Miracle-dz/UniFAR-FTUs huggingface dataset:
Enzymatic Verification and Comparative Analysis of Carrageenan Metabolism Pathways in Marine Bacterium Flavobacterium algicola

Carrageenan is the main polysaccharide of red macroalgae and is composed of d-AHG and d-Gal. The carrageenan PUL (CarPUL)-encoded enzymes exist in many marine bacteria for decomposing carrageenan to provide self-growth. Here, the related enzymes in Flavobacterium algicola for metabolizing carrageenan were characterized for describing the catabolic pathways, notably, although the specific polysaccharide hydrolases existed that were like previous studies. ABSTRACT Marine bacteria usually contain polysaccharide utilization loci (PUL) for metabolizing red algae polysaccharides. They are of great significance in the carbon cycle of the marine ecosystem, as well as in supporting marine heterotrophic bacterial growth. Here, we described the whole κ-carrageenan (KC), ι-carrageenan (IC), and partial λ-carrageenan (LC) catabolic pathways in a marine Gram-negative bacterium, Flavobacterium algicola, which is involved carrageenan polysaccharide hydrolases, oligosaccharide sulfatases, oligosaccharide glycosidases, and the 3,6-anhydro-d-galactose (d-AHG) utilization-related enzymes harbored in the carrageenan-specific PUL. In the pathways, the KC and IC were hydrolyzed into 4-sugar-unit oligomers by specific glycoside hydrolases. Then, the multifunctional G4S sulfatases would remove their nonreducing ends’ G4S sulfate groups, while the ι-neocarratetrose (Nι4) product would further lose the nonreducing end of its DA2S group. Furthermore, the neocarrageenan oligosaccharides (NCOSs) with no G4S and DA2S groups in their nonreducing ends would completely be decomposed into d-Gal and d-AHG. Finally, the released d-AHG would enter the cytoplasmic four-step enzymatic process, and an l-rhamnose-H+ transporter (RhaT) was preliminarily verified for the function for transportation of d-AHG. Moreover, comparative analysis with the reported carrageenan metabolism pathways further implied the diversity of microbial systems for utilizing the red algae carrageenan. IMPORTANCE Carrageenan is the main polysaccharide of red macroalgae and is composed of d-AHG and d-Gal. The carrageenan PUL (CarPUL)-encoded enzymes exist in many marine bacteria for decomposing carrageenan to provide self-growth. Here, the related enzymes in Flavobacterium algicola for metabolizing carrageenan were characterized for describing the catabolic pathways, notably, although the specific polysaccharide hydrolases existed that were like previous studies. A multifunctional G4S sulfatase also existed, which was devoted to the removal of G4S or G2S sulfate groups from three kinds of NCOSs. Additionally, the transformation of three types of carrageenans into two monomers, d-Gal and d-AHG, occurred outside the cell with no periplasmic reactions that existed in previously reported pathways. These results help to clarify the diversity of marine bacteria using macroalgae polysaccharides.
Campylobacter positivity and public health risks in live bird markets in Busia, Kenya: A value chain analysis

