Failing to fail: MUM effect and its implications in education

It is important to carry out the assessment of students correctly and in an unbiased way; but more importantly, the assessment decisions should also be conveyed to the students in an unbiased manner, even if the decisions are negative ones. But here lies the catch – many a times, assessors tend to shy away from conveying such negative decisions properly to the students due to various reasons. This article is an effort to identify such reasons and their implications in the education scenario.

Leptin-mediated suppression of food intake by conserved Glp1r-expressing neurons prevents obesity

The adipose-derived hormone leptin acts via its receptor (LepRb) in the brain to control energy balance. A previously unidentified population of GABAergic hypothalamic LepRb neurons plays key roles in the restraint of food intake and body weight by leptin. To identify markers for candidate populations of LepRb neurons in an unbiased manner, we performed single-nucleus RNA-sequencing of enriched mouse hypothalamic LepRb cells, as well as with total hypothalamic cells from multiple mammalian species. In addition to identifying known LepRb neuron types, this analysis identified several previously unrecognized populations of hypothalamic LepRb neurons. Many of these populations display strong conservation across species, including GABAergic Glp1r-expressing LepRb (LepRbGlp1r) neurons that express more Lepr and respond more robustly to exogenous leptin than other LepRb populations. Ablating LepRb from these cells provoked hyperphagic obesity without impairing energy expenditure. Conversely, reactivating LepRb in Glp1r-expressing cells decreased food intake and body weight in otherwise LepRb-null mice. Furthermore, LepRb reactivation in GABA neurons improved energy balance in LepRb-null mice, and this effect required the expression of LepRb in GABAergic Glp1r-expressing neurons. Thus, the conserved GABAergic LepRbGlp1r neuron population plays crucial roles in the control of food intake and body weight by leptin.

Insights into the changes in the proteome of Alzheimer disease elucidated by a meta-analysis

Mass spectrometry (MS)-based proteomics is a powerful tool to explore pathogenic changes of a disease in an unbiased manner and has been used extensively in Alzheimer disease (AD) research. Here, by performing a meta-analysis of high-quality proteomic studies, we address which pathological changes are observed consistently and therefore most likely are of great importance for AD pathogenesis. We retrieved datasets, comprising a total of 21,588 distinct proteins identified across 857 postmortem human samples, from ten studies using labeled or label-free MS approaches. Our meta-analysis findings showed significant alterations of 757 and 1,195 proteins in AD in the labeled and label-free datasets, respectively. Only 33 proteins, some of which were associated with synaptic signaling, had the same directional change across the individual studies. However, despite alterations in individual proteins being different between the labeled and the label-free datasets, several pathways related to synaptic signaling, oxidative phosphorylation, immune response and extracellular matrix were commonly dysregulated in AD. These pathways represent robust changes in the human AD brain and warrant further investigation.

Application of BigData technology to improve the efficiency of Arctic shelf fields development

Development processes in the Arctic zone require that a set of tasks related to the development or improvement of technologies, as well as to the optimization of project management methods be solved. It has been noted that in order to solve the tasks, fast updated Big Data is needed, the timely acquisition and processing of which will allow for unbiased assessment of the current situation, taking appropriate management decisions, and prompt adjusting as new factors arise. It has been concluded that the introduction of Big Data technology is considered to be the most efficient Industry 4.0 tool for geological survey, and data arrays on the state of exploration of the territories and the results of exploration drilling can serve as the basis for an information model of oil and gas exploration. It has also been found that the array accumulated by subsoil users in the course of scientific research makes it possible to significantly increase the state of exploration of the natural Arctic environment and assess in an unbiased manner the natural processes that occur in the areas of the northern seas. Based on the analysis of the collected data, to predict the state of the natural environment and further develop optimal technical and managerial solutions for the development of the Arctic fields is possible.

LiveCellMiner: A New Tool to Analyze Mitotic Progression

Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.

Identification of antimalarial targets of chloroquine by a combined deconvolution strategy of ABPP and MS-CETSA

Background Malaria is a devastating infectious disease that disproportionally threatens hundreds of millions of people in developing countries. In the history of anti-malaria campaign, chloroquine (CQ) has played an indispensable role, however, its mechanism of action (MoA) is not fully understood. Methods We used the approach of photo-affinity labeling (PAL) in the design of a chloroquine probe and developed a combined deconvolution strategy – activity-based protein profiling (ABPP) and mass spectrometry-coupled cellular thermal shift assay (MS-CESTA) – that identified the protein targets of chloroquine in an unbiased manner in this study. Results We developed a novel photo-affinity chloroquine analog probe (CQP), which retains the antimalarial activity in the nanomole range, and identified a total of 40 proteins that specifically interacted and photo-crosslinked with CQP, which was inhibited in the presence of excess CQ. Using MS-CETSA, we identified 83 candidate interacting proteins out of a total of 3375 measured parasite proteins. Together, we identified 8 proteins as the most potential hits which were commonly identified by both methods. Conclusions We found that CQ could disrupt glycolysis and energy metabolism of malarial parasites through direct binding with some of the key enzymes, a new mechanism that is different from its known inhibitory effect of hemozoin formation. This is the first report of identifying chloroquine antimalarial targets by a parallel usage of labeled (ABPP) and label-free (MS-CETSA) methods.

