Miracle-dz/UniFAR-FTUs huggingface dataset:
"Love hurts": romantic attachment and depressive symptoms in pregnant adolescent and young adult couples.

OBJECTIVE The current study investigates the relationship between romantic attachment style and depressive symptoms between both members of pregnant adolescent and young adult couples. METHOD Participants were 296 pregnant young females (mean age = 18.7) and their male partners (mean age = 21.3; 592 total participants) who were recruited from obstetrics and gynecology clinics in Connecticut. The dimensions of avoidant and anxious romantic attachment were assessed using the Experiences in Close Relationships Inventory. Depressive symptoms were measured using the Center for Epidemiological Studies-Depression Scale. RESULTS Results showed that avoidant attachment and anxious attachment were significantly positively related to depressive symptoms. Multilevel modeling for partner effects revealed that anxious attachment and depressive symptoms in partners were significantly positively associated with depressive symptoms CONCLUSION Findings underscore the importance of considering couples-based approaches to supporting the transition to parenthood and developing the necessary self and relationship skills to manage attachment needs and relationship challenges.
Role of KIFC3 motor protein in Golgi positioning and integration

KIFC3, a microtubule (MT) minus end–directed kinesin superfamily protein, is expressed abundantly and is associated with the Golgi apparatus in adrenocortical cells. We report here that disruption of the kifC3 gene induced fragmentation of the Golgi apparatus when cholesterol was depleted. Analysis of the reassembly process of the Golgi apparatus revealed bidirectional movement of the Golgi fragments in both wild-type and kifC3 −/− cells. However, we observed a markedly reduced inwardly directed motility of the Golgi fragments in cholesterol-depleted kifC3 −/− cells compared with either cholesterol-depleted wild-type cells or cholesterol-replenished kifC3 −/− cells. These results suggest that (a) under the cholesterol-depleted condition, reduced inwardly directed motility of the Golgi apparatus results in the observed Golgi scattering phenotype in kifC3 −/− cells, and (b) cholesterol is necessary for the Golgi fragments to attain sufficient inwardly directed motility by MT minus end–directed motors other than KIFC3, such as dynein, in kifC3 −/− cells. Furthermore, we showed that Golgi scattering was much more drastic in kifC3 −/− cells than in wild-type cells to the exogenous dynamitin expression even in the presence of cholesterol. These results collectively demonstrate that KIFC3 plays a complementary role in Golgi positioning and integration with cytoplasmic dynein.
An empirical study of morphing on behavior-based network traffic classification

With the rapid advancement of traffic classification techniques, a countermeasure against them called network traffic morphing, which aims at masking traffic to degrade the performance of traffic identification and classification, has emerged. Although several morphing strategies have been proposed as promising approaches, very few works, however, have investigated their impact on the actual traffic classification performance. This work sets out to fulfill this gap from an empirical study point of view. It takes into account different morphing strategies exerted on packet size PS and/or inter-arrival time IAT and evaluates them by simulation. The impact is evaluated by using three popularity used classification algorithms, including C4.5, Support Vector Machines , and Naive Bayes, with various performance metrics considered. The results show that not all morphing strategies can effectively thwart traffic classification. Different morphing strategies perform distinctively in degrading traffic identification, among which the integration of PS and IAT morphings is the best, and the PS-based method alone is the worst. Furthermore, the three classifiers also exhibit distinct robustness to the morphing, with C4.5 being the most robust and Naive Bayes being the weakest. Finally, our study shows that classifiers can learn nontrivial information merely from the traffic direction patterns, which partially explains the weak protection of PS-based morphing methods because they fail to take the direction patterns into consideration. Copyright © 2013 John Wiley & Sons, Ltd.
A putative Toll/interleukin‐1 receptor domain protein from Helicobacter pylori is dimeric in solution and interacts with human Toll‐like receptor adaptor myeloid differentiation primary response 88

Helicobacter pylori, an important human pathogen, is capable of causing persistent infection with minimal immune response. The first line of defense during H. pylori infection is through gastric epithelial cells that present TLR, A family of bacterial proteins that share homology with the Toll/IL‐1 receptor (TIR) domain were identified. Bacterial TIR proteins (BTP) mimic human TIR domain proteins and act on myeloid differentiation primary response gene 88 (MyD88) signaling pathways to suppress TLR signaling. H. pylori may also produce a similar protein. A putative H. pylori BTP was found based on sequence homology. The corresponding gene hp1437 was inserted into an expression vector in fusion with an N‐terminal cleavable 6his‐tag. The recombinant protein, 6his‐HP1437, was purified using nickel affinity chromatography with a yield of 8 mg/L culture. Oligomerization of HP1437 was investigated by size‐exclusion chromatography. It was found that HP1437 forms dimers in solution similar to other BTPs. Furthermore, glutathione S‐transferase pull down assays identified an interaction between HP1437 and human TIR domain adaptor MyD88. These findings suggest that HP1437 has the characteristic features of BTPs and may play a direct role in reducing immune response against H. pylori by binding to MyD88 and pave the way for an in‐depth characterization of this putative novel H. pylori virulence factor.
Analyzing the Trajectories of Patients with Sepsis using Process Mining

