Miracle-dz/UniFAR-FTUs huggingface dataset:
dRNASb: a systems biology approach to decipher dynamics of host–pathogen interactions using temporal dual RNA-seq data

Infection triggers a dynamic cascade of reciprocal events between host and pathogen wherein the host activates complex mechanisms to recognise and kill pathogens while the pathogen often adjusts its virulence and fitness to avoid eradication by the host. The interaction between the pathogen and the host results in large-scale changes in gene expression in both organisms. Dual RNA-seq, the simultaneous detection of host and pathogen transcripts, has become a leading approach to unravelling complex molecular interactions between the host and the pathogen and is particularly informative for intracellular organisms. The amount of in vitro and in vivo dual RNA-seq data is rapidly growing, which demands computational pipelines to effectively analyse such data. In particular, holistic, systems-level, and temporal analyses of dual RNA-seq data are essential to enable further insights into the host–pathogen transcriptional dynamics and potential interactions. Here, we developed an integrative network-driven bioinformatics pipeline, dRNASb, a systems biology-based computational pipeline to analyse temporal transcriptional clusters, incorporate molecular interaction networks (e.g. protein-protein interactions), identify topologically and functionally key transcripts in host and pathogen, and associate host and pathogen temporal transcriptome to decipher potential between-species interactions. The pipeline is applicable to various dual RNA-seq data from different species and experimental conditions. As a case study, we applied dRNASb to analyse temporal dual RNA-seq data of Salmonella -infected human cells, which enabled us to uncover genes contributing to the infection process and their potential functions and to identify putative associations between host and pathogen genes during infection. Overall, dRNASb has the potential to identify key genes involved in bacterial growth or host defence mechanisms for future uses as therapeutic targets.
Effects of saccadic eye movements on episodic & semantic memory fluency in older and younger participants

ABSTRACT Research has demonstrated that performing a sequence of saccadic horizontal eye movements prior to retrieval facilitates performance on tests of episodic memory. This has been observed in both laboratory tasks of retention and autobiographical memory. To date, the work has centred on performance in younger individuals. This paper extends previous investigations by examining the effects of saccadic eye movements in older persons. Autobiographical episodic and semantic memory fluency was assessed in younger (age range 18–35, mean = 22.50), and older (age range 55–87, mean = 70.35) participants following saccadic (vs. fixation control) manipulations. The main effects of eye movements and age were found for episodic autobiographical memory (greater fluency after eye movements and in younger participants). Semantic autobiographical memory showed a main effect of age (greater fluency in younger participants), whereas general semantic memory showed no effect of age or eye movement. These findings indicate that saccadic horizontal eye movements can enhance episodic personal memory in older individuals. This has implications as a technique to improve autobiographical recollection in the elderly and as an adjunct in reminiscence therapy.
Exploring brainstem auditory evoked potentials and mental development index as early indicators of autism spectrum disorders in high‐risk infants

This study of infants from Hubei Province, China examined brainstem auditory evoked potentials (BAEP) and mental development index (MDI) as possible early indicators associated with autism spectrum disorders (ASD). The 34 ASD cases and 102 controls who had recovered from perinatal conditions were matched for age, sex, gestational age, birth weight and maternal age. BAEP absolute latencies (AL) I, III, V and interpeak latencies (IPL) I‐III, III‐V, I‐V were compared in ASD cases and controls at ages 1, 3 and 6 months. MDI scores were compared in these infants from 1 month to 2 years old. Multiple logistic regression analysis was performed to test associations among ASD, BAEP and MDI. Results showed BAEP AL I, V and IPL III‐V prolonged in the ASD group (p < 0.001), and MDI scores in ASD cases sharply declining from 12 to 24 months (p < 0.001). Regression analysis revealed odds ratios (OR) indicating that ASD was likely associated with abnormal values of BAEP AL I at 1 and 3 months (ORAL I: 4.27; ORAL I: 4.13), and AL V at 6 months (ORAL V: 7.85). Lower MDI scores (MDI < 80) in infants at 1, 3, and 6 months were likely associated with ASD (ORMDI: 2.58; ORMDI: 3.83; ORMDI: 4.87). These data show that abnormal BAEP values and low MDI scores are independent factors associated with ASD, and that monitoring of BAEP and MDI during infancy might facilitate screening for ASD development.
Semirational bioengineering of AAV vectors with increased potency and specificity for systemic gene therapy of muscle disorders

