antibody, type of antigen retrieval, and blocking agents. We recommend utilization of MiB1 antibody in dilution validated by each laboratory; antigen retrieval should ideally be done by microwaving and blocking should be done by 5% serum. [3]
The Importance of TOR Kinase in Plant Development

TARGET OF RAPAMYCIN (TOR) kinase has been recognised as a key developmental regulator in both plants and animals. Despite their distinct developmental programmes, all eukaryotes studied possess a functional TOR kinase, which integrates environmental and nutrient signals to direct growth and development. This is particularly important in plants, as they are sessile and must sense and respond to external signals to coordinate multicellular growth appropriately. Thus, the investigation of TOR is essential for plant developmental studies in the context of the resources available for growth. Recently, links have been shown between TOR and plant development from embryogenesis through to senescence, however more investigation is crucial to fully elucidate TOR function in each developmental process.
BedSect: An Integrated Web Server Application to Perform Intersection, Visualization, and Functional Annotation of Genomic Regions From Multiple Datasets

A large number of genomic regions captured during next generation sequencing (NGS) data analyses are generally overlapped to answer a variety of biological questions. Though several command-line tools are available to perform such analysis, there is lack of a comprehensive web server application to perform the genomic region intersections from multiple datasets, to generate a plot for the subsets of the overlapped regions and to perform integrated functional annotation. To address this gap, we have developed a user-friendly integrated web server application i.e. Bedsect, where users can upload genomic regions from multiple datasets without any file number limitation and perform intersection analysis along with visualization of the intersection regions as UpSet and Correlation plot using integrated Shiny application. Bedsect also integrates GREAT for functional annotation, gene ontology, and biological pathways enrichment analysis from identified unique as well as intersected genomic regions. These genomic regions can be further uploaded in the UCSC genome browser for visualization of the results as custom tracks directly from the tool. Bedsect is available at http://imgsb.org/bedsect/.
Attachment-Based Family Therapy for Adolescent Substance Use: A Move to the Level of Systems

This paper provides an account of the theoretical basis of a family-based intervention called Behaviour Exchange and Systems Therapy (BEST). The model described here has also been applied to adolescents with substance abuse and other mental health problems such as depression and anxiety disorders in both children and adolescents. Evaluative studies of the model have been published including randomised clinical trials as well as qualitative analyses. The current paper discusses a theory of the family system as a discourse and represents an integration of aspects of attachment, psychoanalytic, and systems theories. Key concepts elaborated are the attachment-family system, the family as a single discourse, the use of segregation as a defense in relation to trauma and loss and its manifestation in a family narrative, and the role of the family secure base in affect regulation. The paper also briefly describes specific treatment techniques that are derived from the theoretical model. Our approach has wide application as a discourse focused treatment for children and adolescents using a family systems approach. Future work requires the comparison of this model to similar attachment-based models of intervention for children and families, further development and validation of measures able to be used for whole families in a clinical setting, and further empirical demonstration of treatment efficacy in a variety of clinical settings.
The Role of Macrophages in Aortic Dissection

Aortic dissection (AD) is a fatal disease that accounts for a large proportion of aortic-related deaths and has an incidence of about 3–4 per 100,000 individuals every year. Recent studies have found that inflammation plays an important role in the development of AD, and that macrophages are the hub of inflammation in the aortic wall. Aortic samples from AD patients reveal a large amount of macrophage infiltration. The sites of macrophage infiltration and activity vary throughout the different stages of AD, with involvement even in the tissue repair phase of AD. Angiotensin II has been shown to be an important factor in the stimulation of macrophage activity. Stimulated macrophages can secrete metalloproteinases, inflammatory factors and other substances to cause matrix destruction, smooth muscle cell apoptosis, neovascularization and more, all of which destroy the aortic wall structure. At the same time, there are a number of factors that regulate macrophages to reduce the formation of AD and induce the repair of torn aortic tissues. The aim of this review is to take a close look at the roles of macrophages throughout the course of AD disease.
A Concise Review of Gradient Models in Mechanics and Physics

The various mathematical models developed over the years to interpret the behavior of materials and corresponding processes they undergo were based on observations and experiments made at that time. Classical laws for solids (Hooke) and fluids (Navier–Stokes) form the basis of current technology. The discovery of new phenomena with the aid of newly developed experimental probes have led to various modifications of these laws, especially at small scales. The emergence of nanotechnology is ultimately connected with the design of novel tools for observation and measurements, as well as with the development of new methods and approaches for quantification and understanding. This paper first reviews the author's previously developed weakly non-local or gradient models for elasticity, diffusion, and plasticity. It then proposes a similar extension for fluids and electrodynamics. Finally, it suggests a gradient modification of Newton's law of gravity, with a possible connection to the strong force of elementary particle physics.
Biological and Technical Challenges in Unraveling the Role of N-Glycans in Immune Receptor Regulation

N-glycosylation of membrane receptors is important for a wide variety of cellular processes. In the immune system, loss or alteration of receptor glycosylation can affect pathogen recognition, cell-cell interaction, and activation as well as migration. This is not only due to aberrant folding of the receptor, but also to altered lateral mobility or aggregation capacity. Despite increasing evidence of their biological relevance, glycosylation-dependent mechanisms of receptor regulation are hard to dissect at the molecular level. This is due to the intrinsic complexity of the glycosylation process and high diversity of glycan structures combined with the technical limitations of the current experimental tools. It is still challenging to precisely determine the localization and site-occupancy of glycosylation sites, glycan micro- and macro-heterogeneity at the individual receptor level as well as the biological function and specific interactome of receptor glycoforms. In addition, the tools available to manipulate N-glycans of a specific receptor are limited. Significant progress has however been made thanks to innovative approaches such as glycoproteomics, metabolic engineering, or chemoenzymatic labeling. By discussing examples of immune receptors involved in pathogen recognition, migration, antigen presentation, and cell signaling, this Mini Review will focus on the biological importance of N-glycosylation for receptor functions and highlight the technical challenges for examination and manipulation of receptor N-glycans.