ONLINE PROFESSIONAL LEARNING NETWORKS

As technological advancements are made in the social media world, more people are connecting for professional development this way. This study served as an update to a 2011 study conducted by Dr. Hilary Risser. The previous study established a base network of teachers that used blogs to communicate educational practices. The purpose of this investigation is to analyze the differences, similarities, and benefits of online versus face-to-face communication. Interviews with multiple math and science teachers were conducted first with an online survey, and followed up via Skype. Their blogs were examined to identify connections between these teachers so that a new network of communication could be established. Preliminary results show that since 2011, networks have grown. Moving forward, the contents of each blog will be assessed. One future goal is that the conclusion of this study could lead to better equipped online social media for education professionals to grow

Audience monitor:an open source tool for tracking audience mobility in front of pervasive displays

Understanding an audience's behavior is an important aspect of evaluating display installations. In particular, it is important to understand how people move around in the vicinity of displays, including viewer transitions from noticing a display, through approach, to final use of the display. Despite the importance of measuring viewer mobility patterns, there are still relatively few low-cost tools that can be used with research display deployments to capture detailed spatial and temporal behavior of an audience. In this paper, we present an approach to audience monitoring that uses an off-the-shelf depth sensor and open source computer vision algorithms to monitor the space in front of a digital display, tracking presence and movements of both passers-by and display users. We believe that our approach can help display researchers evaluate their public display deployments and improve the level of quantitative data underpinning our field

Photogrammetry-based Texture Analysis of a Volcaniclastic Outcrop-peel: Low-cost Alternative to TLS and Automation Potentialities using Haar Wavelet and Spatial-Analysis Algorithms

Numerous progress has been made in the field of applied photogrammetry in the last decade, including the usage of close-range photogrammetry as a mean of conservation and record of outcrops. In the present contribution, we use the SfM-MVS method combined with a wavelet decomposition analysis of the surface, in order to relate it to morphological and surface roughness data. The results demonstrated that wavelet decomposition and RMS could provide a rapid insight on the location of coarser materials and individual outliers, while arithmetic surface roughness were more useful to detect units or layers that are similar on the outcrop. The method also emphasizes the fact that the automation of the process does not allows clear distinction between any artefact crack or surface change and that human supervision is still essential despite the original goal of automating the outcrop surface analysis

Advances in field-based high-throughput photosynthetic phenotyping

Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis

A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.

Although cancer genomes are replete with noncoding mutations, the effects of these mutations remain poorly characterized. Here we perform an integrative analysis of 930 tumor whole genomes and matched transcriptomes, identifying a network of 193 noncoding loci in which mutations disrupt target gene expression. These 'somatic eQTLs' (expression quantitative trait loci) are frequently mutated in specific cancer tissues, and the majority can be validated in an independent cohort of 3,382 tumors. Among these, we find that the effects of noncoding mutations on DAAM1, MTG2 and HYI transcription are recapitulated in multiple cancer cell lines and that increasing DAAM1 expression leads to invasive cell migration. Collectively, the noncoding loci converge on a set of core pathways, permitting a classification of tumors into pathway-based subtypes. The somatic eQTL network is disrupted in 88% of tumors, suggesting widespread impact of noncoding mutations in cancer

The promises of large language models for protein design and modeling.

The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the language of proteins invite the application and adaptation of LLMs to protein modelling and design. Considering the impressive results of GPT-4 and other recently developed LLMs in processing, generating and translating human languages, we anticipate analogous results with the language of proteins. Indeed, protein language models have been already trained to accurately predict protein properties, generate novel functionally characterized proteins, achieving state-of-the-art results. In this paper we discuss the promises and the open challenges raised by this novel and exciting research area, and we propose our perspective on how LLMs will affect protein modeling and design

Jeans Instability in a Tidally Disrupted Halo Satellite Galaxy

We use a hybrid test particle/N-body simulation to integrate 4 million massless test particle trajectories within a fully self-consistent 10^5 particle N-body simulation. The number of massless particles allows us to resolve fine structure in the spatial distribution and phase space of a dwarf galaxy as it is disrupted in the tidal field of a Milky Way type galaxy. The tidal tails exhibit nearly periodic clumping or a smoke-like appearance. By running simulations with different satellite particle mass, halo particle mass, number of massive and massless particles and with and without a galaxy disk, we have determined that the instabilities are not due to numerical noise, amplification of structure in the halo, or shocking as the satellite passes through the disk of the Galaxy. We measure Jeans wavelengths and growth timescales in the tidal tail and show that the Jeans instability is a viable explanation for the clumps. We find that the instability causes velocity perturbations of order 10 km/s. Clumps in tidal tails present in the Milky Way could be seen in stellar radial velocity surveys as well as number counts. We find that the unstable wavelength growth is sensitive to the simulated mass of dark matter halo particles. A simulation with a smoother halo exhibits colder and thinner tidal tails with more closely spaced clumps than a simulation with more massive dark matter halo particles. Heating by the halo particles increases the Jeans wavelength in the tidal tail affecting substructure development, suggesting an intricate connection between tidal tails and dark matter halo substructure.Comment: 15 pages, 7 figures, submitted to MNRAS, May 25 201

Mechanisms of innate immune activation by gluten peptide p31-43 in mice

Celiac disease (CD) is an immune-mediated enteropathy triggered by gluten in genetically susceptible individuals. Innate immunity contributes to the pathogenesis of CD, but the mechanisms remain poorly understood. Although previous in vitro work suggests that gliadin peptide p31-43 acts as an innate immune trigger, the underlying pathways are unclear and have not been explored in vivo. Here we show that intraluminal delivery of p31-43 induces morphological changes in the small intestinal mucosa of normal mice consistent with those seen in CD, including increased cell death and expression of inflammatory mediators. The effects of p31-43 were dependent on MyD88 and type I IFNs, but not Toll-like receptor 4 (TLR4), and were enhanced by coadministration of the TLR3 agonist polyinosinic:polycytidylic acid. Together, these results indicate that gliadin peptide p31-43 activates the innate immune pathways in vivo, such as IFN-dependent inflammation, relevant to CD. Our findings also suggest a common mechanism for the potential interaction between dietary gluten and viral infections in the pathogenesis of CD

Domain Model Explains Propagation Dynamics and Stability of Histone H3K27 and H3K36 Methylation Landscapes

Chromatin states must be maintained during cell proliferation to uphold cellular identity and genome integrity. Inheritance of histone modifications is central in this process. However, the histone modification landscape is challenged by incorporation of new unmodified histones during each cell cycle, and the principles governing heritability remain unclear. We take a quantitative computational modeling approach to describe propagation of histone H3K27 and H3K36 methylation states. We measure combinatorial H3K27 and H3K36 methylation patterns by quantitative mass spectrometry on subsequent generations of histones. Using model comparison, we reject active global demethylation and invoke the existence of domains defined by distinct methylation endpoints. We find that H3K27me3 on pre-existing histones stimulates the rate of de novo H3K27me3 establishment, supporting a read-write mechanism in timely chromatin restoration. Finally, we provide a detailed quantitative picture of the mutual antagonism between H3K27 and H3K36 methylation and propose that it stabilizes epigenetic states across cell division