Neogene sediments in the northern Chilean forearc display a wide range of near syndepositional structures. Analysis of the origin and distribution of these structures in space and time offers new insights into the development of the forearc basins. The structures are described in detail and show many features associated with soft-sediment deformation, pseudo-diapirism and slope failure. Synsedimentary deformation reached a peak in the Late Miocene to Early Pliocene while the sediments were saturated in a largely plastic state, and many of the structures were probably triggered by seismic shock. Late-stage tilting of the forearc generated shear stresses in the sediments leading to slumping and sliding. Base-level revision and drainage incision led to sediment bypass and cessation of lacustrine sedimentation that was not necessarily linked to climate change. Compaction and dewatering of the basins caused transition of the sediments from a plastic to a brittle state. The age and distribution of structures associated with seismicity appears to correlate with increasing subduction erosion and westward drift of South America but not with basin subsidence, shortening rates or plate convergence. This suggests that upper crustal deformation is at least partly decoupled from plate movement.

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V ery few functions seem as well-positioned to create value as the corporate strategy function. Even the name, corporate strategy, suggests access to critical information and decision-makers, as well as distinctive contributions to the organization's most important decisions. Yet many corporate strategy managers find that their contributions are limited and they are unable to have significant, tangible impact. The value of the corporate strategy function is questioned as a result, and senior executives are faced with the question ''How can I increase the impact of my corporate strategy function?'' Our research shows that companies can improve the impact of their corporate strategy function, but choosing to do so requires a significant commitment to address the organization structure, processes and people competencies that limit the function's ability to have impact. Approach The authors conducted qualitative interviews of corporate or business unit strategy executives and senior managers from 11 different companies representing the manufacturing, electric/gas utility, petroleum and retail industries in the United States and Canada. The interviews focused on understanding the scope of the function, the nature of its work, and the process, organization, and people characteristics of the function. The interviews were synthesized into a simple, 2 £ 2 matrix that characterizes corporate strategy functions and sheds light on how their impact can be increased. Corporate strategy contributions Contributions are the work that the strategy function performs. Examples include managing the annual planning process, developing/maintaining relevant methodologies (e.g. business

Chronic exertional compartment syndrome (CECS) is a condition occurring most frequently in the lower limbs and often requires corrective surgery to alleviate symptoms. Amongst military personnel, the success rates of this surgery can be as low as 20%, presenting a challenge in determining whether surgery is worthwhile. In this study, the data of 132 fasciotomies for CECS was analysed and using combinatorial feature selection methods, coupled with input from clinicians, identified a set of key clinical features contributing to the occupational outcomes of surgery. Features were utilised to develop a machine learning model for predicting return-to-work outcomes 12-months post-surgery. An AUC of 0.85 ± 0.08 was achieved using a linear-SVM, trained using 6 features (height, mean arterial pressure, pre-surgical score on the exercise-induced leg pain questionnaire, time from initial presentation to surgery, and whether a patient had received a prior surgery for CECS). To facilitate trust and transparency, interrogation strategies were used to identify reasons why certain patients were misclassified, using instance hardness measures. Model interrogation revealed that patient difficulty was associated with an overlap in the clinical characteristics of surgical outcomes, which was best handled by XGBoost and SVM-based models. The methodology was compiled into a machine learning framework, termed AITIA, which can be applied to other clinical problems. AITIA extends the typical machine learning pipeline, integrating the proposed interrogation strategy, allowing to user to reason and decide whether to trust the developed model based on the sensibility of its decision-making.

Non-clear cell renal cell carcinomas (non-ccRCCs) represent ~15-20% RCCs cases comprising nearly 20 different disease subtypes and a wide spectrum of clinical behavior from benign to highly aggressive course. Clinically, metastatic non-ccRCC patients, regardless of subtypes with distinct genomic aberrations, are all treated with the same standard of care therapies, underscoring the need for precision therapeutic strategies. Diagnostic challenges also exist as benign and malignant entities often display overlapping histomorphologies that current diagnostic cytokeratin markers cannot resolve. Therefore, identification of more reliable diagnostic and prognostic non-ccRCC biomarkers remains an unmet need in this field. As part of the Clinical Tumor Analysis Consortium (CPTAC), we performed integrative analysis of multi-omics data including genomic next generation sequencing-based whole exome, whole genome, RNAseq, snRNAseq and mass spectrometry-based proteomics, post translational modifications (glycosylation and phosphorylation) and metabolomic profiles generated by CPTAC. The composition of the kidney tumor cohort (n=151) included 103 ccRCC, 15 oncocytomas, 13 papillary RCC (PRCC), 11 other rare tumors and 8 unclassified RCCs. Our multi-omic analysis revealed both unique and shared molecular features of RCC subtypes. We characterized proteogenomic, PTM and glycoproteome impact of genome instability (GI), a feature that is associated with poor prognosis in both ccRCC and non-ccRCC and affects 10-15% of cases. These analyses identified new prognostic signatures, outlier targetable kinase expression patterns, kinase-substrate relationships and differential protein glycosylation events. Glycoproteome analysis also revealed variation in cell-type specific marker expression among RCC subtypes such as FUT8 (core-fucosyltransferase) associated protein glycosylation in PRCC. Integrative analysis of snRNA-seq data predicted diverse tumor cell-of-origin and stratified RCC subtype specific proteogenomic signatures. Differential expression analysis revealed several novel diagnostic makers including MAPRE3, GPNMB, PIGR, SOSTDC1. These biomarkers were validated by IHC and their addition to existing panels results in improved diagnostic specificity. Metabolic characterization revealed RCC subtype-specific differences and increased oncometabolite SAICAR in oncocytomas that may have functional significance. The valuable proteogenomic data resource we generated contains several rare tumor types that are hard to obtain for proteogenomic characterization at the scale described here, and will certainly aid in future pan-RCC studies. Citation Format: Ginny Xiaohe Li, Yi Hsiao, Lijun Chen, Rahul Mannan, Yuping Zhang, Francesca Petralia, Hanbyul Cho, Noshad Hosseini, Anna Calinawan, Yize Li, Shankara Anand, Aniket Dagar, Yifat Geffen, Felipe V. Leprevost, Anne Le, Sean Ponce, Michael Schnaubelt, Nataly Naser Al Deen, Wagma Caravan, Andrew Houston, Chandan Kumar-Sinha, Xiaoming Wang, Seema Chugh, Gilbert S. Omenn, Daniel W. Chan, Christopher Ricketts, Rohit Mehra, Arul Chinnaiyan, Li Ding, Marcin Cieslik, Hui Zhang, Saravana M. Dhanasekaran, Alexey I. Nesvizhskii. Comprehensive proteogenomic characterization of rare kidney tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3127.