What is with all the plastic garbage everywhere and where does it go?

Despite countless efforts in recycling, awareness, and limited legislation, plastic pollution has continued to spiral out of control due to massive production/use and the inability to naturally decompose. Plastic pollution ends up accumulating in the environment in places like sediment and surface water. Unfortunately, another reservoir for plastic pollution is local compost, or decayed organic matter used for agricultural processes. A substantial amount of micro- and macro- plastics has been collected from both the local compost and along and around roads in Valparaiso. Plastic waste was collected at numerous roadside locations throughout the Salt Creek watershed in the Porter County area; it was quantified and classified by both recycling numbers and via infrared (IR) spectroscopy. Compost samples were collected and analyzed in the same manner, this time also accounting for microplastics. The compost was sieved with two and five micron sieves. A solution of zinc chloride (1.4 g/mL) was utilized to separate the microplastics via density separation. This was then further processed via H2O2/UV advanced oxidation to eliminate excessive organic matter and to isolate the microplastics and microfibers further. The results will identify the various types of plastics and their magnitude in the local environment, in both the local compost and throughout the Salt Creek watershed of Porter County, Indiana. These results can establish knowledge of how different types and sizes of plastics migrate throughout the environment, and provide citizens with ways to reduce the garbage

What is with all the plastic garbage everywhere and where does it go?

Despite countless efforts in recycling, awareness, and limited legislation, plastic pollution has continued to spiral out of control due to massive production/use and the inability to naturally decompose. Plastic pollution ends up accumulating in the environment in places like sediment and surface water. Unfortunately, another reservoir for plastic pollution is local compost, or decayed organic matter used for agricultural processes. A substantial amount of micro- and macro- plastics has been collected from both the local compost and along and around roads in Valparaiso. Plastic waste was collected at numerous roadside locations throughout the Salt Creek watershed in the Porter County area; it was quantified and classified by both recycling numbers and via infrared (IR) spectroscopy. Compost samples were collected and analyzed in the same manner, this time also accounting for microplastics. The compost was sieved with two and five micron sieves. A solution of zinc chloride (1.4 g/mL) was utilized to separate the microplastics via density separation. This was then further processed via H2O2/UV advanced oxidation to eliminate excessive organic matter and to isolate the microplastics and microfibers further. The results will identify the various types of plastics and their magnitude in the local environment, in both the local compost and throughout the Salt Creek watershed of Porter County, Indiana. These results can establish knowledge of how different types and sizes of plastics migrate throughout the environment, and provide citizens with ways to reduce the garbage

Malaysia Airlines flight MH370 search data reveal geomorphology and seafloor processes in the remote southeast Indian Ocean

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Marine Geology 395 (2018): 301-319, doi:10.1016/j.margeo.2017.10.014.A high-resolution multibeam echosounder (MBES) dataset covering over 279,000 km2 was acquired in the southeastern Indian Ocean to assist the search for Malaysia Airlines Flight 370 (MH370) that disappeared on 8 March 2014. The data provided an essential geospatial framework for the search and is the first large-scale coverage of MBES data in this region. Here we report on geomorphic analyses of the new MBES data, including a comparison with the Global Seafloor Geomorphic Features Map (GSFM) that is based on coarser resolution satellite altimetry data, and the insights the new data provide into geological processes that have formed and are currently shaping this remote deepsea area. Our comparison between the new MBES bathymetric model and the latest global topographic/bathymetric model (SRTM15_plus) reveals that 62% of the satellite-derived data points for the study area are comparable with MBES measurements within the estimated vertical uncertainty of the SRTM15_plus model (± 100 m). However, > 38% of the SRTM15_plus depth estimates disagree with the MBES data by > 100 m, in places by up to 1900 m. The new MBES data show that abyssal plains and basins in the study area are significantly more rugged than their representation in the GSFM, with a 20% increase in the extent of hills and mountains. The new model also reveals four times more seamounts than presented in the GSFM, suggesting more of these features than previously estimated for the broader region. This is important considering the ecological significance of high-relief structures on the seabed, such as hosting high levels of biodiversity. Analyses of the new data also enabled sea knolls, fans, valleys, canyons, troughs, and holes to be identified, doubling the number of discrete features mapped. Importantly, mapping the study area using MBES data improves our understanding of the geological evolution of the region and reveals a range of modern sedimentary processes. For example, a large series of ridges extending over approximately 20% of the mapped area, in places capped by sea knolls, highlight the preserved seafloor spreading fabric and provide valuable insights into Southeast Indian Ridge seafloor spreading processes, especially volcanism. Rifting is also recorded along the Broken Ridge – Diamantina Escarpment, with rift blocks and well-bedded sedimentary bedrock outcrops discernible down to 2400 m water depth. Modern ocean floor sedimentary processes are documented by sediment mass transport features, especially along the northern margin of Broken Ridge, and in pockmarks (the finest-scale features mapped), which are numerous south of Diamantina Trench and appear to record gas and/or fluid discharge from underlying marine sediments. The new MBES data highlight the complexity of the search area and serve to demonstrate how little we know about the vast areas of the ocean that have not been mapped with MBES. The availability of high-resolution and accurate maps of the ocean floor can clearly provide new insights into the Earth's geological evolution, modern ocean floor processes, and the location of sites that are likely to have relatively high biodiversity

Survey and evaluation of hypertension machine learning research

Background: Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision‐making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results: The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension‐related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real‐time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions: Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption

“We come together as one…and hope for solidarity to live on”: On designing technologies for activism and the commemoration of lost lives

On the International Day to End Violence Against Sex Workers (IDEVASW), sex worker rights advocates and support services commemorate lives lost due to violence. In this paper we describe and reflect on a Feminist Participatory Action Research project that supported the activities of IDEVASW over two years in North East England. Working alongside a charity that provides services to women who are sex workers or have experienced sexual exploitation, we co-organised the first activist march on the day. As researchers and service providers, we present detailed reflections on the use of digital technologies during the public activist march, a private service for commemoration, and the development of a semi-public archive to collect experiences of the day. We develop three implications for the design of digital technologies for activism and the commemoration of lost lives: as catalysts for reflection and opportunities to layer experience

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

DNA polymerase ɛ and δ exonuclease domain mutations in endometrial cancer

Accurate duplication of DNA prior to cell division is essential to suppress mutagenesis and tumour development. The high fidelity of eukaryotic DNA replication is due to a combination of accurate incorporation of nucleotides into the nascent DNA strand by DNA polymerases, the recognition and removal of mispaired nucleotides (proofreading) by the exonuclease activity of DNA polymerases δ and ɛ, and post-replication surveillance and repair of newly synthesized DNA by the mismatch repair (MMR) apparatus. While the contribution of defective MMR to neoplasia is well recognized, evidence that faulty DNA polymerase activity is important in cancer development has been limited. We have recently shown that germline POLE and POLD1 exonuclease domain mutations (EDMs) predispose to colorectal cancer (CRC) and, in the latter case, to endometrial cancer (EC). Somatic POLE mutations also occur in 5–10% of sporadic CRCs and underlie a hypermutator, microsatellite-stable molecular phenotype. We hypothesized that sporadic ECs might also acquire somatic POLE and/or POLD1 mutations. Here, we have found that missense POLE EDMs with good evidence of pathogenic effects are present in 7% of a set of 173 endometrial cancers, although POLD1 EDMs are uncommon. The POLE mutations localized to highly conserved residues and were strongly predicted to affect proofreading. Consistent with this, POLE-mutant tumours were hypermutated, with a high frequency of base substitutions, and an especially large relative excess of G:C>T:A transversions. All POLE EDM tumours were microsatellite stable, suggesting that defects in either DNA proofreading or MMR provide alternative mechanisms to achieve genomic instability and tumourigenesis

Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution

Immune evasion is a hallmark of cancer. Losing the ability to present neoantigens through human leukocyte antigen (HLA) loss may facilitate immune evasion. However, the polymorphic nature of the locus has precluded accurate HLA copy-number analysis. Here, we present loss of heterozygosity in human leukocyte antigen (LOHHLA), a computational tool to determine HLA allele-specific copy number from sequencing data. Using LOHHLA, we find that HLA LOH occurs in 40% of non-small-cell lung cancers (NSCLCs) and is associated with a high subclonal neoantigen burden, APOBEC-mediated mutagenesis, upregulation of cytolytic activity, and PD-L1 positivity. The focal nature of HLA LOH alterations, their subclonal frequencies, enrichment in metastatic sites, and occurrence as parallel events suggests that HLA LOH is an immune escape mechanism that is subject to strong microenvironmental selection pressures later in tumor evolution. Characterizing HLA LOH with LOHHLA refines neoantigen prediction and may have implications for our understanding of resistance mechanisms and immunotherapeutic approaches targeting neoantigens. Video Abstract [Figure presented] Development of the bioinformatics tool LOHHLA allows precise measurement of allele-specific HLA copy number, improves the accuracy in neoantigen prediction, and uncovers insights into how immune escape contributes to tumor evolution in non-small-cell lung cancer