Naming Human Diseases: Ethical Principles of Curating Exclusive Substitute for Inopportune Nosology BackgroundIn the medical sphere, understanding naming conventions strengthen the integrity of naming human diseases remains nominal rather than substantial yet. Since the current nosology-based standard for human diseases could not offer a one-size-fits-all corrective mechanism, many idiomatic but flawed names frequently appear in scientific literature and news outlets at the cost of sociocultural impacts.

ObjectiveWe attempt to examine the ethical oversights of current naming practices and propose heuristic rationales and approaches to determine a pithy name instead of an inopportune nosology.

MethodsFirst, we examined the compiled global online news volumes and emotional tones on some inopportune nosology like German measles, Middle Eastern Respiratory Syndrome, Spanish flu, Hong Kong flu, and Huntingtons disease in the wake of COVID-19. Second, we prototypically scrutinize the lexical dynamics and pathological differentials of German measles and common synonyms by leveraging the capacity of the Google Books Ngram Corpus. Third, we demonstrated the empirical approaches to curate an exclusive substitute for an anachronistic nosology German measles based on deep learning models and post-hoc explanations.

ResultsThe infodemiological study shows that the public informed the offensive names with extremely negative tones in textual and visual narratives. The findings of the historiographical study indicate that many synonyms of German measles did not survive, while German measles became an anachronistic usage, and rubella has taken the dominant place since 1994. The PubMedBERT model could identify rubella as a potential substitution for German measles with the highest semantic similarity. The results of the semantic drift experiments further indicate that rubella tends to survive during the ebb and flow of semantic drift.

ConclusionsOur findings indicate that the nosological evolution of anachronistic names could result in sociocultural impacts without a corrective mechanism. To mitigate such impacts, we introduce some ethical principles for formulating an improved naming scheme. Based on deep learning models and post-hoc explanations, our illustrated experiments could provide hallmark references to the remedial mechanism of naming practices and pertinent credit allocations.
"Adopt a Microorganism" methodology enhances the discourse richness of high school students in both hybrid learning and remote education The Internet has changed the way teachers and students access information and build knowledge. The recent COVID-19 pandemic has created challenges for both teachers and students and a demand for new methodologies of remote learning. In the life sciences, mixing online content with practical activities represents an even greater challenge. In microbiology, the implementation of an active teaching methodology, the #Adopt project, based on the social network Facebook(R), represents an excellent option for connecting remote education with classroom activities. In 2020, the version applied in high school, "Adopt a Microorganism", was adapted to meet the demands of emergency remote education owing to the suppression of face-to-face activities caused by the pandemic. In the present study, we assessed how the change in methodology impacted the discourse richness of students from high school integrated with technical education in the Business Administration program of the Federal Institute of Sao Paulo, Sorocaba Campus. Three questionnaires related to the groups of microorganisms (Archaea, Bacteria, Virus, Fungi, and Protozoan) were applied. The students responses in the 2019 and 2020 classes were compared concerning content richness and multiplicity of concepts through the application of the Shannon diversity index, an approach that is generally used to assess biodiversity in different environments. The observed results suggest that remote learning provided students with a conceptual basis and richness of content equivalent to that achieved by students subjected to the hybrid teaching model. In conclusion, this study suggests that the #Adopt project methodology increases students discourse richness in microbiology even without face-to-face traditional classes.
Cellular feedback to organotelluranes displays modulation of antioxidant proteins gene expression Organotelluranes RT3 and RT4 are thiol reagents that induce mitochondrial transition pore (MTP) opening in a sensitive and insensitive manner to cyclosporin A. Although RT3 and RT4 promote glutathione depletion, paradoxically, they are also an efficient antioxidant for membrane lipids. These compounds antagonistic effects elicited the challenging question of how the gene expression of antioxidant enzymes would respond to treatment with these compounds. The influence of RT3 and RT4 on antioxidant enzyme expression was investigated in cultured aortic smooth muscle cells (ASMC). RT3 and RT4 promoted disruption of ionic calcium homeostasis, mitochondrial transmembrane potential ({Delta}{Psi}), and cell death in a dose-dependent manner. The cell death mechanisms responded qualitatively to the increase of the organotellurane concentration and changed from apoptosis to necrosis. RT3 and RT4 increased the expression of thioredoxin significantly. RT3 also increased the expression of glutaredoxin and glutathione peroxidase, slightly the catalase expression without significant effects on SOD expression. The results are consistent with GSH and protein thiol depletion and discussed based on the cell toxicity mechanism exhibited by these compounds.



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org.highwire.dtl.DTLVardef@52bb82org.highwire.dtl.DTLVardef@19476d9org.highwire.dtl.DTLVardef@6aa99org.highwire.dtl.DTLVardef@1aa605e_HPS_FORMAT_FIGEXP  M_FIG C_FIG The variant Senescence-Associated-Secretory-Phenotype induced by centrosome amplification constitutes a pathway that activates Hypoxia-Inducible-Factor-1α. The Senescence-Associated-Secretory Phenotype (SASP) promote paracrine invasion however may also suppress tumour growth, thus generating complex phenotypic outcomes. Although centrosome amplification can induce proliferation arrest, the subsequent fate of cells with centrosome amplification remains elusive. Here, we report that centrosome amplification induces a variant SASP, constituting a pathway activating paracrine invasion. The centrosome amplification SASP is non-canonical as it lacks detectable DNA damage or prominent NF-{kappa}B activation. Instead, involving Rac activation and reactive oxygen species production. Consequently, inducing hypoxia-inducible factor 1 and associated genes, which includes pro-migratory factors such as ANGPTL4. Since senescent cells also have poor fitness, it is tempting to speculate that centrosome amplification induced SASP is one explanation for why extra centrosomes promote malignancy in some experimental models but are neutral or inhibitory in others. Assessment and Optimization of the Interpretability of Machine Learning Models Applied to Transcriptomic Data Explainable artificial intelligence aims to interpret how the machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, the understandings of the applicability of these model explainers to biological data are still lacking. In this study, we comprehensively evaluated multiple explainers by interpreting pretrained models of predicting tissue types from transcriptomic data, and by identifying top contributing genes from each sample with the greatest impacts on model prediction. To improve the reproducibility and interpretability of results generated by model explainers, we proposed a series of optimization strategies for each explainer on two different model architectures of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN). We observed three groups of explainer and model architecture combinations with high reproducibility. Group II, which contains three model explainers on aggregated MLP models, identified top contributing genes in different tissues that exhibited tissue-specific manifestation and were potential cancer biomarkers. In summary, our work provides novel insights and guidance for exploring biological mechanisms using explainable machine learning models. Genetic analysis of DinG-family helicase YoaA and its interaction with replication clamp-loader protein HolC in E. coli The XP-D/DinG family of DNA helicases contribute to genomic stability in all three domains of life. We investigate here the role of one of these proteins,YoaA, of Escherichia coli. In E. coli,YoaA aids tolerance to the nucleoside azidothymidine (AZT), a DNA replication inhibitor and physically interacts with a subunit of the DNA polymerase III holoenzyme, HolC. We map the residues of YoaA required for HolC interaction to its C-terminus by yeast two-hybrid analysis. We propose that this interaction competes with HolCs interaction with HolD and the rest of the replisome;YoaA indeed inhibits growth when overexpressed, dependent on this interaction region. By gene fusions we show YoaA is repressed by LexA and induced in response to DNA damage as part of the SOS response. Induction of YoaA by AZT is biphasic with an immediate response after treatment and a slower response that peaks in the late log phase of growth. This growth-phase dependent induction by AZT is not blocked by lexA3 (Ind-), which normally negates its self-cleavage, implying another means to induce the DNA damage response that responds to the nutritional state of the cell. We propose that YoaA helicase activity increases access to the 3 nascent strand during replication; consistent with this,YoaA appears to aid removal of potential A-to-T transversion mutations in ndk mutants, which are prone to nucleotide misincorporation. YoaA and its paralog DinG also may initiate template-switching that leads to deletions between tandem repeats in DNA.