A Genome-Phenome Association study in native microbiomes identifies a mechanism for cytosine modification in DNA and RNA

Shotgun metagenomic sequencing is a powerful approach to study microbiomes in an unbiased manner and of increasing relevance for identifying novel enzymatic functions. However, the potential of metagenomics to relate from microbiome composition to function has thus far been underutilized. Here, we introduce the Metagenomics Genome-Phenome Association (MetaGPA) study framework, which allows linking genetic information in metagenomes with a dedicated functional phenotype. We applied MetaGPA to identify enzymes associated with cytosine modifications in environmental samples. From the 2365 genes that met our significance criteria, we confirm known pathways for cytosine modifications and proposed novel cytosine-modifying mechanisms. Specifically, we characterized and identified a novel nucleic acid modifying enzyme, 5-hydroxymethylcytosine carbamoyltransferase, that catalyzes the formation of a previously unknown cytosine modification, 5-carbamoyloxymethylcytosine, in DNA and RNA. Our work introduces MetaGPA as a novel and versatile tool for advancing functional metagenomics.

Evolution of SARS-CoV-2 Key Mutations in Vienna Detected by Large Scale Screening Program

Currently countries across the globe are preparing for the fourth wave of SARS-CoV-2 infections, which is mainly driven by the rapid spread of novel SARS-CoV-2 variants. Austria and, in particular, the capital city of Vienna, witnessed a disproportionally steep rise in SARS-CoV-2 infection rates during the last wave of infections. By the end of January 2021, the government of Vienna launched an innovative, state-wide SARS-CoV-2 screening program based on PCR analysis of self-collected mouthwash samples. More than 400,000 mouthwash samples were collected in Vienna during the third wave of infection from January to March 2021. All preanalytical and analytical steps were carried out in a highly standardized manner at a single certified testing center. SARS-CoV-2 specific PCR analysis revealed in these samples a positivity rate of 0.43%. The relative proportion of N501Y positive virus samples increased continually to 68% of weekly samples. Mutation K417N was detected only in three samples. With this study, we were able to map the temporal occurrence of SARS-CoV-2 variants in a highly unbiased manner. Positivity rates and variant prevalence rates in this study were lower than in other nationwide programs. The results presented in this study indicate that actual virus prevalence tends to be overestimated by surveillance programs such as results of cluster analysis or contact tracing programs.

Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning

Adrenocortical carcinoma (ACC) is a rare disease, associated with poor survival. Several “multiple-omics” studies characterizing ACC on a molecular level identified two different clusters correlating with patient survival (C1A and C1B). We here used the publicly available transcriptome data from the TCGA-ACC dataset (n = 79), applying machine learning (ML) methods to classify the ACC based on expression pattern in an unbiased manner. UMAP (uniform manifold approximation and projection)-based clustering resulted in two distinct groups, ACC-UMAP1 and ACC-UMAP2, that largely overlap with clusters C1B and C1A, respectively. However, subsequent use of random-forest-based learning revealed a set of new possible marker genes showing significant differential expression in the described clusters (e.g., SOAT1, EIF2A1). For validation purposes, we used a secondary dataset based on a previous study from our group, consisting of 4 normal adrenal glands and 52 benign and 7 malignant tumor samples. The results largely confirmed those obtained for the TCGA-ACC cohort. In addition, the ENSAT dataset showed a correlation between benign adrenocortical tumors and the good prognosis ACC cluster ACC-UMAP1/C1B. In conclusion, the use of ML approaches re-identified and redefined known prognostic ACC subgroups. On the other hand, the subsequent use of random-forest-based learning identified new possible prognostic marker genes for ACC.

Regulations Matter: Epistemic Monopoly, Domination, Patents, and the Public Interest

This paper argues that regulatory agencies have a responsibility to further the public interest when they determine the conditions under which new technological products may be commercialized. As a case study, this paper analyzes the US 9th Circuit Court’s ruling on the efforts of the US Environmental Protection Agency (EPA) to regulate an herbicide meant for use with seed that are genetically modified to be tolerant of the chemical. Using that case, it is argued that when regulatory agencies evaluate new technological products, they have an obligation to draw on data, analyses, and evaluations from a variety of credible epistemic sources, and not rely solely or even primarily on the technology developer. Otherwise, they create conditions for their own domination and that of the polity by the technology developer. Moreover, in the interest of advancing the public interest, regulatory agencies must evaluate new technologies in a substantively and procedurally unbiased manner.