Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of a NoSQL Graph DBMS for a Hospital Social Network

Nowadays, the possibility of using social media in the healthcare domain is attracting the attention of many clinical professionals all around the world. In this panorama, many Healthcare Social Network (HSN) platforms are emerging with the purpose to enhance patient care and education. However, many clinical operators are reluctant to use them because they do not fulfil their requirements and are looking at the possibility to develop their own HSN platforms in order to perform social science studies. In this context, one of the major issue is the management of generated big data presenting a huge amount of relations and for this reason, traditional Relational Database management Systems (RDBMSs) are not adequate. The objective of this preliminary scientific work is to prove that a NoSQL graph DBMS can address such an issue, paving the way toward future social science studies. Experiments results show that Neo4j, i.e., one of the major NoSQL graph DBMS, simplifies the management of HSN data also guaranteeing acceptable performances in the perspective of future social science studies.
It’s Hard to Avoid Avoidance: Uncoupling the Evolutionary Connection between Plant Growth, Productivity and Stress “Tolerance”

In the last 100 years, agricultural developments have favoured selection for highly productive crops, a fact that has been commonly associated with loss of key traits for environmental stress tolerance. We argue here that this is not exactly the case. We reason that high yield under near optimal environments came along with hypersensitization of plant stress perception and consequently early activation of stress avoidance mechanisms, such as slow growth, which were originally needed for survival over long evolutionary time periods. Therefore, mechanisms employed by plants to cope with a stressful environment during evolution were overwhelmingly geared to avoid detrimental effects so as to ensure survival and that plant stress “tolerance” is fundamentally and evolutionarily based on “avoidance” of injury and death which may be referred to as evolutionary avoidance (EVOL-Avoidance). As a consequence, slow growth results from being exposed to stress because genes and genetic programs to adjust growth rates to external circumstances have evolved as a survival but not productivity strategy that has allowed extant plants to avoid extinction. To improve productivity under moderate stressful conditions, the evolution-oriented plant stress response circuits must be changed from a survival mode to a continued productivity mode or to avoid the evolutionary avoidance response, as it were. This may be referred to as Agricultural (AGRI-Avoidance). Clearly, highly productive crops have kept the slow, reduced growth response to stress that they evolved to ensure survival. Breeding programs and genetic engineering have not succeeded to genetically remove these responses because they are polygenic and redundantly programmed. From the beginning of modern plant breeding, we have not fully appreciated that our crop plants react overly-cautiously to stress conditions. They over-reduce growth to be able to survive stresses for a period of time much longer than a cropping season. If we are able to remove this polygenic redundant survival safety net we may improve yield in moderately stressful environments, yet we will face the requirement to replace it with either an emergency slow or no growth (dormancy) response to extreme stress or use resource management to rescue crops under extreme stress (or both).
Does OpenFlow Really Decouple The Data Plane from The Control Plane?

Software Defined Networks (SDNs) offer flexibility to current networks, allowing operators to manage network elements using software on an external server. SDNs are founded on a key feature: the separation of the control plane from the data plane. OpenFlow is the most popular SDN southbound interface today. However, does OpenFlow really decouple the data plane from the control plane? This is the leading question in this work. The literature has sought to quantity the impact of OpenFlow commands from control plane on data plane performance. Particularly, we argue that it is possible to damage the date plane by too many flow updates. Attackers, for instance, can use this effect in a cloud environment to reduce the performance of a collocated virtual network. However, it is not clear what is the exact impact of this coupling on production hardware and software switches. We investigate this through experiments, under representative scenarios, and propose a threshold mechanism to mitigate the effect of malicious administrators. We have observed that both hardware and software switches suffer from this limitation, presenting an average RTT degradation of up to 12.35% in the hardware switch, and 25.9% on the software switch. Finally, the proposed mechanism mitigates the lack of decoupling and malicious behavior.
Optimized CF Recommendation Algorithm Based on Users' Characteristics and Trust

