Tuning target selection algorithms to improve galaxy redshift estimates

We showcase machine learning (ML) inspired target selection algorithms to determine which of all potential targets should be selected first for spectroscopic follow up. Efficient target selection can improve the ML redshift uncertainties as calculated on an independent sample, while requiring less targets to be observed. We compare the ML targeting algorithms with the Sloan Digital Sky Survey (SDSS) target order, and with a random targeting algorithm. The ML inspired algorithms are constructed iteratively by estimating which of the remaining target galaxies will be most difficult for the machine learning methods to accurately estimate redshifts using the previously observed data. This is performed by predicting the expected redshift error and redshift offset (or bias) of all of the remaining target galaxies. We find that the predicted values of bias and error are accurate to better than 10-30% of the true values, even with only limited training sample sizes. We construct a hypothetical follow-up survey and find that some of the ML targeting algorithms are able to obtain the same redshift predictive power with 2-3 times less observing time, as compared to that of the SDSS, or random, target selection algorithms. The reduction in the required follow up resources could allow for a change to the follow-up strategy, for example by obtaining deeper spectroscopy, which could improve ML redshift estimates for deeper test data.Comment: 16 pages, 9 figures, updated to match MNRAS accepted version. Minor text changes, results unchange

Stacking for machine learning redshifts applied to SDSS galaxies

We present an analysis of a general machine learning technique called 'stacking' for the estimation of photometric redshifts. Stacking techniques can feed the photometric redshift estimate, as output by a base algorithm, back into the same algorithm as an additional input feature in a subsequent learning round. We shown how all tested base algorithms benefit from at least one additional stacking round (or layer). To demonstrate the benefit of stacking, we apply the method to both unsupervised machine learning techniques based on self-organising maps (SOMs), and supervised machine learning methods based on decision trees. We explore a range of stacking architectures, such as the number of layers and the number of base learners per layer. Finally we explore the effectiveness of stacking even when using a successful algorithm such as AdaBoost. We observe a significant improvement of between 1.9% and 21% on all computed metrics when stacking is applied to weak learners (such as SOMs and decision trees). When applied to strong learning algorithms (such as AdaBoost) the ratio of improvement shrinks, but still remains positive and is between 0.4% and 2.5% for the explored metrics and comes at almost no additional computational cost.Comment: 13 pages, 3 tables, 7 figures version accepted by MNRAS, minor text updates. Results and conclusions unchange

Anomaly detection for machine learning redshifts applied to SDSS galaxies

We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates. Anomalous training examples may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies with one or more poorly measured photometric quantity. We select 2.5 million 'clean' SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730 'anomalous' galaxies with spectroscopic redshift measurements which are flagged as unreliable. We contaminate the clean base galaxy sample with galaxies with unreliable redshifts and attempt to recover the contaminating galaxies using the Elliptical Envelope technique. We then train four machine learning architectures for redshift analysis on both the contaminated sample and on the preprocessed 'anomaly-removed' sample and measure redshift statistics on a clean validation sample generated without any preprocessing. We find an improvement on all measured statistics of up to 80% when training on the anomaly removed sample as compared with training on the contaminated sample for each of the machine learning routines explored. We further describe a method to estimate the contamination fraction of a base data sample.Comment: 13 pages, 8 figures, 1 table, minor text updates to macth MNRAS accepted versio

A molecular clone of Chronic Bee Paralysis Virus (CBPV) causes mortality in honey bee pupae (Apis mellifera)

: Among the many diseases compromising the well-being of the honey bee (Apis mellifera) the chronic paralysis syndrome of adult honey bees is one of the best described. The causative agent, chronic bee paralysis virus (CBPV), is a positive sense, single-stranded RNA virus with a segmented genome. Segment 1 encodes three putative open reading frames (ORFs), including the RNA-dependent RNA polymerase and other non-structural protein coding regions. Segment 2 encodes four putative ORFs, which contain the genes of supposed structural proteins. In this study, we established a reverse genetic system for CBPV by molecular cloning of DNA copies of both genome segments. CBPV rescue was studied in imago and honey bee pupae infection models. Virus replication and progeny virus production was only initiated when capped RNAs of both genome segments were injected in honey bees. As injection of these clonal RNAs caused clinical symptoms similar to wild-type CBPV infection, we conclude that the novel molecular clone fulfilled Koch's postulates. Our virus clone will enable in-depth analysis of CBPV pathogenesis and help to increase knowledge about this important honey bee disease

Comparison of PET/CT-based eligibility according to VISION and TheraP trial criteria in end-stage prostate cancer patients undergoing radioligand therapy

Background Two randomized clinical trials demonstrated the efficacy of prostate-specific membrane antigen (PSMA) radioligand therapy (PSMA RLT) in metastatic castration-resistant prostate cancer (mCRPC). While the VISION trial used criteria within PSMA PET/CT for inclusion, the TheraP trial used dual tracer imaging including FDG PET/CT. Therefore, we investigated whether the application of the VISION criteria leads to a benefit in overall survival (OS) or progression-free survival (PFS) for men with mCRPC after PSMA RLT. Methods Thirty-five men with mCRPC who had received PSMA RLT as a last-line option and who had undergone pretherapeutic imaging with FDG and [68Ga]Ga-PSMA I&T or [18F]PSMA-1007 were studied. Therapeutic eligibility was retrospectively evaluated using the VISION and TheraP study criteria. Results 26 of 35 (74%) treated patients fulfilled the VISION criteria (= VISION+) and only 17 of 35 (49%) fulfilled the TheraP criteria (= TheraP+). Significantly reduced OS and PFS after PSMA RLT was observed in patients rated VISION− compared to VISION+ (OS: VISION−: 3 vs. VISION+: 12 months, hazard ratio (HR) 3.1, 95% confidence interval (CI) 1.0–9.1, p < 0.01; PFS: VISION−: 1 vs. VISION+: 5 months, HR 2.7, 95% CI 1.0–7.8, p < 0.01). For patients rated TheraP−, no significant difference in OS but in PFS was observed compared to TheraP+ patients (OS: TheraP−: 5.5 vs. TheraP+: 11 months, HR 1.6, 95% CI 0.8–3.3, p = 0.2; PFS: TheraP−: 1 vs. TheraP+: 6 months, HR 2.2, 95% CI 1.0–4.5, p < 0.01). Conclusion Retrospective application of the inclusion criteria of the VISION study leads to a benefit in OS and PFS after PSMA RL, whereas TheraP criteria appear to be too strict in patients with end-stage prostate cancer. Thus, performing PSMA PET/CT including a contrast-enhanced CT as proposed in the VISION trial might be sufficient for treatment eligibility of end-stage prostate cancer patients

White matter microstructural changes in adolescent anorexia nervosa including an exploratory longitudinal study

AbstractBackgroundAnorexia nervosa (AN) often begins in adolescence, however, the understanding of the underlying pathophysiology at this developmentally important age is scarce, impeding early interventions. We used diffusion tensor imaging (DTI) to investigate microstructural white matter (WM) brain changes including an experimental longitudinal follow-up.MethodsWe acquired whole brain diffusion-weighted brain scans of 22 adolescent female hospitalized patients with AN at admission and nine patients longitudinally at discharge after weight rehabilitation. Patients (10–18years) were compared to 21 typically developing controls (TD). Tract-based spatial statistics (TBSS) were applied to compare fractional anisotropy (FA) across groups and time points. Associations between average FA values of the global WM skeleton and weight as well as illness duration parameters were analyzed by multiple linear regression.ResultsWe observed increased FA in bilateral frontal, parietal and temporal areas in AN patients at admission compared to TD. Higher FA of the global WM skeleton at admission was associated with faster weight loss prior to admission. Exploratory longitudinal analysis showed this FA increase to be partially normalized after weight rehabilitation.ConclusionsOur findings reveal a markedly different pattern of WM microstructural changes in adolescent AN compared to most previous results in adult AN. This could signify a different susceptibility and reaction to semi-starvation in the still developing brain of adolescents or a time-dependent pathomechanism differing with extend of chronicity. Higher FA at admission in adolescents with AN could point to WM fibers being packed together more closely

Filariasis of the Axilla in a Patient Returning from Travel Abroad: A Case Report

Background: The term filariasis comprises a group of parasitic infections caused by helminths belonging to different genera in the superfamily Filaroidea. The human parasites occur mainly in tropical and subtropical regions, but filariae are also found in temperate climates, where they can infect wild and domestic animals. Humans are rarely infected by these zoonotic parasites. Patients and Methods: A 55-year-old patient presented with a new-onset, subcutaneous, non-tender palpable mass in the right axilla. Ultrasonography showed a 1.3-cm, solid, singular encapsulated node. Sonography of the breast on both sides, axilla and lymphatic drainage on the left side, lymphatic drainage on the right side, and mammography on both sides were without pathological findings. The node was excised under local anesthesia as the patient refused minimal invasive biopsy. Results: On histopathological examination, the tail of a parasite of the group of filariae was found. The patient revealed that she had stayed in Africa and Malaysia for professional reasons. 6 months before the time of diagnosis, she had also suffered from a fever and poor general condition after a trip abroad. The patient was referred for further treatment to the Institute for Tropical Medicine at the University of Dusseldorf, where a treatment with ivermectin was conducted on the basis of positive staining with antibodies against filariae. Conclusion: Our case demonstrates the importance of interdisciplinary collaboration between breast center, pathology, and other specialties such as microbiology and tropical medicine

Immune Checkpoint Profiling in Humanized Breast Cancer Mice Revealed Cell-Specific LAG-3/PD-1/TIM-3 Co-Expression and Elevated PD-1/TIM-3 Secretion

Checkpoint blockade is particularly based on PD-1/PD-L1-inhibiting antibodies. However, an efficient immunological tumor defense can be blocked not only by PD-(L)1 but also by the presence of additional immune checkpoint molecules. Here, we investigated the co-expression of several immune checkpoint proteins and the soluble forms thereof (e.g., PD-1, TIM-3, LAG-3, PD-L1, PD-L2 and others) in humanized tumor mice (HTM) simultaneously harboring cell line-derived (JIMT-1, MDA-MB-231, MCF-7) or patient-derived breast cancer and a functional human immune system. We identified tumor-infiltrating T cells with a triple-positive PD-1, LAG-3 and TIM-3 phenotype. While PD-1 expression was increased in both the CD4 and CD8 T cells, TIM-3 was found to be upregulated particularly in the cytotoxic T cells in the MDA-MB-231-based HTM model. High levels of soluble TIM-3 and galectin-9 (a TIM-3 ligand) were detected in the serum. Surprisingly, soluble PD-L2, but only low levels of sPD-L1, were found in mice harboring PD-L1-positive tumors. Analysis of a dataset containing 3039 primary breast cancer samples on the R2 Genomics Analysis Platform revealed increased TIM-3, galectin-9 and LAG-3 expression, not only in triple-negative breast cancer but also in the HER2+ and hormone receptor-positive breast cancer subtypes. These data indicate that LAG-3 and TIM-3 represent additional key molecules within the breast cancer anti-immunity landscape

How can we measure endometriosis-associated pelvic pain?

Purpose: The aim of our work was to explore which of the most commonly used pain scales is best suited to assess treatment success in endometriosis therapy and, therefore, qualifies best to be used as primary endpoint for clinical studies in this indication. Methods: We compared patient‘s responses on the different pain scales Visual Analog Scale, Biberoglu and Behrman Score, and SF-36 Bodily Pain Subscale with the Clinical Global Impression score. Parametric and non-parametric correlation coefficients and effect sizes were calculated. Results: A total of 428 patients with endometriosis-associated pelvic pain from three studies were included in our analyses. Their mean age was 31.4±6.3years and their mean pain score on the visual analog scale was 58.1±21.9 at baseline. The highest correlation with the Clinical Global Impression score was observed for the visual analog scale followed by the B&B pelvic pain item. The highest effect sizes were found for dysmenorrhea and SF-36 bodily pain subscale followed by the visual analog scale. Conclusions: A general measure of endometriosis-related pain can be recommended as primary endpoint in clinical trials to assess painful symptoms of endometriosis. In addition, a disease-specific quality of life tool is recommended to help interpret impact on patients‘ daily activities

Neoadjuvant radiotherapy in ER+, HER2+, and triple-negative -specific breast cancer based humanized tumor mice enhances anti-PD-L1 treatment efficacy

Pre-operative radiation therapy is not currently integrated into the treatment protocols for breast cancer. However, transforming immunological “cold” breast cancers by neoadjuvant irradiation into their “hot” variants is supposed to elicit an endogenous tumor immune defense and, thus, enhance immunotherapy efficiency. We investigated cellular and immunological effects of sub-lethal, neoadjuvant irradiation of ER pos., HER2 pos., and triple-negative breast cancer subtypes in-vitro and in-vivo in humanized tumor mice (HTM). This mouse model is characterized by a human-like immune system and therefore facilitates detailed analysis of the mechanisms and efficiency of neoadjuvant, irradiation-induced “in-situ vaccination”, especially in the context of concurrently applied checkpoint therapy. Similar to clinical appearances, we observed a gradually increased immunogenicity from the luminal over the HER2-pos. to the triple negative subtype in HTM indicated by an increasing immune cell infiltration into the tumor tissue. Anti-PD-L1 therapy divided the HER2-pos. and triple negative HTM groups into responder and non-responder, while the luminal HTMs were basically irresponsive. Irradiation alone was effective in the HER2-pos. and luminal subtype-specific HTM and was supportive for overcoming irresponsiveness to single anti-PD-L1 treatment. The treatment success correlated with a significantly increased T cell proportion and PD-1 expression in the spleen. In all subtype-specific HTM combination therapy proved most effective in diminishing tumor growth, enhancing the immune response, and converted non-responder into responder during anti-PD-L1 therapy. In HTM, neoadjuvant irradiation reinforced anti-PD-L1 checkpoint treatment of breast cancer in a subtype –specific manner. According to the “bench to bedside” principle, this study offers a vital foundation for clinical translating the use of neoadjuvant irradiation in the context of checkpoint therapy