snu-aidas/HVMap-dataset huggingface dataset:
Increased human intestinal barrier permeability plasma biomarkers zonulin and FABP2 correlated with plasma LPS and altered gut microbiome in anxiety or depression.

We read with interest the recent work by Uhde et al, which demonstrated that physically asymptomatic non-coeliac gluten/ wheat sensitivity involves compromised intestinal epithelium barrier dysfunction in conjunction with systemic immune activation events. We also read with interest the recent work by Marchesi et al, which comprehensively reviewed the role of microbiota in physical disorders of the gut and extra-gut organs. But common to these Gut papers was the lack of accounting for anxiety and depression, comorbidities often experienced in gastroenterology clinics. Patients who are otherwise physically asymptomatic often do not explicitly divulge these mental disorders, or the disorders are unintentionally overlooked, yet they report ‘diminished quality of life’. In response to this gap, we explored roles of dysbiosis and gut barrier integrity in individuals physically asymptomatic for gastrointestinal distress, yet nonetheless experiencing mental distress. We hypothesised that anxiety and depressive disorders are linked to human gut dysbiosis with microbiota that secrete lipopolysaccharide (LPS) endotoxin into plasma, which in conjunction with compromised gut barrier integrity has systemic manifestations including the brain. We further hypothesised that this correlates with altered intestinal epithelium paracellular integrity molecules discharged into blood as biomarkers, namely zonulin and intestinal fatty acidbinding protein-2 (FABP2). Zonulin is the master regulator of endothelial and epithelium tight junctions, modulating both gut and blood–brain barriers. We analysed stool faecal microbiota and plasma in 50 subjects who self-reported to be asymptomatic for gastrointestinal physical disorders; 22 volunteers met Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria for a depressive disorder or an anxiety disorder (DEP/ ANX), and 28 more were control reference subjects (NODEPANX). Subjects refrained from probiotics and antibiotics during 30 days prior to providing samples (see online supplementary methods). LEfSe analyses (online supplementary figures S1 and S2) revealed gut dysbiosis in the DEP/ANX subjects, in contrast to microbiome of control subjects. PICRUSt analyses (figure 1) predicted KEGG orthologue gene differences consistent with a pathophysiological gut metagenomic signature in DEP/ANX versus NODEPANX microbiome. Notable in figure 1 were over-represented LPS biosynthesis genes in the gut microbiome of PostScript Gut Online First, published on August 16, 2017 as 10.1136/gutjnl-2017-314759
Family presence during resuscitation and/or invasive procedures in the Emergency Department: one size does not fit all.
Security for Online Social Networks : Challenges

nline social networks (OSNs) [1, 2] such as Facebook, MySpace, and Twitter enable people to stay in touch with their contacts, reconnect with old acquaintances, and establish new relationships with other people based on shared features such as communities, hobbies, interests, and overlaps in friendship circles. Recent years have seen unprecedented growth in the application of OSNs, with about 300 OSN systems collecting information on more than half a billion registered users [2]. As a result, OSNs store a huge amount of possibly sensitive and private information on users and their interactions. This information is usually private and intended for the eyes of a specific audience only. However, the popularity of OSNs attracts not only faithful users but parties with rather adverse interests as well [3, 4]. The diversification and sophistication of purposes and usage patterns of OSNs inevitably introduce privacy infringement risks to all OSN users as a result of information exchange and sharing on the Internet. It is therefore not surprising that stories about privacy breaches by Facebook and MySpace appear repeatedly in mainstream media [3–5]. Regardless of the purpose of an OSN, one of the main motivations for users to join an OSN, create a profile, and use different applications offered by the OSN is the possibility to easily share information with selected contacts or the public, and facilitate social interactions between the users of OSNs. Disclosing personal information in OSNs is a double-edged sword [6]. On one hand, information exposure is usually a plus, even a must, if people want to participate in social communities. Visibility of users' profiles and public display of connections (friend lists) are necessary for implementing core functionalities of OSNs such as social search and social traver-sal. On the other hand, leakage of personal information, especially one's identity, may invite malicious attacks from the real world and cyberspace, such as stalking, reputation slander, personalized spamming, and phishing [7]. Despite the risks, many of the privacy and access control mechanisms of today's OSNs are purposefully weak to make joining the OSN and sharing information easy. We believe that more effective and flexible security mechanisms are therefore required for the safety of OSN users as well as the continued thriving of OSNs. In this article we present a general framework for assessing the security and privacy of current and next-generation OSNs. Whereas others have considered specific mechanisms for improving OSN users' security and privacy, …
Model-based fault-detection and diagnosis - status and applications

