Miracle-dz/UniFAR-FTUs huggingface dataset:
Insight into the Mechanism of the OH-induced Reaction of Ketoprofen: A Combined DFT Simulation and Experimental Study

The OH-induced reactions are the most important reactions for the degradation of pharmaceutical and personal care proucts (PPCPs) in advanced oxidation process (AOP) systems. In this work, the transformation mechanisms of a typical PPCP, ketprofen (KP), are investigated in detail by combined density functional theory (DFT) simulations and laboratory experiments. We find that Habstraction pathways are preferred in the OH-initiated reactions, and the influence of the hydrogen bond between the OH radical and the carboxyl group cannot be neglected. The subsequent decarboxylation and decomposition reactions are likely to occur. In addition, under highly acidic condition, the protonation of the carbonyl group in KP or in other benzophenone-like compounds might inhibit the decomposition reactions. Meanwhile, the simulation results are well supported by the Fenton experiments.
Bioactivity and efficacy of a hyperimmune bovine colostrum product- Travelan, against shigellosis in a non-Human primate model (Macaca mulatta)

Infectious diarrhea is a World Health Organization public health priority area due to the lack of effective vaccines and an accelerating global antimicrobial resistance crisis. New strategies are urgently needed such as immunoprophylactic for prevention of diarrheal diseases. Hyperimmune bovine colostrum (HBC) is an established and effective prophylactic for infectious diarrhea. The commercial HBC product, Travelan® (Immuron Ltd, Australia) targets multiple strains of enterotoxigenic Escherichia coli (ETEC) is highly effective in preventing diarrhea in human clinical studies. Although Travelan® targets ETEC, preliminary studies suggested cross-reactivity with other Gram-negative enteric pathogens including Shigella and Salmonella species. For this study we selected an invasive diarrheal/dysentery-causing enteric pathogen, Shigella, to evaluate the effectiveness of Travelan®, both in vitro and in vivo. Here we demonstrate broad cross-reactivity of Travelan® with all four Shigella spp. (S. flexneri, S. sonnei, S. dysenteriae and S. boydii) and important virulence factor Shigella antigens. Naïve juvenile rhesus macaques (NJRM) were randomized, 8 dosed with Travelan® and 4 with a placebo intragastrically twice daily over 6 days. All NJRM were challenged with S. flexneri 2a strain 2457T on the 4th day of treatment and monitored for diarrheal symptoms. All placebo-treated NJRM displayed acute dysentery symptoms within 24–36 hours of challenge. Two Travelan®-treated NJRM displayed dysentery symptoms and six animals remained healthy and symptom-free post challenge; resulting in 75% efficacy of prevention of shigellosis (p = 0.014). These results strongly indicate that Travelan® is functionally cross-reactive and an effective prophylactic for shigellosis. This has positive implications for the prophylactic use of Travelan® for protection against both ETEC and Shigella spp. diarrheal infections. Future refinement and expansion of pathogens recognized by HBC including Travelan® could revolutionize current management of gastrointestinal infections and outbreaks in travelers’ including military, peacekeepers, humanitarian workers and in populations living in endemic regions of the world.
Usefulness of the Quick-Sepsis Organ Failure Assessment Score in Cardiovascular Intensive Care Unit to Predict Prognosis in Acute Coronary Syndrome

