Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Team Enablers and Team Cohesion on Project Team Success

The aim of this study was to explore the influences of team enablers (team autonomy and organizational support) and team cohesion dimensions on project team success and to test the mediating effect of team cohesion dimensions in this relation. PLS-SEM was implemented in the study carried out in 110 project teams with 343 team members in a multinational financial institution. The measurement model was tested for validity and reliability. Because the analysis was performed at team level, aggregation of the survey responses to team level were done by calculation of arithmetic mean. The findings of the research revealed that organizational support was a significant antecedent of team task cohesion and team effectiveness. Team task cohesion mediated the link between organizational support and team effectiveness. Although the significant direct positive influences of team autonomy on success dimensions were confirmed, it appeared that none of the dimensions of team cohesion was influenced by team autonomy. In line with the literature, positive influences of task cohesion on effectiveness and innovation were supported, but the influence of social cohesion on team success was immaterial.
Overexpression of Arabidopsis H+-pyrophosphatase improves the growth of alfalfa under long-term salinity, drought conditions and phosphate deficiency

Alfalfa planting is threatened by limited arable land, salinization, water shortage, and soil nutrient deprivation. To deal with this challenge, we previously introduced the Arabidopsis type I H+-pyrophosphatase gene AVP1 into alfalfa and found that transgenic lines exhibited enhanced tolerance to short-term salinity or drought. In this study, the growth performances of two transgenic lines were further investigated under long-term salinity or drought conditions, as well as under phosphate deficiency (low-Pi). Compared with wild-type (WT) plants, the transgenic alfalfa showed better growth performance with taller plants and more biomass accumulation after being treated with either long-term salinity, long-term drought, or low-Pi. Most importantly, the overexpression of AVP1 significantly increased the root dry weight and the root/shoot ratio of transgenic alfalfa. A more robust root system facilitates the transgenic alfalfa to absorb nutrients, and in turn promotes the growth of the plants. Whether being treated with low-Pi or not, transgenic plants showed higher total phosphorus concentrations by 16.5–35.5% than WT plants. This study laid a foundation for breeding alfalfa cultivars adapted to saline, arid and nutrient-deprived marginal land.
Visual cycle protein RPE65 persists in new retinal cells during retinal regeneration of adult newt

Adult newts can regenerate their entire retina through transdifferentiation of the retinal pigment epithelium (RPE). The objective of this study was to redescribe the retina regeneration process by means of modern biological techniques. We report two different antibodies (RPE‐No.112 and MAB5428) that recognize the newt homolog of RPE65, which is involved in the visual cycle and exclusively label the RPE cell‐layer in the adult newt eye. We analyzed the process of retinal regeneration by immunohistochemistry and immunoblotting and propose that this process should be divided into nine stages. We found that the RPE65 protein is present in the RPE‐derived new retinal rudiment at 14 days postoperative (po) and in the regenerating retinas at the 3–4 cell stage (19 days po). These observations suggest that certain characteristics of RPE cells overlap with those of retinal stem/progenitor cells during the period of transdifferentiation. However, RPE65 protein was not detected in either retinal stem/progenitor cells in the ciliary marginal zone (CMZ) of adult eyes or in neuroepithelium present during retina development, where it was first detected in differentiated RPE. Moreover, the gene expression of RPE65 was drastically downregulated in the early phase of transdifferentiation (by 10 days po), while those of Connexin43 and Pax‐6, both expressed in regenerating retinas, were differently upregulated. These observations suggest that the RPE65 protein in the RPE‐derived retinal rudiment may represent the remainder after protein degradation or discharge rather than newly synthesized protein. J. Comp. Neurol. 40:391–407, 2006. © 2006 Wiley‐Liss, Inc.
Motion tracking in the liver: validation of a method based on 4D ultrasound using a nonrigid registration technique.

PURPOSE Treatments like radiotherapy and focused ultrasound in the abdomen require accurate motion tracking, in order to optimize dosage delivery to the target and minimize damage to critical structures and healthy tissues around the target. 4D ultrasound is a promising modality for motion tracking during such treatments. In this study, the authors evaluate the accuracy of motion tracking in the liver based on deformable registration of 4D ultrasound images. METHODS The offline analysis was performed using a nonrigid registration algorithm that was specifically designed for motion estimation from dynamic imaging data. The method registers the entire 4D image data sequence in a groupwise optimization fashion, thus avoiding a bias toward a specifically chosen reference time point. Three healthy volunteers were scanned over several breathing cycles (12 s) from three different positions and angles on the abdomen; a total of nine 4D scans for the three volunteers. Well-defined anatomic landmarks were manually annotated in all 96 time frames for assessment of the automatic algorithm. The error of the automatic motion estimation method was compared with interobserver variability. The authors also performed experiments to investigate the influence of parameters defining the deformation field flexibility and evaluated how well the method performed with a lower temporal resolution in order to establish the minimum frame rate required for accurate motion estimation. RESULTS The registration method estimated liver motion with an error of 1 mm (75% percentile over all datasets), which was lower than the interobserver variability of 1.4 mm. The results were only slightly dependent on the degrees of freedom of the deformation model. The registration error increased to 2.8 mm with an eight times lower temporal resolution. CONCLUSIONS The authors conclude that the methodology was able to accurately track the motion of the liver in the 4D ultrasound data. The authors believe that the method has potential in interventions on moving abdominal organs such as MR or ultrasound guided focused ultrasound therapy and radiotherapy, pending the method is enabled to run in real-time. The data and the annotations used for this study are made publicly available for those who would like to test other methods on 4D liver ultrasound data.
Gray scale and color flow Doppler characterization of uterine tumors.

