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Activation of PINK1 and Parkin in response to mitochondrial damage initiates a response that includes phosphorylation of RAB7A at Ser72. Rubicon is a RAB7A binding negative regulator of autophagy. The structure of the Rubicon:RAB7A complex suggests that phosphorylation of RAB7A at Ser72 would block Rubicon binding. Indeed, in vitro phosphorylation of RAB7A by TBK1 abrogates Rubicon:RAB7A binding. Pacer, a positive regulator of autophagy, has an RH domain with a basic triad predicted to bind an introduced phosphate. Consistent with this, Pacer-RH binds to phosho-RAB7A but not to unphosphorylated RAB7A. In cells, mitochondrial depolarization reduces Rubicon:RAB7A colocalization whilst recruiting Pacer to phospho-RAB7A-positive puncta. Pacer knockout reduces Parkin mitophagy with little effect on bulk autophagy or Parkin-independent mitophagy. Rescue of Parkin-dependent mitophagy requires the intact pRAB7A phosphate-binding basic triad of Pacer. Together these structural and functional data support a model in which the TBK1-dependent phosphorylation of RAB7A serves as a switch, promoting mitophagy by relieving Rubicon inhibition and favoring Pacer activation.

El final de los regímenes socialistas de tipo soviético en el Este de Europa se produjo de una manera relativamente pacífica, salvo en el caso de Rumanía. En dicho país, la dictadura se había caracterizado por el uso de la represión para solventar cualquier ápice de oposición y su final llegó mediante los sucesos revolucionarios de diciembre de 1989, donde un importante número de personas perdieron la vida. Las nuevas autoridades, que ocuparon el vacío de poder que había dejado el Partido Comunista Rumano, emplearon los rumores y el temor para controlar a la población y alzarse como los dirigentes y salvadores de la Revolución. Una parte sustancial de ellos habían participado en la política del régimen anterior y conocían perfectamente dichas técnicas. Sin embargo, no podían seguir basándose simplemente en ello y en los meses siguientes comenzaron a emplear, cuando lo consideraron oportuno, distintas actuaciones violentas para acallar a las voces disonantes con ellos, mientras que la imagen del país y su credibilidad democrática se desmoronaba en el exterior. Por ello, a lo largo de las páginas de este artículo vamos a examinar cómo las nuevas autoridades rumanas utilizaron la violencia para hacerse con el control político del país y mantenerse en el mismo.

Quantitative dynamic contrast-enhanced (DCE) MRI as a promising method for the prediction of breast cancer response to neoadjuvant chemotherapy (NAC) has been demonstrated mostly in single-center and single-vendor platform studies. This preliminary study reports the initial experience in implementing quantitative breast DCE-MRI in multi-center (MC) and multi-vendor platform (MP) settings to predict NAC response. MRI data, including B1 mapping, variable flip angle (VFA) measurements of native tissue R1 (R1,0), and DCE-MRI, were acquired during NAC at three sites using 3T systems with Siemens, Philips, and GE platforms, respectively. High spatiotemporal resolution DCE-MRI was performed using similar vendor product sequences with k-space undersampling during acquisition and view sharing during reconstruction. A breast phantom was used for quality assurance/quality control (QA/QC) across sites. The Tofts model (TM) and shutter-speed model (SSM) were used for pharmacokinetic (PK) analysis of the DCE data. Additionally, tumor region of interest (ROI)- vs. voxel-based analyses in combination with the use of VFA-measured R1,0vs. fixed, literature-reported R1,0 were investigated to determine the optimal analysis approach. Results from 15 patients who completed the study are reported. Voxel-based PK analysis using fixed R1,0 was deemed the optimal approach, which allowed the inclusion of data from one vendor platform where VFA measurements produced ≥100% overestimation of R1,0. The semi-quantitative signal enhancement ratio (SER) and quantitative PK parameters outperformed the tumor longest diameter (LD) in the prediction of pathologic complete response (pCR) vs. non-pCR after the first NAC cycle, whereas Ktrans consistently provided more accurate predictions than both SER and LD after the first NAC cycle and at the NAC midpoint. Both TM and SSM Ktrans and kep were excellent predictors of response at the NAC midpoint with ROC AUC >0.90, while the SSM parameters (AUC ≥0.80) performed better than their TM counterparts (AUC