Triage of patients with acute coronary syndrome (ACS) at high risk of in-hospital complications is essential. In this study, we evaluated the quick sepsis organ failure assessment (qSOFA) score as a tool for predicting the prognosis of 964 patients admitted to the cardiovascular intensive care unit (CICU) with ACS over a 4-year period. In total, out of 964 patients included, with a percentage of 4.6% for 30-day mortality. The risk of 30-day mortality was independently associated with qSOFA ≥ 2 at admission (hazard ratio = 2.76, 95% CI 1.32-5.74, p = 0.007). For MACEs, qSOFA ≥ 2 at admission was a predictive factor with (odds ratio = 2.42, 95% CI 1.37–4.36, p = .002). A qSOFA ≥ 2 on admission had an AUC of 0.729 (95% CI [0.694, 0.762]), with a good specificity of 91.6%. For 30-day mortality, an AUC of 0.759 (95%CI [0.726, 0.792]) for cardiogenic shock with specificity of 92.5%. For MACEs, an AUC of 0.702 (95% CI [0.64, 0.700] with a specificity of 95%. Concerning the different scores tested, we found no significant difference between the Zwolle score and the qSOFA score for predicting prognosis, whereas the CADILLAC score was better than qSOFA for predicting 30-day mortality (AUC = 0.829 and De long test = 0.03). However, there was no difference between qSOFA and CADILLAC scores for predicting cardiogenic shock (De Long test at 0.08). This is the first study to evaluate qSOFA as a predictive score for 30-day mortality and MACEs, and the results are very encouraging, particularly for cardiogenic shock.
A combination of oxygenation and driving pressure can provide valuable information in predicting the risk of mortality in ARDS patients

Background Acute respiratory distress syndrome (ARDS) is a common life-threatening condition in critically ill patients. Itis also an important public health issue because it can cause substantial mortality and health care burden worldwide. The objective of this study was to investigate therisk factors that impact ARDS mortality in a medical center in Taiwan. Methods This was a single center, observational study thatretrospectively analyzed data from adults in 6 intensive care units (ICUs) at Taichung Veterans General Hospital in Taiwan from 1st October, 2018to30th September, 2019. Patients needing invasive mechanical ventilation and meeting the Berlin definition criteria were included for analysis. Results A total of 1,778 subjects were screened in 6 adult ICUs and 370 patients fulfilled the criteria of ARDS in the first 24 hours of the ICU admission. Among these patients, the prevalenceof ARDS was 20.8% and the overall hospital mortality rate was 42.2%. The mortality rates of mild, moderate and severe ARDS were 35.9%, 43.9% and 46.5%, respectively. In a multivariate logistic regression model, combination of driving pressure (DP) > 14cmH2O and oxygenation (P/F ratio)≤150 was an independent predictor of mortality (OR2.497, 95% CI 1.201–5.191, p = 0.014). Patients with worse oxygenation and a higher driving pressure had the highest hospital mortality rate(p<0.0001). Conclusions ARDS is common in ICUs and the mortality rate remains high. Combining oxygenation and respiratory mechanics may better predict the outcomes of these ARDS patients.
The complete chloroplast genome of Sophora alopecuroides (Fabaceae)

Abstract Sophora alopecuroides L. (Fabaceae) is an important medicinal and forage herb species with good economic value. Using an Illumina platform, we sequenced its complete chloroplast genome. Our study reveals that S. alopecuroides have a typical chloroplast genome of 154,108 bp in length, comprised of a large single-copy region (LSC) of 84,221 bp, a small single-copy region (SSC) of 18,139 bp, and inverted repeat regions (IRs) of 258,74 bp. A total of 129 genes, 81 of which are protein coding genes, 38 tRNA genes, eight rRNA genes, and two pseudogenes were identified. Phylogenetic analysis indicates that S. alopecuroides is closely related to the species of Ammopiptanthus mongolicus.
University of Rome Carbon-14 Dates XIV

This list includes dates measured from January 1974 to June 1975 with previously described CO2 proportional counters (Alessio et al, 1970b). All archaeologic and geologic samples except for Arslantepe series come from Italian territory. Chemical apparatus for CO2 preparation and purification is unchanged (Alessio et al, 1970b). Data recording devices have recently been replaced with integrated circuits built entirely at the Lab. The logic of the circuitry was also modified to provide more stable devices and a higher relative figure of merit A/√B of each counter (A = net activity of modern standard, B = background). Consequently, the resolution of age measurements has increased appreciably. Present values of A/√B for the 4 counters in use are shown in Table 1.
Predicting Optimal Patient-Specific Postoperative Facial Landmarks for Patients with Craniomaxillofacial Deformities

