Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis.

BACKGROUND: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality. OBJECTIVE: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data. METHODS: In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips. RESULTS: Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance. CONCLUSIONS: Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems.

Artificial Intelligence to Determine Fetal Sex.

OBJECTIVE: This proof-of-concept study assessed how confidently an artificial intelligence (AI) model can determine the sex of a fetus from an ultrasound image. STUDY DESIGN: Analysis was performed using 19,212 ultrasound image slices from a high-volume fetal sex determination practice. This dataset was split into a training set (11,769) and test set (7,443). A computer vision model was trained using a transfer learning approach with EfficientNetB4 architecture as base. The performance of the computer vision model was evaluated on the hold out test set. Accuracy, Cohen's Kappa and Multiclass Receiver Operating Characteristic area under the curve (AUC) were used to evaluate the performance of the model. RESULTS: The AI model achieved an Accuracy of 88.27% on the holdout test set and a Cohen's Kappa score 0.843. The ROC AUC score for Male was calculated to be 0.896, for Female a score of 0.897, for Unable to Assess a score of 0.916, and for Text Added a score of 0.981 was achieved. CONCLUSION: This novel AI model proved to have a high rate of fetal sex capture that could be of significant use in areas where ultrasound expertise is not readily available. KEY POINTS: · This is the first proof-of-concept AI model to determine fetal sex.. · This study adds to the growing research in ultrasound AI.. · Our findings demonstrate AI integration into obstetric care..

Routine Molecular Profiling in Both Resectable and Unresectable Pancreatic Adenocarcinoma: Relevance of Cytologic Samples.

BACKGROUND & AIMS: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease, for which it is crucial to promptly detect actionable and prognostic alterations to drive specific therapeutic decisions, regardless of tumor resectability status. Endoscopic ultrasonography-guided fine-needle aspiration (EUS-FNA) is of key importance for PDAC diagnosis and can contribute significantly to tumor molecular profiling. METHODS: Comprehensive genomic profile by targeted next-generation sequencing (NGS) was performed on 2 independent PDAC patient cohorts. Cohort 1 consisted of 77 patients with resectable PDAC for whom the histologic sample at the time of resection was available; for 56 patients cytologic specimens at the time of diagnosis also were obtained by EUS-FNA. Cohort 2 consisted of 20 patients with unresectable PDAC, for whom only the EUS-FNA cytologic sample was available. RESULTS: In cohort 1, a complete concordant mutational profile between the cytologic sample at diagnosis and the corresponding histologic specimen after surgery was observed in 88% of the cases, proving the ability to detect potential clinically relevant alterations in cytologic samples by NGS analysis. Notably, clinically actionable mutations were identified in 20% of patients. In cohort 2, comprehensive mutational profiling was obtained successfully for all samples. Consistent with the findings of cohort 1, KRAS, TP53, CDKN2A, and SMAD4 were the most altered genes. Most importantly, 15% of the patients harbored actionable mutations. CONCLUSIONS: Our findings show the feasibility of an NGS approach using both surgical specimens and cytologic samples. The model proposed in this study can be included successfully in the clinical setting for comprehensive molecular profiling of all PDAC patients irrespective of their surgical eligibility.

The Viability of an Artificial Intelligence/Machine Learning Prediction Model to Determine Candidates for Knee Arthroplasty.