CF (Collaborative filtering) algorithm has the widest and most successful applications in personalized recommendations. However, due to its over-reliance on the users' historical data, it is difficult to avoid data sparseness and cold start issues. The data sparseness and cold start may cause poor recommendation accuracy of the collaborative filtering algorithm. A hybrid optimal collaborative filtering algorithm based on users' characteristics and trust is proposed in this paper. In the process of users' similarity calculation, the age and gender of users' characteristics are introduced to make the determination of nearest neighbor more accurate. Besides, in order to improve the recommendation accuracy of the traditional CF recommendation algorithm, the trust relationship is introduced into the prediction score by measuring the users' trust, and this improvement will be used in the recommendation of new items in order to improve the recommendation accuracy of the traditional CF recommendation algorithm. The experimental results of Movie lens data set show that the improved recommendation accuracy of the recommendation system can be achieved by the proposed algorithm. Also, the problems of cold start and sparse data can be solved effectively.
Mixed Likelihood Gaussian Process Latent Variable Model

We present the Mixed Likelihood Gaussian process latent variable model (GP-LVM), capable of modeling data with attributes of different types. The standard formulation of GP-LVM assumes that each observation is drawn from a Gaussian distribution, which makes the model unsuited for data with e.g. categorical or nominal attributes. Our model, for which we use a sampling based variational inference, instead assumes a separate likelihood for each observed dimension. This formulation results in more meaningful latent representations, and give better predictive performance for real world data with dimensions of different types.
A Comparative Analysis of Bloom Filter-based Routing Protocols for Information-Centric Networks

Bloom filter-based routing protocols for Named Data Networking (NDN) aim at facilitating content discovery in NDN. In this paper, we compare the performance of two Bloom filterbased routing protocols, namely BFR and COBRA. BFR is a push-based routing protocol that works based on Bloom filterbased content advertisements, while COBRA is a pull-based routing protocol that operates based on route traces left from previously retrieved content objects, which are stored in Stable Bloom Filters. In this paper, we show that BFR outperforms COBRA in terms of average memory needed for storing routing updates, average round-trip delay, normalized communication overhead, total Interest communication overhead, and mean hit distance.
Stereotactic Lesioning of the Thalamic Vo Nucleus for the Treatment of Writer's Cramp (Focal Hand Dystonia)

Writer's cramp (focal hand dystonia) is a sporadic focal dystonia that affects a specific part of the upper limb causing excessive co-contraction of antagonistic muscles. It usually presents as a task-specific dystonia, including, among others, writing of a character or playing a musical instrument. Although treatments for writer's cramp exist, medical therapy often results in unsatisfactory outcomes in patients with this type of dystonia. However, accumulating evidence suggests that long-term and complete remission of various types of focal hand dystonia can be achieved with stereotactic ablation or deep brain stimulation of the thalamic ventral-oralis complex (Vo) nucleus, which includes both the ventralis oralis posterior and anterior nuclei of the thalamus. Following the striking therapeutic success of Vo thalamotomy in patients with medically-refractory writer's cramp, we here introduce the use of stereotactic lesioning of the thalamic Vo nucleus for the treatment of this focal type of dystonia. Our findings identified patients with disabling writer's cramp (i.e., it prevents their success in their professional careers) to be good candidates for positive outcome with this surgical technique.
Intestinal probiotics restore the ecological fitness decline of Bactrocera dorsalis by irradiation

