Presume It Not: True Causes in the Search for the Basis of Heredity

Kyle Stanford has recently given substance to the problem of unconceived alternatives, which challenges the reliability of inference to the best explanation (IBE) in remote domains of nature. Conjoined with the view that IBE is the central inferential tool at our disposal in investigating these domains, the problem of unconceived alternatives leads to scientific anti-realism. We argue that, at least within the biological community, scientists are now and have long been aware of the dangers of IBE. We re-analyze the nineteenth-century study of inheritance and development (Stanford’s case study) and extend it into the twentieth century, focusing in particular on both classical Mendelian genetics and the studies by Avery et al. on the chemical nature of the hereditary substance. Our extended case studies show the preference of the biological community for a different methodological standard: the vera causa ideal, which requires that purported causes be shown on non-explanatory grounds to exist and be competent to produce their effects. On this basis, we defend a prospective realism about the biological sciences

Presume It Not: True Causes in the Search for the Basis of Heredity

Kyle Stanford has recently given substance to the problem of unconceived alternatives, which challenges the reliability of inference to the best explanation (IBE) in remote domains of nature. Conjoined with the view that IBE is the central inferential tool at our disposal in investigating these domains, the problem of unconceived alternatives leads to scientific anti-realism. We argue that, at least within the biological community, scientists are now and have long been aware of the dangers of IBE. We re-analyze the nineteenth-century study of inheritance and development (Stanford’s case study) and extend it into the twentieth century, focusing in particular on both classical Mendelian genetics and the studies by Avery et al. on the chemical nature of the hereditary substance. Our extended case studies show the preference of the biological community for a different methodological standard: the vera causa ideal, which requires that purported causes be shown on non-explanatory grounds to exist and be competent to produce their effects. On this basis, we defend a prospective realism about the biological sciences

The Well-Being of a University: The Relationship Between Gratitude and Organizational Commitment on Faculty Members’ Intention to Stay

Retaining good employees can be difficult, and no organization is immune. This study investigated the relationship between gratitude and organizational commitment to determine if they can predict the intention to stay among university faculty. Specifically, this study analyzed gratitude, a construct that has not been researched much in the workplace. There was a significant relationship between gratitude on organizational commitment. Gratitude and organizational commitment were important predictors of intention to stay. Gratitude was supported as a moderator between organizational commitment and intention to stay. Organizations should incorporate positive reinforcements to express gratitude to increase organizational commitment and intent to stay

Selenite inhibits notch signaling in cells and mice

Selenium is an essential micronutrient with a wide range of biological effects in mammals. The inorganic form of selenium, selenite, is supplemented to relieve individuals with selenium deficiency and to alleviate associated symptoms. Additionally, physiological and supranutritional selenite have shown selectively higher affinity and toxicity towards cancer cells, highlighting their potential to serve as chemotherapeutic agents or adjuvants. At varying doses, selenite extensively regulates cellular signaling and modulates many cellular processes. In this study, we report the identification of Delta–Notch signaling as a previously uncharacterized selenite inhibited target. Our transcriptomic results in selenite treated primary mouse hepatocytes revealed that the transcription of Notch1, Notch2, Hes1, Maml1, Furin and c-Myc were all decreased following selenite treatment. We further showed that selenite can inhibit Notch1 expression in cultured MCF7 breast adenocarcinoma cells and HEPG2 liver carcinoma cells. In mice acutely treated with 2.5 mg/kg selenite via intraperitoneal injection, we found that Notch1 expression was drastically lowered in liver and kidney tissues by 90% and 70%, respectively. Combined, these results support selenite as a novel inhibitor of Notch signaling, and a plausible mechanism of inhibition has been proposed. This discovery highlights the potential value of selenite applied in a pathological context where Notch is a key drug target in diseases such as cancer, fibrosis, and neurodegenerative disorders

The material properties of a bacterial-derived biomolecular condensate tune biological function in natural and synthetic systems

