Update to the Vitamin C, Thiamine and Steroids in Sepsis (VICTAS) protocol: statistical analysis plan for a prospective, multicenter, double-blind, adaptive sample size, randomized, placebo-controlled, clinical trial.

BACKGROUND: Observational research suggests that combined therapy with Vitamin C, thiamine and hydrocortisone may reduce mortality in patients with septic shock. METHODS AND DESIGN: The Vitamin C, Thiamine and Steroids in Sepsis (VICTAS) trial is a multicenter, double-blind, adaptive sample size, randomized, placebo-controlled trial designed to test the efficacy of combination therapy with vitamin C (1.5 g), thiamine (100 mg), and hydrocortisone (50 mg) given every 6 h for up to 16 doses in patients with respiratory or circulatory dysfunction (or both) resulting from sepsis. The primary outcome is ventilator- and vasopressor-free days with mortality as the key secondary outcome. Recruitment began in August 2018 and is ongoing; 501 participants have been enrolled to date, with a planned maximum sample size of 2000. The Data and Safety Monitoring Board reviewed interim results at N = 200, 300, 400 and 500, and has recommended continuing recruitment. The next interim analysis will occur when N = 1000. This update presents the statistical analysis plan. Specifically, we provide definitions for key treatment and outcome variables, and for intent-to-treat, per-protocol, and safety analysis datasets. We describe the planned descriptive analyses, the main analysis of the primary end point, our approach to secondary and exploratory analyses, and handling of missing data. Our goal is to provide enough detail that our approach could be replicated by an independent study group, thereby enhancing the transparency of the study. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03509350. Registered on 26 April 2018

Venture funding for science-based African health innovation

<p>Abstract</p> <p>Background</p> <p>While venture funding has been applied to biotechnology and health in high-income countries, it is still nascent in these fields in developing countries, and particularly in Africa. Yet the need for implementing innovative solutions to health challenges is greatest in Africa, with its enormous burden of communicable disease. Issues such as risk, investment opportunities, return on investment requirements, and quantifying health impact are critical in assessing venture capital’s potential for supporting health innovation. This paper uses lessons learned from five venture capital firms from Kenya, South Africa, China, India, and the US to suggest design principles for African health venture funds.</p> <p>Discussion</p> <p>The case study method was used to explore relevant funds, and lessons for the African context. The health venture funds in this study included publicly-owned organizations, corporations, social enterprises, and subsidiaries of foreign venture firms. The size and type of investments varied widely. The primary investor in four funds was the International Finance Corporation. Three of the funds aimed primarily for financial returns, one aimed primarily for social and health returns, and one had mixed aims. Lessons learned include the importance of measuring and supporting both social and financial returns; the need to engage both upstream capital such as government risk-funding and downstream capital from the private sector; and the existence of many challenges including difficulty of raising capital, low human resource capacity, regulatory barriers, and risky business environments. Based on these lessons, design principles for appropriate venture funding are suggested.</p> <p>Summary</p> <p>Based on the cases studied and relevant experiences elsewhere, there is a case for venture funding as one support mechanism for science-based African health innovation, with opportunities for risk-tolerant investors to make financial as well as social returns. Such funds should be structured to overcome the challenges identified, be sustainable in the long run, attract for-profit private sector funds, and have measurable and significant health impact. If this is done, the proposed venture approach may have complementary benefits to existing initiatives and encourage local scientific and economic development while tapping new sources of funding.</p

Impact flux on Jupiter: From superbolides to large-scale collisions

Context. Regular observations of Jupiter by a large number of amateur astronomers have resulted in the serendipitous discovery of short bright flashes in its atmosphere, which have been proposed as being caused by impacts of small objects. Three flashes were detected: one on June 3, 2010, one on August 20, 2010, and one on September 10, 2012. Aims. We show that the flashes are caused by impacting objects that we characterize in terms of their size, and we study the flux of small impacts on Jupiter. Methods. We measured the light curves of these atmospheric airbursts to extract their luminous energy and computed the masses and sizes of the objects. We ran simulations of impacts and compared them with the light curves. We analyzed the statistical significance of these events in the large pool of Jupiter observations. Results. All three objects are in the 5-20 m size category depending on their density, and they released energy comparable to the recent Chelyabinsk airburst. Model simulations approximately agree with the interpretation of the limited observations. Biases in observations of Jupiter suggest a rate of 12-60 similar impacts per year and we provide software tools for amateurs to examine the faint signature of impacts in their data to increase the number of detected collisions. Conclusions. The impact rate agrees with dynamical models of comets. More massive objects (a few 100 m) should impact with Jupiter every few years leaving atmospheric dark debris features that could be detectable about once per decade

