Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of polymer-encapsulated metal nanoparticles as surface-enhanced Raman scattering probes.

In recent years, surface-enhanced Raman scattering (SERS) probes have gained popularity as an alternative to fluorescent labels such as organic fluorophores and quantum dots. The nanometer-sized SERS labels usually consist of a layer of Raman reporter molecules coated on the surface of metal nanoparticles (NPs), which act as the source of enhancement. Compared with fluorescent labels, Raman reporters on metal NPs are resistant to photo-bleaching owing to the quenching of fluorescence excited states by the metal surface and to the short lifetime of Raman virtual energy states. Furthermore, multiple Raman reporters can be excited with a single light source of choice (such as near infrared light for human tissue) to give multiple sets of narrow peaks characteristic of the individual reporters. These advantages render great promise for the SERS labels to be widely applied in biomedical systems. Direct attachment of Raman reporters onto metal NPs has been well established, producing nanoprobes that were used for various applications. However, the efficiency and reliability of the nanoprobes are often compromised by ligand dissociation or exchange. In addition, the exposed Raman reporters could be easily influenced by variations in the chemical or biological environments. A variety of encapsulation methods were, therefore, developed to enhance the stability of the nanoprobes by coating the nanoparticles with biomolecules (such as bovine serum albumin), polymers,
Enabling communication and cooperation in bio-nanosensor networks: toward innovative healthcare solutions

Bio-nanosensors and communication at the nanoscale are a promising paradigm and technology for the development of a new class of ehealth solutions. While recent communication technologies such as mobile and wireless combined with medical sensors have allowed new successful eHealth applications, another level of innovation is required to deliver scalable and cost-effective solutions via developing devices that operate and communicate directly inside the body. This work presents the application of nano technology for the development of miniaturized bio-nanosensors that are able to communicate and exchange information about sensed molecules or chemical compound concentration and therefore draw a global response in the case of health anomalies. Two communication techniques are reviewed: electromagnetic wireless communication in the terahertz band and molecular communication. The characteristics of these two modes of communication are highlighted, and a general architecture for bio-nanosensors is proposed along with examples of cooperation schemes. An implementation of the bio-nanosensor part of the nanomachine is presented along with some experimental results of sensing biomolecules. Finally, a general example of coordination among bio-nanomachines using both communication technologies is presented, and challenges in terms of communication protocols, data transmission, and coordination among nanomachines are discussed.
Effects of acute alcohol consumption on the perception of eye gaze direction

Alcohol consumption is associated with increases in aggressive behaviour, but the mechanisms underlying this relationship are poorly understood. One mechanism by which alcohol consumption may influence behaviour is via alterations in the processing of social cues such as gaze. We investigated the effects of acute alcohol consumption on the perception of gaze, using a task in which participants determined whether a stimulus face was looking towards or away from them. Gaze direction varied across trials, allowing calculation of a threshold at which participants considered gaze to switch from direct to averted. Target faces varied in both sex and attractiveness. Thirty social drinkers attended three randomized experimental sessions. At each session, participants consumed 0.0, 0.2 or 0.4 g/kg alcohol, and completed the gaze perception task. A significant three-way interaction involving target sex, participant sex and alcohol dose indicated that alcohol increased the cone of gaze for females viewing male targets (i.e. females were biased towards making a direct gaze judgement), but decreased the cone of gaze for males viewing male targets. Our data indicate that alcohol consumption influences gaze perception, but that these effects vary across sex of both stimulus and rater. These effects may have important implications for alcohol-related violence.
Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications

Mobile cloud computing, with its promise to meet the urgent need for richer applications and services of resource-constrained mobile devices, is emerging as a new computing paradigm and has recently attracted significant attention. However, there is no clear definition and no well defined scope for mobile cloud computing due to commercial hype, and diverse ways of combining cloud computing and mobile applications. This article makes the first attempt to present a survey of mobile cloud computing from the perspective of its intended usages. Specifically, we introduce three common mobile cloud architectures and classify comprehensive existing work into two fundamental categories: computation offloading and capability extending. Considering the energy bottleneck and user context of mobile devices, we discuss the research challenges and opportunities of introducing cloud computing to assist mobile devices, including energy-efficient interactions, virtual machine migration overhead, privacy, and security. Moreover, we demonstrate three real-world applications enabled by mobile cloud computing, in order to stimulate further discussion and development of this emerging field.
Frequency-domain cross-layer diversity techniques: an efficient way of coping with lost packets in broadband wireless systems