Intracellular phase separation is emerging as a universal principle for organizing biochemical reactions in time and space. It remains incompletely resolved how biological function is encoded in these assemblies and whether this depends on their material state. The conserved intrinsically disordered protein PopZ forms condensates at the poles of the bacterium Caulobacter crescentus, which in turn orchestrate cell-cycle regulating signaling cascades. Here we show that the material properties of these condensates are determined by a balance between attractive and repulsive forces mediated by a helical oligomerization domain and an expanded disordered region, respectively. A series of PopZ mutants disrupting this balance results in condensates that span the material properties spectrum, from liquid to solid. A narrow range of condensate material properties supports proper cell division, linking emergent properties to organismal fitness. We use these insights to repurpose PopZ as a modular platform for generating tunable synthetic condensates in human cells

Situating dissemination and implementation sciences within and across the translational research spectrum

The efficient and effective movement of research into practice is acknowledged as crucial to improving population health and assuring return on investment in healthcare research. The National Center for Advancing Translational Science which sponsors Clinical and Translational Science Awards (CTSA) recognizes that dissemination and implementation (D&I) sciences have matured over the last 15 years and are central to its goals to shift academic health institutions to better align with this reality. In 2016, the CTSA Collaboration and Engagement Domain Task Force chartered a D&I Science Workgroup to explore the role of D&I sciences across the translational research spectrum. This special communication discusses the conceptual distinctions and purposes of dissemination, implementation, and translational sciences. We propose an integrated framework and provide real-world examples for articulating the role of D&I sciences within and across all of the translational research spectrum. The framework\u27s major proposition is that it situates D&I sciences as targeted sub-sciences of translational science to be used by CTSAs, and others, to identify and investigate coherent strategies for more routinely and proactively accelerating research translation. The framework highlights the importance of D&I thought leaders in extending D&I principles to all research stages

Status of the CRESST Dark Matter Search

The CRESST experiment aims for a detection of dark matter in the form of WIMPs. These particles are expected to scatter elastically off the nuclei of a target material, thereby depositing energy on the recoiling nucleus. CRESST uses scintillating CaWO4 crystals as such a target. The energy deposited by an interacting particle is primarily converted to phonons which are detected by transition edge sensors. In addition, a small fraction of the interaction energy is emitted from the crystals in the form of scintillation light which is measured in coincidence with the phonon signal by a separate cryogenic light detector for each target crystal. The ratio of light to phonon energy permits the discrimination between the nuclear recoils expected from WIMPs and events from radioactive backgrounds which primarily lead to electron recoils. CRESST has shown the success of this method in a commissioning run in 2007 and, since then, further investigated possibilities for an even better suppression of backgrounds. Here, we report on a new class of background events observed in the course of this work. The consequences of this observation are discussed and we present the current status of the experiment.Comment: Proceedings of the 13th International Workshop on Low Temperature Detectors, 4 pages, 3 figure

Composite CaWO4 Detectors for the CRESST-II Experiment

CRESST-II, standing for Cryogenic Rare Events Search with Superconducting Thermometers phase II, is an experiment searching for Dark Matter. In the LNGS facility in Gran Sasso, Italy, a cryogenic detector setup is operated in order to detect WIMPs by elastic scattering off nuclei, generating phononic lattice excitations and scintillation light. The thermometers used in the experiment consist of a tungsten thin-film structure evaporated onto the CaWO4 absorber crystal. The process of evaporation causes a decrease in the scintillation light output. This, together with the need of a big-scale detector production for the upcoming EURECA experiment lead to investigations for producing thermometers on smaller crystals which are glued onto the absorber crystal. In our Run 31 we tested composite detectors for the first time in the Gran Sasso setup. They seem to produce higher light yields as hoped and could provide an additional time based discrimination mechanism for low light yield clamp events.Comment: Proceedings of the Thirteenth International Workshop on Low Temperature Detectors 4 pages, 9 figure

Validation of automated artificial intelligence segmentation of optical coherence tomography images

PURPOSE To benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera. METHODS A convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results. RESULTS The CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders' compartmentalization was higher than the mean score for intra-grader group comparison. CONCLUSION The proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders

Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

Machine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization ('neural recording'). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications