Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Evolution of Animal Proteins over Development: Support for the Darwin Selection Opportunity Hypothesis of Evo-Devo

A driving hypothesis of Evo-Devo is that animal morphological diversity is shaped both by adaptation and by developmental constraints. Here we have tested Darwin’s “selection opportunity” hypothesis, according to which high evolutionary divergence in late development is due to strong positive selection. We contrasted it to a “developmental constraint” hypothesis, according to which late development is under relaxed negative selection. Indeed, the highest divergence between species, both at the morphological and molecular levels, is observed late in embryogenesis and post-embryonically. To distinguish between adaptation and relaxation hypotheses, we investigated the evidence of positive selection on protein-coding genes in relation to their expression over development, in fly Drosohila melanogaster, zebrafish Danio rerio, and mouse Mus musculus. First, we found that genes specifically expressed in late development have stronger signals of positive selection. Second, over the full transcriptome, genes with evidence for positive selection trend to be expressed in late development. Finally, genes involved in pathways with cumulative evidence of positive selection have higher expression in late development. Overall, there is a consistent signal that positive selection mainly affects genes and pathways expressed in late embryonic development and in adult. Our results imply that the evolution of embryogenesis is mostly conservative, with most adaptive evolution affecting some stages of post-embryonic gene expression, and thus post-embryonic phenotypes. This is consistent with the diversity of environmental challenges to which juveniles and adults are exposed.
Treatment Recommendations in Oncology Visits: Implications for Patient Agency and Physician Authority

ABSTRACT Although oncology is a major site for clinician‒patient treatment negotiation requiring a careful balance of potentially competing viewpoints, little is known about how clinicians promote their treatment recommendations to patients and what the manner of promotion tells us about the oncologist‒patient relationship. Utilizing an already-established schema of coding treatment recommendations, I draw on 61 treatment recommendations to examine treatment decision-making in oncology. This paper investigates how physicians balance asserting their authority while at the same time attending to patient agency and involvement in decision-making. Taking this one step further, this paper explores how physicians negotiate decision-making with patients given that they occupy a liminal state between obligations to policy imperatives and commitments to their professional knowledge and technical expertise. How do they do this, and what accounts for this? To answer these questions, this paper analyzes the ways in which physicians present treatment recommendations and the treatment contexts in which they are made.
Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations

Semantic parsing from denotations faces two key challenges in model training: (1) given only the denotations (e.g., answers), search for good candidate semantic parses, and (2) choose the best model update algorithm. We propose effective and general solutions to each of them. Using policy shaping, we bias the search procedure towards semantic parses that are more compatible to the text, which provide better supervision signals for training. In addition, we propose an update equation that generalizes three different families of learning algorithms, which enables fast model exploration. When experimented on a recently proposed sequential question answering dataset, our framework leads to a new state-of-the-art model that outperforms previous work by 5.0% absolute on exact match accuracy.
Automated bird sound recognition in realistic settings

We evaluated the effectiveness of an automated bird sound identification system in a situation that emulates a realistic, typical application. We trained classification algorithms on a crowd-sourced collection of bird audio recording data and restricted our training methods to be completely free of manual intervention. The approach is hence directly applicable to the analysis of multiple species collections, with labelling provided by crowd-sourced collection. We evaluated the performance of the bird sound recognition system on a realistic number of candidate classes, corresponding to real conditions. We investigated the use of two canonical classification methods, chosen due to their widespread use and ease of interpretation, namely a k Nearest Neighbour (kNN) classifier with histogram-based features and a Support Vector Machine (SVM) with time-summarisation features. We further investigated the use of a certainty measure, derived from the output probabilities of the classifiers, to enhance the interpretability and reliability of the class decisions. Our results demonstrate that both identification methods achieved similar performance, but we argue that the use of the kNN classifier offers somewhat more flexibility. Furthermore, we show that employing an outcome certainty measure provides a valuable and consistent indicator of the reliability of classification results. Our use of generic training data and our investigation of probabilistic classification methodologies that can flexibly address the variable number of candidate species/classes that are expected to be encountered in the field, directly contribute to the development of a practical bird sound identification system with potentially global application. Further, we show that certainty measures associated with identification outcomes can significantly contribute to the practical usability of the overall system.
Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models

Text normalization is an important enabling technology for several NLP tasks. Recently, neural-network-based approaches have outperformed well-established models in this task. However, in languages other than English, there has been little exploration in this direction. Both the scarcity of annotated data and the complexity of the language increase the difficulty of the problem. To address these challenges, we use a sequence-to-sequence model with character-based attention, which in addition to its self-learned character embeddings, uses word embeddings pre-trained with an approach that also models subword information. This provides the neural model with access to more linguistic information especially suitable for text normalization, without large parallel corpora. We show that providing the model with word-level features bridges the gap for the neural network approach to achieve a state-of-the-art F1 score on a standard Arabic language correction shared task dataset.
Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach

Contemporary deep learning based medical image segmentation algorithms require hours of annotation labor by domain experts. These data hungry deep models perform sub-optimally in the presence of limited amount of labeled data. In this paper, we present a data efficient learning framework using the recent concept of Generative Adversarial Networks; this allows a deep neural network to perform significantly better than its fully supervised counterpart in low annotation regime. The proposed method is an extension of our previous work with the addition of a new unsupervised adversarial loss and a structured prediction based architecture. To the best of our knowledge, this work is the first demonstration of an adversarial framework based structured prediction model for medical image segmentation. Though generic, we apply our method for segmentation of blood vessels in retinal fundus images. We experiment with extreme low annotation budget (0.8 - 1.6% of contemporary annotation size). On DRIVE and STARE datasets, the proposed method outperforms our previous method and other fully supervised benchmark models by significant margins especially with very low number of annotated examples. In addition, our systematic ablation studies suggest some key recipes for successfully training GAN based semi-supervised algorithms with an encoder-decoder style network architecture.
Music and substance use: A meta-analytic review

Abstract While previous research has documented a relation between music and substance use among consumers, to date, there are no meta-analytic reviews of the literature, making our meta-analysis the first in this area. Results from 31 studies, yielding a total of 330,652, indicated that music had a significant effect on substance use, with both music format and genre being significant contributors. The effect of music on substance use also varied by substance type. In addition, participant biological sex and location of data collection were found to moderate the effect of music on substance use. Theoretical implications are discussed along with directions for future research.
Midurethral support is also necessary for reflex closure of the urethra

The pathophysiology of female stress urinary incontinence (SUI) is far from unraveled. Capturing all aspects of this bothersome condition in one theory remains challenging. The well‐known Hammock and Integral theories, both from the early 90's, were successful in explaining a large proportion of the observations made in clinical practice. Nevertheless, some (pre)clinical observations cannot be explained by the current understanding. One of the issues concerns the pressure transmission. Is this process really a passive mechanical action, or is an additional active mechanism responsible for urethral closure? The finding that an increase in urethral pressure sometimes precedes and exceeds the increase in intravesical pressure suggests the latter. This concept has never been incorporated in one of the existing theories describing SUI. This is remarkable as a lot of evidence has been generated in recent years that proves involvement of active components. This review aims to provide an additional theory in which an active reflex closure mechanism of the urethra is incorporated.
Robot-assisted laparoscopic cystectomy with intracorporeal urinary diversion vs. open mini-laparotomy cystectomy: evaluation of surgical inflammatory response and immunosuppressive ability of CO2-pneumoperitoneum in an experimental porcine study

Abstract Objects: To compare surgical inflammatory response (SIR) after radical cystectomy (RC) in a porcine model using minimal invasive techniques. Additionally we aimed to investigate the potential immunosuppressive ability of preoperative CO2-pneumoperitoneum (CO2P). Materials and Methods: Forty female landrace pigs were randomized to five groups: Three intervention groups all having a cystectomy and an ileal conduit either done by robot-assisted laparoscopic technique with intracorporeal urinary diversion (RALC) or an open mini-laparotomy with or without prior CO2P (OMC ± CO2P). Two control sham groups with or without prior CO2P (S ± CO2P). Serum samples were obtained preoperatively, immediately postoperative, 24, 48 and 72 hours postoperatively, and the inflammatory mediators CRP, Haptoglobin, Ceruloplasmin, Albumin, Cortisol, IL-4, IL-6, IL-12 and IFN-α were measured. Results: Operative time was significantly longer in RALC compared to open groups (OMC ± CO2P) (p’s < .0001). CRP and Haptoglobin levels were significantly higher for surgical intervention groups (SIG) compared to controls 24, 48 and 72 hours postoperatively (p’s < .001). At 48 hours, CRP was higher for RALC vs OMC + CO2P (p = .029). At 72 hours, Haptoglobin was higher for RALC vs open groups (p’s < .024). Ceruloplasmin, cortisol, albumin, IL-4, IL-6, IL-12 and IFN-α, revealed no significant differences between SIG. Conclusions: No major differences were found between RALC and OMC regarding the degree of tissue trauma quantified by inflammatory markers. Thirty minutes of CO2-insufflation preoperative appears to have a transient immunosuppressive effect of the innate postoperative SIR, whereas prolonged CO2P apparently diminishes this effect.
Cyclophosphamide versus cyclosporine A therapy in steroid-resistant nephrotic syndrome: a retrospective study with a mean 5-year follow-up

Objective To compare the clinical efficacy of cyclophosphamide (CTX) and cyclosporine A (CSA) in initial treatment of children with steroid-resistant nephrotic syndrome (SRNS). Methods Prospectively maintained databases were reviewed to retrospectively compare two cohorts with SRNS that received peroral administration of 2 to 2.5 mg/kg/d CTX for 3 to 6 months or 1 to 5 mg/kg/d CSA for 2 years until the primary analysis cut-off date during 2007 to 2011. The time to first on-study relapse of SRNS was the primary endpoint. The effective rate was the second endpoint. Results A total of 127 children with SRNS were included (CTX-treated cohort: n = 62; CSA-treated cohort: n = 65), with a mean 5-year follow-up. CTX-treated children showed a significantly delayed time to first on-study relapse of SRNS compared with CSA-treated children (hazard ratio 0.66, 95% confidence interval 0.32–1.75). The relapse rate (rate/year) in CTX-treated children (1.1 ± 0.1) at the 24-month follow-up was significantly higher than that with CSA (0.4 ± 0.2). This difference persisted until the final follow-up. Conclusions CSA is associated with a significantly lower relapse rate and significantly higher effective rate compared with CTX, especially in children with minimal change disease.
Accessing Data from Multiple Sources Through Context-Aware Access Control

With the proliferation of cloud-based data and services, accessing data from distributed cloud environments and consequently providing integrated results to the users has become a key challenge, often involving large processing overheads and administrative costs. The traditional, spatial, temporal and other context-sensitive access control models have been applied in different environments in order to access such data and information. Recently, fog-based access control models have also been introduced to overcome the latency and processing issues by moving the execution of application logic from the cloud-level to an intermediary-level through adding computational nodes at the edges of the networks. These existing access control models mostly have been used to access data from centralized sources. However, we have been encountering rapid changes in computing technologies over the last few years, and many organizations need to dynamically control context-sensitive access to cloud data resources from distributed environments. In this article, we propose a new generation of fog-based access control approach, combining the benefits of fog computing and context-sensitive access control solutions. We first formally introduce a general data model and its associated policy and mapping models, in order to access data from distributed cloud sources and to provide integrated results to the users. In particular, we present a unified set of fog-based access control policies with the aim of reducing administrative burdens and processing overheads. We then introduce a unified data ontology together with its reasoning capability by realizing our formal approach. We demonstrate the applicability of our proposal through a prototype testing and several case studies. Experiment results demonstrate the good performance of our approach with respect to our earlier context-sensitive access control approach.
Laser Ablation of the Recipient Area With Platelet-Rich Plasma–Enriched Epidermal Suspension Transplant in Vitiligo Surgery: A Pilot Study

BACKGROUND Noncultured epidermal cell suspension transplantation is a very popular surgical modality for treating vitiligo. However, the procedure can be modified to improve results. OBJECTIVE To study the extent of repigmentation with noncultured trypsinized fragmented epidermal suspension using platelet-rich plasma (PRP) as a suspending agent and pixel erbium yttrium aluminium garnet laser for ablation of recipient area in patients with stable vitiligo. MATERIALS AND METHODS Autologous noncultured tiny epidermal fragments suspended in PRP were grafted on superficially pixel erbium YAG laser-ablated vitiligo lesions in 10 patients. The repigmentation was evaluated over a period of 6 months using a digital image analysis system. RESULTS Repigmentation was observed as early as 2 weeks after the treatment. Of 20 lesions, 12 (60%) showed excellent response; of which 10 lesions (50%) showed complete repigmentation in 8 weeks only. There were no milia or keloid formation, donor-site scarring, stuck on appearance at recipient site or infection. CONCLUSION This study indicates that PRP-enriched epidermal suspension transplant (LA-PEEST) has the potential to improve the rate of healing and repigmentation in vitiligo patches. Further investigations and larger controlled trials are required to establish this technique as a rapid surgical method to restore pigmentation.
Left distal trans‐radial access facilitates earlier discharge post‐coronary angiography

