Miracle-dz/UniFAR-FTUs huggingface dataset:
On the capabilities of pattern classification using a PID concept

We present a concept for increasing the reliability of distance-based pattern classification models, applied to time-variant processes. In fault detection, static pattern classification is often used to differentiate between normal sensor data indicating ordinary uncritical states for the monitored process and abnormal data, indicating states that need to be resolved by maintenance or even dangerous states, that require the process to be switched off. Depending on the process and its environment, it can be difficult to classify, properly, each state the process can be in by employing sensor data. Sensor hardware limitations may also cause uncertainty when gathering the data for different process states. Inspired by conventional linear control theory, we propose a Proportional-Integral-Derivative (PID) concept which allows the determination of a relevance factor for a current classification, by consulting previous data and expert knowledge. It is based on the assumption that features of sensor data have different alteration rates, when transitioning into different states. The PID concept can be applied to several classical distance-based pattern recognition approaches such as Kohonen's Learning Vector Quantization (LVQ) or the k-means algorithm and allows them to classify abnormal data more reliably. The performance of this PID concept is demonstrated with test data sets. Problems that might occur when applying the PID concept to real applications and their solution are pointed out.
The Elucidation of Planar Aesthetic Curves

A compact formula for Logarithmic Curvature Histogram (LCH) and its gradient for planar curves have been proposed. Using these entities and the analysis of Generalized Cornu Spiral (GCS), the mathematical definition for a curve to be aesthetic has been introduced to overcome the ambiguity that occurs in measuring the beauty of a curve. In the last section, detailed examples are shown on how LCH and its gradient represented as a straight line equation can be used to measure the aesthetic value of planar curves.
Epidemiological connectivity between humans and animals across an urban landscape

Significance In this study, we conducted a comparative analysis of bacterial gene sharing across urban human and animal populations. Because the genes we targeted (called “mobile genetic elements”) are shared horizontally between bacteria, their population structure can be used to infer bacterial transmission between hosts. By uncovering characteristics of human and animal populations and the urban environments in which they live that promote cross-species sharing of Escherichia coli–borne genes, this study sheds important light on the conditions that could facilitate spillover of pathogens. A clear understanding of the socioecological determinants of cross-species transmission is crucial for design of public health interventions aimed at limiting human exposure to novel animal-borne pathogens in rapidly developing cities.
The Role of Endoplasmic Reticulum Stress Response in Liver Regeneration

Exposure to hepatotoxic chemicals is involved in liver disease-related morbidity and mortality worldwide. The liver responds to damage by triggering compensatory hepatic regeneration. Physical agent or chemical-induced liver damage disrupts hepatocyte proteostasis, including endoplasmic reticulum (ER) homeostasis. Post liver injury ER experiences a homeostatic imbalance, followed by active ER stress response signaling. Activated ER stress response causes selective upregulation of stress response genes and downregulation of many hepatocyte genes. Acetaminophen overdose, CCl4, acute and chronic alcohol exposure, and physical injury activates the ER stress response, but details about the cellular consequences of the ER stress response on liver regeneration remain unclear. The current data indicate that inhibiting the ER stress response after partial hepatectomy-induced liver damage promotes liver regeneration, whereas inhibiting the ER stress response after chemical-induced hepatotoxicity impairs liver regeneration. This review summarizes key findings and emphasizes the knowledge gaps in role of ER stress in injury and regeneration.
Nano‐delivery systems as a promising therapeutic potential for epilepsy: Current status and future perspectives

Epilepsy is a common chronic neurological disorder caused by aberrant neuronal electrical activity. Antiseizure medications (ASMs) are the first line of treatment for people with epilepsy (PWE). However, their effectiveness may be limited by their inability to cross the blood–brain barrier (BBB), among many other potential underpinnings for drug resistance in epilepsy. Therefore, there is a need to overcome this issue and, hopefully, improve the effectiveness of ASMs. Recently, synthetic nanoparticle‐based drug delivery systems have received attention for improving the effectiveness of ASMs due to their ability to cross the BBB. Furthermore, exosomes have emerged as a promising generation of drug delivery systems because of their potential benefits over synthetic nanoparticles. In this narrative review, we focus on various synthetic nanoparticles that have been studied to deliver ASMs. Furthermore, the benefits and limitations of each nano‐delivery system have been discussed. Finally, we discuss exosomes as potentially promising delivery tools for treating epilepsy.
SIRS, SOFA, qSOFA, and NEWS in the diagnosis of sepsis and prediction of adverse outcomes: a systematic review and meta-analysis