The sterile insect technique (SIT) as an eco‐friendly and reliable strategy has been used to control populations of insect pests of agricultural, veterinary and human health importance. Successful applications of SIT rely on the high‐level ecological fitness of sterile males. A suitable and stable gut microbiome can contribute to the ecological fitness of insect by influencing their physiology, biochemistry and development processes. Here, we show that a shift in the gut bacterial composition and structure by sterilizing irradiation, characterized by a decrease in the major gut microbiota community Enterobacteriaceae, an expansion of the minor members (e.g., Bacillaceae) and a higher richness and diversity, is tightly linked to radiation‐induced ecological fitness (male mating competitiveness, flight capacity, survival rate and life span) decline in Bactrocera dorsalis (Hendel) sterile males. Function prediction of gut microbiota indicated that changes in microbiome taxonomy tend to drive microbiome functional shifts. A higher nutrient consumption of the flourishing minor gut microbiota may cause a decline in nutrients and energy metabolic activity of host and then result in the reduced ecological fitness of irradiated flies. Furthermore, we found that a gut bacterial strain Klebsiella oxytoca (BD177) can restore ecological fitness by improving food intake and increasing haemolymph sugar and amino acid levels of irradiated B. dorsalis flies. Our findings suggest that gut symbiont‐based probiotics can be used as agents for reversing radiation‐induced ecological fitness decrease.
A Virtual Prototype Semihosting Approach for Early Simulation of Cyber-Physical Systems

An early design space exploration of Cyber-Physical Systems (CPS) is a challenging task due to multi-domain areas and tight interaction of computing systems (cyber) and environment actuation, communication and sensing (physical). The CPS designer must be able to rapidly evaluate various solutions and to verify if project constraints were met. In this paper, we propose a CPS development framework based on Virtual Prototype (VP) to provide a high level of abstraction models to accurately simulate CPS behavior, using target independent semihosting interfaces and assessing performance and timing constraints. The experiments show a high timing accuracy in CoreMark and Dhrystone compute-intensive benchmarks and a low overhead in I/O intensive tasks for semihosting access of host resources, such as files and memory. An ECG heart rate detection case study was implemented using heterogeneous interfaces to demonstrate how hardware, human and software components can be seamlessly integrated to exchange real world data.
Scalable Fogbed for Fog Computing Emulation

The fog computing model integrates cloud services into the network on a widely distributed level to overcome challengers of cloud-based IoT platforms. The deployment of IoT solutions employing a fog architecture requires a decentralized and scalable computing infrastructure, which places networking, compute and storage resources in a hierarchy of levels arranged between the data source and the cloud. Despite recent advances in fog platforms, there exists no readily available testbed which can help researchers to design and evaluate fog applications on a truly IoT scale. The current fog prototyping tools are adapted from cloud middleware or network simulators to enable the evaluation of fog solutions in limited environments. This paper presents a framework and toolset integration that uses the Fogbed emulator to enable fog distributed testbeds in virtualized environments. Unlike current approaches, the proposed framework allows for the deployment and testing of fog components in a scalable way. Its design is compatible with real world technologies and meets the requirements of low cost, flexible setup and supports third-party systems through standard interfaces. A case study is presented to demonstrate the emulation of fog distributed components and services using a cluster approach. In addition, future developments and research directions are discussed.
Early Recurrent Ischemic Lesions in Patients With Cryptogenic Stroke and Patent Foramen Ovale: An Observational Study

Background: Randomized controlled trials indicate that patent foramen ovale (PFO) closure reduces risk of stroke recurrence in patients with cryptogenic stroke and PFO. However, the optimal time point for PFO closure is unknown and depends on the risk of stroke recurrence. Objective: We aimed to investigate risk of early new ischemic lesions on cerebral magnetic resonance imaging (MRI) in cryptogenic stroke patients with and without PFO. Methods: Cryptogenic stroke patients underwent serial MRI examinations within 1 week after symptom onset to detect early new ischemic lesions. Diffusion-weighted imaging (DWI) lesions were delineated, co-registered, and analyzed visually for new hyperintensities by raters blinded to clinical details. A PFO was classified as stroke-related in patients with PFO and a Risk of Paradoxical Embolism (RoPE) score >5 points. Results: Out of 80 cryptogenic stroke patients, risk of early recurrent DWI lesions was not significantly different in cryptogenic stroke patients with and without PFO. Similar results were observed in patients ≤60 years of age. Patients with a stroke-related PFO even had a significantly lower risk of early recurrent ischemic lesions compared to all other patients with cryptogenic stroke (unadjusted odds ratio 0.23 [95% confidence interval 0.06–0.87], P = 0.030). Conclusion: Our data argue against a high risk of early stroke recurrence in patients with cryptogenic stroke and PFO.
Label-free imaging of amyloid plaques in Alzheimer’s disease with stimulated Raman scattering microscopy

