Miracle-dz/UniFAR-FTUs huggingface dataset:
Phenotypic plasticity of Drosophila suzukii wing to developmental temperature: implications for flight

ABSTRACT Phenotypic plasticity has been proposed as a mechanism that facilitates the success of biological invasions. In order to test the hypothesis of an adaptive role for plasticity in invasions, particular attention should be paid to the relationship between the focal plastic trait, the environmental stimulus and the functional importance of the trait. The Drosophila wing is particularly amenable to experimental studies of phenotypic plasticity. Wing morphology is known for its plastic variation under different experimental temperatures, but this plasticity has rarely been investigated in a functional context of flight. Here, we investigate the effect of temperature on wing morphology and flight in the invasive pest species Drosophila suzukii. Although the rapid invasion of both Europe and North America was most likely facilitated by human activities, D. suzukii is also expected to disperse actively. By quantifying wing morphology and individual flight trajectories of flies raised under different temperatures, we tested whether (1) invasive populations of D. suzukii show higher phenotypic plasticity than their native counterparts, and (2) wing plasticity affects flight parameters. Developmental temperature was found to affect both wing morphology and flight parameters (in particular speed and acceleration), leaving open the possibility of an adaptive value for wing plasticity. Our results show no difference in phenotypic plasticity between invasive and native populations, rejecting a role for wing plasticity in the invasion success. Summary: The effect of wing plasticity to developmental temperature on flight was studied in Drosophila suzukii. Results indicate that cold-reared flies are capable of faster flight. The role of wing shape and size is discussed, along with the adaptive role of phenotypic plasticity.
Immune Cells at the Fetomaternal Interface: How the Microenvironment Modulates Immune Cells To Foster Fetal Development

Immune cells adapt their phenotypic and functional characteristics in response to the tissue microenvironment within which they traffic and reside. The fetomaternal interface, consisting of placental trophoblasts and the maternal decidua, is a highly specialized tissue with a unique and time-limited function: to nourish and support development of the semiallogeneic fetus and protect it from inflammatory or immune-mediated injury. It is therefore important to understand how immune cells within these tissues are educated and adapt to fulfill their biological functions. This review article focuses on the local regulatory mechanisms ensuring that both innate and adaptive immune cells appropriately support the early events of implantation and placental development through direct involvement in promoting immune tolerance of fetal alloantigens, suppressing inflammation, and remodeling of maternal uterine vessels to facilitate optimal placental function and fetal growth.
Convolutional Memory Blocks for Depth Data Representation Learning

Compared to natural RGB images, data captured by 3D / depth sensors (e.g., Microsoft Kinect) have different properties, e.g., less discriminable in appearance due to lacking color / texture information. Applying convolutional neural networks (CNNs) on these depth data would lead to unsatisfying learning efficiency, i.e., requiring large amounts of annotated training data for convergence. To address this issue, this paper proposes a novel memory network module, called Convolutional Memory Block (CMB), which empowers CNNs with the memory mechanism on handling depth data. Different from the existing memory networks that store long / short term dependency from sequential data, our proposed CMB focuses on modeling the representative dependency (correlation) among non-sequential samples. Specifically, our CMB consists of one internal memory (i.e., a set of feature maps) and three specific controllers, which enable a powerful yet efficient memory manipulation mechanism. In this way, the internal memory, being implicitly aggregated from all previous inputted samples, can learn to store and utilize representative features among the samples. Furthermore, we employ our CMB to develop a concise framework for predicting articulated pose from still depth images. Comprehensive evaluations on three public benchmarks demonstrate significant superiority (about 6%) of our framework over all the compared methods. More importantly, thanks to the enhanced learning efficiency, our framework can still achieve satisfying results using 50% less training data.
Epidemiology of Suicide and the Psychiatric Perspective

Suicide is a worldwide phenomenon. This review is based on a literature search of the World Health Organization (WHO) databases and PubMed. According to the WHO, in 2015, about 800,000 suicides were documented worldwide, and globally 78% of all completed suicides occur in low- and middle-income countries. Overall, suicides account for 1.4% of premature deaths worldwide. Differences arise between regions and countries with respect to the age, gender, and socioeconomic status of the individual and the respective country, method of suicide, and access to health care. During the second and third decades of life, suicide is the second leading cause of death. Completed suicides are three times more common in males than females; for suicide attempts, an inverse ratio can be found. Suicide attempts are up to 30 times more common compared to suicides; they are however important predictors of repeated attempts as well as completed suicides. Overall, suicide rates vary among the sexes and across lifetimes, whereas methods differ according to countries. The most commonly used methods are hanging, self-poisoning with pesticides, and use of firearms. The majority of suicides worldwide are related to psychiatric diseases. Among those, depression, substance use, and psychosis constitute the most relevant risk factors, but also anxiety, personality-, eating- and trauma-related disorders as well as organic mental disorders significantly add to unnatural causes of death compared to the general population. Overall, the matter at hand is relatively complex and a significant amount of underreporting is likely to be present. Nevertheless, suicides can, at least partially, be prevented by restricting access to means of suicide, by training primary care physicians and health workers to identify people at risk as well as to assess and manage respective crises, provide adequate follow-up care and address the way this is reported by the media. Suicidality represents a major societal and health care problem; it thus should be given a high priority in many realms.
Pseudo-Boolean Constraints from a Knowledge Representation Perspective

