Structure and composition of the superconducting phase in alkali iron selenide Ky_yFe1.6+x_{1.6+x}Se2_2

We use neutron diffraction to study the temperature evolution of the average structure and local lattice distortions in insulating and superconducting potassium iron selenide K$_y$Fe$_{1.6+x}$Se$_2$. In the high temperature paramagnetic state, both materials have a single phase with crystal structure similar to that of the BaFe$_2$As$_2$ family of iron pnictides. While the insulating K$_y$Fe$_{1.6+x}$Se$_2$ forms a $\sqrt{5}\times\sqrt{5}$ iron vacancy ordered block antiferromagnetic (AF) structure at low-temperature, the superconducting compounds spontaneously phase separate into an insulating part with $\sqrt{5}\times\sqrt{5}$ iron vacancy order and a superconducting phase with chemical composition of K$_z$Fe$_{2}$Se$_2$ and BaFe$_2$As$_2$ structure. Therefore, superconductivity in alkaline iron selenides arises from alkali deficient K$_z$Fe$_{2}$Se$_2$ in the matrix of the insulating block AF phase.Comment: 10 pages, 5 figure

Team Building Without Boundaries

Team building can be challenging when participants are from the same discipline or sub-discipline, but needs special attention when participants use a different vocabulary and have different cultural views on what constitutes viable problems and solutions. Essential to No Boundary Thinking (NBT) teams is proper formulation of the problem to be solved, and a basic tenant is that the NBT team must come together with diverse perspectives to decide the problem before solutions can be considered. Given that participants come with different views on problem formulation and solution, it is important to consider a robust process for team formation and maintenance. This takes extra effort and time, but scholars studying teams of experts with diverse training have found that they are better positioned to be successful in solving even deep and difficult problems especially if they have learned to work well with each other. At this workshop we will discuss principles that scholars who have worked in NBT teams have discovered as effective. We will then engage with the workshop participants to consider discuss these principles and brainstorm to consider other approaches

NBT (No-Boundary Thinking): Needed To Attend To Ethical Implications Of Data And AI

In this era of Big Data and AI, expertise in multiple aspects of data, computing, and the domains of application is needed. This calls for teams of experts with different training and perspectives. Because data analysis can have serious ethical implications, it is important that these teams are well and deeply integrated. No-Boundary Thinking (NBT) teams can provide support for team formation and maintenance, thereby attending to the many dimensions of the ethics of data and analysis. In this NBT workshop session, we discuss the ethical concerns that arise from the use of data and AI, and the implications for team building; and provide and brainstorm suggestions for ethical data enabled science and AI

OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation

Current 3D open-vocabulary scene understanding methods mostly utilize well-aligned 2D images as the bridge to learn 3D features with language. However, applying these approaches becomes challenging in scenarios where 2D images are absent. In this work, we introduce a completely new pipeline, namely, OpenIns3D, which requires no 2D image inputs, for 3D open-vocabulary scene understanding at the instance level. The OpenIns3D framework employs a "Mask-Snap-Lookup" scheme. The "Mask" module learns class-agnostic mask proposals in 3D point clouds. The "Snap" module generates synthetic scene-level images at multiple scales and leverages 2D vision language models to extract interesting objects. The "Lookup" module searches through the outcomes of "Snap" with the help of Mask2Pixel maps, which contain the precise correspondence between 3D masks and synthetic images, to assign category names to the proposed masks. This 2D input-free, easy-to-train, and flexible approach achieved state-of-the-art results on a wide range of indoor and outdoor datasets with a large margin. Furthermore, OpenIns3D allows for effortless switching of 2D detectors without re-training. When integrated with state-of-the-art 2D open-world models such as ODISE and GroundingDINO, superb results are observed on open-vocabulary instance segmentation. When integrated with LLM-powered 2D models like LISA, it demonstrates a remarkable capacity to process highly complex text queries, including those that require intricate reasoning and world knowledge. Project page: https://zheninghuang.github.io/OpenIns3D/Comment: 24 pages, 16 figures, 13 tables. Project page: https://zheninghuang.github.io/OpenIns3D

Retroviral replicating vector-mediated gene therapy achieves long-term control of tumor recurrence and leads to durable anticancer immunity.