Chromosomal evolution in the South American Riodinidae (Lepidoptera: Papilionoidea)

Peer reviewe

Spatial constraints dictate glial territories at murine neuromuscular junctions

Spatial competition between glial cells causes them to partition neuromuscular junctions into discrete domains within a synapse

Solitary median maxillary central incisor (SMMCI) syndrome

Solitary median maxillary central incisor syndrome (SMMCI) is a complex disorder consisting of multiple, mainly midline defects of development resulting from unknown factor(s) operating in utero about the 35th–38th day(s) from conception. It is estimated to occur in 1:50,000 live births. Aetiology is uncertain. Missense mutation in the SHH gene (I111F) at 7q36 may be associated with SMMCI. The SMMCI tooth differs from the normal central incisor, in that the crown form is symmetric; it develops and erupts precisely in the midline of the maxillary dental arch in both primary and permanent dentitions. Congenital nasal malformation (choanal atresia, midnasal stenosis or congenital pyriform aperture stenosis) is positively associated with SMMCI. The presence of an SMMCI tooth can predict associated anomalies and in particular the serious anomaly holoprosencephaly. Common congenital anomalies associated with SMMCI are: severe to mild intellectual disability, congenital heart disease, cleft lip and/or palate and less frequently, microcephaly, hypopituitarism, hypotelorism, convergent strabismus, oesophageal and duodenal atresia, cervical hemivertebrae, cervical dermoid, hypothyroidism, scoliosis, absent kidney, micropenis and ambiguous genitalia. Short stature is present in half the children. Diagnosis should be made by eight months of age, but can be made at birth and even prenatally at 18–22 weeks from the routine mid-trimester ultrasound scan. Management depends upon the individual anomalies present. Choanal stenosis requires emergency surgical treatment. Short stature may require growth hormone therapy. SMMCI tooth itself is mainly an aesthetic problem, which is ideally managed by combined orthodontic, prosthodontic and oral surgical treatment; alternatively, it can be left untreated

Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning

Many protein engineering problems involve finding mutations that produce proteins with a particular function. Computational active learning is an attractive approach to discover desired biological activities. Traditional active learning techniques have been optimized to iteratively improve classifier accuracy, not to quickly discover biologically significant results. We report here a novel active learning technique, Most Informative Positive (MIP), which is tailored to biological problems because it seeks novel and informative positive results. MIP active learning differs from traditional active learning methods in two ways: (1) it preferentially seeks Positive (functionally active) examples; and (2) it may be effectively extended to select gene regions suitable for high throughput combinatorial mutagenesis. We applied MIP to discover mutations in the tumor suppressor protein p53 that reactivate mutated p53 found in human cancers. This is an important biomedical goal because p53 mutants have been implicated in half of all human cancers, and restoring active p53 in tumors leads to tumor regression. MIP found Positive (cancer rescue) p53 mutants in silico using 33% fewer experiments than traditional non-MIP active learning, with only a minor decrease in classifier accuracy. Applying MIP to in vivo experimentation yielded immediate Positive results. Ten different p53 mutations found in human cancers were paired in silico with all possible single amino acid rescue mutations, from which MIP was used to select a Positive Region predicted to be enriched for p53 cancer rescue mutants. In vivo assays showed that the predicted Positive Region: (1) had significantly more (p<0.01) new strong cancer rescue mutants than control regions (Negative, and non-MIP active learning); (2) had slightly more new strong cancer rescue mutants than an Expert region selected for purely biological considerations; and (3) rescued for the first time the previously unrescuable p53 cancer mutant P152L

The mitochondrial genome sequence of the ciliate Paramecium caudatum reveals a shift in nucleotide composition and codon usage within the genus Paramecium

<p>Abstract</p> <p>Background</p> <p>Despite the fact that the organization of the ciliate mitochondrial genome is exceptional, only few ciliate mitochondrial genomes have been sequenced until today. All ciliate mitochondrial genomes are linear. They are 40 kb to 47 kb long and contain some 50 tightly packed genes without introns. Earlier studies documented that the mitochondrial guanine + cytosine contents are very different between <it>Paramecium tetraurelia </it>and all studied <it>Tetrahymena </it>species. This raises the question of whether the high mitochondrial G+C content observed in <it>P. tetraurelia </it>is a characteristic property of <it>Paramecium </it>mtDNA, or whether it is an exception of the ciliate mitochondrial genomes known so far. To test this question, we determined the mitochondrial genome sequence of <it>Paramecium caudatum </it>and compared the gene content and sequence properties to the closely related <it>P. tetraurelia</it>.</p> <p>Results</p> <p>The guanine + cytosine content of the <it>P. caudatum </it>mitochondrial genome was significantly lower than that of <it>P. tetraurelia </it>(22.4% vs. 41.2%). This difference in the mitochondrial nucleotide composition was accompanied by significantly different codon usage patterns in both species, i.e. within <it>P. caudatum </it>clearly A/T ending codons dominated, whereas for <it>P. tetraurelia </it>the synonymous codons were more balanced with a higher number of G/C ending codons. Further analyses indicated that the nucleotide composition of most members of the genus <it>Paramecium </it>resembles that of <it>P. caudatum </it>and that the shift observed in <it>P. tetraurelia </it>is restricted to the <it>P. aurelia </it>species complex.</p> <p>Conclusions</p> <p>Surprisingly, the codon usage bias in the <it>P. caudatum </it>mitochondrial genome, exemplified by the effective number of codons, is more similar to the distantly related <it>T. pyriformis </it>and other single-celled eukaryotes such as <it>Chlamydomonas</it>, than to the closely related <it>P. tetraurelia</it>. These differences in base composition and codon usage bias were, however, not reflected in the amino acid composition. Most probably, the observed picture is best explained by a hitherto unknown (neutral or adaptive) mechanism that increased the guanine + cytosine content in <it>P. tetraurelia </it>mtDNA on the one hand, and strong purifying selection on the ancestral amino acid composition on the other hand. These contradicting forces are counterbalanced by a considerably altered codon usage pattern.</p

Ensemble-Based Computational Approach Discriminates Functional Activity of p53 Cancer and Rescue Mutants

The tumor suppressor protein p53 can lose its function upon single-point missense mutations in the core DNA-binding domain (“cancer mutants”). Activity can be restored by second-site suppressor mutations (“rescue mutants”). This paper relates the functional activity of p53 cancer and rescue mutants to their overall molecular dynamics (MD), without focusing on local structural details. A novel global measure of protein flexibility for the p53 core DNA-binding domain, the number of clusters at a certain RMSD cutoff, was computed by clustering over 0.7 µs of explicitly solvated all-atom MD simulations. For wild-type p53 and a sample of p53 cancer or rescue mutants, the number of clusters was a good predictor of in vivo p53 functional activity in cell-based assays. This number-of-clusters (NOC) metric was strongly correlated (r2 = 0.77) with reported values of experimentally measured ΔΔG protein thermodynamic stability. Interpreting the number of clusters as a measure of protein flexibility: (i) p53 cancer mutants were more flexible than wild-type protein, (ii) second-site rescue mutations decreased the flexibility of cancer mutants, and (iii) negative controls of non-rescue second-site mutants did not. This new method reflects the overall stability of the p53 core domain and can discriminate which second-site mutations restore activity to p53 cancer mutants

Coordinating Environmental Genomics and Geochemistry Reveals Metabolic Transitions in a Hot Spring Ecosystem

We have constructed a conceptual model of biogeochemical cycles and metabolic and microbial community shifts within a hot spring ecosystem via coordinated analysis of the “Bison Pool” (BP) Environmental Genome and a complementary contextual geochemical dataset of ∼75 geochemical parameters. 2,321 16S rRNA clones and 470 megabases of environmental sequence data were produced from biofilms at five sites along the outflow of BP, an alkaline hot spring in Sentinel Meadow (Lower Geyser Basin) of Yellowstone National Park. This channel acts as a >22 m gradient of decreasing temperature, increasing dissolved oxygen, and changing availability of biologically important chemical species, such as those containing nitrogen and sulfur. Microbial life at BP transitions from a 92°C chemotrophic streamer biofilm community in the BP source pool to a 56°C phototrophic mat community. We improved automated annotation of the BP environmental genomes using BLAST-based Markov clustering. We have also assigned environmental genome sequences to individual microbial community members by complementing traditional homology-based assignment with nucleotide word-usage algorithms, allowing more than 70% of all reads to be assigned to source organisms. This assignment yields high genome coverage in dominant community members, facilitating reconstruction of nearly complete metabolic profiles and in-depth analysis of the relation between geochemical and metabolic changes along the outflow. We show that changes in environmental conditions and energy availability are associated with dramatic shifts in microbial communities and metabolic function. We have also identified an organism constituting a novel phylum in a metabolic “transition” community, located physically between the chemotroph- and phototroph-dominated sites. The complementary analysis of biogeochemical and environmental genomic data from BP has allowed us to build ecosystem-based conceptual models for this hot spring, reconstructing whole metabolic networks in order to illuminate community roles in shaping and responding to geochemical variability