We study pseudo-Boolean constraints (PBC) and their special case cardinality constraints (CARD) from the perspective of knowledge representation. To this end, the succinctness of PBC and CARD is compared to that of many standard propositional languages. Moreover, we determine which queries and transformations are feasible in polynomial time when knowledge is represented by PBC or CARD, and which are not (unconditionally or unless P = NP). In particular, the advantages and disadvantages compared to CNF are discussed.
Clinical characteristics and treatment outcomes in IgE multiple myeloma: A case‐control study

occurred in none (0%) of the 17 patients receiving preemptive full systemic heparinization; by contrast, the particular complication occurred in 15 (26%) of 58 patients receiving low systemic heparinization and 17 (40%) of 43 patients receiving no systemic anticoagulation (P = .002). Of note, there was no difference, among the different treatment groups, in age (P = .35) and sex (P = .12) distribution, presence or absence of pre-splenectomy thrombosis history (P = .52), platelet (P = .26) or leukocyte (P = .8) counts or major hemorrhagic events prior or after the institution of anticoagulant therapy (Table 1). At the time of this writing, 95 (79%) deaths and 30 (25%) leukemic transformations were documented; postsplenectomy overall and leukemiafree survivals were not affected by either thrombosis history before splenectomy or the occurrence of early thrombotic event following splenectomy (P > .2 in all instances). This study provides evidence that suggests the antithrombotic superiority of full therapeutic dose heparin therapy, compared to treatment with lower dose heparin or no systemic anticoagulation, in preventing early thrombotic complications, following splenectomy in MF. The fact that >90% of these incidents constituted venous thrombosis is in line with consistent with the observation regarding the most optimal treatment strategy; furthermore, it is possible that the procoagulant nature of the underlying disease (ie, myeloproliferative neoplasm often associated with JAK2 mutation) might have contributed to the need for higher heparin dose. A similar argument can be made to explain the difference in the incidence of postsplenectomy thrombosis between patients receiving low-dose heparin and those not receiving systemic anticoagulation. The retrospective nature of our study dictates that our observations should be considered preliminary but worthy of further perusal in a controlled setting.
Diabetes Medication Use in Association with Survival among Patients of Breast, Colorectal, Lung, or Gastric Cancer

Purpose Studies suggest that regular use of metformin may decrease cancer mortality. We investigated the association between diabetes medication use and cancer survival. Materials and Methods The current study includes 633 breast, 890 colorectal, 824 lung, and 543 gastric cancer cases identified from participants of two population-based cohort studies in Shanghai. Information on diabetes medication use was obtained by linking to electronic medical records. The associations between diabetes medication use (metformin, sulfonylureas, and insulin) and overall and cancer-specific survival were evaluated using time-dependent Cox proportional hazards models. Results After adjustment for clinical characteristics and treatment factors, use of metformin was associated with better overall survival among colorectal cancer patients (hazards ratio [HR], 0.55; 95% confidence interval [CI], 0.34 to 0.88) and for all four types of cancer combined (HR, 0.75; 95% CI, 0.57 to 0.98). Ever use of insulin was associated with worse survival for all cancer types combined (HR, 1.89; 95% CI, 1.57 to 2.29) and for the four cancer types individually. Similar associations were seen for diabetic patients. Sulfonylureas use was associated with worse overall survival for breast or gastric cancer (HR, 2.87; 95% CI, 1.22 to 6.80 and HR, 2.05; 95% CI, 1.09 to 3.84, respectively) among diabetic patients. Similar association patterns were observed between diabetes medication use and cancer-specific survival. Conclusion Metformin was associated with improved survival among colorectal cancer cases, while insulin use was associated with worse survival among patients of four major cancers. Further investigation on the topic is needed given the potential translational impact of these findings.
Inconsistency-Tolerant Ontology-Based Data Access Revisited: Taking Mappings into Account