Process mining techniques analyze processes based on event data. We analyzed the trajectories of patients in a Dutch hospital from their registration in the emergency room until their discharge. We considered a sample of 1050 patients with symptoms of a sepsis condition, which is a life-threatening condition. We extracted an event log that includes events on activities in the emergency room, admission to hospital wards, and discharge. The event log was enriched with data from laboratory tests and triage checklists. We try to automatically discover a process model of the patient trajectories, we check conformance to medical guidelines for sepsis patients, and visualize the flow of patients on a de-jure process model. The lessons-learned from this analysis are: (1) process mining can be used to clarify the patient flow in a hospital; (2) process mining can be used to check the daily clinical practice against medical guidelines; (3) process discovery methods may return unsuitable models that are difficult to understand for stakeholders; and (4) process mining is an iterative process, e.g., data quality issues are often discovered and need to be addressed.
QSO+Galaxy Association and Discrepant Redshifts in NEQ3

Spectroscopy and deep imaging of the group NEQ3 are presented. This system is formed by three compact objects with relative separations of ~2. ″6 and ~2. ″8 and a lenticular galaxy at ~17'' from the geometric center of the group. A diffuse filament is located on a line joining the three compact objects and the main galaxy. Analysis of these observations confirms the redshift previously known for three of the objects (z = 0.1239 for the main galaxy, and z = 0.1935 and 0.1939 for two of the compact objects). We have also determined the previously unknown redshift of the third compact object as z = 0.2229. Using the relative strength and width of the main spectral lines we have classified the compact objects as two H II galaxies and one QSO (the object at z = 0.1935). With cross-correlation techniques, we have tentatively estimated the redshift of the filament as z = 0.19 (although a weaker component also appears at z = 0.12), so that it is probably associated with the halo of the two compact objects at this redshift. The two objects at redshift ~0.19 represent possibly one of the more clear examples of starburst (and perhaps QSO activity) driven by interaction. These considerations and the relation between these objects and the other two at reshifts ~0.12 and ~0.22 make the nature of the system intriguing, being difficult to explain the whole association in conventional scenarios.
A Genre Classification Plug-in for Data Collection

This demonstration illustrates how the methods developed in the MIR community can be used to provide real-time feedback to music users. By creating a genre classifier plugin for a popular media player we present users with relevant information as they play their songs. The plug-in can furthermore be used as a data collection platform. After informed consent from a selected set of users the plug-in will report on music consumption behavior back to a central server.
New species of Tulasnella associated with terrestrial orchids in Australia

Recent studies using sequence data from eight sequence loci and coalescent-based species delimitation methods have revealed several species-level lineages of Tulasnella associated with the orchid genera Arthrochilus, Caleana, Chiloglottis, and Drakaea in Australia. Here we formally describe three of those species, Tulasnella prima, T. secunda, and T. warcupii spp. nov., as well as an additional Tulasnella species associated with Chiloglottis growing in Sphagnum, T. sphagneti sp. nov. Species were identified by phylogenetic analyses of the ITS with up to 1.3 % sequence divergence within taxa and a minimum of 7.6 % intraspecific divergence. These new Tulasnella (Tulasnellaceae, Cantharellales) species are currently only known from orchid hosts, with each fungal species showing a strong relationship with an orchid genus. In this study, T. prima and T. sphagneti associate with Chiloglottis, while T. secunda associates with Drakaea and Caleana, and T. warcupii associates with Arthrochilus oreophilus.
Integrated Data and Process Management: Finally?

