Emotional engagements predict and enhance social cognition in young chimpanzees

Social cognition in infancy is evident in coordinated triadic engagements, that is, infants attending jointly with social partners and objects. Current evolutionary theories of primate social cognition tend to highlight species differences in cognition based on human-unique cooperative motives. We consider a developmental model in which engagement experiences produce differential outcomes. We conducted a 10-year-long study in which two groups of laboratory-raised chimpanzee infants were given quantifiably different engagement experiences. Joint attention, cooperativeness, affect, and different levels of cognition were measured in 5- to 12-month-old chimpanzees, and compared to outcomes derived from a normative human database. We found that joint attention skills significantly improved across development for all infants, but by 12 months, the humans significantly surpassed the chimpanzees. We found that cooperativeness was stable in the humans, but by 12 months, the chimpanzee group given enriched engagement experiences significantly surpassed the humans. Past engagement experiences and concurrent affect were significant unique predictors of both joint attention and cooperativeness in 5- to 12-month-old chimpanzees. When engagement experiences and concurrent affect were statistically controlled, joint attention and cooperation were not associated. We explain differential social cognition outcomes in terms of the significant influences of previous engagement experiences and affect, in addition to cognition. Our study highlights developmental processes that underpin the emergence of social cognition in support of evolutionary continuity

Only two out of five articles by New Zealand researchers are free-to-access: a multiple API study of access, citations, cost of Article Processing Charges (APC), and the potential to increase the proportion of open access

We studied journal articles published by researchers at all eight New Zealand universities in 2017 to determine how many were freely accessible on the web. We wrote software code to harvest data from multiple sources, code that we now share to enable others to reproduce our work on their own sample set. In May 2019, we ran our code to determine which of the 2017 articles were open at that time and by what method; where those articles would have incurred an Article Processing Charge (APC) we calculated the cost if those charges had been paid. Where articles were not freely available we determined whether the policies of publishers in each case would have allowed deposit in a non-commercial repository (Green open access). We also examined citation rates for different types of access. We found that, of our 2017 sample set, about two out of every five articles were freely accessible without payment or subscription (41%). Where research was explicitly said to be funded by New Zealand’s major research funding agencies, the proportion was slightly higher at 45%. Where open articles would have incurred an APC we estimated an average cost per article of USD1,682 (for publications where all articles require an APC, that is, Gold open access) and USD2,558 (where APC payment is optional, Hybrid open access) at a total estimated cost of USD1.45m. Of the paid options, Gold is by far more common for New Zealand researchers (82% Gold, 18% Hybrid). In terms of citations, our analysis aligned with previous studies that suggest a correlation between publications being freely accessible and, on balance, slightly higher rates of citation. This is not seen across all types of open access, however, with Diamond OA achieving the lowest rates. Where articles were not freely accessible we found that a very large majority of them (88% or 3089 publications) could have been legally deposited in an institutional repository. Similarly, only in a very small number of cases had a version deposited in the repository of a New Zealand university made the difference between the publication being freely accessible or not (125 publications). Given that most New Zealand researchers support research being open, there is clearly a large gap between belief and practice in New Zealand’s research ecosystem

Comprehensive molecular characterization of urachal adenocarcinoma reveals commonalities with colorectal cancer, including a hypermutable phenotype

Purpose Urachal adenocarcinoma is a rare type of primary bladder adenocarcinoma that comprises less than 1% of all bladder cancers. The low incidence of urachal adenocarcinomas does not allow for an evidence-based approach to therapy. Transcriptome profiling of urachal adenocarcinomas has not been previously reported.Wehypothesized that an in-depth molecular understanding of urachal adenocarcinoma would uncover rational therapeutic strategies. Patients and Methods We performed targeted exon sequencing and global transcriptome profiling of 12 urachal tumors to generate a comprehensive molecular portrait of urachal adenocarcinoma. A single patient with an MSH6 mutation was treated with the anti-programmed death-ligand 1 antibody, atezolizumab. Results Urachal adenocarcinoma closely resembles colorectal cancer at the level of RNA expression, which extends previous observations that urachal tumors harbor genomic alterations that are found in colorectal adenocarcinoma. A subset of tumors was found to have alterations in genes that are associated with microsatellite instability (MSH2 and MSH6) and hypermutation (POLE).Apatient with anMSH6mutation was treated withimmunecheckpoint blockade, which resulted in stable disease. Conclusion Because clinical trials are next to impossible for patients with rare tumors, precision oncology may be an important adjunct for treatment decisions. Our findings demonstrate that urachal adenocarcinomas molecularly resemble colorectal adenocarcinomas at the level ofRNA expression, are the first report, to our knowledge, of MSH2andMSH6mutations in this disease, and support the consideration of immune checkpoint blockade as a rational therapeutic treatment of this exceedingly rare tumor

