Miracle-dz/UniFAR-FTUs huggingface dataset:
GWAS meta-analysis of 16 790 patients with Barrett’s oesophagus and oesophageal adenocarcinoma identifies 16 novel genetic risk loci and provides insights into disease aetiology beyond the single marker level

Objective Oesophageal cancer (EC) is the sixth leading cause of cancer-related deaths. Oesophageal adenocarcinoma (EA), with Barrett’s oesophagus (BE) as a precursor lesion, is the most prevalent EC subtype in the Western world. This study aims to contribute to better understand the genetic causes of BE/EA by leveraging genome wide association studies (GWAS), genetic correlation analyses and polygenic risk modelling. Design We combined data from previous GWAS with new cohorts, increasing the sample size to 16 790 BE/EA cases and 32 476 controls. We also carried out a transcriptome wide association study (TWAS) using expression data from disease-relevant tissues to identify BE/EA candidate genes. To investigate the relationship with reported BE/EA risk factors, a linkage disequilibrium score regression (LDSR) analysis was performed. BE/EA risk models were developed combining clinical/lifestyle risk factors with polygenic risk scores (PRS) derived from the GWAS meta-analysis. Results The GWAS meta-analysis identified 27 BE and/or EA risk loci, 11 of which were novel. The TWAS identified promising BE/EA candidate genes at seven GWAS loci and at five additional risk loci. The LDSR analysis led to the identification of novel genetic correlations and pointed to differences in BE and EA aetiology. Gastro-oesophageal reflux disease appeared to contribute stronger to the metaplastic BE transformation than to EA development. Finally, combining PRS with BE/EA risk factors improved the performance of the risk models. Conclusion Our findings provide further insights into BE/EA aetiology and its relationship to risk factors. The results lay the foundation for future follow-up studies to identify underlying disease mechanisms and improving risk prediction.
Natural and induced variations in transcriptional regulator genes result in low‐nicotine phenotypes in tobacco

SUMMARY In tobacco, the homologous ETHYLENE RESPONSE FACTOR (ERF) transcription factors ERF199 and ERF189 coordinate the transcription of multiple metabolic genes involved in nicotine biosynthesis. Natural alleles at the NIC1 and NIC2 loci greatly affect alkaloid accumulation and overlap with ERF199 and ERF189 in the tobacco genome, respectively. In this study, we identified several low‐nicotine tobacco varieties lacking ERF199 or ERF189 from a tobacco germplasm collection. We characterized the sequence of these new nic1 and nic2 alleles, as well as the previously defined alleles nic1‐1 and nic2‐1. Moreover, we examined the influence of different nic alleles on alkaloid contents and expression levels of genes related to nicotine biosynthesis. We also demonstrated that the deletion of a distal genomic region attenuates ERF199 expression, resulting in a moderately negative effect on the alkaloid phenotype. Our study provides new insights into the regulation of nicotine biosynthesis and novel genetic resources to breed low‐nicotine tobacco.
Uncovering translation roadblocks during the development of a synthetic tRNA

Abstract Ribosomes are remarkable in their malleability to accept diverse aminoacyl-tRNA substrates from both the same organism and other organisms or domains of life. This is a critical feature of the ribosome that allows the use of orthogonal translation systems for genetic code expansion. Optimization of these orthogonal translation systems generally involves focusing on the compatibility of the tRNA, aminoacyl-tRNA synthetase, and a non-canonical amino acid with each other. As we expand the diversity of tRNAs used to include non-canonical structures, the question arises as to the tRNA suitability on the ribosome. Specifically, we investigated the ribosomal translation of allo-tRNAUTu1, a uniquely shaped (9/3) tRNA exploited for site-specific selenocysteine insertion, using single-molecule fluorescence. With this technique we identified ribosomal disassembly occurring from translocation of allo-tRNAUTu1 from the A to the P site. Using cryo-EM to capture the tRNA on the ribosome, we pinpointed a distinct tertiary interaction preventing fluid translocation. Through a single nucleotide mutation, we disrupted this tertiary interaction and relieved the translation roadblock. With the continued diversification of genetic code expansion, our work highlights a targeted approach to optimize translation by distinct tRNAs as they move through the ribosome.
Revisiting socio-economic inequalities in sedentary leisure time in Sweden: An intersectional analysis of individual heterogeneity and discriminatory accuracy (AIHDA)

