Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatially Defined Drug Targeting by in Situ Host–Guest Chemistry in a Living Animal

Ensuring effective drug concentration specifically at sites of need, while limiting systemic side effects, remains a challenge in the discovery and use of new drug molecules. Carriers targeted through biological affinity (e.g., antibodies) afford a common means of drug localization, yet often deliver considerably less than 1% of an administered drug to a desired site in the body. We report on an alternative targeting paradigm using pendant guest motifs to direct molecules to sites distinguished by a hydrogel bearing a high density of a complementary cucurbituril supramolecular host. Host–guest affinity (Keq) of 1012 M–1 serves to spatially localize ∼4% of a model small molecule within hours of its administration in mice. These high-affinity interactions furthermore ensure long-lasting retention of the model compound at the site of interest, and the site can be serially targeted upon repeated dosing. This supramolecular homing axis extends the localization of small molecule payloads beyond injectable hydrogels, enabling targeting of modified biomaterials. This approach also has promising therapeutic utility, improving efficacy of a guest-modified chemotherapeutic agent in a tumor model.
Manganese Uptake by A549 Cells is Mediated by Both ZIP8 and ZIP14

The alveolar epithelia of the lungs require manganese (Mn) as an essential nutrient, but also provide an entry route for airborne Mn that can cause neurotoxicity. Transporters involved in Mn uptake by alveolar epithelial cells are unknown. Recently, two members of the Zrt- and Irt-like protein (ZIP) family of metal transporters, ZIP8 and ZIP14, have been identified as crucial Mn importers in vivo. ZIP8 is by far most abundantly expressed in the lungs, whereas ZIP14 expression in the lungs is low compared to other tissues. We hypothesized that Mn uptake by alveolar epithelial cells is primarily mediated by ZIP8. To test our hypothesis, we used A549 cells, a type II alveolar cell line. Mirroring the in vivo situation, A549 cells expressed higher levels of ZIP8 than cell models for the liver, intestines, and kidney. Quantification of ZIP8 and ZIP14 revealed a strong enrichment of ZIP8 over ZIP14 in A549 cells. Using siRNA technology, we identified ZIP8 and ZIP14 as the major transporters mediating Mn uptake by A549 cells. To our surprise, knockdown of either ZIP8 or ZIP14 impaired Mn accumulation to a similar extent, which we traced back to similar amounts of ZIP8 and ZIP14 at the plasma membrane. Our study highlights the importance of both ZIP8 and ZIP14 in Mn metabolism of alveolar epithelial cells.
Depression‐related disturbances in rat maternal behaviour are associated with altered monoamine levels within mesocorticolimbic structures

The ability of mothers to sensitively attune their maternal responses to the needs of their developing young is fundamental to a healthy mother‐young relationship. The biological mechanisms that govern how mothers adjust caregiving to the dynamic changes in the demands of the young remain an open question. In the present study, we examined whether changes in monoamine levels, within discrete mesocorticolimbic structures involved in cognitive and motivational processes key to parenting, modulate this flexibility in caregiving across the postpartum period. The present study used a Wistar‐Kyoto (WKY) animal model of depression and control Sprague‐Dawley (SD) rats, which differ dramatically in their cognitive, motivational, and parenting performance. Levels of the monoamine neurotransmitters, dopamine, noradrenaline and serotonin, as well as their major metabolites, were measured within the medial prefrontal cortex, striatum, nucleus accumbens and medial preoptic area of SD and WKY mothers at early (postpartum day [PPD]7‐8), late (PPD15‐16) and weaning (PPD25) postpartum stages using high‐performance liquid chromatography with electrochemical detection. Consistent with our prior work, we find that caregiving of SD mothers declined as the postpartum period progressed. Relative to nulliparous females, early postpartum mothers had lower intracellular concentrations of monoamines, as well as lower noradrenaline turnover, and an elevated serotonin turnover within most structures. Postpartum behavioural trajectories subsequently corresponded to a progressive increase in all three monoamine levels within multiple structures. Compared to SD mothers, WKY mothers were inconsistent and disorganised in caring for their offspring and exhibit profound deficits in maternal behaviour. Additionally, WKY mothers had generally lower levels of all three monoamines, as well as different patterns of change across the postpartum period, compared to SD mothers, suggesting dysfunctional central monoamine pathways in WKY mothers as they transition and experience motherhood. Taken together, the results of the present study suggest a role for monoamines at multiple mesocorticolimbic structures with repect to modulating caregiving behaviours attuned to the changing needs of the young.
High‐fat diet reduces novelty‐induced expression of activity‐regulated cytoskeleton‐associated protein

Chronic high‐fat‐diet (HFD) consumption can lead to the development of brain insulin resistance, which then exerts deleterious effects on learning and memory. Activity‐regulated cytoskeleton‐associated protein (Arc) is a memory‐related protein, and its expression can be induced by insulin stimulation. In HFD‐fed animals, their basal Arc protein levels in cerebral cortex and hippocampus are reduced. However, the effects of HFD on novelty‐induced Arc protein expression that is important for cognitive function is still unknown. In the present study, after feeding HFD (60% kcal from fat) for 5 weeks, mice developed brain insulin resistance and had a significant reduction in the novelty‐induced but not the basal Arc protein levels in their hippocampi. Further experiments were performed in primary rat hippocampal neurons. The results show that, under the condition of neuronal insulin resistance, acute insulin stimulation induced less activation of the phosphatidylinositol 3‐kinase/protein kinase B/p70 ribosomal S6 kinase (PI3K/Akt/p70S6K) pathway, resulting in reduced induction of Arc protein expression. Accordingly, it is suggested that following HFD feeding, the reduction in novelty‐induced Arc protein expression in animal's hippocampus is probably related to a suppressed activation of the PI3K/Akt/p70S6K pathway due to the existence of brain insulin resistance.
American heart association’s cardiovascular health metrics and risk of cardiovascular disease mortality among a middle-aged male Scandinavian population

Abstract Background The burden of cardiovascular disease (CVD) prompted the American Heart Association to develop a cardiovascular health (CVH) metric as a measure to assess the cardiovascular status of the population. We aimed to assess the association between CVH scores and the risk of CVD mortality among a middle-aged Finnish population. Methods We employed the prospective population-based Kuopio Ischemic Heart Disease cohort study comprising of middle-aged men (42–60 years). CVH scores were computed among 2607 participants at baseline and categorized as optimum (0–4), average (5–9), or inadequate (10–14) CVH. Multivariate cox regression models were used to estimate the hazard ratios (HR) and 95% confidence intervals (CIs) of CVH score for cardiovascular mortality. Results During a median follow-up period of 25.8 years, 609 CVD mortality cases were recorded. The risk of CVD mortality increased gradually with increasing CVH score across the range 3–14 (p-value for non-linearity =.77). Men with optimum CVH score had HR (95% CI) for CVD mortality of 0.30 (CI 0.21 – 0.42, p < .0001) compared to those with inadequate CVH score after adjustment for conventional cardiovascular risk factors. Conclusions CVH score was strongly and continuously associated with the risk of CVD mortality among middle-aged Finnish population and this was independent of other conventional risk factors. Key messages Achieving optimum cardiovascular health score reduces the risk of cardiovascular mortality. Adopting the American Heart Association’s cardiovascular health metrics is a welcome approach for public health awareness and monitoring of cardiovascular health among Scandinavian population.
Blueprint model and language for engineering cloud applications

The research presented in this thesis is positioned within the domain of engineering CSBAs. Its contribution is twofold: (1) a uniform specification language, called the Blueprint Specification Language (BSL), for specifying cloud services across several cloud vendors and (2) a set of associated techniques, called the Blueprint Manipulation Techniques (BMTs), for publishing, querying, and composing cloud service specifications with aim to support the flexible design and configuration of an CSBA.
Pragmatic approach using OAuth mechanism for IoT device authorization in cloud

