Miracle-dz/UniFAR-FTUs huggingface dataset:
Linking neuroanatomical abnormalities in autism spectrum disorder with gene expression of candidate ASD genes: A meta-analytic and network-oriented approach

Background Autism spectrum disorder (ASD) is a set of developmental conditions with widespread neuroanatomical abnormalities and a strong genetic basis. Although neuroimaging studies have indicated anatomical changes in grey matter (GM) morphometry, their associations with gene expression remain elusive. Methods Here, we aim to understand how gene expression correlates with neuroanatomical atypicalities in ASD. To do so, we performed a coordinate-based meta-analysis to determine the common GM variation pattern in the autistic brain. From the Allen Human Brain Atlas, we selected eight genes from the SHANK, NRXN, NLGN family and MECP2, which have been implicated with ASD, particularly in regards to altered synaptic transmission and plasticity. The gene expression maps for each gene were built. We then assessed the correlation between the gene expression maps and the GM alteration maps. Lastly, we projected the obtained clusters of GM alteration-gene correlations on top of the canonical resting state networks, in order to provide a functional characterization of the structural evidence. Results We found that gene expression of most genes correlated with GM alteration (both increase and decrease) in regions located in the default mode network. Decreased GM was also correlated with gene expression of some ASD genes in areas associated with the dorsal attention and cerebellar network. Lastly, single genes were found to be significantly correlated with increased GM in areas located in the somatomotor, limbic and ganglia/thalamus networks. Conclusions This approach allowed us to combine the well beaten path of genetic and brain imaging in a novel way, to specifically investigate the relation between gene expression and brain with structural damage, and individuate genes of potential interest for further investigation in the functional domain.
Open-source automated insulin delivery systems and formal healthcare: A qualitative study of challenges in the interaction between service-users with type 1 diabetes and healthcare professionals

Objectives To explore the challenges that open-source automated insulin delivery systems pose to the encounter between service-users and healthcare professionals and how these challenges might be addressed in the future. Methods Five adult service-users and six healthcare professionals participated in semi-structured interviews. A six-step thematic analysis was conducted, the focus being on challenges and needs for improvement of the interactions. Results The analysis revealed three overarching challenges and wishes: (1) Lack of exchange of experiences. Service-users are reticent about disclosing their use of the systems to healthcare professionals. (2) Unclear ethical responsibility. Service-users are aware of their responsibility and guidelines can help to alleviate healthcare professional's legal responsibility; however, ambiguities around ethical responsibility for healthcare professionals remain an impediment to meaningful clinical interactions. (3) Unresolved individual and mutual expectations. Service-users expect better access to technology and focus on psychosocial aspects. In contrast, healthcare professionals fear the burden associated with the rising technical expectations. Conclusion Transparency and openness towards open-source systems need to be part of service-user and healthcare professionals interaction. Requirements for future care include improved knowledge about the systems, an openminded approach towards user-driven initiatives among healthcare professionals, and a focus on psychosocial aspects in the interaction.
A deliberative public engagement study on heritable human genome editing among South Africans: Study results

This paper reports the results of a public engagement study on heritable human genome editing (HHGE) carried out in South Africa, which was conducted in accordance with a study protocol that was published in this journal in 2021. This study is novel as it is the first public engagement study on HHGE in Africa. It used a deliberative public engagement (DPE) methodology, entailing inter alia that measures were put in place to ensure that potential participants became informed about HHGE, and that deliberations between the participants were facilitated with the aim of seeking consensus. A diverse group of 30 persons was selected to participate in the DPE study, which took place via Zoom over three consecutive weekday evenings. The main results are: Provided that HHGE is safe and effective, an overwhelming majority of participants supported allowing the use of HHGE to prevent genetic health conditions and for immunity against TB and HIV/Aids, while significant majorities opposed allowing HHGE for enhancement. The dominant paradigm during the deliberations was balancing health benefits (and associated improvements in quality of life) with unforeseen health risks (such as loss of natural immunity). The seriousness of a health condition emerged as the determining factor for the policy choice of whether to allow an application of HHGE. More generally, equal access to HHGE qua healthcare service featured as an important value, and it was uncontested that the South African government should allocate resources to promote scientific research into HHGE. These results are aligned with the policy principles for regulating HHGE in South Africa suggested by Thaldar et al. They call for urgent revision of South African ethics guidelines that currently prohibit research on HHGE, and for dedicated HHGE legal regulations that provide a clear and comprehensive legal pathway for researchers who intend to conduct HHGE research and clinical trials.
Thalamocortical functional connectivity and cannabis use in men with childhood attention-deficit/hyperactivity disorder

