Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Neuroscience

reality to the fiction depending on their own engrossment and/or the suggestiveness of the play. Therefore transition from the reality actually grasped from sensory information to the represented reality is made possible owing to the potency of the phrasing and acting, promoted by the stage director's artistic concept and envisioning of the drama. In his Poetics, Aristotle emphasized the key function of both writing and staging, and the distinction between theater and narrative; " tragedy, is an imitation of an action that is serious, complete, and of a certain magnitude; in language embellished with each kind of artistic ornament, the several kinds being found in separate parts of the play; in the form of action, not of narrative " (Aristotle, 350 B.C.E). Another essential component, which may assist in the shift from actual reality to fiction in live theater deals with the suspension of disbelief based on the agreement by the spectators to provisionally suspend their judgment. According to Coleridge (1817) who coined the notion to support non-realistic elements in literature, the " willing suspension of disbelief " necessitates " to transfer from our inward nature a human interest and a semblance of truth " to enjoy a fantastic story. The ability for a spectator of live theater to enter a fictional reality would therefore imply, first a readiness to sacrifice realism, even logic and believability, and second a capacity to " contextualize " immediate sensory information processing. The first condition is a matter of culture. Like more recent forms of live arts, i.e., happenings or visual performing arts, theater is a time-based form of
Human Neuroscience

The posterior parietal cortex is related to both action knowledge (e.g., motor) and internal representation of actions (e.g., cognition; Creem-Regehr, 2009). Our own work has identified a left-hemisphere network for differentiation of matching and mismatching tool–object pairings (Mizelle and Wheaton, 2010b). Specifically, activation was seen in superior temporal cortex, insula, posterior cingulate, and precuneus in distinguishing mismatching versus matching tool–object pairs. This network was not active in evaluation of matching versus mismatching environmental image pairings, thus strongly suggesting a discrete network for detecting incorrect tool–object relationships. Others have evaluated the understanding of tool similarity based on action relatedness (comparing tools used in the same way) or functional relatedness (comparing tools used in the same context; Canessa et al., 2008), and highlighted the importance of retrosplenial and inferotemporal cortex in understanding functional properties of tools. Further, processing of information related to object manipulation and function has been shown to engage similar neural structures (Boronat et al., 2005), suggesting the conceptual identification of objects may involve binding the perceptual input (e.g., identity) and output (e.g., usage) representations of those objects. Others using ERP analyses have identified the N400 effect in response to identification of anomalous tool use (Sitnikova et al., 2003, 2008). Similarly, this response has been seen in extracting movement-related semantic information, such as identifying the incorrect conclusion of an action sequence (Reid and Striano, 2008) and in determining uncooperative hand–hand interactions (Shibata et al., 2009).
STOCHASTIC EPIDEMIC MODELING FOR THE H1N1 INFLUENZA IN KOREA

We present computational methods of the stochastic epidemic models such as stochastic simulation algorithm(SSA) and moment closure method(MCM) and apply the methods to an H1N1 influenza model. Computational efficiency is an important issue for stochastic modeling when one simulate the model with different situations and parameters. In general, the exact stochastic simulation algorithm(SSA) requires intensive and expensive computations especially when the infection process is fast and there is a large number of population . In that case, the moment closure method(MCM) is a good alternative way of computation for stochastic modeling. A MCM is useful approximation method to find the moments by truncating the higher order from the ODE system of moments. There have been some moment closure methods described in [15,19–21]. We compare the results between deterministic and stochastic models in terms of the spread of the influenza. We show that there is a considerable discrepancy between the results of stochastic and deterministic models especially when a small number of either infective or susceptible individuals is initially. H1N1 INFLUENZA MODEL We construct an SLIAR model with treatment for modeling the spread of H1N1 influenza. We apply the response strategies implemented in South Korea in 2009 2010 to the model. Treatment includes vaccination of susceptible class and antiviral treatment of latent, infective, and asymptomatically infective class. The SLIAR model has five compartments; susceptible(S), latent(L), infectious and symptomatic(I), asymptomatic but infective(A), recovered(R) We add the treatment compartment into each class for SLIAR, called ST (t), LT (t), IT (t) and AT (t), respectively [4]. The system of ODEs is described by S ′ =−βSQ− γS ST ′ =−σsβSTQ+ γS L′ = βSQ− κL− φLL LT ′ =σsβSTQ− κTLT + φLL I ′ = pκL− αI − φII IT ′ = pτκTLT − αT IT + φII A′ =(1− p)κL− ηA− φAA AT ′=(1− pτ)κTLT − ηTAT + φAA R′ = fαI + fTαT IT + ηA+ ηTAT Q = I + σIIT + δA+ δσAAT where S(0) = S0, ST (0) = 0, I(0) = I0, L(0) = LT (0) = A(0) = AT (0) = IT (0) = R(0) = D(0) = 0.
Study on determination points of vitrinite random reflectance for mixed coal in GB/T 6948-2008

Microscopical determination of the reflectance of vitrinite in coal is essential testing method in coking blending process in recent years,determining points is one of the important factors deciding testing precision and testing efficiency.Coking coal in practical production is taken as testing object in this paper,and the determination result precision of vitrinite reflectance with different determination points is studied.
Identification of Potential Biomarkers of Septic Shock Based on Pathway and Transcriptome Analyses of Immune-Related Genes

Immunoregulation is crucial to septic shock (SS) but has not been clearly explained. Our aim was to explore potential biomarkers for SS by pathway and transcriptional analyses of immune-related genes to improve early detection. GSE57065 and GSE95233 microarray data were used to screen differentially expressed genes (DEGs) in SS. Gene Ontology and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses of DEGs were performed, and correlations between immune cell and pathway enrichment scores were analyzed. The predictive value of candidate genes was evaluated by receiver operating characteristic (ROC) curves. GSE66099, GSE4607, and GSE13904 datasets were used for external validation. Blood samples from six patients and six controls were collected for validation by qRT-PCR and western blotting. In total, 550 DEGs in SS were identified; these genes were involved in the immune response, inflammation, and infection. Immune-related pathways and levels of infiltration of CD4 + TCM, CD8 + T cells, and preadipocytes differed between SS cases and controls. Seventeen genes were identified as potential biomarkers of SS (areas under ROC curves >0.9). The downregulation of CD8A, CD247, CD3G, LCK, and HLA-DRA in SS was experimentally confirmed. We identified several immune-related biomarkers in SS that may improve early identification of disease risk.
Identification of potential biomarkers and immune infiltration characteristics in recurrent implantation failure using bioinformatics analysis

Introduction Recurrent implantation failure (RIF) is a frustrating challenge because the cause is unknown. The current study aims to identify differentially expressed genes (DEGs) in the endometrium on the basis of immune cell infiltration characteristics between RIF patients and healthy controls, as well as to investigate potential prognostic markers in RIF. Methods GSE103465, and GSE111974 datasets from the Gene Expression Omnibus database were obtained to screen DEGs between RIF and control groups. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes Pathway analysis, Gene Set Enrichment Analysis, and Protein-protein interactions analysis were performed to investigate potential biological functions and signaling pathways. CIBERSORT was used to describe the level of immune infiltration in RIF, and flow cytometry was used to confirm the top two most abundant immune cells detected. Results 122 downregulated and 66 upregulated DEGs were obtained between RIF and control groups. Six immune-related hub genes were discovered, which were involved in Wnt/-catenin signaling and Notch signaling as a result of our research. The ROC curves revealed that three of the six identified genes (AKT1, PSMB8, and PSMD10) had potential diagnostic values for RIF. Finally, we used cMap analysis to identify potential therapeutic or induced compounds for RIF, among which fulvestrant (estrogen receptor antagonist), bisindolylmaleimide-ix (CDK and PKC inhibitor), and JNK-9L (JNK inhibitor) were thought to influence the pathogenic process of RIF. Furthermore, our findings revealed the level of immune infiltration in RIF by highlighting three signaling pathways (Wnt/-catenin signaling, Notch signaling, and immune response) and three potential diagnostic DEGs (AKT1, PSMB8, and PSMD10). Conclusion Importantly, our findings may contribute to the scientific basis for several potential therapeutic agents to improve endometrial receptivity.
Survey of Siscowet Lake Trout at Their Maximum Depth in Lake Superior

ABSTRACT The siscowet Salvelinus namaycush is a deepwater morphotype of lake trout in Lake Superior. As part of a standardized lake-wide survey in 2006 to assess siscowet populations, bottom-set, multi-mesh gill nets were fished at 36.6 m depth intervals from near shore areas to the deepest waters in south-central Lake Superior. Siscowet length distributions, diet compositions, and sea lamprey wounding rates were compared for three depth zones: shallow (< 200 m), deep (200–394 m), and deepest (395–399 m). There were 39 siscowets collected in proximity to Lake Superior's greatest recorded depth of 405 m. To our knowledge, this is the greatest depth that fish have been collected in the Great Lakes. Higher proportions of siscowets ≤ 500 mm were caught in the shallow zone compared to deeper zones. Deepwater sculpins were the dominant prey for small siscowets (< 600 mm) across all depth zones. The diet of large siscowets (≥ 600 mm) among all depth zones comprised mostly of coregonines and burbot Lota lota. Terrestrial insects were observed in the diet of siscowets in all depth zones, indicating migration to the surface. Type A sea lamprey wounding rates were higher for large (≥ 600 mm) than small siscowets among all depth zones. The highest wounding rate was observed on large siscowets in the deep zone. Recent work indicates that siscowets are the most abundant lake trout form and this research indicates that siscowets use the maximum depths of Lake Superior.
Machine Learning-Based Classification of Mango Pulp Weevil Activity Utilizing an Acoustic Sensor