Orthognathic surgery traditionally focuses on correcting skeletal abnormalities and malocclusion, with the expectation that an optimal facial appearance will naturally follow. However, this skeletal-driven approach can lead to undesirable facial aesthetics and residual asymmetry. To address these issues, a soft-tissue-driven planning method has been proposed. This innovative method bases bone movement estimates on the targeted ideal facial appearance, thus increasing the surgical plan's accuracy and effectiveness. This study explores the initial phase of implementing a soft-tissue-driven approach, simulating the patient's optimal facial look by repositioning deformed facial landmarks to an ideal state. The algorithm incorporates symmetrization and weighted optimization strategies, aligning projected optimal landmarks with standard cephalometric values for both facial symmetry and form, which are integral to facial aesthetics in orthognathic surgery. It also includes regularization to preserve the patient's original facial characteristics. Validated using retrospective analysis of data from both preoperative patients and normal subjects, this approach effectively achieves not only facial symmetry, particularly in the lower face, but also a more natural and normalized facial form. This novel approach, aligning with soft-tissue-driven planning principles, shows promise in surpassing traditional methods, potentially leading to enhanced facial outcomes and patient satisfaction in orthognathic surgery.
Using the effective weak mixing angle as an input parameter in SMEFT

We implement electroweak renormalisation schemes involving the effective weak mixing angle to NLO in Standard Model Effective Field Theory (SMEFT). After developing the necessary theoretical machinery, we analyse a select set of electroweak precision observables in such input schemes. An attractive feature is that large corrections from top-quark loops appearing in other schemes are absorbed into the definition of the effective weak mixing angle. On the other hand, the renormalisation condition which achieves this involves a large number of flavour-specific SMEFT couplings between the $Z$ boson and charged leptons, motivating simple flavour assumptions such as minimal flavour violation for practical applications. The results of this paper provide a valuable new component for estimating systematic uncertainties in SMEFT fits by performing analyses in multiple input schemes.
Conformalised data synthesis with statistical quality guarantees

With the proliferation of ever more complicated Deep Learning architectures, data synthesis is a highly promising technique to address the demand of data-hungry models. However, reliably assessing the quality of a 'synthesiser' model's output is an open research question with significant associated risks for high-stake domains. To address this challenge, we have designed a unique confident data synthesis algorithm that introduces statistical confidence guarantees through a novel extension of the Conformal Prediction framework. We support our proposed algorithm with theoretical proofs and an extensive empirical evaluation of five benchmark datasets. To show our approach's versatility on ubiquitous real-world challenges, the datasets were carefully selected for their variety of difficult characteristics: low sample count, class imbalance and non-separability, and privacy-sensitive data. In all trials, training sets extended with our confident synthesised data performed at least as well as the original, and frequently significantly improved Deep Learning performance by up to +65% F1-score.
Delayed and Fast-rising Radio Flares from an Optical and X-Ray-detected Tidal Disruption Event in the Center of a Dwarf Galaxy

AT 2018cqh is a unique tidal disruption event (TDE) discovered in a dwarf galaxy. Both the light-curve fitting and galaxy scaling relationships suggest a central black hole mass in the range of 5.9 < logM BH/M ☉ < 6.4. The r-band peak luminosity is ∼ 1043 erg s−1, making AT 2018cqh relatively faint among known optical TDEs. A delayed X-ray brightening was found around 590 days after the optical discovery but shows an unusually long time rising to peak over at least 558 days, which could be coming from delayed accretion of a newly forming debris disk. We report the discovery of delayed radio flares around 1105 days since its discovery, characterized by an initial steep rise of ≳175 days, a flattening lasting about 544 days, and a phase with another steep rise. The rapid rise in radio flux coupled with the slow decay in the X-ray emission points to a delayed launching of outflow, perhaps due to a transition in the accretion state. However, known accretion models can hardly explain the origins of the secondary radio flare that is rising even more rapidly in comparison with the initial one. If confirmed, AT 2018cqh would be a rare faint TDE in a dwarf galaxy exhibiting optical, X-ray, and radio flares. We call for continued multifrequency radio observations to monitor its spectral and temporal evolution, which may help to reveal new physical processes that are not included in standard TDE models.
Deep learning with plasma plume image sequences for anomaly detection and prediction of growth kinetics during pulsed laser deposition

Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We developed a multi-output (2$+$1)D convolutional neural network regression model that extracts deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results are the first demonstration of how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment

Deep learning-based video quality assessment (deep VQA) has demonstrated significant potential in surpassing conventional metrics, with promising improvements in terms of correlation with human perception. However, the practical deployment of such deep VQA models is often limited due to their high computational complexity and large memory requirements. To address this issue, we aim to significantly reduce the model size and runtime of one of the state-of-the-art deep VQA methods, RankDVQA, by employing a two-phase workflow that integrates pruning-driven model compression with multi-level knowledge distillation. The resulting lightweight full reference quality metric, RankDVQA-mini, requires less than 10% of the model parameters compared to its full version (14% in terms of FLOPs), while still retaining a quality prediction performance that is superior to most existing deep VQA methods. The source code of the RankDVQA-mini has been released at https://chenfeng-bristol.github. io/RankDVQA-mini/ for public evaluation.
A PnP Algorithm for Two-Dimensional Pose Estimation

We propose a PnP algorithm for a camera constrained to two-dimensional motion (applicable, for instance, to many wheeled robotics platforms). Leveraging this assumption allows accuracy and performance improvements over 3D PnP algorithms due to the reduction in search space dimensionality. It also reduces the incidence of ambiguous pose estimates (as, in most cases, the spurious solutions fall outside the plane of movement). Our algorithm finds an approximate solution by solving a polynomial system and refines its prediction iteratively to minimize the reprojection error. The algorithm compares favorably to existing 3D PnP algorithms in terms of accuracy, performance, and robustness to noise.
Shannon capacity, Lovász theta number and the Mycielski construction

We investigate the effect of the well-known Mycielski construction on the Shannon capacity of graphs and on one of its most prominent upper bounds, the (complementary) Lov\'asz theta number. We prove that if the Shannon capacity of a graph, the distinguishability graph of a noisy channel, is attained by some finite power, then its Mycielskian has strictly larger Shannon capacity than the graph itself. For the complementary Lov\'asz theta function we show that its value on the Mycielskian of a graph is completely determined by its value on the original graph, a phenomenon similar to the one discovered for the fractional chromatic number by Larsen, Propp and Ullman. We also consider the possibility of generalizing our results on the Sperner capacity of directed graphs and on the generalized Mycielsky construction. Possible connections with what Zuiddam calls the asymptotic spectrum of graphs are discussed as well.
QQESPM: A Quantitative and Qualitative Spatial Pattern Matching Algorithm

The Spatial Pattern Matching (SPM) query allows for the retrieval of Points of Interest (POIs) based on spatial patterns defined by keywords and distance criteria. However, it does not consider the connectivity between POIs. In this study, we introduce the Qualitative and Quantitative Spatial Pattern Matching (QQ-SPM) query, an extension of the SPM query that incorporates qualitative connectivity constraints. To answer the proposed query type, we propose the QQESPM algorithm, which adapts the state-of-the-art ESPM algorithm to handle connectivity constraints. Performance tests comparing QQESPM to a baseline approach demonstrate QQESPM's superiority in addressing the proposed query type.
Leaky Waveguide Antennas for Downlink Wideband THz Communications

