Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrasound location of ground-glass opacity during thoracoscopic surgery

Abstract OBJECTIVES Application of video-assisted thoracoscopy brought lung surgery into the minimally invasive era; the lack of tactile feedback using VATS, remains a disadvantage because surgeons are unable to locate lesions with a finger or device. This study aimed to investigate the effectiveness, the applicability and the utility of intraoperative ultrasound (IU), for the localization of small ground-glass opacity (GGO) lesions in the parenchyma, as a guide in finding their margins in a deflated lung. MATERIALS AND METHODS We included 15 consecutive patients undergoing diagnostic resection of GGOs via VATS in the Thoracic Surgery Unit of the University of ‘Luigi Vanvitelli’ of Naples from November 2019 to December 2021. They were under general anaesthesia, when the lung had been collapsed, the probe was placed in the region where the target lesion was thought to reside on the basis of low-dose computed tomography scanning. GGO could be identified their sizes, echo levels and posterior echo was recorded by IU when the lung was completely deflated. RESULTS We conducted a retrospective single-centre study. All GGOs were identified by IU. The mean size and depth were 14.1 ± 0.5 and 4.8 ± 0.3 mm, respectively. Six (40%) lesions had hyperechoic patterns, 9 (60%) had mixed echogenicity where the hyperechoic patterns were irregularly mixed with hypoechoic patterns. The final diagnoses included 2 (15%) atypical adenomatous hyperplasia; 2 (15%) adenocarcinomas in situ; 3 (23%) minimally invasive adenocarcinomas and 6 (46%) invasive adenocarcinomas. CONCLUSIONS The results of our study showed that IU could safely and effectively detect GGOs.
A hybrid of 1-deoxynojirimycin and benzotriazole induces preferential inhibition of butyrylcholinesterase (BuChE) over acetylcholinesterase (AChE)

Abstract The synthesis of four heterodimers in which the copper(I)-catalysed azide-alkyne cycloaddition was employed to connect a 1-deoxynojirimycin moiety with a benzotriazole scaffold is reported. The heterodimers were investigated as inhibitors against acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE). The heterodimers displayed preferential inhibition (> 9) of BuChE over AChE in the micromolar concentration range (IC50 = 7–50 µM). For the most potent inhibitor of BuChE, Cornish-Bowden plots were used, which demonstrated that it behaves as a mixed inhibitor. Modelling studies of the same inhibitor demonstrated that the benzotriazole and 1-deoxynojirimycin moiety is accommodated in the peripheral anionic site and catalytic anionic site, respectively, of AChE. The binding mode to BuChE was different as the benzotriazole moiety is accommodated in the catalytic anionic site.
Chronological analysis of the gut microbiome for efficacy of atezolizumab‐based immunotherapy in non‐small cell lung cancer: Protocol for a multicenter prospective observational study

Cancer immunotherapy with immune checkpoint inhibitors (ICIs) is an innovative treatment for non‐small cell lung cancer (NSCLC). Recently, the specific composition of the gut microbiome before initiation of cancer immunotherapy has been highlighted as a predictive biomarker in patients undergoing cancer immunotherapy, mainly in the US or Europe. However, the fact gut microbiome status is completely different in races or countries has been revealed. In addition, how the microbiome composition and diversity chronologically change during cancer immunotherapy is still unclear.
Vaccine target and carrier molecule nontypeable Haemophilus influenzae protein D dimerizes like the close Escherichia coli GlpQ homolog but unlike other known homolog dimers

