Miracle-dz/UniFAR-FTUs huggingface dataset:
A three-dimensional transient computational study of 532-nm laser thermal ablation in a geometrical model representing prostate tissue

Abstract Objective: Laser with 532-nm wavelength (GreenLightTM) is clinically approved to treat benign prostatic hyperplasia (BPH). However, low rate of tissue ablation and excessive thermal coagulation are shortcomings of this therapy. The goal of this study was to use a mathematical model to identify clinically viable laser settings that have the potential to improve treatment time and outcomes. Methods: A three-dimensional transient computational model was developed, validated against analytical and experimental results, and utilized to investigate the response of tissues subjected to continuous-wave and pulsed lasers emitting 532-nm light (GreenLightTM laser). The impact of laser power (10–125 W), pulse duration (100 ns and 100 µs) and pulse frequency (10 and 100 Hz) on tissue ablation and coagulation rates and sizes was explored. Results: Good agreement between the computational model and analytical and experimental results was found. Continuous-wave laser results in 13% less coagulation zone thickness and 10% higher ablation rate than the low frequency pulsed laser. With increasing laser power; ablation rate is expected to increase linearly, while coagulation zone thickness is expected to increase asymptotically. Pulse frequency influence on tissue ablation and coagulation is relevant at high power, but pulse duration is found to have minimal effect at all powers. Conclusions: Laser thermal tissue ablation employing continuous wave mode lasers outperforms that employing pulsed mode lasers. Laser power settings should be carefully selected to maximize the rate of tissue ablation and minimize tissue coagulation.
Machine learning determination of atomic dynamics at grain boundaries

Significance A machine learning method is used to analyze the atomic structures that rearrange within the grain boundaries of polycrystals. The method readily separates the atomic structures into those that rarely rearrange and those that often rearrange. The likelihood of an atom rearranging under a thermal fluctuation is correlated with free volume and potential energy but is not entirely attributable to those quantities. A machine-learned quantity allows estimation of the energy barrier to rearrangement for particular atoms. The grain boundary atoms that rearrange most have more possible rearrangement trajectories rather than much-reduced energy barriers, as in bulk glasses. The work suggests that polycrystal plasticity can be studied in part from the local atomic structural environments without traditional classification of microstructure. In polycrystalline materials, grain boundaries are sites of enhanced atomic motion, but the complexity of the atomic structures within a grain boundary network makes it difficult to link the structure and atomic dynamics. Here, we use a machine learning technique to establish a connection between local structure and dynamics of these materials. Following previous work on bulk glassy materials, we define a purely structural quantity (softness) that captures the propensity of an atom to rearrange. This approach correctly identifies crystalline regions, stacking faults, and twin boundaries as having low likelihood of atomic rearrangements while finding a large variability within high-energy grain boundaries. As has been found in glasses, the probability that atoms of a given softness will rearrange is nearly Arrhenius. This indicates a well-defined energy barrier as well as a well-defined prefactor for the Arrhenius form for atoms of a given softness. The decrease in the prefactor for low-softness atoms indicates that variations in entropy exhibit a dominant influence on the atomic dynamics in grain boundaries.
BET bromodomain inhibitor birabresib in mantle cell lymphoma: in vivo activity and identification of novel combinations to overcome adaptive resistance

Background The outcome of patients affected by mantle cell lymphoma (MCL) has improved in recent years, but there is still a need for novel treatment strategies for these patients. Human cancers, including MCL, present recurrent alterations in genes that encode transcription machinery proteins and of proteins involved in regulating chromatin structure, providing the rationale to pharmacologically target epigenetic proteins. The Bromodomain and Extra Terminal domain (BET) family proteins act as transcriptional regulators of key signalling pathways including those sustaining cell viability. Birabresib (MK-8628/OTX015) has shown antitumour activity in different preclinical models and has been the first BET inhibitor to successfully undergo early clinical trials. Materials and methods The activity of birabresib as a single agent and in combination, as well as its mechanism of action was studied in MCL cell lines. Results Birabresib showed in vitro and in vivo activities, which appeared mediated via downregulation of MYC targets, cell cycle and NFKB pathway genes and were independent of direct downregulation of CCND1. Additionally, the combination of birabresib with other targeted agents (especially pomalidomide, or inhibitors of BTK, mTOR and ATR) was beneficial in MCL cell lines. Conclusion Our data provide the rationale to evaluate birabresib in patients affected by MCL.
Performance of distributed algorithms for QoS in wireless ad hoc networks: arbitrary networks under the primary interference model, and line networks under the protocol interference model

Consider a wireless network where each communication link has a minimum bandwidth quality-of-service requirement. Certain pairs of wireless links interfere with each other due to being in the same vicinity, and this interference is modeled by a conflict graph. Given the conflict graph and link bandwidth requirements, the objective is to determine, using only localized information, whether the demands of all the links can be satisfied. At one extreme, each node knows the demands of only its neighbors; at the other extreme, there exists an optimal, centralized scheduler that has global information. The present work interpolates between these two extremes by quantitatively characterizing the tradeoff between the degree of decentralization and the performance of the distributed algorithm. This open problem is resolved for the primary interference model, and the following general result is obtained: if each node knows the demands of all links in a ball of radius $d$ centered at the node, then there is a distributed algorithm whose performance is away from that of an optimal, centralized algorithm by a factor of at most $(2d+3)/(2d+2)$. It is shown that for line networks under the protocol interference model, the row constraints are a factor of at most $3$ away from optimal. Both bounds are best possible.
Mitochondrial Morphology, Function and Homeostasis Are Impaired by Expression of an N-terminal Calpain Cleavage Fragment of Ataxin-3

Alterations in mitochondrial morphology and function have been linked to neurodegenerative diseases, including Parkinson disease, Alzheimer disease and Huntington disease. Metabolic defects, resulting from dysfunctional mitochondria, have been reported in patients and respective animal models of all those diseases. Spinocerebellar Ataxia Type 3 (SCA3), another neurodegenerative disorder, also presents with metabolic defects and loss of body weight in early disease stages although the possible role of mitochondrial dysfunction in SCA3 pathology is still to be determined. Interestingly, the SCA3 disease protein ataxin-3, which is predominantly localized in cytoplasm and nucleus, has also been associated with mitochondria in both its mutant and wildtype form. This observation provides an interesting link to a potential mitochondrial involvement of mutant ataxin-3 in SCA3 pathogenesis. Furthermore, proteolytic cleavage of ataxin-3 has been shown to produce toxic fragments and even overexpression of artificially truncated forms of ataxin-3 resulted in mitochondria deficits. Therefore, we analyzed the repercussions of expressing a naturally occurring N-terminal cleavage fragment of ataxin-3 and the influence of an endogenous expression of the S256 cleavage fragment in vitro and in vivo. In our study, expression of a fragment derived from calpain cleavage induced mitochondrial fragmentation and cristae alterations leading to a significantly decreased capacity of mitochondrial respiration and contributing to an increased susceptibility to apoptosis. Furthermore, analyzing mitophagy revealed activation of autophagy in the early pathogenesis with reduced lysosomal activity. In conclusion, our findings indicate that cleavage of ataxin-3 by calpains results in fragments which interfere with mitochondrial function and mitochondrial degradation processes.
Clinical, personality, and neurodevelopmental phenotypes in borderline personality disorder: a family study

Abstract Background Borderline personality disorder (BPD) is characterized by a heterogeneous clinical phenotype that emerges from interactions among genetic, biological, neurodevelopmental, and psychosocial factors. In the present family study, we evaluated the familial aggregation of key clinical, personality, and neurodevelopmental phenotypes in probands with BPD (n = 103), first-degree biological relatives (n = 74; 43% without a history of psychiatric disorder), and non-psychiatric controls (n = 99). Methods Participants were assessed on DSM-IV psychiatric diagnoses, symptom dimensions of emotion dysregulation and impulsivity, ‘big five’ personality traits, and neurodevelopmental characteristics, as part of a larger family study on neurocognitive, biological, and genetic markers in BPD. Results The most common psychiatric diagnoses in probands and relatives were major depression, substance use disorders, post-traumatic stress disorder, anxiety disorders, and avoidant personality disorder. There was evidence of familial aggregation for specific dimensions of impulsivity and emotion dysregulation, and the big five traits neuroticism and conscientiousness. Both probands and relatives reported an elevated neurodevelopmental history of attentional and behavioral difficulties. Conclusions These results support the validity of negative affectivity- and impulse-spectrum phenotypes associated with BPD and its familial risk. Further research is needed to investigate the aggregation of neurocognitive, neural and genetic factors in families with BPD and their associations with core phenotypes underlying the disorder.
Transfer Metric Learning: Algorithms, Applications and Outlooks

Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data relationship. It is critical in many machine learning, pattern recognition and data mining algorithms, and usually require large amount of label information (such as class labels or pair/triplet constraints) to achieve satisfactory performance. However, the label information may be insufficient in real-world applications due to the high-labeling cost, and DML may fail in this case. Transfer metric learning (TML) is able to mitigate this issue for DML in the domain of interest (target domain) by leveraging knowledge/information from other related domains (source domains). Although achieved a certain level of development, TML has limited success in various aspects such as selective transfer, theoretical understanding, handling complex data, big data and extreme cases. In this survey, we present a systematic review of the TML literature. In particular, we group TML into different categories according to different settings and metric transfer strategies, such as direct metric approximation, subspace approximation, distance approximation, and distribution approximation. A summarization and insightful discussion of the various TML approaches and their applications will be presented. Finally, we indicate some challenges and provide possible future directions.
The Replication Hypothesis along the Take-Off Run and a System of Equilibrium Equations at the Lift-Off of a Protobird

An extant bird resorts to flapping and running along its take-off run to generate lift and thrust in order to reach the minimum required wing velocity speed required for lift-off. This paper introduces the replication hypothesis that posits that the variation of lift relative to the thrust generated by the flapping wings of an extant bird, along its take-off run, replicates the variation of lift relative to the thrust by the flapping wings of a protobird as it evolves towards sustained flight. The replication hypothesis combines experimental data from extant birds with evidence from the paleontological record of protobirds to come up with a physics-based model of its evolution towards sustained flight while scaling down the time span from millions of years to a few seconds. A second hypothesis states that the vertical and horizontal forces acting on a protobird when it first encounters lift-off are in equilibrium as the protobird exerts its maximum available power for flapping, equaling its lift with its weight, and its thrust with its drag.
Comparison of Tissue Loading Before and After the Creation of a Continuous Density Gradient in Porcine Islet Purification

The purification step is one of the most important and difficult procedures in islet isolation for pancreatic islet transplantation. We previously reported that a purification method using large plastic bottles effectively achieved a high yield of islets from the porcine pancreas. In this study, we evaluated the impact of the timing of tissue loading on porcine islet purification using large plastic bottles. One method involved loading digested tissue after creating a continuous density gradient (tissue after gradient [TAG]). The other method involved loading digested tissue before creating a continuous density gradient (tissue before gradient [TBG]). There were no significant differences between TAG and TBG in terms of the islet yield, rates of viability and purity, score, and in the stimulation index after purification. Furthermore, there were no marked differences in the attainability or suitability of post-transplantation normoglycemia. Our study shows the equivalency of these two methods of islet purification.
How to effectively communicate risks to diverse consumers

The article outlines how to efficiently communicate risks described in numerical values to consumers. The issue is vital and its relevance is related to communication that applies numeric values is an essential part of informing consumers about hazards and advantages of food products; however, a lot of consumers have difficulty perceiving digital information about risks due to various reasons. Consequently, such people assess risks inadequately and can't make well justi-fied decisions based on quantitative information. The authors explain that to remove numerical information and to re-place it with verbal description is not advisable as it doesn't allow to solve an issue of efficient risk communication. They also give examples that illustrate how wrong communication tactics can lead to risks aggravation or underestimation. The authors apply certain propositions to prove efficiency of tested risk communication formats, such as standard catego-ries, visual aids, conventional symbols, etc. It was detected that visual aids, or graphic images of information on risks, can eliminate a lot of problems and result in much better understanding of risks and decisions among diverse people. Such means are quite efficient when it comes to people with limited abilities related to perception of linguistic and numerical information, elderly people, highly educated people and population in general. The authors also give some posi-tive examples on how information was adapted for diverse consumers. They come to a conclusion that well-thought-out and transparent risk communication formats that incorporate natural cognitive strategies can make risk communications much easier. Better understanding, in its turn, often leads to conscious decision making by consumers and health-oriented decisions, intentions, and behavior.
Improving Myocardium Segmentation in Cardiac CT Angiography using Spectral Information

Accurate segmentation of the left ventricle myocardium in cardiac CT angiography (CCTA) is essential for e.g. the assessment of myocardial perfusion. Automatic deep learning methods for segmentation in CCTA might suffer from differences in contrast-agent attenuation between training and test data due to non-standardized contrast administration protocols and varying cardiac output. We propose augmentation of the training data with virtual mono-energetic reconstructions from a spectral CT scanner which show different attenuation levels of the contrast agent. We compare this to an augmentation by linear scaling of all intensity values, and combine both types of augmentation. We train a 3D fully convolutional network (FCN) with 10 conventional CCTA images and corresponding virtual mono-energetic reconstructions acquired on a spectral CT scanner, and evaluate on 40 CCTA scans acquired on a conventional CT scanner. We show that training with data augmentation using virtual mono-energetic images improves upon training with only conventional images (Dice similarity coefficient (DSC) 0.895 $\pm$ 0.039 vs. 0.846 $\pm$ 0.125). In comparison, training with data augmentation using linear scaling improves the DSC to 0.890 $\pm$ 0.039. Moreover, combining the results of both augmentation methods leads to a DSC of 0.901 $\pm$ 0.036, showing that both augmentations lead to different local improvements of the segmentations. Our results indicate that virtual mono-energetic images improve the generalization of an FCN used for myocardium segmentation in CCTA images.
Long-Term Effects of Fibrin Conduit with Human Mesenchymal Stem Cells and Immunosuppression after Peripheral Nerve Repair in a Xenogenic Model

Introduction: Previously we showed that a fibrin glue conduit with human mesenchymal stem cells (hMSCs) and cyclosporine A (CsA) enhanced early nerve regeneration. In this study long term effects of this conduit are investigated. Methods: In a rat model, the sciatic nerve was repaired with fibrin conduit containing fibrin matrix, fibrin conduit containing fibrin matrix with CsA treatment and fibrin conduit containing fibrin matrix with hMSCs and CsA treatment, and also with nerve graft as control. Results: At 12 weeks 34% of motoneurons of the control group regenerated axons through the fibrin conduit. CsA treatment alone or with hMSCs resulted in axon regeneration of 67% and 64% motoneurons respectively. The gastrocnemius muscle weight was reduced in the conduit with fibrin matrix. The treatment with CsA or CsA with hMSCs induced recovery of the muscle weight and size of fast type fibers towards the levels of the nerve graft group. Discussion: The transplantation of hMSCs for peripheral nerve injury should be optimized to demonstrate their beneficial effects. The CsA may have its own effect on nerve regeneration.
Image-to-Video Person Re-Identification by Reusing Cross-modal Embeddings

Image-to-video person re-identification identifies a target person by a probe image from quantities of pedestrian videos captured by non-overlapping cameras. Despite the great progress achieved,it's still challenging to match in the multimodal scenario,i.e. between image and video. Currently,state-of-the-art approaches mainly focus on the task-specific data,neglecting the extra information on the different but related tasks. In this paper,we propose an end-to-end neural network framework for image-to-video person reidentification by leveraging cross-modal embeddings learned from extra information.Concretely speaking,cross-modal embeddings from image captioning and video captioning models are reused to help learned features be projected into a coordinated space,where similarity can be directly computed. Besides,training steps from fixed model reuse approach are integrated into our framework,which can incorporate beneficial information and eventually make the target networks independent of existing models. Apart from that,our proposed framework resorts to CNNs and LSTMs for extracting visual and spatiotemporal features,and combines the strengths of identification and verification model to improve the discriminative ability of the learned feature. The experimental results demonstrate the effectiveness of our framework on narrowing down the gap between heterogeneous data and obtaining observable improvement in image-to-video person re-identification.
Methods to Measure MDSC Immune Suppressive Activity In Vitro and In Vivo

This unit presents methods to assess the immunosuppressive properties of immunoregulatory cells of myeloid origin, such as myeloid‐derived suppressor cells (MDSCs), both in vitro and in vivo in mice, as well as in biological samples from cancer patients. These methods could be adapted to test the impact of different suppressive populations on T cell activation, proliferation, and cytotoxic activity; moreover, they could be useful to assess the influence exerted by genetic modifications, chemical inhibitors, and drugs on immune suppressive pathways © 2018 by John Wiley & Sons, Inc.
Collective evolution of weights in wide neural networks

We derive a nonlinear integro-differential transport equation describing collective evolution of weights under gradient descent in large-width neural-network-like models. We characterize stationary points of the evolution and analyze several scenarios where the transport equation can be solved approximately. We test our general method in the special case of linear free-knot splines, and find good agreement between theory and experiment in observations of global optima, stability of stationary points, and convergence rates.