Willingness to Engage in Collective Action After the Killing of an Unarmed Black Man: Differential Pathways for Black and White Individuals

This cross-sectional survey study examined the underlying psychosocial constructs of Black (n = 163) and White (n = 246) university students\u27 willingness to endorse racially motivated collective action. Consistent with the defensive motivation system model, we expected the police shooting of an unarmed Black American to activate concerns about personal safety, thereby eliciting negative affect, lack of forgiveness of the perpetrator, and motivation to engage in collective action. This path model was expected for both Black and White participants, with stronger associations among Black participants. In the full model, Black participants identified more with the victim and indicated greater personal threat, which led to (1) more negative affect and greater endorsement of collective action and (2) greater avoidance of the shooter and greater endorsement of collective action. In the Black participants model, collective action was explained by identifying with the victim and feeling personally threatened. In the White participants model, collective action was explained by three pathways stemming from identifying with the victim and personal threat, including negative affect, seeking avoidance, and seeking revenge. The results indicate different mechanisms to explain Black and White individuals\u27 motivation to endorse collective action to prevent police-involved shootings of unarmed Black Americans

Charting the low-loss region in Electron Energy Loss Spectroscopy with machine learning

Exploiting the information provided by electron energy-loss spectroscopy (EELS) requires reliable access to the low-loss region where the zero-loss peak (ZLP) often overwhelms the contributions associated to inelastic scatterings off the specimen. Here we deploy machine learning techniques developed in particle physics to realise a model-independent, multidimensional determination of the ZLP with a faithful uncertainty estimate. This novel method is then applied to subtract the ZLP for EEL spectra acquired in flower-like WS$_2$ nanostructures characterised by a 2H/3R mixed polytypism. From the resulting subtracted spectra we determine the nature and value of the bandgap of polytypic WS$_2$, finding $E_{\rm BG} = 1.6_{-0.2}^{+0.3}\,{\rm eV}$ with a clear preference for an indirect bandgap. Further, we demonstrate how this method enables us to robustly identify excitonic transitions down to very small energy losses. Our approach has been implemented and made available in an open source Python package dubbed EELSfitter.Comment: 37 pages, 14 figures. The EELSfitter code is available from https://github.com/LHCfitNikhef/EELSfitte

Microsatellite instability and defects in mismatch repair proteins: a new aetiology for Sertoli cell‐only syndrome

Microsatellite instability is characteristic of certain types of cancer, and is present in rodents lacking specific DNA mismatch repair proteins. These azoospermic mice exhibit spermatogenic defects similar to some human testicular failure patients. Therefore, we hypothesized that microsatellite instability due to deficiencies in mismatch repair genes might be an unrecognized aetiology of human testicular failure. Because these azoospermic patients are candidates for testicular sperm extraction and ICSI, transmission of mismatch repair defects to the offspring is possible. Seven microsatellite loci were analysed for instability in specimens from 41 testicular failure patients and 20 controls. Blood and testicular DNA were extracted from patient and control specimens, and amplified by PCR targeting seven microsatellite loci. DNA fragment length was analysed with an ABI Prism 310 Genotyping Machine and GeneScan software. Immunohistochemistry was performed on paraffinized testis biopsy sections and cultured testicular fibroblasts from each patient to determine if expression of the mismatch repair proteins hMSH2 and hMLH1 was normal in both somatic and germline cells. Results demonstrate that microsatellite instability and DNA mismatch repair protein defects are present in some azoospermic men, predominantly in Sertoli cell‐only patients (P < 0.01 and P < 0.05 respectively). This provides evidence of a previously unrecognized aetiology of testicular failure that may be associated with cancer predispositio

Charting the low-loss region in Electron Energy Loss Spectroscopy with machine learning

Exploiting the information provided by electron energy-loss spectroscopy (EELS) requires reliable access to the low-loss region where the zero-loss peak (ZLP) often overwhelms the contributions associated to inelastic scatterings off the specimen. Here we deploy machine learning techniques developed in particle physics to realise a model-independent, multidimensional determination of the ZLP with a faithful uncertainty estimate. This novel method is then applied to subtract the ZLP for EEL spectra acquired in flower-like WS$_2$ nanostructures characterised by a 2H/3R mixed polytypism. From the resulting subtracted spectra we determine the nature and value of the bandgap of polytypic WS$_2$, finding $E_{\rm BG} = 1.6_{-0.2}^{+0.3}\,{\rm eV}$ with a clear preference for an indirect bandgap. Further, we demonstrate how this method enables us to robustly identify excitonic transitions down to very small energy losses. Our approach has been implemented and made available in an open source Python package dubbed EELSfitter

Deep learning prediction of proton and photon dose distributions for paediatric abdominal tumours

OBJECTIVE: Dose prediction using deep-learning networks prior to radiotherapy might lead to more efficient modality selections. The study goal was to predict proton and photon dose distributions based on the patient-specific anatomy and to assess their clinical usage for paediatric abdominal tumours. MATERIAL &METHODS: Data from 80 patients with neuroblastoma or Wilms' tumour was included. Pencil beam scanning (PBS) (5mm/3%) and volumetric-modulated arc therapy (VMAT) plans (5mm) were robustly optimized on the internal target volume (ITV). Separate 3-dimensional patch-based U-net networks were trained to predict PBS and VMAT dose distributions. Doses, planning-computed tomography images and relevant optimization masks (ITV, vertebra and organs-at-risk) of 60 patients were used for training with a 5-fold cross validation. The networks' performance was evaluated by computing the relative error between planned and predicted dose-volume histogram (DVH) parameters for 20 inference patients. In addition, the organs-at-risk mean dose difference between modalities was calculated using planned and predicted dose distributions (ΔDmean= DVMAT-DPBS). Two radiation oncologists performed a blind PBS/VMAT modality selection based on either planned or predicted ΔDmean. RESULTS: Average DVH differences between planned and predicted dose distributions were ≤|6%|for both modalities. The networks classified the organs-at-risk difference as a gain (ΔDmean>0) with 98% precision. An identical modality selection based on planned compared to predicted ΔDmean was made for 18/20 patients. CONCLUSION: Deep-learning networks for accurate prediction of proton and photon dose distributions for abdominal paediatric tumours were established. These networks allowing fast dose visualization might aid in identifying the optimal radiotherapy technique when experience and/or resources are unavailable

Fate specification and tissue-specific cell cycle control of the <i>Caenorhabditis elegans</i> intestine

Coordination between cell fate specification and cell cycle control in multicellular organisms is essential to regulate cell numbers in tissues and organs during development, and its failure may lead to oncogenesis. In mammalian cells, as part of a general cell cycle checkpoint mechanism, the F-box protein β-transducin repeat-containing protein (β-TrCP) and the Skp1/Cul1/F-box complex control the periodic cell cycle fluctuations in abundance of the CDC25A and B phosphatases. Here, we find that the Caenorhabditis elegans β-TrCP orthologue LIN-23 regulates a progressive decline of CDC-25.1 abundance over several embryonic cell cycles and specifies cell number of one tissue, the embryonic intestine. The negative regulation of CDC-25.1 abundance by LIN-23 may be developmentally controlled because CDC-25.1 accumulates over time within the developing germline, where LIN-23 is also present. Concurrent with the destabilization of CDC-25.1, LIN-23 displays a spatially dynamic behavior in the embryo, periodically entering a nuclear compartment where CDC-25.1 is abundant

Review - Late toxicity of abdominal and pelvic radiotherapy for childhood cancer

As survival improves in childhood cancer, prevention of late treatment-related toxicity in survivors becomes increasingly relevant. Radiotherapy is an important contributor to late toxicity. Therefore, minimizing radiation exposure to normal tissues is an important step towards improving the long-term therapeutic window of childhood cancer treatment. Since children are growing and developing, they are particularly vulnerable to radiation exposure. This makes the 'as low as reasonably achievable (ALARA)' principle even more important. In order to guide and achieve clinically meaningful dose reductions through advanced and emerging radiation techniques, it is important to investigate age-dependent relationships between radiation exposure to healthy tissues and late radiation-induced toxicity. In this review, we provide an overview of literature on the association between radiotherapy dose and late toxicity after abdominal and pelvic irradiation in childhood cancer. With this information, we aim to aid in decision-making regarding radiotherapy for childhood cancer. (c) 2022 The Author(s). Published by Elsevier B.V. Radiotherapy and Oncology 170 (2022) 27-36 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Long-term survivors of early breast cancer treated with chemotherapy are characterized by a pro-inflammatory biomarker profile compared to matched controls

Background: Chemo- and radiotherapy for breast cancer (BC) can lead to cardiotoxicity even years after the initial treatment. The pathophysiology behind these late cardiac effects is poorly understood. Therefore, we studied a large panel of biomarkers from different pathophysiological domains in long-term BC survivors, and compared these to matched controls. Methods and results: In total 91 biomarkers were measured in 688 subjects: 342 BC survivors stratified either to treatment with chemotherapy ± radiotherapy (n = 170) or radiotherapy alone (n = 172) and matched controls. Mean age was 59 ± 9 years and 65 ± 8 years for women treated with chemotherapy ± radiotherapy and radiotherapy alone, respectively, with a mean time since treatment of 11 ± 5.5 years. No biomarkers were differentially expressed in survivors treated with radiotherapy alone vs. controls (P for all >0.1). In sharp contrast, a total of 19 biomarkers were elevated, relative to controls, in BC survivors treated with chemotherapy ± radiotherapy after correction for multiple comparisons (P <0.05 for all). Network analysis revealed upregulation of pathways relating to collagen degradation and activation of matrix metalloproteinases. Furthermore, several inflammatory biomarkers including growth differentiation factor 15, monocyte chemoattractant protein 1, chemokine (C-X-C motif) ligand 16, tumour necrosis factor super family member 13b and proprotein convertase subtilisin/kexin type 9, elevated in survivors treated with chemotherapy, showed an independent association with lower left ventricular ejection fraction. Conclusion: Breast cancer survivors treated with chemotherapy ± radiotherapy show a distinct biomarker profile associated with mild cardiac dysfunction even 10 years after treatment. These results suggest that an ongoing pro-inflammatory state and activation of matrix metalloproteinases following initial treatment with chemotherapy might play a role in the observed cardiac dysfunction in late BC survivors