The mango pulp weevil (MPW) is an aggressive pest that mates seasonally according to the cycle of the mango fruit. After discovering the existence of the mango pulp weevil in Palawan, the island has been under quarantine for exporting mangoes. Detection of the pest proves difficult as the pest does not leave a physical sign that the mango has been damaged. Infested mangoes are wasted as they cannot be sold due to damage. This study serves as a base study for non-invasive mango pulp weevil detection using MATLAB machine learning and audio feature extraction tools. Acoustic sensors were evaluated for best-fit use in the study. The rationale for selecting the acoustic sensors includes local availability and accessibility. Among the three sensors tested, the MEMS sensor had the best result. The data for acoustic frequency are acquired using the selected sensor, which is placed inside a soundproof chamber to minimize the noise and isolate the sound produced by each activity. The identified activity of the adult mango pulp weevil includes walking, resting, and mating. The Mel-frequency cepstral coefficient (MFCC) was used for feature extraction of the recorded audio and training of the SVM classifier. The study achieved 89.81% overall accuracy in characterizing mango pulp weevil activity.
Machine Learning-Based Classification of Road Accident Severity in India: A Comparative Analysis of Algorithms

In India, road accidents pose a serious threat to public health and safety, thus it's crucial to accurately categorize their severity so that first responders and policymakers can address the problem. In this research, a dataset of past accident records in India is used to test several machine-learning algorithms for accident severity classification. The purpose of this study is to evaluate existing categorization algorithms and determine which one performed the best. Several different machine learning methods like k-nearest neighbors, support vector machines, neural networks, etc. have been tried out. In addition, models are optimized by selecting relevant features to use. The findings demonstrate that the neural network approach beat the other techniques, with 90% accuracy in properly categorizing the severity of traffic accidents. Using machine learning algorithms to reliably categorize the severity of traffic accidents in India is a promising area of research, as shown by the results of this study. When it comes to enhancing emergency response and creating evidence-based policy for road safety in India, the neural network technique has shown to be very accurate.
Book Reviews: Word Sense Disambiguation: Algorithms and Applications, edited by Eneko Agirre and Philip Edmonds

This is the first book to cover the entire topic of word sense disambiguation (WSD) including: all the major algorithms, techniques, performance measures, results, philosophical issues, and applications. Leading researchers in the field have contributed chapters that synthesize and provide an overview of past and state-of-the-art research across the field. The editors have carefully organized the chapters into sub-topics. Researchers and lecturers will learn about the full range of what has been done and where the field is headed. Developers will learn which technique(s) will apply to their particular application, how to build and evaluate systems, and what performance to expect. An accompanying Website provides links to resources for WSD and a searchable index of the book.
Patient Characteristics Associated with HCV Treatment Adherence, Treatment Completion, and Sustained Virologic Response in HIV Coinfected Patients

Background. Hepatitis C (HCV) treatment efficacy among HIV patients is limited by poor treatment adherence and tolerance, but few studies have examined the psychosocial determinants of treatment adherence and outcomes. Methods. Chart abstracted and survey data were collected on 72 HIV patients who had received pegylated interferon and ribavirin to assess correlates of treatment adherence, completion, and sustained virologic response (SVR). Results. Nearly half (46%) the sample had active psychiatric problems and 13% had illicit drug use at treatment onset; 28% reported <100% treatment adherence, 38% did not complete treatment (mostly due to virologic nonresponse), and intent to treat SVR rate was 49%. Having a psychiatric diagnosis was associated with nonadherence, while better HCV adherence was associated with both treatment completion and SVR. Conclusions. Good mental health may be an indicator of HCV treatment adherence readiness, which is in turn associated with treatment completion and response, but further research is needed with new HCV treatments emerging.
Concomitant investigation of crustacean amphipods lipidome and metabolome during molting stage by Zeno SWATH Data-Independent Acquisition coupled with Electron Activated Dissociation and machine learning

