Constraints on Supersymmetric Dark Matter for Heavy Scalar Superpartners

We study the constraints on neutralino dark matter in minimal low energy supersymmetry models and the case of heavy lepton and quark scalar superpartners. For values of the Higgsino and gaugino mass parameters of the order of the weak scale, direct detection experiments are already putting strong bounds on models in which the dominant interactions between the dark matter candidates and nuclei are governed by Higgs boson exchange processes, particularly for positive values of the Higgsino mass parameter mu. For negative values of mu, there can be destructive interference between the amplitudes associated with the exchange of the standard CP-even Higgs boson and the exchange of the non-standard one. This leads to specific regions of parameter space which are consistent with the current experimental constraints and a thermal origin of the observed relic density. In this article we study the current experimental constraints on these scenarios, as well as the future experimental probes, using a combination of direct and indirect dark matter detection and heavy Higgs and electroweakino searches at hadron colliders.Comment: 32 pages, 13 figure

Case report: successful response to bevacizumab combined with erlotinib for a novel FH gene mutation hereditary leiomyoma and renal cell carcinoma

FH-deficient Renal Cell Carcinoma (FH-deficient RCC) are inherited tumors caused by mutations in the fumarate hydratase (FH) gene, which plays a role in the tricarboxylic acid cycle. These mutations often result in aggressive forms of renal cell carcinoma (RCC) and other tumors. Here, we present a case of FH-deficient RCC in a 43-year-old woman with a history of uterine fibroids. She exhibited a new heterozygous mutation in exon six of the FH gene (c.799_803del, c.781_796del). The patient had multiple bone metastases and small subcutaneous nodules in various areas such as the shoulders, back, and buttocks. Biopsy of a subcutaneous nodule on the right side revealed positive expression of 2-succinate-cysteine (2SC), and FH staining indicated FH expression deletion. The patient underwent treatment with a combination of erlotinib and bevacizumab, which resulted in significant efficacy with moderate side effects. This treatment combination may be recommended as a standard regimen. This case underscores the importance of genetic testing in patients with advanced renal cancer to enhance diagnostic accuracy. Furthermore, it provides insights into potential treatment approaches for FH-deficient RCC

Constraints on supersymmetric dark matter for heavy scalar superpartners

We study the constraints on neutralino dark matter in minimal low energy supersymmetry models and the case of heavy lepton and quark scalar superpartners. For values of the Higgsino and gaugino mass parameters of the order of the weak scale, direct detection experiments are already putting strong bounds on models in which the dominant interactions between the dark matter candidates and nuclei are governed by Higgs boson exchange processes, particularly for positive values of the Higgsino mass parameter μ. For negative values of μ, there can be destructive interference between the amplitudes associated with the exchange of the standard CP-even Higgs boson and the exchange of the nonstandard one. This leads to specific regions of parameter space which are consistent with the current experimental constraints and a thermal origin of the observed relic density. In this article, we study the current experimental constraints on these scenarios, as well as the future experimental probes, using a combination of direct and indirect dark matter detection and heavy Higgs and electroweakino searches at hadron colliders

Case report: Concurrent intrathecal and intravenous pembrolizumab for metastatic melanoma with leptomeningeal disease

Leptomeningeal disease (LMD) is a serious cancer complication associated with poor prognosis. Approximately 5%–25% of patients with melanoma develop LMD. Currently, no standard treatment protocol exists and very few cases have been reported. Despite ongoing advances in new therapies, treatment options for LMD remain limited. Herein, we report a case of intrathecal pembrolizumab administration in a patient with melanoma and LMD. Intrathecal pembrolizumab administration was feasible and safe at the doses tested. Drawing from this case, along with our expertise and the existing evidence on systemic immunotherapy, we propose that an immunotherapy approach involving intrathecal administration for patients with LMD from melanoma warrants additional exploration in clinical trials

Spatial Uncertainty-Aware Semi-Supervised Crowd Counting

Semi-supervised approaches for crowd counting attract attention, as the fully supervised paradigm is expensive and laborious due to its request for a large number of images of dense crowd scenarios and their annotations. This paper proposes a spatial uncertainty-aware semi-supervised approach via regularized surrogate task (binary segmentation) for crowd counting problems. Different from existing semi-supervised learning-based crowd counting methods, to exploit the unlabeled data, our proposed spatial uncertainty-aware teacher-student framework focuses on high confident regions' information while addressing the noisy supervision from the unlabeled data in an end-to-end manner. Specifically, we estimate the spatial uncertainty maps from the teacher model's surrogate task to guide the feature learning of the main task (density regression) and the surrogate task of the student model at the same time. Besides, we introduce a simple yet effective differential transformation layer to enforce the inherent spatial consistency regularization between the main task and the surrogate task in the student model, which helps the surrogate task to yield more reliable predictions and generates high-quality uncertainty maps. Thus, our model can also address the task-level perturbation problems that occur spatial inconsistency between the primary and surrogate tasks in the student model. Experimental results on four challenging crowd counting datasets demonstrate that our method achieves superior performance to the state-of-the-art semi-supervised methods

Counting with Adaptive Auxiliary Learning

This paper proposes an adaptive auxiliary task learning based approach for object counting problems. Unlike existing auxiliary task learning based methods, we develop an attention-enhanced adaptively shared backbone network to enable both task-shared and task-tailored features learning in an end-to-end manner. The network seamlessly combines standard Convolution Neural Network (CNN) and Graph Convolution Network (GCN) for feature extraction and feature reasoning among different domains of tasks. Our approach gains enriched contextual information by iteratively and hierarchically fusing the features across different task branches of the adaptive CNN backbone. The whole framework pays special attention to the objects' spatial locations and varied density levels, informed by object (or crowd) segmentation and density level segmentation auxiliary tasks. In particular, thanks to the proposed dilated contrastive density loss function, our network benefits from individual and regional context supervision in terms of pixel-independent and pixel-dependent feature learning mechanisms, along with strengthened robustness. Experiments on seven challenging multi-domain datasets demonstrate that our method achieves superior performance to the state-of-the-art auxiliary task learning based counting methods. Our code is made publicly available at: https://github.com/smallmax00/Counting_With_Adaptive_Auxiliar

Fabrication of one-dimensional Ag/multiwalled carbon nanotube nano-composite

Composite made of multiwalled carbon nanotubes coated with silver was fabricated by an electroless deposition process. The thickness of silver layer is about 40 to 60 nm, characterized as nano-crystalline with (111) crystal orientation along the nanotube's axial direction. The characterization of silver/carbon nanotube [Ag/CNT] nanowire has shown the large current carrying capability, and the electric conductivity is similar to the pure silver nanowires that Ag/CNT would be promising as building blocks for integrated circuits

Secure Payment Authentication That Provides Strong Customer Authentication

Multi-factor verification steps currently used for authenticating online purchases, e.g., one-time codes sent to a phone, can prove to be a hurdle for some customers. This disclosure describes a strong customer authentication technique, referred to as secure payment authentication (SPA), that enables users to authenticate online transactions using device-bound tokens. Authentication is driven by payment service providers, and a simple device unlock can confirm a transaction. Strong customer authentication is made possible with just a single (or even zero) click. Cross-device authentication can be enabled, such that a customer can authenticate themselves on a payment app on a mobile device while performing transactions on a second device such as a laptop, etc