Bioengineering of viral vectors for therapeutic gene delivery is a pivotal strategy to reduce doses, facilitate manufacturing, and improve efficacy and patient safety. Here, we engineered myotropic adeno-associated viral (AAV) vectors via a semirational, combinatorial approach that merges AAV capsid and peptide library screens. We first identified shuffled AAVs with increased specificity in the murine skeletal muscle, diaphragm, and heart, concurrent with liver detargeting. Next, we boosted muscle specificity by displaying a myotropic peptide on the capsid surface. In a mouse model of X-linked myotubular myopathy, the best vectors—AAVMYO2 and AAVMYO3—prolonged survival, corrected growth, restored strength, and ameliorated muscle fiber size and centronucleation. In a mouse model of Duchenne muscular dystrophy, our lead capsid induced robust microdystrophin expression and improved muscle function. Our pipeline is compatible with complementary AAV genome bioengineering strategies, as demonstrated here with two promoters, and could benefit many clinical applications beyond muscle gene therapy.
The whole-cell pertussis vaccine imposes a broad effector B cell response in mouse heterologous prime-boost settings

ÍSince the introduction of new generation pertussis vaccines, resurgence of pertussis has been observed in many developed countries. Former whole-cell pertussis (wP) vaccines are able to protect against disease and transmission but have been replaced in several industrialized countries because of their reactogenicity and adverse effects. Current acellular pertussis (aP) vaccines, made of purified proteins of Bordetella pertussis, are efficient at preventing disease but fail to induce long-term protection from infection. While the systemic and mucosal T cell immunity induced by the 2 types of vaccines has been well described, much less is known concerning B cell responses. Taking advantage of an inducible activation-induced cytidine deaminase fate-mapping mouse model, we compared effector and memory B cells induced by the 2 classes of vaccines and showed that a stronger and broader memory B cell and plasma cell response was achieved by a wP prime. We also observed that homologous or heterologous vaccine combinations that include at least 1 wP administration, even as a booster dose, were sufficient to induce this broad effector response, thus highlighting its dominant imprint on the B cell profile. Finally, we describe the settlement of memory B cell populations in the lung following subcutaneous wP prime vaccination.
Deficiency of inhibitory TLR4 homolog RP105 exacerbates fibrosis

Activation of TLR4 by its cognate damage-associated molecular patterns (DAMPs) elicits potent profibrotic effects and myofibroblast activation in systemic sclerosis (SSc), while genetic targeting of TLR4 or its DAMPs in mice accelerates fibrosis resolution. To prevent aberrant DAMP/TLR4 activity, a variety of negative regulators evolved to dampen the magnitude and duration of the signaling. These include radioprotective 105 kDa (RP105), a transmembrane TLR4 homolog that competitively inhibits DAMP recognition of TLR4, blocking TLR4 signaling in immune cells. The role of RP105 in TLR4-dependent fibrotic responses in SSc is unknown. Using unbiased transcriptome analysis of skin biopsies, we found that levels of both TLR4 and its adaptor protein MD2 were elevated in SSc skin and significantly correlated with each other. Expression of RP105 was negatively associated with myofibroblast differentiation in SSc. Importantly, RP105-TLR4 association was reduced, whereas TLR4-TLR4 showed strong association in fibroblasts from patients with SSc, as evidenced by PLA assays. Moreover, RP105 adaptor MD1 expression was significantly reduced in SSc skin biopsies and explanted SSc skin fibroblasts. Exogenous RP105-MD1 abrogated, while loss of RP105 exaggerated, fibrotic cellular responses. Importantly, ablation of RP105 in mice was associated with augmented TLR4 signaling and aggravated skin fibrosis in complementary disease models. Thus, we believe RP105-MD1 to be a novel cell-intrinsic negative regulator of TLR4-MD2–driven sustained fibroblast activation, representing a critical regulatory network governing the fibrotic process. Impaired RP105 function in SSc might contribute to persistence of progression of the disease.
Estimation and prediction of convection-diffusion-reaction systems from point measurement