Background DIA (Data-Independent Acquisition) is a powerful technique in Liquid Chromatography coupled with high-resolution tandem Mass Spectrometry (LC-MS/MS) in metabolomics and lipidomics, for comprehensive profiling of molecules in biological samples. It provides unbiased coverage, improved reproducibility, and quantitative accuracy compared to Data-Dependent Acquisition (DDA). Combined with the Zeno trap and Electron-Activated Dissociation (EAD), DIA enhances data quality and structural elucidation compared to conventional fragmentation under CID. These tools were applied to study the lipidome and metabolome of the freshwater amphipod Gammarus fossarum, successfully discriminating stages and highlighting significant biological features. Despite being underused, DIA, along with the Zeno trap and EAD, holds great potential for advancing research in the omics field. Results DIA combined with the Zeno trap enhances detection reproducibility compared to conventional DDA, improving fragmentation spectra quality and putative identifications. LC coupled with Zeno-SWATH-DIA methods were used to study molecular changes in reproductive cycle of female gammarids. Multivariate data analysis including Principal Component Analysis and Partial Least Square Discriminant Analysis successfully identified statistically significant features responsible for group segregation. EAD fragmentation helped to identify unknown features and to confirm their molecular structure using fragmentation spectra DB annotation or machine learning. EAD database matching accurately annotated five glycerophospholipids, including the position of double bonds on fatty acid chain moieties. SIRIUS database predicted structures of unknown features based on experimental fragmentation spectra to compensate for database incompleteness. Significance Reproducible detection of features and confident identification of putative compounds are pivotal stages within analytical pipelines. The DIA approach combined with Zeno pulsing and EAD synergistically enhances detection sensitivity while providing orthogonal fragmentation information, thereby facilitating a comprehensive and insightful exploration of pertinent biological molecules associated with the reproductive cycle of gammarids. The developed methodology holds great promises for identifying biomarkers of significance in the field of environmental assessment. Highlights Advancements in Data-Independent-Acquisition methods for lipidomics and metabolomics. Zeno pulsing improves sensitivity in fragment detection and compound identification. Comprehensive structure elucidation of lipids and metabolites using EAD fragmentation.
Sparsity-driven autofocus for multipass SAR tomography

Synthetic aperture radar (SAR) systems produce high resolution, two dimensional imaging of areas of environmental interest. SAR interferometry and tomography enables these techniques to extend to three dimensional imaging by exploiting multiple SAR images with diversity in space and time. These techniques require accurate phase information over multiple images as the data is extremely sensitive to deviations from the reference track, therefore to enable interferometry and tomography an accurate autofocus solution is required. This paper investigates phase errors resulting from navigational uncertainties in multipass spotlight SAR imaging and uses techniques from the field of compressive sensing to achieve an autofocus solution. The proposed algorithm builds on previous autofocus work by expanding it to the multipass case and jointly recovers phase errors for all images simultaneously, making it extremely useful for interferometry and tomography techniques. The algorithm described uses pixels that are stable in all SAR images to gain an autofocus solution as these are the pixels that are the focus for analysis using tomography. This is unlike conventional autofocus, which just works on an image-by-image basis. The tools of compressive sensing can be used to concurrently select pixels for bright image elements that are stable and coherent over all images, as these pixels are sparse in the image domain, and calculate the phase errors present in each pass. Using the multipass data after autofocus, height distributions for scatterers in single pixels are determined for simulated forest scenes at X-band. The performance of the autofocus algorithm is examined through numerical simulations and is also applied to real data collected from Selex ES’s airborne, X-band, experimental SAR system. The experimental results demonstrate that the algorithm effectively achieves an autofocus solution. By finding the vertical distribution of two scatterers in a single pixel over simulated forestry images we can determine if compressive sensing can be utilized to gain information on scatterers below the canopy at X-band in future studies.
Spatial Rearrangement of Manufacturing Industrial Zones Based on MCDM in Mojokerto Regency. Case Study Northern Mojokerto Region

Mojokerto Regency is part of the national strategic area (Gerbangkertasusila) in East Java. Gerbangkertasusila serves as the driving force behind East Java’s economy, with the manufacturing industry becoming the prime mover as it contributes to 2022 GRDP by 56.68 percent. The spatial plan of Mojokerto Regency includes the development of an industrial estate in the northern part, specifically in Jetis and Dawarblandong Districts, covering a total area of 8.553 hectares. However, the government’s attempt to make industrial estates has only been realized by 2.65% since 2012. This research aims to assess the suitability of space allocation within the industrial estates in the northern region of Mojokerto. The research method uses spatial analysis approach, multi criteria decision making (MCDM). Spatial data were obtained through RBI and Bhumi ATR, also Mojokerto Regency’s RTRW as comparative data to assess the suitability of industrial locations. This study also considers other spatial aspects, such as projections of land use based on cellular automata, population, infrastructure, transportation, and physical conditions. Analysis result shows that the allocation of space requirements for industrial estate development is 1,081 hectares or 12.64 percent of the original plan. The area is much smaller than the planned area in the Mojokerto Regency’s RTRW. The study identifies four potential locations for Industrial Estate development in the northern Mojokerto region, consisting of two in Jetis District and two in Dawarblandong District. The research results can be an input for the industrial cluster development in the northern Mojokerto region.