Targeting KRAS in NSCLC: Old Failures and New Options for “Non-G12c” Patients

Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) gene mutations are among the most common driver alterations in non-small cell lung cancer (NSCLC). Despite their high frequency, valid treatment options are still lacking, mainly due to an intrinsic complexity of both the protein structure and the downstream pathway. The increasing knowledge about different mutation subtypes and co-mutations has paved the way to several promising therapeutic strategies. Despite the best results so far having been obtained in patients harbouring KRAS exon 2 p.G12C mutation, even the treatment landscape of non-p.G12C KRAS mutation positive patients is predicted to change soon. This review provides a comprehensive and critical overview of ongoing studies into NSCLC patients with KRAS mutations other than p.G12C and discusses future scenarios that will hopefully change the story of this disease.

Technical Note—A Permutation-Dependent Separability Approach for Capacitated Two-Echelon Inventory Systems

Analyzing Capacitated Two-Echelon Systems with Permutation-Dependent Separability Capacitated multiechelon systems are common in practice due to the escalating costs of labor and advanced manufacturing technology. However, identifying the optimal replenishment policies for such systems is a largely open area of research due to the intrinsic complexity, especially when there is an upstream bottleneck. In “A Permutation-Dependent Separability Approach for Capacitated Two-Echelon Inventory Systems”, Shen, Yu, and Huh propose a new approach, that is, permutation-dependent separability, to tackle a capacitated two-echelon system in which the capacity of upstream stage can be the bottleneck. They show that the value function for the capacitated two-echelon system in each period is permutation-dependent separable, and that for each echelon, a permutation-dependent echelon base stock policy is optimal. The authors also develop efficient solution procedures on how to obtain the optimal policy.

Wnt5A Signaling Blocks Progression of Experimental Visceral Leishmaniasis

Visceral Leishmaniasis, caused by L. donovani infection is fatal if left untreated. The intrinsic complexity of visceral leishmaniasis complicated further by the increasing emergence of drug resistant L. donovani strains warrants fresh investigations into host defense schemes that counter infections. Accordingly, using a mouse model of experimental visceral leishmaniasis we explored the utility of host Wnt5A in restraining L. donovani infection, using both antimony sensitive and antimony resistant L. donovani strains. We found that Wnt5A heterozygous (Wnt5A +/-) mice are more susceptible to L. donovani infection than their wild type (Wnt5A +/+) counterparts as depicted by the respective Leishman Donovan Units (LDU) enumerated from the liver and spleen harvested from infected mice. Higher LDU in Wnt5A +/- mice correlated with increased level of plasma gammaglobulin, liver granuloma and disorganization of splenic germinal centers. Progression of infection in mice by both antimony sensitive and antimony resistant strains of L. donovani could be prevented by activation of Wnt5A signaling as evident from the lowered LDU and gammaglobulin level, and intactness of splenic germinal centers through intravenous administration of rWnt5A prior to L. donovani infection. Wnt5A mediated blockade of L. donovani infection correlated with the preservation of splenic macrophages and activated T cells, and a TH1 like cytokine thrust. Taken together our results indicate that depletion of Wnt5A promotes susceptibility to visceral leishmaniasis and revamping Wnt5A signaling in the host is able to curb L. donovani infection irrespective of antimony sensitivity or resistance and mitigate the progression of visceral leishmaniasis.

Metamodels’ Development for High Pressure Die Casting of Aluminum Alloy

Simulation is a very useful tool in the design of the part and process conditions of high-pressure die casting (HPDC), due to the intrinsic complexity of this manufacturing process. Usually, physics-based models solved by finite element or finite volume methods are used, but their main drawback is the long calculation time. In order to apply optimization strategies in the design process or to implement online predictive systems, faster models are required. One solution is the use of surrogate models, also called metamodels or grey-box models. The novelty of the work presented here lies in the development of several metamodels for the HPDC process. These metamodels are based on a gradient boosting regressor technique and derived from a physics-based finite element model. The results show that the developed metamodels are able to predict with high accuracy the secondary dendrite arm spacing (SDAS) of the cast parts and, with good accuracy, the misrun risk and the shrinkage level. Results obtained in the predictions of microporosity and macroporosity, eutectic percentage, and grain density were less accurate. The metamodels were very fast (less than 1 s); therefore, they can be used for optimization activities or be integrated into online prediction systems for the HPDC industry. The case study corresponds to several parts of aluminum cast alloys, used in the automotive industry, manufactured by high-pressure die casting in a multicavity mold.

Decomposition Methods for Tourism Demand Forecasting: A Comparative Study

Decomposition methods are extensively used for processing the complex patterns of tourism demand data. Given tourism demand data’s intrinsic complexity, it is critical to theoretically understand how different decomposition methods provide solutions. However, a comprehensive comparison of decomposition methods in tourism demand forecasting is still lacking. Hence, this study systematically investigates the forecasting performance of decomposition methods in tourism demand. Nine popular decomposition methods and six forecasting methods are employed, and their forecasting performance is compared. With Hong Kong visitor arrivals from eight major sources as a sample, three main conclusions are obtained from empirical results. First, all the decomposition methods generally outperform benchmark at all horizons, in both the level and directional forecasting. Second, decomposition methods can be divided into four categories based on forecasting accuracy. Finally, variational mode decomposition method is consistently superior to other eight decomposition methods and can provide the best forecasts in all cases.

Orchestrating and sharing large multimodal data for transparent and reproducible research

Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA (orcestra.ca), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies.

Seeing Complexity To Continue to Understand Emotions

Commentary on Michael S. Brady’s book, Emotion: The Basics, indicating that it offers an overview of the field of philosophy of emotions while raising awareness about the intrinsic complexity of the issues in emotion research. This makes it possible to show how emotion research is inevitably tied to specific philosophical assumptions. Three illustrations are discussed that hopefully also testify that, as Brady states, the philosophy of emotion is inevitably tied to the question of what it means to do philosophy.

Artificial Intelligence for Pigment Classification Task in the Short-Wave Infrared Range

Hyperspectral reflectance imaging in the short-wave infrared range (SWIR, “extended NIR”, ca. 1000 to 2500 nm) has proven to provide enhanced characterization of paint materials. However, the interpretation of the results remains challenging due to the intrinsic complexity of the SWIR spectra, presenting both broad and narrow absorption features with possible overlaps. To cope with the high dimensionality and spectral complexity of such datasets acquired in the SWIR domain, one data treatment approach is tested, inspired by innovative development in the cultural heritage field: the use of a pigment spectral database (extracted from model and historical samples) combined with a deep neural network (DNN). This approach allows for multi-label pigment classification within each pixel of the data cube. Conventional Spectral Angle Mapping and DNN results obtained on both pigment reference samples and a Buddhist painting (thangka) are discussed.

Anharmonic DFT Study of Near-Infrared Spectra of Caffeine: Vibrational Analysis of the Second Overtones and Ternary Combinations

Anharmonic quantum chemical calculations were employed to simulate and interpret a near-infrared (NIR) spectrum of caffeine. First and second overtones, as well as binary and ternary combination bands, were obtained, accurately reproducing the lineshape of the experimental spectrum in the region of 10,000–4000 cm−1 (1000–2500 nm). The calculations enabled performing a detailed analysis of NIR spectra of caffeine, including weak bands due to the second overtones and ternary combinations. A highly convoluted nature of NIR spectrum of caffeine was unveiled, with numerous overlapping bands found beneath the observed spectral lineshape. To properly reflect that intrinsic complexity, the band assignments were provided in the form of heat maps presenting the contributions to the NIR spectrum from various kinds of vibrational transitions. These contributions were also quantitatively assessed in terms of the integral intensities. It was found that the combination bands provide the decisively dominant contributions to the NIR spectrum of caffeine. The first overtones gain significant importance between 6500–5500 cm−1, while the second overtones are meaningful in the higher wavenumber regions, particularly in the 10,000–7000 cm−1 region. The obtained detailed band assignments enabled deep interpretation of the absorption regions of caffeine identified in the literature as meaningful for analytical applications of NIR spectroscopy focused on quantitative analysis of caffeine content in drugs and natural products.

In Search of Climate Politics

In what ways is climate change political? This book addresses this key - but oddly neglected - question. It argues that in order to answer it we need to understand politics in a three-fold way: as a site of authoritative, public decision-making; as a question of power; and as a conflictual phenomenon. Recurring themes center on de- and re-politicization, and a tension between attempts to simplify climate change to a single problem and its intrinsic complexity. These dynamics are driven by processes of capital accumulation and their associated subjectivities. The book explores these arguments through an analysis of a specific city - Ottawa - which acts as a microcosm of these broader processes. It provides detailed analyses of conflicts over urban planning, transport, and attempts by city government and other institutions to address climate change. The book will be valuable for students and researches looking at the politics of climate change.