To address the problem of authorization that enables a third-party service to access a resource server in a secured manner (a token based authorization without exposing user id and password), there has been research called OAuth framework. The OAuth framework gives permission for restricted access to a third party entity in a token based access control mechanism. This framework is widely used as a de-facto standard. The companies such as Google, Facebook, LinkedIn, and Microsoft are using the OAuth mechanism to enable third-party services to access their resources in a secured manner. However, the OAuth mechanism is operating on HTTP(HyperText Transfer Protocol) that assumes a server in the cloud, which adapts the OAuth mechanism, will primarily operates using HTTP. The de-facto standard protocol used by IoT(Internet of Things) devices is CoAP(Constrained Application Protocol) that is designed to be used by a simple, in other word limited, device. In the case of IoT cloud that interconnects IoT devices and related applications mainly use CoAP over TCP(Transmission Control Protocol). This paper describes the development of a pragmatic approach to authorize an application accessing IoT devices using CoAP and inter-working with already deployed HTTP based authorization servers using OAuth mechanism. The developed architecture using the proposed approach has been evaluated using the real-world device, i.e. Samsung air conditioner, with Samsung, Facebook and Github accounts. This work has become the open source IoT cloud reference implementation of OCF (Open Connectivity Foundation).
Face Recognition in Automotive Applications

Already available Advanced driver-assistance systems technologies, like lane departure, cruse control, blind spot detection, etc. are constructing a model of the external environment, helping to increase the security in traffic. With the advance of the autonomous driving, the car makers are shifting the focus also into the interior of the car. Face recognition, i.e. recognizing a person, based on a picture, which is highly available on social platforms(e.g. Facebook) poses a challenge when it has to be implemented in an automotive system, due to smaller computational power and lower memory currently available in such system. This paper explores the possibility to implement a face recognition on an automotive embedded system. The system is composed from a infrared camera, 1MPixel resolution, and an image processing unit based on TDA3x
Control of Chaos in BLDC Motor Drive

This paper deals with the chaotic behavior of Brushless DC Motor (BLDCM) which is highly reliable, stable and efficient, but is a typical multivariable nonlinear system. The chaotic behavior of the BLDC motor can change the reliability and stability of the BLDC motor. In this paper the BLDCM system and its mathematical model has been dealt with. Finally the delayed feedback control is applied to control the chaos in BLDCM using PYRAGAS’ METHOD OF CONTROL.
Speech Enhancement Using RNN models and Ideal Exponential Mask

This paper aims at Speech Enhancement using Mask- Approximation method. An Ideal Exponential Mask has been proposed to increase the accuracy of the model. The Mel Frequency Cepstral Coefficient is extracted from the speech signal as a feature, which is fed into different models as input. Considering speech as a sequential data, different Recurrent Neural Networks, such as Long Short Term memory and Gated Recurrent Unit, have been used to train the model. Later, considering that the speech signal depends on both the past and future sequence, Bidirectional models have been used. The Signal to Distortion Ratio and Short Time Object Intelligibility has been calculated to measure the accuracy of different models and different masks. A maximum Short Time Object Intelligibility of 0.87 has been achieved using the new mask.
WiLabel: Behavior-Based Room Type Automatic Annotation for Indoor Floorplan

The growing indoor location based services (LBS) applications enhance the requirement of room type annotation. Existing room type annotations are either depending on additional sensors or prone to privacy disclosure. We proposed a method called WiLabel-based on channel state information (CSI) alone. By analyzing the CSI fluctuation, we adopt the percentage of nonzero elements (PEM) algorithm to classify indoor scene and design a zero prior knowledge behavior recognition method to achieve behavior perception in the fewer-person scene, then, design a behavior-based decision tree classifier to determine the room type. The evaluation results from 84 rooms of the college building and mall show that the WiLabel can achieve an average accuracy of 90.5% superior to others.
An Event-local View: Emotion Interplay in the Underlying Social Graph of a Literary Text

The traditional view of a social graph is static, though the graph itself evolves with events. An event-local view can capture this dynamic evolution and offer an event-specific local view of the social graph. In this paper, a novel framework is proposed to offer such a view of the underlying social network of a literary text accentuated with the emotion inter-play. It does this by integrating several pieces together i.e. extraction of character names with co-reference resolution, sentiment based windowing to identify main events in the text, detection of the underlying social graph of characters based on co-occurrence in the context of an event and supervised learning to detect emotions between characters. It delivers an integrated visualization to display the emotion interplay between main protagonists in the underlying social network in an event-specific manner. This is in contrast to the traditional static global views of the social graph made by analyzing the full text. Our work, by adopting an event-local view successfully captures the dynamic and evolving aspect of the underlying social graph.
A Fault Resilient Consensus Protocol for Large Permissioned Blockchain Networks

Permissioned blockchains have evolved as an alternative to permissionless blockchains for various closed business environments. In this paper, we develop FRChain, a scalable and high-performant consensus protocol for permissioned blockchains, which is resilient to different types of node and network failures. FRChain uses collective signing over multicast trees for block propagation and block validation. The protocol ensures safety and liveness as long as a majority of the nodes can participate in the protocol correctly. Further, we also demonstrate a technique for replacing failed nodes with correct ones. We have implemented and tested FRChain spread across a 5000 node blockchain network over two different data centers – (a) 35 Softlayer Cloud VMs located across Melbourne, Milan and San Jose, (b) Amazon VPC with 30 Amazon EC2 instances spread across Mumbai, Singapore, Tokyo and Frankfurt. Our experiments show that FRChain is scalable in terms of both transaction throughput and network size.
LiDAR-Enhanced Connected Infrastructures Sensing and Broadcasting High-Resolution Traffic Information Serving Smart Cities

Connected-vehicle system is an important component of smart cities. The complete benefits of connected-vehicle technologies need the real-time information of all vehicles and other road users. However, the existing connected-vehicle deployments obtain the real-time status of connected vehicles, but without knowing the unconnected traffic since there are still many unconnected vehicles and pedestrians on the roads. Therefore, it is urgent to find an approach to collect the high-resolution real-time status of unconnected road users. When it is difficult for all vehicles, pedestrians, and bicyclists to broadcast their real-time status in the near future, enhancing the traffic infrastructures to actively sense and broadcast each road user’s status is an intuitive solution to fill the data gap. This paper introduces a new-generation LiDAR-enhanced connected infrastructures that can actively sense the high-resolution status of surrounding traffic participants with roadside LiDAR sensors and broadcast connected-vehicle messages through DSRC roadside units. The system architecture, the LiDAR data processing procedure, the data communication, and the first pilot implementation at an intersection in Reno, Nevada are included in this paper. This research is the start of the new-generation connected infrastructures serving connected/autonomous vehicles with the roadside LiDAR sensors. It will accelerate the deployment of the connected network for the smart cities to improve traffic safety, mobility, and fuel efficiency.
A Systematic Literature Review of Indoor Position System Accuracy and Implementation

the development of localization techniques has grown rapidly, especially localization techniques that use satellites known as the Global Positioning System. Localization techniques using this satellite cannot work in buildings; therefore research for localization techniques in buildings has been the focus of researchers in the last decade. Localization techniques in the area that cannot be covered by satellite can be split into two groups namely techniques signal using Bluetooth Low Energy (BLE) and techniques using the access point or Wi-Fi. Most researcher still face problems in localization technique in building known as Indoor Position System (IPS) implementation, especially to escalation the precision of techniques. In this paper, we presented a assessment of the papers (2006-2016) on indoor position system that focus on accuracy and placement of sensor for both BLE and Wi-Fi. This paper is a Systematic Literature Review (SLR) establishes a fusion exploration based on the exploration model by PRISMA checklist. The finishing identifies of chosen major papers for the initially phase had 25 major papers; the complete texts of 25 major papers were explored. Then, we implemented the synthesis to construct parameter and aspects that affect accuracy of indoor position system implementation for BLE and Wi-Fi. Base on review, we can conclude that the fingerprint of sensor play important role to increase accuracy of system.
DiRP: Distributed Intelligent Rendezvous Point for Multicast Control Plane