Different procedures with respect to estimation and prediction of systems characterized by convection, diffusion and reactions on the basis of point measurement data, have been studied. Two applications of these convection-diffusion-reaction (CDR) systems have been used as a case study of the proposed estimation and prediction methods. One is a climate room for bulk storage of agricultural produce (Case A) and the other is a UV disinfection process used in water treatment, food industry and greenhouse cultivation (Case B). An essential step in the implementation of estimation and prediction for these types of systems is model reduction. The proposed procedures not only differ by the nature of the estimation and prediction method, but also with respect to early or late model reduction. In the context of this thesis, early model reduction encompasses approximation of the infinite-dimensional system to finite-dimensional form before estimation and prediction is worked out, whereas in late model reduction, the approximation step is applied after synthesis of an infinite-dimensional estimator (observer) or predictor. The first contribution of this thesis is an identification approach with output-error (OE) modelling techniques that links important physical parameters in a reduced order model to the OE parameters. This technique is illustrated by Case A, using real experimental data. Local parametric sensitivity analysis shows how physical parameters affect the dominant time constant in an identified, first order output-error model. The second contribution is a realization approach from a discrete-time linear finite-dimensional system affine in parameters to linear regressive form. The resulting linear regression form allows the formulation of a convex parameter estimation and prediction problem. Such an approach is attractive for reduced order, discretized CDR models with specific boundary conditions. For such models, it turns out that the response and regressor functions can be formulated explicitly as functions of the number of compartments, sensor and actuator location. Once available, they can further be used for a priori identifiability checks, parameter and input sensitivity analysis. Results are illustrated by two diffusion examples with different boundary conditions. Finally, the last contributions are a static and a dynamic boundary observer for CDR systems. Detectability and observability results aid in the design of a static gain boundary observer of an infinite-dimensional system where only boundary measurements are available. The dynamic observer is synthesized by formulating an H∞-filtering problem in a linear fractional transformation framework in order to cope with disturbances on the input and output of the system. Both observer synthesis approaches are illustrated by a CDR model of Case B.
Tuning structural properties, morphology and magnetic characteristics of nanostructured Ni‐Co‐Fe/ITO ternary alloys by galvanostatic pretreatment process

In this research, the structural properties, surface morphology, and magnetic characteristics of nanostructured ternary ferromagnetic alloys grown by a cost‐effective and effortless two‐step electrochemical deposition method on indium tin oxide (ITO) substrates with and without a galvanostatic pretreatment process (GPP) were examined. The GPP was applied at various pretreatment current densities (PCDs) such as −10, −20, and − 30 mA/cm2. The effect of the PCD on the Ni, Co, and Fe contents is found to be insignificant and all resultant Ni‐Co‐Fe thin films show an abnormal co‐deposition. The films have nano‐sized crystallites ranging from 17.3 to 19.6 nm and showed a face‐centered cubic structure with the [111] preferential growth. Compared to the non‐GPP applied Ni‐Co‐Fe film, growing the ternary Ni‐Co‐Fe film on ITO at the PCD of −30 mA/cm2 causes an improvement in the crystal quality and a reduction in the particle size from 150 ± 50 to 70 ± 20 nm. A decrement in the surface roughness and coercivity was also achieved by applying the GPP at the PCD of −30 mA/cm2, but the opposite is true for the GPP performed at the PCD of −10 mA/cm2. The GPP has an effect on the magnetic Squareness Ratio (SQR), but the influence of the PCD on the SQR parameter is negligible. The obtained findings reveal that the properties of the Ni‐Co‐Fe/ITO ternary alloys can be tuned through the GPP applied in various PCDs.
Unsupervised Multi-Latent Space RL Framework for Video Summarization in Ultrasound Imaging