The aim of this study was to investigate gray scale and color flow characteristics of a group of patients with a suspected uterine pathologic condition. One hundred and twenty‐two consecutive patients at the Women's Cancer Center, University of Minnesota, undergoing transvaginal sonography and color flow Doppler imaging for suspected uterine corpus abnormality made up the study group. After gray scale morphologic assessment, color flow Doppler imaging of the tumor and uterus was performed, including the ipsilateral uterine artery. Malignant tumors were confirmed pathologically in all 35 patients who had them. In comparing patients with benign versus malignant tumors, gray scale morphologic assessment confirmed that malignant uterine tumors (31 endometrial cancers and four sarcomas) were more likely to have a thickened echoic endometrium (P = < 0.0001), be enlarged (P = 0.004), to be retroverted (P = 0.02), and to lack a subendometrial halo (P < 0.0001). Patients with four benign and 13 malignant tumors demonstrated increased flow when assessed by CFD. The calculated sensitivity of increased color flow in predicting malignancy was 39%, with a specificity of 92%, a positive predictive value of 77%, and a negative predictive value of 71%. No difference existed between the benign and malignant groups for the systolic, diastolic, and mean velocities and for the calculated pulsatility index and resistive index in both sampled uterine and intramyometrial or tumor vessels. (ABSTRACT TRUNCATED AT 250 WORDS)
LET US NOT BE UNFAIR TO ASEXUALS: THEIR EPHEMERALITY MAY BE EXPLAINED BY NEUTRAL MODELS WITHOUT INVOKING ANY EVOLUTIONARY CONSTRAINTS OF ASEXUALITY

Phylogenetic studies typically demonstrate lower evolutionary ages of clones, relative to their sexual ancestors. This has often been attributed to heightened extinction risk of asexual organisms. We previously criticized such interpretations and demonstrated that the life span of clones is ultimately limited by neutral drift depending on the rate at which new clones are spawned into an asexual community of a finite size. Therefore, it is important to investigate whether the natural rates of such influxes are sufficiently high to account for the relative ephemerality of clones without assuming their increased extinction rate. I applied the neutral clonal turnover model to phylogenies of polyploid asexual ferns and simulated the coalescent trees over a wide range of demographic structures and sampling schemes. On parameterizing the model with biologically relevant estimates of population sizes and plant polyploidization rates, simulated clonal assemblages appeared younger than their sexual counterparts even in the absence of selection against clones. Therefore, differences observed between the ages of sexual and clonal lineages may be explained by the neutral clonal turnover. Researchers should consider the possibility that natural clones may get lost by neutral drift before their fate could eventually be affected by any long‐term constraints of asexuality.
CNN Patch-Based Voting for Fingerprint Liveness Detection

Biometric identification systems based on fingerprints are vulnerable to attacks that use fake replicas of real fingerprints. One possible countermeasure to this issue consists in developing software modules capable of telling the liveness of an input image and, thus, of discarding fakes prior to the recognition step. This paper presents a fingerprint liveness detection method founded on a patch-based voting approach. Fingerprint images are first segmented to discard background information. Then, small-sized foreground patches are extracted and processed by a well-know Convolutional Neural Network model adapted to the problem at hand. Finally, the patch scores are combined to draw the final fingerprint label. Experimental results on well-established benchmarks demonstrate a promising performance of the proposed method compared with several state-of-the-art algorithms.
Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective

To maximize cumulative user engagement (e.g. cumulative clicks) in sequential recommendation, it is often needed to tradeoff two potentially conflicting objectives, that is, pursuing higher immediate user engagement (e.g., click-through rate) and encouraging user browsing (i.e., more items exposured). Existing works often study these two tasks separately, thus tend to result in sub-optimal results. In this paper, we study this problem from an online optimization perspective, and propose a flexible and practical framework to explicitly tradeoff longer user browsing length and high immediate user engagement. Specifically, by considering items as actions, user's requests as states and user leaving as an absorbing state, we formulate each user's behavior as a personalized Markov decision process (MDP), and the problem of maximizing cumulative user engagement is reduced to a stochastic shortest path (SSP) problem. Meanwhile, with immediate user engagement and quit probability estimation, it is shown that the SSP problem can be efficiently solved via dynamic programming. Experiments on real-world datasets demonstrate the effectiveness of the proposed approach. Moreover, this approach is deployed at a large E-commerce platform, achieved over 7% improvement of cumulative clicks.
Effects of dietary energy density and digestible protein:energy ratio on de novo lipid synthesis from dietary protein in gilthead sea bream (Sparus aurata) quantified with stable isotopes

The effects of varying dietary digestible protein (DP) and digestible energy (DE) content on performance, nutrient retention efficiency and the de novo lipogenesis of DP origin were examined in triplicate groups of gilthead sea bream (Sparus aurata), fed nine extruded experimental diets. In order to trace the metabolic fate of dietary protein, 1·8 % fishmeal was replaced with isotope-labelled whole protein (>98 % 13C). The experiment was divided into a growth period lasting 89 d, growing fish from approximately 140 to 350 g, followed by a 3 d period feeding isotope-enriched diets. Isotope ratio MS was applied to quantify the 13C enrichment of whole-body lipid from dietary DP. Between 18·6 and 22·4 % of the carbon derived from protein was recovered in the lipid fraction of the fish, and between 21·6 and 30·3 % of the total lipid deposited could be attributed to dietary protein. DP retention was significantly improved by reductions in dietary DP:DE ratio, while the opposite was true for apparent digestible lipid retention. Both overall DE retention and whole-body proximate composition of whole fish were largely unaffected by dietary treatments, while feed conversion ratios were significantly improved with increasing dietary energy density. The present study suggests that gilthead sea bream efficiently utilises dietary nutrients over a wide range of DP:DE ratios and energy densities. In addition, they appear to endeavour a certain body energy status rather than maximising growth, which in the present trial was apparent from inherently high de novo lipogenesis originating from DP.
Convolutional Neural Network Based on Spiral Arrangement of Features and Its Application in Bearing Fault Diagnosis

With the coming of artificial intelligence and the era of big data, convolutional neural network (CNN) has become one of the research hotspots in many scientific fields. However, there exist serious edge information loss problems in the information transmission process of CNN. Therefore, in order to suppress the loss of important information during the information transmission process, new methods are proposed which use the singular value decomposition (SVD) algorithm based on the phase space reconstruction to analyze the bearing vibration signal. Singular values are regarded as the features for evaluating the bearing’s health condition. Then, the features radiate from the center around to form a spiral matrix as the input of the CNN, which can effectively resist information loss problems during the information delivery process. In order to verify the performance of the proposed methods better than the conventional ones, experiments are carried out. This paper shows that the proposed methods have excellent performances in the field of bearing fault diagnosis.
Early Labor Initiation With Oral PGE2 After Premature Rupture of the Membranes at Term

Two groups of healthy women at term, who were not in labor 3 hours after premature rupture of the membranes, were studied. In one group labor induction with oral prostaglandin E2 (PGE2) was begun 3 hours after rupture, and in the other group intravenous oxytocin induction was begun 12 hours after rupture. PGE2 was successful in initiating active labor in 88% of women treated. Of the women who were observed for 12 hours, one-half began labor spontaneously during that time. Women in whom labor was induced with PGE2 given 3 hours after rupture of the membranes had a shorter interval of rupture to delivery, a lower cesarean section rate, and shorter postpartum hospitalization. Although significant bradycardia did not occur in fetuses of those women given PGE2, 10% of infants whose mothers were receiving oxytocin were delivered by cesarean section for this reason. It is concluded that oral PGE2 is safe and effective for induction of labor in women with premature membrane rupture. The benefits, to both mother and fetus, of a shorter latent period are discussed.
Reasoning about effects: from lists to cyber-physical agents

Theories for reasoning about programs with effects initially focused on basic manipulation of lists and other mutable data. The next challenge was to consider higher-order programming, adding functions as first class objects to mutable data. Reasoning about actors added the challenge of dealing with distributed open systems of entities interacting asynchronously. The advent of cyber-physical agents introduces the need to consider uncertainty, faults, physical as well as logical effects. In addition cyber-physical agents have sensors and actuators giving rise to a much richer class of effects with broader scope: think of self-driving cars, autonomous drones, or smart medical devices. This paper gives a retrospective on reasoning about effects highlighting key principles and techniques and closing with challenges for future work.
Capturing Broadband Light in a Compact Bound State in the Continuum