Abstract Live bird markets (LBMs) provide integral hubs for 95% of poultry produced for food. Surveillance systems in LBMs serving smallholder farmers in sub‐saharan Africa are often non‐functional, and data about public health risks and emerging pathogens are lacking. Studies in Kenya have reported 29–44% Campylobacter prevalence in poultry. We analysed such LBMs in Kenya for likely transmission of Campylobacter from poultry to humans. We conducted a cross‐sectional survey among 186 live poultry traders (LPTs) in 14 LBMs in a region with widespread backyard poultry systems. A pretested structured questionnaire was administered to all LPTs having regular contacts with poultry to gather market data and risk information on campylobacteriosis. Campylobacter was detected in individual cloacal cultures and identified through PCR. The median score obtained from the outcome of risk assessment dichotomized respondents into high and low risk categories. We performed logistic regression at 95% confidence interval (CI) to compare market characteristics and Campylobacter positivity to risk categories to identify LBM‐associated public health risks. Markets had a median of 13 traders, and mean age of 46.3 ± 13.7 years. Majority 162/186 (87.1%) were males. Market behavioural processes by LPTs varied: Only 58.6% LPTs held bird species separate; onsite slaughter (38.7%); encountered sick‐bird (93%) and dead‐bird (83%) amidst limited health inspection (31.2%). Campylobacter positivity in live birds was 43/112 (38.4%, 95% CI: 29.4–48.1). Risk information on campylobacteriosis was low 41/114 (36%, 95% CI: 27.2–45.5). Sanitary risks were related to accumulation of litter (adjusted prevalence odds ratio [aPOR]: 19.67, 95% CI: 3.01–128.52). Accessing hand‐wash facilities (aPOR: .32, 95% CI: .13–.78) and access to information (aPOR: .24, 95% CI: .09–.61) were protective. Sanitary risks were related to poor hygiene. LBMs could be central surveillance sites for Campylobacter. Public health authorities/actors should consider appropriate targeting to improve sanitary measures and Campylobacter control strategies.
Never‐breastfed children face a higher risk of suboptimal cognition at 2 years of corrected age: A multinational cohort of very preterm children

Abstract In a cohort of children born very preterm (VPT), we investigated the association between breast milk feeding (BMF) initiation and its duration on cognitive development at 2 years of corrected age. Data were obtained from the Effective Perinatal Intensive Care in Europe population‐based prospective cohort of children born <32 weeks of gestation, in 11 European countries, in 2011–2012. The study sample included 4323 children. Nonverbal cognitive ability was measured applying the Parental Report of Children's Abilities, except for France where the problem‐solving domain of the Ages & Stages Questionnaire was used. Verbal cognition was based on the number of words the child could say. To determine the association between BMF (mother's own milk) and nonverbal and verbal cognition (outcome categorized as optimal and suboptimal), adjusted risk ratios (aRRs) were estimated fitting Poisson regression models, with inverse probability weights to account for nonresponse bias. Overall, 16% and 11% of the children presented suboptimal nonverbal and verbal cognition, respectively. Never BMF was associated with a significantly increased risk for suboptimal nonverbal (aRR = 1.29, 95% confidence interval [CI] = 1.09–1.53) and verbal (aRR = 1.45, 95% CI = 1.09–1.92) cognitive development compared with those ever breastfed, after adjustment for perinatal and sociodemographic characteristics. Compared with children breastfed 6 months or more, children with shorter BMF duration exhibited a statistically nonsignificant elevated aRR. VPT children fed with breast milk had both improved nonverbal and verbal cognitive development at 2 years in comparison with never breastfed, independently of perinatal and sociodemographic characteristics. This study encourages targeted interventions to promote BMF among these vulnerable children.
Spectroscopic observation of materials under dynamic conditions

A new method is developed for direct real-time visualization of shock generation, propagation, and convergence in a sample. The approach opens up new possibilities for controlling the shock parameters and allows one to access pressures in the multiple gigapascal range. Optical generation of shock waves is followed by optical measurement of sample response during and after shock propagation. In this approach, a shock wave is generated that propagates laterally in the plane of the sample (perpendicular to the direction of the optical beam) rather than through the sample plane as in a more conventional approach. The optical configuration and sample geometry make shock wave formation and propagation directly accessible to optical imaging and spectroscopic probes with wavelengths ranging from UV to far-IR. With proper shaping of the optical shock generation pulse, focusing of the shock response can be initiated to provide increased shock pressure. The method has been validated through measurements of shock propagation in liquid water that illustrate some of the possibilities for shock generation, control, and measurement, and demonstrate the utility and potential of the new technique. The charge-coupled device (CCD) and streak camera images recorded provide for the first time a direct dynamic picture of cylindrical shock convergence within the nanosecond time window. This unique technique enables rapid and direct measurement of the dynamic shock responses of advanced materials and structures to diagnose and subsequently optimize their readiness in mitigating blast threats. Thesis Supervisor: Keith A. Nelson Title: Professor of Chemistry Thesis Supervisor: Edwin L. Thomas Title: Morris Cohen Professor of Materials Science and Engineering
Long-range accurate ranging of millimeter-wave retro-reflective tags in high mobility