SCISSOR: a framework for identifying structural changes in RNA transcripts

High-throughput sequencing protocols such as RNA-seq have made it possible to interrogate the sequence, structure and abundance of RNA transcripts at higher resolution than previous microarray and other molecular techniques. While many computational tools have been proposed for identifying mRNA variation through differential splicing/alternative exon usage, challenges in its analysis remain. Here, we propose a framework for unbiased and robust discovery of aberrant RNA transcript structures using short read sequencing data based on shape changes in an RNA-seq coverage profile. Shape changes in selecting sample outliers in RNA-seq, SCISSOR, is a series of procedures for transforming and normalizing base-level RNA sequencing coverage data in a transcript independent manner, followed by a statistical framework for its analysis (https://github.com/hyochoi/SCISSOR). The resulting high dimensional object is amenable to unsupervised screening of structural alterations across RNA-seq cohorts with nearly no assumption on the mutational mechanisms underlying abnormalities. This enables SCISSOR to independently recapture known variants such as splice site mutations in tumor suppressor genes as well as novel variants that are previously unrecognized or difficult to identify by any existing methods including recurrent alternate transcription start sites and recurrent complex deletions in 3′ UTRs

Tracking Odor Plumes in a Laminar Wind Field with Bio-Inspired Algorithms

We introduce a novel bio-inspired odor source localization algorithm (surge- cast) for environments with a main wind flow and compare it to two well-known algorithms. With all three algorithms, systematic experiments with real robots are carried out in a wind tunnel under laminar flow conditions. The algorithms are compared in terms of distance overhead when tracking the plume up to the source, but a variety of other experimental results and some theoretical considerations are provided as well. We conclude that the surge-cast algorithm yields significantly better performance than the casting algorithm, and slightly better performance than the surge-spiral algorithm

Scaling Tests of the Cross Section for Deeply Virtual Compton Scattering

We present the first measurements of the \vec{e}p->epg cross section in the deeply virtual Compton scattering (DVCS) regime and the valence quark region. The Q^2 dependence (from 1.5 to 2.3 GeV^2) of the helicity-dependent cross section indicates the twist-2 dominance of DVCS, proving that generalized parton distributions (GPDs) are accessible to experiment at moderate Q^2. The helicity-independent cross section is also measured at Q^2=2.3 GeV^2. We present the first model-independent measurement of linear combinations of GPDs and GPD integrals up to the twist-3 approximation.Comment: 5 pages, 4 figures, 2 tables. Text shortened for publication. References added. One figure remove

Measuring and understanding adherence in a home-based exercise intervention during chemotherapy for early breast cancer

Purpose: Ensuring and measuring adherence to prescribed exercise regimens are fundamental challenges in intervention studies to promote exercise in adults with cancer. This study reports exercise adherence in women who were asked to walk 150 min/week throughout chemotherapy treatment for early breast cancer. Participants were asked to wear a FitbitTM throughout their waking hours, and Fitbit steps were uploaded directly into study computers. Methods: Descriptive statistics are reported, and both unadjusted and multivariable linear regression models were used to assess associations between participant characteristics, breast cancer diagnosis, treatment, chemotherapy toxicities, and patient-reported symptoms with average Fitbit steps/week. Results: Of 127 women consented to the study, 100 had analyzable Fitbit data (79%); mean age was 48 and 31% were non-white. Mean walking steps were 3956 per day. Nineteen percent were fully adherent with the target of 6686 steps/day and an additional 24% were moderately adherent. In unadjusted analysis, baseline variables associated with fewer Fitbit steps were: non-white race (p = 0.012), high school education or less (p = 0.0005), higher body mass index (p = 0.0024), and never/almost never drinking alcohol (p = 0.0048). Physical activity variables associated with greater Fitbit steps were: pre-chemotherapy history of vigorous physical activity (p = 0.0091) and higher self-reported walking minutes/week (p < 0.001), and higher outcome expectations from exercise (p = 0.014). Higher baseline anxiety (p = 0.03) and higher number of chemotherapy-related symptoms rates “severe/very severe” (p = 0.012) were associated with fewer steps. In multivariable analysis, white race was associated with 12,146 greater Fitbit steps per week (p = 0.004), as was self-reported walking minutes prior to start of chemotherapy (p < 0.0001). Conclusions: Inexpensive commercial-grade activity trackers, with data uploaded directly into research computers, enable objective monitoring of home-based exercise interventions in adults diagnosed with cancer. Analysis of the association of walking steps with participant characteristics at baseline and toxicities during chemotherapy can identify reasons for low/non-adherence with prescribed exercise regimens

UNMASC: Tumor-only variant calling with unmatched normal controls

Despite years of progress, mutation detection in cancer samples continues to require significant manual review as a final step. Expert review is particularly challenging in cases where tumors are sequenced without matched normal control DNA. Attempts have been made to call somatic point mutations without a matched normal sample by removing well-known germline variants, utilizing unmatched normal controls, and constructing decision rules to classify sequencing errors and private germline variants. With budgetary constraints related to computational and sequencing costs, finding the appropriate number of controls is a crucial step to identifying somatic variants. Our approach utilizes public databases for canonical somatic variants as well as germline variants and leverages information gathered about nearby positions in the normal controls. Drawing from our cohort of targeted capture panel sequencing of tumor and normal samples with varying tumortypes and demographics, these served as a benchmark for our tumor-only variant calling pipeline to observe the relationship between our ability to correctly classify variants against a number of unmatched normals. With our benchmarked samples, approximately ten normal controls were needed to maintain 94% sensitivity, 99% specificity and 76% positive predictive value, far outperforming comparable methods. Our approach, called UNMASC, also serves as a supplement to traditional tumor with matched normal variant calling workflows and can potentially extend to other concerns arising from analyzing next generation sequencing data