Miracle-dz/UniFAR-FTUs huggingface dataset:
The long-term recurrence rate and survival of obstructive left-sided colon cancer patients : a stent as a bridge to surgery

Background: a colonic stent as a bridge to elective surgery for left-sided malignant colonic obstruction is an alternative to the classical treatment. The aim of our study was to evaluate the recurrence rate as well as the morbidity and mortality of this treatment. Patients and methods: patients admitted to the Emergency Department with left-sided malignant colonic obstruction between June 2006 and January 2014 were analyzed in a retrospective observational study. Patients who underwent self-expanding metallic stent placement via endoscopy as a bridge to surgery were included. The observation period was performed until May 2017. Results: fifty-three patients were treated with a colonic stent as a bridge to surgery; nine patients died during the postoperative period. The deceased patients were more frequently male (100% in the deceased vs 62% in the non-deceased, p = 0.02), with a more advanced age (81.4 ± 5.1 vs 71.6 ± 10.8, p < 0.001), lower hemoglobin levels on admission (12.9 vs 13.6 p < 0.001), a greater number of leukocytes (12,918 vs 9,437, p < 0.001) and greater coagulopathy (INR 1.6 vs 1, p < 0.001). Eight patients had a distant relapse with a median disease-free survival of 19.1 months. The variables were compared according to the appearance of distant disease and the mean age was lower in patients with a recurrence (65.9 ± 11.3 vs 74.9 ± 9.9, p < 0.001). Conclusions: the results of the use of a stent as a bridge to curative surgery in patients with obstructive left colon cancer in our hospital is comparable to previous studies.
Semi-blind source separation with multichannel variational autoencoder

This paper proposes a multichannel source separation technique called the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture. By training the CVAE using the spectrograms of training examples with source-class labels, we can use the trained decoder distribution as a universal generative model capable of generating spectrograms conditioned on a specified class label. By treating the latent space variables and the class label as the unknown parameters of this generative model, we can develop a convergence-guaranteed semi-blind source separation algorithm that consists of iteratively estimating the power spectrograms of the underlying sources as well as the separation matrices. In experimental evaluations, our MVAE produced better separation performance than a baseline method.
Inference of Users Demographic Attributes based on Homophily in Communication Networks

Over the past decade, mobile phones have become prevalent in all parts of the world, across all demographic backgrounds. Mobile phones are used by men and women across a wide age range in both developed and developing countries. Consequently, they have become one of the most important mechanisms for social interaction within a population, making them an increasingly important source of information to understand human demographics and human behaviour. In this work we combine two sources of information: communication logs from a major mobile operator in a Latin American country, and information on the demographics of a subset of the users population. This allows us to perform an observational study of mobile phone usage, differentiated by age groups categories. This study is interesting in its own right, since it provides knowledge on the structure and demographics of the mobile phone market in the studied country. We then tackle the problem of inferring the age group for all users in the network. We present here an exclusively graph-based inference method relying solely on the topological structure of the mobile network, together with a topological analysis of the performance of the algorithm. The equations for our algorithm can be described as a diffusion process with two added properties: (i) memory of its initial state, and (ii) the information is propagated as a probability vector for each node attribute (instead of the value of the attribute itself). Our algorithm can successfully infer different age groups within the network population given known values for a subset of nodes (seed nodes). Most interestingly, we show that by carefully analysing the topological relationships between correctly predicted nodes and the seed nodes, we can characterize particular subsets of nodes for which our inference method has significantly higher accuracy.
SC79, the AKT Activator Protects Cerebral Ischemia in a Rat Model of Ischemia/Reperfusion Injury