The adaptor protein (AP) complexes not only form the inner layer of clathrin coats but also have clathrin-independent roles in membrane traffic whose mechanisms are unknown. HIV-1 Nef hijacks AP-1 to sequester major histocompatibility complex class I (MHC-I), evading immune detection. We found that AP-1:Arf1:Nef:MHC-I forms a coat on tubulated membranes without clathrin and determined its structure. The coat assembles via Arf1 dimer interfaces. AP-1-positive tubules are enriched in cells upon clathrin knockdown. Nef localizes preferentially to AP-1 tubules in cells, explaining how Nef sequesters MHC-I. Coat contact residues are conserved across Arf isoforms and the Arf-dependent AP complexes AP-1, AP-3, and AP-4. Thus, AP complexes can self-assemble with Arf1 into tubular coats without clathrin or other scaffolding factors. The AP-1:Arf1 coat defines the structural basis of a broader class of tubulovesicular membrane coats as an intermediate in clathrin vesicle formation from internal membranes and as an MHC-I sequestration mechanism in HIV-1 infection.

In this study, we leverage sentiment analysis to investigate public perception towards environmental issues as conveyed through global news articles and its potential implications on the transition to a low-carbon economy. Utilizing an extensive corpus of news articles sourced globally, we deploy Natural Language Processing (NLP) techniques to quantify sentiment in these articles, capturing public sentiment’s dynamism and complexity towards various environmental issues. Our methodology involves sentiment scoring of key aspects like “climate change”, “climate policy”, “renewable energy”, “solar energy”, “wind energy”, and “environmental impact” which facilitated a detailed sentiment trend analysis over time. We also incorporated a Latent Dirichlet Allocation (LDA) model to conduct topic modelling, identifying five major topics recurring in the discourse. Our correlation analysis uncovers interesting relationships such as a positive correlation between sentiment scores of “low carbon” and “electric cars”, and a negative correlation between “greenhouse gas emissions” and “electric cars". The findings indicate that public sentiment towards environmental issues is not only multifaceted but also evolving, with significant implications for policy-making and stakeholder engagement in the low-carbon transition. These results exemplify sentiment analysis as a powerful tool in understanding public perception, providing actionable insights for researchers, policymakers, and stakeholders involved in environmental issues and the low-carbon economy transition.

Background Suppression of background parenchymal enhancement (BPE) is commonly observed after neoadjuvant chemotherapy (NAC) at contrast-enhanced breast MRI. It was hypothesized that nonsuppressed BPE may be associated with inferior response to NAC. Purpose To investigate the relationship between lack of BPE suppression and pathologic response. Materials and Methods A retrospective review was performed for women with menopausal status data who were treated for breast cancer by one of 10 drug arms (standard NAC with or without experimental agents) between May 2010 and November 2016 in the Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2, or I-SPY 2 TRIAL (NCT01042379). Patients underwent MRI at four points: before treatment (T0), early treatment (T1), interregimen (T2), and before surgery (T3). BPE was quantitatively measured by using automated fibroglandular tissue segmentation. To test the hypothesis effectively, a subset of examinations with BPE with high-quality segmentation was selected. BPE change from T0 was defined as suppressed or nonsuppressed for each point. The Fisher exact test and the Z tests of proportions with Yates continuity correction were used to examine the relationship between BPE suppression and pathologic complete response (pCR) in hormone receptor (HR)-positive and HR-negative cohorts. Results A total of 3528 MRI scans from 882 patients (mean age, 48 years ± 10 [standard deviation]) were reviewed and the subset of patients with high-quality BPE segmentation was determined (T1, 433 patients; T2, 396 patients; T3, 380 patients). In the HR-positive cohort, an association between lack of BPE suppression and lower pCR rate was detected at T2 (nonsuppressed vs suppressed, 11.8% [six of 51] vs 28.9% [50 of 173]; difference, 17.1% [95% CI: 4.7, 29.5]; P = .02) and T3 (nonsuppressed vs suppressed, 5.3% [two of 38] vs 27.4% [48 of 175]; difference, 22.2% [95% CI: 10.9, 33.5]; P = .003). In the HR-negative cohort, patients with nonsuppressed BPE had lower estimated pCR rate at all points, but the P values for the association were all greater than .05. Conclusions In hormone receptor-positive breast cancer, lack of background parenchymal enhancement suppression may indicate inferior treatment response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

ObjectiveThe A6702 multisite trial confirmed that apparent diffusion coefficient (ADC) measures can improve breast MRI accuracy and reduce unnecessary biopsies, but also found that technical issues rendered many lesions non-evaluable on diffusion-weighted imaging (DWI). This secondary analysis investigated factors affecting lesion evaluability and impact on diagnostic performance.MethodsThe A6702 protocol was IRB-approved at 10 institutions; participants provided informed consent. In total, 103 women with 142 MRI-detected breast lesions (BI-RADS assessment category 3, 4, or 5) completed the study. DWI was acquired at 1.5T and 3T using a four b-value, echo-planar imaging sequence. Scans were reviewed for multiple quality factors (artifacts, signal-to-noise, misregistration, and fat suppression); lesions were considered non-evaluable if there was low confidence in ADC measurement. Associations of lesion evaluability with imaging and lesion characteristics were determined. Areas under the receiver operating characteristic curves (AUCs) were compared using bootstrapping.ResultsThirty percent (42/142) of lesions were non-evaluable on DWI; 23% (32/142) with image quality issues, 7% (10/142) with conspicuity and/or localization issues. Misregistration was the only factor associated with non-evaluability (P = 0.001). Smaller (≤10 mm) lesions were more commonly non-evaluable than larger lesions (p

Background The Eastern Cooperative Oncology Group and American College of Radiology Imaging Network Cancer Research Group A6702 multicenter trial helped confirm the potential of diffusion-weighted MRI for improving differential diagnosis of suspicious breast abnormalities and reducing unnecessary biopsies. A prespecified secondary objective was to explore the relative value of different approaches for quantitative assessment of lesions at diffusion-weighted MRI. Purpose To determine whether alternate calculations of apparent diffusion coefficient (ADC) can help further improve diagnostic performance versus mean ADC values alone for analysis of suspicious breast lesions at MRI. Materials and Methods This prospective trial (ClinicalTrials.gov identifier: NCT02022579) enrolled consecutive women (from March 2014 to April 2015) with a Breast Imaging Reporting and Data System category of 3, 4, or 5 at breast MRI. All study participants underwent standardized diffusion-weighted MRI (b = 0, 100, 600, and 800 sec/mm2). Centralized ADC measures were performed, including manually drawn whole-lesion and hotspot regions of interest, histogram metrics, normalized ADC, and variable b-value combinations. Diagnostic performance was estimated by using the area under the receiver operating characteristic curve (AUC). Reduction in biopsy rate (maintaining 100% sensitivity) was estimated according to thresholds for each ADC metric. Results Among 107 enrolled women, 81 lesions with outcomes (28 malignant and 53 benign) in 67 women (median age, 49 years; interquartile range, 41-60 years) were analyzed. Among ADC metrics tested, none improved diagnostic performance versus standard mean ADC (AUC, 0.59-0.79 vs AUC, 0.75; P = .02-.84), and maximum ADC had worse performance (AUC, 0.52; P < .001). The 25th-percentile ADC metric provided the best performance (AUC, 0.79; 95% CI: 0.70, 0.88), and a threshold using median ADC provided the greatest reduction in biopsy rate of 23.9% (95% CI: 14.8, 32.9; 16 of 67 BI-RADS category 4 and 5 lesions). Nonzero minimum b value (100, 600, and 800 sec/mm2) did not improve the AUC (0.74; P = .28), and several combinations of two b values (0 and 600, 100 and 600, 0 and 800, and 100 and 800 sec/mm2; AUC, 0.73-0.76) provided results similar to those seen with calculations of four b values (AUC, 0.75; P = .17-.87). Conclusion Mean apparent diffusion coefficient calculated with a two-b-value acquisition is a simple and sufficient diffusion-weighted MRI metric to augment diagnostic performance of breast MRI compared with more complex approaches to apparent diffusion coefficient measurement. © RSNA, 2020 Online supplemental material is available for this article.

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.

Abstract Introduction Breast cancer patients treated with neoadjuvant chemotherapy (NACT) are at risk of recurrence depending on clinicopathological characteristics. This preliminary study aimed to investigate the predictive performances of quantitative dynamic contrast-enhanced (DCE) MRI parameters, alone and in combination with clinicopathological variables, for prediction of recurrence in patients treated with NACT. Methods Forty-seven patients underwent pre- and post-NACT MRI exams including high spatiotemporal resolution DCE-MRI. The Shutter-Speed model was employed to perform pharmacokinetic analysis of the DCE-MRI data and estimate the Ktrans, ve, kep, and τi parameters. Univariable logistic regression was used to assess predictive accuracy for recurrence for each MRI metric, while Firth logistic regression was used to evaluate predictive performances for models with multi-clinicopathological variables and in combination with a single MRI metric or the first principal components of all MRI metrics. Results Pre- and post-NACT DCE-MRI parameters performed better than tumor size measurement in prediction of recurrence, whether alone or in combination with clinicopathological variables. Combining post-NACT Ktrans with residual cancer burden and age showed the best improvement in predictive performance with ROC AUC = 0.965. Conclusion Accurate prediction of recurrence pre- and/or post-NACT through integration of imaging markers and clinicopathological variables may help improve clinical decision making in adjusting NACT and/or adjuvant treatment regimens to reduce the risk of recurrence and improve survival outcome.

PurposeTo determine the in vitro accuracy, test-retest repeatability, and interplatform reproducibility of T1 quantification protocols used for dynamic contrast-enhanced MRI at 1.5 and 3 T.MethodsA T1 phantom with 14 samples was imaged at eight centers with a common inversion-recovery spin-echo (IR-SE) protocol and a variable flip angle (VFA) protocol using seven flip angles, as well as site-specific protocols (VFA with different flip angles, variable repetition time, proton density, and Look-Locker inversion recovery). Factors influencing the accuracy (deviation from reference NMR T1 measurements) and repeatability were assessed using general linear mixed models. Interplatform reproducibility was assessed using coefficients of variation.ResultsFor the common IR-SE protocol, accuracy (median error across platforms = 1.4-5.5%) was influenced predominantly by T1 sample (P

We present the case of a young woman without a medical history who presented with a giant right atrial, transtricuspid, and right ventricular mass and in a severe clinical state. Multimodal imaging raised the suspicion of primary cardiac angiosarcoma. Due to rapid hemodynamic and respiratory deterioration, we were forced to perform surgical removal of the mass with a concomitant reconstruction of the involved right heart structures, only 48 h after presentation. The postoperative course was uneventful, and the patient was discharged from the intensive care unit 2 days later. Radical surgical resection with reconstruction of the resected heart structures was the only possible salvage option for giant angiosarcoma, which led to hemodynamic instability. Followed by chemotherapy, this radical approach may prolong survival.

We compile a sample of spectroscopically and photometrically selected cluster galaxies from four high-redshift galaxy clusters (1.59 < z < 1.71) from the Spitzer Adaptation of the Red-Sequence Cluster Survey (SpARCS), and a comparison field sample selected from the UKIDSS Deep Survey. Using near-infrared imaging from the Hubble Space Telescope, we classify potential mergers involving massive (M∗ ≥ 3 × 1010M⊙) cluster members by eye, based on morphological properties such as tidal distortions, double nuclei, and projected near neighbors within 20 kpc. With a catalog of 23 spectroscopic and 32 photometric massive cluster members across the four clusters and 65 spectroscopic and 26 photometric comparable field galaxies, we find that after taking into account contamination from interlopers, 11+7.0-5.6% of the cluster members are involved in potential mergers, compared to 24.7+5.3-4.6% of the field galaxies. We see no evidence of merger enhancement in the central cluster environment with respect to the field, suggesting that galaxy-galaxy merging is not a stronger source of galaxy evolution in cluster environments compared to the field at these redshifts.

Quantitative dynamic contrast-enhanced (DCE) MRI as a promising method for the prediction of breast cancer response to neoadjuvant chemotherapy (NAC) has been demonstrated mostly in single-center and single-vendor platform studies. This preliminary study reports the initial experience in implementing quantitative breast DCE-MRI in multi-center (MC) and multi-vendor platform (MP) settings to predict NAC response. MRI data, including B1 mapping, variable flip angle (VFA) measurements of native tissue R1 (R1,0), and DCE-MRI, were acquired during NAC at three sites using 3T systems with Siemens, Philips, and GE platforms, respectively. High spatiotemporal resolution DCE-MRI was performed using similar vendor product sequences with k-space undersampling during acquisition and view sharing during reconstruction. A breast phantom was used for quality assurance/quality control (QA/QC) across sites. The Tofts model (TM) and shutter-speed model (SSM) were used for pharmacokinetic (PK) analysis of the DCE data. Additionally, tumor region of interest (ROI)- vs. voxel-based analyses in combination with the use of VFA-measured R1,0 vs. fixed, literature-reported R1,0 were investigated to determine the optimal analysis approach. Results from 15 patients who completed the study are reported. Voxel-based PK analysis using fixed R1,0 was deemed the optimal approach, which allowed the inclusion of data from one vendor platform where VFA measurements produced ≥100% overestimation of R1,0. The semi-quantitative signal enhancement ratio (SER) and quantitative PK parameters outperformed the tumor longest diameter (LD) in the prediction of pathologic complete response (pCR) vs. non-pCR after the first NAC cycle, whereas Ktrans consistently provided more accurate predictions than both SER and LD after the first NAC cycle and at the NAC midpoint. Both TM and SSM Ktrans and kep were excellent predictors of response at the NAC midpoint with ROC AUC >0.90, while the SSM parameters (AUC ≥0.80) performed better than their TM counterparts (AUC <0.80) after the first NAC cycle. The initial experience of this ongoing study indicates the importance of QA/QC using a phantom and suggests that deploying voxel-based PK analysis using a fixed R1,0 may mitigate random errors from R1,0 measurements across platforms and potentially eliminate the need for B1 and VFA acquisitions in MC and MP trials. [ABSTRACT FROM AUTHOR]

We have discovered an optically rich galaxy cluster at z = 1.7089 with star formation occurring in close proximity to the central galaxy. The system, SpARCS104922.6+564032.5, was detected within the Spitzer Adaptation of the red-sequence Cluster Survey, and confirmed through Keck-MOSFIRE spectroscopy. The rest-frame optical richness of Ngal (500 kpc) = 30 8 implies a total halo mass, within 500 kpc, of ∼3.8 1.2 1014 Mo, comparable to other clusters at or above this redshift. There is a wealth of ancillary data available, including Canada-France-Hawaii Telescope optical, UKIRT-K, Spitzer-IRAC/MIPS, and Herschel-SPIRE. This work adds submillimeter imaging with the SCUBA2 camera on the James Clerk Maxwell Telescope and near-infrared imaging with the Hubble Space Telescope. The mid/far-infrared (M/FIR) data detect an Ultra-luminous Infrared Galaxy spatially coincident with the central galaxy, with LIR = 6.2 0.9 1012 Lo. The detection of polycyclic aromatic hydrocarbons at z = 1.7 in a Spitzer-IRS spectrum of the source implies the FIR luminosity is dominated by star formation (an Active Galactic Nucleus contribution of 20%) with a rate of ∼860 130 Mo yr-1. The optical source corresponding to the IR emission is likely a chain of >10 individual clumps arranged as "beads on a string" over a linear scale of 66 kpc. Its morphology and proximity to the Brightest Cluster Galaxy (BCG) imply a gas-rich interaction at the center of the cluster triggered the star formation. This system indicates that wet mergers may be an important process in forming the stellar mass of BCGs at early times.

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