Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical, dermoscopic and histopathological findings in localized human monkeypox: a case from northern Italy

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The influence of candidates’ physical attributes on patient ratings in simulated assessments of clinical practice

Abstract Background We have previously shown that clinical examiners’ scoring is not negatively impacted when a candidate has a tattoo, unnatural hair colour, or a regional accent. We investigated whether these physical attributes in exam candidates impact patient scoring. Methods Simulated/real patients were randomly assigned to watch five videos of simulated candidate performances of a cranial nerve examination: clear fail, borderline, good, ‘clear pass’ without an attribute, and ‘clear pass’ with one of the attributes (tattoo, purple hair, accent). Participants scored domains of communication and professionalism. We compared scores for the clear pass candidates with and without attributes. Results One hundred and eighty three patients participated. The total scores for the candidates with tattoos and purple hair were higher than the candidate with no physical attribute (p < 0.001). For the candidate with a Liverpool English accent no difference was identified (p = 0.120). Conclusions The presence of certain physical attributes (tattoos or purple hair) was associated with higher scores given by patients to candidates in a simulated physical examination station.
Screening for Depressive Symptoms in Patients with Diabetic Foot using (CES-D) Scale: A Cross-Sectional Study

Aim: The aim of this study was to assess levels of depressive symptoms present in patients with diabetic foot. Methods: A convenience sampling method was used to recruit 108 patients with diabetic foot. After having completed the Centre for Epidemiologic Studies Depression Scale CES-D scale, the patients' demographic data and medical history were collected using pre-structured forms. Results: Of the entire sample, 38.9% have CES-D score ≥27, which indicates risk of major depression. Logistic regression analysis showed that retinopathy was significantly associated with increased depressive symptoms among diabetic foot patients (odds ratio 3.41(p=0.017)). Taking supplement therapy and not taking a combination of oral hypoglycemic agent and insulin treatment were significantly associated with higher depressive symptoms (odds ratio 3.38 (p=0.022), 2.83 (p=0.030)), respectively. Patients with primary education level have the highest odds ratio among all factors associated with risk of major depression (OR, 4.07; p=0.003). Conclusions: The risk of major deression among patients with diabetic foot in Jordan is high compared to general diabetic population. This was associated with low educational level, retinopathy, taking supplemt therapy, but not taking a combination of oral hypoglycemic agent and insulin. There is a need for routine screening for depressive symptoms in patients with diabetic foot to help in the prevention, early detection of depression and even referral to a psychiatrist.
On the Robustness of Bayesian Neural Networks to Adversarial Attacks

Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications. Despite significant efforts, both practical and theoretical, training deep learning models robust to adversarial attacks is still an open problem. In this paper, we analyse the geometry of adversarial attacks in the large-data, overparameterized limit for Bayesian Neural Networks (BNNs). We show that, in the limit, vulnerability to gradient-based attacks arises as a result of degeneracy in the data distribution, i.e., when the data lies on a lower-dimensional submanifold of the ambient space. As a direct consequence, we demonstrate that in this limit BNN posteriors are robust to gradient-based adversarial attacks. Crucially, we prove that the expected gradient of the loss with respect to the BNN posterior distribution is vanishing, even when each neural network sampled from the posterior is vulnerable to gradient-based attacks. Experimental results on the MNIST, Fashion MNIST, and half moons datasets, representing the finite data regime, with BNNs trained with Hamiltonian Monte Carlo and Variational Inference, support this line of arguments, showing that BNNs can display both high accuracy on clean data and robustness to both gradient-based and gradient-free based adversarial attacks.
Subband-based Generative Adversarial Network for Non-parallel Many-to-many Voice Conversion

Voice conversion is to generate a new speech with the source content and a target voice style. In this paper, we focus on one general setting, i.e., non-parallel many-to-many voice conversion, which is close to the real-world scenario. As the name implies, non-parallel many-to-many voice conversion does not require the paired source and reference speeches and can be applied to arbitrary voice transfer. In recent years, Generative Adversarial Networks (GANs) and other techniques such as Conditional Variational Autoencoders (CVAEs) have made considerable progress in this field. However, due to the sophistication of voice conversion, the style similarity of the converted speech is still unsatisfactory. Inspired by the inherent structure of mel-spectrogram, we propose a new voice conversion framework, i.e., Subband-based Generative Adversarial Network for Voice Conversion (SGAN-VC). SGAN-VC converts each subband content of the source speech separately by explicitly utilizing the spatial characteristics between different subbands. SGAN-VC contains one style encoder, one content encoder, and one decoder. In particular, the style encoder network is designed to learn style codes for different subbands of the target speaker. The content encoder network can capture the content information on the source speech. Finally, the decoder generates particular subband content. In addition, we propose a pitch-shift module to fine-tune the pitch of the source speaker, making the converted tone more accurate and explainable. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art performance on VCTK Corpus and AISHELL3 datasets both qualitatively and quantitatively, whether on seen or unseen data. Furthermore, the content intelligibility of SGAN-VC on unseen data even exceeds that of StarGANv2-VC with ASR network assistance.
Revealing Unfair Models by Mining Interpretable Evidence