Aims: Swedish public health reports have repeatedly provided information about socio-economic inequalities in sedentary leisure time, despite that, in the interest of health equity, physical activity should be equally distributed in the population. Such public health reports, however, neither consider the intersection of multiple socio-demographic factors nor the individual heterogeneity around group averages. Drawing on intersectionality theory, this study aimed to revisit previous findings on sedentary leisure time from Swedish public health surveys and demonstrate how the analysis of individual heterogeneity and discriminatory accuracy (AIHDA) can be used for analysing complex health inequalities. Methods: Using data from Swedish national public health surveys (2004–2015), we applied the AIHDA to define 72 intersectional groups by categories of age, gender, educational achievement, migration status and household composition. We then calculated (a) the absolute and relative risk of sedentary leisure time and (b) the discriminatory accuracy (DA) of the intersectional grouping. Results: The average risk of sedentary leisure time ranged from 5.8% among native-born, highly educated, young women living alone to 41.0% among immigrated young men, living alone, with low education. The risk was higher in strata comprising immigrated people with low education and lower in strata including native-born, highly educated people. However, the DA of the grouping was poor, indicating a substantial overlap of individual risk between groups. Conclusions: Using the AIHDA and drawing on intersectionality, this study provides an improved mapping of the socio-economic distribution of sedentary leisure time in Sweden, with the poor DA suggesting universal rather than targeted physical activity interventions.
Asian American Vaccination, Testing, and Other Healthcare Knowledge & Behaviors during COVID-19, A Systematic Review

ABSTRACT Novel COVID-19 variants continue to endanger global public health. Increasing COVID-19 vaccination, healthcare-related preventative behaviors, and general knowledge rates are all critical in halting COVID-19 spread. We evaluated Asian American COVID-19 healthcare-related behaviors and knowledge, due to the dearth of knowledge in this area and the unique social factor of COVID-19 related discrimination; discriminatory acts during the pandemic may play a role in COVID-19 related behavior adherence. Following PRISMA-P protocol, we conducted a systematic review. The search strategy combined synonyms of health-care behaviors and knowledge. Reviewers synthesized key themes across articles and assessed studies utilizing modified Newcastle–Ottawa criteria. Of the 2,518 articles, 32 were selected. Asian Americans reported greater COVID-19 vaccination willingness and decreased COVID-19 testing relative to other racial groups. Common COVID-19 vaccination concerns included vaccination side effects, long-term safety, and distrust of COVID-19 information sources. Asian Americans had high COVID-19 preventative behavior rates including mask-wearing, handwashing, and social isolation compared to other ethnic groups. Asian Americans, conversely, had lower COVID-19-related healthcare knowledge and telemedicine adoption levels relative to other participants. This systematic review informs public health officials and clinicians of COVID-19 related healthcare knowledge and behaviors in the Asian American population. Equipped with this knowledge, public health officials can better target messaging about vaccine safety concerns to the Asian American community and recognize the importance of tailoring COVID-19 educational materials to the heterogeneous Asian American subpopulations. This systematic review also provides insight into the unique telemedicine challenges physicians may face when engaging with Asian American patients.
Nearest Neighbour Corner Points Matching Detection Algorithm

