Acidosis-mediated increase in IFN-γ-induced PD-L1 expression on cancer cells as an immune escape mechanism in solid tumors.

Immune checkpoint inhibitors have revolutionized cancer therapy, yet the efficacy of these treatments is often limited by the heterogeneous and hypoxic tumor microenvironment (TME) of solid tumors. In the TME, programmed death-ligand 1 (PD-L1) expression on cancer cells is mainly regulated by Interferon-gamma (IFN-γ), which induces T cell exhaustion and enables tumor immune evasion. In this study, we demonstrate that acidosis, a common characteristic of solid tumors, significantly increases IFN-γ-induced PD-L1 expression on aggressive cancer cells, thus promoting immune escape. Using preclinical models, we found that acidosis enhances the genomic expression and phosphorylation of signal transducer and activator of transcription 1 (STAT1), and the translation of STAT1 mRNA by eukaryotic initiation factor 4F (elF4F), resulting in an increased PD-L1 expression. We observed this effect in murine and human anti-PD-L1-responsive tumor cell lines, but not in anti-PD-L1-nonresponsive tumor cell lines. In vivo studies fully validated our in vitro findings and revealed that neutralizing the acidic extracellular tumor pH by sodium bicarbonate treatment suppresses IFN-γ-induced PD-L1 expression and promotes immune cell infiltration in responsive tumors and thus reduces tumor growth. However, this effect was not observed in anti-PD-L1-nonresponsive tumors. In vivo experiments in tumor-bearing IFN-γ-/- mice validated the dependency on immune cell-derived IFN-γ for acidosis-mediated cancer cell PD-L1 induction and tumor immune escape. Thus, acidosis and IFN-γ-induced elevation of PD-L1 expression on cancer cells represent a previously unknown immune escape mechanism that may serve as a novel biomarker for anti-PD-L1/PD-1 treatment response. These findings have important implications for the development of new strategies to enhance the efficacy of immunotherapy in cancer patients.

A machine learning approach that measures pH using acidoCEST MRI of iopamidol.

Tumor acidosis is an important biomarker for aggressive tumors, and extracellular pH (pHe) of the tumor microenvironment can be used to predict and evaluate tumor responses to chemotherapy and immunotherapy. AcidoCEST MRI measures tumor pHe by exploiting the pH-dependent chemical exchange saturation transfer (CEST) effect of iopamidol, an exogenous CT agent repurposed for CEST MRI. However, all pH fitting methodologies for acidoCEST MRI data have limitations. Herein we present results of the application of machine learning for extracting pH values from CEST Z-spectra of iopamidol. We acquired 36,000 experimental CEST spectra from 200 phantoms of iopamidol prepared at five concentrations, five T1 values, and eight pH values at five temperatures, acquired at six saturation powers and six saturation times. We also acquired T1 , T2 , B1 RF power, and B0 magnetic field strength supplementary MR information. These MR images were used to train and validate machine learning models for the tasks of pH classification and pH regression. Specifically, we tested the L1-penalized logistic regression classification (LRC) model and the random forest classification (RFC) model for classifying the CEST Z-spectra for thresholds at pH 6.5 and 7.0. Our results showed that both RFC and LRC were effective for pH classification, although the RFC model achieved higher predictive value, and improved the accuracy of classification accuracy with CEST Z-spectra with a more limited set of saturation frequencies. Furthermore, we used LASSO and random forest regression (RFR) models to explore pH regression, which showed that the RFR model achieved higher accuracy and precision for estimating pH across the entire pH range of 6.2-7.3, especially when using a more limited set of features. Based on these results, machine learning for analysis of acidoCEST MRI is promising for eventual in vivo determination of tumor pHe.

Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors.