The popularity of machine learning has increased the risk of unfair models getting deployed in high-stake applications, such as justice system, drug/vaccination design, and medical diagnosis. Although there are effective methods to train fair models from scratch, how to automatically reveal and explain the unfairness of a trained model remains a challenging task. Revealing unfairness of machine learning models in interpretable fashion is a critical step towards fair and trustworthy AI. In this paper, we systematically tackle the novel task of revealing unfair models by mining interpretable evidence (RUMIE). The key idea is to find solid evidence in the form of a group of data instances discriminated most by the model. To make the evidence interpretable, we also find a set of human-understandable key attributes and decision rules that characterize the discriminated data instances and distinguish them from the other non-discriminated data. As demonstrated by extensive experiments on many real-world data sets, our method finds highly interpretable and solid evidence to effectively reveal the unfairness of trained models. Moreover, it is much more scalable than all of the baseline methods.
Estimation and Compensation of Process Induced Variations in Nanoscale Tunnel Field Effect Transistors (TFETs) for Improved Reliability

— Tunnel Field Effect Transistors (TFET) have extremely low leakage current, exhibit excellent subthreshold swing and are less susceptible to short channel effects. However, TFETs do face certain special challenges, particularly with respect to the process induced variations in (i) the channel length and (ii) the thickness of the silicon thin-film and the gate oxide. This paper, for the first time, studies the impact of the above process variations on the electrical characteristics of a Double Gate Tunnel Field Effect Transistor (DGTFET). Using two dimensional device simulations, we propose the Strained Double Gate Tunnel Field Effect Transistor (SDGTFET) with high-k gate dielectric as a possible solution for effectively compensating the process induced variations in the on-current, threshold voltage and subthreshold-swing improving the reliability of the DGTFET.
Endocannabinoid signaling in astrocytes

The study of the astrocytic contribution to brain functions has been growing in popularity in the neuroscience field. In the last years, and especially since the demonstration of the involvement of astrocytes in synaptic functions, the astrocyte field has revealed multiple functions of these cells that seemed inconceivable not long ago. In parallel, cannabinoid investigation has also identified different ways by which cannabinoids are able to interact with these cells, modify their functions, alter their communication with neurons and impact behavior. In this review, we will describe the expression of different endocannabinoid system members in astrocytes. Moreover, we will relate the latest findings regarding cannabinoid modulation of some of the most relevant astroglial functions, namely calcium (Ca2+) dynamics, gliotransmission, metabolism, and inflammation.
Using adopted individuals to partition indirect maternal genetic effects into prenatal and postnatal effects on offspring phenotypes

Maternal genetic effects can be defined as the effect of a mother’s genotype on the phenotype of her offspring, independent of the offspring’s genotype. Maternal genetic effects can act via the intrauterine environment during pregnancy and/or via the postnatal environment. In this manuscript, we present a simple extension to the basic adoption design that uses structural equation modelling (SEM) to partition maternal genetic effects into prenatal and postnatal effects. We examine the power, utility and type I error rate of our model using simulations and asymptotic power calculations. We apply our model to polygenic scores of educational attainment and birth weight associated variants, in up to 5,178 adopted singletons, 943 trios, 2687 mother-offspring pairs, 712 father-offspring pairs and 347,980 singletons from the UK Biobank. Our results show the expected pattern of maternal genetic effects on offspring birth weight, but unexpectedly large prenatal maternal genetic effects on offspring educational attainment. Sensitivity and simulation analyses suggest this result may be at least partially due to adopted individuals in the UK Biobank being raised by their biological relatives. We show that accurate modelling of these sorts of cryptic relationships is sufficient to bring type I error rate under control and produce asymptotically unbiased estimates of prenatal and postnatal maternal genetic effects. We conclude that there would be considerable value in following up adopted individuals in the UK Biobank to determine whether they were raised by their biological relatives, and if so, to precisely ascertain the nature of these relationships. These adopted individuals could then be incorporated into informative statistical genetics models like the one described in our manuscript to further elucidate the genetic architecture of complex traits and diseases.
Simultaneous Total Ankle Replacement and Contralateral Ankle Arthrodesis for Bilateral Ankle Osteoarthritis: A Retrospective Study Focused on Clinical Outcomes and Cost‐effectiveness