THz communications are expected to play a profound role in future wireless systems. The current trend of the extremely massive multiple-input multiple-output (MIMO) antenna architectures tends to be costly and power inefficient when implementing wideband THz communications. An emerging THz antenna technology is leaky wave antenna (LWA), which can realize frequency selective beamforming with a single radiating element. In this work, we explore the usage of LWAs technology for wideband multi-user THz communications. We propose a model for the LWA signal processing that is physically compliant facilitating studying LWA-aided communication systems. Focusing on downlink systems, we propose an alternating optimization algorithm for jointly optimizing the LWA configuration along with the signal spectral power allocation to maximize the sum-rate performance. Our numerical results show that a single LWA can generate diverse beampatterns at THz, exhibiting performance comparable to costly fully digital MIMO arrays.
Maximizing Nash Social Welfare under Two-Sided Preferences

The maximum Nash social welfare (NSW)---which maximizes the geometric mean of agents' utilities---is a fundamental solution concept with remarkable fairness and efficiency guarantees. The computational aspects of NSW have been extensively studied for *one-sided* preferences where a set of agents have preferences over a set of resources. Our work deviates from this trend and studies NSW maximization for *two-sided* preferences, wherein a set of workers and firms, each having a cardinal valuation function, are matched with each other. We provide a systematic study of the computational complexity of maximizing NSW for many-to-one matchings under two-sided preferences. Our main negative result is that maximizing NSW is NP-hard even in a highly restricted setting where each firm has capacity 2, all valuations are in the range {0,1,2}, and each agent positively values at most three other agents. In search of positive results, we develop approximation algorithms as well as parameterized algorithms in terms of natural parameters such as the number of workers, the number of firms, and the firms' capacities. We also provide algorithms for restricted domains such as symmetric binary valuations and bounded degree instances.
Using eye tracking to investigate what native Chinese speakers notice about linguistic landscape images

Linguistic landscape is an important field in sociolinguistic research. Eye tracking technology is a common technology in psychological research. There are few cases of using eye movement to study linguistic landscape. This paper uses eye tracking technology to study the actual fixation of the linguistic landscape and finds that in the two dimensions of fixation time and fixation times, the fixation of native Chinese speakers to the linguistic landscape is higher than that of the general landscape. This paper argues that this phenomenon is due to the higher information density of linguistic landscapes. At the same time, the article also discusses other possible reasons for this phenomenon.
Detection and Defense of Unlearnable Examples

Privacy preserving has become increasingly critical with the emergence of social media. Unlearnable examples have been proposed to avoid leaking personal information on the Internet by degrading the generalization abilities of deep learning models. However, our study reveals that unlearnable examples are easily detectable. We provide theoretical results on linear separability of certain unlearnable poisoned dataset and simple network-based detection methods that can identify all existing unlearnable examples, as demonstrated by extensive experiments. Detectability of unlearnable examples with simple networks motivates us to design a novel defense method. We propose using stronger data augmentations coupled with adversarial noises generated by simple networks, to degrade the detectability and thus provide effective defense against unlearnable examples with a lower cost. Adversarial training with large budgets is a widely-used defense method on unlearnable examples. We establish quantitative criteria between the poison and adversarial budgets, which determine the existence of robust unlearnable examples or the failure of the adversarial defense.
Representational constraints underlying similarity between task-optimized neural systems

Neural systems, artificial and biological, show similar representations of inputs when optimized to perform similar tasks. In visual systems optimized for tasks similar to object recognition, we propose that representation similarities arise from the constraints imposed by the development of abstractions in the representation across the processing stages. To study the effect of abstraction hierarchy of representations across different visual systems, we constructed a two-dimensional space in which each neural representation is positioned based on its distance from the pixel space and the class space. Trajectories of representations in all the task-optimized visual neural networks start close to the pixel space and gradually move towards higher abstract representations, such as object categories. We also observe that proximity in this abstraction space predicts the similarity of neural representations between visual systems. The gradual similar change of the representations suggests that the similarity across different task-optimized systems could arise from constraints on representational trajectories.