Miracle-dz/UniFAR-FTUs huggingface dataset:
Phosphohistone-H3 and Ki67: Useful Markers in Differentiating Dermatofibroma From Dermatofibrosarcoma Protuberans and Atypical Fibrohistiocytic Lesions

Abstract: Dermatofibromas (DF) are common, benign, skin tumors, usually easily differentiated from dermatofibrosarcoma protuberans (DFSP) by the presence of a relative low cellularity, lesser degree of infiltration of subcutaneous tissue, and immunohistochemical pattern (eg, FXIIIa in DF and CD34 in DFSP). Atypical fibrohistiocytic lesions (AFL) have features intermediate to DF and DFSP (trunk location, storiform pattern, infiltration of the subcutaneous tissue, and focal expression of both CD34 and Factor XIIIa). It is unclear if mitotic counts/degree of proliferation is helpful to distinguish DF from DFSP. To study the mitotic rate and proliferation index in DF, AFL/DFSP, anti-ki67, and anti-PHH3 were performed on 10 cases of DF (including 4 cellular DF), 10 standard DFSP, and 2 AFL. The proliferation index and mitotic figures were counted per square millimeter in a “hotspot” (in a fashion similar to mitotic counts in melanoma). All cases of DF showed much higher Ki67 proliferation index (P = 0.0001) along with increased mitotic figures both on H&E and with anti-PHH3 (P = 0.0001) when compared to AFL/DFSP. Our data indicate that DF has a higher proliferation index and mitotic counts when compared to superficial/peripheral portion of AFL and DFSP. This finding may be helpful in the differential diagnosis among these fibrohistiocytic lesions.
Shining a light on the dark world of plant root–microbe interactions

Interactions between bacteria and roots are critical to the terrestrial ecosystem. The zone of soil immediately surrounding roots is known as the rhizosphere and the surface of the root the rhizoplane (1, 2). This region is of paramount importance to the growth and productivity of plants because it is the main area where they interact with an enormously complex microbial community, the microbiome. Soil may contain up to 104 bacterial species and 109 bacteria per g as well as a diverse archaeal and eukaryotic population (3, 4). Although the diversity of bacteria in the rhizosphere is less than in soil, it is much more active because it is under strong plant selection and has access to root exudation. Plants may release up to 20% of their photosynthate via their roots, which is critical in shaping and possibly farming the root microbiome. This microbiome consists of symbionts, commensals, and pathogens that will interact with roots, the soil, and one another to form a root holobiont that determines much of biological and agricultural productivity. Despite the immense importance of the root environment, it is often overlooked because it is challenging to image and study roots that are underground and are difficult to observe without disturbance. For example, compare the ease of sampling shoots relative to roots. One of the principal ways of overcoming this is to image roots with X-ray computed tomography scanning, a powerful way of building 3D images of root growth in a spatial and temporal fashion (5). However, this approach is not suited to examine the critical interaction of microbes with roots. To overcome this, plants can be grown between thinly separated glass sheets, known as rhizotrons, on agar plates or similar artificial media and imaged by a wide range of physical and microscopic techniques. … [↵][1]1Email: philip.poole{at}plants.ox. ac.uk. [1]: #xref-corresp-1-1
Implants for elderly patients