We have determined the 1.8 Å X‐ray crystal structure of nonlipidated (i.e., N‐terminally truncated) nontypeable Haemophilus influenzae (NTHi; H. influenzae) protein D. Protein D exists on outer membranes of H. influenzae strains and acts as a virulence factor that helps invade human cells. Protein D is a proven successful antigen in animal models to treat obstructive pulmonary disease (COPD) and otitis media (OM), and when conjugated to polysaccharides also has been used as a carrier molecule for human vaccines, for example in GlaxoSmithKline Synflorix™. NTHi protein D shares high sequence and structural identify to the Escherichia coli (E. coli) glpQ gene product (GlpQ). E. coli GlpQ is a glycerophosphodiester phosphodiesterase (GDPD) with a known dimeric structure in the Protein Structural Database, albeit without an associated publication. We show here that both structures exhibit similar homodimer organization despite slightly different crystal lattices. Additionally, we have observed both the presence of weak dimerization and the lack of dimerization in solution during size exclusion chromatography (SEC) experiments yet have distinctly observed dimerization in native mass spectrometry analyses. Comparison of NTHi protein D and E. coli GlpQ with other homologous homodimers and monomers shows that the E. coli and NTHi homodimer interfaces are distinct. Despite this distinction, NTHi protein D and E. coli GlpQ possess a triose‐phosphate isomerase (TIM) barrel domain seen in many of the other homologs. The active site of NTHi protein D is located near the center of this TIM barrel. A putative glycerol moiety was modeled in two different conformations (occupancies) in the active site of our NTHi protein D structure and we compared this to ligands modeled in homologous structures. Our structural analysis should aid in future efforts to determine structures of protein D bound to substrates, analog intermediates, and products, to fully appreciate this reaction scheme and aiding in future inhibitor design.
Multi-Aspect Deep Active Attention Network for Healthcare Explainable Adoption

Depression is a serious illness that significantly affects the lives of those affected. Recent studies have looked at the possibility of detecting and diagnosing this mental disorder using user-generated data from various forms of online media. Therefore, we address the issue of detecting sadness in social media by focusing on terms in personal remarks. To overcome the limitations in classifying depression texts, this study aims to develop attention networks that use covert levels of self-attention. Since nodes/words can express properties/emotions of their neighbors, this paper naturally assigns each node in a neighborhood a weight without performing costly matrix operations such as similarity or network architecture knowledge. This paper extends the emotion lexicon by using hypernyms. For this reason, our method is superior to the performance of other designs. According to the results of our experiments, the emotion lexicon combined with an attention network achieves an ROC of 0.87 while maintaining its interpretability and transparency level. Subsequently, the learned embedding is used to display the contribution of each symptom to the activated word, and the psychiatrist is polled to obtain his qualitative agreement with this representation. By using unlabeled forum language, the method increases the rate at which depression symptoms can be identified from information in Internet forums.
A Study of Publicly Available Resources Addressing Legal Data-Sharing Barriers: Systematic Assessment

Background United States data protection laws vary depending on the data type and its context. Data projects involving social determinants of health often concern different data protection laws, making them difficult to navigate. Objective We systematically aggregated and assessed useful online resources to help navigate the data-sharing landscape. Methods We included publicly available resources that discussed legal data-sharing issues with some health relevance and published between 2010 and 2019. We conducted an iterative search with a common string pattern using a general-purpose search engine that targeted 24 different sectors identified by Data Across Sectors for Health. We scored each online resource for its depth of legal and data-sharing discussions and value for addressing legal barriers. Results Out of 3710 total search hits, 2721 unique URLs were reviewed for scope, 322 received full-text review, and 154 were selected for final coding. Legal agreements, consent, and agency guidance were the most widely covered legal topics, with HIPAA (The Health Insurance Portability and Accountability Act), Family Educational Rights and Privacy Act (FERPA), Title 42 of the Code of Federal Regulations Part 2 being the top 3 federal laws discussed. Clinical health care was the most prominent sector with a mention in 73 resources. Conclusions This is the first systematic study of publicly available resources on legal data-sharing issues. We found existing gaps where resources covering certain laws or applications may be needed. The volume of resources we found is an indicator that real and perceived legal issues are a substantial barrier to efforts in leveraging data from different sectors to promote health.
Anthropometric parameters‐derived estimation of muscle mass predicts all‐cause mortality in heart failure patients

Reduction in appendicular skeletal muscle mass index (ASMI) assessed by dual‐energy X‐ray absorptiometry (DEXA) has been shown to be independently associated with a higher mortality rate in patients with heart failure (HF). However, DEXA is not suitable for measurement of muscle mass in a daily clinical setting and in large population‐based studies. The aim of this study was to determine whether ASMI predicted from anthropometric indicators (predicted ASMI) serves as an alternative to DEXA‐measured ASMI for predicting all‐cause death in HF patients.
Almost everywhere convergence of convolution products

Let $(X,\mathcal{B},m,\tau)$ be a dynamical system with $\ds (X,\mathcal{B},m)$ a probability space and $\ds \tau$ an invertible, measure preserving transformation. The present paper deals with the almost everywhere convergence in $\ds{L}^1(X)$ of a sequence of operators of weighted averages. Almost everywhere convergence follows once we obtain an appropriate maximal estimate and once we provide a dense class where convergence holds almost everywhere. The weights are given by convolution products of members of a sequence of probability measures $\ds\{\nu_i\}$ defined on $\ds\mathbb{Z}$. We then exhibit cases of such averages, where convergence fails.
Sestrin2 suppresses ferroptosis to alleviate septic intestinal inflammation and barrier dysfunction

Abstract Objective Alterations in intestinal function play a crucial role in the pathogenesis of sepsis, and the repair of the intestinal barrier is a potential strategy for the treatment of sepsis. Sestrin2 (SESN2), a highly conserved stress-responsive protein, can be induced in response to stress. Aim This paper aimed to explore the role and mechanism of SESN2 in septic intestinal dysfunction. Methods Blood samples were collected from patients with septic intestinal dysfunction, and Caco-2 cells were subjected to lipopolysaccharide (LPS) to construct in vitro models. The expression level of SESN2 was determined in the blood samples and cells. The impacts of SESN2 overexpression on cell inflammation, oxidative stress, barrier integrity, and MAPK/Nrf2 signaling were evaluated. To determine the mediated role of MAPK signaling and ferroptosis, AMPK inhibitor (Compound C) and ferroptosis inducer (erastin) were separately used to treat cells, and the influences on the above aspects in cells were assessed. Results The expression level of SESN2 was down-regulated in patients with septic intestinal dysfunction and LPS-induced cells. SESN2 overexpression was found to suppress cell inflammation and oxidative stress, maintain barrier integrity, and activate AMPK/Nrf2 signaling. Following the AMPK signaling was inhibited or the ferroptosis was triggered, the effects of SESN2 overexpression on the cells were both reversed. Conclusion Reduced SESN2 contributed to inflammatory response and barrier dysfunction in septic intestinal dysfunction by promoting ferroptosis via activating the AMPK/Nrf2 signaling pathway.
Pandemic paranoia in the general population: international prevalence and sociodemographic profile

Abstract Background The term ‘pandemic paranoia’ has been coined to refer to heightened levels of mistrust and suspicion towards other people specifically due to the COVID-19 pandemic. In this study, we examine the international prevalence of pandemic paranoia in the general population and its associated sociodemographic profile. Methods A representative international sample of general population adults (N = 2510) from five sites (USA N = 535, Germany N = 516, UK N = 512, Australia N = 502 and Hong Kong N = 445) were recruited using stratified quota sampling (for age, sex, educational attainment) and completed the Pandemic Paranoia Scale (PPS). Results The overall prevalence rate of pandemic paranoia was 19%, and was highest in Australia and lowest in Germany. On the subscales of the PPS, prevalence was 11% for persecutory threat, 29% for paranoid conspiracy and 37% for interpersonal mistrust. Site and general paranoia significantly predicted pandemic paranoia. Sociodemographic variables (lower age, higher population size and income, being male, employed and no migrant status) explained additional variance and significantly improved prediction of pandemic paranoia. Conclusions Pandemic paranoia was relatively common in a representative sample of the general population across five international sites. Sociodemographic variables explained a small but significant amount of the variance in pandemic paranoia.
Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information