Modern broadband wireless systems demand increasing quality of service (QoS), requiring small error rates and delays which effectively ensure increased throughput. This article focuses on the handling of lost packets and its influence on spectral efficiency. Two time diversity approaches are considered and compared to cope with lost packets: a low-complexity diversity combining Automatic Repeat reQuest (ARQ) scheme employed in a Time Division Multiple Access (TDMA) architecture; and a Network Diversity Multiple Access (NDMA), which is a multi-packet detection approach able to separate simultaneous transmissions using temporal diversity. The comparison among the approaches focuses on error rate, throughput and delay performances, and takes the implementation complexity in consideration.
Control and data signaling decoupled architecture for railway wireless networks

Current implementations of narrowband Global System for Mobile Communications for railway systems are facing significant challenges in meeting the emerging massive capacity demands of passenger services. To extend the capacity, this article presents a control and data signaling decoupled architecture, namely, C/U-plane decoupled architecture for railway wireless networks, in which the relatively important C-plane of passenger services is kept on high-quality lower frequency bands to handle mobility, while the corresponding U-plane is moved to higher frequency bands to gain broader spectra. In this railway wireless network with C/U-plane decoupled architecture, the U-plane and C-plane handovers are also physically decoupled. To achieve the seamless and soft U-plane handover, we introduce a handover scheme based on coordinated multi-point transmission and reception and bi-casting. In addition, channel mappings and physical layer frames are redesigned to facilitate the design. Our study has demonstrated that by decoupling the C/U planes, the network performance is greatly enhanced, leading to a more effective way to provide high speed communications for railway systems.
A comparison of InVivoStat with other statistical software packages for analysis of data generated from animal experiments

InVivoStat is a free-to-use statistical software package for analysis of data generated from animal experiments. The package is designed specifically for researchers in the behavioural sciences, where exploiting the experimental design is crucial for reliable statistical analyses. This paper compares the analysis of three experiments conducted using InVivoStat with other widely used statistical packages: SPSS (V19), PRISM (V5), UniStat (V5.6) and Statistica (V9). We show that InVivoStat provides results that are similar to those from the other packages and, in some cases, are more advanced. This investigation provides evidence of further validation of InVivoStat and should strengthen users’ confidence in this new software package.
Lamotrigine augmentation of serotonin reuptake inhibitors in treatment-resistant obsessive–compulsive disorder: a double-blind, placebo-controlled study

The present 16-week double-blind, randomized, placebo-controlled trial had the aim to explore the efficacy of lamotrigine add-on pharmacotherapy on clinical symptomatology and cognitive functioning in a sample of patients with treatment-resistant obsessive–compulsive disorder (OCD) receiving serotonin reuptake inhibitors (SRIs). After clinical and neurocognitive assessments, patients were randomly allocated to receive, in a double-blind design, 100 mg/day of lamotrigine or a placebo. A final sample of 33 patients completed the study. The results obtained indicate that lamotrigine added to stable SRI treatment substantially improved obsessive–compulsive (Yale–Brown Obsessive Compulsive Scale: obsessions, p < 0.0001; compulsions, p < 0.0001; total score, p < 0.0001), and affective symptoms (Hamilton Rating Scale for Depression p < 0.0001). Regarding cognitive functions, improvement was observed only in Semantic Fluency (p = 0.004). The findings provide evidence that lamotrigine augmentation of SRI treatment is well tolerated and may be proposed as an effective therapeutic strategy to improve outcome in treatment-resistant OCD.
Functional neuroanatomy of emotion processing in major depressive disorder is altered after successful antidepressant therapy

Major Depressive Disorder (MDD) is associated with impaired processing and regulation of emotions. A vast body of research has elucidated the altered neural processes that occur in response to emotional stimuli, while little is known about anticipatory processes. Here we used functional magnetic resonance imaging (fMRI) to investigate neural activation during the presentation and anticipation of negative stimuli. Furthermore, we examined the effects of an 8-week antidepressant treatment with escitalopram. We matched 12 unmedicated MDD patients and 12 healthy control participants to perform a task involving affective pictures. The design of our event-related task consisted of presenting positive, negative, and neutral pictures from the International Affective Picture System (IAPS) across two runs and under opposite conditions. For the ‘expected’ condition, the pictures were cued by a word indicating their emotional valence; whereas the ‘unexpected’ condition had a combination of random letters precede the emotion picture. MDD patients displayed greater amygdala activation when anticipating negative pictures and greater prefrontal activation when confronted with them without the anticipatory cues. After antidepressant treatment, both amygdala and prefrontal activation decreased significantly in the treated MDD patients relative to controls. These findings show that the neural mechanisms of emotion anticipation and processing are altered in patients with MDD and that these alterations are able to normalize after treatment with an antidepressant.
Bloom-filter-aided redundancy elimination in opportunistic communications

