An estimate of glomerular filtration rate has been derived for children from body length (L, in centimeters) and plasma creatinine (Pcr, in milligrams per deciliter): GFR = 0.55 L/Pcr. The near universality of this estimate in children led us to seek a similar formula for estimating GFR in full-term infants during the first year of life. We measured Pcr in 137 healthy infants and performed creatinine clearance (Ccr) studies in 63 of them aged greater than or equal to 5 days. Beyond the first week, Pcr averaged 0.39 +/- 0.01 (0.10 SD) mg/dl. The estimate of GFR from 0.55 L/Pcr overestimated Ccr by 24% (P less than 0.001). Based on the calculation of a new constant from Ccr X Pcr/L, GFR was more accurately estimated from 0.45 L/Pcr (mean difference of Ccr - 0.45 L/Pcr = -0.4 +/- 3.7 (SE) ml/min X 1.73 m2) in full-term infants between 1 and 52 weeks of age. Because the constant 0.45 and Pcr do not change significantly during this period, GFR can be approximated at the bedside from body length of the healthy full-term infant (GFR = 0.45 L/0.39 = 1.1 L).
The mature optic nerve cannot regenerate when injured, leaving victims of traumatic nerve damage or diseases such as glaucoma with irreversible visual losses. Recent studies have identified ways to stimulate retinal ganglion cells to regenerate axons part-way through the optic nerve, but it remains unknown whether mature axons can reenter the brain, navigate to appropriate target areas, or restore vision. We show here that with adequate stimulation, retinal ganglion cells are able to regenerate axons the full length of the visual pathway and on into the lateral geniculate nucleus, superior colliculus, and other visual centers. Regeneration partially restores the optomotor response, depth perception, and circadian photoentrainment, demonstrating the feasibility of reconstructing central circuitry for vision after optic nerve damage in mature mammals.
I watched Phineas Fisher use this technique in his hacking video, and it feels like magic. Basically it is possible to use a dumb netcat shell to upgrade to a full TTY by setting some stty options within your Kali terminal.
Since the 1970s the use of small subunit (SSU) rRNA (SSU rRNA) sequences has revolutionized microbial classification, systematics, and ecology. The SSU rRNA gene has become the most sequenced gene, with hundreds of thousands of its sequences now deposited in public databases. It has become the current 'gold standard' in microbial diversity studies, and for good reasons. For one, it is present in all microbial organisms. For another, the gene sequence is highly conserved at both ends. This enables one to obtain nearly full-length SSU rRNA gene sequences by polymerase chain reaction amplification using 'universal' primers and without having to isolate and culture the organism in question. Until very recently, the vast majority of microbes were identified and classified only by recovering and sequencing their SSU rRNA genes. This single sequence of approximately 1.5 kilobases is often the only information we have about the organism from which it came - the only way we know that it exists in the natural environment.
The situation changed with the advent of genomic sequencing. Each complete genome sequence brings with it the sequences for all protein encoding genes in that organism. Now, not only can one build gene trees based on a favorite protein encoding gene, but also one has the option to concatenate multiple gene sequences to construct trees on the 'genome level'. Possessing more phylogenetic signals, such 'genome trees' or 'super-matrix trees' are less susceptible to the stochastic errors than those built from a single gene . Recent studies attempting to reconstruct the tree of life have demonstrated the power of this approach [8, 9] (for review ). Likewise, genome trees have also been used successfully to reassess the phylogenetic positions of individual species [11, 12]. It is worth pointing out, however, that the genome trees are still susceptible to systematic errors caused by compositional biases, unrealistic evolutionary models, and inadequate taxonomic sampling [7, 13, 14].
With the rapid increase in available genomic sequence data, there is an ever-urgent need for automated phylogenetic analyses using protein sequences. However, automation is frequently accompanied by reduced quality. We introduce here a fully automated method that is not only fast but also is of high quality. The main components of our approach are shown in Figure 1, and their implementation is described in detail in the Material and methods section (below). Designed to align and trim protein sequences rapidly, reliably, and reproducibly, AMPHORA eliminates one of the tightest bottlenecks in large-scale protein phylogenetic inference. It can be used for phylogenetic analyses of single genes or whole genomes.
Although use of SSU rRNA was a landmark advancement in molecular microbial systematics, genome sequences provide an important alternative and complement [11, 12]. Phylogenetic trees built from multiple genes are more robust in resolving taxonomic relationships below the phylum level and hence provide an excellent alternative phylogenetic framework for microbial systematics. Until many more genomes have been sequenced, however, a hybrid approach may be most fruitful. A genome tree built from sequenced genomes can be used as a scaffold; species for which we lack full genome sequences can be placed by comparing their SSU rRNA sequences with those of sequenced species.