Inconsistency-tolerant query answering in the presence of ontologies has received considerable attention in recent years. However, existing work assumes that the data is expressed using the vocabulary of the ontology and is therefore not directly applicable to ontology-based data access (OBDA), where relational data is connected to the ontology via mappings. This motivates us to revisit existing results in the wider context of OBDA with mappings. After formalizing the problem, we perform a detailed analysis of the data complexity of inconsistency-tolerant OBDA for ontologies formulated in DL-Lite and other data-tractable description logics, considering three different semantics (AR, IAR, and brave), two notions of repairs (subset and symmetric difference), and two classes of global-as-view (GAV) mappings. We show that adding plain GAV mappings does not affect data complexity, but there is a jump in complexity if mappings with negated atoms are considered.
The Relevance of Transformational Leadership to Nontraditional Accounting Services: Information Systems Assurance and Business Consulting

This paper examines whether supervisors in nontraditional accounting services (vs. supervisors in financial auditing and tax services) are more likely to acquire transformational leadership skills, and, whether such skills impact subordinates' role clarity and role outcomes. Transformational leadership coincides with the AICPA's Vision Project's definition of leadership skills: the ability to influence, inspire, and motivate others to achieve results. The paper uses role theory to articulate specific links between transformational leader behavior, role clarity, and role outcomes (job satisfaction, organizational commitment, and job performance). Data was obtained through a mail survey to 1,150 AICPA members, with 416 usable responses obtained. Participants who primarily worked in the functional area of information system assurance and business consulting (ISA/BC) perceived that their supervisors demonstrated higher levels of transformational leadership, compared to participants from two other service area.
SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks

Generating texts of different sentiment labels is getting more and more attention in the area of natural language generation. Recently, Generative Adversarial Net (GAN) has shown promising results in text generation. However, the texts generated by GAN usually suffer from the problems of poor quality, lack of diversity and mode collapse. In this paper, we propose a novel framework - SentiGAN, which has multiple generators and one multi-class discriminator, to address the above problems. In our framework, multiple generators are trained simultaneously, aiming at generating texts of different sentiment labels without supervision. We propose a penalty based objective in the generators to force each of them to generate diversified examples of a specific sentiment label. Moreover, the use of multiple generators and one multi-class discriminator can make each generator focus on generating its own examples of a specific sentiment label accurately. Experimental results on four datasets demonstrate that our model consistently outperforms several state-of-the-art text generation methods in the sentiment accuracy and quality of generated texts.
Tractable (Simple) Contests

Much of the work on multi-agent contests is focused on determining the equilibrium behavior of contestants. This capability is essential for the principal for choosing the optimal parameters for the contest (e.g. prize amount). As it turns out, many contests exhibit not one, but many possible equilibria, hence precluding contest design optimization and contestants behavior prediction. In this paper we examine a variation of the classic contest that alleviates this problem by having contestants make the decisions sequentially rather than in parallel. We study this model in the setting of a simple contest, wherein contestants only choose whether or not to participate, while their performance level is exogenously set. We show that by switching to the revised mechanism the principal can not only force her most desired pure-strategies based equilibrium to emerge, but also, at times, end up with an equilibrium offering a greater expected profit. Further, we show that in the modified contest the optimal prize can be effectively computed. The theoretical analysis is complemented by comprehensive experiments with people over Amazon Mechanical Turk. Here, we find that the modified mechanism offers great benefit for the principal, both in terms of an increased over-participation in the contest (compared to theoretical expectations) and increased average profit.
SDMCH: Supervised Discrete Manifold-Embedded Cross-Modal Hashing

Cross-modal hashing methods have attracted considerable attention. Most pioneer approaches only preserve the neighborhood relationship by constructing the correlations among heterogeneous modalities. However, they neglect the fact that the high-dimensional data often exists on a low-dimensional manifold embedded in the ambient space and the relative proximity between the neighbors is also important. Although some methods leverage the manifold learning to generate the hash codes, most of them fail to explicitly explore the discriminative information in the class labels and discard the binary constraints during optimization, generating large quantization errors. To address these issues, in this paper, we present a novel cross-modal hashing method, named Supervised Discrete Manifold-Embedded Cross-Modal Hashing (SDMCH). It can not only exploit the non-linear manifold structure of data and construct the correlation among heterogeneous multiple modalities, but also fully utilize the semantic information. Moreover, the hash codes can be generated discretely by an iterative optimization algorithm, which can avoid the large quantization errors. Extensive experimental results on three benchmark datasets demonstrate that SDMCH outperforms ten state-of-the-art cross-modal hashing methods.
Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

Video surveillance plays a vital role in maintaining the social security although, until now, large uncertainty still exists in danger understanding and recognition, which can be partly attributed to intractable environment changes in the backgrounds. This article presents a brain-inspired computing of attention value of surrounding environment changes (EC) with a processes-based cognition model by introducing a ratio value of EC-implications within considered periods. Theoretical models for computation of warning level of EC-implications to the universal video recognition efficiency (quantified as time cost of implication-ratio variations from to , ) are further established. Imbedding proposed models into the online algorithms is suggested as a future research priority towards precision security for critical applications and, furthermore, schemes for a practical implementation of such integration are also preliminarily discussed.
Enhanced brain activity associated with memory access in highly superior autobiographical memory