Contemporary information systems are generally built on the principle of segregation of data and processes. Data are modeled in terms of entities and relationships while processes are modeled as chains of events and activities. This situation engenders an impedance mismatch between the process layer, the business logic layer and the data layer. We discuss some of the issues that this impedance mismatch raises and analyze how and to what extent these issues are addressed by emerging artifact-centric process management paradigms. 1 The Data Versus Process Divide Data management and process management are both well-trodden fields – but each in its own way. Well-established data analysis and design methods allow data analysts to identify and to capture domain entities and to refine these domain entities down to the level of database schemas in a seamless and largely standardized manner. Concomitantly, database systems and associated middleware enable the development of robust and scalable data-driven applications, while contemporary packaged enterprise systems support hundreds of business activities on top of shared databases. In a similar vein, well-documented and proven process analysis and design methods allow process analysts to identify and to capture process models at different levels of abstraction, ranging from high-level process models suitable for qualitative analysis and organizational redesign down to the level of executable processes that can be deployed in Business Process Management Systems (BPMS). But while data management and process management are each well supported by their own body of mature methods and tools, these methods and tools are at best loosely integrated. For example, when it comes to accessing data, BPMS typically rely on request-response interactions with database applications or packaged enterprise systems. Typically, data fetched from these systems are copied into the “working memory” of the BPMS. The data in this working memory are then used to evaluate business rules relevant to the execution of the process, and to orchestrate both manual and automated work. But the burden of synchronizing the working data maintained by the BPMS with the data maintained by the underlying systems is generally left with the developers. More generally, the “data vs. process” divide leads to an impedance mismatch between the data layer, the business logic layers and the process layer, which in the long run, hinders on the coherence and maintainability of information systems. In particular, the data vs. process divide has the following effects: – Process-related and function-related data redundancy. The BPMS maintains data about the state of the process, since these data are needed in order to enable the
Ultraviolet curable adhesive composition, adhesive film and method for producing adhesive film

The present invention addresses the problem of providing an ultraviolet curable adhesive composition which is capable of providing an adhesive film that has excellent cutting properties and resistance to wet heat whitening. The present invention provides an ultraviolet curable adhesive composition which is characterized by containing: a urethane (meth)acrylate (A) that is obtained by reacting a polyol (a1) that contains a polycarbonate polyol (a1-1), a polyisocyanate (a2) and a (meth)acrylic compound (a3) having an isocyanate group or a hydroxyl group; a (meth)acrylic compound (B) that has two or more (meth)acryloyl groups; a photopolymerization initiator (C); and an organic solvent (D). The present invention also provides a method for producing an adhesive film which is characterized in that an adhesive film is obtained by: applying the above-described ultraviolet curable adhesive composition to a base; then drying the organic solvent (C) so as to obtain an ultraviolet curable adhesive film; and subsequently bonding an adhesive layer of the ultraviolet curable adhesive film to the base and irradiating the ultraviolet curable adhesive film with ultraviolet light after the bonding.
Extracting Opinion Relations from Online Reviews Based on WAM

Data mining process of pattern discovering in large data sets in that the emerging field is sentiment analysis. Opinion mining is the study of analyzing the human‘s opinions, sentiments, and emotion towards the entities such as products, services. The main application of sentiment analysis is collecting the online reviews about the product, social networks informal text. The process of the opinion targets and the opinion words extraction and determining the relations between these words. In previous, the nearest neighbor rules approach was used, the disadvantage of this method was not suitable for long span sentences. The Word Alignment Model is proposed to extract the opinion words and opinion targets from the obtained reviews and the graph based co-ranking algorithm is used to detect the opinion relation through opinion relation graph. While compared with previous used methods, this novel approach effectively decreases the error probability. The results shows the algorithm effectively outperforms when compare to existing methods.
Dynamic Collaborative Filtering With Compound Poisson Factorization

Model-based collaborative filtering analyzes user-item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions. Most collaborative filtering algorithms assume that these latent factors are static, although it has been shown that user preferences and item perceptions drift over time. In this paper, we propose a conjugate and numerically stable dynamic matrix factorization (DCPF) based on compound Poisson matrix factorization that models the smoothly drifting latent factors using Gamma-Markov chains. We propose a numerically stable Gamma chain construction, and then present a stochastic variational inference approach to estimate the parameters of our model. We apply our model to time-stamped ratings data sets: Netflix, Yelp, and this http URL, where DCPF achieves a higher predictive accuracy than state-of-the-art static and dynamic factorization models.
STEPPING STONE MODEL 1349 voter model , and in the work of Cox

We study the stepping stone model on the two-dimensional torus. We prove several new hitting time results for random walks from which we derive some simple approximation formulas for the homozygosity in the stepping stone model as a function of the separation of the colonies and for Wright’s genetic distance FST . These results confirm a result of Crow and Aoki (1984) found by simulation: in the usual biological range of parameters FST grows like the log of the number of colonies. In the other direction, our formulas show that there is significant spatial structure in parts of parameter space where Maruyama and Nei (1971) and Slatkin and Barton (1989) have called the stepping model “effectively panmictic. ”
Vlasov equation of plasma in magnetic field

