Online social network sensors for influenza outbreaks

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 27-28).Previous research has shown strong correlations between postings on the online social network Twitter where users complain of influenza-like symptoms, and clinical data on actual influenza rates. In addition, previous research has shown that more popular individuals in a real-life social network are infected with influenza earlier than average individuals. We collect all flu-related tweets during the 2012-2013 influenza season in order to compare the timing of flu-related tweets from more popular users compared to less popular users. No difference is seen in flu tweet timing between Twitter users with a high number of followers compared to users with a low number of followers. Restricting the Twitter network to bidirectional edges (mutual followings) performs slightly better, but is still not significant. Future work should focus on identifying edges in online social networks that indicate that two users regularly come into close physical proximity.by Katie Elizabeth Everett.M. Eng

Why Do Senior Officers Sometimes Fail in Character? The Leaky Character Reservoir

This article argues senior officers may fail in character because their rate of character development throughout their careers typically decreases as environmental stressors rise. It conceptualizes character as an open system with both gains and leaks over time and integrates existing scholarship on personality and ethical development to create the Leaky Character Reservoir framework, which it then applies to Army officers’ careers. Military leaders will gain a new understanding of character and find specific actions officers, units, and the US Army can undertake to strengthen the character of its senior officers

GFlowNet-EM for learning compositional latent variable models

Latent variable models (LVMs) with discrete compositional latents are an important but challenging setting due to a combinatorially large number of possible configurations of the latents. A key tradeoff in modeling the posteriors over latents is between expressivity and tractable optimization. For algorithms based on expectation-maximization (EM), the E-step is often intractable without restrictive approximations to the posterior. We propose the use of GFlowNets, algorithms for sampling from an unnormalized density by learning a stochastic policy for sequential construction of samples, for this intractable E-step. By training GFlowNets to sample from the posterior over latents, we take advantage of their strengths as amortized variational inference algorithms for complex distributions over discrete structures. Our approach, GFlowNet-EM, enables the training of expressive LVMs with discrete compositional latents, as shown by experiments on non-context-free grammar induction and on images using discrete variational autoencoders (VAEs) without conditional independence enforced in the encoder.Comment: ICML 2023; code: https://github.com/GFNOrg/GFlowNet-E

Life on Mars: Evidence from Martian Meteorites

New data on martian meteorite 84001 as well as new experimental studies show that thermal or shock decomposition of carbonate, the leading alternative non-biologic explanation for the unusual nanophase magnetite found in this meteorite, cannot explain the chemistry of the actual martian magnetites. This leaves the biogenic explanation as the only remaining viable hypothesis for the origin of these unique magnetites. Additional data from two other martian meteorites show a suite of biomorphs which are nearly identical between meteorites recovered from two widely different terrestrial environments (Egyptian Nile bottomlands and Antarctic ice sheets). This similarity argues against terrestrial processes as the cause of these biomorphs and supports an origin on Mars for these features

Presacral malakoplakia presenting as foot drop: a case report

Background: Malakoplakia is a rare condition characterized by inflammatory masses with specific histological characteristics. These soft tissue masses can mimic tumors and tend to develop in association with chronic or recurrent infections, typically of the urinary tract. A specific defect in innate immunity has been described. In the absence of randomized controlled trials, management is based on an understanding of the biology and on case reports. Case presentation: Here we describe a case of presacral malakoplakia in a British Indian woman in her late 30s, presenting with complex unilateral foot drop. Four years earlier, she had suffered a protracted episode of intrapelvic sepsis following a caesarean delivery. Resection of her presacral soft tissue mass was not possible. She received empiric antibiotics, a cholinergic agonist, and ascorbic acid. She responded well to medical management both when first treated and following a recurrence of symptoms after completing an initial 8 months of therapy. Whole exome sequencing of the patient and her parents was undertaken but no clear causal variant was identified. Conclusions: Malakoplakia is uncommon but the diagnosis should be considered where soft tissue masses develop at the site of chronic or recurrent infections. Obtaining tissue for histological examination is key to making the diagnosis. This case suggests that surgical resection is not always needed to achieve a good clinical and radiological outcome

Small-scale proxies for large-scale Transformer training instabilities

Teams that have trained large Transformer-based models have reported training instabilities at large scale that did not appear when training with the same hyperparameters at smaller scales. Although the causes of such instabilities are of scientific interest, the amount of resources required to reproduce them has made investigation difficult. In this work, we seek ways to reproduce and study training stability and instability at smaller scales. First, we focus on two sources of training instability described in previous work: the growth of logits in attention layers (Dehghani et al., 2023) and divergence of the output logits from the log probabilities (Chowdhery et al., 2022). By measuring the relationship between learning rate and loss across scales, we show that these instabilities also appear in small models when training at high learning rates, and that mitigations previously employed at large scales are equally effective in this regime. This prompts us to investigate the extent to which other known optimizer and model interventions influence the sensitivity of the final loss to changes in the learning rate. To this end, we study methods such as warm-up, weight decay, and the $\mu$Param (Yang et al., 2022), and combine techniques to train small models that achieve similar losses across orders of magnitude of learning rate variation. Finally, to conclude our exploration we study two cases where instabilities can be predicted before they emerge by examining the scaling behavior of model activation and gradient norms

A Novel Staphylococcus Podophage Encodes a Unique Lysin with Unusual Modular Design

ABSTRACT Drug-resistant staphylococci, particularly Staphylococcus aureus and Staphylococcus epidermidis, are leading causes of hospital-acquired infections. Bacteriophages and their peptidoglycan hydrolytic enzymes (lysins) are currently being explored as alternatives to conventional antibiotics; however, only a limited diversity of staphylococcal phages and their lysins has yet been characterized. Here, we describe a novel staphylococcal phage and its lysins. Bacteriophage Andhra is the first reported S. epidermidis phage belonging to the family Podoviridae. Andhra possesses an 18,546-nucleotide genome with 20 open reading frames. BLASTp searches revealed that gene product 10 (gp10) and gp14 harbor putative catalytic domains with predicted peptidase and amidase activities, characteristic functions of phage lysins. We purified these proteins and show that both Andhra_gp10 and Andhra_gp14 inhibit growth and degrade cell walls of diverse staphylococci, with Andhra_gp10 exhibiting more robust activity against the panel of cell wall substrates tested. Site-directed mutagenesis of its predicted catalytic residues abrogated the activity of Andhra_gp10, consistent with the presence of a catalytic CHAP domain on its C terminus. The active site location combined with the absence of an SH3b cell wall binding domain distinguishes Andhra_gp10 from the majority of staphylococcal lysins characterized to date. Importantly, close homologs of Andhra_gp10 are present in related staphylococcal podophages, and we propose that these constitute a new class of phage-encoded lysins. Altogether, our results reveal insights into the biology of a rare family of staphylococcal phages while adding to the arsenal of antimicrobials with potential for therapeutic use. IMPORTANCE The spread of antibiotic resistance among bacterial pathogens is inciting a global public health crisis. Drug-resistant Staphylococcus species, especially S. aureus and S. epidermidis, have emerged in both hospital and community settings, underscoring the urgent need for new strategies to combat staphylococcal infections. Bacterial viruses (phages) and the enzymes that they use to degrade bacterial cell walls (lysins) show promise as alternative antimicrobials; however, only a limited variety of staphylococcal phages and their lysins have yet been identified. Here, we report the discovery and characterization of a novel staphylococcal phage, Andhra. We show that Andhra encodes two lysins (Andhra_gp10 and Andhra_gp14) that inhibit growth and degrade the cell walls of diverse staphylococci, including S. aureus and S. epidermidis strains. Andhra and its unique lysins add to the arsenal of antimicrobials with potential for therapeutic use

Distinct Salmonella Enteritidis lineages associated with enterocolitis in high-income settings and invasive disease in low-income settings.

An epidemiological paradox surrounds Salmonella enterica serovar Enteritidis. In high-income settings, it has been responsible for an epidemic of poultry-associated, self-limiting enterocolitis, whereas in sub-Saharan Africa it is a major cause of invasive nontyphoidal Salmonella disease, associated with high case fatality. By whole-genome sequence analysis of 675 isolates of S. Enteritidis from 45 countries, we show the existence of a global epidemic clade and two new clades of S. Enteritidis that are geographically restricted to distinct regions of Africa. The African isolates display genomic degradation, a novel prophage repertoire, and an expanded multidrug resistance plasmid. S. Enteritidis is a further example of a Salmonella serotype that displays niche plasticity, with distinct clades that enable it to become a prominent cause of gastroenteritis in association with the industrial production of eggs and of multidrug-resistant, bloodstream-invasive infection in Africa.This work was supported by the Wellcome Trust. We would like to thank the members of the Pathogen Informatics Team and the core sequencing teams at the Wellcome Trust Sanger Institute (Cambridge, UK). We are grateful to D. Harris for work in managing the sequence data

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine