Mapping Electrophile Chemoselectivity in DalPhos/Nickel N-Arylation Catalysis: The Unusual Influence of Remote Sterics.

We disclose herein our evaluation of competitive (hetero)aryl-X (X: Br > Cl > OTf) reactivity preferences in bisphosphine/Ni-catalyzed C-N cross-coupling catalysis, using furfurylamine as a prototypical nucleophile, and employing DalPhos and DPPF as representative ancillary ligands with established efficacy. Beyond this general (pseudo)halide ranking, other intriguing structure-reactivity trends were noted experimentally, including the unexpected observation that bulky alkyl (e.g., R = tBu) substitution in para-R-aryl-X electrophiles strongly discourages (pseudo)halide reactivity relative to smaller substituents (e.g., nBu, Et, Me), despite being both remote from, and having a similar electronic influence on, the reacting C-X bond; such effects on nickel oxidative addition have not been documented previously and were not observed in our comparator reactions presented herein involving palladium. Density functional theory modeling of such PhPAd-DalPhos/Ni-catalyzed C-N cross-couplings revealed the origins of competitive turnover of C-Br over C-Cl, and possible ways in which bulky para-alkyl substitution might discourage net electrophile uptake/turnover, leading to inversion of halide selectivity.

ALE meta-analyses of voxel-based morphometry studies: Parameter validation via large-scale simulations.

Activation likelihood estimation (ALE) meta-analysis has been applied to structural neuroimaging data since long, but up to now, any systematic assessment of the algorithm's behavior, power and sensitivity has been based on simulations using functional neuroimaging databases as their foundation. Here, we aimed to determine whether the guidelines offered by previous evaluations can be generalized to ALE meta-analyses of voxel-based morphometry (VBM) studies. We ran 365000 distinct ALE analyses filled with simulated experiments, randomly sampling parameters from BrainMap's VBM experiment database. We then examined the algorithm's sensitivity, its susceptibility to spurious convergence, and its susceptibility to excessive contributions by individual experiments. In general, the performance of the ALE algorithm was highly comparable between imaging modalities, with the algorithm's sensitivity and specificity reaching similar levels with structural data as previously observed with functional data. Because of the lower number of foci reported and the higher number of participants usually included in structural experiments, individual studies had, on average, a higher impact towards significant clusters. To prevent significant clusters from being driven by single experiments, we recommend that researchers include at least 23 experiments in a VBM ALE dataset, instead of the previously recommended minimum of n = 17. While these recommendations do not constitute hard borders, running ALE analyses on smaller datasets would require special diligence in assessing and reporting the contributions of experiments to individual clusters.

Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies.

Deep neural networks currently provide the most advanced and accurate machine learning models to distinguish between structural MRI scans of subjects with Alzheimer's disease and healthy controls. Unfortunately, the subtle brain alterations captured by these models are difficult to interpret because of the complexity of these multi-layer and non-linear models. Several heatmap methods have been proposed to address this issue and analyze the imaging patterns extracted from the deep neural networks, but no quantitative comparison between these methods has been carried out so far. In this work, we explore these questions by deriving heatmaps from Convolutional Neural Networks (CNN) trained using T1 MRI scans of the ADNI data set and by comparing these heatmaps with brain maps corresponding to Support Vector Machine (SVM) activation patterns. Three prominent heatmap methods are studied: Layer-wise Relevance Propagation (LRP), Integrated Gradients (IG), and Guided Grad-CAM (GGC). Contrary to prior studies where the quality of heatmaps was visually or qualitatively assessed, we obtained precise quantitative measures by computing overlap with a ground-truth map from a large meta-analysis that combined 77 voxel-based morphometry (VBM) studies independently from ADNI. Our results indicate that all three heatmap methods were able to capture brain regions covering the meta-analysis map and achieved better results than SVM activation patterns. Among them, IG produced the heatmaps with the best overlap with the independent meta-analysis.

Pertuzumab study in the neoadjuvant setting for HER2-positive nonmetastatic breast cancer in Australia (PeRSIA).

The pertuzumab study in the neoadjuvant setting for HER2+ nonmetastatic breast cancer in Australia (PeRSIA-ML39622) is an analysis of safety and effectiveness data from the pertuzumab patient registry. Although the prognosis of patients with early stage HER2+ breast cancer has been greatly improved by advances in chemotherapy approximately 25% to 30% of patients develop recurrent disease. Our study aimed to examine the effectiveness of neoadjuvant pertuzumab on surgical outcomes, describe the medium-term effectiveness outcomes of patients treated with pertuzumab, and describe the planned and actual anticancer treatment regimens that patients received. Deidentified data were collected from the patients' medical records and entered into REDCap, between March 2018 and July 2019 (n = 95). The adverse events (AEs) reported most frequently were diarrhea (20; 21.1%), rash (4; 4.2%), and LVSD (4; 4.2%; two patients during neoadjuvant treatment and two patients during adjuvant treatment). AEs, ≥Grade 3 were diarrhea (2; 2.1%) and LVSD (1; 1.1%). Following surgery, a breast pathological complete response (bpCR) was achieved in 65 patients (70.7%; 95% CI: 60.2%-79.7%) and total pathological complete response (tpCR) in 59 patients (64.1%; 95% CI: 53.4%-73.9%). All patients who did not achieve a tpCR obtained a partial response (33/92, 35.9%). Our study is the first to capture real-world data on the use of pertuzumab in the neoadjuvant setting in Australia. The effectiveness and safety data are consistent with those reported in clinical trials of pertuzumab in patients with HER2+ breast cancer, with no new safety concerns.

Trends in the sample size, statistics, and contributions to the BrainMap database of activation likelihood estimation meta-analyses: An empirical study of 10-year data.

The literature of neuroimaging meta-analysis has been thriving for over a decade. A majority of them were coordinate-based meta-analyses, particularly the activation likelihood estimation (ALE) approach. A meta-evaluation of these meta-analyses was performed to qualitatively evaluate their design and reporting standards. The publications listed from the BrainMap website were screened. Six hundred and three ALE papers published during 2010-2019 were included and analysed. For reporting standards, most of the ALE papers reported their total number of Papers involved and mentioned the inclusion/exclusion criteria on Paper selection. However, most papers did not describe how data redundancy was avoided when multiple related Experiments were reported within one paper. The most prevalent repeated-measures correction methods were voxel-level FDR (54.4%) and cluster-level FWE (33.8%), with the latter quickly replacing the former since 2016. For study characteristics, sample size in terms of number of Papers included per ALE paper and number of Experiments per analysis seemed to be stable over the decade. One-fifth of the surveyed ALE papers failed to meet the recommendation of having >17 Experiments per analysis. For data sharing, most of them did not provide input and output data. In conclusion, the field has matured well in terms of rising dominance of cluster-level FWE correction, and slightly improved reporting on elimination of data redundancy and providing input data. The provision of Data and Code availability statements and flow chart of literature screening process, as well as data submission to BrainMap, should be more encouraged.

PET/CT targeted tissue sampling reveals virus specific dIgA can alter the distribution and localization of HIV after rectal exposure.

Human immunodeficiency virus (HIV) vaccines have not been successful in clinical trials. Dimeric IgA (dIgA) in the form of secretory IgA is the most abundant antibody class in mucosal tissues, making dIgA a prime candidate for potential HIV vaccines. We coupled Positron Emission Tomography (PET) imaging and fluorescent microscopy of 64Cu-labeled, photoactivatable-GFP HIV (PA-GFP-BaL) and fluorescently labeled dIgA to determine how dIgA antibodies influence virus interaction with mucosal barriers and viral penetration in colorectal tissue. Our results show that HIV virions rapidly disseminate throughout the colon two hours after exposure. The presence of dIgA resulted in an increase in virions and penetration depth in the transverse colon. Moreover, virions were found in the mesenteric lymph nodes two hours after viral exposure, and the presence of dIgA led to an increase in virions in mesenteric lymph nodes. Taken together, these technologies enable in vivo and in situ visualization of antibody-virus interactions and detailed investigations of early events in HIV infection.

Imaging local genetic influences on cortical folding.

Recent progress in deciphering mechanisms of human brain cortical folding leave unexplained whether spatially patterned genetic influences contribute to this folding. High-resolution in vivo brain MRI can be used to estimate genetic correlations (covariability due to shared genetic factors) in interregional cortical thickness, and biomechanical studies predict an influence of cortical thickness on folding patterns. However, progress has been hampered because shared genetic influences related to folding patterns likely operate at a scale that is much more local (<1 cm) than that addressed in prior imaging studies. Here, we develop methodological approaches to examine local genetic influences on cortical thickness and apply these methods to two large, independent samples. We find that such influences are markedly heterogeneous in strength, and in some cortical areas are notably stronger in specific orientations relative to gyri or sulci. The overall, phenotypic local correlation has a significant basis in shared genetic factors and is highly symmetric between left and right cortical hemispheres. Furthermore, the degree of local cortical folding relates systematically with the strength of local correlations, which tends to be higher in gyral crests and lower in sulcal fundi. The relationship between folding and local correlations is stronger in primary sensorimotor areas and weaker in association areas such as prefrontal cortex, consistent with reduced genetic constraints on the structural topology of association cortex. Collectively, our results suggest that patterned genetic influences on cortical thickness, measurable at the scale of in vivo MRI, may be a causal factor in the development of cortical folding.

