The Phenomenon of Student Revolt, By Milo Ross

The Phenomenon of Student Revolt was a speech given by Dr. Milo Ross, president of George Fox University to the class of 1969. It touches on many of the main challenges effecting colleges of the time. Please remember that this is a historical document. Some phrases and ideas may be offensive to the modern audience.https://digitalcommons.georgefox.edu/archives_presidents/1003/thumbnail.jp

Coarse-Graining and Self-Dissimilarity of Complex Networks

Can complex engineered and biological networks be coarse-grained into smaller and more understandable versions in which each node represents an entire pattern in the original network? To address this, we define coarse-graining units (CGU) as connectivity patterns which can serve as the nodes of a coarse-grained network, and present algorithms to detect them. We use this approach to systematically reverse-engineer electronic circuits, forming understandable high-level maps from incomprehensible transistor wiring: first, a coarse-grained version in which each node is a gate made of several transistors is established. Then, the coarse-grained network is itself coarse-grained, resulting in a high-level blueprint in which each node is a circuit-module made of multiple gates. We apply our approach also to a mammalian protein-signaling network, to find a simplified coarse-grained network with three main signaling channels that correspond to cross-interacting MAP-kinase cascades. We find that both biological and electronic networks are 'self-dissimilar', with different network motifs found at each level. The present approach can be used to simplify a wide variety of directed and nondirected, natural and designed networks.Comment: 11 pages, 11 figure

A motif-based approach to network epidemics

Networks have become an indispensable tool in modelling infectious diseases, with the structure of epidemiologically relevant contacts known to affect both the dynamics of the infection process and the efficacy of intervention strategies. One of the key reasons for this is the presence of clustering in contact networks, which is typically analysed in terms of prevalence of triangles in the network. We present a more general approach, based on the prevalence of different four-motifs, in the context of ODE approximations to network dynamics. This is shown to outperform existing models for a range of small world networks

Discovering universal statistical laws of complex networks

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their generalisation power, which we identify with large structural variability and absence of constraints imposed by the construction scheme. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This allows, for instance, to infer global features from local ones using regression models trained on networks with high generalisation power. Our results confirm and extend previous findings regarding the synchronisation properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks with good approximation. Finally, we demonstrate on three different data sets (C. elegans' neuronal network, R. prowazekii's metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models

A nose for trouble.

A 63‑year‑old Caucasian non‑smoker presented with purplish, firm, slowly growing plaques on the nose, of 20 years duration. The bigger one involved the upper part of the nose in its entirety [Figure 1], while there were smaller ones on the left and right cheek. All the plaques were asymptomatic and did not worsen after sunlight exposure. His past medical history was unremarkable. Hematological and biochemical parameters were within normal limits. Histological examination revealed a diffuse mid‑dermal cellular infiltrate composed of neutrophils, eosinophils, plasma cells, and lymphocytes. A narrow Grenz zone was present. In the lower part of the reticular dermis, the mixed infiltrate was intermingled with a massive deposition of fascicled pattern collagen fibers and spindle cell

Collective decision-making on triadic graphs

Many real-world networks exhibit community structures and non-trivial clustering associated with the occurrence of a considerable number of triangular subgraphs known as triadic motifs. Triads are a set of distinct triangles that do not share an edge with any other triangle in the network. Network motifs are subgraphs that occur significantly more often compared to random topologies. Two prominent examples, the feedforward loop and the feedback loop, occur in various real-world networks such as gene-regulatory networks, food webs or neuronal networks. However, as triangular connections are also prevalent in communication topologies of complex collective systems, it is worthwhile investigating the influence of triadic motifs on the collective decision-making dynamics. To this end, we generate networks called Triadic Graphs (TGs) exclusively from distinct triadic motifs. We then apply TGs as underlying topologies of systems with collective dynamics inspired from locust marching bands. We demonstrate that the motif type constituting the networks can have a paramount influence on group decision-making that cannot be explained solely in terms of the degree distribution. We find that, in contrast to the feedback loop, when the feedforward loop is the dominant subgraph, the resulting network is hierarchical and inhibits coherent behavior

Systolic blood pressure reduction during the first 24 h in acute heart failure admission: friend or foe?

Aims: Changes in systolic blood pressure (SBP) during an admission for acute heart failure (AHF), especially those leading to hypotension, have been suggested to increase the risk for adverse outcomes. Methods and results: We analysed associations of SBP decrease during the first 24 h from randomization with serum creatinine changes at the last time-point available (72 h), using linear regression, and with 30- and 180-day outcomes, using Cox regression, in 1257 patients in the VERITAS study. After multivariable adjustment for baseline SBP, greater SBP decrease at 24 h from randomization was associated with greater creatinine increase at 72 h and greater risk for 30-day all-cause death, worsening heart failure (HF) or HF readmission. The hazard ratio (HR) for each 1 mmHg decrease in SBP at 24 h for 30-day death, worsening HF or HF rehospitalization was 1.01 [95% confidence interval (CI) 1.00–1.02; P = 0.021]. Similarly, the HR for each 1 mmHg decrease in SBP at 24 h for 180-day all-cause mortality was 1.01 (95% CI 1.00–1.03; P = 0.038). The associations between SBP decrease and outcomes did not differ by tezosentan treatment group, although tezosentan treatment was associated with a greater SBP decrease at 24 h. Conclusions: In the current post hoc analysis, SBP decrease during the first 24 h was associated with increased renal impairment and adverse outcomes at 30 and 180 days. Caution, with special attention to blood pressure monitoring, should be exercised when vasodilating agents are given to AHF patients

Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli

The set of regulatory interactions between genes, mediated by transcription factors, forms a species' transcriptional regulatory network (TRN). By comparing this network with measured gene expression data one can identify functional properties of the TRN and gain general insight into transcriptional control. We define the subnet of a node as the subgraph consisting of all nodes topologically downstream of the node, including itself. Using a large set of microarray expression data of the bacterium Escherichia coli, we find that the gene expression in different subnets exhibits a structured pattern in response to environmental changes and genotypic mutation. Subnets with less changes in their expression pattern have a higher fraction of feed-forward loop motifs and a lower fraction of small RNA targets within them. Our study implies that the TRN consists of several scales of regulatory organization: 1) subnets with more varying gene expression controlled by both transcription factors and post-transcriptional RNA regulation, and 2) subnets with less varying gene expression having more feed-forward loops and less post-transcriptional RNA regulation.Comment: 14 pages, 8 figures, to be published in PLoS Computational Biolog