Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of the Therapeutic Potential of Persimmon Leaf Extract on Prediabetic Subjects

Dietary supplements have exhibited myriads of positive health effects on human health conditions and with the advent of new technological advances, including in the fields of proteomics, genomics, and metabolomics, biological and pharmacological activities of dietary supplements are being evaluated for their ameliorative effects in human ailments. Recent interests in understanding and discovering the molecular targets of phytochemical-gene-protein-metabolite dynamics resulted in discovery of a few protein signature candidates that could potentially be used to assess the effects of dietary supplements on human health. Persimmon (Diospyros kaki) is a folk medicine, commonly used as dietary supplement in China, Japan, and South Korea, owing to its different beneficial health effects including anti-diabetic implications. However, neither mechanism of action nor molecular biomarkers have been discovered that could either validate or be used to evaluate effects of persimmon on human health. In present study, Mass Spectrometry (MS)-based proteomic studies were accomplished to discover proteomic molecular signatures that could be used to understand therapeutic potentials of persimmon leaf extract (PLE) in diabetes amelioration. Saliva, serum, and urine samples were analyzed and we propose that salivary proteins can be used for evaluating treatment effectiveness and in improving patient compliance. The present discovery proteomics study demonstrates that salivary proteomic profile changes were found as a result of PLE treatment in prediabetic subjects that could specifically be used as potential protein signature candidates.
Incest: A Biosocial View

tooth eruption schedules are given. Tooth loss is considered from the aspect of bony changes that accompany it. Suture closure is discussed thoroughly and explained further in an unusual table. While these two factors are major categories of age-related changes in the skull, Rogers does not mention ossification or nonpathological osteoporotic age changes. Each of the preceding three sections is accompanied by a briefdiscussion of recent statistical methods for determining sex, race, and age from a skull, but Rogers passes over any attempt at descriptions and includes only the dimensions necessary for applying various techniques and references to relevant published sources. The accuracy of statistical determinations is also discussed briefly. The chapter concludes with three sections that are so cursory that their inclusion in this volume is questionable. Discussion of forensic aspects of the skull provides minimal information on personal identification of human remains, although facial reproduction is described in its own section. Artificial cranial deformation is illustrated and noted briefly by citation of numerous historical studies. In general, this chapter is the least useful, being poorly organized, with incomplete information, out-of-date references, and few illustrations. Five sections at the end of the book complement the three chapters. The appendix addresses the topic of skull conservation and preservation by providing useful, standard information, but is not comprehensive and relies heavily on references to Olivier. Better treatments of this topic may be found in each of the comparative sources mentioned here, in addition to Lewis and Redfield’s Care of Osteological Collections (Museum Briefs X4, University of Missouri, 1970) among many other sources. The glossary is of exceptional value. The bibliography is satisfactory for the subject matter, but there are few references later than 1970, and none after 1982. The book is very well indexed by subject and by the names ofscholars contributing to the historical development of research on the skull. The Human Skull is bound in classic Charles C Thomas style and is expensive. Aside from chapter 1 and the glossary, which would make good introductory reading selections for firstor second-year students, this book is recommended primarily for laboratory or university libraries as a reference source.
Chinese word sense disambiguation by combining pseudo training data

In supervised methods of word sense disambiguation, sense tagged samples for training classifiers are needed. Since sense tagging for ambiguous words is expensive and labor intensive, it is worth looking for some reasonable substitutes. We suggest a kind of substitute for Chinese sense tagged data, which is called pseudo training data. The suggestion is based on a linguistic phenomenon in Chinese, which some multicharacter-words inherit only one sense and some syntactical features from ambiguous one-character-words. Data derived from unambiguous multicharacter-words are employed as pseudo training data. Pseudo training data have an advantage of being able to be collected automatically. Our experiments show that classifiers trained by not too much pseudo training data outperform classifiers trained by small quantities of sense tagged samples for Chinese ambiguous word senses. Further experiments show combination of a small set of tagged data and a large quantities pseudo training data is a more promising way to word sense disambiguation.
A log‐linear modelling approach to assessing the consistency of ego reports of dyadic outcomes with applications to fertility and sexual partnerships