Background Activation of AKT pathway attenuates brain damage and neuronal apoptosis during cerebral ischemia/reperfusion (I/R) injury. SC79 is a novel, selective and highly-efficient Akt activator. This study aimed to investigate the neuroprotective effect of SC79 against cerebral I/R injury in a rat model, and to explore the possible underlying mechanisms. Material/Methods Male Sprague-Dawley rats received cerebral ischemia for 1 hour, followed by brain reperfusion for 0.5–24 hours. The cerebral I/R injury animal model were treated with SC79 alone or SC79 in combination with LY294002. Western blots were used to detect the levels of expression of phosphatidylinositol AKT (p-Akt), Bax, and bcl-2. Twenty-four hours after cerebral I/R, the degree of brain injury was evaluated by detecting the neurological deficit score (NDS). The infarct rate of brain tissue was observed by TTC (2, 3, 5-triphenyltetrazolium chloride) staining. TUNEL (terminal deoxynucleotidyl transferase-mediated UTP nick end labeling) staining was used to detect cell apoptosis. Results p-Akt was activated during early cerebral I/R at 0.5 hours, and reached the highest levels at 4 hours, then gradually decreased from 6 hours, and reached and maintained the lowest levels at 12–24 hours. Bax expression was gradually increased from 6 hours and reached the highest level at 24 hours. However, bcl-2 expression was gradually increased and reached the highest levels at 4 hours, then gradually decreased from 6 hours, and reached the lowest levels at 24 hours. Administration of SC79 decreased infarct volumes and improved neurological function significantly. LY294002 in combination with SC79 lost the capability of SC79 to resist the cerebral I/R injury. SC79 treatment alone activated p-Akt and promoted anti-apoptotic bcl-2 and inhibited anti-apoptotic Bax expression in middle cerebral artery occlusion (MCAO) mice. However, combined SC79 and LY294002 treatment abolished SC79-induced p-Akt activity, inhibited anti-apoptotic bcl-2 and promoted anti-apoptotic Bax expression in MCAO mice. Furthermore, SC79 treatment alone attenuated apoptotic neuronal cell death, but abolished this effect in SC79 in combination with LY294002 treated groups. Conclusions SC79 significantly increased Akt activation and reduced infarct volume and subsequently improved neurological function in ischemic brain after cerebral I/R injury in rats. These findings suggested that SC79 may be as a neuroprotective drug to be potentially used in the clinic.
Investigation of Novel Partitioned-Primary Hybrid-Excited Flux-Switching Linear Machines

The novel partitioned-primary hybrid-excited flux-switching linear machines (PP-HEFSLMs) with unaligned and aligned structures are proposed, which exhibit advantages of direct-driven, wide speed operation, robust secondary, etc. Unlike conventional hybrid-excited topologies, the proposed PP-HEFSLMs accommodate armature and excitation windings on two separated parts of the partitioned primary, which solves the problem of crowded primary; hence, both flux-adjusting ratio and thrust force density are effectively improved. The topology and operation principle of the two PP-HEFSLMs are introduced in detail first. Based on finite-element method (FEM) analysis, major geometric parameters are investigated and optimized then. For the optimized structures, the electromagnetic performances, i.e., thrust force characteristics, demagnetization, flux-regulation capability, and inductances are calculated and further compared. The results manifest both structures show a satisfactory flux-adjusting ratio particularly for an aligned structure and good fault-tolerant capability due to tiny mutual inductances between phases. Although the thrust density of the aligned structure is relatively low compared with its unaligned counterpart, it can be taken as a mechanical flux-weakening method by aligning permanent magnets with armature teeth. Finally, three-dimensional FEM analysis is performed to validate the two-dimensional FEM predicted results.
Temperature Stability of a Hybrid Polarization-Maintaining Photonic Crystal Fiber Resonator and Its Application in a Resonant Fiber Optic Gyro

A fiber ring resonator (FRR) constructed using a Panda polarization-maintaining fiber does not effectively solve the problem of temperature-related polarization fluctuation, which considerably limits the detection accuracy of the resonant fiber optic gyro. The polarization-maintaining photonic crystal fiber (PM-PCF) can improve the thermal stability of the FRR. In this study, a structure that can simultaneously detect the polarization fluctuation of two FRRs is designed. We analyzed and verified the polarization phase shift errors of these two types of fibers, which are caused by the thermally induced birefringence changes. Theoretical simulation and experimental results confirm that a PM-PCF can be used to optimize the FRR, which can effectively suppress the polarization fluctuation.
Estimation of Deterioration Levels of Transmission Towers via Deep Learning Maximizing Canonical Correlation Between Heterogeneous Features

This paper presents estimation of deterioration levels of transmission towers via deep learning maximizing the canonical correlation between heterogeneous features. In the proposed method, we newly construct a correlation-maximizing deep extreme learning machine (CMDELM) based on a local receptive field (LRF). For accurate deterioration level estimation, it is necessary to obtain semantic information that effectively represents deterioration levels. However, since the amount of training data for transmission towers is small, it is difficult to perform feature transformation by using many hidden layers such as general deep learning methods. In CMDELM-LRF, one hidden layer, which maximizes the canonical correlation between visual features and text features obtained from inspection text data, is newly inserted. Specifically, by using projections obtained by maximizing the canonical correlation as weight parameters of the hidden layer, feature transformation for extracting semantic information is realized without designing many hidden layers. This is the main contribution of this paper. Consequently, CMDELM-LRF realizes accurate deterioration level estimation from a small amount of training data.
Effects of Meditative Movements on Major Depressive Disorder: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: Tai Chi, Qigong, and Yoga are recognized as the most popular complementary approaches for alleviating musculoskeletal pain, improving sleep quality, and reducing blood pressure. The therapeutic effects of these meditative movements for treating major depressive disorder (MDD) is yet to be determined. Therefore, we examined whether meditative movements (Tai Chi, Qigong, and Yoga) are effective for treating MDD. Seven electronic databases (SPORTDiscus, PubMed, PsycINFO, Cochrane Library, Web of Science, CNKI, and Wanfang) were used to search relevant articles. Randomized controlled trials (RCT) using Tai Chi, Qigong or Yoga as intervention for MDD were considered for the meta-analysis (standardized mean difference: SMD). Results: Meta-analysis on 15 fair-to-high quality RCTs showed a significant benefit in favor of meditative movement on depression severity (SMD = −0.56, 95% CI −0.76 to −0.37, p < 0.001, I2 = 35.76%) and on anxiety severity (SMD = −0.46, 95% CI −0.71 to −0.21, p < 0.001, I2 = 1.17%). Meditative movement interventions showed significantly improved treatment remission rate (OR = 6.7, 95% CI 2.38 to 18.86, p < 0.001) and response rate (OR = 5.2, 95% CI 1.73 to 15.59, p < 0.001) over passive controls. Conclusions: Emphasizing the therapeutic effects of meditative movements for treating MDD is critical because it may provide a useful alternative to existing mainstream treatments (drug therapy and psychotherapy) for MDD. Given the fact that meditative movements are safe and easily accessible, clinicians may consider recommending meditative movements for symptomatic management in this population.
Chasing the Fish Oil—Do Bunker Vessels Hold the Key to Fisheries Crime Networks?

Illegal, Unreported and Unregulated (IUU) fishing is recognised as a major threat to the health of the world’s oceans. In addition, the discourse around IUU is recognizing and recommending that IUU be viewed through the lens of transnational crime. Reducing IUU fishing therefore requires a detailed understanding of how fishing fleets function and interact transnationally. However, this is difficult to determine because IUU fishing is by nature a clandestine activity, and often occurs in contexts that, by intention, make monitoring very difficult. Disentangling fleet activities therefore necessitates identifying and understanding how key players or actors function to support or guide IUU activities. In this regard, recent efforts have focused on identifying transhipment activities by finding and following refrigerated cargo (reefer) vessels. We suggest that bunker (fuel) vessels, may provide one solution to understanding and unravelling these IUU networks for two main reasons: they are fewer in number, are known to provide additional support to fishing vessels beyond refuelling. In this respect, bunker vessels are also used to resupply provisions, and crew, thereby facilitating human trafficking and slavery, which is demonstrably rife in certain components of the commercial fishing realm (e.g., long distance or high seas fleets). Here, we illustrate how social network analysis could be used as a methodological lens to expose previously clandestine IUU fishing fleet dynamics. Specifically, we highlight the connectedness of a tanker vessel, and several fishing and reefer vessels, in an area in the Southern Indian Ocean that has high levels of fishing and general shipping.
Myeloid-derived suppressor cells and their role in gynecological malignancies