Significance Recent research has identified human subjects who have highly superior autobiographical memory (HSAM). Here, we investigated, using fMRI, the neural activation induced by retrieval of autobiographical memories (AMs) and semantic memories (SMs) in subjects with HSAM and control subjects. While their brains were being scanned, subjects had to retrieve AMs as well as SMs (e.g., examples of animals). The subjects with HSAM displayed a superior ability to retrieve details of AMs, supported by enhanced activation of several brain regions, including the medial prefrontal cortex and temporoparietal junction, as well as increased connectivity of the prefrontal cortex with the hippocampus, a region well known to be involved in memory representation. These findings suggest that activation of these systems may play a critical role in enabling HSAM. Brain systems underlying human memory function have been classically investigated studying patients with selective memory impairments. The discovery of rare individuals who have highly superior autobiographical memory (HSAM) provides, instead, an opportunity to investigate the brain systems underlying enhanced memory. Here, we carried out an fMRI investigation of a group of subjects identified as having HSAM. During fMRI scanning, eight subjects with HSAM and 21 control subjects were asked to retrieve autobiographical memories (AMs) as well as non-AMs (e.g., examples of animals). Subjects were instructed to signal the “access” to an AM by a key press and to continue “reliving” it immediately after. Compared with controls, individuals with HSAM provided a richer AM recollection and were faster in accessing AMs. The access to AMs was associated with enhanced prefrontal/hippocampal functional connectivity. AM access also induced increased activity in the left temporoparietal junction and enhanced functional coupling with sensory cortices in subjects with HSAM compared with controls. In contrast, subjects with HSAM did not differ from controls in functional activity during the reliving phase. These findings, based on fMRI assessment, provide evidence of interaction of brain systems engaged in memory retrieval and suggest that enhanced activity of these systems is selectively involved in enabling more efficient access to past experiences in HSAM.
Optimal Bidding Strategy for Brand Advertising

Brand advertising is a type of advertising that aims at increasing the awareness of companies or products. This type of advertising is well studied in economic, marketing, and psychological literature; however, there are no studies in the area of computational advertising because the effect of such advertising is difficult to observe. In this study, we consider a real-time biding strategy for brand advertising. Here, our objective to maximizes the total number of users who remember the advertisement, averaged over the time. For this objective, we first introduce a new objective function that captures the cognitive psychological properties of memory retention, and can be optimized efficiently in the online setting (i.e., it is a monotone submodular function). Then, we propose an algorithm for the bid optimization problem with the proposed objective function under the second price mechanism by reducing the problem to the online knapsack constrained monotone submodular maximization problem. We evaluated the proposed objective function and the algorithm in a real-world data collected from our system and a questionnaire survey. We observed that our objective function is reasonable in real-world setting, and the proposed algorithm outperformed the baseline online algorithms.
Efficacy of ivabradine to control ventricular arrhythmias in catecholaminergic polymorphic ventricular tachycardia

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a potentially lethal hereditary disease characterized by complex ventricular arrhythmias provoked by exercise or emotional stress and by a high mortality rate in young individuals. Nadolol alone or in combination with flecainide is the most effective therapy. However, compliance to treatment is often low due to side effects.
Family-Based Mindful Eating Intervention in Adolescents with Obesity: A Pilot Randomized Clinical Trial

Mindfulness has gained attention in the treatment of obesity. However, there is a paucity of data on family-based training in mindful eating in children. The objective of this pilot randomized clinical trial was to evaluate the feasibility and acceptability of a family-based mindful eating intervention (MEI) in adolescents with obesity, and to compare the efficacy of the MEI versus standard dietary counseling (SDC) for decreasing weight and improving cardiometabolic risk markers. Twenty-two adolescents (age 14.5–17.9 years) and parent pairs were randomized to the MEI or SDC. The MEI was administered in four 90-min sessions over 10 weeks and SDC was provided at baseline, 12 weeks, and 24 weeks. Despite the requirement of more frequent visits with the MEI, adolescents and parents attended 100% of the sessions and there were no dropouts in that group. High density lipoprotein (HDL) cholesterol increased in the SDC group, but not in the MEI group. Adolescents receiving the MEI demonstrated an increase in awareness at 24 weeks (p = 0.01) and a decrease in distraction during eating at 12 weeks (p = 0.04), when compared with the SDC group. The family-based MEI showed feasibility and acceptability in adolescents with obesity. Future studies with more intense therapy and larger sample sizes are warranted to examine the role of mindful eating in treating pediatric obesity.