The COVID-19 pandemic has highlighted the need for a tool to speed up triage in ultrasound scans and provide clinicians with fast access to relevant information. To this end, we propose a new unsupervised reinforcement learning (RL) framework with novel rewards to facilitate unsupervised learning by avoiding tedious and impractical manual labelling for summarizing ultrasound videos. The proposed framework is capable of delivering video summaries with classification labels and segmentations of key landmarks which enhances its utility as a triage tool in the emergency department (ED) and for use in telemedicine. Using an attention ensemble of encoders, the high dimensional image is projected into a low dimensional latent space in terms of: a) reduced distance with a normal or abnormal class (classifier encoder), b) following a topology of landmarks (segmentation encoder), and c) the distance or topology agnostic latent representation (autoencoders). The summarization network is implemented using a bi-directional long short term memory (Bi-LSTM) which utilizes the latent space representation from the encoder. Validation is performed on lung ultrasound (LUS), that typically represent potential use cases in telemedicine and ED triage acquired from different medical centers across geographies (India and Spain). The proposed approach trained and tested on 126 LUS videos showed high agreement with the ground truth with an average precision of over 80% and average $\boldsymbol{F}_{{1}}$ score of well over $\boldsymbol{44 \pm 1.7 \%}$. The approach resulted in an average reduction in storage space of 77% which can ease bandwidth and storage requirements in telemedicine.
Maritime autonomous surface ships: can we learn from unmanned aerial vehicle incidents using the perceptual cycle model?

Abstract Interest in Maritime Autonomous Surface Ships (MASS) is increasing as it is predicted that they can bring improved safety, performance and operational capabilities. However, their introduction is associated with a number of enduring Human Factors challenges (e.g. difficulties monitoring automated systems) for human operators, with their ‘remoteness’ in shore-side control centres exacerbating issues. This paper aims to investigate underlying decision-making processes of operators of uncrewed vehicles using the theoretical foundation of the Perceptual Cycle Model (PCM). A case study of an Unmanned Aerial Vehicle (UAV) accident has been chosen as it bears similarities to the operation of MASS through means of a ground-based control centre. Two PCMs were developed; one to demonstrate what actually happened and one to demonstrate what should have happened. Comparing the models demonstrates the importance of operator situational awareness, clearly defined operator roles, training and interface design in making decisions when operating from remote control centres. Practitioner Summary: To investigate underlying decision-making processes of operators of uncrewed vehicles using the Perceptual Cycle Model, by using an UAV accident case study. The findings showed the importance of operator situational awareness, clearly defined operator roles, training and interface design in making decisions when monitoring uncrewed systems from remote control centres.
Three perspectives on the prediction of chemical effects in ecosystems

The increasing production, use and emission of synthetic chemicals into the environment represents a major driver of global change. The large number of synthetic chemicals, limited knowledge on exposure patterns and effects in organisms and their interaction with other global change drivers hamper the prediction of effects in ecosystems. However, recent advances in biomolecular and computational methods are promising to improve our capacity for prediction. We delineate three idealised perspectives for the prediction of chemical effects: the suborganismal, organismal and ecological perspective, which are currently largely separated. Each of the outlined perspectives includes essential and complementary theories and tools for prediction but captures only part of the phenomenon of chemical effects. Links between the perspectives may foster predictive modelling of chemical effects in ecosystems and extrapolation between species. A major challenge for the linkage is the lack of data sets simultaneously covering different levels of biological organisation (here referred to as biological levels) as well as varying temporal and spatial scales. Synthesising the three perspectives, some central aspects and associated types of data seem particularly necessary to improve prediction. First, suborganism‐ and organism‐level responses to chemicals need to be recorded and tested for relationships with chemical groups and organism traits. Second, metrics that are measurable at many biological levels, such as energy, need to be scrutinised for their potential to integrate across levels. Third, experimental data on the simultaneous response over multiple biological levels and spatiotemporal scales are required. These could be collected in nested and interconnected micro‐ and mesocosm experiments. Lastly, prioritisation of processes involved in the prediction framework needs to find a balance between simplification and capturing the essential complexity of a system. For example, in some cases, eco‐evolutionary dynamics and interactions may need stronger consideration. Prediction needs to move from a static to a real‐world eco‐evolutionary view.
Batch Bayesian optimisation via density-ratio estimation with guarantees