ABSTRACT Background We compared Systemic Inflammatory Response Syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), Quick Sepsis-related Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS) for sepsis diagnosis and adverse outcomes prediction. Methods Clinical studies that used SIRS, SOFA, qSOFA, and NEWS for sepsis diagnosis and prognosis assessment were included. Data were extracted, and meta-analysis was performed for outcome measures, including sepsis diagnosis, in-hospital mortality, 7/10/14-day mortality, 28/30-day mortality, and ICU admission. Results Fifty-seven included studies showed good overall quality. Regarding sepsis prediction, SIRS demonstrated high sensitivity (0.85) but low specificity (0.41), qSOFA showed low sensitivity (0.42) but high specificity (0.98), and NEWS exhibited high sensitivity (0.71) and specificity (0.85). For predicting in-hospital mortality, SOFA demonstrated the highest sensitivity (0.89) and specificity (0.69). In terms of predicting 7/10/14-day mortality, SIRS exhibited high sensitivity (0.87), while qSOFA had high specificity (0.75). For predicting 28/30-day mortality, SOFA showed high sensitivity (0.97) but low specificity (0.14), whereas qSOFA displayed low sensitivity (0.41) but high specificity (0.88). Conclusions NEWS independently demonstrates good diagnostic capability for sepsis, especially in high-income countries. SOFA emerges as the optimal choice for predicting in-hospital mortality and can be employed as a screening tool for 28/30-day mortality in low-income countries.
CNAsim: improved simulation of single-cell copy number profiles and DNA-seq data from tumors

Abstract Summary CNAsim is a software package for improved simulation of single-cell copy number alteration (CNA) data from tumors. CNAsim can be used to efficiently generate single-cell copy number profiles for thousands of simulated tumor cells under a more realistic error model and a broader range of possible CNA mechanisms compared with existing simulators. The error model implemented in CNAsim accounts for the specific biases of single-cell sequencing that leads to read count fluctuation and poor resolution of CNA detection. For improved realism over existing simulators, CNAsim can (i) generate WGD, whole-chromosomal CNAs, and chromosome-arm CNAs, (ii) simulate subclonal population structure defined by the accumulation of chromosomal CNAs, and (iii) dilute the sampled cell population with both normal diploid cells and pseudo-diploid cells. The software can also generate DNA-seq data for sampled cells. Availability and implementation CNAsim is written in Python and is freely available open-source from https://github.com/samsonweiner/CNAsim.
Cortical timescales and the modular organization of structural and functional brain networks

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the “timescale” over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.
Beyond the Visual Word Form Area: Characterizing a hierarchical, distributed and bilateral network for visual word processing

Although the left hemisphere (LH) Visual Word Form Area (VWFA) is considered the pre-eminent cortical region engaged in visual text processing, other regions in both hemispheres have also been implicated. To examine the entire circuit, using functional MRI data, we defined ten regions of interest (ROI) in each hemisphere that, based on functional connectivity measures, naturally grouped into early vision, high-level vision, and language clusters. We analysed univariate and multivariate responses to words, inverted words, and consonant strings for ROIs and clusters, and demonstrated modulation by text condition bihemispherically, albeit more strongly and in a larger number of regions in the LH. Graph theory analysis revealed that the high-level vision cluster and, specifically, the VWFA was equivalently connected with both early visual and language clusters in both hemispheres, reflecting its role as a mediator in the circuit. Our findings reveal bihemispheric, stimulus-mediated ROI response flexibility but circuit-level connectivity stability, reflecting the complex contribution of a distributed system for word processing.
Quantifying Contribution of Atmospheric Circulation to Precipitation Variability and Changes in the U.S. Great Plains and Southwest Using Self Organizing Map – Analogue