We propose a log‐linear model to assess the consistency of ego reports of dyadic outcomes. We do so specifically in the context where males and females report on shared events, and we demonstrate how inconsistencies can be assessed by using a log‐linear model that estimates separate mixing totals for each set of reports. This modelling approach immediately allows us to determine where inconsistencies in reports occur. To demonstrate how our method can be easily implemented for survey data, we apply it to both the 1992 National Health and Social Life Survey and the 2002 National Survey of Family Growth. Our analysis identifies inconsistencies in male and female reports of concurrent partnerships and the number of biological children.
Computer-aided classification of multi-types of dementia via convolutional neural networks

With millions of people suffering from dementia worldwide, the global prevalence of dementia has a significant impact on the patients' lives, their caregivers' physical and emotional states, and the global economy. Early diagnosis of dementia helps in finding suitable therapies that reduce or even prevent further deterioration of patients' cognitive abilities. In recent years, state-of-the-art literature has proposed various computer-aided diagnosis systems based on 3-dimensional brain imagery analysis to identify early symptoms of dementia. These systems aim to assist radiologists in increasing the accuracy of diagnoses and reducing false positives. However, the early diagnosis of dementia is a challenging task due to the image quality, noise, and human brain irregularities. The state-of-the-art has focused on differentiating multi-stages of Alzheimer's disease, however, the diagnosis of various types of dementia is still a gap. This paper proposes a deep learning-based computer-aided diagnosis approach for the early detection of multi-type of dementia. To show the performance of the proposed CAD algorithm, three conventional CAD methods are implemented for comparison. The proposed algorithm yields a 74.93% accuracy in early diagnosis of multi-type of dementia and outperforms the state of the art CAD methods.
A Cell-surface Phylome for African Trypanosomes

The cell surface of Trypanosoma brucei, like many protistan blood parasites, is crucial for mediating host-parasite interactions and is instrumental to the initiation, maintenance and severity of infection. Previous comparisons with the related trypanosomatid parasites T. cruzi and Leishmania major suggest that the cell-surface proteome of T. brucei is largely taxon-specific. Here we compare genes predicted to encode cell surface proteins of T. brucei with those from two related African trypanosomes, T. congolense and T. vivax. We created a cell surface phylome (CSP) by estimating phylogenies for 79 gene families with putative surface functions to understand the more recent evolution of African trypanosome surface architecture. Our findings demonstrate that the transferrin receptor genes essential for bloodstream survival in T. brucei are conserved in T. congolense but absent from T. vivax and include an expanded gene family of insect stage-specific surface glycoproteins that includes many currently uncharacterized genes. We also identify species-specific features and innovations and confirm that these include most expression site-associated genes (ESAGs) in T. brucei, which are absent from T. congolense and T. vivax. The CSP presents the first global picture of the origins and dynamics of cell surface architecture in African trypanosomes, representing the principal differences in genomic repertoire between African trypanosome species and provides a basis from which to explore the developmental and pathological differences in surface architectures. All data can be accessed at: http://www.genedb. org/Page/trypanosoma_surface_phylome.
On minimax robust detection of stationary Gaussian signals in white Gaussian noise

The problem of detecting a wide-sense stationary Gaussian signal process embedded in white Gaussian noise, where the power spectral density of the signal process exhibits uncertainty, is investigated. The performance of minimax robust detection is characterized by the exponential decay rate of the miss probability under a Neyman-Pearson criterion with a fixed false alarm probability, as the length of the observation interval grows without bound. A dominance condition is identified for the uncertainty set of spectral density functions, and it is established that, under the dominance condition, the resulting minimax problem possesses a saddle point, which is achievable by the likelihood ratio tests matched to a so-called dominated power spectral density in the uncertainty set. No convexity condition on the uncertainty set is required to establish this result.
Effects of contention window and packet size on the energy efficiency of wireless local area network