AIMS In 2017, Kiemeneij published a paper on distal trans-radial artery access for coronary angiography in 62 patients. This paper proposed several advantages to this method. Since this paper was published, several other papers have been published describing this technique, with less than 200 cases in total described. We performed a non randomized control study of left distal trans-radial access in patients undergoing coronary angiography in our center. METHODS We prospectively identified patients presenting for coronary angiography to our center for enrolment in this study. We recruited 94 patients (47 ldTRA, 47 age and sex matched controls). Pre-defined endpoints for the study were as follows: time until radial compression device (RCD) removal, procedural time, radiation dose, fluoroscopy time, and contrast dose. RESULTS Patient and procedural characteristics did not differ significantly between the two groups. With regard to our primary endpoint, patients undergoing ldTRA required, on average, 69 min less time until removal of the RCD (167.8 ± 30 vs 236.6 ± 63.9 min, P < 0.0001). Procedural length did not vary between groups (28.95 ± 5.89 vs 29.76 ± 8.16 min, P = 0.5824). Similarly, there was no statistically significant difference in radiation dose area product (5032.66 ± 2740 vs 4826 ± 2796 Gy/cm2 , P = 0.7191), contrast dose (82.93 ± 23 vs 92.1 ± 33 mL, P = 0.1215), and fluoroscopy time between the two groups (5.41 ± 3.42 vs 4.82 ± 2.97 min, P = 0.3742). CONCLUSIONS Our study confirms that ldTRA is a feasible technique for diagnostic coronary angiography in a modern cardiac catheterization laboratory. It results in decreased post-procedure radial artery compression time without increasing procedural time or radiation dose.
An Imbalance Modified Deep Neural Network With Dynamical Incremental Learning for Chemical Fault Diagnosis

In this paper, a data-driven fault diagnosis model dealing with chemical imbalanced data streams is investigated. Different faults occur with varied frequencies by continuous arrival in chemical plants, while this issue has been hardly addressed in developing a diagnosis model. A novel incremental imbalance modified deep neural network (incremental-IMDNN) is proposed to promote the fault diagnosis to the imbalanced data stream. The first step in designing the incremental-IMDNN is the employment of an imbalance modified method combined with active learning for the extraction and generation of the most valuable information keeping in view the model feedback. DNN is utilized as a basic diagnosis model to excavate potential information. Then for the continuous arrival of new fault modes, DNN is promoted in an incremental hierarchical way. Unlike the traditional model that trained on a static snapshot of data, this model inherits the existing knowledge and hierarchically expands the diagnosis model by the similarity of faults. Similar faults that are judged by fuzzy clustering merge into a superclass, and every submodel shares the same architecture that is prevalent in previous research, which can be trained in parallel. We validate the performance of the proposed method in a Tennessee Eastman (TE) dataset, and the simulation results indicate that the proposed incremental-IM-DNN is better than the existing methods and possesses significant robustness and adaptability in chemical fault diagnosis.
VISTA Instrument: A PCM-Based Sensor for Organics and Volatiles Characterization by Using Thermogravimetric Technique

VISTA (Volatile In Situ Thermogravimetry Analyser) is a µ-Thermogravimeter sensor developed by Consortium of Italian Institutes. ThermoGravimetric Analysis (TGA) is a widely used technique to monitor thermal processes involving volatile compounds, e.g. deposition/sublimation and absorption/ desorption. The instrument core is composed by a Piezoelectric Crystal Microbalance (PCM), equipped with built-in heater and built-in temperature sensor, and provided of its own Proximity Electronics (PE). The PCM oscillation frequency linearly depends on the mass deposited on its sensible area (according to Sauerbrey equation) while the PCM temperature can be increased by means of integrated heaters. Thus, mass and volatile composition can be inferred by the frequency change and by desorption temperature, respectively. The instrument is divided in two sensor heads: VISTA1, able to monitor outgassing processes in space, and VISTA2, able to reach higher temperatures, studying the dehydration and organics decomposition in minerals in different environmental conditions. An Engineering Model of VISTA1 and a laboratory breadboard of VISTA2 have been developed. Pure organic compounds and contaminant have been characterized by using deposition processes and TGA cycles obtaining some physical-chemical parameters, i.e. enthalpy of sublimation/evaporation, $\Delta \pmb{H}_{sub, evap}$, deposition rates, $\pmb{k}$ and vapor pressures, $P_{vap}$. The instrument concept, the scientific objectives and the laboratory measurements are explained in this work.
"Why not use it more?" Sources of self-efficacy in researchers' use of social media for knowledge sharing

Purpose The purpose of this paper is to investigate the sources of self-efficacy that researchers rely on when using social media for knowledge sharing and to explore how these sources impact their use. Design/methodology/approach The study employed 30 semi-structured interviews with researchers at a major Scottish university. The authors analysed the interview transcriptions using directed content analysis. Findings The researchers relied on the four sources of self-efficacy proposed by Bandura (1977) when using social media for knowledge sharing. These sources lead researchers to use social media effectively and frequently for sharing knowledge, although some may discourage its use. Research limitations/implications It extends the self-efficacy integrative theoretical framework of Bandura (1977) by presenting the relative amount of the influence of these sources for researchers to share their ideas, experiences, questions and research outputs on social media. While the participants included academic staff, postdoctoral researchers, and PhD students, the majority were PhD students. Practical implications The findings can help universities understand how to promote productive use of social media. For example, academic staff who have high personal mastery experience could mentor those who do not. Originality/value This is the first known study to investigate the sources of self-efficacy that impact researchers’ use of social media for knowledge sharing.