The Great Plains and Southwest regions of the U.S. are highly vulnerable to precipitation-related climate disasters such as droughts and floods. In this study, we propose a self-organizing map–analogue (SOMA) approach to empirically quantify the contribution of atmospheric circulation (mid-tropospheric geopotential and column moisture transport) to the regional precipitation anomalies, variability, and multi-decadal changes. Our results indicate that 10 atmospheric circulation contributes significantly to short-term precipitation variability, accounting for 54-61% of the total variance and 62-68% of the amplitude of the mean precipitation anomalies in these regions, though these contributions vary significantly across seasons. The remaining variance is largely influenced by thermodynamically driven factors. As indicated in previous research, Pacific Decadal Oscillation (PDO) is one of the major climate modes influencing the long-term multi-decadal variation of precipitation. By contrasting three multi-decadal periods (1950- 15 1976, 1977-1998, 1999-2021) with shifting PDO phases and linking the phase shift to circulation SOM nodes, we found that circulation changes contribute considerably to the multi-decadal changes of precipitation anomaly in terms of the mean and probability of dry and wet extremes, especially for the Southern GP and Southwest. However, these circulation-induced changes are not totally related to the PDO phase shift (mostly less than half), atmospheric internal variability or anthropogenically induced changes in circulation can also be potential contributors. Our approach 20 improves upon flow analogue and SOM-based methods and provides insights into the contribution of atmospheric circulation to regional precipitation anomalies and variability.
Modelling concentration heterogeneities in streets using the street-network model MUNICH

Abstract. Populations in urban areas are exposed to high local concentrations of pollutants, such as nitrogen dioxide and particulate matter, because of unfavourable dispersion conditions and the proximity to traffic. To simulate these concentrations over cities, models like the street-network model MUNICH (Model of Urban Network of Intersecting Canyons and Highways) rely on parameterizations to represent the air flow and the concentrations of pollutants in streets. In the current version, MUNICH v2.0, concentrations are assumed to be homogeneous in each street segment. A new version of MUNICH, where the street volume is discretized, is developed to represent the street gradients and to better estimate peoples' exposure. Three vertical levels are defined in each street segment. A horizontal discretization is also introduced under specific conditions by considering two zones with a parameterization taken from the Operational Street Pollution Model (OSPM). Simulations are performed over two districts of Copenhagen, Denmark, and one district of greater Paris, France. Results show an improvement in the comparison to observations, with higher concentrations at the bottom of the street, closer to traffic, of pollutants emitted by traffic (NOx, black carbon, organic matter). These increases reach up to 60 % for NO2 and 30 % for PM10 in comparison to MUNICH v2.0. The aspect ratio (ratio between building height and street width) influences the extent of the increase of the first-level concentrations compared to the average of the street. The increase is higher for wide streets (low aspect ratio and often higher traffic) by up to 53 % for NOx and 18 % for PM10. Finally, a sensitivity analysis with regard to the influence of the street network highlights the importance of using the model MUNICH with a network rather than with a single street.
Analytical Model of Particle Motion in Optical Interference Landscapes and Laminar Flow

CONOVER, BRANDON L. Analytical Model of Particle Motion in Optical Interference Landscapes and Laminar Flow. (Under the direction of Assistant Professor Michael J. Escuti). Optically defined oneand two-dimensional potential energy “landscapes” can create characteristic forces and torques on nanoand micro-scale spheroidal particles that may be specifically tailored to meet the manipulation and measurement needs within colloidal hydrodynamic systems. Similar to optical tweezers, optical landscapes are able to selectively sort, trap, mix, align, and order mesoscale particles, yet they hold the potential to perform these tasks on a massively parallel scope. While recent publications have provided both experimental and theoretical support of optical landscapes’ capabilities, none to date have derived an order-of-magnitude approximation of the response of spheroidal particles within them. While almost all analytical models of particle motion reported so far are limited to spheres, many particles of interest are in fact shaped like disks and rods (e.g. blood cells, nanowires). This work advances toward the goal of describing complete spheroidal particle response to laminar flow and general optical landscapes of oneand two-dimensions. Here we derive the optically induced force and torque from first principles, resulting in a model capable of predicting results in agreement with previous experiments and theory. A key prediction of our model is that highly selective trapping within the landscapes can be achieved based on shape, in addition to size as described in the literature. In general, we find that as particles become more oblate or prolate, they become easier to trap as compared to spherical particles with an equivalent volume. Additionally, we find that a trapped elliptical particle will align in its lowest energy orientation as dictated by the shape of the optical landscape. Finally, we provide a brief comparison of oneand two-dimensional landscapes and their effects on spheroidal particles. Analytical Model of Particle Motion in Optical Interference Landscapes and Laminar Flow
CONSTRUCTION COSTS AND PAYBACK TIME OF BIOMASS: A WHOLE PLANT PERSPECTIVE