In the developed world, the large birth cohorts of the so-called baby boomer generation have arrived in medical and dental practices. Often, elderly patients are 'young-old' baby boomers in whom partial edentulism is the predominant indication for implant therapy. However, the generation 85+ years of age represents a new challenge for the dental profession, as their lives are frequently dominated by dependency, multimorbidity and frailty. In geriatric implant dentistry, treatment planning is highly individualized, as interindividual differences become more pronounced with age. Nevertheless, there are four typical indications for implant therapy: (i) avoidance of removable partial prostheses; (ii) preservation of existing removable partial prostheses; (iii) stabilization of Kennedy Class I removable partial prostheses; and (iv) stabilization of complete prostheses. From a surgical point of view, two very important aspects must be considered when planning implant surgery in elderly patients: first, the consistent strive to minimize morbidity; and, second, the fact that coexisting medical risk factors are significantly more common in elderly patients. Modern three-dimensional cone beam computed tomography imaging is often indicated in order to plan minimally invasive implant surgery. Computer-assisted implant surgery might allow flapless implant surgery, which offers a low level of postoperative morbidity and a minimal risk of postsurgical bleeding. Short and reduced-diameter implants are now utilized much more often than a decade ago. Two-stage surgical procedures should be avoided in elderly patients. Implant restorations for elderly patients should be designed so that they can be modified to become low-maintenance prostheses, or even be removed, as a strategy to facilitate oral hygiene and comfort in the final stage of life.
Comparison of normal tissue dose calculation methods for epidemiological studies of radiotherapy patients

Radiation dosimetry is an essential input for epidemiological studies of radiotherapy patients aimed at quantifying the dose–response relationship of late-term morbidity and mortality. Individualised organ dose must be estimated for all tissues of interest located in-field, near-field, or out-of-field. Whereas conventional measurement approaches are limited to points in water or anthropomorphic phantoms, computational approaches using patient images or human phantoms offer greater flexibility and can provide more detailed three-dimensional dose information. In the current study, we systematically compared four different dose calculation algorithms so that dosimetrists and epidemiologists can better understand the advantages and limitations of the various approaches at their disposal. The four dose calculations algorithms considered were as follows: the (1) Analytical Anisotropic Algorithm (AAA) and (2) Acuros XB algorithm (Acuros XB), as implemented in the Eclipse treatment planning system (TPS); (3) a Monte Carlo radiation transport code, EGSnrc; and (4) an accelerated Monte Carlo code, the x-ray Voxel Monte Carlo (XVMC). The four algorithms were compared in terms of their accuracy and appropriateness in the context of dose reconstruction for epidemiological investigations. Accuracy in peripheral dose was evaluated first by benchmarking the calculated dose profiles against measurements in a homogeneous water phantom. Additional simulations in a heterogeneous cylinder phantom evaluated the performance of the algorithms in the presence of tissue heterogeneity. In general, we found that the algorithms contained within the commercial TPS (AAA and Acuros XB) were fast and accurate in-field or near-field, but not acceptable out-of-field. Therefore, the TPS is best suited for epidemiological studies involving large cohorts and where the organs of interest are located in-field or partially in-field. The EGSnrc and XVMC codes showed excellent agreement with measurements both in-field and out-of-field. The EGSnrc code was the most accurate dosimetry approach, but was too slow to be used for large-scale epidemiological cohorts. The XVMC code showed similar accuracy to EGSnrc, but was significantly faster, and thus epidemiological applications seem feasible, especially when the organs of interest reside far away from the field edge.
Relating ASD symptoms to well-being: moving across different construct levels

Abstract Background Little is known about the specific factors that contribute to the well-being (WB) of individuals with autism spectrum disorder (ASD). A plausible hypothesis is that ASD symptomatology has a direct negative effect on WB. In the current study, the emerging tools of network analysis allow to explore the functional interdependencies between specific symptoms of ASD and domains of WB in a multivariate framework. We illustrate how studying both higher-order (total score) and lower-order (subscale) representations of ASD symptomatology can clarify the interrelations of factors relevant for domains of WB. Methods We estimated network structures on three different construct levels for ASD symptomatology, as assessed with the Adult Social Behavior Questionnaire (item, subscale, total score), relating them to daily functioning (DF) and subjective WB in 323 adult individuals with clinically identified ASD (aged 17–70 years). For these networks, we assessed the importance of specific factors in the network structure. Results When focusing on the highest representation level of ASD symptomatology (i.e. a total score), we found a negative connection between ASD symptom severity and domains of WB. However, zooming in on lower representation levels of ASD symptomatology revealed that this connection was mainly funnelled by ASD symptoms related to insistence on sameness and experiencing reduced contact and that those symptom scales, in turn, impact different domains of WB. Conclusions Zooming in across construct levels of ASD symptom severity into subscales of ASD symptoms can provide us with important insights into how specific domains of ASD symptoms relate to specific domains of DF and WB.
Deep Convolutional Recurrent Network for Segmentation-Free Offline Handwritten Japanese Text Recognition

This paper presents a model of Deep Convolutional Recurrent Network (DCRN) for recognizing offline handwritten Japanese text lines without explicit segmentation of characters. Most of traditional offline handwritten Japanese text recognizers perform segmentation of text image into characters before individually recognizing each character. Although segmentation by recognition and context are employed to recover from segmentation errors, errors made at this stage directly make an impact on the performance of the whole system. The DCRN model consists of three parts: a convolutional feature extractor using Convolutional Neural Network (CNN) and sliding window to extract features from text image; recurrent layers using BLSTM to predict pre-frame from an input sequence; and a transcription layer using a CTC-decoder to translate the predictions into the label sequence. Experimental results on the database: TUAT Kondate database demonstrates the effectiveness of the proposed method.
Regional Gray Matter Volume Differences Between Adolescents With ADHD and Typically Developing Controls: Further Evidence for Anterior Cingulate Involvement

Objective: The present study investigated structural brain differences between adolescents with ADHD and matched control participants. Method: Voxel-based morphometry (VBM) using the DARTEL approach was performed to assess regional gray matter (GM) volumes. Additionally, individual performance on tests of attention was recorded to correlate ADHD related cognitive impairments with regional gray matter abnormalities. Results: We found significantly smaller GM volume in subjects with ADHD compared to their matched controls within the anterior cingulate cortex (ACC), the occipital cortex, bilateral hippocampus/amygdala and in widespread cerebellar regions. Further, reductions of the ACC gray matter volume were found to correlate with scores of selective inattention. Conclusion: These findings underline that structural alterations in a widespread cortico-subcortical network seem to underlie the observable attention problems in patients with ADHD.
Ergodic Secrecy Sum-Rate for Downlink Multiuser MIMO Systems With Limited CSI Feedback

This paper investigates the ergodic secrecy sum-rate for the downlink multiuser MIMO system with independent confidential messages, each of which is intended for one of the users and should keep secret from others. We adopt zero-forcing beamforming with limited channel state information (CSI) feedback at the transmitter, and derive the closed-form expression for the ergodic secrecy sum-rate. Moreover, we analyze some of its asymptotic characteristics, which provide guidance on secure transmission design. Finally, all theoretical results are validated by numerical simulations.
An update on potential therapeutic strategies for Parkinson’s disease based on pathogenic mechanisms

ABSTRACT Introduction: Parkinson’s disease is a common neurodegenerative disorder mainly caused by the loss of nigral dopaminergic neurons, of which the pathogenesis remains essentially unknown. Current therapeutic strategies help manage signs and symptoms but have no effect in disease modification. Over the past several decades, scientists have devoted a lot of effort to clarifying the pathological mechanism and searching for new targets for Parkinson’s disease treatment. Areas covered: Treatment of Parkinson’s disease. Expert Commentary: Illustrated in this review are newly found discoveries and evidence that contribute to the understanding of Parkinson’s disease pathogenic mechanism. Also discussed are potential therapeutic strategies that are being studied, including disease-modifying and genetically mediated small molecule compounds, cell- and gene-based therapeutic strategies, immunization strategies and anti-diabetic therapy, which may be very promising therapeutic methods in the future.
Characterizing the Interrelationships of Prescription Opioid and Benzodiazepine Drugs With Worker Health and Workplace Hazards

Objective: Prescription opioid and benzodiazepine drug use, which has risen significantly, can affect worker health. Exploration of the scientific literature assessed (1) interrelationships of such drug use, occupational risk factors, and illness and injury, and (2) occupational and personal risk factor combinations that can affect their use. Methods: The scientific literature from 2000 to 2015 was searched to determine any interrelationships. Results: Evidence for eight conceptual models emerged based on the search yield of 133 articles. These models summarize interrelationships among prescription opioid and benzodiazepine use with occupational injury and illness. Factors associated with the use of these drugs included fatigue, impaired cognition, falls, motor vehicle crashes, and the use of multiple providers. Conclusion: Prescription opioid and benzodiazepine drugs may be both a personal risk factor for work-related injury and a consequence of workplace exposures.
Hollow carbon spheres, synthesis and applications – a review

Hollow carbon spheres (HCSs), also sometimes called carbon capsules, refer to hollow structured carbon particles of millimetre, micron or even nanometre size and correspondingly thin shells. These materials are presently attracting great attention due to their unique properties such as encapsulation ability, controllable permeability, surface functionality, high surface-to-volume ratios, and excellent chemical and thermal stabilities. In many aspects, carbon capsules are therefore superior to their polymer- or metal-based counterparts. In the present review, two main synthetic strategies, hard-templating and soft-templating routes, but also far more uncommon preparation ways are reported and concisely described. The main features of the resultant HCSs are also briefly given. Recent progress in the main application aspects of HCSs, such as adsorption, energy storage and catalysis, among others, is next discussed. Finally, an outlook of their prospects and challenges in terms of controlled synthesis and applications is presented.
Scene Text Eraser

The character information in natural scene images contains various personal information, such as telephone numbers, home addresses, etc. It is a high risk of leakage the information if they are published. In this paper, we proposed a scene text erasing method to properly hide the information via an inpainting convolutional neural network (CNN) model. The input is a scene text image, and the output is expected to be text erased image with all the character regions filled up the colors of the surrounding background pixels. This work is accomplished byaCNNmodelthroughconvolutiontodeconvolutionwithinterconnection process. The training samples and the corresponding inpainting images are considered as teaching signals for training. To evaluate the text erasing performance, the output images are detected by a novel scene text detection method. Subsequently, the same measurement on text detection is utilized for testing the images in benchmark dataset ICDAR2013. Compared with direct text detection way, the scene text erasing process demonstrates a drastically decrease on the precision, recall and f-score. That proves the effectiveness of proposed method for erasing the text in natural scene images.
Cerebrospinal fluid and blood biomarkers for neurodegenerative dementias: An update of the Consensus of the Task Force on Biological Markers in Psychiatry of the World Federation of Societies of Biological Psychiatry

Abstract In the 12 years since the publication of the first Consensus Paper of the WFSBP on biomarkers of neurodegenerative dementias, enormous advancement has taken place in the field, and the Task Force takes now the opportunity to extend and update the original paper. New concepts of Alzheimer’s disease (AD) and the conceptual interactions between AD and dementia due to AD were developed, resulting in two sets for diagnostic/research criteria. Procedures for pre-analytical sample handling, biobanking, analyses and post-analytical interpretation of the results were intensively studied and optimised. A global quality control project was introduced to evaluate and monitor the inter-centre variability in measurements with the goal of harmonisation of results. Contexts of use and how to approach candidate biomarkers in biological specimens other than cerebrospinal fluid (CSF), e.g. blood, were precisely defined. Important development was achieved in neuroimaging techniques, including studies comparing amyloid-β positron emission tomography results to fluid-based modalities. Similarly, development in research laboratory technologies, such as ultra-sensitive methods, raises our hopes to further improve analytical and diagnostic accuracy of classic and novel candidate biomarkers. Synergistically, advancement in clinical trials of anti-dementia therapies energises and motivates the efforts to find and optimise the most reliable early diagnostic modalities. Finally, the first studies were published addressing the potential of cost-effectiveness of the biomarkers-based diagnosis of neurodegenerative disorders.
Artificial Noise Assisted Communication in the Multiuser Downlink: Optimal Power Allocation

We consider the problem of secure communication in the multiuser downlink with an external eavesdropper. The transmitter simultaneously transmits concurrent information signals to the users and an artificial noise (AN) to the eavesdropper. We derive a closed-form expression for the secrecy sum-rate in the large system limit and use it as the objective function to optimize the power allocation between the information signals and the AN to maximize the secrecy sum-rate. We obtain a simple approximation for the optimal power allocation in the high signal-to-noise ratio regime, which proves to be a generic and near-optimal strategy. Analytical and simulation results show that more power needs to be allocated to AN if the system serves fewer users and the eavesdropper has more antennas.
A novel hybrid model-based MPPT algorithm based on artificial neural networks for photovoltaic applications

In this paper, a novel hybrid Maximum Power Point Tracking (MPPT) strategy has been proposed. This algorithm employs an idea similar to the Fractional Short Circuit Current (FSCC) method in order to improve the performance of the conventional Perturbation and Observation (P&O) algorithm. It should be considered that in the conventional FSCC method, the short circuit current needs to be measured at different time instances and hence, the power generated by PV module is not permanently delivered to the load. In the proposed strategy, a model of the PV cell is obtained based on a Multi-Layer Perceptron (MLP) structure. Then, the PV short circuit current is easily approximated using this model and is employed as an auxiliary information for the conventional P&O algorithm in order to simultaneously achieve fast and accurate tracking performance. The output of the proposed algorithm is the reference current value which is then fed to a predictive controller used to control the DC-DC boost converter placed between the PV cell and the load. Finally, the system is simulated in the MATLAB/Simulink environment. The simulation results verify the appropriate performance of the proposed method.
Ethnic Group Differences in Bullying Perpetration: A Meta‐Analysis

We examined ethnic differences in bullying perpetration in order to assess whether ethnic group membership was associated with higher involvement among (1) nonimmigrant and immigrant youth; and (2) White and visible minority youth (i.e., Black, Hispanic, Asian, Indigenous, and Biracial). Fifty-three studies (N = 740,176; 6-18-year-olds) were included in the meta-analysis. Results yielded very small and nonsignificant effect size differences across all group comparisons. Methodological moderator analyses indicated several differences across groups. Our findings provide initial support that the assessment of ethnicity as a descriptive variable is not sufficient to account for group differences in bullying perpetration.
Hyaluronan Modulation Impacts Staphylococcus aureus Biofilm Infection

ABSTRACT Staphylococcus aureus is a leading cause of chronic biofilm infections. Hyaluronic acid (HA) is a large glycosaminoglycan abundant in mammalian tissues that has been shown to enhance biofilm formation in multiple Gram-positive pathogens. We observed that HA accumulated in an S. aureus biofilm infection using a murine implant-associated infection model and that HA levels increased in a mutant strain lacking hyaluronidase (HysA). S. aureus secretes HysA in order to cleave HA during infection. Through in vitro biofilm studies with HA, the hysA mutant was found to accumulate increased biofilm biomass compared to the wild type, and confocal microscopy showed that HA is incorporated into the biofilm matrix. Exogenous addition of purified HysA enzyme dispersed HA-containing biofilms, while catalytically inactive enzyme had no impact. Additionally, induction of hysA expression prevented biofilm formation and also dispersed an established biofilm in the presence of HA. These observations were corroborated in the implant model, where there was decreased dissemination from an hysA mutant biofilm infection compared to the S. aureus wild type. Histopathology demonstrated that infection with an hysA mutant caused significantly reduced distribution of tissue inflammation compared to wild-type infection. To extend these studies, the impact of HA and S. aureus HysA on biofilm-like aggregates found in joint infections was examined. We found that HA contributes to the formation of synovial fluid aggregates, and HysA can disrupt aggregate formation. Taken together, these studies demonstrate that HA is a relevant component of the S. aureus biofilm matrix and HysA is important for dissemination from a biofilm infection.