In this paper, we demonstrate Adaptive Millimetro as an extension of Millimetro, an ultra-low power millimeter-wave (mmWave) retro-reflector presented in [1], for high mobility scenarios. Adaptive Millimetro makes use of automotive radars and enables communication with and accurate localization of roadside infrastructure overextended distances (i.e. >100m). Millimetro achieves this by designing ultra-low-power retro-reflective tags that operate in the mmWave frequency band and can be embedded in road signs, pavements, bi-cycles, or even the clothing of pedestrians. Millimetro addresses the severe path loss problem of mmWave signals by combining coding gain and retro-reflective antenna front-end to achieve long-range operation. However, highly mobile scenarios may still experience unreliable performance due to the Doppler effect changing the received signals. In this paper, we demonstrate a simple solution for robust localization in high mobility by implementing a Moving Target Indication (MTI) filter and an adaptive Kalman filter. We also present an augmented reality app, as an in-car AR platform, that uses Adaptive Millimetro’s algorithms to estimate the tag positions and overlay a virtual box at the estimated locations.
Decreasing the RAG:SAG ratio of granola cereal predictably reduces postprandial glucose and insulin responses: a report of four randomised trials in healthy adults

Abstract Dietary starch contains rapidly (RAG) and slowly available glucose (SAG). To establish the relationships between the RAG:SAG ratio and postprandial glucose, insulin and hunger, we measured postprandial responses elicited by test meals varying in the RAG:SAG ratio in n 160 healthy adults, each of whom participated in one of four randomised cross-over studies (n 40 each): a pilot trial comparing six chews (RAG:SAG ratio 2·4–42·7) and three studies comparing a test granola (TG1-3, RAG:SAG ratio 4·5–5·2) with a control granola (CG1–3, RAG:SAG ratio 54·8–69·3). Within studies, test meals were matched for fat, protein and available carbohydrate. Blood glucose, serum insulin and subjective hunger were measured for 3 h. Data were subjected to repeated-measures analysis of variance (ANOVA). The relationships between the RAG:SAG ratio and postprandial end points were determined by regression analysis. In the pilot trial, 0–2 h glucose incremental areas under the curve (iAUC0–2; primary end point) varied across the six chews (P = 0·014) with each 50 % reduction in the RAG:SAG ratio reducing relative glucose response by 4·0 %. TGs1-3 elicited significantly lower glucose iAUC0–2 than CGs1–3 by 17, 18 and 17 %, respectively (similar to the 15 % reduction predicted by the pilot trial). The combined means ± sem (n 120) for TC and CG were glucose iAUC0–2, 98 ± 4 v. 118 ± 4 mmol × min/l (P < 0·001), and insulin iAUC0–2, 153 ± 9 v. 184 ± 11 nmol × h/l (P < 0·001), respectively. Neither postprandial hunger nor glucose or hunger increments 2 h after eating differed significantly between TG and CG. We concluded that TGs with RAG:SAG ratios <5·5 predictably reduced glycaemic and insulinaemic responses compared with CGs with RAG:SAG ratios >54. However, compared with CG, TG did not reduce postprandial hunger or delay the return of glucose or hunger to baseline.
UnseenNet: Fast Training Detector for Any Unseen Concept