For the improvement of reliability, safety and efficiency advanced methods of supervision, fault detection and fault diagnosis become increasingly important for many technical processes. This holds especially for safety related processes like aircraft, trains, automobiles, power plants and chemical plants. The classical approaches are limit or trend checking of some measurable output variables. Because they do not give a deeper insight and usually do not allow a fault diagnosis, model-based methods of fault detection were developed by using input and output signals and applying dynamic process models. These methods are based, e.g., on parameter estimation, parity equations or state observers. Also signal model approaches were developed. The goal is to generate several symptoms indicating the difference between nominal and faulty status. Based on different symptoms fault diagnosis procedures follow, determining the fault by applying classification or inference methods. This contribution gives a short introduction into the field and shows some applications for an actuator, a passenger car and a combustion engine. Copyright © 2004 IFAC
A Markerless Augmented Reality Approach Based on Real-Time 3 D Reconstruction using Kinect

In this paper we present a markerless augmented reality (MAR) approach based on real-time 3D reconstruction using a low-cost depth camera, the Kinect. We intend to use this MAR approach for 3D medical visualization. In the actual state of developing, our approach is based on the KinectFusion algorithm with some minor adaptations, as long as our goal is not reconstruct scenes but use the reconstructed model as a reference to MAR. Keywords-Augmented Reality; 3D Reconstruction; Kinect;
Model-Driven Game Development Addressing Architectural Diversity and Game Engine-Integration
Stiffness analysis for a 3-PUU parallel kinematic machine

This paper presents the stiffness characteristics of a three-prismatic-universal–universal (3-PUU) translational parallel kinematic machine (PKM). The stiffness matrix is derived intuitively based upon an alternative approach considering actuations and constraints, and the compliances subject to both actuators and legs are involved in the stiffness model. The stiffness performance of the manipulator is evaluated by utilizing the extremum stiffness values, and the influences of design parameters on the stiffness properties are presented, which will be valuable for the architecture design of a 3-PUU PKM. Moreover, the stiffness behavior of the PKM is investigated via the eigenscrew decomposition of the stiffness matrix, which provides a physical interpretation of the PKM stiffness and allows the identification of the stiffness center and compliant axis. 2007 Elsevier Ltd. All rights reserved.
Energy-aware wireless microsensor networks

Self-configuring wireless sensor networks can be invaluable in many civil and military applications for collecting, processing, and disseminating wide ranges of complex environmental data. Because of this, they have attracted considerable research attention in the last few years. The WINS [1] and SmartDust [2] projects, for instance, aim to integrate sensing, computing, and wireless communication capabilities into a small form factor to enable low-cost production of these tiny nodes in large numbers. Several other groups are investigating efficient hardware/software system architectures, signal processing algorithms, and network protocols for wireless sensor networks [3]-[5]. Sensor nodes are battery driven and hence operate on an extremely frugal energy budget. Further, they must have a lifetime on the order of months to years, since battery replacement is not an option for networks with thousands of physically embedded nodes. In some cases, these networks may be required to operate solely on energy scavenged from the environment through seismic, photovoltaic, or thermal conversion. This transforms energy consumption into the most important factor that determines sensor node lifetime. Conventional low-power design techniques [6] and hardware architectures only provide point solutions which are insufficient for these highly energy-constrained systems. Energy optimization, in the case of sensor networks, is much more complex, since it involves not only reducing the energy consumption of a single sensor node but also maximizing the lifetime of an entire network. The network lifetime can be maximized
Future trends of electrical propulsion and implications to ship design

Over the last 10 years the electrical propulsion fleet grew three times faster than the world fleet. This paper provides an overlook of the main drivers supporting this impressive growth and explores supporting drivers for future growth. The paper also explores two emerging technologies in electrical propulsion and distribution and how these influence ship design: DC distribution and energy storage. with a considerable level of accuracy which led to development of Dynamic Positioning (DP). Since electric propulsion is the most suitable solution to cope with the power fluctuation and redundancy requirements from DP systems, the development of offshore segment was the biggest driver for the development of electrical propulsion, reaching more than 50% of the units in 2013. Figure 2 Distribution of electrical propulsion fleet Dredgers – Dredging equipment requires a considerable amount of power and consequently the auxiliary power tends to be quite high, very often higher than the propulsion power. Therefore, the merit of electric propulsion is to reduce the overall installed power on board. Cruise – The main driver for electrical propulsion is space saving and space optimization. By shifting into electrical propulsion it is possible to reduce the space occupied by main propulsion systems and above all to place the different equipment in a way that the spaces reserved for passengers are maximized in terms of volume and quality. Nevertheless, the redundancy levels offered by electrical propulsion and its ability to meet the regulations on this segment have also contributed significantly for the adoption of this technology. Survey vessels – These vessels are often used to tow underwater survey devices for different applications, e.g. hydrographic, seismic, etc. Therefore, the lower underwater noise offered by electrical propulsion is very attractive. In addition, the load requirements of the different operation modes can vary significantly which also favor electric propulsion. LNG carriers – When laden, LNG carriers use their own cargo as fuel which is released by the cargo containment system in a process known as boil-off. Therefore, the propulsion system is selected so that the boil-off gas can be used as fuel. Initially, most vessels were fitted with steam turbines connected to the propulsion shaft through a gearbox. However, the introduction of dual fuel engines led to the adoption of electric propulsion (Hansen et al. 2007). Icebreakers – Although traditional icebreakers use their own weight to break sheets of ice, it is quite often to have blocks of ice reaching the propellers. This causes a sudden drop of the propeller speed and consequently a sudden increase of torque which could easily stall the propulsion engine with mechanical drive. Electrical drives are able to vary the speed and the torque independently and therefore maintain the torque within the engine’s acceptable limits (Ådnanes, 2003).
PESTO: Parameter EStimation TOolbox