Total ankle replacement (TAR) and ankle arthrodesis (AA) are two common surgical treatment options for end‐stage ankle osteoarthritis. However, few reports compare the outcomes of simultaneous TAR and contralateral AA for bilateral ankle osteoarthritis. The aim of this study was to assess changes in pain, joint range of movement (ROM), functional outcomes, patient satisfaction, and cost‐effectiveness following simultaneous TAR and contralateral AA.
Towards Highly Expressive Machine Learning Models of Non-Melanoma Skin Cancer

Pathologists have a rich vocabulary with which they can describe all the nuances of cellular morphology. In their world, there is a natural pairing of images and words. Recent advances demonstrate that machine learning models can now be trained to learn high-quality image features and represent them as discrete units of information. This enables natural language, which is also discrete, to be jointly modelled alongside the imaging, resulting in a description of the contents of the imaging. Here we present experiments in applying discrete modelling techniques to the problem domain of non-melanoma skin cancer, specifically, histological images of Intraepidermal Carcinoma (IEC). Implementing a VQ-GAN model to reconstruct high-resolution (256×256) images of IEC images, we trained a sequence-to-sequence transformer to generate natural language descriptions using pathologist terminology. Combined with the idea of interactive concept vectors available by using continuous generative methods, we demonstrate an additional angle of interpretability. The result is a promising means of working towards highly expressive machine learning systems which are not only useful as predictive/classification tools, but also means to further our scientific understanding of disease.
Conditional Energy-Based Models for Implicit Policies: The Gap between Theory and Practice

We present our findings in the gap between theory and practice of using conditional energy-based models (EBM) as an implicit representation for behavior-cloned policies. We also clarify several subtle, and potentially confusing, details in previous work in an attempt to help future research in this area. We point out key differences between unconditional and conditional EBMs, and warn that blindly applying training methods for one to the other could lead to undesirable results that do not generalize well. Finally, we emphasize the importance of the Maximum Mutual Information principle as a necessary condition to achieve good generalization in conditional EBMs as implicit models for regression tasks.
Epidemiology of urticaria in China: a population-based study

Abstract Background: Urticaria is a common skin disease characterized by episodes of wheals, and it has a negative effect on patients’ quality of life. Large-scale population-based epidemiological studies of urticaria are scarce in China. The aim of this survey was to determine the prevalence, clinical forms, and risk factors of urticaria in the Chinese population. Methods: This survey was conducted in 35 cities from 31 provinces, autonomous regions, and municipalities of China. Two to three communities in each city were selected in this investigation. Participants completed questionnaires and received dermatological examinations. We analyzed the prevalence, clinical forms, and risk factors of urticaria. Results: In total, 44,875 questionnaires were distributed and 41,041 valid questionnaires were collected (17,563 male and 23,478 female participants). The lifetime prevalence of urticaria was 7.30%, with 8.26% in female and 6.34% in male individuals (P < 0.05). The point prevalence of urticaria was 0.75%, with 0.79% in female and 0.71% in male individuals (P < 0.05). Concomitant angioedema was found in 6.16% of patients. Adults had a higher prevalence of urticaria than adolescents and children. Living in urban areas, exposure to pollutants, an anxious or depressed psychological status, a personal and family history of allergy, thyroid diseases, and Helicobacter pylori infection were associated with a higher prevalence of urticaria. Smoking was correlated with a reduced risk of urticaria. Conclusion: This study demonstrated that the lifetime prevalence of urticaria was 7.30% and the point prevalence was 0.75% in the Chinese population; women had a higher prevalence of urticaria than men. Various factors were correlated with urticaria.
Cloning and expression of Burkholderia polyyne biosynthetic gene clusters in Paraburkholderia hosts provides a strategy for biopesticide development

Burkholderia have potential as biocontrol agents because they encode diverse biosynthetic gene clusters (BGCs) for a range of antimicrobial metabolites. Given the opportunistic pathogenicity associated with Burkholderia species, heterologous BGC expression within non‐pathogenic hosts is a strategy to construct safe biocontrol strains. We constructed a yeast‐adapted Burkholderia‐Escherichia shuttle vector (pMLBAD_yeast) with a yeast replication origin 2 μ and URA3 selection marker and optimised it for cloning BGCs using the in vivo recombination ability of Saccharomyces cerevisiae. Two Burkholderia polyyne BGCs, cepacin (13 kb) and caryoynencin (11 kb), were PCR‐amplified as three overlapping fragments, cloned downstream of the pBAD arabinose promoter in pMLBAD_yeast and mobilised into Burkholderia and Paraburkholderia heterologous hosts. Paraburkholderia phytofirmans carrying the heterologous polyyne constructs displayed in vitro bioactivity against a variety of fungal and bacterial plant pathogens similar to the native polyyne producers. Thirteen Paraburkholderia strains with preferential growth at 30°C compared with 37°C were also identified, and four of these were amenable to genetic manipulation and heterologous expression of the caryoynencin construct. The cloning and successful heterologous expression of Burkholderia biosynthetic gene clusters within Paraburkholderia with restricted growth at 37°C opens avenues for engineering non‐pathogenic biocontrol strains.
Developing the Quality in Nutrition Care Model for Dietitians (QUINCE‐MOD) in primary care: A mixed‐method survey of healthcare consumers and professionals

Abstract Background Evidence‐based models are effective tools for informing quality improvement activities in health care. The Quality in Nutrition Care (QUINCE) model is an evidence‐based model that describes quality nutrition care in the primary care setting but has not been applied to primary care dietetics. The present study comprised two phases. In the first phase, the study aimed to evaluate and compare healthcare consumers and professional perspectives on the importance of the 10 statements to understand their relevance for dietetics care. In the second phase, the study aimed to test and enhance the QUINCE model using healthcare consumer and professional responses to statements that were identified as relevant to primary care dietetics in the first phase. Methods Quantitative and qualitative data were collected using an online survey comprising statements describing quality dietetic care adapted from the complexity literature. Quantitative data were analysed using descriptive statistical analysis to investigate statement importance as rated by participants using a six‐point Likert scale. Qualitative content analysis was performed on the qualitative data using a deductive‐inductive approach to identify patterns in the data. The QUINCE model was compared with identified patterns and modified as appropriate. Results One hundred and fourteen consumers and 107 primary care professionals agreed on the importance of seven statements but expressed divergent views on the remaining three statements that addressed: (1) access to medical records; (2) access to performance and safety information; and (3) variation in care between dietitians. The QUINCE model was modified to produce the Quality in Nutrition Care Model for Dietitians (QUINCE‐MOD) describing quality dietetic care in the primary care setting. Conclusions Consumers and professionals share views on some aspects of quality dietetic care but diverge on other aspects. The QUINCE‐MOD is an evidence‐based, complexity‐informed model that describes components of quality relevant to primary care dietetics. The model should be applied at the practice level to reflect the unique perspective of quality at that level.
Comparison of two primer-probe sets of Fusobacterium nucleatum using droplet digital polymerase chain reaction for the detection of colorectal neoplasia from faecal samples

Background: Although faecal DNA testing of Fusobacterium nucleatum (Fn) is expected to be useful for colorectal neoplasia detection, there is no standardized quantification method of Fn. We performed this study to establish a possible standardized method. Methods: In this study, 322 participants including 71 subjects without colorectal neoplasia (control group), 31 patients with non-advanced colorectal adenoma, 93 patients with advanced colorectal adenoma, and 127 patients with colorectal cancer were enrolled. Faecal Fn were quantified by droplet digital PCR (ddPCR) using two PCR primer-probe sets reported previously that are tentatively named Fn1 and Fn2. Fn1 has been used in ddPCR by us and Fn2 has been widely used in quantitative real-time PCR. Results: The Fn copy number using Fn1 was five times higher than that using Fn2, with a linear relationship shown between them. Receiver operating characteristic curve analysis showed the area under the curve (AUC) to be almost the same between Fn1 and Fn2 in discriminating between the control group and the colorectal cancer group (AUC = 0.81 and 0.81, respectively), and between the control/non-advanced colorectal adenoma group and the advanced colorectal adenoma/colorectal cancer group (AUC = 0.74 and 0.74, respectively). Conclusions: As the diagnostic performance was quite similar between Fn1 and Fn2, ddPCR-based Fn testing using Fn1 and Fn2 could be a possible standardized method for a colorectal neoplasia screening test, considering that Fn levels quantified by Fn1 are about five times higher than those by Fn2.
Holin‐assisted bacterial recombinant protein export

A simple generic method for enhancing extracellular protein yields in engineered bacteria is still lacking. Here, we demonstrated that phage‐encoded holin can be used to export proteins to the extracellular medium in both Gram‐negative Escherichia coli and ‐positive Lactococcus lactis. When a putative holin gene LLNZ_RS10380 annotated in the genome of L. lactis NZ9000 (hol380) was recombinantly expressed in E. coli BL21(DE3), the Hol380 oligomerized up to hexamer in the cytoplasmic membrane, yielding membrane pore to allow the passage of cytosolic β‐galatosidase (116 kDa), whose extracellular production reached 54.59 U/μl, accounting for 76.37% of the total activity. However, the overexpressed Hol380 could not release cytosolic proteins across the membrane in L. lactis NZ9000, but increased the secretory production of staphylococcal nuclease to 2.55‐fold and fimbrial adhesin FaeG to 2.40‐fold compared with those guided by signal peptide Usp45 alone. By using a combination of proteomics and transcriptional level analysis, we found that overexpression of the Hol380 raised the accumulation of Ffh and YidC involved in the signal recognition particle pathway in L. lactis, suggesting an alternative road participating in protein secretion. This study proposed a new approach by expressing holin in bacterial cell factories to export target proteins of economic or medical interest.