Opportunistic communication is a promising solution to offload mobile data traffic by exchanging data among mobile devices without the aid of infrastructures. However, considerable redundant traffic can be transmitted in opportunistic communications. In this article, we introduce a protocol-independent redundancy elimination (RE) technique to reduce redundant data traffic at the packet level. Specifically, we propose a Bloom-filter-aided RE (BFRE) to further improve the performance of RE on opportunistic communication links. In BFRE, the receiver predicts the next consecutive data transmissions and delivers the compressed information to the sender by means of a Bloom filter. Then the sender can transmit small encoded packets, and the data traffic can be effectively reduced. Extensive simulation results demonstrate that BFRE can significantly reduce traffic and transmission time by 34 and 23 percent, respectively.
Big data routing in D2D communications with cognitive radio capability

In D2D communications, random contacts can be utilized to exchange data among nodes without the support from infrastructures or central control units. Because of the huge quantity and high mobility of the nodes, the scarcity of the available spectrum severely limits the data delivery capacity in D2D communications. CR technology gives D2D the ability to use idle licensed radio spectra from licensed networks to improve data delivery capacity. The advantages of opportunistic data delivery and CR technology make D2D communications an alternative that provides a complementary technology for big data applications. However, efficient routing algorithm design in D2D communications with CRD2D is nontrivial due to the spatial, temporal, and spectrum limitations introduced by node mobility and the available spectrum bands. This article investigates and analyzes the latest routing algorithms for D2D communications and CR networks. Taking advantage of the integration of D2D and CR techniques, a routing framework with social awareness for big data applications is proposed, which employs the regularities of nodes' mobility and spectrum mobility to improve the performance of data delivery. Open research issues for big data routing in CRD2D networks are also addressed.
Ethnic Differences: Word Descriptors Used By African-American and White Asthma Patients During Induced Bronchoconstriction

Study objectives: To determine if African-American and white patients with asthma (1) differ in the words they use to describe their breathlessness, and (2) differ in their perception of breathlessness. Design: Descriptive cross-sectional design. Setting and participants: The study setting was located in Northern California, an ethnically and economically diverse area. A total of 32 subjects, 16 per group, completed the study. Measurements: All had a provocation concentration of methacholine chloride causing a 30% fall in FEV1 (PC30 )o f< 8 mg/mL. Serial pulmonary function testing was performed. Breathlessness was measured using the Borg scale and the visual analog scale. Word descriptors were measured by an open-ended word descriptor questionnaire. Results: Significant ethnic differences in the words used to describe the sensation of breathlessness were present at PC30. African Americans used upper airway word descriptors: tight throat (p < 0.0004), scared-agitated (p < 0.006), voice tight (p < 0.04), itchy throat (p < 0.03), and tough breath (p < 0.04). Whites used lower airway or chest-wall symptom descriptors: deep breath (p < 0.03), lightheaded (p < 0.03), out of air (p < 0.01), aware of breathing (p < 0.03), and hurts to breathe (p < 0.06). In addition, African Americans required a significantly smaller, 44.3% (mean), dose of methacholine to achieve PC30 (p < 0.02). Conclusion: This study provides valuable new information about ethnicity and the words used to describe breathlessness during airflow obstruction. Asthmatic African Americans used primarily upper airway word descriptors; whites used lower airway or chest-wall word descriptors. Effective symptom monitoring requires asking the correct question and awareness that ethnic differences in the words used to describe breathlessness may exist. (CHEST 2000; 117:935‐943)
Toward end-to-end biomet rics-based security for IoT infrastructure