Summary
PESTO is a widely applicable and highly customizable toolbox for parameter estimation in MathWorks MATLAB. It offers scalable algorithms for optimization, uncertainty and identifiability analysis, which work in a very generic manner, treating the objective function as a black box. Hence, PESTO can be used for any parameter estimation problem, for which the user can provide a deterministic objective function in MATLAB.


Availability and implementation
PESTO is a MATLAB toolbox, freely available under the BSD license. The source code, along with extensive documentation and example code, can be downloaded from https://github.com/ICB-DCM/PESTO/.


Contact
jan.hasenauer@helmholtz-muenchen.de.


Supplementary information
Supplementary data are available at Bioinformatics online.
Air filter particulate loading detection using smartphone audio and optimized ensemble classification

Automotive engine intake filters ensure clean air delivery to the engine, though over time these filters load with contaminants hindering free airflow. Today’s open-loop approach to air filter maintenance has drivers replace elements at predetermined service intervals, causing costly and potentially harmful overand under-replacement. The result is that many vehicles consistently operate with reduced power, increased fuel consumption, or excessive particulate-related wear which may harm the catalyst or damage machined engine surfaces. We present a method of detecting filter contaminant loading from audio data collected by a smartphone and a stand microphone. Our machine learning approach to filter supervision uses Mel-Cepstrum, Fourier and Wavelet features as input into a classification model and applies feature ranking to select the best-differentiating features. We demonstrate the robustness of our technique by showing its efficacy for two vehicle types and different microphones, finding a best result of 79.7% accuracy when classifying a filter into three loading states. Refinements to this technique will help drivers supervise their filters and aid in optimally timing their replacement. This will result in an improvement in vehicle performance, efficiency, and reliability, while reducing the cost of maintenance to vehicle owners. © 2017 Elsevier Ltd. All rights reserved.
A PID Compensator Control for the Synchronous Rectifier Buck DC-DC Converter

This paper aims to design a PID compensator for the control of Synchronous Rectifier (SR) Buck Converter to improve its conversion efficiency under different load conditions. Since the diode rectifier is replaced by a high frequency MOSFET switch, the SR control method itself will be sufficient under heavy load condition, to attain better normal mode performance. However, this technique does not hold well in light load condition, due to increased switching losses. A new control technique accompanied with PID compensator is introduced in the paper will enable synchronous buck converter to realize ZVS, while feeding light loads. This is also least cost and highly efficient easy technique without use of extra auxiliary switches and RLC components. This control technique also proved to be efficient under input voltage variations. Simulation is done in MATLAB Simulink for proving stabilization provided by the PID compensator for synchronous rectifier (SR) buck converter. Keywords—Synchronous Rectifier (SR), Continuous Condu-ction Mode (CCM), Discontinuous Conduction Mode (DCM), Zero Voltage Switching (ZVS), Proportional-IntegralDerivative (PID).
Neural Program Meta-Induction

Most recently proposed methods for Neural Program Induction work under the assumption of having a large set of input/output (I/O) examples for learning any underlying input-output mapping. This paper aims to address the problem of data and computation efficiency of program induction by leveraging information from related tasks. Specifically, we propose two approaches for cross-task knowledge transfer to improve program induction in limited-data scenarios. In our first proposal, portfolio adaptation, a set of induction models is pretrained on a set of related tasks, and the best model is adapted towards the new task using transfer learning. In our second approach, meta program induction, a k-shot learning approach is used to make a model generalize to new tasks without additional training. To test the efficacy of our methods, we constructed a new benchmark of programs written in the Karel programming language [17]. Using an extensive experimental evaluation on the Karel benchmark, we demonstrate that our proposals dramatically outperform the baseline induction method that does not use knowledge transfer. We also analyze the relative performance of the two approaches and study conditions in which they perform best. In particular, meta induction outperforms all existing approaches under extreme data sparsity (when a very small number of examples are available), i.e., fewer than ten. As the number of available I/O examples increase (i.e. a thousand or more), portfolio adapted program induction becomes the best approach. For intermediate data sizes, we demonstrate that the combined method of adapted meta program induction has the strongest performance.
Value creation process in the fast fashion industry . Towards a networking approach < Value co-creation and the changing role of suppliers and customers >