Estimating the average treatment effect (ATE) from observational data is challenging due to selection bias. Existing works mainly tackle this challenge in two ways. Some researchers propose constructing a score function that satisfies the orthogonal condition, which guarantees that the established ATE estimator is"orthogonal"to be more robust. The others explore representation learning models to achieve a balanced representation between the treated and the controlled groups. However, existing studies fail to 1) discriminate treated units from controlled ones in the representation space to avoid the over-balanced issue; 2) fully utilize the"orthogonality information". In this paper, we propose a moderately-balanced representation learning (MBRL) framework based on recent covariates balanced representation learning methods and orthogonal machine learning theory. This framework protects the representation from being over-balanced via multi-task learning. Simultaneously, MBRL incorporates the noise orthogonality information in the training and validation stages to achieve a better ATE estimation. The comprehensive experiments on benchmark and simulated datasets show the superiority and robustness of our method on treatment effect estimations compared with existing state-of-the-art methods.
Depletion of fossil fuel reserves and projections of CO$_2$ concentration in the Earth atmosphere

The paper has been suggested by two observations: 1) the atmospheric CO 2 growth rate is smaller than that ascribed to the emission of fossil fuels combustion, 2) the fossil fuel reserves are finite. The first observation has lead the way to a simple kinetic mode, based on the balance of 1) land/ocean CO 2 absorption and 2) CO 2 anthropogenic emission limited solely by depletion of the present day fossil-fuel reserves, in a business-as-usual scenario. The second observation has suggested to extrapolate past CO 2 emissions by fossil fuel combustion in the future years up to 2200 CE, by constraining emissions to the physical limits of reserves availability. The Meixner curve (hyperbolic secant distribution) has been used to model the pathway of resource exploitation for the three main classes of fossil fuels, crude oil, natural gas and coal. The kinetic model, driven by the extrapolated emissions, has been employed to project the CO 2 atmospheric concentration due to fossil fuel combustion close to the zero-reserve epoch. The result is just the output of simple models tuned on well-known experimental data. Error analysis of literature data provides the method robustness and the relevant uncertainty band. results demonstrate some IPCC of if compared to
Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy

Probabilistic (Bayesian) modeling has experienced a surge of applications in almost all quantitative sciences and industrial areas. This development is driven by a combination of several factors, including better probabilistic estimation algorithms, flexible software, increased computing power, and a growing awareness of the benefits of probabilistic learning. However, a principled Bayesian model building workflow is far from complete and many challenges remain. To aid future research and applications of a principled Bayesian workflow, we ask and provide answers for what we perceive as two fundamental questions of Bayesian modeling, namely (a)"What actually is a Bayesian model? "and (b)"What makes a good Bayesian model? ". As an answer to the first question, we propose the PAD model taxonomy that defines four basic kinds of Bayesian models, each representing some combination of the assumed joint distribution of all (known or unknown) variables (P), a posterior approximator (A), and training data (D). As an answer to the second question, we propose ten utility dimensions according to which we can evaluate Bayesian models holistically, namely, (1) causal consistency, (2) parameter recoverability, (3) predictive performance, (4) fairness, (5) structural faithfulness, (6) parsimony, (7) interpretability, (8) convergence, (9) estimation speed, and (10) robustness. Further, we propose two example utility decision trees that describe hierarchies and trade-offs between utilities depending on the inferential goals that drive model building and testing.
Robust Causal Learning for the Estimation of Average Treatment Effects

Many practical decision-making problems in economics and healthcare seek to estimate the average treatment effect (ATE) from observational data. The Double/Debiased Machine Learning (DML) is one of the prevalent methods to estimate ATE in the observational study. However, the DML estimators can suffer an error-compounding issue and even give an extreme estimate when the propensity scores are misspecified or very close to 0 or 1. Previous studies have overcome this issue through some empirical tricks such as propensity score trimming, yet none of the existing literature solves this problem from a theoretical standpoint. In this paper, we propose a Robust Causal Learning (RCL) method to offset the deficiencies of the DML estimators. Theoretically, the RCL estimators i) are as consistent and doubly robust as the DML estimators, and ii) can get rid of the error-compounding issue. Empirically, the comprehensive experiments show that i) the RCL estimators give more stable estimations of the causal parameters than the DML estimators, and ii) the RCL estimators outperform the traditional estimators and their variants when applying different machine learning models on both simulation and benchmark datasets.
Analysis of the Effect of Time Delay for Unmanned Aerial Vehicles with Applications to Vision Based Navigation