Training of object detection models using less data is currently the focus of existing N-shot learning models in computer vision. Such methods use object-level labels and takes hours to train on unseen classes. There are many cases where we have large amount of image-level labels available for training but cannot be utilized by few shot object detection models for training. There is a need for a machine learning framework that can be used for training any unseen class and can become useful in real-time situations. In this paper, we proposed an"Unseen Class Detector"that can be trained within a very short time for any possible unseen class without bounding boxes with competitive accuracy. We build our approach on"Strong"and"Weak"baseline detectors, which we trained on existing object detection and image classification datasets, respectively. Unseen concepts are fine-tuned on the strong baseline detector using only image-level labels and further adapted by transferring the classifier-detector knowledge between baselines. We use semantic as well as visual similarities to identify the source class (i.e. Sheep) for the fine-tuning and adaptation of unseen class (i.e. Goat). Our model (UnseenNet) is trained on the ImageNet classification dataset for unseen classes and tested on an object detection dataset (OpenImages). UnseenNet improves the mean average precision (mAP) by 10% to 30% over existing baselines (semi-supervised and few-shot) of object detection on different unseen class splits. Moreover, training time of our model is<10 min for each unseen class. Qualitative results demonstrate that UnseenNet is suitable not only for few classes of Pascal VOC but for unseen classes of any dataset or web. Code is available at https://github.com/Asra-Aslam/UnseenNet.
Architectures and Synchronization Techniques for Distributed Satellite Systems: A Survey

Cohesive Distributed Satellite Systems (CDSSs) is a key enabling technology for the future of remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSSs. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSSs. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques for Distributed Satellite Systems (DSSs). First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSSs in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on Machine Learning (ML). Finally, a compilation of current research activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field.
Orion A's complete 3D magnetic field morphology

Magnetic ﬁelds permeate the interstellar medium and are important in the star formation process. Determining the three-dimensional (3D) magnetic ﬁelds of molecular clouds will allow us to better understand their role in the evolution of these clouds and the formation of stars. We fully reconstruct the approximate 3D magnetic ﬁeld morphology of the Orion A molecular cloud (on scales of a few to ∼ 100pc) using Galactic magnetic ﬁeld models, as well as available line-of-sight and plane-of-sky magnetic ﬁeld observations. While previous studies identiﬁed the 3D magnetic ﬁeld morphology of the Orion A cloud as an arc shape, in this study we provide the orientation of this arc-shaped ﬁeld and its plane-of-sky direction for the ﬁrst time. We ﬁnd that this 3D ﬁeld is a tilted, semi-convex (from our point of view) structure and mostly points in the direction of decreasing latitude and longitude on the plane of the sky from our vantage point. The previously identiﬁed bubbles and events in this region were key in shaping this arc-shaped magnetic ﬁeld morphology.
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning

Deep neural networks have seen great success in recent years; however, training a deep model is often challenging as its performance heavily depends on the hyper-parameters used. In addition, finding the optimal hyper-parameter configuration, even with state-of-the-art (SOTA) hyper-parameter optimization (HPO) algorithms, can be time-consuming, requiring multiple training runs over the entire dataset for different possible sets of hyper-parameters. Our central insight is that using an informative subset of the dataset for model training runs involved in hyper-parameter optimization, allows us to find the optimal hyper-parameter configuration significantly faster. In this work, we propose AUTOMATA, a gradient-based subset selection framework for hyper-parameter tuning. We empirically evaluate the effectiveness of AUTOMATA in hyper-parameter tuning through several experiments on real-world datasets in the text, vision, and tabular domains. Our experiments show that using gradient-based data subsets for hyper-parameter tuning achieves significantly faster turnaround times and speedups of 3$\times$-30$\times$ while achieving comparable performance to the hyper-parameters found using the entire dataset.
Magnetic Field Reversal around an Active Fast Radio Burst

The environment of actively repeating fast radio bursts (FRBs) has been shown to be complex and varying. The recently localized FRB 20190520B is extremely active, has the largest confirmed host dispersion measure, and is only the second FRB source associated with a compact, persistent radio source (PRS). The main tracer of the magneto-ionic environments is the rotation measure (RM), a path-integral of the line-of-sight component of magnetic field strength (B) and electron density, which does not allow a direct probe of the B-field configuration. Here we report direct evidence for a B-field reversal based on the observed sign change and extreme variation of FRB 20190520B's RM, which changed from $\sim10000$ rad m$^{-2}$ to $\sim-16000$ rad m$^{-2}$ between June 2021 and January 2022. Such extreme RM reversal has never been observed before in any FRB nor in any astronomical object. The implied short-term change of the B-field configuration in or around the FRB could be due to the vicinity of massive black holes, or a magnetized companion star in binary systems, or a young supernova remnant along the line of sight.
Recursive Variational Quantum Compiling

