Diagnostics for typhoid fever: current perspectives and future outlooks for product development and access

Typhoid is an enteric disease caused by Salmonella Typhi. Like many febrile illnesses, typhoid presents with nonspecific symptoms. In routine healthcare settings in low- and middle-income countries, typhoid fever is suspected and treated empirically. Though many diagnostic tests are available for typhoid diagnosis, there are currently no diagnostic tests that meet ideal requirements for sensitivity, specificity, speed, and cost-effectiveness. With introduction of typhoid conjugate vaccine, it is essential to explore the current and future typhoid approach in the context of use case and access to ensure their utilization for disease control

Differential expression of Toll-like receptors and inflammatory cytokines in ovine interdigital dermatitis and footrot.

Footrot is a common inflammatory bacterial disease affecting the health and welfare of sheep worldwide. The pathogenesis of footrot is complex and multifactorial. The primary causal pathogen is the anaerobic bacterium Dichelobacter nodosus, with Fusobacterium necrophorum also shown to play a key role in disease. Since immune-mediated pathology is implicated, the aim of this research was to investigate the role of the host response in interdigital dermatitis (ID) and footrot. We compared the expression of Toll-like receptors (TLRs) and pro-inflammatory cytokines and the histological appearance of clinically normal in comparison to ID and footrot affected tissues. Severe ID and footrot were characterised by significantly increased transcript levels of pro-inflammatory cytokines TNFα and IL1β and the pattern recognition receptors TLR2 and TLR4 in the interdigital skin. This was reflected in the histopathological appearance, with ID and footrot presenting progressive chronic-active pododermatitis with a mixed lymphocytic and neutrophilic infiltration, gradually increasing from a mild form in clinically normal feet, to moderate in ID and to a focally severe form with frequent areas of purulence in footrot. Stimulation with F. necrophorum and/or D. nodosus extracts demonstrated that dermal fibroblasts, the resident cell type of the dermis, also contribute to the inflammatory response to footrot bacteria by increased expression of TNFα, IL1β and TLR2. Overall, ID and footrot lead to a local inflammatory response given that expression levels of TLRs and IL1β were dependent on the disease state of the foot not the animal.This is the final version published by Elsevier in Veterinary Immunology and Immunopathology under a CC BY license. It was originally published here: http://www.sciencedirect.com/science/article/pii/S0165242714001706

A triadic framework for collaborative consumption (CC): Motives, activities and resources & capabilities of actors

Collaborative consumption (CC) is an increasingly prevalent form of exchange. CC occurs within a triangle of actors: a platform provider (e.g., Uber), a peer service provider (e.g., an Uber driver) and a customer. The platform provider’s main role is matchmaking, so that a customer can access assets of a peer service provider. This paper has three objectives. First, this article identifies three criteria to delineate CC from related constructs such as access-based consumption, sharing or renting. Second, it introduces a literature-based framework explicating the roles of the actors in the CC triangle along three dimensions: motives, activities and resources and capabilities. Third, it highlights areas for further research, such as the dynamics of CC, context-dependent motives and the emergence of professional (peer) service providers

Consistent image-based measurement and classification of skin color

Little prior image processing work has addressed estimation and classification of skin color in a manner that is independent of camera and illuminant. To this end, we first present new methods for 1) fast, easy-to-use image color correction, with specialization toward skin tones, and 2) fully automated estimation of facial skin color, with robustness to shadows, specularities, and blemishes. Each of these is validated independently against ground truth, and then combined with a classification method that successfully discriminates skin color across a population of people imaged with several different cameras. We also evaluate the effects of image quality and various algorithmic choices on our classification performance. We believe our methods are practical for relatively untrained operators, using inexpensive consumer equipment

From Therapeutic Factors to Mechanisms of Change in the Creative Arts Therapies:A Scoping Review

Empirical studies in the creative arts therapies (CATs; i.e., art therapy, dance/movement therapy, drama therapy, music therapy, psychodrama, and poetry/bibliotherapy) have grown rapidly in the last 10 years, documenting their positive impact on a wide range of psychological and physiological outcomes (e.g., stress, trauma, depression, anxiety, and pain). However, it remains unclear how and why the CATs have positive effects, and which therapeutic factors account for these changes. Research that specifically focuses on the therapeutic factors and/or mechanisms of change in CATs is only beginning to emerge. To gain more insight into how and why the CATs influence outcomes, we conducted a scoping review (Nstudies = 67) to pinpoint therapeutic factors specific to each CATs discipline, joint factors of CATs, and more generic common factors across all psychotherapy approaches. This review therefore provides an overview of empirical CATs studies dealing with therapeutic factors and/or mechanisms of change, and a detailed analysis of these therapeutic factors which are grouped into domains. A framework of 19 domains of CATs therapeutic factors is proposed, of which the three domains are composed solely of factors unique to the CATs: “embodiment,” “concretization,” and “symbolism and metaphors.” The terminology used in change process research is clarified, and the implications for future research, clinical practice, and CATs education are discussed

Color correction of uncalibrated images for the classification of human skin color

Images of a scene captured with multiple cameras will have different color values due to variations in capture and color rendering across devices. We present a method to accurately retrieve color information from uncalibrated images taken under uncontrolled lighting conditions with an unknown device and no access to raw data, but with a limited number of reference colors in the scene. The method is used to assess skin tones. A subject is imaged with the calibration target in the scene. This target is extracted and its color values are used to compute a color correction transform that is applied to the entire image. We establish that the best mapping is done using a target consisting of skin colored patches representing a range of human skin colors. We show that color information extracted from images is well correlated with color data derived from spectral measurements of skin. We also show that skin color can be consistently measured across cameras with different color rendering and resolutions ranging from 0.1 Mpixels to 4.0 Mpixels

Cell Phones as Imaging Sensors

Camera phones are ubiquitous, and consumers have been adopting them faster than any other technology in modern history. When connected to a network, though, they are capable of more than just picture taking: Suddenly, they gain access to the power of the cloud. We exploit this capability by providing a series of image-based personal advisory services. These are designed to work with any handset over any cellular carrier using commonly available Multimedia Messaging Service (MMS) and Short Message Service (SMS) features. Targeted at the unsophisticated consumer, these applications must be quick and easy to use, not requiring download capabilities or preplanning. Thus, all application processing occurs in the back-end system (i.e., as a cloud service) and not on the handset itself. Presenting an image to an advisory service in the cloud, a user receives information that can be acted upon immediately. Two of our examples involve color assessment – selecting cosmetics and home décor paint palettes; the third provides the ability to extract text from a scene. In the case of the color imaging applications, we have shown that our service rivals the advice quality of experts. The result of this capability is a new paradigm for mobile interactions — image-based information services exploiting the ubiquity of camera phones

Necessary condition for steerability of arbitrary two-qubit states with loss

Einstein-Podolsky-Rosen steering refers to the quantum phenomenon whereby the state of a system held by one party can be "steered" into different states at the will of another, distant, party by performing different local measurements. Although steering has been demonstrated in a number of experiments involving qubits, the question of which two-qubit states are steerable remains an open theoretical problem. Here, we derive a necessary condition for any two-qubit state to be steerable when the steering party suffers from a given probability of qubit loss. Our main result finds application in one-way steering demonstrations that rely upon loss. Specifically, we apply it to a recent experiment on one-way steering with projective measurements and POVMs, reported by Wollmann et. al. [Phys. Rev. Lett., 116, 160403 (2016)].Comment: 7 pages, 1 figur

Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression

BACKGROUND: Deep sequencing of transposon mutant libraries (or TnSeq) is a powerful method for probing essentiality of genomic loci under different environmental conditions. Various analytical methods have been described for identifying conditionally essential genes whose tolerance for insertions varies between two conditions. However, for large-scale experiments involving many conditions, a method is needed for identifying genes that exhibit significant variability in insertions across multiple conditions. RESULTS: In this paper, we introduce a novel statistical method for identifying genes with significant variability of insertion counts across multiple conditions based on Zero-Inflated Negative Binomial (ZINB) regression. Using likelihood ratio tests, we show that the ZINB distribution fits TnSeq data better than either ANOVA or a Negative Binomial (in a generalized linear model). We use ZINB regression to identify genes required for infection of M. tuberculosis H37Rv in C57BL/6 mice. We also use ZINB to perform a analysis of genes conditionally essential in H37Rv cultures exposed to multiple antibiotics. CONCLUSIONS: Our results show that, not only does ZINB generally identify most of the genes found by pairwise resampling (and vastly out-performs ANOVA), but it also identifies additional genes where variability is detectable only when the magnitudes of insertion counts are treated separately from local differences in saturation, as in the ZINB model