IMPORTANCEMaintaining genomic stability is crucial for all living organisms. Replication of DNA frequently encounters barriers that must be removed to complete genome duplication. Balancing DNA synthesis with its repair is critical and not entirely understood at a mechanistic level.The YoaA protein, studied here, is required for certain types of DNA repair and interacts in an alternative manner with proteins that catalyze DNA replication. YoaA is part of the well-studied LexA-regulated response to DNA damage, the SOS response. We describe an unusual feature of its regulation that promotes induction after DNA damage as the culture begins to experience starvation. Replication fork repair integrates both DNA damage and nutritional signals. We also show that YoaA affects genomic stability. Neuroinvasion and neurotropism by SARS-CoV-2 variants in the K18-hACE2 mouse Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) not only affects the respiratory tract but also causes neurological symptoms such as loss of smell and taste, headache, fatigue or severe cerebrovascular complications. Using transgenic mice expressing human angiotensin-converting enzyme 2 (hACE2) we investigated the spatiotemporal distribution and pathomorphological features in the CNS following intranasal infection with SARS-CoV-2 variants, also after prior influenza A virus infection. Apart from Omicron, we found all variants to frequently spread to and within the CNS. Infection was restricted to neurons and appeared to spread from the olfactory bulb mainly in basally orientated regions in the brain and into the spinal cord, independent of ACE2 expression and without evidence of neuronal cell death, axonal damage or demyelination. However, microglial activation, microgliosis and a mild macrophage and T cell dominated inflammatory response was consistently observed, accompanied by apoptotic death of endothelial, microglial and immune cells, without their apparent infection. Microgliosis and immune cell apoptosis indicate a potential role of microglia for pathogenesis and viral effect in COVID-19 and possible impairment of neurological functions, especially in long COVID. These data may also be informative for the selection of therapeutic candidates, and broadly support investigation of agents with adequate penetration into relevant regions of the CNS. Sperm Histone H3 Lysine 4 tri-methylation serves as a metabolic sensor of paternal obesity and is associated with the inheritance of metabolic dysfunction ObjectiveParental environmental exposures can strongly influence descendant risks for adult disease. How paternal obesity changes the sperm chromatin leading to the acquisition of metabolic disease in offspring remains controversial and ill-defined. The objective of this study was to assess: (1) whether obesity induced by a high-fat diet alters sperm histone methylation; (2) whether paternal obesity can induce metabolic disturbances across generations; (3) whether there could be cumulative damage to the sperm epigenome leading to enhanced metabolic dysfunction in descendants; and (4) whether obesity-sensitive regions associate with embryonic epigenetic and transcriptomic profiles. Using a genetic mouse model of epigenetic inheritance, we investigated the role of histone H3 lysine 4 methylation (H3K4me3) in the paternal transmission of metabolic dysfunction. This transgenic mouse overexpresses the histone demethylase enzyme KDM1A in the developing germline and has an altered sperm epigenome at the level of histone H3K4 methylation. We hypothesized that challenging transgenic sires with a high-fat diet would further erode the sperm epigenome and lead to enhanced metabolic disturbances in the next generations.

MethodsTo assess whether paternal obesity can have inter- or transgenerational impacts, and if so, to identify potential mechanisms of this non-genetic inheritance, we used wildtype C57BL/6NCrl and transgenic males with a pre-existing altered sperm epigenome. To induce obesity, sires were fed either a control or high-fat diet (10% or 60% kcal fat, respectively) for 10-12 weeks, then bred to wildtype C57BL/6NCrl female fed a regular diet. F1 and F2 descendants were characterized for metabolic phenotypes by examining the effects of paternal obesity by sex, on body weight, fat mass distribution, the liver transcriptome, intraperitoneal glucose and insulin tolerance tests. To determine whether obesity altered the F0 sperm chromatin, native chromatin immunoprecipitation-sequencing targeting H3K4me3 was performed. To gain insight into mechanisms of paternal transmission, we compared our sperm H3K4me3 profiles with embryonic and placental chromatin states, histone modification and gene expression profiles.

ResultsObesity-induced alterations in H3K4me3 occurred at genes implicated in metabolic, inflammatory, and developmental processes. These processes were associated with offspring metabolic dysfunction and corresponded to genes enriched for H3K4me3 in embryos, and overlapped embryonic and placenta gene expression profiles. Transgenerational susceptibility to metabolic disease was only observed when obese F0 had a pre-existing modified sperm epigenome. This coincided with increased H3K4me3 alterations in sperm and more severe phenotypes affecting their offspring.