Literature data were compiled on the construction costs of plant biomass, that is, the amount of glucose required to construct one gram ofbiomass. No evidence was found for more than a slight variation in construction costs, with leaves of herbaceous species somewhat more expensive to produce than stems or roots. There is little or no variation due to environmental differences, or in construction costs between ecologically contrast· ing groups ofspecies. However, most research in this area has focused on leaves. Differ· ences between woody and herbaceous species may become apparent when construction costs of whole plants are consiaered. It has been suggested that the payback time ofleaves, i.e., the time a leaf requires to meet its own construction costs, rather than the construction costs themselves, vary for ecologically contrasting species. Although correct, such an approach ignores the relevance of stem and roots and their associated costs for the functioning of a leaf. From a whole plant perspective, payback time is identical to the relative growth rate of a plant. As such, this concept is closely related to part of Grime's plant strategy theory, where relative growth rate is considered to be one of the differentiating key factors.
False Discovery Rates, Higher Criticism and Related Methods in High-Dimensional Multiple Testing

The technical advancements in genomics, functional magnetic-resonance and other areas of scientific research seen in the last two decades have led to a burst of interest in multiple testing procedures. A driving factor for innovations in the field of multiple testing has been the problem of large scale simultaneous testing. There, the goal is to uncover lower–dimensional signals from high–dimensional data. Mathematically speaking, this means that the dimension d is usually in the thousands while the sample size n is relatively small (max. 100 in general, often due to cost constraints) — a characteristic commonly abbreviated as d n. In my thesis I look at several multiple testing problems and corresponding procedures from a false discovery rate (FDR) perspective, a methodology originally introduced in a seminal paper by Benjamini and Hochberg (2005). FDR analysis starts by fitting a two–component mixture model to the observed test statistics. This mixture consists of a null model density and an alternative component density from which the interesting cases are assumed to be drawn. In the thesis I proposed a new approach called log–FDR to the estimation of false discovery rates. Specifically, my new approach to truncated maximum likelihood estimation yields accurate null model estimates. This is complemented by constrained maximum likelihood estimation for the alternative density using log–concave density estimation. A recent competitor to the FDR is the method of “Higher Criticism”. It has been and strongly advocated in the context of variable selection in classification which is deeply linked to multiple comparisons. Hence, I also looked at variable selection in class prediction which can be viewed as a special signal identification problem. Both FDR methods and Higher Criticism can be highly useful for signal identification. This is discussed in the context of variable selection in linear discriminant analysis (LDA), a popular classification method. FDR methods are not only useful for multiple testing situations in the strict sense, they are also applicable to related problems. I looked at several kinds of applications of FDR in linear classification. I present and extend statistical techniques related to effect size estimation using false discovery rates and showed how to use these for variable selection. The resulting fdr–effect method proposed for effect size estimation is shown to work as well as competing approaches while being conceptually simple and computationally v
Examining very-long-term variability in X-ray binaries: are ultraluminous X-ray sources intermediate mass black holes?

The very-long-term (weeks to years) timing properties of high mass X-ray bina- ries (HMXBs) featuring black holes, particularly the so-called ultraluminous X-ray sources (ULXs), could be linked to the coupled precession of their accretion disks and relativistic jets. These modulations arise in environments that feature super- Eddington mass transfer onto their central compact objects, inviting comparisons with the archetype microquasar SS433. Measuring superorbital periods is difficult, especially for extragalactic sources, and this work highlights the need for long-term monitoring campaigns with good sampling plans. ULXs could host the long-sought intermediate-mass black holes. Alternately, they might be stellar-mass black holes that are either undergoing hyperaccretion, exhibiting beamed emission along our line of sight, or possibly a combination of these two scenarios, in which case ULXs would be a natural extension of the HMXB population. Utilizing a multiwaveband approach, we probe the long-term timing properties of HMXBs to learn if these modulations are a universal feature of ULXs. We examine the very-long-term variability of X-ray binaries (XRBs) in the nearby galaxy M87. Constraining the variability of XRBs depends on obtaining meaningful lightcurves, which can only be done for the most luminous sources and with limited time resolution. Understanding the longer-term variability of extragalactic XRBs requires multi-epoch Chandra observations, which remain rare. We used archival Chandra data to search the long-term (∼10 year) light curves of 8 of the brightest XRBs in M87 for periodic modulations ranging from 30 days to 5 years. Our analysis shows that some of these sources exceed the ULX threshold luminosity 10^39 erg/s, but the relatively poor sampling makes it difficult to detect any very-long-term periods that might be present. Finally, a spectroscopic analysis of a recently discovered HMXB found in the Large Magellanic Cloud and within a supernova remnant is presented. Its nebular environment invites comparisons to both SS433 and the ULX NGC 5408 X-1. The best explanation for the observed X-ray and optical properties is a HMXB containing a black hole undergoing wind accretion from its massive early-type donor.