Myeloid-derived suppressor cells are a heterogeneous population of immature myeloid cells that exhibit immunosuppressive activity (they block the proliferation and activity of both T cells and natural killer cells). In addition to their role in suppressing immune responses, myeloid-derived suppressor cells directly stimulate tumor cell proliferation, metastasis, and angiogenesis. In the area of gynecological cancer, increased numbers of circulating myeloid-derived suppressor cells or tumor-infiltrating myeloid-derived suppressor cells have been detected, and the increased frequencies of myeloid-derived suppressor cells are associated with a poor prognosis. Thus, the successful myeloid-derived suppressor cells depletion may hold the key to maximizing existing anti-cancer therapies and improving the prognosis of gynecological cancer. In this review, we summarize current knowledge regarding myeloid-derived suppressor cells biology, clinical significance of myeloid-derived suppressor cells, and the potential myeloid-derived suppressor cells–targeting strategies in gynecological cancer.
Meta-Analysis of NOS3 G894T Polymorphisms with Air Pollution on the Risk of Ischemic Heart Disease Worldwide

The purpose of this updated meta-analysis was to investigate the effect of nitric oxide synthase-3 (NOS3) G894T polymorphisms, air pollution and their interaction on ischemic heart disease (IHD) risk across populations worldwide. Recursive partition trees, nonlinear association curve fit and geographic information system maps were incorporated to verify results of conventional pooled analyses for sources of heterogeneity. Results from 61 studies (16,219 cases, 12,222 controls) revealed a significant increased relative risk (RR) of IHD associated with NOS3 894 polymorphisms TT (RR = 1.44) and GT (RR = 1.37). Subgroup analysis revealed that the TT polymorphism genotype had significantly increased risk of IHD in Caucasian, East Asian, South Asian, and Middle Eastern populations (all p < 0.05). It is important to point out that many countries demonstrated an average risk of greater than two, which identifies the NOS3 894 TT polymorphism as a potential causal factor and biological marker of IHD, based on criteria for strong evidence used in international consensus panels. These 10 countries include Ukraine, the United Kingdom, Brazil, Chile, Japan, South Korea, India, Iran, Egypt and Morocco. For these countries with elevated risk (RR > 2) from the NOS3 894 TT polymorphism, meta-predictive analysis demonstrated an increasing trend in air pollution association with increased NOS3 894 polymorphisms. Further studies are needed to explore the complexity of the associations among NOS3 gene polymorphisms per population stratifications within countries, detailed air pollution data for added specificity for geographic location across time, and disease risk.
Using triangles and latent factor cosine similarity prior to improve community detection in multi‐relational social networks

Community detection is a key to understanding the structure of complex networks. Communities, or clusters, are groups of vertices having higher probability of being connected to each other than to the members in other groups. Considering the importance of triangle structures, we first propose σ‐tensor to model ordinary relationships and triangle relationships simultaneously. Then, we propose a simple but effective latent factor prior, ie, latent factor cosine similarity prior, to improve community detection. The latent factor cosine similarity prior is a kind of statistics of the well‐defined synthetic multi‐relational social networks. It is based on a key observation that most latent feature factors of intra‐group members in these networks are highly similar according to cosine similarity measure. Using this prior along with the RESCAL tensor factorization model, we can obtain a superior latent feature factor matrix. Moreover, N‐RESCAL model, a variant of RESCAL model, and its corresponding algorithm N‐RESCAL‐ALS are proposed for the simplicity and the removal of the limit of cosine similarity. Once the latent factor matrix is obtained by factorizing σ‐tensor using N‐RESCAL model, we apply agglomerative clustering algorithm for community discovery. We call this framework as TNRA. Experiment results on several real‐world datasets are surprisingly promising, clearly demonstrating the power of the proposed prior and the effectiveness of our proposed methods.
Predictive Torque and Rotor Flux Control of a DFIG-DC System for Torque Ripple Compensation and Loss Minimization

The severe torque ripple normally occurring in the doubly fed induction generator dc (DFIG-dc) system can cause premature failure of mechanical components and shorten the life of the drive train. This paper addresses the torque ripple issue by proposing a predictive direct torque control strategy, which delivers at the same time torque ripple suppression and minimization of losses. The existing control algorithms for torque ripple mitigation are mostly based on resonant controllers and repetitive control forcing the compensation signal either through the current chain or directly into the rotor voltage commands. All these techniques lead to structures with multiple controllers whose tuning is not straightforward. Furthermore, they are very sensitive to the operating frequency, making optimized operation with variable frequency highly challenging. Conversely, the proposed algorithm predicts directly the best rotor voltage space vector to minimize torque ripple and track a prescribed rotor flux amplitude to minimize losses, with no current control chain. As confirmed by simulations and experiments, the strategy allows large stator frequency variations as required by the optimal flux command for minimum losses, whilst ensuring effective torque ripple compensation.
Inductive Multi-Hypergraph Learning and Its Application on View-Based 3D Object Classification

The wide 3D applications have led to increasing amount of 3D object data, and thus effective 3D object classification technique has become an urgent requirement. One important and challenging task for 3D object classification is how to formulate the 3D data correlation and exploit it. Most of the previous works focus on learning optimal pairwise distance metric for object comparison, which may lose the global correlation among 3D objects. Recently, a transductive hypergraph learning has been investigated for classification, which can jointly explore the correlation among multiple objects, including both the labeled and unlabeled data. Although these methods have shown better performance, they are still limited due to 1) a considerable amount of testing data may not be available in practice and 2) the high computational cost to test new coming data. To handle this problem, considering the multi-modal representations of 3D objects in practice, we propose an inductive multi-hypergraph learning algorithm, which targets on learning an optimal projection for the multi-modal training data. In this method, all the training data are formulated in multi-hypergraph based on the features, and the inductive learning is conducted to learn the projection matrices and the optimal multi-hypergraph combination weights simultaneously. Different from the transductive learning on hypergraph, the high cost training process is off-line, and the testing process is very efficient for the inductive learning on hypergraph. We have conducted experiments on two 3D benchmarks, i.e., the NTU and the ModelNet40 data sets, and compared the proposed algorithm with the state-of-the-art methods and traditional transductive multi-hypergraph learning methods. Experimental results have demonstrated that the proposed method can achieve effective and efficient classification performance. We also note that the proposed method is a general framework and has the potential to be applied in other applications in practice.
HPWL Formulation for Analytical Placement Using Gaussian Error Function

Placement is a crucial stage in the physical design of VLSI(Very Large Scale Integration). Analytical placer at this stage determines optical physical locations of cells in the chip while minimizing the total half-perimeter wirelength(HPWL) of the nets. The constraints imposed while minimizing the total HPWL of nets are non-overlapping of blocks, congestion, delay etc. This paper introduces a recursive smooth wirelength model for HPWL of a net using Gaussian error function, which can be used by analytical placers. The novelty of this model lies in approximating closely the HPWL of a net than other state-of-the-art wirelength models in the literature including log-sum-exponent(LSE), weighted average(WA), CHMAX and absolute wirelength(ABSWL) models. The HPWL accuracy of the proposed model for global and detailed placements for IBM ISPD 2004 benchmark suite shows less than 1% and 0.5% absolute errors in total wirelength in average respectively.