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Cancer immunotherapy has transformed the treatment landscape for numerous malignancies and is emerging as an increasingly revolutionary and promising approach for cancer treatment. Clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) screening, a potent tool for unbiased functional genomic screening, has unveiled novel molecular targets for cancer immunotherapy. CRISPR/Cas9 screening facilitates the identification of targets in both cancer cells and immune cells, including T and natural killer cells. Various CRISPR screening methods such as in vitro and in vivo screening, along with diverse techniques including CRISPR-ko, CRISPRi, and CRISPRa screening, are utilized for target identification across various cancer models. In vivo CRISPR screening replicates human cancer by preserving the native microenvironment and facilitating high-throughput analysis of intricate cancer processes. Clustered regularly interspaced palindromic repeats (CRISPR)-based technology, a powerful toolset for the unbiased functional genomic screening of biological processes, has facilitated several scientific breakthroughs in the biomedical field. Cancer immunotherapy has advanced the treatment of numerous malignancies that previously had restricted treatment options or unfavorable outcomes. In the realm of cancer immunotherapy, the application of CRISPR/CRISPR-associated protein 9 (Cas9)-based genetic perturbation screening has enabled the identification of genes, biomarkers, and signaling pathways that govern various cancer immunoreactivities, as well as the development of effective immunotherapeutic targets. In this review, we summarize the advances in CRISPR/Cas9-based screening for cancer immunotherapy and outline the immunotherapeutic targets identified via CRISPR screening based on cancer-type classification. [ABSTRACT FROM AUTHOR]

Simultaneous noninvasive and invasive electrophysiological recordings provide a unique opportunity to achieve a comprehensive understanding of human brain activity, much like a Rosetta stone for human neuroscience. In this review we focus on the increasingly-used powerful combination of intracranial electroencephalography (iEEG) with scalp electroencephalography (EEG) or magnetoencephalography (MEG). We first provide practical insight on how to achieve these technically challenging recordings. We then provide examples from clinical research on how simultaneous recordings are advancing our understanding of epilepsy. This is followed by the illustration of how human neuroscience and methodological advances could benefit from these simultaneous recordings. We conclude with a call for open data sharing and collaboration, while ensuring neuroethical approaches and argue that only with a true collaborative approach the promises of simultaneous recordings will be fulfilled. • Complementary views of simultaneous scalp EEG / MEG and intracranial EEG in humans. • Simultaneous recordings are advancing our understanding of epilepsy. • Simultaneous recordings are improving our understanding of human neuroscience. • Simultaneous recordings provide a ground truth for source localization. • Data sharing within neuroethical boundaries is essential to advance the field. [ABSTRACT FROM AUTHOR]

The constellation of Earth-observing satellites continuously collects measurements of scattered radiance, which must be transformed into geophysical parameters in order to answer fundamental scientific questions about the Earth. Retrieval of these parameters requires highly flexible, accurate, and fast forward and inverse radiative transfer models. Existing forward models used by the remote sensing community are typically accurate and fast, but sacrifice flexibility by assuming the atmosphere or ocean is composed of plane-parallel layers. Monte Carlo forward models can handle more complex scenarios such as 3D spatial heterogeneity, but are relatively slower. We propose looking to the computer graphics community for inspiration to improve the statistical efficiency of Monte Carlo forward models and explore new approaches to inverse models for remote sensing. In Part 1 of this work, we examine the evolution of radiative transfer models in computer graphics and highlight recent advancements that have the potential to push forward models in remote sensing beyond their current periphery of realism. • A Rosetta Stone for the remote sensing and computer graphics communities. • We highlight recent advances in Monte Carlo radiative transfer in computer graphics. • We extend a new computer graphics framework, Mitsuba 3, to perform benchmark tests. • Our framework's error is on par with existing codes on benchmark tests. [ABSTRACT FROM AUTHOR]

