Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

Orientational control of fimE expression in Escherichia coli.

Phase-variable expression of type 1 fimbriae is, in part, controlled by site-specific DNA inversion of the fim switch in Escherichia coli. Of the two fim recombinases (FimB and FimE) that catalyse the inversion reaction, FimE exhibits a strong bias for phase switching from the ON to the OFF orientation. The specificity associated with fimE is the result of two different mechanisms: (i) FimE exhibits a preference for the invertible element in the ON orientation as substrate for recombination; (ii) the invertible element in the OFF orientation acts in cis to inhibit recombinase activity (orientational control). We show here that the invertible element negatively regulates fimE, even though expression of a fimE-lacZYA transcriptional fusion is unaffected by orientational control. The fimE transcript extends into the invertible region and hence switch ON-specific and switch OFF-specific mRNA contain different sequences. Furthermore, we show that orientational control is suppressed by the insertion of a structured RNA (tRNA(Gly)) between fimE and the fim switch, indicating that the switch OFF-specific mRNA is inactivated by 3' to 5' degradation. Analysis of the fim switch reveals that it contains two inhibitory elements that exert orientational control independently

Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study

BackgroundData on patients with COVID-19 who have cancer are lacking. Here we characterise the outcomes of a cohort of patients with cancer and COVID-19 and identify potential prognostic factors for mortality and severe illness.MethodsIn this cohort study, we collected de-identified data on patients with active or previous malignancy, aged 18 years and older, with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from the USA, Canada, and Spain from the COVID-19 and Cancer Consortium (CCC19) database for whom baseline data were added between March 17 and April 16, 2020. We collected data on baseline clinical conditions, medications, cancer diagnosis and treatment, and COVID-19 disease course. The primary endpoint was all-cause mortality within 30 days of diagnosis of COVID-19. We assessed the association between the outcome and potential prognostic variables using logistic regression analyses, partially adjusted for age, sex, smoking status, and obesity. This study is registered with ClinicalTrials.gov, NCT04354701, and is ongoing.FindingsOf 1035 records entered into the CCC19 database during the study period, 928 patients met inclusion criteria for our analysis. Median age was 66 years (IQR 57-76), 279 (30%) were aged 75 years or older, and 468 (50%) patients were male. The most prevalent malignancies were breast (191 [21%]) and prostate (152 [16%]). 366 (39%) patients were on active anticancer treatment, and 396 (43%) had active (measurable) cancer. At analysis (May 7, 2020), 121 (13%) patients had died. In logistic regression analysis, independent factors associated with increased 30-day mortality, after partial adjustment, were: increased age (per 10 years; partially adjusted odds ratio 1·84, 95% CI 1·53-2·21), male sex (1·63, 1·07-2·48), smoking status (former smoker vs never smoked: 1·60, 1·03-2·47), number of comorbidities (two vs none: 4·50, 1·33-15·28), Eastern Cooperative Oncology Group performance status of 2 or higher (status of 2 vs 0 or 1: 3·89, 2·11-7·18), active cancer (progressing vs remission: 5·20, 2·77-9·77), and receipt of azithromycin plus hydroxychloroquine (vs treatment with neither: 2·93, 1·79-4·79; confounding by indication cannot be excluded). Compared with residence in the US-Northeast, residence in Canada (0·24, 0·07-0·84) or the US-Midwest (0·50, 0·28-0·90) were associated with decreased 30-day all-cause mortality. Race and ethnicity, obesity status, cancer type, type of anticancer therapy, and recent surgery were not associated with mortality.InterpretationAmong patients with cancer and COVID-19, 30-day all-cause mortality was high and associated with general risk factors and risk factors unique to patients with cancer. Longer follow-up is needed to better understand the effect of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments.FundingAmerican Cancer Society, National Institutes of Health, and Hope Foundation for Cancer Research

A Systematic Framework to Rapidly Obtain Data on Patients with Cancer and COVID-19: CCC19 Governance, Protocol, and Quality Assurance

When the COVID-19 pandemic began, formal frameworks to collect data about affected patients were lacking. The COVID-19 and Cancer Consortium (CCC19) was formed to collect granular data on patients with cancer and COVID-19 at scale and as rapidly as possible. CCC19 has grown from five initial institutions to 125 institutions with >400 collaborators. More than 5,000 cases with complete baseline data have been accrued. Future directions include increased electronic health record integration for direct data ingestion, expansion to additional domestic and international sites, more intentional patient involvement, and granular analyses of still-unanswered questions related to cancer subtypes and treatments. When the COVID-19 pandemic began, formal frameworks to collect data about affected patients were lacking. The COVID-19 and Cancer Consortium (CCC19) was formed to collect granular data on patients with cancer and COVID-19 at scale and as rapidly as possible. CCC19 has grown from five initial institutions to 125 institutions with >400 collaborators. More than 5,000 cases with complete baseline data have been accrued. Future directions include increased electronic health record integration for direct data ingestion, expansion to additional domestic and international sites, more intentional patient involvement, and granular analyses of still-unanswered questions related to cancer subtypes and treatments