Accurate detection towards the corners plays an important part in camera calibration. To deal with the instability and inaccuracies of present corner detection algorithm, the nearest neighbour corners match- ing detection algorithms was brought forward. First, it dilates the binary image of the photographed pictures, searches and reserves quadrilateral outline of the image. Second, the blocks which accord with chess-board-corners are classified into a class. If too many blocks in class, it will be deleted; if not, it will be added, and then let the midpoint of the two vertex coordinates be the rough position of corner. At last, it precisely locates the position of the corners. The Experimental results have shown that the algorithm has obvious ad- vantages on accuracy and validity in corner detection, and it can give security for camera calibration in traffic accident measurement.
Point Cloud Attacks in Graph Spectral Domain: When 3D Geometry Meets Graph Signal Processing

With the increasing attention in various 3D safety-critical applications, point cloud learning models have been shown to be vulnerable to adversarial attacks. Although existing 3D attack methods achieve high success rates, they delve into the data space with point-wise perturbation, which may neglect the geometric characteristics. Instead, we propose point cloud attacks from a new perspective-the graph spectral domain attack, aiming to perturb graph transform coefficients in the spectral domain that correspond to varying certain geometric structures. Specifically, leveraging on graph signal processing, we first adaptively transform the coordinates of points onto the spectral domain via graph Fourier transform (GFT) for compact representation. Then, we analyze the influence of different spectral bands on the geometric structure, based on which we propose to perturb the GFT coefficients via a learnable graph spectral filter. Considering the low-frequency components mainly contribute to the rough shape of the 3D object, we further introduce a low-frequency constraint to limit perturbations within imperceptible high-frequency components. Finally, the adversarial point cloud is generated by transforming the perturbed spectral representation back to the data domain via the inverse GFT. Experimental results demonstrate the effectiveness of the proposed attack in terms of both the imperceptibility and attack success rates.
Berry-curvature-induced linear magnetotransport in magnetic Weyl semimetals

Magnetotransport such as the giant magnetoresistance and Hall effect lies at the heart of fundamental physics and technologies. Recently, some experiments have clearly demonstrated linear magnetotransport (LMT) proportional to magnetic field but the underlying physical mechanism is still unclear. In this work, we show that Berry curvature effect is a new mechanism dominating the LMT. The Berry-curvature-induced LMT widely exists in 66 out of 122 magnetic point groups. For typical magnetic Weyl semimetals Co$_3$Sn$_2$S$_2$ and ferromagnetic MnBi$_2$Te$_4$, Berry curvature induces LMT conductivities reaching orders of $10^4$ and $10^2$ ${\rm \Omega^{-1}m^{-1}}$ per tesla, respectively, which are tunable through magnetization canting induced by moderate magnetic fields. We further reveal that Berry-curvature-induced LMT can be detected by Hall effect and especially intrinsic magnetoresistance exceeding $100\%$ per tesla insensitive to the sample quality. Our results agree with recent experiments and uncover the important role of Berry curvature in LMT.
Modelling Social Context for Fake News Detection: A Graph Neural Network Based Approach

Detection of fake news is crucial to ensure the authenticity of information and maintain the news ecosystem's reliability. Recently, there has been an increase in fake news content due to the recent proliferation of social media and fake content generation techniques such as Deep-Fake. The majority of the existing modalities of fake news detection focus on content-based approaches. However, most of these techniques fail to deal with ultra-realistic synthesized media produced by generative models. Our recent studies find that the propagation characteristics of authentic and fake news are distinguishable, irrespective of their modalities. In this regard, we have investigated the auxiliary information based on social context to detect fake news. This paper has analyzed the social context of fake news detection with a hybrid graph neural network-based approach. This hybrid model is based on integrating a graph neural network on the propagation of news and bi-directional encoder representations from the transformers model on news content to learn the text features. Thus this proposed approach learns the content as well as the context features and hence able to outperform the baseline models with an f1-score of 0.91 on Politifact and 0.93 on the Gossipcop dataset, respectively
Planning and Learning: A Review of Methods involving Path-Planning for Autonomous Vehicles