BACKGROUND: The purpose of this study is to assess the viability of a knee arthroplasty prediction model using 3-view X-rays that helps determine if patients with knee pain are candidates for total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), or are not arthroplasty candidates. METHODS: Analysis was performed using radiographic and surgical data from a high-volume joint replacement practice. The dataset included 3 different X-ray views (anterior-posterior, lateral, and sunrise) for 2,767 patients along with information of whether that patient underwent an arthroplasty surgery (UKA or TKA) or not. This resulted in a dataset including 8,301 images from 2,707 patients. This dataset was then split into a training set (70%) and holdout test set (30%). A computer vision model was trained using a transfer learning approach. The performance of the computer vision model was evaluated on the holdout test set. Accuracy and multiclass receiver operating characteristic area under curve was used to evaluate the performance of the model. RESULTS: The artificial intelligence model achieved an accuracy of 87.8% on the holdout test set and a quadratic Cohen's kappa score of 0.811. The multiclass receiver operating characteristic area under curve score for TKA was calculated to be 0.97; for UKA a score of 0.96 and for No Surgery a score of 0.98 was achieved. An accuracy of 93.8% was achieved for predicting Surgery versus No Surgery and 88% for TKA versus not TKA was achieved. CONCLUSION: The artificial intelligence/machine learning model demonstrated viability for predicting which patients are candidates for a UKA, TKA, or no surgical intervention.

Data blinding for the nEDM experiment at PSI.

Psychological bias towards, or away from, prior measurements or theory predictions is an intrinsic threat to any data analysis. While various methods can be used to try to avoid such a bias, e.g. actively avoiding looking at the result, only data blinding is a traceable and trustworthy method that can circumvent the bias and convince a public audience that there is not even an accidental psychological bias. Data blinding is nowadays a standard practice in particle physics, but it is particularly difficult for experiments searching for the neutron electric dipole moment (nEDM), as several cross measurements, in particular of the magnetic field, create a self-consistent network into which it is hard to inject a false signal. We present an algorithm that modifies the data without influencing the experiment. Results of an automated analysis of the data are used to change the recorded spin state of a few neutrons within each measurement cycle. The flexible algorithm may be applied twice (or more) to the data, thus providing the option of sequentially applying various blinding offsets for separate analysis steps with independent teams. The subtle manner in which the data are modified allows one subsequently to adjust the algorithm and to produce a re-blinded data set without revealing the initial blinding offset. The method was designed for the 2015/2016 measurement campaign of the nEDM experiment at the Paul Scherrer Institute. However, it can be re-used with minor modification for the follow-up experiment n2EDM, and may be suitable for comparable projects elsewhere.

Measurement of the Permanent Electric Dipole Moment of the Neutron.

We present the result of an experiment to measure the electric dipole moment (EDM) of the neutron at the Paul Scherrer Institute using Ramsey's method of separated oscillating magnetic fields with ultracold neutrons. Our measurement stands in the long history of EDM experiments probing physics violating time-reversal invariance. The salient features of this experiment were the use of a ^{199}Hg comagnetometer and an array of optically pumped cesium vapor magnetometers to cancel and correct for magnetic-field changes. The statistical analysis was performed on blinded datasets by two separate groups, while the estimation of systematic effects profited from an unprecedented knowledge of the magnetic field. The measured value of the neutron EDM is d_{n}=(0.0±1.1_{stat}±0.2_{sys})×10^{-26} e.cm.

Characterization of a novel breast cancer cell line derived from a metastatic bone lesion of a breast cancer patient.

PURPOSE: We aimed to generate and characterize a novel cell line from a breast cancer bone metastasis to better study the progression of the disease. METHODS: The cell line, P7731, was derived from a metastatic bone lesion of a breast cancer patient and assessed for marker expression. P7731 was analyzed for DNA copy number variation, somatic mutations, and gene expression and was compared with the primary tumor. RESULTS: P7731 cells are negative for estrogen receptor alpha (ERα), progesterone receptor (PR), and HER2 (triple-negative); strongly express vimentin (100% of cells positive) and also express cytokeratins 8/18 and 19 but at lower frequencies. Flow cytometry indicates P7731 cells are predominantly CD44+/CD49f+/EpCAM-, consistent with a primitive, mesenchymal-like phenotype. The cell line is tumorigenic in immunocompromised mice. Exome sequencing identified a total of 45 and 76 somatic mutations in the primary tumor and cell line, respectively, of which 32 were identified in both samples and included mutations in known driver genes PIK3CA, TP53, and ARID1A. P7731 retains the DNA copy number alterations present in the matching primary tumor. Homozygous deletions detected in the cell line and in the primary tumor were found in regions containing three known (CDKN2A, CDKN2B, and CDKN1B) and 23 putative tumor suppressor genes. Cell line-specific gene amplification coupled with mRNA expression analysis revealed genes and pathways with potential pro-metastatic functions. CONCLUSION: This novel human breast cancer-bone metastasis cell line will be a useful model to study aspects of breast cancer biology, particularly metastasis-related changes from breast to bone.

Multidimensional phenotyping of breast cancer cell lines to guide preclinical research.

PURPOSE: Cell lines are extremely useful tools in breast cancer research. Their key benefits include a high degree of control over experimental variables and reproducibility. However, the advantages must be balanced against the limitations of modelling such a complex disease in vitro. Informed selection of cell line(s) for a given experiment now requires essential knowledge about molecular and phenotypic context in the culture dish. METHODS: We performed multidimensional profiling of 36 widely used breast cancer cell lines that were cultured under standardised conditions. Flow cytometry and digital immunohistochemistry were used to compare the expression of 14 classical breast cancer biomarkers related to intrinsic molecular profiles and differentiation states: EpCAM, CD24, CD49f, CD44, ER, AR, HER2, EGFR, E-cadherin, p53, vimentin, and cytokeratins 5, 8/18 and 19. RESULTS: This cell-by-cell analysis revealed striking heterogeneity within cultures of individual lines that would be otherwise obscured by analysing cell homogenates, particularly amongst the triple-negative lines. High levels of p53 protein, but not RNA, were associated with somatic mutations (p = 0.008). We also identified new subgroups using the nanoString PanCancer Pathways panel (730 transcripts representing 13 canonical cancer pathways). Unsupervised clustering identified five groups: luminal/HER2, immortalised ('normal'), claudin-low and two basal clusters, distinguished mostly by baseline expression of TGF-beta and PI3-kinase pathway genes. CONCLUSION: These features are compared with other published genotype and phenotype information in a user-friendly reference table to help guide selection of the most appropriate models for in vitro and in vivo studies, and as a framework for classifying new patient-derived cancer cell lines and xenografts.

Expression of MAGE-A and NY-ESO-1 cancer/testis antigens is enriched in triple-negative invasive breast cancers.

AIMS: A better understanding of the expression of cancer/testis antigens (CTAs) in breast cancer might enable the identification of new immunotherapy options, especially for triple-negative (TN) tumours, which lack expression of the conventional therapeutic targets oestrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. The aim of this study was to quantify the expression of MAGE-A and NY-ESO-1 CTAs in breast cancer, and relate this to known clinicopathological parameters. METHODS AND RESULTS: We surveyed MAGE-A and NY-ESO-1 expression in an unselected cohort of 367 breast tumours (of which 65 were TN), with accompanying clinical follow-up data, by using immunohistochemical analysis of tissue microarrays. Relevant to their potential as vaccine targets in breast cancer, MAGE-A was expressed in 13% of cases, and NY-ESO-1 in 3.8%, with the majority of tumours showing fairly homogeneous staining within individual tissue cores (~85% of cases with staining in >75% of tumour cells). Most NY-ESO-1-positive cases also expressed MAGE-A (P = 2.06 × 10-9 ), and both were strongly associated with the TN phenotype (P < 0.0001), with the most proliferative and poorly differentiated cases, in paticular, showing genomic instability. This was characterised by coexpression of c-Kit and TTK, and overexpression of p53. CONCLUSIONS: MAGE-A and NY-ESO-1 are frequently expressed in TN breast cancer (~47% and 17% of TN cases, respectively), suggesting that targeting them could be feasible in this patient group. Expression is reasonably homogeneous in positive cases, suggesting that immunohistochemical analysis of tissue biopsies would be a reliable companion biomarker.

High content screening application for cell-type specific behaviour in heterogeneous primary breast epithelial subpopulations.

BACKGROUND: The complex interaction between multiple cell types and the microenvironment underlies the diverse pathways to carcinogenesis and necessitates sophisticated approaches to in vitro hypotheses testing. The combination of mixed culture format with high content immunofluorescence screening technology provides a powerful platform for observation of cell type specific behavior. METHODS: We have developed a versatile, high-throughput method for assessing cell-type specific responses. In addition to the specificity and sensitivity offered traditionally by immunofluorescent detection in flow cytometry, the 'in-cell' analysis method we describe provides the added benefits of higher throughput and the ability to analyse protein subcellular localisation in situ. Furthermore, elimination of the cell dissociation step allows for more immediate analysis of responses to specific extrinsic stimuli. We applied this method to investigate ionising radiation treatment response in normal breast epithelial cells, measuring growth rate, cell cycle response and double-strand DNA breaks. RESULTS: The 'in-cell' analysis approach elucidated several interesting donor and cell-type specific differences. Notably, in response to ionizing radiation we observed differential expression in luminal and basal-like cells of a member of the APOBEC enzyme family, recently identified as a critical driver of an oncogenic signature. Our findings suggest that this enzyme is active in the normal breast epithelium during DNA damage response. CONCLUSIONS: We demonstrate the practical application of a new method for assessing cell-type specific change in mixed cultures, especially the analysis of normal primary cultures, overcoming a major technical issue of dissecting the response of multiple cell types in a heterogeneous population.

Achieving ultra-low and -uniform residual magnetic fields in a very large magnetically shielded room for fundamental physics experiments.

High-precision searches for an electric dipole moment of the neutron (nEDM) require stable and uniform magnetic field environments. We present the recent achievements of degaussing and equilibrating the magnetically shielded room (MSR) for the n2EDM experiment at the Paul Scherrer Institute. We present the final degaussing configuration that will be used for n2EDM after numerous studies. The optimized procedure results in a residual magnetic field that has been reduced by a factor of two. The ultra-low field is achieved with the full magnetic-field-coil system, and a large vacuum vessel installed, both in the MSR. In the inner volume of ∼1.4m3, the field is now more uniform and below 300 pT. In addition, the procedure is faster and dissipates less heat into the magnetic environment, which in turn, reduces its thermal relaxation time from 12h down to 1.5h.

Assessment of ORAI1-mediated basal calcium influx in mammary epithelial cells.

BACKGROUND: The entry of calcium ions into mammary gland epithelial cells is one of the least well-understood processes in the transport of calcium into milk during lactation. The store-operated calcium entry channel ORAI1, has been suggested as a potential mechanism for the entry of Ca(2+) into mammary gland epithelial cells from the maternal blood supply during lactation. The down regulation of the canonical ORAI1 activator STIM1 during lactation suggests that other known ORAI activators such as STIM2 and SPCA2 may be important during lactation. RESULTS: Differentiation of HC11 mammary gland epithelial cells was associated with enhanced basal Ca(2+) influx. Silencing of Orai1 abolished this enhancement of Ca(2+) influx. Stim2 had a modest effect on Ca(2+) influx in this in vitro model of lactation, whereas Stim1 and Spca2 silencing had no effect. Despite pronounced increases in Spca2 mRNA during lactation there was no change in the generation of the alternative splice product generated by Mist1, which increases during lactation. CONCLUSIONS: These studies support the hypothesis that lactation is associated with a remodelling of Ca(2+) influx and this is associated with enhancement of basal Ca(2+) influx. This enhanced Ca(2+) influx appears to occur through the calcium channel Orai1.

SNORD-host RNA Zfas1 is a regulator of mammary development and a potential marker for breast cancer.

Long noncoding RNAs (lncRNAs) are increasingly recognized to play major regulatory roles in development and disease. To identify novel regulators in breast biology, we identified differentially regulated lncRNAs during mouse mammary development. Among the highest and most differentially expressed was a transcript (Zfas1) antisense to the 5' end of the protein-coding gene Znfx1. In vivo, Zfas1 RNA is localized within the ducts and alveoli of the mammary gland. Zfas1 intronically hosts three previously undescribed C/D box snoRNAs (SNORDs): Snord12, Snord12b, and Snord12c. In contrast to the general assumption that noncoding SNORD-host transcripts function only as vehicles to generate snoRNAs, knockdown of Zfas1 in a mammary epithelial cell line resulted in increased cellular proliferation and differentiation, while not substantially altering the levels of the SNORDs. In support of an independent function, we also found that Zfas1 is extremely stable, with a half-life >16 h. Expression analysis of the SNORDs revealed these were expressed at different levels, likely a result of distinct structures conferring differential stability. While there is relatively low primary sequence conservation between Zfas1 and its syntenic human ortholog ZFAS1, their predicted secondary structures have similar features. Like Zfas1, ZFAS1 is highly expressed in the mammary gland and is down-regulated in breast tumors compared to normal tissue. We propose a functional role for Zfas1/ ZFAS1 in the regulation of alveolar development and epithelial cell differentiation in the mammary gland, which, together with its dysregulation in human breast cancer, suggests ZFAS1 as a putative tumor suppressor gene.

A large 'Active Magnetic Shield' for a high-precision experiment: nEDM collaboration.

We present a novel Active Magnetic Shield (AMS), designed and implemented for the n2EDM experiment at the Paul Scherrer Institute. The experiment will perform a high-sensitivity search for the electric dipole moment of the neutron. Magnetic-field stability and control is of key importance for n2EDM. A large, cubic, 5 m side length, magnetically shielded room (MSR) provides a passive, quasi-static shielding-factor of about 105 for its inner sensitive volume. The AMS consists of a system of eight complex, feedback-controlled compensation coils constructed on an irregular grid spanned on a volume of less than 1000 m3 around the MSR. The AMS is designed to provide a stable and uniform magnetic-field environment around the MSR, while being reasonably compact. The system can compensate static and variable magnetic fields up to ±50µT (homogeneous components) and ±5µT/m (first-order gradients), suppressing them to a few µT in the sub-Hertz frequency range. The presented design concept and implementation of the AMS fulfills the requirements of the n2EDM experiment and can be useful for other applications, where magnetically silent environments are important and spatial constraints inhibit simpler geometrical solutions.

Gene expression profiling of tumour epithelial and stromal compartments during breast cancer progression.

The progression of ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) marks a critical step in the evolution of breast cancer. There is some evidence to suggest that dynamic interactions between the neoplastic cells and the tumour microenvironment play an important role. Using the whole-genome cDNA-mediated annealing, selection, extension and ligation assay (WG-DASL, Illumina), we performed gene expression profiling on 87 formalin-fixed paraffin-embedded (FFPE) samples from 17 patients consisting of matched IDC, DCIS and three types of stroma: IDC-S (<3 mm from IDC), DCIS-S (<3 mm from DCIS) and breast cancer associated-normal stroma (BC-NS; >10 mm from IDC or DCIS). Differential gene expression analysis was validated by quantitative real time-PCR, immunohistochemistry and immunofluorescence. The expression of several genes was down-regulated in stroma from cancer patients relative to normal stroma from reduction mammoplasties. In contrast, neoplastic epithelium underwent more gene expression changes during progression, including down regulation of SFRP1. In particular, we observed that molecules related to extracellular matrix (ECM) remodelling (e.g. COL11A1, COL5A2 and MMP13) were differentially expressed between DCIS and IDC. COL11A1 was overexpressed in IDC relative to DCIS and was expressed by both the epithelial and stromal compartments but was enriched in invading neoplastic epithelial cells. The contributions of both the epithelial and stromal compartments to the clinically important scenario of progression from DCIS to IDC. Gene expression profiles, we identified differential expression of genes related to ECM remodelling, and specifically the elevated expression of genes such as COL11A1, COL5A2 and MMP13 in epithelial cells of IDC. We propose that these expression changes could be involved in facilitating the transition from in situ disease to invasive cancer and may thus mark a critical point in disease development.

Novice-performed point-of-care ultrasound for home-based imaging.