The impact of left ventricular (LV) diastolic dysfunction (DD) on the outcome of patients with heart failure was established over three decades ago. Nevertheless, the relevance of LVDD for critically ill patients admitted to the intensive care unit has seen growing interest recently, and LVDD is associated with poor prognosis. Whilst an assessment of LV diastolic function is desirable in critically ill patients, treatment options for LVDD are very limited, and pharmacological possibilities to rapidly optimize diastolic function have not been found yet. Hence, a proactive approach might have a substantial role in improving the outcomes of these patients. Recalling historical Egyptian parallelism suggesting that Doppler echocardiography has been the "Rosetta stone" to decipher the study of LV diastolic function, we developed a potentially useful acronym for physicians at the bedside to optimize the management of critically ill patients with LVDD with the application of the bundle. We summarized the bundle under the acronym of the famous ancient Egyptian pharaoh CHEOPS: C hest Ultrasound, combining information from echocardiography and lung ultrasound; HE modynamics assessment, with careful evaluation of heart rate and rhythm, as well as afterload and vasoactive drugs; OP timization of mechanical ventilation and pulmonary circulation, considering the effects of positive end-expiratory pressure on both right and left heart function; S tabilization, with cautious fluid administration and prompt fluid removal whenever judged safe and valuable. Notably, the CHEOPS bundle represents experts' opinion and are not targeted at the initial resuscitation phase but rather for the optimization and subsequent period of critical illness. [ABSTRACT FROM AUTHOR]

The high-temperature superconductivity in layered cuprates discovered by Bednorz and Müller arrived as a shock. Gradually, the presence of competing orders, such as antiferromagnetism and charge order, were discovered; however, the relationship to the superconductivity has been confusing. It so happens that the original cuprate superconductor family La 2 − x Ba x CuO 4 contains all of the relevant phases, with extreme competition among them, and analysis of these phases provides strong clues to the nature of the superconductivity in cuprates. [ABSTRACT FROM AUTHOR]

Pedotransfer functions (PTFs) have been widely used to predict soil hydraulic parameters in favor of expensive laboratory or field measurements. Rosetta (Schaap et al., 2001, denoted as Rosetta1) is one of many PTFs and is based on artificial neural network (ANN) analysis coupled with the bootstrap re-sampling method which allows the estimation of van Genuchten water retention parameters (van Genuchten, 1980, abbreviated here as VG), saturated hydraulic conductivity ( K s ), and their uncertainties. In this study, we present an improved set of hierarchical pedotransfer functions (Rosetta3) that unify the water retention and K s submodels into one. Parameter uncertainty of the fit of the VG curve to the original retention data is used in the ANN calibration procedure to reduce bias of parameters predicted by the new PTF. One thousand bootstrap replicas were used to calibrate the new models compared to 60 or 100 in Rosetta1, thus allowing the uni-variate and bi-variate probability distributions of predicted parameters to be quantified in greater detail. We determined the optimal weights for VG parameters and K s , the optimal number of hidden nodes in ANN, and the number of bootstrap replicas required for statistically stable estimates. Results show that matric potential-dependent bias was reduced significantly while root mean square error ( RMSE ) for water content were reduced modestly; RMSE for K s was increased by 0.9% (H3 w ) to 3.3% (H5 w ) in the new models on log scale of K s compared with the Rosetta1 model. It was found that estimated distributions of parameters were mildly non-Gaussian and could instead be described rather well with heavy-tailed α-stable distributions. On the other hand, arithmetic means had only a small estimation bias for most textures when compared with the mean-like “shift” parameter of the α-stable distributions. Arithmetic means and (co-)variances are therefore still recommended as summary statistics of the estimated distributions. However, it may be necessary to parameterize the distributions in different ways if the new estimates are used in stochastic analyses of vadose zone flow and transport. Rosetta1 and Posetta3 were implemented in the python programming language, and the source code as well as additional documentation is available at: http://www.cals.arizona.edu/research/rosettav3.html . [ABSTRACT FROM AUTHOR]

Proteins, the main cell machinery which play a major role in nearly every cellular process, have always been a central focus in biology. We live in the post-genomic era, and inferring information from massive data sets is a steadily growing universal challenge. The increasing availability of fully sequenced genomes can be regarded as the ‘Rosetta Stone’ of the protein universe, allowing the understanding of genomes and their evolution, just as the original Rosetta Stone allowed Champollion to decipher the ancient Egyptian hieroglyphics. In this review, we consider aspects of the protein domain architectures repertoire that are closely related to those of human languages and aim to provide some insights about the language of proteins. [ABSTRACT FROM AUTHOR]

Who deciphered the Rosetta Stone and the Egyptian hieroglyphs? The usual answer is Jean-François Champollion, beginning in 1822. But ever since that time, Egyptologists have debated the role of his rival, the polymath Thomas Young, the first person to publish a partially correct translation of the Rosetta Stone. A recent BBC television dramatisation rekindled the controversy by presenting Champollion as a ‘lone genius’ who succeeded independently of Young. While there is no doubt that Champollion deciphered the hieroglyphic script as a whole, the evidence suggests that Young''s early detailed study of the Rosetta Stone created the conceptual framework that made possible Champollion''s later breakthrough. [Copyright &y& Elsevier]