Purpose – Quick response, short product life cycles, customer-centric businesses, agile supply chains and reduction of lead times are considered to be the core business strategies in the sector of fast fashion. The work is an attempt to identify the most revealing management and organizational tools that support the final value creation and service delivery processes in the international industry of fast fashion Design/Methodology/approach – In order to fulfill such a purpose, the paper detects the sector through the recent developments from Service-Dominant (S-D) Logic and Network Theory scientific proposals Findings – Value is a co-created performance, requiring several actors participation. In the fast fashion, such actors are represented by textile businesses, providers, retailers, stores, customers. They all interact within a supply chain becoming a value chain in which stakeholders interact with each other contributing to the final value generation. At the light of this research, it seems to be restrictive to identify in the lead time reduction the success factor of the fast fashion businesses, but it should be pursued in politics of integration, interaction, co-creation and sharing throughout the chain. Fast fashion is an example of totally integrated global chain working and performing as a whole business in which every single component co-creates the final mutual benefit Practical implications – Textile organizations taking part in the network represented by fast fashion are called to re-organize their activities in function of a highly developed integration level and so to move towards a networking approach, fostering the mutual exchange of resources in order to co-create a common value Originality/value – The paper adds value to the study of the fast fashion industry, valorizing the importance of the co-creating perspective within the supply chain management
IoT-based continuous glucose monitoring system: A feasibility study

Health monitoring systems based on Internet-of-things (IoT) have been recently introduced to improve the quality of health care services. However, the number of advanced IoT-based continuous glucose monitoring systems is small and the existing systems have several limitations. In this paper we study feasibility of invasive and continuous glucose monitoring (CGM) system utilizing IoT based approach. We designed an IoT-based system architecture from a sensor device to a back-end system for presenting real-time glucose, body temperature and contextual data (i.e. environmental temperature) in graphical and human-readable forms to end-users such as patients and doctors. In addition, nRF communication protocol is customized for suiting to the glucose monitoring system and achieving a high level of energy efficiency. Furthermore, we investigate energy consumption of the sensor device and design energy harvesting units for the device. Finally, the work provides many advanced services at a gateway level such as a push notification service for notifying patient and doctors in case of abnormal situations (i.e. too low or too high glucose level). The results show that our system is able to achieve continuous glucose monitoring remotely in real-time. In addition, the results reveal that a high level of energy efficiency can be achieved by applying the customized nRF component, the power management unit and the energy harvesting unit altogether in the sensor device. c © 2017 The Authors. Published by E sevier B.V. i ilit f t f re ce r ra hairs.
Adaptive Deep Brain Stimulation In Advanced Parkinson Disease

OBJECTIVE
Brain-computer interfaces (BCIs) could potentially be used to interact with pathological brain signals to intervene and ameliorate their effects in disease states. Here, we provide proof-of-principle of this approach by using a BCI to interpret pathological brain activity in patients with advanced Parkinson disease (PD) and to use this feedback to control when therapeutic deep brain stimulation (DBS) is delivered. Our goal was to demonstrate that by personalizing and optimizing stimulation in real time, we could improve on both the efficacy and efficiency of conventional continuous DBS.


METHODS
We tested BCI-controlled adaptive DBS (aDBS) of the subthalamic nucleus in 8 PD patients. Feedback was provided by processing of the local field potentials recorded directly from the stimulation electrodes. The results were compared to no stimulation, conventional continuous stimulation (cDBS), and random intermittent stimulation. Both unblinded and blinded clinical assessments of motor effect were performed using the Unified Parkinson's Disease Rating Scale.


RESULTS
Motor scores improved by 66% (unblinded) and 50% (blinded) during aDBS, which were 29% (p = 0.03) and 27% (p = 0.005) better than cDBS, respectively. These improvements were achieved with a 56% reduction in stimulation time compared to cDBS, and a corresponding reduction in energy requirements (p < 0.001). aDBS was also more effective than no stimulation and random intermittent stimulation.


INTERPRETATION
BCI-controlled DBS is tractable and can be more efficient and efficacious than conventional continuous neuromodulation for PD.