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In 1842, the pioneering French photographer Joseph-Philibert Girault de Prangey (1804-1892) set out eastward across the Mediterranean with a custom-built camera to explore ancient lands that were largely unknown to the Western world. This book is the first to fully consider the hundreds of daguerreotypes that resulted from his three-year journey, many of which were made using innovative techniques that fascinate photographers to this day. The images, including the first-known photographic documentation of significant locations, offer tangible evidence of historic sites, many of which have since been destroyed, in places such as Greece, Italy, Egypt, Turkey, Syria, and Jerusalem. They are remarkable and unparalleled portraits of a world gone by. Copiously illustrated and featuring a geographic glossary of the sites and images, Monumental Journey sheds new light on the arc of Girault's career, the vibrant orientalist milieu of 19th-century France that shaped his work, and his inventive contributions to the nascent field of photography. It introduces modern audiences to a brilliant yet enigmatic talent, as well as the stunning images, many published here for the time, that make a major contribution to the histories of both photography and eastern Mediterranean.--Exhibition: The Metropolitan Museum of Art, New York (30.10-12.05.2019); Musâee d'Orsay, Paris, France (17.06-13.10.2019).

The ciliate Tetrahymena thermophila is a model organism for molecular and cellular biology. Like other ciliates, this species has separate germline and soma functions that are embodied by distinct nuclei within a single cell. The germline-like micronucleus (MIC) has its genome held in reserve for sexual reproduction. The soma-like macronucleus (MAC), which possesses a genome processed from that of the MIC, is the center of gene expression and does not directly contribute DNA to sexual progeny. We report here the shotgun sequencing, assembly, and analysis of the MAC genome of T. thermophila, which is approximately 104 Mb in length and composed of approximately 225 chromosomes. Overall, the gene set is robust, with more than 27,000 predicted protein-coding genes, 15,000 of which have strong matches to genes in other organisms. The functional diversity encoded by these genes is substantial and reflects the complexity of processes required for a free-living, predatory, single-celled organism. This is highlighted by the abundance of lineage-specific duplications of genes with predicted roles in sensing and responding to environmental conditions (e.g., kinases), using diverse resources (e.g., proteases and transporters), and generating structural complexity (e.g., kinesins and dyneins). In contrast to the other lineages of alveolates (apicomplexans and dinoflagellates), no compelling evidence could be found for plastid-derived genes in the genome. UGA, the only T. thermophila stop codon, is used in some genes to encode selenocysteine, thus making this organism the first known with the potential to translate all 64 codons in nuclear genes into amino acids. We present genomic evidence supporting the hypothesis that the excision of DNA from the MIC to generate the MAC specifically targets foreign DNA as a form of genome self-defense. The combination of the genome sequence, the functional diversity encoded therein, and the presence of some pathways missing from other model organisms makes T. thermophila an ideal model for functional genomic studies to address biological, biomedical, and biotechnological questions of fundamental importance.

Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model's predictions to clinical experts during interpretation. Our dataset consisted of 1,370 knee MRI exams performed at Stanford University Medical Center between January 1, 2001, and December 31, 2012 (mean age 38.0 years; 569 [41.5%] female patients). The majority vote of 3 musculoskeletal radiologists established reference standard labels on an internal validation set of 120 exams. We developed MRNet, a convolutional neural network for classifying MRI series and combined predictions from 3 series per exam using logistic regression. In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the receiver operating characteristic curve (AUC) values of 0.937 (95% CI 0.895, 0.980), 0.965 (95% CI 0.938, 0.993), and 0.847 (95% CI 0.780, 0.914), respectively, on the internal validation set. We also obtained a public dataset of 917 exams with sagittal T1-weighted series and labels for ACL injury from Clinical Hospital Centre Rijeka, Croatia. On the external validation set of 183 exams, the MRNet trained on Stanford sagittal T2-weighted series achieved an AUC of 0.824 (95% CI 0.757, 0.892) in the detection of ACL injuries with no additional training, while an MRNet trained on the rest of the external data achieved an AUC of 0.911 (95% CI 0.864, 0.958). We additionally measured the specificity, sensitivity, and accuracy of 9 clinical experts (7 board-certified general radiologists and 2 orthopedic surgeons) on the internal validation set both with and without model assistance. Using a 2-sided Pearson's chi-squared test with adjustment for multiple comparisons, we found no significant differences between the performance of the model and that of unassisted general radiologists in detecting abnormalities. General radiologists achieved significantly higher sensitivity in detecting ACL tears (p-value = 0.002; q-value = 0.019) and significantly higher specificity in detecting meniscal tears (p-value = 0.003; q-value = 0.019). Using a 1-tailed t test on the change in performance metrics, we found that providing model predictions significantly increased clinical experts' specificity in identifying ACL tears (p-value < 0.001; q-value = 0.006). The primary limitations of our study include lack of surgical ground truth and the small size of the panel of clinical experts. Our deep learning model can rapidly generate accurate clinical pathology classifications of knee MRI exams from both internal and external datasets. Moreover, our results support the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation. Further research is needed to validate the model prospectively and to determine its utility in the clinical setting.

Recent research on ocean health has found large predator abundance to be a key element of ocean condition. Fisheries can impact large predator abundance directly through targeted capture and indirectly through incidental capture of nontarget species or bycatch. However, measures of the global nature of bycatch are lacking for air-breathing megafauna. We fill this knowledge gap and present a synoptic global assessment of the distribution and intensity of bycatch of seabirds, marine mammals, and sea turtles based on empirical data from the three most commonly used types of fishing gears worldwide. We identify taxa-specific hotspots of bycatch intensity and find evidence of cumulative impacts across fishing fleets and gears. This global map of bycatch illustrates where data are particularly scarce—in coastal and small-scale fisheries and ocean regions that support developed industrial fisheries and millions of small-scale fishers—and identifies fishing areas where, given the evidence of cumulative hotspots across gear and taxa, traditional species or gear-specific bycatch management and mitigation efforts may be necessary but not sufficient. Given the global distribution of bycatch and the mitigation success achieved by some fleets, the reduction of air-breathing megafauna bycatch is both an urgent and achievable conservation priority.

Although diabetes results in part from a deficiency of normal pancreatic beta cells, inducing human beta cells to regenerate is difficult. Reasoning that insulinomas hold the “genomic recipe” for beta cell expansion, we surveyed 38 human insulinomas to obtain insights into therapeutic pathways for beta cell regeneration. An integrative analysis of whole-exome and RNA-sequencing data was employed to extensively characterize the genomic and molecular landscape of insulinomas relative to normal beta cells. Here, we show at the pathway level that the majority of the insulinomas display mutations, copy number variants and/or dysregulation of epigenetic modifying genes, most prominently in the polycomb and trithorax families. Importantly, these processes are coupled to co-expression network modules associated with cell proliferation, revealing candidates for inducing beta cell regeneration. Validation of key computational predictions supports the concept that understanding the molecular complexity of insulinoma may be a valuable approach to diabetes drug discovery. Diabetes results in part from a deficiency of functional pancreatic beta cells. Here, the authors study the genomic and epigenetic landscapes of human insulinomas to gain insight into possible pathways for therapeutic beta cell regeneration, highlighting epigenetic genes and pathways.

An increasing number of applied disciplines are utilizing evidence-based frameworks to review and disseminate the effectiveness of management and policy interventions. The rationale is that increased accessibility of the best available evidence will provide a more efficient and less biased platform for decision making. We argue that there are significant benefits for conservation in using such a framework, but the scientific community needs to undertake and disseminate more systematic reviews before the full benefit can be realized. We devised a set of guidelines for undertaking formalized systematic review, based on a health services model. The guideline stages include planning and conducting a review, including protocol formation, search strategy, data inclusion, data extraction, and analysis. Review dissemination is addressed in terms of current developments and future plans for a Web-based open-access library. By the use of case studies we highlight critical modifications to guidelines for protocol formulation, data-quality assessment, data extraction, and data synthesis for conservation and environmental management. Ecological data presented significant but soluble challenges for the systematic review process, particularly in terms of the quantity, accessibility, and diverse quality of available data. In the field of conservation and environmental management there needs to be further engagement of scientists and practitioners to develop and take ownership of an evidence-based framework.

The apolipoprotein E (APOE) - ε4 allele is the strongest genetic risk factor for late-onset, sporadic Alzheimer's disease, likely increasing risk by altering amyloid-β (Aβ) accumulation. We recently demonstrated that the low-density lipoprotein receptor (LDLR) is a major apoE receptor in the brain that strongly regulates amyloid plaque deposition. In the current study, we sought to understand the mechanism by which LDLR regulates Aβ accumulation by altering Aβ clearance from brain interstitial fluid. We hypothesized that increasing LDLR levels enhances blood–brain barrier-mediated Aβ clearance, thus leading to reduced Aβ accumulation. Using the brain Aβ efflux index method, we found that blood–brain barrier-mediated clearance of exogenously administered Aβ is enhanced with LDLR overexpression. We next developed a method to directly assess the elimination of centrally derived, endogenous Aβ into the plasma of mice using an anti-Aβ antibody that prevents degradation of plasma Aβ, allowing its rate of appearance from the brain to be measured. Using this plasma Aβ accumulation technique, we found that LDLR overexpression enhances brain-to-blood Aβ transport. Together, our results suggest a unique mechanism by which LDLR regulates brain-to-blood Aβ clearance, which may serve as a useful therapeutic avenue in targeting Aβ clearance from the brain.

Abstract Context Pituitary corticotroph adenomas are rare tumors that can be associated with excess adrenocorticotropin (ACTH) and adrenal cortisol production, resulting in the clinically debilitating endocrine condition Cushing disease. A subset of corticotroph tumors behave aggressively, and genomic drivers behind the development of these tumors are largely unknown. Objective To investigate genomic drivers of corticotroph tumors at risk for aggressive behavior. Design Whole-exome sequencing of patient-matched corticotroph tumor and normal deoxyribonucleic acid (DNA) from a patient cohort enriched for tumors at risk for aggressive behavior. Setting Tertiary care center Patients Twenty-seven corticotroph tumors from 22 patients were analyzed. Twelve tumors were macroadenomas, of which 6 were silent ACTH tumors, 2 were Crooke’s cell tumors, and 1 was a corticotroph carcinoma. Intervention Whole-exome sequencing. Main outcome measure Somatic mutation genomic biomarkers. Results We found recurrent somatic mutations in USP8 and TP53 genes, both with higher allelic fractions than other somatic mutations. These mutations were mutually exclusive, with TP53 mutations occurring only in USP8 wildtype (WT) tumors, indicating they may be independent driver genes. USP8-WT tumors were characterized by extensive somatic copy number variation compared with USP8-mutated tumors. Independent of molecular driver status, we found an association between invasiveness, macroadenomas, and aneuploidy. Conclusions Our data suggest that corticotroph tumors may be categorized into a USP8-mutated, genome-stable subtype versus a USP8-WT, genome-disrupted subtype, the latter of which has a TP53-mutated subtype with high level of chromosome instability. These findings could help identify high risk corticotroph tumors, namely those with widespread CNV, that may need closer monitoring and more aggressive treatment.

The development of native-like HIV-1 envelope (Env) trimer antigens has enabled the induction of neutralizing antibody (NAb) responses against neutralization-resistant HIV-1 strains in animal models. However, NAb responses are relatively weak and narrow in specificity. Displaying antigens in a multivalent fashion on nanoparticles (NPs) is an established strategy to increase their immunogenicity. Here we present the design and characterization of two-component protein NPs displaying 20 stabilized SOSIP trimers from various HIV-1 strains. The two-component nature permits the incorporation of exclusively well-folded, native-like Env trimers into NPs that self-assemble in vitro with high efficiency. Immunization studies show that the NPs are particularly efficacious as priming immunogens, improve the quality of the Ab response over a conventional one-component nanoparticle system, and are most effective when SOSIP trimers with an apex-proximate neutralizing epitope are displayed. Their ability to enhance and shape the immunogenicity of SOSIP trimers make these NPs a promising immunogen platform. Nanoparticles are a promising approach to increase immunogenicity of protein antigens for vaccines. Here, Brouwer et al . design self-assembling, two-component protein NPs that present native-like SOSIP trimers of HIV envelope protein and determine immunogenicity in a small animal model.