Amide proton transfer-weighted (APTw) MR imaging shows promise as a biomarker of brain tumor status. Currently used APTw MRI pulse sequences and protocols vary substantially among different institutes, and there are no agreed-on standards in the imaging community. Therefore, the results acquired from different research centers are difficult to compare, which hampers uniform clinical application and interpretation. This paper reviews current clinical APTw imaging approaches and provides a rationale for optimized APTw brain tumor imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods. We expect that these consensus recommendations will become the first broadly accepted guidelines for APTw imaging of brain tumors on 3 T MRI systems from different vendors. This will allow more medical centers to use the same or comparable APTw MRI techniques for the detection, characterization, and monitoring of brain tumors, enabling multi-center trials in larger patient cohorts and, ultimately, routine clinical use.

Quantitative Apparent Diffusion Coefficients From Peritumoral Regions as Early Predictors of Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer.

BACKGROUND: Pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in triple-negative breast cancer (TNBC) is a strong predictor of patient survival. Edema in the peritumoral region (PTR) has been reported to be a negative prognostic factor in TNBC. PURPOSE: To determine whether quantitative apparent diffusion coefficient (ADC) features from PTRs on reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) predict the response to NAST in TNBC. STUDY TYPE: Prospective. POPULATION/SUBJECTS: A total of 108 patients with biopsy-proven TNBC who underwent NAST and definitive surgery during 2015-2020. FIELD STRENGTH/SEQUENCE: A 3.0 T/rFOV single-shot diffusion-weighted echo-planar imaging sequence (DWI). ASSESSMENT: Three scans were acquired longitudinally (pretreatment, after two cycles of NAST, and after four cycles of NAST). For each scan, 11 ADC histogram features (minimum, maximum, mean, median, standard deviation, kurtosis, skewness and 10th, 25th, 75th, and 90th percentiles) were extracted from tumors and from PTRs of 5 mm, 10 mm, 15 mm, and 20 mm in thickness with inclusion and exclusion of fat-dominant pixels. STATISTICAL TESTS: ADC features were tested for prediction of pCR, both individually using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC), and in combination in multivariable models with k-fold cross-validation. A P value < 0.05 was considered statistically significant. RESULTS: Fifty-one patients (47%) had pCR. Maximum ADC from PTR, measured after two and four cycles of NAST, was significantly higher in pCR patients (2.8 ± 0.69 vs 3.5 ± 0.94 mm2 /sec). The top-performing feature for prediction of pCR was the maximum ADC from the 5-mm fat-inclusive PTR after cycle 4 of NAST (AUC: 0.74; 95% confidence interval: 0.64, 0.84). Multivariable models of ADC features performed similarly for fat-inclusive and fat-exclusive PTRs, with AUCs ranging from 0.68 to 0.72 for the cycle 2 and cycle 4 scans. DATA CONCLUSION: Quantitative ADC features from PTRs may serve as early predictors of the response to NAST in TNBC. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 4.

Nothing Wrong with the Analysis of Clades in Comparative Evolutionary Studies: A Reply to Poe et al.

In a recent paper, Poe et al. assert that scientists should abandon clade-based approaches, particularly those using named taxonomic ranks. Poe et al. attempt to demonstrate that clade selection can have effects on the results of evolutionary analyses but unfortunately fall short of making any robust conclusions. Here, we demonstrate that the assertions made by Poe et al. have two important flaws: (i) an erroneous view of modern phylogenetic comparative methods; and (ii) a lack of statistical rigor in their analyses. We repeat Poe et al.'s analysis but using appropriate phylogenetic comparative approaches. We demonstrate that results remain consistent regardless of the clade definition. We go on to discuss the value of taxonomic groupings and how they can provide meaningful units of comparison in evolutionary study. Unlike the disheartening suggestion to abandon the use of clades, scientists can instead continue to use phylogenetic " corrections" that are already the standard for most comparative evolutionary analyses. [Comparative methods; evolution; phylogeny; taxonomy.].

Dominant words rise to the top by positive frequency-dependent selection.