In this paper, we analyze the effect of time delay dynamics on controller design for Unmanned Aerial Vehicles (UAVs) with vision based navigation. Time delay is an inevitable phenomenon in cyber-physical systems, and has important implications on controller design and trajectory generation for UAVs. The impact of time delay on UAV dynamics increases with the use of the slower vision based navigation stack. We show that the existing models in the literature, which exclude time delay, are unsuitable for controller tuning since a trivial solution for minimizing an error cost functional always exists. The trivial solution that we identify suggests use of infinite controller gains to achieve optimal performance, which contradicts practical findings. We avoid such shortcomings by introducing a novel nonlinear time delay model for UAVs, and then obtain a set of linear decoupled models corresponding to each of the UAV control loops. The cost functional of the linearized time delay model of angular and altitude dynamics is analyzed, and in contrast to the delay-free models, we show the existence of finite optimal controller parameters. Due to the use of time delay models, we experimentally show that the proposed model accurately represents system stability limits. Due to time delay consideration, we achieved a tracking results of RMSE 5.01 cm when tracking a lemniscate trajectory with a peak velocity of 2.09 m/s using visual odometry (VO) based UAV navigation, which is on par with the state-of-the-art.
Trust in Language Grounding: a new AI challenge for human-robot teams

The challenge of language grounding is to fully understand natural language by grounding language in real-world referents. While AI techniques are available, the widespread adoption and effectiveness of such technologies for human-robot teams relies critically on user trust. This survey provides three contributions relating to the newly emerging field of trust in language grounding, including a) an overview of language grounding research in terms of AI technologies, data sets, and user interfaces; b) six hypothesised trust factors relevant to language grounding, which are tested empirically on a human-robot cleaning team; and c) future research directions for trust in language grounding.
More on the super domination number of graphs

Let G = ( V, E ) be a simple graph. A dominating set of G is a subset S ⊆ V such that every vertex in V \ S is adjacent to at least one vertex in S . A dominating set S is called a super dominating set of G , if for every vertex u ∈ S = V \ S , there exists a v ∈ S such that N ( v ) ∩ S = { u } . The cardinality of a minimum size super dominating set of G is the super domination number of G . The problem of ﬁnding the super domination number of a graph is NP -hard. In this paper, we continue the study of the super domination number of graphs and present tight bounds for neighbourhood corona product, r -gluing, and Haj´os sum of two graphs. We present inﬁnite families of graphs attaining our bounds. Finally, we give the exact number of minimum size super dominating sets for some graph classes.
Error-rate in Viterbi decoding of a duobinary signal in presence of noise and distortions: theory and simulation

The Viterbi algorithm, presented in 1967, allows a maximum likelihood decoding of partial response codes. This study focuses on the duobinary code which is the first member of this family and has been specified for the digital part of television systems recommended by International Organizations. Up to now the error-rate, which is the main criterion of the performance, has been evaluated by simulation. Although there exist theoretical bounds, these bounds are not satisfactory for a channel such as broadcasting (by terrestrial transmitters, cable networks or satellite) which is strongly impaired by noise, and linear and non-linear distortions. Analytical methods, verified by simulation, are presented here in order to evaluate the theoretical and exact values of the error-rate, in the form of series of numerical integrations, for a transmission in baseband or in radio-frequency with quadriphase modulation (or AM/VSB for cable networks) and coherent demodulation, in presence of noise and several distortions. This methodology can be later extended to other partial response codes, to convolutional codes and their concatenations.