ConclusionsOur data suggest sperm H3K4me3 might serve as a metabolic sensor that connects paternal diet with offspring phenotypes via the placenta. This non-DNA based knowledge of inheritance has the potential to improve our understanding of how environment shapes heritability and may lead to novel routes for the prevention of disease. This study highlights the need to further study the connection between the sperm epigenome, placental development and childrens health. Predicting microbiome compositions from species assemblages through deep learning. Microbes can form complex communities that perform critical functions in maintaining the integrity of their environment or their hosts well-being. Rationally managing these microbial communities requires improving our ability to predict how different species assemblages affect the final species composition of the community. However, making such a prediction remains challenging because of our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. To overcome this challenge, here we present a deep learning framework that automatically learns the map between species assemblages and community compositions from training data only, without knowledge of any of the above processes. First, we systematically validate our framework using synthetic data generated by classical population dynamics models. Then, we apply it to experimental data of both in vitro and in vivo communities, including ocean and soil microbial communities, Drosophila melanogaster gut microbiota, and human gut and oral microbiota. In particular, we show how our framework learns to perform accurate out-of-sample predictions of complex community compositions from a small number of training samples. Our results demonstrate how deep learning can enable us to understand better and potentially manage complex microbial communities. Engineering of a fluorescent chemogenetic reporter with tunable color for advanced live-cell imaging Biocompatible fluorescent reporters with spectral properties spanning the entire visible spectrum are indispensable tools for imaging the biochemistry of living cells and organisms in real time. Here, we present the engineering of a fluorescent chemogenetic reporter with tunable optical and spectral properties. A collection of live-cell compatible fluorogenic chromophores with various electronic properties enables to generate bimolecular fluorescent assemblies that cover the visible spectrum from blue to red using a single protein tag engineered and optimized by directed evolution and rational design. We showed that the ability to tune the fluorescence color and properties through simple molecular modulation provides an unprecedent experimental versatility for imaging proteins in live cells, including delicate cultured hippocampal neurons, and in multicellular organisms. The ability to tune the spectral properties and fluorescence performance enables to match the spectral specifications and requirements of the most advanced imaging techniques, and allowed us to achieve efficient stimulated emission depletion (STED) nanoscopy of fusion proteins in live cells and live primary cultured neurons. Morphology of immatures of the thelytokous ant, Monomorium triviale Wheeler (Formicidae: Myrmicinae: Solenopsidini) with descriptions of the extraordinary last-instar queen larvae The ant genus Monomorium is one of the most species-rich but taxonomically problematic groups in the hyperdiverse subfamily Myrmicinae. An East Asian species, M. triviale Wheeler, produces both reproductive queens and sterile workers via obligate thelytokous parthenogenesis. Here, we describe the immature forms of M. triviale based on light and scanning electron microscopy observations, with a note on the striking caste dimorphism in the last larval instar. The last-instar queen larvae were easily recognized by their large size, "aphaenogastroid" body shape, and rows of doorknob-like tubercles on the lateral and dorsal body surface. This type of queen-specific structure has not been found in ants in general, let alone congeneric species found in Japan. In stark contrast to the queen larvae, worker larvae showed a "pheidoloid" body shape and a body surface similar to other ants. The worker larvae were estimated to have three instars, consistent with previously described congeners. The pupae of both castes had no cocoon, a characteristic commonly described in other Myrmicinae species. In total, the developmental period from egg to adult worker averaged 59 days under 25{degrees}C. We discuss possible functions of the tubercles of queen larvae based on previous studies. Dramatic consequences of reducing erythrocyte membrane cholesterol on Plasmodium falciparum Cholesterol is the most abundant lipid in the erythrocyte. During its blood stage development, the malaria parasite establishes an active cholesterol gradient across the various membrane systems within the infected erythrocyte. Interestingly, some antimalarial compounds have recently been shown to disrupt cholesterol homeostasis in intraerythrocytic stages of Plasmodium falciparum. These studies point to the importance of cholesterol for parasite growth. Previously, reduction of cholesterol from the erythrocyte membrane by treatment with methyl-{beta}-cyclodextrin (M{beta}CD) was shown to inhibit parasite invasion and growth. In addition, M{beta}CD treatment of trophozoite stage P. falciparum was shown to result in parasite expulsion from the host cell. We have revisited these phenomena by using live video microscopy, ultrastructural analysis, and response to antimalarial compounds. By using time-lapse video microscopy of fluorescently tagged parasites, we show that M{beta}CD treatment for just 30 min causes dramatic expulsion of the trophozoite stage parasites. This forceful expulsion occurs within 10 sec. Remarkably, the plasma membrane of the host cell from which the parasite has been expelled does not appear to be compromised. The parasitophorous vacuolar membrane (PVM) continued to surround the extruded parasite, but the PVM appeared damaged. Treatment with antimalarial compounds targeting PfATP4 or PfNCR1 prevented M{beta}CD-mediated extrusion of the parasites, pointing to a potential role of cholesterol dynamics underlying the expulsion phenomena. We also confirmed the essential role of erythrocyte plasma membrane cholesterol for invasion and growth of P. falciparum. This defect can be partially complemented by cholesterol and desmosterol but not with epicholesterol, revealing stereospecificity underlying cholesterol function. Overall, our studies advance previous observations and reveal unusual cell biological features underlying cholesterol depletion of infected erythrocyte plasma membrane.

