Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence & susceptibility to fluconazole of Candida species causing vulvovaginitis.

BACKGROUND & OBJECTIVE Vulvovaginal candidiasis is an important cause of morbidity in women of reproductive age. This study was carried out to determine the species prevalence and susceptibility pattern to fluconazole of yeasts isolated from the vagina of symptomatic women. METHODS This prospective study was conducted in a rural primary health care center of north India from May 2003 to April 2004 and included 601 married, sexually active women (18-49 yr) with the self reported symptoms of vaginal discharge and/or genital itching and/or genital burning. Specific aetiology of the genitourinary symptoms including candidal infection were determined. Specimens from the lateral wall of vagina were subjected to direct wet mount microscopy and fungal culture on Sabouraud's dextrose agar. Susceptibility testing to fluconazole was carried out using broth microdilution method. RESULTS Yeasts were isolated in 111 (18.5%) women and these consisted of Candida glabrata (56, 50.4%), C. albicans (39, 35.1%), C. tropicalis (12, 10.8%), C. krusei (3, 2.7%) and C. parapsilosis (1, 0.9%). Susceptibility testing carried out on 30 representative isolates (15 C. glabrata, 10 C. albicans, 4 C. tropicalis and 1 C. parapsilosis) revealed that 21 isolates (70%) were susceptible (MIC, < or = 8 microg/ml) to fluconazole while 9 (30%) were susceptible-dose dependent (S-DD, MIC 16-32 microg/ml). INTERPRETATION & CONCLUSION Our findings suggest a low prevalence of fluconazole resistance in vaginal candida isolates in our population. However, a high prevalence of non-albicans candida species and increased dose-dependent resistance in these isolates necessitates vigilance since this may warrant a change in the optimal therapy of non-albicans candida vaginitis.
Automated facial landmark detection, comparison and visualization

Anthropometric facial landmarks and their detection has wide application in anthropology and forensic science. More specifically, these landmarks play an important role in facial comparison, in the analysis of morphological changes during human growth and in searching for the variability of human faces (e.g., sexual dimorphism). The automatic extraction of landmarks is a challenging area and specific visualization methods serving for the comparison are crucial for the correct interpretation of the results. In this paper we introduce a novel system which incorporates a newly proposed algorithm for automatic landmark detection. The algorithm is based on surface curvature and symmetric profile extraction. The landmarks are then used in the Generalized Procrustes analysis for facial comparison. Our system also introduces appropriate visualization methods supporting the presentation and understandability of the results.
Visual Fashion-Product Search at SK Planet

We build a large-scale visual search system which finds similar product images given a fashion item. Defining similarity among arbitrary fashion-products is still remains a challenging problem, even there is no exact ground-truth. To resolve this problem, we define more than 90 fashion-related attributes, and combination of these attributes can represent thousands of unique fashion-styles. The fashion-attributes are one of the ingredients to define semantic similarity among fashion-product images. To build our system at scale, these fashion-attributes are again used to build an inverted indexing scheme. In addition to these fashion-attributes for semantic similarity, we extract colour and appearance features in a region-of-interest (ROI) of a fashion item for visual similarity. By sharing our approach, we expect active discussion on that how to apply current computer vision research into the e-commerce industry.
The Dorsolateral Prefrontal Cortex Contributes to Successful Relational Memory Encoding

Results from neuroimaging studies of long-term memory (LTM) encoding have contributed to the view that the ventrolateral prefrontal cortex (VLPFC) contributes to successful LTM formation, whereas the dorsolateral prefrontal cortex (DLPFC) does not. We hypothesized that the DLPFC does contribute to LTM, but under specific circumstances. That is, the DLPFC may be critical for building relationships between items during on-line processing, and this may promote LTM for associations between items. We used event-related functional magnetic resonance imaging (fMRI) to test this hypothesis by examining brain activity during sequential encoding of unrelated word pairs. During presentation of the second (“target”) word in each pair, subjects either made a semantic judgment specific to the target word (“item-specific” trials), or a semantic judgment that involved a comparison between the target word and the first word in the pair (“relational” trials). Behaviorally, recognition memory for target words was equivalent between the two trial types but associative recognition of studied word pairs was significantly greater for relational trials. fMRI results showed that DLPFC activity was greater during relational compared with item-specific encoding and that DLPFC activity predicted successful memory for associations but not successful item memory. Activity in the VLPFC was also greater for relational compared with item-specific encoding, but VLPFC activation predicted successful memory for both associations and items. These results support the view that the DLPFC may contribute to LTM through its role in active processing of relationships during encoding, whereas the VLPFC may have a more general role in promoting successful LTM formation.
ANALYTICAL AND NUMERICAL STUDY OF NATURAL CONVECTION HEAT TRANSFER IN A VERTICAL POROUS ANNULUS