Although our phylogeny-based phylotyping is fully automated, it still requires many more steps than, and is slower than, similarity based phylotyping methods such as a MEGAN . Is it worth the trouble? Similarity based phylotyping works by searching a query sequence against a reference database such as NCBI nr and deriving taxonomic information from the best matches or 'hits'. When species that are closely related to the query sequence exist in the reference database, similarity-based phylotyping can work well. However, if the reference database is a biased sample or if it contains no closely related species to the query, then the top hits returned could be misleading . Furthermore, similarity-based methods require an arbitrary similarity cut-off value to define the top hits. Because individual bacterial genomes and proteins can evolve at very different rates, a universal cut-off that works under all conditions does not exist. As a result, the final results can be very subjective.
We are in the process of adding more proteins to our initial database of 31 markers, including the commonly used protein markers RecA, HSP70, and EF-Tu. Ideally, a probability based method that evaluates the positional homology of the multiple sequence alignment could be developed to automate fully the process of masking. Major expansion will also require systematic assessment of many other protein families for their suitability as phylogenetic markers. For metagenomic phylotyping, the marker genes do not have to be single-copy or universal, but they must have been reasonably well sampled, have sufficient phylogenetic signal, and not be frequently exchanged between distantly related lineages. Until we learn more about the extent of lateral gene transfer in natural microbial communities, we caution against using every protein sequence collected in metagenomics studies for microbial diversity study.
Currently, SSU rRNA is still the most powerful phylogenetic marker because of the number of sequences available and the scope of taxonomic coverage. However, the imminent arrival of thousands of microbial genome sequences will vastly expand the amount of data available for alternative protein phylogenetic markers, thus presenting us with both a challenge and an opportunity. We have developed AMPHORA, a fully automated method for phylogenetic inference using multiple protein markers. AMPHORA offers speed, reliability, and high quality analyses. By eliminating the need for time consuming manual curation of sequence alignments, it removes one of the tightest bottlenecks in large-scale protein phylogenetic inference. We demonstrated its usefulness for automating both the construction of genome trees and the assignment of phylotypes to environmental metagenomic data. We believe such a phylogenomic approach will be valuable in helping us to make sense of rapidly accumulating microbial genomic data.
BRIG output image of a simulated draft E. coli O157:H7 str. Sakai genome. Figure 1 shows a draft E. coli genome compared against 27 other prokaryote genomes (the full list of genomes is described in Table 1). The reference genome is an ordered set of contigs, assembled using GS De Novo Assembler (454 Life Sciences/Roche) version 2.3, from simulated sequencing reads generated by MetaSim  based on the E. coli O157:H7 str. Sakai genome [GenBank:BA000007]. After assembly contigs were ordered against the complete E. coli O157:H7 Sakai genome using Mauve . The innermost rings show GC skew (purple/green) and GC content (black). The third innermost ring shows genome coverage (brown); genome regions with coverage more than one standard deviation ( 41) from the mean coverage ( 94) are represented as blue spikes. Contig boundaries are shown outside this ring as alternating red and blue bars. The remaining rings show BLAST comparisons of 27 other complete E. coli and Salmonella genomes against the simulated draft genome assembly (in several cases, multiple genome comparisons are collapsed into a single ring, Table 1). The outermost ring highlights the Sakai prophage, and prophage-like (Sp & SpLE) regions as described by Hayashi et al. , shown in navy blue and fuchsia, respectively. SpLE 4, containing the locus of enterocyte effacement, is shown in green.
Figure 3 represents the presence of protein encoding genes within each query genome as a full and vividly coloured bar (e.g. see the E. coli O157:H7 strains for the translated espD gene). Gene absence can be observed as a blank/white region, like any of the results for E. coli K12 MG1665, whose genome does not carry the LEE. Variation in the translated sequences will have a lower sequence identity compared to the reference genome and appear with a fully coloured but slightly faded bar, as seen in Figure 3 for E. coli O103:H2 and C. rodentium when searching for EspZ, or where the bar is not fully coloured, such as for E. coli O111:H- and O127:H6 when searching for EspH. As with any BRIG image, percentage identity cut-off values can be customised to alter the dynamic range of colour shown in each ring. The annotations in Figure 3 illustrate a feature of BRIG where users can opt to load the FASTA headings from a multi-FASTA reference sequence and use these headers to annotate their image. 350c69d7ab