BackgroundProdrug-activator gene therapy with Toca 511, a tumor-selective retroviral replicating vector (RRV) encoding yeast cytosine deaminase, is being evaluated in recurrent high-grade glioma patients. Nonlytic retroviral infection leads to permanent integration of RRV into the cancer cell genome, converting infected cancer cell and progeny into stable vector producer cells, enabling ongoing transduction and viral persistence within tumors. Cytosine deaminase in infected tumor cells converts the antifungal prodrug 5-fluorocytosine into the anticancer drug 5-fluorouracil, mediating local tumor destruction without significant systemic adverse effects.MethodsHere we investigated mechanisms underlying the therapeutic efficacy of this approach in orthotopic brain tumor models, employing both human glioma xenografts in immunodeficient hosts and syngeneic murine gliomas in immunocompetent hosts.ResultsIn both models, a single injection of replicating vector followed by prodrug administration achieved long-term survival benefit. In the immunodeficient model, tumors recurred repeatedly, but bioluminescence imaging of tumors enabled tailored scheduling of multicycle prodrug administration, continued control of disease burden, and long-term survival. In the immunocompetent model, complete loss of tumor signal was observed after only 1-2 cycles of prodrug, followed by long-term survival without recurrence for >300 days despite discontinuation of prodrug. Long-term survivors rejected challenge with uninfected glioma cells, indicating immunological responses against native tumor antigens, and immune cell depletion showed a critical role for CD4+ T cells.ConclusionThese results support dual mechanisms of action contributing to the efficacy of RRV-mediated prodrug-activator gene therapy: long-term tumor control by prodrug conversion-mediated cytoreduction, and induction of antitumor immunity

Competence of graph convolutional network in anti-money laundering in Bitcoin Blockchain

Graph networks are extensively used as an essential framework to analyse the interconnections between transactions and capture illicit behaviour in Bitcoin blockchain. Due to the complexity of Bitcoin transaction graph, the prediction of illicit transactions has become a challenging problem to unveil illicit services over the network. Graph Convolutional Network, a graph neural network based spectral approach, has recently emerged and gained much attention regarding graph-structured data. Previous research has highlighted the degraded performance of the latter approach to predict illicit transactions using, a Bitcoin transaction graph, so-called Elliptic data derived from Bitcoin blockchain. Motivated by the previous work, we seek to explore graph convolutions in a novel way. For this purpose, we present a novel approach that is modelled using the existing Graph Convolutional Network intertwined with linear layers. Concisely, we concatenate node embeddings obtained from graph convolutional layers with a single hidden layer derived from the linear transformation of the node feature matrix and followed by Multi-layer Perceptron. Our approach is evaluated using Elliptic data, wherein efficient accuracy is yielded. The proposed approach outperforms the original work of same data set

Toona sinensis

Toona sinensis leaf (TSL) is commonly used as a vegetable and in spice in Asia. In this study, feeding with aqueous extract of TSL (TSL-A) alleviated oxidative stress and recovered the motility and functions of sperm in rats under oxidative stress. Protein expressions in testes identified by proteomic analysis and verified by Western blot demonstrated that TSL-A not only downregulated the level of glutathione transferase mu6 (antioxidant system), heat shock protein 90 kDa-β (protein misfolding repairing system), cofilin 2 (spermatogenesis), and cyclophilin A (apoptosis) but also upregulated crease3-hydroxy-3-methylglutaryl-coenzyme A synthase 2 (steroidogenesis), heat shock glycoprotein 96, and pancreatic trypsin 1 (sperm-oocyte interaction). These results indicate that TSL-A promotes the functions of sperm and testes via regulating multiple testicular proteins in rats under oxidative stress, suggesting that TSL-A is a valuable functional food supplement to improve functions of sperm and testes for males under oxidative stress

Sunitinib in combination with docetaxel in patients with advanced solid tumors: a phase I dose-escalation study

PURPOSE: Sunitinib in combination with docetaxel enhances antitumor activity in xenograft models of human breast and non-small cell lung cancer. We assessed the maximum tolerated doses (MTDs), safety, pharmacokinetic profiles, and preliminary efficacy of sunitinib plus docetaxel in patients with advanced solid tumors. METHODS: In this phase I study, successive patient cohorts received sunitinib 25, 37.5, or 50 mg/day for 4 weeks of a 6-week cycle (Schedule 4/2, 4 weeks on, 2 weeks off) or for 2 weeks of a 3-week cycle (Schedule 2/1, 2 weeks on, 1 week off) with docetaxel 60 or 75 mg/m(2) IV q21d to determine the MTDs of this treatment combination. RESULTS: Fifty patients enrolled: 10 on Schedule 4/2 and 40 on Schedule 2/1. MTDs were established as sunitinib 25 mg on Schedule 4/2 with docetaxel 60 mg/m(2) q21d, and as sunitinib 37.5 mg on Schedule 2/1 with docetaxel 75 mg/m(2) q21d. On Schedule 2/1, the most frequent dose-limiting toxicity was neutropenia (±fever; grade [G]3/4, n = 5) and the most common G3/4 non-hematologic adverse event (AE) was fatigue (G3, n = 8). Hematologic AEs were managed with growth factor support in 11 of 23 (48%) patients treated at Schedule 2/1 MTD. Three patients achieved a partial response at the Schedule 2/1 MTD. There were no pharmacokinetic drug–drug interactions with either schedule. CONCLUSIONS: Oral sunitinib 37.5 mg/day on Schedule 2/1 with docetaxel 75 mg/m(2) IV q21d is a clinically feasible regimen with a manageable safety profile, no pharmacokinetic drug–drug interactions, and shows antitumor activity in patients with advanced solid tumors