This short review aims to make the reader familiar with state-of-the-art works relating to planning, scheduling and learning. First, we study state-of-the-art planning algorithms. We give a brief introduction of neural networks. Then we explore in more detail graph neural networks, a recent variant of neural networks suited for processing graph-structured inputs. We describe briefly the concept of reinforcement learning algorithms and some approaches designed to date. Next, we study some successful approaches combining neural networks for path-planning. Lastly, we focus on temporal planning problems with uncertainty.
Are Neighbors Enough? Multi-Head Neural n-gram can be Alternative to Self-attention

Impressive performance of Transformer has been attributed to self-attention, where dependencies between entire input in a sequence are considered at every position. In this work, we reform the neural $n$-gram model, which focuses on only several surrounding representations of each position, with the multi-head mechanism as in Vaswani et al.(2017). Through experiments on sequence-to-sequence tasks, we show that replacing self-attention in Transformer with multi-head neural $n$-gram can achieve comparable or better performance than Transformer. From various analyses on our proposed method, we find that multi-head neural $n$-gram is complementary to self-attention, and their combinations can further improve performance of vanilla Transformer.
Entanglement between a trapped-ion qubit and a 780-nm photon via quantum frequency conversion

Future quantum networks will require the ability to produce matter-photon entanglement at photon frequencies not naturally emitted from the matter qubit. This allows for a hybrid network architecture, where these photons can couple to other tools and quantum technologies useful for tasks such as multiplexing, routing, and storage, but which operate at wavelengths different from that of the matter qubit source, while also reducing network losses. Here, we demonstrate entanglement between a trapped ion and a 780 nm photon, a wavelength which can interact with neutral-Rb-based quantum networking devices. A single barium ion is used to produce 493 nm photons, entangled with the ion, which are then frequency converted to 780 nm while preserving the entanglement. We generate ion-photon entanglement with fidelities $\geq$ 0.93(2) and $\geq$ 0.84(2) for 493 nm and 780 nm photons respectively with the fidelity drop arising predominantly from a reduction in the signal-noise of our detectors at 780 nm compared with at 493 nm.
Trajectory Planning of Cellular-Connected UAV for Communication-Assisted Radar Sensing

Being a key technology for beyond fifth-generation wireless systems, joint communication and radar sensing (JCAS) utilizes the reflections of communication signals to detect foreign objects and deliver situational awareness. A cellular-connected unmanned aerial vehicle (UAV) is uniquely suited to form a mobile bistatic synthetic aperture radar (SAR) with its serving base station (BS) to sense over large areas with superb sensing resolutions at no additional requirement of spectrum. This paper designs this novel BS-UAV bistatic SAR platform, and optimizes the flight path of the UAV to minimize its propulsion energy and guarantee the required sensing resolutions on a series of interesting landmarks. A new trajectory planning algorithm is developed to convexify the propulsion energy and resolution requirements by using successive convex approximation and block coordinate descent. Effective trajectories are obtained with a polynomial complexity. Extensive simulations reveal that the proposed trajectory planning algorithm outperforms significantly its alternative that minimizes the flight distance of cellular-aided sensing missions in terms of energy efficiency and effective consumption fluctuation. The energy saving offered by the proposed algorithm can be as significant as 55%.
Fault Detection and Classification of Aerospace Sensors using a VGG16-based Deep Neural Network

Compared with traditional model-based fault detection and classiﬁcation (FDC) methods, deep neural networks (DNN) prove to be eﬀective for the aerospace sensors FDC problems. However, time being consumed in training the DNN is excessive, and explainability analysis for the FDC neural network is still under-whelming. A concept known as imageﬁcation-based intelligent FDC has been studied in recent years. This concept advocates to stack the sensors measurement data into an image format, the sensors FDC issue is then transformed to abnormal regions detection problem on the stacked image, which may well borrow the recent advances in the machine vision vision realm. Although promising results have been claimed in the imageﬁcation-based intelligent FDC researches, due to the low size of the stacked image, small convolutional kernels and shallow DNN layers were used, which hinders the FDC performance. In this paper, we ﬁrst propose a data augmentation method which inﬂates the stacked image to a larger size (correspondent to the VGG16 net developed in the machine vision realm). The FDC neural network is then trained via ﬁne-tuning the VGG16 directly. To truncate and compress the FDC net size (hence its running time), we perform model pruning on the ﬁne-tuned net. Class activation mapping (CAM) method is also adopted for explainability analysis of the FDC net to verify its internal operations. Via data augmentation, ﬁne-tuning from VGG16, and model pruning, the FDC net developed in this paper claims an FDC accuracy 98.90% across 4 aircraft at 5 ﬂight conditions (running time 26 ms). The CAM results also verify the FDC net w.r. t. its internal operations.
Lecture Notes on Neural Information Retrieval

These lecture notes focus on the recent advancements in neural information retrieval, with particular emphasis on the systems and models exploiting transformer networks. These networks, originally proposed by Google in 2017, have seen a large success in many natural language processing and information retrieval tasks. While there are many fantastic textbook on information retrieval and natural language processing as well as specialised books for a more advanced audience, these lecture notes target people aiming at developing a basic understanding of the main information retrieval techniques and approaches based on deep learning. These notes have been prepared for a IR graduate course of the MSc program in Artificial Intelligence and Data Engineering at the University of Pisa, Italy.
Magnetically charged Euler-Heisenberg black holes with scalar hair

We study the Einstein-Euler-Heisenberg theory in the presence of a self interacting scalar field, minimally coupled to gravity. We solve analytically the field equations for the magnetically charged case and we obtain novel magnetically charged hairy black holes. The scalar field dresses the black hole with a secondary scalar hair. The hairy black hole develops three horizons when Euler-Heisenberg parameter and the magnetic charge are small and the horizon radius is getting large when the scalar charge and the gravitational mass are large. The presence of matter and the magnetic field outside the horizon of the black hole increases the temperature only for small black holes. Calculating the heat capacity we show that the asymptotically AdS Euler-Heisenberg hairy black hole undergoes a second order phase transition and then it is stabilized. Also the weak energy condition is violated for the asymptotically AdS Euler-Heisenberg hairy black hole.
Dynamic Shielding for Reinforcement Learning in Black-Box Environments

It is challenging to use reinforcement learning (RL) in cyber-physical systems due to the lack of safety guarantees during learning. Although there have been various proposals to reduce undesired behaviors during learning, most of these techniques require prior system knowledge, and their applicability is limited. This paper aims to reduce undesired behaviors during learning without requiring any prior system knowledge. We propose dynamic shielding : an extension of a model-based safe RL technique called shielding using data-driven automata learning . The dynamic shielding technique constructs an approximate system model in parallel with RL using a variant of the RPNI algorithm and sup-presses undesired explorations due to the shield constructed from the learned model. Through this combination, potentially unsafe actions can be foreseen before the agent experiences them. Experiments show that our dynamic shield signiﬁcantly decreases the number of undesired events during training. and experiment results.
Beyond Visuals: Examining the Experiences of Geoscience Professionals With Vision Disabilities in Accessing Data Visualizations

Data visualizations are ubiquitous in all disciplines and have become the primary means of analysing data and communicating insights. However, the predominant reliance on visual encoding of data con-tinues to create accessibility barriers for people who are blind/vision impaired resulting in their under representation in Science, Tech-nology, Engineering and Mathematics (STEM) disciplines. This research study seeks to understand the experiences of professionals who are blind/vision impaired in one such STEM discipline (geo-sciences) in accessing data visualizations. In-depth, semi-structured interviews with seven professionals were conducted to examine the accessibility barriers and areas for improvement to inform acces-sibility research pertaining to data visualizations through a socio-technical lens. A reflexive thematic analysis revealed the negative impact of visualizations in influencing their career path, lack of data exploration tools for research, barriers in accessing works of peers and mismatched pace of visualization and accessibility research. The article also includes recommendations from the participants to address some of these accessibility barriers.