The IoT is the next generation of innovation in the smart world, which has the potential to improve safety, security, and the quality of our daily lives through pervasive communication and computation. Currently, we have observed that a plethora of interconnected smartphones, devices, and sensors are deployed for providing personalized services (e.g., social media, smart home, health monitoring) at any time from anywhere. The personalized services offered by IoT, although enhancing the quality of our lives, have serious challenges of securing networks and data in transit, as every day a myriad of devices and services are connected to the IoT. However, existing security solutions, such as two-factor authentication based on passwords along with second-level protection may not be efficient and reliable for providing end-to-end secure communication solutions among different devices and services connected to the IoT. To this end, this article proposes an end-to-end secure IoT-based solution using biometrics and pairing-based cryptography. Because of the uniqueness of one's biometric traits (e.g., face, fingerprint, palm, iris, voice, heartbeat), a biometric-based security solution is less vulnerable to security breaches for IoT systems or infrastructure. We present a biometric- based IoT infrastructure comprising four layers, and for each layer, we discuss possible security challenges along with the corresponding countermeasures. Finally, we provide a case study of face-based biometric recognition, where sensors or smartphones capture a face image and securely transmit it to the IoT platform to provide end-to end security.
Location privacy attacks and defenses in cloud-enabled internet of vehicles

As one of the promising branches of the Internet of Things, the cloud-enabled Internet of Vehicles (CE-IoV) is envisioned to serve as an essential data sensing, exchanging, and processing platform with powerful computing and storage capabilities for future intelligent transportation systems. The CE-IoV shows great promise for various emerging applications. In order to ensure uninterrupted and high-quality services, a vehicle should move with its own VM via live VM migration to obtain real-time location-based services. However, the live VM migration may lead to unprecedented location privacy challenges. In this article, we study location privacy issues and defenses in CE-IoV. We first present two kinds of unexplored VM mapping attacks, and thus design a VM identifier replacement scheme and a pseudonym-changing synchronization scheme to protect location privacy. We carry out simulations to evaluate the performance of the proposed schemes. Numerical results show that the proposed schemes are effective and efficient with high quality of privacy.
Multi-Traffic Scene Perception Based on Supervised Learning

Traffic accidents are particularly serious on a rainy day, a dark night, an overcast and/or rainy night, a foggy day, and many other times with low visibility conditions. Present vision driver assistance systems are designed to perform under good-natured weather conditions. Classification is a methodology to identify the type of optical characteristics for vision enhancement algorithms to make them more efficient. To improve machine vision in bad weather situations, a multi-class weather classification method is presented based on multiple weather features and supervised learning. First, underlying visual features are extracted from multi-traffic scene images, and then the feature was expressed as an eight-dimensions feature matrix. Second, five supervised learning algorithms are used to train classifiers. The analysis shows that extracted features can accurately describe the image semantics, and the classifiers have high recognition accuracy rate and adaptive ability. The proposed method provides the basis for further enhancing the detection of anterior vehicle detection during nighttime illumination changes, as well as enhancing the driver’s field of vision on a foggy day.
Electric-Field Energy Harvesting in Wireless Networks

Electric-field energy harvesting (EFEH) can be considered as an emerging and promising alternative for self-sustainable next-generation WSNs. Unlike conventional harvesting methods that rely on ambient variables, EFEH provides more reliable and durable operation as it is operable with any voltage-applied conductive material. Therefore, it is better suited for advanced throughput and applications requiring a certain QoS. In this article, we introduce this newly emerging WSN paradigm, and focus on enabling EFEH technology for smart grid architectures, such as home, building, and near area networks, where the field intensity is relatively low. To this end, a practical methodology and a general use implementation framework have been developed for low-voltage applications by regarding compelling design issues and challenging source scarcity. The proposed double-layer harvester model is experimentally evaluated. Its performance in terms of implementation flexibility, sensor lifetime, and communication throughput is investigated. In addition, current challenges, open issues, and future research directions are discussed for the design of more enhanced EFEH wireless networks.
Secure and Efficient Product Information Retrieval in Cloud Computing

Cloud computing is a promising information technique (IT) that can organize a large amount of IT resources in an efficient and flexible manner. Increasingly numerous companies plan to move their local data management systems to the cloud and store and manage their product information on cloud servers. An accompanying challenge is how to protect the security of the commercially confidential data, while maintaining the ability to search the data. In this paper, a privacy-preserving data search scheme is proposed, that can support both the identifier-based and feature-based product searches. Specifically, two novel index trees are constructed and encrypted, that can be searched without knowing the plaintext data. Analysis and simulation results demonstrate the security and efficiency of our scheme.