Disruptions of the cortico-striato-thalamo-cortical circuit has been implicated in both attention-deficit/hyperactivity disorder and substance use disorder. Given the high prevalence of cannabis use among patients with attention-deficit/hyperactivity disorder, we set out to investigate the relationship between the two in the thalamus. We analyzed resting-state functional magnetic resonance imaging data obtained from the Addiction Connectome Preprocessed Initiative Multimodal Treatment Study of Attention-Deficit/Hyperactivity Disorder database. Functional connectivity maps were extracted to compare thalamic connectivity among adults who had been diagnosed with attention-deficit/hyperactivity disorder during childhood according to whether or not they used cannabis. The study participants included 18 cannabis users and 15 cannabis non-users with childhood attention-deficit/hyperactivity disorder. Our results revealed that adults with attention-deficit/hyperactivity disorder who used cannabis (n = 18) had significantly decreased functional connectivity between the thalamus and parietal regions, which was particularly prominent in the inferior parietal areas, in comparison with those who did not use cannabis (n = 15). Left thalamic functional connectivity with the inferior parietal and middle frontal areas and right thalamic functional connectivity with the inferior parietal and superior frontal areas were increased in non-users of cannabis with attention-deficit/hyperactivity disorder compared with a local normative comparison group (n = 7). In conclusion, adults with a childhood history of attention-deficit/hyperactivity disorder who do not use cannabis often have relatively stronger thalamoparietal and thalamofrontal connectivity, which may help reduce the risk of cannabis use.
Community Connectedness as a Moderator of the Association between Intersectional Microaggressions and Alcohol Use among Sexual and Gender Minoritized People of Color

Abstract Background: Researchers have documented associations between discrete conceptualizations of microaggressions (e.g., sexual identity, gender identity, and racial identity microaggressions) and alcohol use among sexual and gender minoritized people of color (SGM-POC). However, little is known about the association between intersectional microaggressions and alcohol use among SGM-POC. Moreover, protective factors such as community connectedness have been examined via similar discrete conceptualizations instead of examining SGM-POC community connectedness with other SGM-POC individuals. Objectives: The purpose of this study was to explore the association between intersectional microaggressions and alcohol use among SGM-POC and test whether different types of community connectedness moderated this association. Methods: Cross-sectional data were collected from a sample of 267 SGM-POC individuals. Four moderation analyses were done to analyze whether different types of community connectedness (sexual identity, racial identity, gender identity, and SGM-POC identity community connectedness) were moderators of the association of intersectional microaggressions and alcohol use. Results: Intersectional microaggressions were significantly positively correlated with alcohol use. Furthermore, SGM-POC community connectedness moderated this association such that the association was stronger for individuals with higher levels of SGM-POC community connectedness, but not lower levels of SGM-POC community connectedness. Conclusions: These findings showcase the importance of assessing for intersectional microaggressions as a risk factor for alcohol use. Similarly, the findings suggest that SGM-POC community connectedness may be a protective factor against alcohol use for SGM-POC.
Balancing Domain Decomposition for Non-stationary Incompressible Flow Problems Using a Characteristic-curve Method

Numerical solutions for large scale models face the problem of poor convergence and for non-stationary problems the situation becomes worse. To speed up the convergence, an algorithm to perform the Balancing Domain Decomposition (BDD) preconditioning for non-stationary incompressible flow problems is constructed. The problem caused by the presence of nonlinear convection term in the non-stationary Navier-Stokes equations, which complicates the solving of the equations numerically, is solved by a characteristic-curve method. The method is advantageous as it renders the matrix for linear equations symmetric, thus enabling the Conjugate Gradient (CG) method to be used to solve the interface problem of the Domain Decomposition Method (DDM). The related algorithms are all implemented in parallel by the Hierarchical Domain Decomposition Method (HDDM) and the program has the capability of solving non-stationary problems with up to 10 million degrees of freedom (DOF). The BDD algorithm together with characteristic-curve method is applied to a benchmark problem, a three-dimensional driven cavity flow model, and the results are compared with available solutions.
AN HRP STUDY OF LOCALIZATION AND TOPOGRAPHICAL MAPPING OF THE MOTONEURONS SUPPLYING THE RAT SERRATUS ANTERIOR MUSCLE

Shoulder movements are a complex motor act, controlled by many muscles attached to the scapula and the vertebral column. These muscles are innervating by motoneurons (Mns) located in the ventral horn of the spinal cord. Motoneurons of the ventral horn are arranged such that several groups of Mns can be distinguished in a transverse section through the spinal cord. The anatomical or functional implication of these groups is still unknown. The present research was designed to determine the location and topographical mapping of Mns to the rat serratus anterior (SA) muscle. Thirty young adult male rats weighing 180-250g were used in this study. The location and distribution of the Mns in the rat cervical spinal cord to the serratus anterior (SA) muscle of the rat have been studied using horseradish peroxidase (HRP) intramuscular injection and the application of a HRP nerve dipping method to the long thoracic nerve. Following the intramuscular injection of HRP and also a nerve dipping for the long thoracic nerve, labeled motoneurons were identified ipsilaterally to the side of HRP application forming two main cell columns extended rostrocaudally in the ventral horn of both sides. The column G5 extended longitudinally from the caudal half of the fifth (C5) to the rostral half of the seventh (C7) cervical segments in group five. The column G7, located mainly in the rostral half of the C7 in group seven. The serratus anterior labeled motoneurons showed a rostrocaudal somatotopic distribution in each of the columns G5 and G7. These results suggest that there is a specific functional role for motoneurons, and also that motoneurons are arranged in the form of a motor pool with a musculotopic pattern of distribution corresponding to the embryonic origin and anatomical position of the muscles they innervate to.
Shimony–Wolf states and hidden coherences in classical light

The classical theory of polarisation coherence is briefly summarised and then extended. The extension is motivated by the recognition that the traditional theory of two-point coherence provides only what we identify as ‘diagonal’ correlation functions and their associated two-point coherence matrices. It is pointed out that a wider focus is possible when taking account of the three-sector vector space underlying all two-point coherences in classical optics. This reveals the possibility of observing a new type of ‘off-diagonal’ correlations that arise when the correlation functions under investigation are associated with points in two distinct vector spaces, pairs of points that are not analogous to the pairs of space points or time points that underlie traditional measures of spatial and temporal coherence. Quantum theory has experience with correlations engaging such ‘cross-sector’ coherences, for example in tests of Bell inequalities, and the quantum formulations are shown to be easily adopted by classical theory without incorporating quantum features in the optical signals. The familiar theory of classical coherence that is associated with the pioneering work of Emil Wolf is extended in conformance with three criteria advanced by Abner Shimony to obtain formulas for correlation functions and for the Bell measure of coherence. Values of greater than the standard upper limit are predicted for certain classical Shimony–Wolf fields, indicating strong cross-sector coherence, but only when standard measures of coherence such as degree of polarisation are minimised. Experimental results confirming the predictions for cross-sector coherence are exhibited.
Carbon Nanotube Growth Technologies Using Tantalum Barrier Layer for Future ULSIs with Cu/Low-k Interconnect Processes

We succeeded in developing carbon nanotube (CNT) vias specifically adapted for the copper interconnect process used in ultra large-scale integrated circuits. The CNTs were grown selectively on titanium films using Co catalyst films. The use of tantalum enabled CNTs to be grown on Cu lines and prevented any increase in the sheet resistance of the Cu lines. A Cu wire/CNT via/Cu wire structure was fabricated and low resistance of the via was demonstrated. In addition, tests showed that a high current density of about 106 A/cm2 flowed into the CNT via for 125 hours.
Clinical outcomes of the ACURATE neo2 transcatheter heart valve: a prospective, multicenter, observational, post-market surveillance study.

BACKGROUND The next-generation ACURATE neo2 transcatheter aortic valve was designed for simplified implantation and to mitigate the risk of paravalvular leak (PVL) compared to the earlier device. AIMS To collect clinical outcomes and device performance data, including echocardiography and 4-dimensional computed tomography (4D-CT) data, with the ACURATE neo2 transcatheter heart valve in patients with severe aortic stenosis (AS). METHODS ACURATE neo2 PMCF is a single-arm, multicentre study of patients with severe AS treated in routine clinical practice. The primary safety endpoint was all-cause mortality at 30-days. The primary imaging endpoint was hypo-attenuated leaflet thickening (HALT), measured by core laboratory-adjudicated 4D-CT at 30 days. Secondary endpoints included VARC safety endpoints, procedural success, and evaluation of valve performance via core laboratory-adjudicated echocardiography. RESULTS The study enrolled 250 patients at 18 European centres (mean age: 80.8 years; 63.6% female; mean STS score: 2.9±2.0%); 246 (98.4%) were successfully treated with ACURATE neo2. The 30-day rates for mortality and disabling stroke were 0.8% and 0%, respectively. The new permanent pacemaker implantation rate was 6.5%. HALT >50% was present in 9.3% of patients at 30 days. Valve haemodynamics improved from baseline to 30 days (mean aortic valve gradient: from 47.6±14.5 mmHg to 8.6±3.9 mmHg; mean aortic valve area: from 0.7±0.2 cm2 to 1.6±0.4 cm2). At 30 days, PVL was evaluated as none/trace in 79.2% of patients, mild in 18.9%, moderate in 1.9%, and severe in 0%. CONCLUSIONS The study results support the safety and efficacy of TAVI with ACURATE neo2 in patients in routine clinical practice.
Evaluation of microaneurysms as predictors of therapeutic response to anti-VEGF therapy in patients with DME

Administration of intravitreal anti-vascular endothelial growth factor (anti-VEGF) therapy is the first-line therapy for diabetic macular oedema (DME). However, some patients show no or insufficient response to repeated anti-VEGF injections. Therefore, it is necessary to identify factors that can predict this resistance against anti-VEGF treatment. Presence of microaneurysms (MAs) is a predictor of the development and progression of DME, but its relationship with the treatment response to the anti-VEGF agents is not well known. Therefore, we aimed to elucidate the relationship between the distribution of MAs and the response to anti-VEGF therapy in patients with DME. The number of MAs was measured before anti-VEGF therapy in each region using fluorescein angiography, indocyanine green angiography (IA), and optical coherence tomography angiography. Patients with DME were divided into the responder and non-responder groups after three loading phases. Differences in the distribution of MAs between the groups were investigated. Pre-treatment IA revealed more MAs in the nasal area in the non-responder group than in the responder group (10.7 ± 10.7 and 5.7 ± 5.7, respectively, in the nasal macula) (1.4 ± 2.1 and 0.4 ± 0.7, respectively, in the nasal fovea). Whereas, pre-treatment FA and OCTA could not reveal significantly difference between the groups. Detection of MAs in the nasal macula using pre-treatment IA may indicate resistance to anti-VEGF therapy. We recommend the clinicians confirm the presence of MAs in the nasal macula, as shown by IA, as a predictor of therapeutic response to anti-VEGF therapy in patients with treatment naive DME.
Cirrus, Transport, and Mixing in the Tropical Upper Troposphere

AbstractThe impact of cloud radiative heating on transport time scales from the tropical upper troposphere to the stratosphere is studied in two-dimensional numerical simulations. Clouds are idealized as sources of radiative heating and are stochastically distributed in space and time. A spatial probability function constrains clouds to occur in only part of the domain to depict heterogeneously distributed clouds in the atmosphere.The transport time from the lower to upper boundaries (age of air) is evaluated with trajectories. The spectra of age of air obtained in the simulations are bimodal, with the first mode composed of trajectories that remain in the cloudy part of the domain during their passages from the lower to upper boundaries, and the second mode composed of the remaining trajectories that visit the cloud-free regions. For the first group of trajectories only, the mean age scales inversely with the time-mean radiative heating in cloudy air, and the one-dimensional advection–diffusion equation .
A Data-driven Pricing Scheme for Optimal Routing through Artificial Currencies

Mobility systems often suffer from a high price of anarchy due to the uncontrolled behavior of selfish users. This may result in societal costs that are significantly higher compared to what could be achieved by a centralized system-optimal controller. Monetary tolling schemes can effectively align the behavior of selfish users with the system-optimum. Yet, they inevitably discriminate the population in terms of income. Artificial currencies were recently presented as an effective alternative that can achieve the same performance, whilst guaranteeing fairness among the population. However, those studies were based on behavioral models that may differ from practical implementations. This paper presents a data-driven approach to automatically adapt artificial-currency tolls within repetitive-game settings. We first consider a parallel-arc setting whereby users commute on a daily basis from an individual origin to an individual destination, choosing a route in exchange of an artificial-currency price or reward, while accounting for the impact of the choices of the other users on travel discomfort. Second, we devise a model-based reinforcement learning controller that autonomously learns the optimal pricing policy by interacting with the proposed framework considering the closeness of the observed aggregate flows to a desired system-optimal distribution as a reward function. Our numerical results show that the proposed data-driven pricing scheme can effectively align the users' flows with the system optimum, significantly reducing the societal costs with respect to the uncontrolled flows (by about 15% and 25% depending on the scenario), and respond to environmental changes in a robust and efficient manner.
Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review

Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective screening method. Dozens of studies over the last few years have demonstrated that a variety of molecular biomarkers can be predicted from H&E alone using the advancements of deep learning: molecular alterations, genomic subtypes, protein biomarkers, and even the presence of viruses. This article reviews the diverse applications across cancer types and the methodology to train and validate these models on whole slide images. From bottom-up to pathologist-driven to hybrid approaches, the leading trends include a variety of weakly supervised deep learning-based approaches, as well as mechanisms for training strongly supervised models in select situations. While results of these algorithms look promising, some challenges still persist, including small training sets, rigorous validation, and model explainability. Biomarker prediction models may yield a screening method to determine when to run molecular tests or an alternative when molecular tests are not possible. They also create new opportunities in quantifying intratumoral heterogeneity and predicting patient outcomes.
Comment on"Amplified emission and lasing in photonic time crystals"

1Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea 2Center for Theoretical Physics of Complex Systems, Institute for Basic Science (IBS) Daejeon 34126, Republic of Korea 3Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea 4Department of Material Sciences and Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea 5Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea