Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Lionel A. Mandell, Richard G. Wunderink, Antonio Anzueto, John G. Bartlett, G. Douglas Campbell, Nathan C. Dean, Scott F. Dowell, Thomas M. File, Jr. Daniel M. Musher, Michael S. Niederman, Antonio Torres, and Cynthia G. Whitney McMaster University Medical School, Hamilton, Ontario, Canada; Northwestern University Feinberg School of Medicine, Chicago, Illinois; University of Texas Health Science Center and South Texas Veterans Health Care System, San Antonio, and Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas; Johns Hopkins University School of Medicine, Baltimore, Maryland; Division of Pulmonary, Critical Care, and Sleep Medicine, University of Mississippi School of Medicine, Jackson; Division of Pulmonary and Critical Care Medicine, LDS Hospital, and University of Utah, Salt Lake City, Utah; Centers for Disease Control and Prevention, Atlanta, Georgia; Northeastern Ohio Universities College of Medicine, Rootstown, and Summa Health System, Akron, Ohio; State University of New York at Stony Brook, Stony Brook, and Department of Medicine, Winthrop University Hospital, Mineola, New York; and Cap de Servei de Pneumologia i Allergia Respiratoria, Institut Clinic del Torax, Hospital Clinic de Barcelona, Facultat de Medicina, Universitat de Barcelona, Institut d’Investigacions Biomediques August Pi i Sunyer, CIBER CB06/06/0028, Barcelona, Spain.

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Infectious Diseases Society of America/American Thoracic Society Consensus Guidelines on the Management of Community-Acquired Pneumonia in Adults

To revise 1998 recommendations on childhood obesity, an Expert Committee, comprised of representatives from 15 professional organizations, appointed experienced scientists and clinicians to 3 writing groups to review the literature and recommend approaches to prevention, assessment, and treatment. Because effective strategies remain poorly defined, the writing groups used both available evidence and expert opinion to develop the recommendations. Primary care providers should universally assess children for obesity risk to improve early identification of elevated BMI, medical risks, and unhealthy eating and physical activity habits. Providers can provide obesity prevention messages for most children and suggest weight control interventions for those with excess weight. The writing groups also recommend changing office systems so that they support efforts to address the problem. BMI should be calculated and plotted at least annually, and the classification should be integrated with other information such as growth pattern, familial obesity, and medical risks to assess the child’s obesity risk. For prevention, the recommendations include both specific eating and physical activity behaviors, which are likely to promote maintenance of healthy weight, but also the use of patient-centered counseling techniques such as motivational interviewing, which helps families identify their own motivation for making change. For assessment, the recommendations include methods to screen for current medical conditions and for future risks, and methods to assess diet and physical activity behaviors. For treatment, the recommendations propose 4 stages of obesity care; the first is brief counseling that can be delivered in a health care office, and subsequent stages require more time and resources. The appropriateness of higher stages is influenced by a patient’s age and degree of excess weight. These recommendations recognize the importance of social and environmental change to reduce the obesity epidemic but also identify ways healthcare providers and health care systems can be part of broader efforts.

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Expert Committee Recommendations Regarding the Prevention, Assessment, and Treatment of Child and Adolescent Overweight and Obesity: Summary Report

The study subjects were followed for 3324 person-years (median, 1.2 years; maximum, 2.8 years). Of these subjects, 10 were found to have been infected with HIV at en rollment, and 100 became infected during follow-up (36 in the FTC–TDF group and 64 in the placebo group), indicating a 44% reduction in the incidence of HIV (95% confidence interval, 15 to 63; P = 0.005). In the FTC–TDF group, the study drug was detected in 22 of 43 of seronegative subjects (51%) and in 3 of 34 HIV-infected subjects (9%) (P<0.001). Nausea was reported more frequently during the first 4 weeks in the FTC–TDF group than in the placebo group (P<0.001). The two groups had similar rates of serious adverse events (P = 0.57). Conclusions Oral FTC–TDF provided protection against the acquisition of HIV infection among the subjects. Detectable blood levels strongly correlated with the prophylactic effect. (Funded by the National Institutes of Health and the Bill and Melinda Gates Foun dation; ClinicalTrials.gov number, NCT00458393.)

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Preexposure Chemoprophylaxis for HIV Prevention in Men Who Have Sex with Men

https://doi.org/10.1016/j.jvacx.2022.100249

Agent-based model of the impact of higher influenza vaccine efficacy on seasonal influenza burden

Background The COVID-19 pandemic continues to affect the United States and the world. Media reports have suggested that the wave of the alpha variant in the Spring of 2021 in the US caused more cases among younger patients and racial and ethnic subgroups. Approach We analyzed electronic health record data from a multihospital health system to test whether younger patients accounted for more cases and more severe disease, and whether racial disparities are widening. We compared demographics, patient characteristics, and hospitalization variables for patients admitted from November 2020 through January 2021 to those admitted in March and April 2021. Results We analyzed data for 37, 502 unique inpatients and outpatients at 21 hospitals from November 1, 2020 to April 30, 2021. Compared to patients from November through January, those with positive tests in March and April were younger and less likely to die. Among patients under age 50, those with positive tests in March and April were three times as likely to be hospitalized and twice as likely to require ICU admission or mechanical ventilation. Individuals identified as Black represented a greater proportion of cases and hospitalizations in March and April as compared to November through January. Conclusions We found that relative COVID-19 hospitalization rates for younger individuals and individuals identified as Black were rising over time. These findings have important implications for ongoing public health measures to mitigate the impact of the pandemic.

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Longitudinal changes in age and race of patients with SARS-CoV-2 in a multi-hospital health system

We performed whole genome sequencing on SARS-CoV-2 from 59 vaccinated individuals from southwest Pennsylvania who tested positive between February and September, 2021. A comparison of mutations among vaccine breakthrough cases to a time-matched control group identified potential adaptive responses of SARS-CoV-2 to vaccination.

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Genomic characterization of SARS-CoV-2 from vaccine breakthrough cases in Allegheny County, Pennsylvania

Objetivo: Avaliar o perfil de resistencia do Mycobacterium tuberculosis isolado de pacientes da cidade de Beira, Sofala, Mocambique. Metodo: Estudo descritivo transversal, que analisou dados secundarios de pacientes que tiveram cultura positiva para M. tuberculosis MGIT960-BACTEC™. Para cada isolado foram revistos dados demogra- ficos do respetivo paciente, a sorologia para o virus da imunodeficiencia humana (HIV), o resultado do teste rapido molecular (TRM) - GeneXpertα-Cepheid e o teste de fenotipico de sensibilidade aos farmacos (TS) de primeira linha. Os testes foram realizados no Laboratorio de Referencia para tuberculose na cidade de Beira, Mocambique no periodo de Janeiro de 2014 a Marco de 2015. Resultados: Foram analisadas 87 isolados de M. tuberculosis de pacientes da provincia de Sofala, Mocambique. Desses, 33/87 (37,9%) apresentaram algum tipo de resistencia do bacilo aos farmacos anti-TB, enquanto que em 54/87 (62,1%) a resistencia nao foi detectada. Dos 33 isolados com resistencia: 18/33 (54,7%) foram classificados como multidrogarresistentes (TB-MDR), 11/33 (33,3%) monorresistentes a isoniazida(INH), e 2/33 (6%) monorresistentes a rifampicina (RMP) e 2/33 (6%) resistentes ao Etambutol. Do total de isolados com resistencia a INH, 5/11 (42%) tambem apresentavam resistencia a estreptomicina (Sm). De 49 pacientes que fizeram sorologia para HIV, 30/49 (61,2%) tinham infeccao confirmada pelo HIV, sendo esta duas vezes mais frequente entre os pacientes portadores do Mycobacterium tuberculosis resistente do que naqueles com bacilo sensivel. Todos isolados resistentes a RIF e INH eram pacientes com historia previa de tratamento para TB . Conclusao: Observa-se uma alta proporcao de coinfeccao HIV/TB entre pacientes portador de cepas do M. tuberculosis monorresistente ou MDR. Alem de alta taxa de monorresistencia a isoniazida, e a associacao entre resistencia concomitante a INH e Sm

https://www.revistas.usp.br/rmrp/article/download/122725/119210

Lee H. Harrison

Papers

Agent-based model of the impact of higher influenza vaccine efficacy on seasonal influenza burden

Longitudinal changes in age and race of patients with SARS-CoV-2 in a multi-hospital health system

Genomic characterization of SARS-CoV-2 from vaccine breakthrough cases in Allegheny County, Pennsylvania

Characterization of resistance profile of Mycobacterium tuberculosis isolates from patients with tuberculosis in Beira, Sofala - Mozambique