Hemodynamic and metabolic correspondence of resting-state voxel-based physiological metrics in healthy adults.

Voxel-based physiological (VBP) variables derived from blood oxygen level dependent (BOLD) fMRI time-course variations include: amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity (ReHo). Although these BOLD-derived variables can detect between-group (e.g. disease vs control) spatial pattern differences, physiological interpretations are not well established. The primary objective of this study was to quantify spatial correspondences between BOLD VBP variables and PET measurements of cerebral metabolic rate and hemodynamics, being well-validated physiological standards. To this end, quantitative, whole-brain PET images of metabolic rate of glucose (MRGlu; 18FDG) and oxygen (MRO2; 15OO), blood flow (BF; H215O) and blood volume (BV; C15O) were obtained in 16 healthy controls. In the same subjects, BOLD time-courses were obtained for computation of ALFF, fALFF and ReHo images. PET variables were compared pair-wise with BOLD variables. In group-averaged, across-region analyses, ALFF corresponded significantly only with BV (R = 0.64; p < 0.0001). fALFF corresponded most strongly with MRGlu (R = 0.79; p < 0.0001), but also significantly (p < 0.0001) with MRO2 (R = 0.68), BF (R = 0.68) and BV (R=0.68). ReHo performed similarly to fALFF, with significant strong correspondence (p < 0.0001) with MRGlu (R = 0.78), MRO2 (R = 0.54), and, but less strongly with BF (R = 0.50) and BV (R=0.50). Mutual information analyses further clarified these physiological interpretations. When conditioned by BV, ALFF retained no significant MRGlu, MRO2 or BF information. When conditioned by MRGlu, fALFF and ReHo retained no significant MRO2, BF or BV information. Of concern, however, the strength of PET-BOLD correspondences varied markedly by brain region, which calls for future investigation on physiological interpretations at a regional and per-subject basis.

Meta-analysis in human neuroimaging: computational modeling of large-scale databases.

Spatial normalization--applying standardized coordinates as anatomical addresses within a reference space--was introduced to human neuroimaging research nearly 30 years ago. Over these three decades, an impressive series of methodological advances have adopted, extended, and popularized this standard. Collectively, this work has generated a methodologically coherent literature of unprecedented rigor, size, and scope. Large-scale online databases have compiled these observations and their associated meta-data, stimulating the development of meta-analytic methods to exploit this expanding corpus. Coordinate-based meta-analytic methods have emerged and evolved in rigor and utility. Early methods computed cross-study consensus, in a manner roughly comparable to traditional (nonimaging) meta-analysis. Recent advances now compute coactivation-based connectivity, connectivity-based functional parcellation, and complex network models powered from data sets representing tens of thousands of subjects. Meta-analyses of human neuroimaging data in large-scale databases now stand at the forefront of computational neurobiology.

Evaluation of thresholding methods for activation likelihood estimation meta-analysis via large-scale simulations.

In recent neuroimaging studies, threshold-free cluster enhancement (TFCE) gained popularity as a sophisticated thresholding method for statistical inference. It was shown to feature higher sensitivity than the frequently used approach of controlling the cluster-level family-wise error (cFWE) and it does not require setting a cluster-forming threshold at voxel level. Here, we examined the applicability of TFCE to a widely used method for coordinate-based neuroimaging meta-analysis, Activation Likelihood Estimation (ALE), by means of large-scale simulations. We created over 200,000 artificial meta-analysis datasets by independently varying the total number of experiments included and the amount of spatial convergence across experiments. Next, we applied ALE to all datasets and compared the performance of TFCE to both voxel-level and cluster-level FWE correction approaches. All three multiple-comparison correction methods yielded valid results, with only about 5% of the significant clusters being based on spurious convergence, which corresponds to the nominal level the methods were controlling for. On average, TFCE's sensitivity was comparable to that of cFWE correction, but it was slightly worse for a subset of parameter combinations, even after TFCE parameter optimization. cFWE yielded the largest significant clusters, closely followed by TFCE, while voxel-level FWE correction yielded substantially smaller clusters, showcasing its high spatial specificity. Given that TFCE does not outperform the standard cFWE correction but is computationally much more expensive, we conclude that employing TFCE for ALE cannot be recommended to the general user.