A puzzle of language is how speakers come to use the same words for particular meanings, given that there are often many competing alternatives (e.g., "sofa," "couch," "settee"), and there is seldom a necessary connection between a word and its meaning. The well-known process of random drift-roughly corresponding in this context to "say what you hear"-can cause the frequencies of alternative words to fluctuate over time, and it is even possible for one of the words to replace all others, without any form of selection being involved. However, is drift alone an adequate explanation of a shared vocabulary? Darwin thought not. Here, we apply models of neutral drift, directional selection, and positive frequency-dependent selection to explain over 417,000 word-use choices for 418 meanings in two natural populations of speakers. We find that neutral drift does not in general explain word use. Instead, some form of selection governs word choice in over 91% of the meanings we studied. In cases where one word dominates all others for a particular meaning-such as is typical of the words in the core lexicon of a language-word choice is guided by positive frequency-dependent selection-a bias that makes speakers disproportionately likely to use the words that most others use. This bias grants an increasing advantage to the common form as it becomes more popular and provides a mechanism to explain how a shared vocabulary can spontaneously self-organize and then be maintained for centuries or even millennia, despite new words continually entering the lexicon.

Tumor necrosis by pretreatment breast MRI: association with neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC).

PURPOSE: To determine if tumor necrosis by pretreatment breast MRI and its quantitative imaging characteristics are associated with response to NAST in TNBC. METHODS: This retrospective study included 85 TNBC patients (mean age 51.8 ± 13 years) with MRI before NAST and definitive surgery during 2010-2018. Each MRI included T2-weighted, diffusion-weighted (DWI), and dynamic contrast-enhanced (DCE) imaging. For each index carcinoma, total tumor volume including necrosis (TTV), excluding necrosis (TV), and the necrosis-only volume (NV) were segmented on early-phase DCE subtractions and DWI images. NV and %NV were calculated. Percent enhancement on early and late phases of DCE and apparent diffusion coefficient were extracted from TTV, TV, and NV. Association between necrosis with pathological complete response (pCR) was assessed using odds ratio (OR). Multivariable analysis was used to evaluate the prognostic value of necrosis with T stage and nodal status at staging. Mann-Whitney U tests and area under the curve (AUC) were used to assess performance of imaging metrics for discriminating pCR vs non-pCR. RESULTS: Of 39 patients (46%) with necrosis, 17 had pCR and 22 did not. Necrosis was not associated with pCR (OR, 0.995; 95% confidence interval [CI] 0.4-2.3) and was not an independent prognostic factor when combined with T stage and nodal status at staging (P = 0.46). None of the imaging metrics differed significantly between pCR and non-pCR in patients with necrosis (AUC < 0.6 and P > 0.40). CONCLUSION: No significant association was found between necrosis by pretreatment MRI or the quantitative imaging characteristics of tumor necrosis and response to NAST in TNBC.

Functional Tumor Volume by Fast Dynamic Contrast-Enhanced MRI for Predicting Neoadjuvant Systemic Therapy Response in Triple-Negative Breast Cancer.

BACKGROUND: Dynamic contrast-enhanced (DCE) MRI is useful for diagnosis and assessment of treatment response in breast cancer. Fast DCE MRI offers a higher sampling rate of contrast enhancement curves in comparison to conventional DCE MRI, potentially characterizing tumor perfusion kinetics more accurately for measurement of functional tumor volume (FTV) as a predictor of treatment response. PURPOSE: To investigate FTV by fast DCE MRI as a predictor of neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). STUDY TYPE: Prospective. POPULATION/SUBJECTS: Sixty patients with biopsy-confirmed TNBC between December 2016 and September 2020. FIELD STRENGTH/SEQUENCE: A 3.0 T/3D fast spoiled gradient echo-based DCE MRI ASSESSMENT: Patients underwent MRI at baseline and after four cycles (C4) of NAST, followed by definitive surgery. DCE subtraction images were analyzed in consensus by two breast radiologists with 5 (A.H.A.) and 2 (H.S.M.) years of experience. Tumor volumes (TV) were measured on early and late subtractions. Tumors were segmented on 1 and 2.5-minute early phases subtractions and FTV was determined using optimized signal enhancement thresholds. Interpolated enhancement curves from segmented voxels were used to determine optimal early phase timing. STATISTICAL TESTS: Tumor volumes were compared between patients who had a pathologic complete response (pCR) and those who did not using the area under the receiver operating curve (AUC) and Mann-Whitney U test. RESULTS: About 26 of 60 patients (43%) had pCR. FTV at 1 minute after injection at C4 provided the best discrimination between pCR and non-pCR, with AUC (95% confidence interval [CI]) = 0.85 (0.74,0.95) (P < 0.05). The 1-minute timing was optimal for FTV measurements at C4 and for the change between C4 and baseline. TV from the early phase at C4 also yielded a good AUC (95%CI) of 0.82 (0.71,0.93) (P < 0.05). DATA CONCLUSION: FTV and TV measured at 1 minute after injection can predict response to NAST in TNBC. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: 4.

