Evidence for Dirac Fermions in a honeycomb lattice based on silicon

Silicene, a sheet of silicon atoms in a honeycomb lattice, was proposed to be a new Dirac-type electron system similar as graphene. We performed scanning tunneling microscopy and spectroscopy studies on the atomic and electronic properties of silicene on Ag(111). An unexpected $\sqrt{3}\times \sqrt{3}$ reconstruction was found, which is explained by an extra-buckling model. Pronounced quasi-particle interferences (QPI) patterns, originating from both the intervalley and intravalley scattering, were observed. From the QPI patterns we derived a linear energy-momentum dispersion and a large Fermi velocity, which prove the existence of Dirac Fermions in silicene.Comment: 6 pages, 4 figure

Immunotherapy for Renal Cell Carcinoma

Despite the rapid development of therapeutic modalities for advanced or metastatic renal cell carcinoma (mRCC) over the past decade to include traditional immunotherapy, such as high-dose interleukin-2 and interferon-α, as well as a number of targeted antiangiogenic therapies, mRCC continues to be associated with poor prognosis. Currently, immunotherapy has seen tremendous development in the form of immune checkpoint inhibition and vaccines at a dizzying pace, which are being studied in mRCC and are showing promise as important steps in the management of this disease. With so many drugs available to clinicians and patients, properly integrating immunotherapy especially immune checkpoint blockade (ICB) into the treatment paradigm is challenging. Emerging research with additional ICB agents and novel combination strategies is likely to further impact clinical decision-making. The further development of biomarkers for predicting a response is required to achieve optimal efficacy with these therapeutic interventions. This chapter summarizes the current landscape of standard and emerging immune therapeutics and other modalities for mRCC

Definition of Multi-State Weighted k-out-of-n: F System

International audienceThe Multi-state Weighted k-out-of-n System model is the generalization of the Multi-state k-out-of-n System model, which finds wide applications in industry. However only Multi-state Weighted k-out-of-n: G System models have been defined and studied in most recent research works. The mirror image of the Multi-state Weighted k-out-of-n: G System - the Multi-state Weighted k-out-of-n: F System has not been clearly defined and discussed. In this short communication, the basic definition of the Multi-state Weighted k-out-of-n: F System model is proposed. The relationship between the Multi-state Weighted k-out-of-n: G System and the Multi-state Weighted k-out-of-n: F System is also analyzed

Recipe for single-pair-Weyl-points phonons carrying the same chiral charges

Recently, Wang et al. [Phys. Rev. B, 106, 195129 (2022)] challenged a widely held belief in the field of Weyl physics, demonstrating that single-pair-Weyl-points (SP-WPs) can exist in nonmagnetic spinless systems, contrary to previous assumptions that they could only exist in magnetic systems. Wang et al. observed that the SP-WPs with opposite and even chiral charges (i.e., |C| = 2 or 4) could also exist in nonmagnetic spinless systems. In this Letter, we present a novel finding in which SP-WPs have a partner, namely a charged nodal surface, in nonmagnetic spinless systems. In contrast to previous observations, we show that the SP-WPs can have uneven chiral charges (i.e., |C| = 1). We identify 6 (out of 230) space groups (SGs) that contain such SP-WPs by searching the encyclopedia of emergent particles in three-dimensional crystals. Our finds were confirmed through the phonon spectra of two specific materials Zr3O (with SG 182) and NaPH2NO3 (with SG 173). This discovery broadens the range of materials that can host SP-WPs and applies to other nonmagnetic spinless crystals

Shifting from Population-wide to Personalized Cancer Prognosis with Microarrays

The era of personalized medicine for cancer therapeutics has taken an important step forward in making accurate prognoses for individual patients with the adoption of high-throughput microarray technology. However, microarray technology in cancer diagnosis or prognosis has been primarily used for the statistical evaluation of patient populations, and thus excludes inter-individual variability and patient-specific predictions. Here we propose a metric called clinical confidence that serves as a measure of prognostic reliability to facilitate the shift from population-wide to personalized cancer prognosis using microarray-based predictive models. The performance of sample-based models predicted with different clinical confidences was evaluated and compared systematically using three large clinical datasets studying the following cancers: breast cancer, multiple myeloma, and neuroblastoma. Survival curves for patients, with different confidences, were also delineated. The results show that the clinical confidence metric separates patients with different prediction accuracies and survival times. Samples with high clinical confidence were likely to have accurate prognoses from predictive models. Moreover, patients with high clinical confidence would be expected to live for a notably longer or shorter time if their prognosis was good or grim based on the models, respectively. We conclude that clinical confidence could serve as a beneficial metric for personalized cancer prognosis prediction utilizing microarrays. Ascribing a confidence level to prognosis with the clinical confidence metric provides the clinician an objective, personalized basis for decisions, such as choosing the severity of the treatment