The paper introduces an analytical model to investigate the energy efficiency of the IEEE 802.11 distributed coordinated function (DCF). Many factors, such as the number of contending nodes, packet size, contention window, packet transmission collision probability and channel condition, that affect the energy efficiency of 802.11 DCF have been considered. We determine the packet transmission probability by a method simpler than the existing Markov chain model. The paper identifies the tradeoff in choosing packet size to optimize the energy efficiency of DCF in error-prone environments. The effects of contention window and packet size on the energy efficiency are obtained for both DCF basic mode and DCF with four-way handshaking. It is shown that under error-prone environments, optimal packet size can improve more on the energy efficiency than optimal contention window. Combining both optimal contention window and optimal packet size can achieve the maximum optimization. To validate our analytical results, we have done extensive simulations, and they seem to match very well with the presented analytical results.
What do we learn from rating metaphors?

What makes some metaphors easier to understand than others? Theoretical accounts of metaphor processing appeal to dimensions like conventionality and aptness to explain variability in metaphor comprehensibility. In a typical experiment, one group of naive participants rates a set of metaphoric sentences along these dimensions, while another is timed reading the same sentences. Then, the ratings are used to predict response times in order to identify the most relevant linguistic dimension for metaphor comprehension. However, surprisingly high correlations between ratings of theoretically orthogonal constructs and the results of an experiment in which a context manipulation affected ratings of metaphor conventionality and aptness suggest that these measures should be treated as dependent, rather than explanatory, variables. We discuss the implications of this perspective for theories of language processing.
The Construction of a Web-Based Learning Platform from the Perspective of Computer Support for Collaborative Design

The purpose of this study is to construct a web-based Computer Support for Collaborative Design CSCD learning platform based on theories related to a constructivist learning environment model, mind mapping and computer-supported collaborative learning. The platform conforms to the needs of design students and provides effective tools for interaction and collaborative learning by integrating mind-mapping tools into a learning environment that utilizes CSCD, a computer-assisted support system that can support and enhance group collaboration. The establishment of the CSCD learning platform represents a significant advance beyond the fixed functions and existing models of current online learning platforms and is the only learning platform in the world that focuses on learners in design departments. The platform offers outstanding user-friendly functions and innovative technology. In terms of funding, technical ability, human resources, organizational strategies, and risk analysis and evaluations, the learning platform is also worthy of expansion and implementation.
Skin Lesion Segmentation Using Clustering Techniques

Cluster analysis has been widely used in various disciplines such as pattern recognition, computer vision, and data mining. In this work we investigate the applicability of two spatial clustering algorithms, namely DBSCAN and STING, to a new problem domain: Color segmentation of skin lesion (tumor) images. Automated segmentation is a key step in the computerized analysis of skin lesion images since the accuracy of the subsequent steps (feature extraction, classification, etc.) crucially depends on the accuracy of this very first step. In this paper, we develop two unsupervised methods for segmentation of skin lesion images: one based on DBSCAN clustering algorithm and the other based on STING clustering algorithm. Experiments on a database of over hundred skin lesion images show that DBSCAN-based segmentation algorithm performs significantly better than the STING-based one.
Superstrings with intrinsic torsion

We analyze the necessary and sufficient conditions for the preservation of supersymmetry for bosonic geometries of the form ${R}^{1,9\ensuremath{-}d}\ifmmode\times\else\texttimes\fi{}{M}_{d},$ in the common Neveu-Schwarz\char21{}Neveu-Schwarz (NS-NS) sector of type II string theory and also type I or heterotic string theory. The results are phrased in terms of the intrinsic torsion of G structures and provide a comprehensive classification of static supersymmetric backgrounds in these theories. Generalized calibrations naturally appear since the geometries can always arise as solutions describing NS or type I or heterotic fivebranes wrapping calibrated cycles. Some new solutions are presented. In particular we find $d=6$ examples with a fibered structure which preserve $\mathcal{N}=1,2,3$ supersymmetry in type II and include compact type I or heterotic geometries.
Population Genetic Consequences of the Allee Effect and the Role of Offspring-Number Variation

