Programmable biomaterials for dynamic and responsive drug delivery

Biomaterials are continually being designed that enable new methods for interacting dynamically with cell and tissues, in turn unlocking new capabilities in areas ranging from drug delivery to regenerative medicine. In this review, we explore some of the recent advances being made in regards to programming biomaterials for improved drug delivery, with a focus on cancer and infection. We begin by explaining several of the underlying concepts that are being used to design this new wave of drug delivery vehicles, followed by examining recent materials systems that are able to coordinate the temporal delivery of multiple therapeutics, dynamically respond to changing tissue environments, and reprogram their bioactivity over time

The hair follicle: an underutilized source of cells and materials for regenerative medicine

The hair follicle is one of only two structures within the adult body that selectively degenerates and regenerates, making it an intriguing organ to study and use for regenerative medicine. Hair follicles have been shown to influence wound healing, angiogenesis and neurogenesis, and harbor distinct populations of stem cells; this has led to cells from the follicle being used in clinical trials for tendinosis and chronic ulcers. In addition, keratin produced by the follicle in the form of a hair fiber provides an abundant source of biomaterials for regenerative medicine. In this review, we provide an overview of the structure of a hair follicle, explain the role of the follicle in regulating the microenvironment of skin and the impact on wound healing, explore individual cell types of interest for regenerative medicine, and cover several applications of keratin-based biomaterials

Spontaneous Regeneration of the Mandible after Hemimandibulectomy: Report of a Case

Mandibular defects may result from many conditions such as trauma, inflammatory diseases and tumors. There are rare cases reported in the literature that have demonstrated spontaneous bone regeneration after resection of the mandible. Several factors such as age, preservation of the periosteum and genetics seem to influence spontaneous bone regeneration capacity in individuals. Evaluation of these factors may lead to a better understanding of the mechanism of spontaneous bone regeneration and also help to create new methods for bone reconstruction. The purpose of this article was to describe the spontaneous regeneration of the hemi-mandible with a well shaped condyle and coronoid after resecting a mandibular pathologic lesion in a young man

A SPARQL Query Transformation Rule Language — Application to Retrieval and Adaptation in Case-Based Reasoning

International audienceThis paper presents SQTRL, a language for transformation rules for SPARQL queries, a tool associated with it, and how it can be applied to retrieval and adaptation in case-based reasoning (CBR). Three applications of SQTRL are presented in the domains of cooking and digital humanities. For a CBR system using RDFS for representing cases and domain knowledge, and SPARQL for its query language, case retrieval with SQTRL consists in a minimal modification of the query so that it matches at least a source case. Adaptation based on the modification of an RDFS base can also be handled with the help of this tool. SQTRL and its tool can therefore be used for several goals related to CBR systems based on the semantic web standards RDFS and SPARQL

A neural circuit model of decision uncertainty and change-of-mind

Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have been proposed, there is no neural model that can compute decision uncertainty and explain its effects on change-of-mind. We propose a neuronal circuit model that computes decision uncertainty while accounting for a variety of behavioural and neural data of decision confidence and change-of-mind, including testable model predictions. Our theoretical analysis suggests that change-of-mind occurs due to the presence of a transient uncertainty-induced choice-neutral stable steady state and noisy fluctuation within the neuronal network. Our distributed network model indicates that the neural basis of change-of-mind is more distinctively identified in motor-based neurons. Overall, our model provides a framework that unifies decision confidence and change-of-mind

Continuous Evolution of Statistical Estimators for Optimal Decision-Making

In many everyday situations, humans must make precise decisions in the presence of uncertain sensory information. For example, when asked to combine information from multiple sources we often assign greater weight to the more reliable information. It has been proposed that statistical-optimality often observed in human perception and decision-making requires that humans have access to the uncertainty of both their senses and their decisions. However, the mechanisms underlying the processes of uncertainty estimation remain largely unexplored. In this paper we introduce a novel visual tracking experiment that requires subjects to continuously report their evolving perception of the mean and uncertainty of noisy visual cues over time. We show that subjects accumulate sensory information over the course of a trial to form a continuous estimate of the mean, hindered only by natural kinematic constraints (sensorimotor latency etc.). Furthermore, subjects have access to a measure of their continuous objective uncertainty, rapidly acquired from sensory information available within a trial, but limited by natural kinematic constraints and a conservative margin for error. Our results provide the first direct evidence of the continuous mean and uncertainty estimation mechanisms in humans that may underlie optimal decision making

Ultra-Rapid Categorization of Fourier-Spectrum Equalized Natural Images: Macaques and Humans Perform Similarly

BACKGROUND: Comparative studies of cognitive processes find similarities between humans and apes but also monkeys. Even high-level processes, like the ability to categorize classes of object from any natural scene under ultra-rapid time constraints, seem to be present in rhesus macaque monkeys (despite a smaller brain and the lack of language and a cultural background). An interesting and still open question concerns the degree to which the same images are treated with the same efficacy by humans and monkeys when a low level cue, the spatial frequency content, is controlled. METHODOLOGY/PRINCIPAL FINDINGS: We used a set of natural images equalized in Fourier spectrum and asked whether it is still possible to categorize them as containing an animal and at what speed. One rhesus macaque monkey performed a forced-choice saccadic task with a good accuracy (67.5% and 76% for new and familiar images respectively) although performance was lower than with non-equalized images. Importantly, the minimum reaction time was still very fast (100 ms). We compared the performances of human subjects with the same setup and the same set of (new) images. Overall mean performance of humans was also lower than with original images (64% correct) but the minimum reaction time was still short (140 ms). CONCLUSION: Performances on individual images (% correct but not reaction times) for both humans and the monkey were significantly correlated suggesting that both species use similar features to perform the task. A similar advantage for full-face images was seen for both species. The results also suggest that local low spatial frequency information could be important, a finding that fits the theory that fast categorization relies on a rapid feedforward magnocellular signal

Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening

Forward genetic screens are powerful tools for the unbiased discovery and functional characterization of specific genetic elements associated with a phenotype of interest. Recently, the RNA-guided endonuclease Cas9 from the microbial CRISPR (clustered regularly interspaced short palindromic repeats) immune system has been adapted for genome-scale screening by combining Cas9 with pooled guide RNA libraries. Here we describe a protocol for genome-scale knockout and transcriptional activation screening using the CRISPR-Cas9 system. Custom- or ready-made guide RNA libraries are constructed and packaged into lentiviral vectors for delivery into cells for screening. As each screen is unique, we provide guidelines for determining screening parameters and maintaining sufficient coverage. To validate candidate genes identified by the screen, we further describe strategies for confirming the screening phenotype, as well as genetic perturbation, through analysis of indel rate and transcriptional activation. Beginning with library design, a genome-scale screen can be completed in 9-15 weeks, followed by 4-5 weeks of validation.Paul & Daisy Soros Fellowships for New Americans (New York, N.Y.)McGovern Institute for Brain Research at MIT (Friends of McGovern Institute Fellowship)Massachusetts Institute of Technology. Poitras Center for Affective Disorders ResearchUnited States. Department of Energy (Computational Science Graduate Fellowship)National Institute of Mental Health (U.S.) (5DP1-MH100706)National Institute of Mental Health (U.S.) (1R01-MH110049)New York Stem Cell FoundationPoitras FoundationSimons FoundationPaul G. Allen Family FoundationVallee FoundationTom HarrimanB. Metcalf

Search for Dark Matter and Supersymmetry with a Compressed Mass Spectrum in the Vector Boson Fusion Topology in Proton-Proton Collisions at root s=8 TeV

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