ImportanceMalaria remains a major challenge in much of the world. Symptoms of malaria are caused by the growth of parasites belonging to Plasmodium spp. inside the red blood cells (RBC), leading to their destruction. The parasite depends upon its host for much of its nutritional need. Cholesterol is a major lipid in the RBC plasma membrane, which is the only source of this lipid for malarial parasites. We have previously shown that certain new antimalarial compounds disrupt cholesterol homeostasis in P. falciparum. Here we use live time-lapse video microscopy to show dramatic expulsion of the parasite from the host RBC when the cholesterol content of the RBC is reduced. Remarkably, this expulsion is inhibited by the antimalarials that disrupt lipid homeostasis. We also show stereospecificity of cholesterol in supporting parasite growth inside RBC. Overall, these results point to a critical role of cholesterol in physiology of malaria parasites. Simultaneous Detection of Dopamine and Serotonin with carbon-based electrodes Graphite-based materials, like pyrolyzed carbon electrodes, are widely used as implantable electrochemical sensors, for the detection of neurotransmitters, neuromodulators, and gaseous species, thanks to their strong mechanical properties, superior electron-transfer kinetics, and in-vivo stability. Electrochemical properties of graphite can be improved by coating them with carbon nanotubes (CNTs) which improves sensitivity, selectivity, and resistance to biofouling. Although several types of electrodes have been developed to detect biologically relevant targets like monoamines, multiplexed sensing of dopamine and serotonin is not yet widely available. Herein we introduce pyrolyzed carbon electrodes coated with CNTs for fast scan cyclic voltammetry for simultaneous detection of dopamine and serotonin with a sensitivity of 52{+/-}8nA/M and 5{+/-}17nA/M, respectively. Serotonin shows a broad oxidation peak at 0.68V{+/-}0.12V. When dopamine and serotonin are probed simultaneously at 10 Hz, dopamine oxidizes at 0.1V{+/-} 0.1 and serotonin oxidizes at 0.78V{+/-}0.12 and dopamine reduces at -0.35V{+/-}0.1 and serotonin at 0.1V{+/-}0.2 V. Thus the sensors shows discrimination between dopamine and serotonin and are suitable for simultaneous detection of these monoamines. A Hierarchy of Biomolecular Proportional-Integral-Derivative Feedback Controllers for Robust Perfect Adaptation and Dynamic Performance Proportional-Integral-Derivative (PID) feedback controllers have been the most widely used controllers in industry for almost a century due to their good performance, simplicity, and ease of tuning. Motivated by their success in various engineering disciplines, PID controllers recently found their way into synthetic biology, where the design of feedback molecular control systems has been identified as an important goal. In this paper, we consider the mathematical realization of PID controllers via biomolecular interactions. We propose an array of topologies that offer a compromise between simplicity and high performance. We first demonstrate that different Proportional-Integral (PI) controllers exhibit different capabilities for enhancing the dynamics and reducing variance (cell-to-cell variability). Next, we introduce several derivative controllers that are realized based on incoherent feedforward loops acting in a feedback configuration. Alternatively, we show that differentiators can be realized by placing molecular integrators in a negative feedback loop--an arrangement that can then be augmented by PI components to yield PID feedback controllers. We demonstrate that the derivative component can be exploited for enhancing system stability, dramatically increasing the molecular control systems dynamic performance, and for reducing the noise effect on the output of interest. The PID controller features are established through various deterministic and stochastic analyses as well as numerical simulations. Finally, we provide an experimental demonstration using a recently developed hybrid setup, the cyberloop, where the controller is implemented in silico to control a biological genetic circuit in vivo. The large array of novel biomolecular PID controllers introduced here forms a basis for the design and construction of advanced high-performance biomolecular control systems that robustly regulate the dynamics of living systems. Platelet transcriptome yields progressive markers in chronic myeloproliferative neoplasms and identifies putative targets of therapy Predicting disease progression remains a particularly challenging endeavor in chronic degenerative disorders and cancer, thus limiting early detection, risk stratification, and preventive interventions. Here, profiling the spectrum of chronic myeloproliferative neoplasms (MPNs) as a model, we identify the blood platelet transcriptome as a proxy for highly sensitive progression biomarkers that also enables prediction of advanced disease via machine learning algorithms. Using RNA sequencing (RNA-seq), we derive disease-relevant gene expression in purified platelets from 120 peripheral blood samples constituting two time-separated cohorts of patients diagnosed with one of three MPN subtypes at sample acquisition - essential thrombocythemia, ET (n=24), polycythemia vera, PV (n=33), and primary or post ET/PV secondary myelofibrosis, MF (n=42), and healthy donors (n=21). The MPN platelet transcriptome reveals an incremental molecular reprogramming that is independent of patient driver mutation status or therapy and discriminates each clinical phenotype. Leveraging this dataset that shows a characteristic progressive expression gradient across MPN, we develop a machine learning model (Lasso-penalized regression) and predict advanced subtype MF at high accuracy and under two conditions of external validation: i) temporal: our two Stanford cohorts, AUC-ROC of 0.96; and ii) geographical: independently published data of an additional n=25 MF and n=46 healthy donors, AUC-ROC of 0.97). Lasso-derived signatures offer a robust core set of < 5 MPN transcriptome markers that are progressive in expression. Mechanistic insights from our data highlight impaired protein homeostasis as a prominent driver of MPN evolution, with persistent integrated stress response. We also identify JAK inhibitor-specific signatures and other interferon, proliferation, and proteostasis-associated markers as putative targets for MPN-directed therapy. Our platelet transcriptome snapshot of chronic MPNs demonstrates a proof of principle for disease risk stratification and progression beyond genetic data alone, with potential utility in other progressive disorders.

HighlightsLeveraging two independent and mutually validating MPN patient cohorts, we identify progressive transcriptomic markers that also enable externally validated prediction in MPNs.

Our platelet RNA-Seq data identifies impaired protein homeostasis as prominent in MPN progression and offers putative targets of therapy.

VISUAL ABSTRACT

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org.highwire.dtl.DTLVardef@72dc16org.highwire.dtl.DTLVardef@cf5096org.highwire.dtl.DTLVardef@b3d460org.highwire.dtl.DTLVardef@3c0ddc_HPS_FORMAT_FIGEXP  M_FIG C_FIG Heterologous vaccination regimens with self-amplifying RNA and Adenoviral COVID vaccines induce robust immune responses in mice Several vaccines have demonstrated efficacy against SARS-CoV-2 mediated disease, yet there is limited data on the immune response induced by heterologous vaccination regimens using alternate vaccine modalities. Here, we present a detailed description of the immune response, in mice, following vaccination with a self-amplifying RNA (saRNA) vaccine and an adenoviral vectored vaccine (ChAdOx1 nCoV-19/AZD1222) against SARS-CoV-2. We demonstrate that antibody responses are higher in two dose heterologous vaccination regimens than single dose regimens. Neutralising titres after heterologous prime-boost were at least comparable or higher than the titres measured after homologous prime boost vaccination with viral vectors. Importantly, the cellular immune response after a heterologous regimen is dominated by cytotoxic T cells and Th1+ CD4 T cells which is superior to the response induced in homologous vaccination regimens in mice. These results underpin the need for clinical trials to investigate the immunogenicity of heterologous regimens with alternate vaccine technologies. An Evolutionary Process Without Variation and Selection Natural selection successfully explains how organisms accumulate adaptive change despite that traits acquired over a lifetime are eliminated at the end of each generation. However, in some domains that exhibit cumulative, adaptive change--e.g., cultural evolution, and earliest life--acquired traits are retained; these domains do not face the problem that Darwins theory was designed to solve. Lack of transmission of acquired traits occurs when germ cells are protected from environmental change, due to a self-assembly code used in two distinct ways: (i) actively interpreted during development to generate a soma, and (ii) passively copied without interpretation during reproduction to generate germ cells. Early life and cultural evolution appear not to involve a self-assembly code used in these two ways. We suggest that cumulative, adaptive change in these domains is due to a lower-fidelity evolutionary process, and model it using Reflexively Autocatalytic and Foodset-generated networks. We refer to this more primitive evolutionary process as Self-Other Reorganisation (SOR) because it involves internal self-organising and self-maintaining processes within entities, as well as interaction between entities. SOR encompasses learning but in general operates across groups. We discuss the relationship between SOR and Lamarckism, and illustrate a special case of SOR without variation. Deep learning can predict multi-omic biomarkers from routine pathology images: A systematic large-scale study We assessed the pan-cancer predictability of multi-omic biomarkers from haematoxylin and eosin (H&E)-stained whole slide images (WSI) using deep learning (DL) throughout a systematic study. A total of 13,443 DL models predicting 4,481 multi-omic biomarkers across 32 cancer types were trained and validated. The investigated biomarkers included a broad range of genetic, transcriptomic, proteomic, and metabolic alterations. Furthermore, established markers relevant for prognosis, molecular subtypes and clinical outcomes were included. Overall, we established the general feasibility of predicting multi-omic markers directly from routine histology images with DL across solid cancer types, where 50% of the models could perform at an area under the curve (AUC) of more than 0.633 (with 25% of the models having an AUC larger than 0.711). A wide range of biomarkers were detectable from routine histology images across all investigated cancer types, with a mean AUC of at least 0.62 in almost all malignancies. Strikingly, we observed that biomarker predictability was mostly consistent and not dependent on sample size and class ratio, suggesting a degree of true predictability inherent in histomorphology. Together, the results of our study show the potential of DL to predict a multitude of biomarkers across the omics spectrum using only routine slides. This paves the way for accelerating diagnosis and developing more precise treatments for cancer patients. Predictive coding across the left fronto-temporal hierarchy during language comprehension We used MEG and ERPs to track the time-course and localization of evoked activity produced by expected, unexpected plausible and implausible words during incremental language comprehension. We interpret the full pattern of evoked responses and functional connectivity within a hierarchical predictive coding framework in which evoked activity reflects residual information (error) that is passed up and down the cortical hierarchy. Between 300-500ms, the three conditions produced progressively larger responses within left temporal cortex (lexico-semantic prediction error), while implausible inputs produced a selectively enhanced response within left inferior frontal cortex (event-level prediction error). Between 600-1000ms, unexpected plausible words activated left inferior frontal and middle temporal cortices (feedback activity that induced