Trapping and storing light for arbitrary time lengths in open cavities is a major goal of nanophotonics, with potential applications ranging from energy harvesting to optical information processing. Unfortunately, however, the resonance lifetime of conventional open resonators remains finite even in the limit of vanishing material absorption, as a result of radiation loss. In this context, bound states in the continuum (BiCs) have provided a unique way to achieve unbounded resonance lifetimes despite the presence of compatible radiation channels. However, physical constraints such as reciprocity, linearity, and delay-bandwidth limits prevent the possibility to externally excite such ideal bound states and make them interact with broadband sources. Here, we overcome these limitations and theoretically demonstrate that subwavelength open resonators undergoing a suitable temporal modulation can efficiently capture a broadband incident wave into a nonradiating eigenmode of the structure, leading to the first example of a BiC that is accessible to broadband light. Our findings unveil the dynamic capabilities of bound states in the continuum and extend their reach and potential impact for different applications.
On Simple Methods for Shape Reconstruction of Unknown Scatterers

Efficient and reliable reconstruction of location and shape of dielectric targets via microwave imaging is relevant in many applications. In this respect, the linear sampling method is an effective candidate to pursue this task. However, despite its simplicity and computational effectiveness, still its use is restricted to the mathematical community wherein it has been originally developed. Starting from this observation, in this paper we propose and test a simple and original "physical" interpretation of the linear sampling methods, which shows its relationship with electromagnetic focusing problems. Taking advantage of this result we discuss merits and limitations of the method and suggest new guidelines for a successful application. The analysis is supported with results against experimental data
The Erosion of Expert Control Through Censure Episodes

Organizations depend on experts to oversee and execute complex tasks. When faced with pressures to reduce their dependence on experts, managers encounter a control paradox: they require experts to explicate the very knowledge and discretionary approaches that are the basis of their control for the purpose of undercutting this control. Experts rarely consent to such a situation; therefore, attempts to reduce dependence on experts and control their work are more often aspirational than actual. Drawing on an ethnography of an organization that was required by a government agency to transfer the work responsibilities of experts to employees throughout the organization, this paper describes how a network of actors developed a discursive, political process to renegotiate control of expert work practices. Through censure episodes, long-standing and largely successful expert practices were examined one by one and relabeled as problematic in relation to established goals. The constructed breaches opened expert practices to evaluation, questioning, and eventual delegitimation within the organization. This process depended on the introduction of new roles that revised dependencies and generated new resources. This paper contributes to the understanding of control in organizations by theorizing how the emergent, symbolic work of censure episodes are a means of gradually subverting expert control. Further, these struggles are reconceptualized as multiple-role negotiations rather than bilateral manager-expert struggles.
Single-Layer Graph Convolutional Networks For Recommendation

Graph Convolutional Networks (GCNs) and their variants have received significant attention and achieved start-of-the-art performances on various recommendation tasks. However, many existing GCN models tend to perform recursive aggregations among all related nodes, which arises severe computational burden. Moreover, they favor multi-layer architectures in conjunction with complicated modeling techniques. Though effective, the excessive amount of model parameters largely hinder their applications in real-world recommender systems. To this end, in this paper, we propose the single-layer GCN model which is able to achieve superior performance along with remarkably less complexity compared with existing models. Our main contribution is three-fold. First, we propose a principled similarity metric named distribution-aware similarity (DA similarity), which can guide the neighbor sampling process and evaluate the quality of the input graph explicitly. We also prove that DA similarity has a positive correlation with the final performance, through both theoretical analysis and empirical simulations. Second, we propose a simplified GCN architecture which employs a single GCN layer to aggregate information from the neighbors filtered by DA similarity and then generates the node representations. Moreover, the aggregation step is a parameter-free operation, such that it can be done in a pre-processing manner to further reduce red the training and inference costs. Third, we conduct extensive experiments on four datasets. The results verify that the proposed model outperforms existing GCN models considerably and yields up to a few orders of magnitude speedup in training, in terms of the recommendation performance.
Texture Interpolation for Probing Visual Perception

Texture synthesis models are important tools for understanding visual processing. In particular, statistical approaches based on neurally relevant features have been instrumental in understanding aspects of visual perception and of neural coding. New deep learning-based approaches further improve the quality of synthetic textures. Yet, it is still unclear why deep texture synthesis performs so well, and applications of this new framework to probe visual perception are scarce. Here, we show that distributions of deep convolutional neural network (CNN) activations of a texture are well described by elliptical distributions and therefore, following optimal transport theory, constraining their mean and covariance is sufficient to generate new texture samples. Then, we propose the natural geodesics (i.e. the shortest path between two points) arising with the optimal transport metric to interpolate between arbitrary textures. Compared to other CNN-based approaches, our interpolation method appears to match more closely the geometry of texture perception, and our mathematical framework is better suited to study its statistical nature. We apply our method by measuring the perceptual scale associated to the interpolation parameter in human observers, and the neural sensitivity of different areas of visual cortex in macaque monkeys.