A study is made of quasi-steady state natural convection in a vertical annular porous layer when the inner wall is heated by a constant heat flux and the other walls are maintained adiabatic. On the basis of the Darcy-Oberbeck-Boussinesq equations, the problem is solved analytically in the limil of a long shallow annulus (A ≫ 1), where A is the aspect (height-to-gap width) ratio. The analytical solution for the Row and heat transfer, based on a parallel flow assumption, is derived in terms of the Darcy-Rayleigh number R and radius ratio K. A numerical study of the same phenomenon, obtained by solving the complete system of governing equations, is conducted to assess the validity of the analytical results. It is demonstrated that the analytical solution predicts well the flow structure and the heat transfer for a wide range of R and K. In the boundary layer regime it is shown that the Nusselt number, based on the gap width of porous annulus, is Nu=R 2/5[2K 2lnK−(K 2 − 1)]/[2(K − 1)2(K + 1)6/5].
Probability-based adaptive forwarding strategy in named data networking

Forwarding strategy is an important research problem in named data networking (NDN). A probability-based adaptive forwarding (PAF) strategy is introduced in this paper. Ant colony optimization is customized to a dynamic NDN environment to compute the selection probabilities. In addition, we use a statistical model in PAF to compute timeout for the retransmission mechanism at the NDN layer. PAF also provides network operators a number of parameters to tune for different network scenarios. Simulation results show PAF can achieve load balance and is adaptive to network condition changes.
Impact of vitamin and mineral supplement withdrawal and wheat middling inclusion on finishing pig growth performance, fecal mineral concentration, carcass characteristics, and the nutrient content and oxidative stability of pork.

A study was conducted to determine if supplement withdrawal (omission of dietary vitamin and trace mineral premixes and a two-thirds reduction in dietary inorganic phosphorus) for 28 d preslaughter and the feeding of wheat middlings (dietary concentrations of 5, 15, and 30% from weaning to 16, 16 to 28, and 28 kg to slaughter, respectively) affect growth performance, carcass characteristics, and fecal mineral concentrations ofthe pig, as well as the nutrient content and oxidative stability of the longissimus dorsi muscle. Crossbred pigs (n = 64) were blocked by weight and assigned to one of four dietary treatments in a 2 x 2 factorial design (with or without supplement withdrawal, and with or without wheat middlings). Supplement withdrawal and wheat middling inclusion did not influence average daily gain (ADG), average daily feed intake, gain/feed, or carcass traits, except for a decrease (P < 0.01) in the ADG of pigs from 28 to 65 kg when fed wheat middlings. Supplement withdrawal decreased (P < 0.01) fecal Ca, P, Cu, Fe, Mn, and Zn concentrations. In diets containing full vitamin and mineral supplementation, wheat middling inclusion decreased (P < 0.01) fecal Ca, Cu, Fe, and Zn concentrations and increased (P < 0.01) fecal Mn. Supplement withdrawal decreased (P < 0.05) concentrations of riboflavin, niacin, and P in the longissimus dorsi muscle, but did not affect longissimus dorsi thiamin, vitamin E, Fe, Cu, Zn, and Ca concentrations. Inclusion of wheat middlings increased (P < 0.04) longissimus dorsi thiamin, niacin, riboflavin, and vitamin E concentrations and decreased (P < 0.04) Cu concentrations. However, wheat middling inclusion did not affect (P > 0.05) longissimus dorsi Ca, P, Fe, and Zn concentrations. Dietary treatment did not affect either Cu/Zn superoxide dismutase or glutathione peroxidase activity in the longissimus dorsi. The results from this study indicate that supplement withdrawal and dietary wheat middling inclusion alter pork nutrient content and fecal mineral concentration, but not the oxidative stability of pork.
Modeling Gene Expression with Differential Equations

We propose a differential equation model for gene expression and provide two methods to construct the model from a set of temporal data. We model both transcription and translation by kinetic equations with feedback loops from translation products to transcription. Degradation of proteins and mRNAs is also incorporated. We study two methods to construct the model from experimental data: Minimum Weight Solutions to Linear Equations (MWSLE), which determines the regulation by solving under-determined linear equations, and Fourier Transform for Stable Systems (FTSS), which refines the model with cell cycle constraints. The results suggest that a minor set of temporal data may be sufficient to construct the model at the genome level. We also give a comprehensive discussion of other extended models: the RNA Model, the Protein Model, and the Time Delay Model.
Image Classification with Segmentation Graph Kernels