Variational quantum compiling algorithms \cite{qaqc} aim to compress quantum circuits into parameterized ansatzes utilizing a hybrid quantum-classical approach. In this article a variant of VQC named the recursive variational quantum compiling algorithm (RVQC) is proposed. In contrast with existing VQC algorithms, where one is required to execute the full target circuit in order to compile it, RVQC works by dividing the target circuit into $N$ parts and recursively compresses one part at a time into the parameterized ansatz. Since smaller parts are being compressed at a time, the circuit depth required to implement the algorithm is not dependent on the depth of the target circuit, but on the depth of the ansatz and the depth of each of the $N$ parts. We are hence able to compress the target circuit by executing circuits of smaller depths than the target circuit itself. To show that this approach has an edge over the standard VQC method for sufficiently large circuit depths, RVQC was compared with VQC on a noise model of the IBM Santiago device with the goal of compressing a randomly generated five-qubit circuit of depth 5000. It was shown that VQC was not able to converge within 500 epochs of training. On the other hand, RVQC was able to converge to a fidelity ($0.88$) within a total of 500 epochs when splitting the target circuit into $N = 5$ parts.
A Personal Computer for a Distrustful World

Personal computer owners often want to be able to run security-critical programs on the same machine as other untrusted and potentially malicious programs. While ostensibly trivial, this requires users to trust hardware and sys-tem software to correctly sandbox malicious programs, trust that is often misplaced. Our goal is to minimize the number and complexity of hardware and software components that a computer owner needs to trust to withstand adversarial inputs. We present a hardware design, called the split-trust machine model, which is composed of statically-partitioned, physically-isolated trust domains. We introduce a few simple, formally-veriﬁed hardware components to enable a program to gain provably exclusive and simultaneous access to both computation and I/O on a temporary basis. To manage this hardware, we present OctopOS, an OS composed of mutually distrustful subsystems. We present a prototype of this machine (hardware and OS) on a CPU-FPGA board and show that it incurs a small hardware cost compared to modern SoCs. For security-critical programs, we show that this machine signiﬁcantly reduces the required trust compared to mainstream TEEs, and for normal programs, we show that it achieves similar performance as a legacy machine.
Cicero: A Declarative Grammar for Responsive Visualization

Designing responsive visualizations can be cast as applying transformations to a source view to render it suitable for a different screen size. However, designing responsive visualizations is often tedious as authors must manually apply and reason about candidate transformations. We present Cicero, a declarative grammar for concisely specifying responsive visualization transformations which paves the way for more intelligent responsive visualization authoring tools. Cicero’s flexible specifier syntax allows authors to select visualization elements to transform, independent of the source view’s structure. Cicero encodes a concise set of actions to encode a diverse set of transformations in both desktop-first and mobile-first design processes. Authors can ultimately reuse design-agnostic transformations across different visualizations. To demonstrate the utility of Cicero, we develop a compiler to an extended version of Vega-Lite, and provide principles for our compiler. We further discuss the incorporation of Cicero into responsive visualization authoring tools, such as a design recommender.
Dense lineability and spaceability in certain subsets of ℓ∞$\ell _{\infty }$

We investigate dense lineability and spaceability of subsets of ℓ∞$\ell _\infty$ with a prescribed number of accumulation points. We prove that the set of all bounded sequences with exactly countably many accumulation points is densely lineable in ℓ∞$\ell _\infty$ , thus complementing a recent result of Papathanasiou who proved the same for the sequences with continuum many accumulation points. We also prove that these sets are spaceable. We then consider the same problems for the set of bounded non‐convergent sequences with a finite number of accumulation points. We prove that such a set is densely lineable in ℓ∞$\ell _\infty$ and that it is nevertheless not spaceable. The said problems are also studied in the setting of ideal convergence and in the space Rω$\mathbb {R}^\omega$ .