Mental health predictive systems typically model language as if from a single context (e.g. Twitter posts, status updates, or forum posts) and often limited to a single level of analysis (e.g. either the message-level or userlevel). Here, we bring these pieces together to explore the use of open-vocabulary (BERT embeddings, topics) and theoretical features (emotional expression lexica, personality) for the task of suicide risk assessment on support forums (the CLPsych-2019 Shared Task). We used dual context based approaches (modeling content from suicide forums separate from other content), built over both traditional ML models as well as a novel dual RNN architecture with user-factor adaptation. We find that while affect from the suicide context distinguishes with no-risk from those with "anyrisk", personality factors from the non-suicide contexts provide distinction of the levels of risk: low, medium, and high risk. Within the shared task, our dual-context approach (listed as SBU-HLAB in the official results) achieved state-of-the-art performance predicting suicide risk using a combination of suicide-context and non-suicide posts (Task B), achieving an F1 score of 0.50 over hidden test set labels.

Abstract-This paper proposes a nonlinear pose observer designed directly on the Lie group structure of the Special Euclidean group SE(3). We use a gradient-based observer design approach and ensure that the derived observer innovation can be implemented from position measurements. We prove local exponential stability of the error and instability of the non-zero critical points. Simulations indicate that the observer is indeed almost globally stable as would be expected.

No abstract

During the last few decades, nanotechnology has established many essential applications in the biomedical field and in particular for cancer therapy. Not only can nanodelivery systems address the shortcomings of conventional chemotherapy such as limited stability, non-specific biodistribution and targeting, poor water solubility, low therapeutic indices, and severe toxic side effects, but some of them can also provide simultaneous combination of therapies and diagnostics. Among the various therapies, the combination of chemo-and photothermal therapy (CT-PTT) has demonstrated synergistic therapeutic efficacies with minimal side effects in several preclinical studies. In this regard, inorganic nanostructures have been of special interest for CT-PTT, owing to their high thermal conversion efficiency, application in bio-imaging, versatility, and ease of synthesis and surface modification. In addition to being used as the first type of CT-PTT agents, they also include the most novel CT-PTT systems as the potentials of new inorganic nanomaterials are being more and more discovered. Considering the variety of inorganic nanostructures introduced for CT-PTT applications, enormous effort is needed to perform translational research on the most promising nanomaterials and to comprehensively evaluate the potentials of newly introduced ones in preclinical studies. This review provides an overview of most novel strategies used to employ inorganic nanostructures for cancer CT-PTT as well as cancer imaging and discusses current challenges and future perspectives in this area.

No abstract

No abstract

Autologous grafts, as the gold standard for vascular bypass procedures, associated with several problems that limit their usability, so tissue engineered vessels have been the subject of an increasing number of works. Nevertheless, gathering all of the desired characteristics of vascular scaffolds in the same construct has been a big challenge for scientists. Herein, a composite silk-based vascular scaffold (CSVS) was proposed to consider all the mechanical, structural and biological requirements of a small-diameter vascular scaffold. The scaffold’s lumen composed of braided silk fiber-reinforced silk fibroin (SF) sponge covalently heparinized (H-CSVS) using Hydroxy-Iron Complexes (HICs) as linkers. The highly porous SF external layer with pores above 60 μm was obtained by lyophilization. Silk fibers were fully embedded in scaffold’s wall with no delamination. The H-CSVS exhibited much higher burst pressure and suture retention strength than native vessels while comparable elastic modulus and compliance. H-CSVSs presented milder hemolysis in vitro and significant calcification resistance in subcutaneous implantation compared to non-heparinized ones. The in vitro antithrombogenic activity was sustained for over 12 weeks. The cytocompatibility was approved using endothelial cells (ECs) and vascular smooth muscle cells (SMCs) in vitro. Therefore, H-CSVS demonstrates a promising candidate for engineering of small-diameter vessels.

The paper addresses the problem of distributed filtering with guaranteed convergence properties using minimumenergy filtering and H∞ filtering methodologies. A linear state space plant model is considered observed by a network of communicating sensors, in which individual sensor measurements may lead to an unobservable filtering problem. However, each filter locally shares estimates, that are subject to disturbances, with its respective neighboring filters to produce an estimate of the plant state. The minimum-energy strategy of the proposed local filter leads to a locally optimal time-varying filter gain facilitating the transient and the asymptotic convergence of the estimation error, with guaranteed H∞ performance. The filters are implementable using only the local measurements and information from the neighboring filters subject to disturbances.A key idea of the proposed algorithm is to locally approximate the neighboring estimates, that are not directly accessible, considering them as disturbance contaminated versions of the plant state. The proposed algorithm imposes minimal communication load on the network and is scalable to larger sensor networks. I. INTRODUCTIONThere is considerable interest in the literature in multiagent systems that are capable of performing control and filtering related tasks in a cooperative manner. Applications range from military aerial fleets, monitoring and maintenance agents in industrial applications to biological applications. In this paper, in particular, we are concerned with distributed filtering using a network of filters that estimate the state of a plant using disturbance contaminated local measurements. An interesting case is when these filters individually have difficulty providing an accurate and complete estimate of the plant, a problem that is resolved by using information from the neighbouring filters.Kalman filtering is the focus of many of the existing methods proposed for distributed filtering. An early result on this subject by Durrant-Whyte et. al [1], [2] provides an exact decentralized formulation for the multi-sensor version of the Kalman filter. This formulation avoids the requirement of a central processing or communication unit with which each sensor has to communicate its information for calculating the state estimate of the plant. The decentralized scheme is robust to sensor failure and network changes, reduces the communication load and allows for faster information processing. The decentralized Kalman filter proposed in [1], [2] however, requires an all-to-all communication of state error information and variance error information between the sensors; this impairs its scalability to bigger networks. OlfatiSaber [3] proposed a distributed Kalman filter algorithm