top-down shifts of event-level and lexico-semantic representations), while highly implausible inputs activated posterior fusiform and medial temporal cortices, supporting orthographic reanalysis and new learning. Therefore, predictive coding may provide a unifying theory that links language comprehension to other domains of cognition. A multivariate view of cognitive performance reveals positive correlation in the Trinidadian Guppy (Poecilia reticulata) Cognitive variation is common among-individuals and can be consistent across time and context. From an evolutionary perspective, among-individual variation is important as a pre-requisite for natural selection and adaptive evolution. Selection is widely hypothesized to favor high cognitive performance but directional selection should erode variation over time, how then is cognitive variation maintained? As selection does not act on traits in isolation, covariance among specific cognitive traits and/or other aspects of phenotype (e.g. personality) could result in fitness trade-offs that are important in shaping evolutionary dynamics. Here we test this using Trinidadian guppies (Poecilia reticulata), using a multivariate approach by characterising the correlation structure among task-specific cognitive performance measures and a personality trait. We estimate the among-individual correlation matrix (ID) in performance across three cognitive tasks; colour association learning task; motor learning task; reversal learning task, and the personality trait  boldness measured as emergence time from a shelter. We found no support for trade-offs among performance in these tasks. Nor do we find evidence of hypothesised speed-accuracy trade-offs within the association learning task. Rather we find strong positive correlation structure in ID, with 57% of variation explained by the leading eigen vector. While noting that non-cognitive factors and assay composition may affect the structure of ID, we suggest our findings are consistent with the g-model of cognitive performance variation, in which a dominant axis of variation loads positively on all performance measures. Thus, we add to a growing body of support for general variation among individuals in animal cognitive ability. Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition The teeth of sharks famously form a series of transversely-organised files with conveyor-belt replacement that are borne directly on the jaw cartilages, in contrast to the dermal plate-borne dentition of bony fishes that undergoes site-specific replacement. A major obstacle in understanding how this system evolved is the poorly understood relationships of the earliest chondrichthyans and the profusion of morphologically and terminologically diverse bones, cartilages, splints and whorls that they possess. Here we use tomographic methods to investigate mandibular structures in several early branching  acanthodian-grade stem-chondrichthyans. We show that the dentigerous jaw bones of disparate genera of ischnacanthids are united by a common construction, being growing bones with non-shedding dentition. Mandibular splints, which support the ventro-lateral edge of the Meckels cartilage in some taxa, are formed from dermal bone and may be an acanthodid synapomorphy. We demonstrate that the teeth of Acanthodopsis are borne directly on the mandibular cartilage and that this taxon is deeply nested within an edentulous radiation, representing an unexpected independent origin of teeth. Many or even all of the range of unusual oral structures may be apomorphic, but they should nonetheless be considered when building hypotheses of tooth and jaw evolution, both in chondrichthyans and more broadly. Phenomics data processing: Extracting dose-response curve parameters from high-resolution temperature courses and repeated field-based wheat height measurements Temperature is a main driver of plant growth and development. New phenotyping tools enable quantifying the temperature response of hundreds of genotypes. Yet, for field-derived data, temperature response modeling bears flaws and pitfalls concerning the interpretation of derived parameters. In this study, climate data from five growing seasons with differing temperature distributions served as starting point for a growth simulation of wheat stem elongation, based on a four-parametric temperature response function (Wang-Engel) including all cardinal temperatures. In a novel approach, we re-extracted dose-responses from the simulation by combining high-resolution (hours) temperature courses with low-resolution (days) height data. The collection of such data is common in field phenotyping platforms. To take advantage of the lack of supra-optimal temperatures during the stem elongation, simpler (linear and asymptotic) models to predict temperature-response parameters were investigated. The asymptotic model extracted the base temperature of growth and the maximum absolute growth rate with high precision, whereas simpler, linear models failed to do so. Additionally, the asymptotic model provided a proxy estimate for the optimum temperature. However, when including seasonally changing cardinal temperatures, the prediction accuracy of the asymptotic model was strongly reduced. In a field study with three winter wheat varieties, significant differences were found for all three asymptotic dose-response curve parameters. We conclude that the asymptotic model based on high-resolution temperature courses is suitable to extract meaningful parameters from field-based data. Elucidating Microscopic Events Driven by GTP Hydrolysis Reaction in Ras-GAP System with Semi-reactive Molecular Dynamics Simulation: Alternative Role of Phosphate Binding Loop as Mechanical Energy Storage ATPase and GTPase have been widely found as chemical energy-mechanical work transducers, whereas the physicochemical mechanisms are not satisfactorily understood. We addressed the problem by examining John Ross conjecture that repulsive Coulomb interaction between ADP/GDP and inorganic phosphate (Pi) does the mechanical work upon the system. We effectively simulated the consequence of GTP hydrolysis reaction in a complex system of Rat sarcoma (Ras) and GTPase activation protein (GAP) in the framework of classical molecular dynamics by switching force field parameters between the reactant and product systems. We then observed ca. 5 kcal/mol raise of potential energy about the phosphate-binding loop (P-loop) in Ras protein, indicating that the mechanical work generated via the GTP hydrolysis is converted into the local interaction energy and stored in the P-loop. Interestingly, this local energy storage in the P-loop depends on neither impulsive nor consecutive collisions of GDP and Pi with P-loop. Instead, GTP-GDP conversion itself does work on the Ras system, elevating the potential energy. These observations encourage us to challenge a conjecture previously given by Ross. We assert that triphosphate nucleotide hydrolyses do mechanical work by producing emergent steric interaction accompanied with relaxation, namely, a shift of biomolecular system to non-equilibrium state on the reshaped potential energy landscape. Recalling the universality of the P-loop motif among GTPases and ATPases, the observations that we obtained through this study would progress physicochemical understanding for the operating principles of GTP/ATP hydrolysis-driven biological nano-machines. Outer hair cell electro-mechanical power conversion Outer hair cells are the cellular motors in the mammalian inner ear responsible for sensitive high-frequency hearing. Motor function requires expression of the protein prestin in the OHC lateral membrane and mechano-electrical transduction in the apical hair bundle. In the present report, electrical power consumption and mechanical power output are determined using previously published voltage clamp data from isolated OHCs and membrane patches. Results reveal that power output by the prestin-motor complex peaks at a best frequency much higher than implied by the low-pass character of nonlinear capacitance, and much higher than the whole-cell resistive-capacitive corner frequency. High frequency power output is enabled by a -90{degrees} shift in the phase of prestin-dependent electrical charge displacement, manifested electrically as emergence of imaginary-valued nonlinear capacitance. Synthesizing VERDICT maps from standard DWI data using GANs VERDICT maps have shown promising results in clinical settings discriminating normal from malignant tissue and identifying specific Gleason grades non-invasively. However, the quantitative estimation of VERDICT maps requires a specific diffusion-weighed imaging (DWI) acquisition. In this study we investigate the feasibility of synthesizing VERDICT maps from standard DWI data from multi-parametric (mp)- MRI by employing conditional generative adversarial networks (GANs). We use data from 67 patients who underwent both standard DWI-MRI and VERDICT MRI and rely on correlation analysis and mean squared error to quantitatively evaluate the quality of the synthetic VERDICT maps. Quantitative results show that the mean values of tumour areas in the synthetic and the real VERDICT maps were strongly correlated while qualitative results indicate that our method can generate realistic VERDICT maps that could supplement mp-MRI assessment for better diagnosis. The joint evolution of animal movement and competition strategies Competition typically takes place in a spatial context, but eco-evolutionary models rarely address the joint evolution of movement and competition strategies. Here we investigate a spatially explicit producer-scrounger model where consumers can either forage on a heterogeneous resource landscape or steal resource items from conspecifics (kleptoparasitism). We consider three scenarios: (1) a population of foragers in the absence of kleptoparasites; (2) a population of consumers that are either specialized on foraging or on kleptoparasitism; and (3) a population of individuals that can fine-tune their behavior by switching between foraging and kleptoparasitism depending on local conditions. By means of individual-based simulations, we study the joint evolution of movement and competition strategies, and we investigate the implications on the resource landscape and the distribution of consumers over this landscape. In all scenarios and for all parameters considered, movement and competition strategies evolved rapidly and consistently across replicate simulations. The evolved movement and resource exploitation patterns differ considerably across the three scenarios. For example, foragers are attracted by conspecifics in scenario (1), while they are repelled by conspecifics in scenario (2). Generally the movement strategies of kleptoparasites differ markedly from those of foragers, but even within each class of consumers polymorphisms emerge, corresponding to pronounced differences in movement patterns. In all scenarios, the distribution of consumers over resources differs substantially from  ideal free predictions. We show that this is related to the intrinsic difficulty of moving effectively on a depleted landscape with few reliable cues for movement. Our study emphasises the advantages of a mechanistic approach when studying competition in a spatial context, and suggests how evolutionary modelling can be integrated with current work in animal movement ecology.