We propose a family of kernels between images, defined as kernels between their respective segmentation graphs. The kernels are based on soft matching of subtree-patterns of the respective graphs, leveraging the natural structure of images while remaining robust to the associated segmentation process uncertainty. Indeed, output from morphological segmentation is often represented by a labelled graph, each vertex corresponding to a segmented region, with edges joining neighboring regions. However, such image representations have mostly remained underused for learning tasks, partly because of the observed instability of the segmentation process and the inherent hardness of inexact graph matching with uncertain graphs. Our kernels count common virtual substructures amongst images, which enables to perform efficient supervised classification of natural images with a support vector machine. Moreover, the kernel machinery allows us to take advantage of recent advances in kernel-based learning: (i) semi-supervised learning reduces the required number of labelled images, while (ii) multiple kernel learning algorithms efficiently select the most relevant similarity measures between images within our family.
Attributed String Matching with Merging for Shape Recognition

A new structural approach to shape recognition using attributed string matching with merging is proposed. After illustrating the disadvantages of conventional symbolic string matching using changes, deletions, and insertions, attributed strings are suggested for matching. Each attributed string is an ordered sequence of shape boundary primitives, each representing a basic boundary structural unit, line segment, with two types of numerical attributes, length and direction. A new type of primitive edit operation, called merge, is then introduced, which can be used to combine and then match any number of consecutive boundary primitives in one shape with those in another. The resulting attributed string matching with merging approach is shown useful for recognizing distorted shapes. Experimental results prove the feasibility of the proposed approach for general shape recognition. Some possible extensions of the approach are also included.
Numerical simulation and analysis of an electroactuated MEMS capacitive switch using finite element method

The MEMS capacitive switch based on fixed-fixed microbeam have garnered significant attention due to their geometric simplicity and broad applicability, and the accurate models should be developed to predict their electromechanical behaviors. The improved macromodel of the fixed-fixed microbeam of MEMS capacitive switch is presented in this paper, the numerical analysis of mechanical characterizations of the MEMS capacitive switches under electric actuation are performed by the finite element discretization method, and the performances of static and dynamic of MEMS capacitive switch are obtained. The numerical results show that, with only a few nodes used in the computation, the finite element discretization method gives the identical results to other numerical methods, such as the shooting method and experiments. Moreover, the proposed model can offer proper and convenient approach for numerical calculations, and promote design of MEMS devices.
A static cost analysis for a higher-order language

We develop a static complexity analysis for a higher-order functional language with structural list recursion. The complexity of an expression is a pair consisting of a cost and a potential. The former is defined to be the size of the expression's evaluation derivation in a standard big-step operational semantics. The latter is a measure of the "future" cost of using the value of that expression. A translation function {. } maps target expressions to complexities. Our main result is the following Soundness Theorem: If {t} is a term in the target language, then the cost component of {t} is an upper bound on the cost of evaluating t. The proof of the Soundness Theorem is formalized in Coq, providing certified upper bounds on the cost of any expression in the target language.
Automated hearing screening for preschool children

Objectives To establish the reliability and validity of an automated hearing screening test system for preschoolers and to investigate the risk factors for hearing loss. Methods The study used a cross-sectional design in a comparative study of subjects. The automated hearing screening test was performed in 6231 preschoolers (3–7 years old) from 41 kindergartens in Shanghai, China. Sensitivity and specificity of the automated test were investigated with subjects who failed the screening test and a group of control subjects. Results Six hundred and forty-seven children were referred for diagnostic audiological assessment after the automated pure-tone screening test. Using the conventional pure-tone audiometry as the “gold standard,” the sensitivity and specificity of the automated hearing screening test increased from 0.33 and 0.15 in the youngest age group to 0.95 and 1.00 in the oldest age group, respectively. There was no statistically significant gender effect, but there was an age effect (χ2 = 22.96, p < 0.01). In the univariate analysis of 104 cases and 201 controls, significantly elevated odds ratios (OR) for hearing loss were correlated with universal newborn hearing screening test results (OR = 28.15, p < 0.01), parental suspicion of hearing loss (OR = 10.10, p < 0.05), and attendance at preschool before the age of five (OR = 0.51, p < 0.05). Conclusions The results suggest that the automated hearing screening test system can be used in screening programmes for preschoolers aged older than five years.
A Practical Platform for Cube-Attack-like Cryptanalyses

Recently, various cryptanalysis methods related to Cube Attack have attracted a lot of interest. We designed a practical platform to perform such cryptanalysis attacks. We also developed a web-based application at http://cube-attack.appspot. com/, which is open to public for simple testing and verification. In this paper, we focus on linearity testing and try to verify the data provided in several papers. Some interesting results produced in our work indicate certain improper assumptions were made in these papers.
Analog-Digital Self-Learning Fuzzy Spiking Neural Network in Image Processing Problems