Misfolded proteins in amyloid plaques in transgenic Alzheimer’s disease mouse brains are visualized directly without labeling. One of the key pathological features of Alzheimer’s disease (AD) is the existence of extracellular deposition of amyloid plaques formed with misfolded amyloid-β (Aβ). The conformational change of proteins leads to enriched contents of β sheets, resulting in remarkable changes of vibrational spectra, especially the spectral shifts of the amide I mode. Here, we applied stimulated Raman scattering (SRS) microscopy to image amyloid plaques in the brain tissue of an AD mouse model. We have demonstrated the capability of SRS microscopy as a rapid, label-free imaging modality to differentiate misfolded from normal proteins based on the blue shift (~10 cm−1) of amide I SRS spectra. Furthermore, SRS imaging of Aβ plaques was verified by antibody staining of frozen thin sections and fluorescence imaging of fresh tissues. Our method may provide a new approach for studies of AD pathology, as well as other neurodegenerative diseases associated with protein misfolding.
Performance Analysis of Maximum Likelihood Estimation for Transmit Power Based on Signal Strength Model

We study theoretical performance of Maximum Likelihood (ML) estimation for transmit power of a primary node in a wireless network with cooperative receiver nodes. The condition that the consistence of an ML estimation via cooperative sensing can be guaranteed is firstly defined. Theoretical analysis is conducted on the feasibility of the consistence condition regarding an ML function generated by independent yet not identically distributed random variables. Numerical experiments justify our theoretical discoveries.
Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning

In hierarchical reinforcement learning a major challenge is determining appropriate low-level policies. We propose an unsupervised learning scheme, based on asymmetric self-play from Sukhbaatar et al. (2018), that automatically learns a good representation of sub-goals in the environment and a low-level policy that can execute them. A high-level policy can then direct the lower one by generating a sequence of continuous sub-goal vectors. We evaluate our model using Mazebase and Mujoco environments, including the challenging AntGather task. Visualizations of the sub-goal embeddings reveal a logical decomposition of tasks within the environment. Quantitatively, our approach obtains compelling performance gains over non-hierarchical approaches.
BAP1 regulation of the key adaptor protein NCoR1 is critical for γ-globin gene repression

In this study, Yu et al. found that nuclear receptor corepressor-1 (NCoR1) is a critical component of the DRED repressor that acts as a scaffold to unite the DNA-binding and epigenetic enzyme components (e.g. DNMT1 and LSD1) that elicit DRED function. They also describe a potent new regulator of γ-globin repression: The deubiquitinase BAP1 is a component of the repressor complex whose activity maintains NCoR1 at sites in the β-globin locus, and BAP1 inhibition in erythroid cells massively induces γ-globin synthesis.
Efficacy of gelatin tannate for acute diarrhea in children: a systematic review and meta-analysis.

AIM To investigate by meta-analysis the efficacy of gelatin tannate (GT), a mucosal barrier protector, in children with acute gastroenteritis. METHODS A comprehensive literature search was conducted. Studies were selected according to PICO: Participants: children aged 0-12 years with acute diarrhea; Intervention: GT; Comparison: oral rehydration solution and/or placebo; Outcomes: diarrhea-related outcomes. RESULTS Three published randomized controlled trials were identified of pediatric diarrhea treated with GT (n = 203) or control (n = 204). GT significantly (p < 0.01) reduced stool frequency at 12 h in two randomized controlled trials. A significant treatment effect (risk ratio = 0.74; p < 0.01) in favor of GT was found for the exploratory composite outcome of 'diarrhea or liquid stools at 24 h' in three studies. Risk ratios in a single study which reported the percentage of patients with liquid stools at 12, 24 and 48 h favored GT at all time points. No significant differences were found between GT and control for patients with diarrhea at 12 or 24 h or for duration of diarrhea. CONCLUSION GT improved stool frequency and stool consistency in children with acute diarrhea, although further well-controlled studies would be useful to confirm a beneficial treatment effect.
Discriminative Co-Occurrence of Concept Features for Action Recognition

We present a new method for action recognition that employs the co-occurrence of concept features as semantic geometric context. Firstly, the semantic concept codebook is learnt by an improved subspace clustering, then the spatio-temporal interest points are labelled as meaningful features, namely concept features. After that, Multi-scale co-occurrence statistics that embeds the relative distance and direction of pairwise concept features is constructed. Unlike the traditional k-means, the features labelled by the concept codebook well represent the ingredients of objects and ensure temporal consistency. Moreover, the relative layout is the semantic geometric context that describes the changes of geometric relationships. Using the popular KTH and UCF-sports action datasets, experimental results show that the relative layouts combined with the STIPs have discriminative power for action recognition. Our method obtains promising recognition performance compared with other state-of-the-art algorithms.
Transcatheter arterial chemoembolization monotherapy vs combined transcatheter arterial chemoembolization–percutaneous microwave coagulation therapy for massive hepatocellular carcinoma (≥10 cm)

Background The prognosis of massive hepatocellular carcinomas (MHCCs; ≥10 cm) remains worse. Purpose The aim of this study was to evaluate the clinical benefits of transcatheter arterial chemoembolization (TACE) or TACE combined with percutaneous microwave coagulation therapy (PMCT) and the long-term survival rate of MHCC patients treated with these techniques. Patients and methods A retrospective study was performed using data involving 102 MHCC patients admitted to the Second Hospital of Nanjing from September 2010 to August 2015. The median interval between treatments and overall survival (OS) was hierarchically analyzed using log-rank tests. Multivariate analysis was done using Cox regression model analysis. Results The median survival time of MHCC patients was 3 months (range, 1–10 months) in the palliative group, 3 months (range, 1–39 months) in the TACE group, and 7.5 months (range, 3–30 months) in the TACE–PMCT group (P=0.038). The 6-, 12-, and 18-month OS rates for MHCC patients were 15%, 0%, and 0% in the palliative group, 30%, 25.63%, and 17.97% in the TACE group, and 50%, 41.67%, and 16.67% in the TACE–PMCT group, respectively (P=0.0467). In addition, TACE sessions had positive correlation with the survival time of MHCC patients (rho = 0.462, P<0.001). TACE treatment more than three times (HR =0.145, P<0.001) was an independent predictor of the survival of MHCC patients, which was identified by the Cox regression model analysis. Conclusions These results indicated that TACE–PMCT treatment in MHCC patients had advantages in prolonging OS and improving liver function. Multiple TACE treatments might be a suitable treatment for the MHCC patients.
Fog Computing Architecture: Survey and Challenges

Emerging technologies that generate a huge amount of data such as the Internet of Things (IoT) services need latency-aware computing platforms to support time critical applications. Due to the on-demand services and scalability features of cloud computing, Big Data application processing is done in the cloud infrastructure. Managing Big Data applications exclusively in the cloud is not an efficient solution for latency -sensitive applications related to smart transportation systems, healthcare solutions, emergency response systems and content delivery applications. Thus, the fog -computing paradigm that allows applications to perform computing operations in-between the cloud and the end devices has emerged. In fog architecture, IoT devices and sensors are connected to the fog devices which are located in close proximity to the users, and it is also responsible for intermediate computation and storage. Most computations will be done on the edge by eliminating full dependencies on the cloud resources. In this chapter, we investigate and survey fog-computing architecture which have been proposed over the past few years. Moreover, we study the requirements of IoT applications and platforms, and the limitations faced by cloud systems when executing IoT applications. Finally, we review current research works that particularly focus on Big Data application execution on fog and address several open challenges as well as future-research directions.