HEATING IN VASCULAR TISSUE AND FLOW-THROUGH TISSUE PHANTOMS INDUCED BY FOCUSED ULTRASOUND

High intensity focused ultrasound (HIFU) can be used to control bleeding, both from individual blood vessels as well as from gross damage to the capillary bed. This process, called acoustic hemostasis, is being studied in the hope that such a method would ultimately provide a lifesaving treatment during the so-called "golden hour", a brief grace period after a severe trauma in which prompt therapy can save the life of an injured person. Thermal effects play a major role in occlusion of small vessels and also appear to contribute to the sealing of punctures in major blood vessels. However, aggressive ultrasound-induced tissue heating can also impact healthy tissue and can lead to deleterious mechanical bioeffects. Moreover, the presence of vascularity can limit one’s ability to elevate the temperature of blood vessel walls owing to convective heat transport. In an effort to better understand the heating process in tissues with vascular structure we have developed a numerical simulation that couples models for ultrasound propagation, acoustic streaming, ultrasound heating and blood cooling in Newtonian viscous media. The 3-D simulation allows for the study of complicated biological structures and insonation geometries. We have also undertaken a series of in vitro experiments, in non-uniform flow-through tissue phantoms, designed to provide a ground truth verification of the model predictions. The calculated and measured results were compared over a range of values for insonation pressure, insonation time, and flow rate; we show good agreement between predictions and measurements. We then conducted a series of simulations that address two limiting problems of interest: hemostasis in small and large vessels. We employed realistic human tissue properties and considered more complex geometries. Results show that the heating pattern in and around a blood vessel is different for different vessel sizes, flow rates and for varying beam orientations relative to the flow axis. Complete occlusion and wall- puncture sealing are both possible depending on the exposure conditions. These results concur with prior clinical observations and may prove useful for planning of a more effective procedure in HIFU treatments.Defense Advanced Research Projects Agency, the U. S. Army, and the Center for Subsurface Sensing and Imaging Systems

Receptor-like cytoplasmic kinase ScRIPK in sugarcane regulates disease resistance and drought tolerance in Arabidopsis

IntroductionReceptor-like cytoplastic kinases (RLCKs) are known in many plants to be involved in various processes of plant growth and development and regulate plant immunity to pathogen infection. Environmental stimuli such as pathogen infection and drought restrict the crop yield and interfere with plant growth. However, the function of RLCKs in sugarcane remains unclear.Methods and resultsIn this study, a member of the RLCK VII subfamily, ScRIPK, was identified in sugarcane based on sequence similarity to the rice and Arabidopsis RLCKs. ScRIPK was localized to the plasma membrane, as predicted, and the expression of ScRIPK was responsive to polyethylene glycol treatment and Fusarium sacchari infection. Overexpression of ScRIPK in Arabidopsis enhanced drought tolerance and disease susceptibility of seedlings. Moreover, the crystal structure of the ScRIPK kinase domain (ScRIPK KD) and the mutant proteins (ScRIPK-KD K124R and ScRIPK-KD S253A|T254A) were characterized in order to determine the activation mechanism. We also identified ScRIN4 as the interacting protein of ScRIPK.DiscussionOur work identified a RLCK in sugarcane, providing a potential target for sugarcane responses to disease infection and drought, and a structural basis for kinase activation mechanisms

Enhancing the Spin–Orbit Coupling in Fe3O4 Epitaxial Thin Films by Interface Engineering

10.1021/acsami.6b0947884027353-2735

From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities

Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide gap between the performance of recent MLLM-based applications and the expectation of the broad public, even though the most powerful OpenAI's GPT-4 and Google's Gemini have been deployed. This paper strives to enhance understanding of the gap through the lens of a qualitative study on the generalizability, trustworthiness, and causal reasoning capabilities of recent proprietary and open-source MLLMs across four modalities: ie, text, code, image, and video, ultimately aiming to improve the transparency of MLLMs. We believe these properties are several representative factors that define the reliability of MLLMs, in supporting various downstream applications. To be specific, we evaluate the closed-source GPT-4 and Gemini and 6 open-source LLMs and MLLMs. Overall we evaluate 230 manually designed cases, where the qualitative results are then summarized into 12 scores (ie, 4 modalities times 3 properties). In total, we uncover 14 empirical findings that are useful to understand the capabilities and limitations of both proprietary and open-source MLLMs, towards more reliable downstream multi-modal applications

Adaptive Co-attention Network for Named Entity Recognition in Tweets

In this study, we investigate the problem of named entity recognition for tweets. Named entity recognition is an important task in natural language processing and has been carefully studied in recent decades.  Previous named entity recognition methods usually only used the textual content when processing tweets. However, many tweets contain not only textual content, but also images. Such visual information is also valuable in the name entity recognition task. To make full use of textual and visual information, this paper proposes a novel method to process tweets that contain multimodal information. We extend a bi-directional long short term memory network with conditional random fields and an adaptive co-attention network to achieve this task.  To evaluate the proposed methods, we constructed a large scale labeled dataset that contained multimodal tweets. Experimental results demonstrated that the proposed method could achieve a better performance than the previous methods in most cases