Bayesian optimisation (BO) algorithms have shown remarkable success in applications involving expensive black-box functions. Traditionally BO has been set as a sequential decision-making process which estimates the utility of query points via an acquisition function and a prior over functions, such as a Gaussian process. Recently, however, a reformulation of BO via density-ratio estimation (BORE) allowed reinterpreting the acquisition function as a probabilistic binary classifier, removing the need for an explicit prior over functions and increasing scalability. In this paper, we present a theoretical analysis of BORE's regret and an extension of the algorithm with improved uncertainty estimates. We also show that BORE can be naturally extended to a batch optimisation setting by recasting the problem as approximate Bayesian inference. The resulting algorithms come equipped with theoretical performance guarantees and are assessed against other batch and sequential BO baselines in a series of experiments.
The SpeakIn System Description for CNSRC2022

This report describes our speaker veriﬁcation systems for the tasks of the CN-Celeb Speaker Recognition Challenge 2022 (CNSRC 2022). This challenge includes two tasks, namely speaker veriﬁcation(SV) and speaker retrieval(SR). The SV task involves two tracks: ﬁxed track and open track . In the ﬁxed track, we only used CN-Celeb.T as the training set. For the open track of the SV task and SR task, we added our open-source audio data. The ResNet-based, RepVGG-based, and TDNN-based architectures were developed for this challenge. Global statistic pooling structure and MQMHA pooling structure were used to aggregate the frame-level features across time to obtain utterance-level representation. We adopted AM-Softmax and AAM-Softmax combined with the Sub-Center method to clas-sify the resulting embeddings. We also used the Large-Margin Fine-Tuning strategy to further improve the model performance. In the backend, Sub-Mean and AS-Norm were used. In the SV task ﬁxed track, our system was a fusion of ﬁve models, and two models were fused in the SV task open track. And we used a single system in the SR task. Our approach leads to superior performance and comes the 1st place in the open track of the SV task, the 2nd place in the ﬁxed track of the SV task, and the 3rd place in the SR task.
Caught in the crossfire: Fears of Chinese–American scientists

Significance Our study reveals the widespread fear among scientists of Chinese descent in the United States arising from conducting routine research and academic activities. If this fear is not alleviated, there are significant risks of underutilization of scientific talent as well as losing scientific talent to China and other countries. Addressing the fear of US-based scientists of Chinese descent and making the American academic environment welcoming and attractive to all will help retain and attract scientific talent and strengthen the US global leadership in science and technology in the long run.
Convolutional Bayesian Kernel Inference for 3D Semantic Mapping

Robotic perception is currently at a cross-roads between modern methods, which operate in an efficient latent space, and classical methods, which are mathematically founded and provide interpretable, trustworthy results. In this paper, we introduce a Convolutional Bayesian Kernel Inference (Con-vBKI) layer which learns to perform explicit Bayesian inference within a depthwise separable convolution layer to maximize efficency while maintaining reliability simultaneously. We apply our layer to the task of real-time 3D semantic mapping, where we learn semantic-geometric probability distributions for LiDAR sensor information and incorporate semantic predictions into a global map. We evaluate our network against state-of-the-art semantic mapping algorithms on the KITTI data set, demonstrating improved latency with comparable semantic label inference results.