Computer-aided Detection of Brain Metastases in T1-weighted MRI for Stereotactic Radiosurgery Using Deep Learning Single-Shot Detectors.

Background Brain metastases are manually identified during stereotactic radiosurgery (SRS) treatment planning, which is time consuming and potentially challenging. Purpose To develop and investigate deep learning (DL) methods for detecting brain metastasis with MRI to aid in treatment planning for SRS. Materials and Methods In this retrospective study, contrast material-enhanced three-dimensional T1-weighted gradient-echo MRI scans from patients who underwent gamma knife SRS from January 2011 to August 2018 were analyzed. Brain metastases were manually identified and contoured by neuroradiologists and treating radiation oncologists. DL single-shot detector (SSD) algorithms were constructed and trained to map axial MRI slices to a set of bounding box predictions encompassing metastases and associated detection confidences. Performances of different DL SSDs were compared for per-lesion metastasis-based detection sensitivity and positive predictive value (PPV) at a 50% confidence threshold. For the highest-performing model, detection performance was analyzed by using free-response receiver operating characteristic analysis. Results Two hundred sixty-six patients (mean age, 60 years ± 14 [standard deviation]; 148 women) were randomly split into 80% training and 20% testing groups (212 and 54 patients, respectively). For the testing group, sensitivity of the highest-performing (baseline) SSD was 81% (95% confidence interval [CI]: 80%, 82%; 190 of 234) and PPV was 36% (95% CI: 35%, 37%; 190 of 530). For metastases measuring at least 6 mm, sensitivity was 98% (95% CI: 97%, 99%; 130 of 132) and PPV was 36% (95% CI: 35%, 37%; 130 of 366). Other models (SSD with a ResNet50 backbone, SSD with focal loss, and RetinaNet) yielded lower sensitivities of 73% (95% CI: 72%, 74%; 171 of 234), 77% (95% CI: 76%, 78%; 180 of 234), and 79% (95% CI: 77%, 81%; 184 of 234), respectively, and lower PPVs of 29% (95% CI: 28%, 30%; 171 of 581), 26% (95% CI: 26%, 26%; 180 of 681), and 13% (95% CI: 12%, 14%; 184 of 1412). Conclusion Deep-learning single-shot detector models detected nearly all brain metastases that were 6 mm or larger with limited false-positive findings using postcontrast T1-weighted MRI. © RSNA, 2020 See also the editorial by Kikinis and Wells in this issue.

Early Detection of Pancreatic Intraepithelial Neoplasias (PanINs) in Transgenic Mouse Model by Hyperpolarized 13C Metabolic Magnetic Resonance Spectroscopy.

While pancreatic cancer (PC) survival rates have recently shown modest improvement, the disease remains largely incurable. Early detection of pancreatic cancer may result in improved outcomes and therefore, methods for early detection of cancer, even premalignant lesions, may provide more favorable outcomes. Pancreatic intraepithelial neoplasias (PanINs) have been identified as premalignant precursor lesions to pancreatic cancer. However, conventional imaging methods used for screening high-risk populations do not have the sensitivity to detect PanINs. Here, we have employed hyperpolarized metabolic imaging in vivo and nuclear magnetic resonance (1H-NMR) metabolomics ex vivo to identify and understand metabolic changes, towards enabling detection of early PanINs and progression to advanced PanINs lesions that precede pancreatic cancer formation. Progression of disease from tissue containing predominantly low-grade PanINs to tissue with high-grade PanINs showed a decreasing alanine/lactate ratio from high-resolution NMR metabolomics ex vivo. Hyperpolarized magnetic resonance spectroscopy (HP-MRS) allows over 10,000-fold sensitivity enhancement relative to conventional magnetic resonance. Real-time HP-MRS was employed to measure non-invasively changes of alanine and lactate metabolites with disease progression and in control mice in vivo, following injection of hyperpolarized [1-13C] pyruvate. The alanine-to-lactate signal intensity ratio was found to decrease as the disease progressed from low-grade PanINs to high-grade PanINs. The biochemical changes of alanine transaminase (ALT) and lactate dehydrogenase (LDH) enzyme activity were assessed. These results demonstrate that there are significant alterations of ALT and LDH activities during the transformation from early to advanced PanINs lesions. Furthermore, we demonstrate that real-time conversion kinetic rate constants (kPA and kPL) can be used as metabolic imaging biomarkers of pancreatic premalignant lesions. Findings from this emerging HP-MRS technique can be translated to the clinic for detection of pancreatic premalignant lesion in high-risk populations.

Machine learning improves classification of preclinical models of pancreatic cancer with chemical exchange saturation transfer MRI.

PURPOSE: We sought to assess whether machine learning-based classification approaches can improve the classification of pancreatic tumor models relative to more simplistic analysis methods, using T1 relaxation, CEST, and DCE MRI. METHODS: The T1 relaxation time constants, % CEST at five saturation frequencies, and vascular permeability constants from DCE MRI were measured from Hs 766 T, MIA PaCa-2, and SU.86.86 pancreatic tumor models. We used each of these measurements as predictors for machine learning classifier algorithms. We also used principal component analysis to reduce the dimensionality of entire CEST spectra and DCE signal evolutions, which were then analyzed using classification methods. RESULTS: The T1 relaxation time constants, % CEST amplitudes at specific saturation frequencies, and the relative Ktrans and kep values from DCE MRI could not classify all three tumor types. However, the area under the curve from DCE signal evolutions could classify each tumor type. Principal component analysis was used to analyze the entire CEST spectrum and DCE signal evolutions, which predicted the correct tumor model with 87.5% and 85.1% accuracy, respectively. CONCLUSIONS: Machine learning applied to the entire CEST spectrum improved the classification of the three tumor models, relative to classifications that used % CEST values at single saturation frequencies. A similar improvement was not attained with machine learning applied to T1 relaxation times or DCE signal evolutions, relative to more simplistic analysis methods.

Assessments of tumor metabolism with CEST MRI.

Chemical exchange saturation transfer (CEST) is a relatively new contrast mechanism for MRI. CEST MRI exploits a specific MR frequency (chemical shift) of a molecule while generating an image with good spatial resolution using standard MRI techniques, combining the specificity of MRS with the spatial resolution of MRI. Many CEST MRI acquisition methods have been developed to improve analyses of tumor metabolism. GluCEST, CrCEST, and LATEST can map glutamate, creatine, and lactate, which are important metabolites involved in tumor metabolism. GlucoCEST MRI tracks the pharmacokinetics of glucose transport and cell internalization within tumors. CatalyCEST MRI detects enzyme catalysis that changes a substrate CEST agent. AcidoCEST MRI measures extracellular pH of the tumor microenvironment by exploiting a ratio of two pH-dependent CEST signals. This review describes each technique, the technical issues involved with CEST MRI and each specific technique, and the merits and challenges associated with applying each CEST MRI technique to study tumor metabolism.

A Comparison of Iron Oxide Particles and Silica Particles for Tracking Organ Recellularization.

Reseeding of decellularized organ scaffolds with a patient's own cells has promise for eliminating graft versus host disease. This study investigated whether ultrasound imaging or magnetic resonance imaging (MRI) can track the reseeding of murine liver scaffolds with silica-labeled or iron-labeled liver hepatocytes. Mesoporous silica particles were created using the Stöber method, loaded with Alexa Flour 647 fluorophore, and conjugated with protamine sulfate, glutamine, and glycine. Fluorescent iron oxide particles were obtained from a commercial source. Liver cells from donor mice were loaded with the silica particles or iron oxide particles. Donor livers were decellularized and reperfused with silica-labeled or iron-labeled cells. The reseeded livers were longitudinally analyzed with ultrasound imaging and MRI. Liver biopsies were imaged with confocal microscopy and scanning electron microscopy. Ultrasound imaging had a detection limit of 0.28 mg/mL, while MRI had a lower detection limit of 0.08 mg/mL based on particle weight. The silica-loaded cells proliferated at a slower rate compared to iron-loaded cells. Ultrasound imaging, MRI, and confocal microscopy underestimated cell numbers relative to scanning electron microscopy. Ultrasound imaging had the greatest underestimation due to coarse resolution compared to the other imaging modalities. Despite this underestimation, both ultrasound imaging and MRI successfully tracked the longitudinal recellularization of liver scaffolds.

A comparison of exogenous and endogenous CEST MRI methods for evaluating in vivo pH.

PURPOSE: Extracellular pH (pHe) is an important biomarker for cancer cell metabolism. Acido-chemical exchange saturation transfer (CEST) MRI uses the contrast agent iopamidol to create spatial maps of pHe. Measurements of amide proton transfer exchange rates (kex ) from endogenous CEST MRI were compared to pHe measurements by exogenous acido-CEST MRI to determine whether endogenous kex could be used as a proxy for pHe measurements. METHODS: Spatial maps of pHe and kex were obtained using exogenous acidoCEST MRI and an endogenous CEST MRI analyzed with the omega plot method, respectively, to evaluate mouse kidney, a flank tumor model, and a spontaneous lung tumor model. The pHe and kex results were evaluated using pixelwise comparisons. RESULTS: The kex values obtained from endogenous CEST measurements did not correlate with the pHe results from exogenous CEST measurements. The kex measurements were limited to fewer pixels and had a limited dynamic range relative to pHe measurements. CONCLUSION: Measurements of kex with endogenous CEST MRI cannot substitute for pHe measurements with acidoCEST MRI. Whereas endogenous CEST MRI may still have good utility for evaluating some specific pathologies, exogenous acido-CEST MRI is more appropriate when evaluating pathologies based on pHe values. Magn Reson Med 79:2766-2772, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Clinical applications of chemical exchange saturation transfer (CEST) MRI.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has been developed and employed in multiple clinical imaging research centers worldwide. Selective radiofrequency (RF) saturation pulses with standard 2D and 3D MRI acquisition schemes are now routinely performed, and CEST MRI can produce semiquantitative results using magnetization transfer ratio asymmetry (MTRasym ) analysis while accounting for B0 inhomogeneity. Faster clinical CEST MRI acquisition methods and more quantitative acquisition and analysis routines are under development. Endogenous biomolecules with amide, amine, and hydroxyl groups have been detected during clinical CEST MRI studies, and exogenous CEST agents have also been administered to patients. These CEST MRI tools show promise for contributing to assessments of cerebral ischemia, neurological disorders, lymphedema, osteoarthritis, muscle physiology, and solid tumors. This review summarizes the salient features of clinical CEST MRI protocols and critically evaluates the utility of CEST MRI for these clinical imaging applications. LEVEL OF EVIDENCE: 5 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:11-27.

Adaptive evolution toward larger size in mammals.

The notion that large body size confers some intrinsic advantage to biological species has been debated for centuries. Using a phylogenetic statistical approach that allows the rate of body size evolution to vary across a phylogeny, we find a long-term directional bias toward increasing size in the mammals. This pattern holds separately in 10 of 11 orders for which sufficient data are available and arises from a tendency for accelerated rates of evolution to produce increases, but not decreases, in size. On a branch-by-branch basis, increases in body size have been more than twice as likely as decreases, yielding what amounts to millions and millions of years of rapid and repeated increases in size away from the small ancestral mammal. These results are the first evidence, to our knowledge, from extant species that are compatible with Cope's rule: the pattern of body size increase through time observed in the mammalian fossil record. We show that this pattern is unlikely to be explained by several nonadaptive mechanisms for increasing size and most likely represents repeated responses to new selective circumstances. By demonstrating that it is possible to uncover ancient evolutionary trends from a combination of a phylogeny and appropriate statistical models, we illustrate how data from extant species can complement paleontological accounts of evolutionary history, opening up new avenues of investigation for both.

Bantu expansion shows that habitat alters the route and pace of human dispersals.

Unlike most other biological species, humans can use cultural innovations to occupy a range of environments, raising the intriguing question of whether human migrations move relatively independently of habitat or show preferences for familiar ones. The Bantu expansion that swept out of West Central Africa beginning ∼5,000 y ago is one of the most influential cultural events of its kind, eventually spreading over a vast geographical area a new way of life in which farming played an increasingly important role. We use a new dated phylogeny of ∼400 Bantu languages to show that migrating Bantu-speaking populations did not expand from their ancestral homeland in a "random walk" but, rather, followed emerging savannah corridors, with rainforest habitats repeatedly imposing temporal barriers to movement. When populations did move from savannah into rainforest, rates of migration were slowed, delaying the occupation of the rainforest by on average 300 y, compared with similar migratory movements exclusively within savannah or within rainforest by established rainforest populations. Despite unmatched abilities to produce innovations culturally, unfamiliar habitats significantly alter the route and pace of human dispersals.

Q&A: What is human language, when did it evolve and why should we care?

Human language is unique among all forms of animal communication. It is unlikely that any other species, including our close genetic cousins the Neanderthals, ever had language, and so-called sign 'language' in Great Apes is nothing like human language. Language evolution shares many features with biological evolution, and this has made it useful for tracing recent human history and for studying how culture evolves among groups of people with related languages. A case can be made that language has played a more important role in our species' recent (circa last 200,000 years) evolution than have our genes.

A biomarker-responsive T2ex MRI contrast agent.

PURPOSE: This study investigated a fundamentally new type of responsive MRI contrast agent for molecular imaging that alters T2 exchange (T2ex ) properties after interacting with a molecular biomarker. METHODS: The contrast agent Tm-DO3A-oAA was treated with nitric oxide (NO) and O2 . The R1 and R2 relaxation rates of the reactant and product were measured with respect to concentration, temperature, and pH. Chemical exchange saturation transfer (CEST) spectra of the reactant and product were acquired using a 7 Tesla (T) MRI scanner and analyzed to estimate the chemical exchange rates and r2ex relaxivities. RESULTS: The reaction of Tm-DO3A-oAA with NO and O2 caused a 6.4-fold increase in the r2 relaxivity of the agent, whereas r1 relaxivity remained unchanged, which demonstrated that Tm-DO3A-oAA is a responsive T2ex agent. The effects of pH and temperature on the r2 relaxivities of the reactant and product supported the conclusion that the product's benzimidazole ligand caused the agent to have a fast chemical exchange rate relative to the slow exchange rate of the reactant's ortho-aminoanilide ligand. CONCLUSIONS: T2ex MRI contrast agents are a new type of responsive agent that have good detection sensitivity and specificity for detecting a biomarker, which can serve as a new tool for molecular imaging. Magn Reson Med 77:1665-1670, 2017. © 2016 International Society for Magnetic Resonance in Medicine.