Patient-performed point-of-care ultrasound (POCUS) may be feasible for use in home-based healthcare. We investigated whether novice users can obtain lung ultrasound (LUS) images via self-scanning with similar interpretability and quality as experts. Adult participants with no prior medical or POCUS training, who were capable of viewing PowerPoint slides in their home and who could hold a probe to their chest were recruited. After training, volunteers self-performed 8-zone LUS and saved images using a hand-held POCUS device in their own home. Each 8-zone LUS scan was repeated by POCUS experts. Clips were independently viewed and scored by POCUS experts blinded to performing sonographers. Quality and interpretability scores of novice- and expert-obtained LUS images were compared. Thirty volunteers with average age of 42.8 years (Standard Deviation (SD) 15.8), and average body mass index of 23.7 (SD 3.1) were recruited. Quality of novice and expert scans did not differ (median score 2.6, interquartile range (IQR) 2.3-2.9 vs. 2.8, IQR 2.3-3.0, respectively p = 0.09). Individual zone quality also did not differ (P > 0.05). Interpretability of LUS was similar between expert and novice scanners (median 7 zones interpretable, IQR 6-8, for both groups, p = 0.42). Interpretability of novice-obtained scans did not differ from expert scans (median 7 out of 8 zones, IQR 6-8, p = 0.42). Novice-users can self-obtain interpretable, expert-quality LUS clips with minimal training. Patient-performed LUS may be feasible for outpatient home monitoring.

Artificial intelligence for renal cancer: From imaging to histology and beyond.

Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%-17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation.

The very large n2EDM magnetically shielded room with an exceptional performance for fundamental physics measurements.

We present the magnetically shielded room (MSR) for the n2EDM experiment at the Paul Scherrer Institute, which features an interior cubic volume with each side of length 2.92 m, thus providing an accessible space of 25 m3. The MSR has 87 openings of diameter up to 220 mm for operating the experimental apparatus inside and an intermediate space between the layers for housing sensitive signal processing electronics. The characterization measurements show a remanent magnetic field in the central 1 m3 below 100 pT and a field below 600 pT in the entire inner volume, up to 4 cm to the walls. The quasi-static shielding factor at 0.01 Hz measured with a sinusoidal 2 µT peak-to-peak signal is about 100 000 in all three spatial directions and increases rapidly with frequency to reach 108 above 1 Hz.

Breast cancer stem cells: treatment resistance and therapeutic opportunities.

The clinical and pathologic heterogeneity of human breast cancer has long been recognized. Now, molecular profiling has enriched our understanding of breast cancer heterogeneity and yielded new prognostic and predictive information. Despite recent therapeutic advances, including the HER2-specific agent, trastuzumab, locoregional and systemic disease recurrence remain an ever-present threat to the health and well being of breast cancer survivors. By definition, disease recurrence originates from residual treatment-resistant cells, which regenerate at least the initial breast cancer phenotype. The discovery of the normal breast stem cell has re-ignited interest in the identity and properties of breast cancer stem-like cells and the relationship of these cells to the repopulating ability of treatment-resistant cells. The cancer stem cell model of breast cancer development contrasts with the clonal evolution model, whereas the mixed model draws on features of both. Although the origin and identity of breast cancer stem-like cells is contentious, treatment-resistant cells survive and propagate only because aberrant and potentially druggable signaling pathways are recruited. As a means to increase the rates of breast cancer cure, several approaches to specific targeting of the treatment-resistant cell population exist and include methods for addressing the problem of radioresistance in particular.

The design of the n2EDM experiment: nEDM Collaboration.

We present the design of a next-generation experiment, n2EDM, currently under construction at the ultracold neutron source at the Paul Scherrer Institute (PSI) with the aim of carrying out a high-precision search for an electric dipole moment of the neutron. The project builds on experience gained with the previous apparatus operated at PSI until 2017, and is expected to deliver an order of magnitude better sensitivity with provision for further substantial improvements. An overview is of the experimental method and setup is given, the sensitivity requirements for the apparatus are derived, and its technical design is described.