The linearized Vlasov equation for a plasma system in a constant external magnetic field and the corresponding linear Vlasov operator are studied. The solution of the Vlasov equation is found by the resolvent method. The spectrum and eigenfunctions of the Vlasov operator are also found. The spectrum of this operator consists of two parts: one is continuous and real; the other is discrete and complex. Interestingly, the real eigenvalues are uncountably infinitely degenerate, which causes difficulty in solving this initial value problem by using the conventional eigenfunction expansion method. It also breaks the natural relation between the eigenfunctions and the resolvent solution in which the eigenfunctions can normally be considered as the coefficients of e-i t in the Laplace (or resolvent) solution.
Sleep-Wake Circadian Activity Rhythms and Fatigue in Family Caregivers of Oncology Patients

Background:Little is known about the relationships between sleep/wake circadian activity rhythms and fatigue in family caregivers (FCs) of oncology patients. Objectives:The objectives of this study were to describe values for nocturnal sleep/rest, daytime wake/activity, and circadian activity rhythm parameters measured using actigraphy and to evaluate the relationships between these subjective and objective measures of sleep disturbance and self-reported fatigue severity, in a sample of FCs of oncology patients. Methods:Family caregivers (n = 103) completed self-report measures for sleep disturbance (ie, Pittsburgh Sleep Quality Index, General Sleep Disturbance Scale) and fatigue (Lee Fatigue Scale) and wore wrist actigraphs for 48 hours prior to beginning radiation therapy. Spearman rank correlations were calculated between variables. Results:Approximately 40% to 60% of FCs experienced sleep disturbance depending on whether clinically significant cutoffs for the subjective or objective measures were used to calculate occurrence rates. In addition, these FCs reported moderate levels of fatigue. Only a limited number of significant correlations were found between the subjective and objective measures of sleep disturbance. Significant positive correlations were found between fatigue and subjective, but not objective measures of sleep disturbance. The amplitude of circadian activity rhythm was not related to any objective sleep measure but was correlated with self-report of longer sleep-onset latency. Conclusions:A significant percentage of FCs experience clinically meaningful disturbances in sleep-wake circadian activity rhythms. These disturbances occur primarily in sleep maintenance. Implications for Practice:Family caregivers need to be assessed, along with patients, for sleep disturbance, and appropriate interventions initiated for them and for the patient.
Optimization of 3D woven preform for improved mechanical performance

For structural design applications, through-thickness characteristics of reinforcement played a vital role, which is why 3D woven preforms are recommended for such applications. These characteristics are mainly dependent on the fiber and yarn positioning in reinforcement. Although research has been conducted for characterizing woven composites, special attention has not been made on weave pattern parameter which directly affects the mechanical performance of composites. In this research work, 3D orthogonal layer to layer and through thickness woven structures with different interlocking patterns have been thoroughly studied for their mechanical properties, thickness, air permeability and areal density. Natural fibers when used with biodegradable matrix find use in structural, as well as low to medium impact applications for automobiles. Jute yarn was used to produce four-layered 3D woven structures, as synthetic fibers will not give a biodegradable composite part. The focus of this study is to optimize weave pattern, which is robust in design, degradable preforms and easy to reproduce. The main objective of this research focused on the effectiveness of weaving patterns on physical and mechanical properties as well as to optimize the weave pattern for optimum performance. Grey relational analysis was used for the optimization of the robust weave pattern. The results showed that hybrid structures can be useful for improving the properties of the orthogonal layer to layer and through thickness woven structures. It was also noted that weft-way 3D woven structures can provide comparable mechanical properties with warp-way 3D woven structures.
WEFEST: Word Embedding Feature Extension for Short Text Classification

Short text classification is a crucial task for information retrieval, social medial text categorization, and many other applications. In reality, due to the inherent sparsity and the limited information available in the short texts, learning and classifying short texts is a significant challenge. In this paper, we propose a new framework, WEFEST, which expands short texts using word embedding for classification. WEFEST is rooted on the deep language model, which learns a new word embedding space, by using word correlations, such that semantically related words also have close feature vectors in the new space. By using word embedding features to help expand the short tests, WEFEST can enrich the word density in the short texts for effective learning, by following three major steps. First, each short text in the training dataset is enriched by using pre-trained word feature embedding. Then the semantic similarity between two short texts is calculated by using the statistical frequency information retrieved from the trained model. Finally, we use the nearest neighbor algorithm to achieve short text classification. Experimental results on Chinese news title dataset validate the effectiveness of the proposed method.