A strong demographic Allee effect in which the expected population growth rate is negative below a certain critical population size can cause high extinction probabilities in small introduced populations. But many species are repeatedly introduced to the same location and eventually one population may overcome the Allee effect by chance. With the help of stochastic models, we investigate how much genetic diversity such successful populations harbor on average and how this depends on offspring-number variation, an important source of stochastic variability in population size. We find that with increasing variability, the Allee effect increasingly promotes genetic diversity in successful populations. Successful Allee-effect populations with highly variable population dynamics escape rapidly from the region of small population sizes and do not linger around the critical population size. Therefore, they are exposed to relatively little genetic drift. It is also conceivable, however, that an Allee effect itself leads to an increase in offspring-number variation. In this case, successful populations with an Allee effect can exhibit less genetic diversity despite growing faster at small population sizes. Unlike in many classical population genetics models, the role of offspring-number variation for the population genetic consequences of the Allee effect cannot be accounted for by an effective-population-size correction. Thus, our results highlight the importance of detailed biological knowledge, in this case on the probability distribution of family sizes, when predicting the evolutionary potential of newly founded populations or when using genetic data to reconstruct their demographic history.
A nuclear factor (NF2d9) that binds to the male-specific P450 (Cyp 2d-9) gene in mouse liver

Expression of the Cyp 2d-9 (steroid 16 alpha-hydroxylase) gene in mouse liver is male specific in such Mus musculus domesticus strains as FVB/N, whereas the corresponding P450 genes in the wild mouse species Mus spretus are not sex specific in their expression. These parental differences in the gene expressions were independently inherited in F1 offspring from crosses of FVB/N and M. spretus. A 5' flanking sequence (-110CTC CTCCCTATTCCGGGCC-92) was defined as a regulatory element (named SDI-A1) for the domestic Cyp 2d-9 promoter. The nucleotide which corresponds to T at position -99 within SDI-A1 was found to be substituted with C in the wild mouse P450 genes. The placing of C at position -99 abolished the transcriptional activity of SDI-A1 in HepG2 cells as well as the binding of SDI-A1 to a nuclear factor. This factor (designated NF2d9) was purified from mouse nuclear extracts, and its cDNA cloned. The purified NF2d9 bound to SDI-A1 but not to the mutated SDI-A1 with C at position -99. The deduced amino acid sequence revealed that NF2d9 is 72 and 94% identical to mouse CP2 and human LBP-1a, respectively. NF2d9 thus belongs to the CP2 family and is the mouse homolog of human LBP-1a, which modulates human immunodeficiency virus type 1 transcription. Anti-NF2d9, which was raised against the bacterially expressed protein, supershifted the SDI-A1 complex with the liver nuclear extract. Both the bacterially expressed and in vitro-translated NF2d9 inhibited SDI-A1 complex formation, although they did not bind to SDI-A1 directly. The results, therefore, indicate that the domestic Cyp 2d-9 gene can be regulated through a specific association of NF2d9 with SDI-A1.
Accelerated immunopathological response of mice infected with Mycobacterium tuberculosis disrupted in the mce1 operon negative transcriptional regulator

Mycobacterium tuberculosis causes a variety of clinical outcomes determined by host as well as bacterial factors. M. tuberculosis disrupted in the mce1 operon causes increased mortality in immunocompetent mice. This operon is negatively regulated by mce1R (Rv0165c). We studied the role of mce1R in infection outcome in mice. At 5 × 104 tail vein infectious dose, the median survival time (MST) of mice infected with the mce1R mutant M. tuberculosis H37Rv was 293 days, while mice infected with the wild‐type H37Rv survived more than 350 days (P < 0.0001). At a higher dose (5 × 106), the MST of mutant‐infected mice was 32 days, compared with 127 days for wild type‐infected mice (P < 0.0001). With either tail vein or aerosol infection, mutant‐infected mice developed larger granulomatous lesions in their lungs than mice infected with the wild type. Mutant‐infected mice were unable to control the bacterial burden in the first 4 weeks of infection, but even after achieving control later, these mice succumbed to granulomatous pneumonia. These observations suggest that the early deregulated expression of the mce1 operon products determines later granulomatous tissue response. mce1 operon may homeostatically regulate the cell wall architecture in vivo that elicits a steady‐state granuloma tissue response permitting M. tuberculosis to establish a long‐term infection.
PAF promotes stemness and radioresistance of glioma stem cells

Significance Glioblastoma multiforme (GBM) is uniformly lethal and shows resistance to all forms of therapy. Glioma stem cells (GSCs) have been shown to support GBM maintenance and exhibit enhanced resistance to ionizing radiation, a cornerstone of GBM therapy. This study establishes that proliferating cell nuclear antigen-associated factor (PAF) depletion profoundly reduces GSC frequency and tumorigenicity, in part, by down-regulating DNA replication and pyrimidine metabolism. Moreover, PAF depletion impairs error-prone DNA translesion synthesis (TLS) and enhances sensitivity of GSCs to radiation treatment. Pharmacological impairment of DNA replication and TLS diminished GSC maintenance and radioresistance, illuminating a potential GBM treatment strategy of combined TLS inhibition and radiation therapy. An integrated genomic and functional analysis to elucidate DNA damage signaling factors promoting self-renewal of glioma stem cells (GSCs) identified proliferating cell nuclear antigen (PCNA)-associated factor (PAF) up-regulation in glioblastoma. PAF is preferentially overexpressed in GSCs. Its depletion impairs maintenance of self-renewal without promoting differentiation and reduces tumor-initiating cell frequency. Combined transcriptomic and metabolomic analyses revealed that PAF supports GSC maintenance, in part, by influencing DNA replication and pyrimidine metabolism pathways. PAF interacts with PCNA and regulates PCNA-associated DNA translesion synthesis (TLS); consequently, PAF depletion in combination with radiation generated fewer tumorspheres compared with radiation alone. Correspondingly, pharmacological impairment of DNA replication and TLS phenocopied the effect of PAF depletion in compromising GSC self-renewal and radioresistance, providing preclinical proof of principle that combined TLS inhibition and radiation therapy may be a viable therapeutic option in the treatment of glioblastoma multiforme (GBM).
Effects of Irrelevant Alternatives in Relative Performance Evaluation

In this study we examine the effect of the presence of irrelevant performance information on the rank order decisions made by supervisors in relative per formance evaluation (RPE). Specifically, we investigate the effect of two types of irrelevant per formance information patterns in what has been termed an asymmetric dominated decoy and a viable d ecoy. We also examine whether relative performance information (RPI) size (evaluating 3 versu s 9 subordinates at a time) can moderate the adverse influence of irrelevant information. The empi rical results from our experiment support that the asymmetrically dominated decoy information pattern (where an additional subordinate is included in the RPE whose performance is similar to and is domi nated by one of the original subordinates – referred to as the target subordinate) can increase t he rank ordering of the target subordinate. Contrary to our expectation, we do not find that the viab le decoy information pattern (where an additional subordinate is included in the RPE whose performanc e is partially dominating the target subordinate) has a significant influence on the rank ordering de cisions of subordinates. Our results also provide support for an interaction between the decoy inform ation and RPI size for the asymmetric dominated decoy such that the rank order effect is only prese nt when the RPI size is small (evaluating 3 subordi nates in our case). Our study informs designers of accounting information systems in several ways.