Multicast is widely deployed in data centers for point-to-multi-point communications. It has an established set of control protocols such as IGMP and PIM that has limitations around the lack of bandwidth awareness when establishing the multicast trees. This could lead to over-subscription of network links and packet loss impacting user quality of experience. Other existing multicast issues are related to path setup time as delay translates to deterioration of user experience. In this paper, we present and implement the DiRP algorithm utilizing distributed decision making architecture to optimize multicast tree formation while maintaining low path setup time. The system is implemented using off-the-shelve commercially available switches. the proposed DiRP algorithm maintains the creation and removal of source trees based on bandwidth requirements. We test the algorithm using Cisco’s Nexus commercially available switches. Testing results confirm that the DiRP Algorithm is able to setup multicast tree paths based on available bandwidth while maintaining distributed decision making in the fabric to lower path setup time. The system offers substantially lower path setup time compared to centralized systems while maintaining bandwidth awareness when setting up the fabric.
Breast Cancer Diagnosis Using Deep Learning Algorithm

The cancer is the most dangerous diseases in the world, its mainly effective for women. So, our prime target must be curing the cancer through scientific investigation and the second main target should be early detection of cancer because the early detection of cancer can be helpful for remove the cancer completed. After reviewed 41 papers we found that several techniques are available for cancer detection. In this paperwe proposedDeep Leaning algorithm neural network for diagnosed breast cancer using Wisconsin Breast Cancer database. The paper shows how we can use deep learning technology for diagnosis breast cancer using UCI Dataset. Because deep learning techniques almost used for high task objective Computer Vision, Image processing, Medical Diagnosis, Neural Language Processing. But in this paper, we are applying deep learning technology on the Wisconsin Breast Cancer Database and we have seen that is very beneficial for us for diagnosis breast cancer with accuracy 99.67%. This paper is divided in three parts first we have collect dataset and applied pre-processing algorithm for scaled and filter data then we have split dataset in training and testing purpose and generate some graph for visualization data. In last implement model on training dataset and achieved accuracy 99.67%. So, we have seen deep learning technology is a good way for diagnosis breast cancer with Wisconsin Breast Dataset. This database provides 569 rows and 30 features in the dataset. In this paper we have used 11 features for diagnosis breast cancer that we have got after pre-processing. But before train model we have applied some pre-processing algorithm like Label Encoder, Normalizerand StandardScalerfor scaled dataset then applied model and achieved accuracy. In this paper we also compare deep learning algorithm with other machine learning and seen our proposed system is proved best from others machine learning algorithm.
Building Logistic Spiking Neuron Models Using Analytical Approach

Spiking neuron models are inspired by biological neurons. They can simulate the neuronal activities of the mammalian brains, such as spiking (integrator) and periodic oscillation (resonator). A spiking neural network consisting of a cluster of spiking neurons can be used to simulate the collective dynamic behaviors of a brain neural network. This paper presents step-by-step analyses for the non-linear dynamics of mathematical spiking neuron models and sets forth a novel spiking model based on logistic function using an analytical approach. The logistic function is a well-known one-dimensional dynamical system and can generate spiking or periodic oscillation based on the system parameter. The novel spiking neural model is a combination of the integrate-and-fire and the quadratic integrate-and-fire neuron models with an added parameter to control the neural dynamics in order to generate stable, periodic, or chaotic neural behavior with flexibility. The analytical approach presented in this paper can be applied extensively to the design and analyses of multi-dimensional neuron models. The goal of this research project is to understand the dynamical behaviors of biological neurons in order to design biologically inspired spiking neuron model for building artificial intelligence, treating cognitive disorders, and advancing the scientific frontiers of brain research.