Computational intelligence provides a variety of means that can perform complex image processing in a rather effective way. Among them, self-learning systems, especially selflearning artificial neural networks (self-organizing maps, ART neural networks, ‘BrainState-in-a-Box’ neuromodels, etc.) (Haykin, 1999) and fuzzy clustering systems (fuzzy cmeans, algorithms of Gustafson-Kessel, Yager-Filev, Klawonn-Hoeppner, etc) (Bezdek et al., 2005; Sato-Ilic & Jain, 2006), occupy a significant place as they make it possible to solve a data processing problem in the absence of a priori knowledge of it. While there are many artificial neural networks that can be successfully used in image processing tasks, the most prominent of them are networks of a new, the third generation, commonly known as spiking neural networks (Maass & Bishop, 1998; Gerstner & Kistler, 2002). On the one hand, spiking neural networks are biologically more plausible than neural networks of the previous generations that is of fundamental importance for computational intelligence from theoretical point of view. On the other hand, networks of spiking neurons appeared to be computationally more powerful than conventional neural networks (Maass, 1997b). In addition, complex data processing via artificial neural networks of the second generation is time consuming due to multi-epoch learning; instead, spiking neural networks can perform the same processing tasks much faster as they require a few learning epochs only (Bohte et al., 2002; Berredo, 2005; Meftah et al., 2008; Lindblad & Kinser, 2005). All these facts are causing considerable interest in networks of spiking neurons as a powerful computational intelligence tool for image processing Although spiking neural networks are becoming a popular computational intelligence tool for various technical problems solving, their architecture and functioning are treated in terms of neurophysiology rather than in terms of any technical sciences apparatus in the most research works on engineering subjects. Yet none technically plausible description of spiking neurons functioning has been provided. In contrast to artificial neural networks, fuzzy logic systems are capable of performing accurate and efficient data processing under a priori and current uncertainty, particularly if classes to be separated overlap one another. Integrating artificial neural networks and fuzzy systems together allows of combining capabilities of both in a synergetic way (Jang et al., 1997), thus producing hybrid intelligent systems that achieve high performance and reliability in real life problems solving, particularly in image processing. Obviously,
Patients’ “gut feelings” about symptoms should be taken seriously, say researchers

Some patients report not being taken seriously by doctors and this can deter them from making a repeat consultation for persistent symptoms, a qualitative analysis published in the British Journal of General Practice has found.1 The researchers analysed free text comments at the end of a recent International Cancer Benchmarking Partnership survey carried out in Wales. Of the 905 patients with newly diagnosed breast, ovarian, colorectal, or lung cancer who returned the questionnaire, 530 included comments in the free text section. The analysis showed a range of factors that patients …
A community change in the algal endosymbionts of a scleractinian coral following a natural bleaching event: field evidence of acclimatization

The symbiosis between reef-building corals and their algal endosymbionts (zooxanthellae of the genus Symbiodinium) is highly sensitive to temperature stress, which makes coral reefs vulnerable to climate change. Thermal tolerance in corals is known to be substantially linked to the type of zooxanthellae they harbour and, when multiple types are present, the relative abundance of types can be experimentally manipulated to increase the thermal limits of individual corals. Although the potential exists for this to translate into substantial thermal acclimatization of coral communities, to date there is no evidence to show that this takes place under natural conditions. In this study, we show field evidence of a dramatic change in the symbiont community of Acropora millepora, a common and widespread Indo-Pacific hard coral species, after a natural bleaching event in early 2006 in the Keppel Islands (Great Barrier Reef). Before bleaching, 93.5% (n=460) of the randomly sampled and tagged colonies predominantly harboured the thermally sensitive Symbiodinium type C2, while the remainder harboured a tolerant Symbiodinium type belonging to clade D or mixtures of C2 and D. After bleaching, 71% of the surviving tagged colonies that were initially C2 predominant changed to D or C1 predominance. Colonies that were originally C2 predominant suffered high mortality (37%) compared with D-predominant colonies (8%). We estimate that just over 18% of the original A. millepora population survived unchanged leaving 29% of the population C2 and 71% D or C1 predominant six months after the bleaching event. This change in the symbiont community structure, while it persists, is likely to have substantially increased the thermal tolerance of this coral population. Understanding the processes that underpin the temporal changes in symbiont communities is key to assessing the acclimatization potential of reef corals.
Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants

Next‐generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost‐effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two‐stage design. Two‐stage designs include a broad‐based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow‐up; third, the impact of extreme and random sampling in (Phase 2) replication. We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false‐negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well‐cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling. Genet. Epidemiol. 35: 236‐246, 2011. © 2011 Wiley‐Liss, Inc.