Miracle-dz/UniFAR-FTUs huggingface dataset:
Plasma metagenomic sequencing to detect and quantify bacterial DNA in ICU patients suspected of sepsis: A proof-of-principle study

BACKGROUND Timely recognition of sepsis and identification of pathogens can improve outcomes in critical care patients but microbial cultures have low accuracy and long turnaround times. In this proof-of-principle study, we describe metagenomic sequencing and analysis of nonhuman DNA in plasma. We hypothesized that quantitative analysis of bacterial DNA (bDNA) levels in plasma can enable detection and monitoring of pathogens. METHODS We enrolled 30 patients suspected of sepsis in the surgical trauma intensive care unit and collected plasma samples at the time of diagnostic workup for sepsis (baseline), and 7 days and 14 days later. We performed metagenomic sequencing of plasma DNA and used computational classification of sequencing reads to detect and quantify total and pathogen-specific bDNA fraction. To improve assay sensitivity, we developed an enrichment method for bDNA based on size selection for shorter fragment lengths. Differences in bDNA fractions between samples were evaluated using t test and linear mixed-effects model, following log transformation. RESULTS We analyzed 72 plasma samples from 30 patients. Twenty-seven samples (37.5%) were collected at the time of infection. Median total bDNA fraction was 1.6 times higher in these samples compared with samples with no infection (0.011% and 0.0068%, respectively, p < 0.001). In 17 patients who had active infection at enrollment and at least one follow-up sample collected, total bDNA fractions were higher at baseline compared with the next sample (p < 0.001). Following enrichment, bDNA fractions increased in paired samples by a mean of 16.9-fold. Of 17 samples collected at the time when bacterial pathogens were identified, we detected pathogen-specific DNA in 13 plasma samples (76.5%). CONCLUSION Bacterial DNA levels in plasma are elevated in critically ill patients with active infection. Pathogen-specific DNA is detectable in plasma, particularly after enrichment using selection for shorter fragments. Serial changes in bDNA levels may be informative of treatment response. LEVEL OF EVIDENCE Epidemiologic/Prognostic, Level V.
Tissue‐specific gene expression shows a cynipid wasp repurposes oak host gene networks to create a complex and novel parasite‐specific organ

Every organism on Earth depends on interactions with other organisms to survive. In each of these interactions, an organism must utilize the limited toolbox of genes and proteins it possesses to successfully manipulate or cooperate with another species, but it can also co‐opt the genome machinery of its partner to expand its available tools. Insect‐induced plant galls are an extreme example of this, wherein an insect hijacks the plant's genome to direct the initiation and development of galls consisting of plant tissue. However, previous transcriptomic studies have not evaluated individual tissues within a gall to determine the full extent to which a galling insect manipulates its host plant. Here we demonstrate that the cynipid wasp Dryocosmus quercuspalustris creates a complex parasite‐specific organ from red oak tissue via massive changes in host gene expression. Our results show that the gall wasp is not merely modifying oak leaf tissue but creating extensive changes in gene expression between galled and ungalled tissue (differential expression in 28% of genes) and distinct gall tissue types (20% of genes). The outer gall tissue shows increases in various plant defence systems, which is consistent with its predicted functional role of protecting the wasp larva. The inner larval capsule shows suppression of large parts of the plant innate immune system and evidence for the wasp utilizing the plant's RNA interference mechanisms, which may be a potential mechanism for the wasp's control on gall growth.
Porin threading drives receptor disengagement and establishes active colicin transport through Escherichia coli OmpF

Bacteria deploy weapons to kill their neighbours during competition for resources and to aid survival within microbiomes. Colicins were the first such antibacterial system identified, yet how these bacteriocins cross the outer membrane (OM) of Escherichia coli is unknown. Here, by solving the structures of translocation intermediates via cryo‐EM and by imaging toxin import, we uncover the mechanism by which the Tol‐dependent nuclease colicin E9 (ColE9) crosses the bacterial OM. We show that threading of ColE9’s disordered N‐terminal domain through two pores of the trimeric porin OmpF causes the colicin to disengage from its primary receptor, BtuB, and reorganises the translocon either side of the membrane. Subsequent import of ColE9 through the lumen of a single OmpF subunit is driven by the proton‐motive force, which is delivered by the TolQ‐TolR‐TolA‐TolB assembly. Our study answers longstanding questions, such as why OmpF is a better translocator than OmpC, and reconciles the mechanisms by which both Tol‐ and Ton‐dependent bacteriocins cross the bacterial outer membrane.
Development of the Caregiving Burden Scale for Family Caregivers of Children with Cancer

Due to the burden of caregiving, family caregivers of children with cancer suffer from physical and psychosocial health problems. The aim of this study was to develop a Caregiving Burden Scale for Family Caregivers of Children with Cancer (CBSFC-CC) and to assess its psychometric properties. A methodological, descriptive study design was conducted. The study sample consisted of 217 family caregivers caring for children with cancer between the ages of 0 and 18 years. The item pool of the scale was created using the Delphi Technique. The Cronbach’s alpha coefficient for the overall scale was 0.93. The item-total score correlations ranged from 0.424 to 0.645. The exploratory factor analysis showed that the scale explained 68.34% of the total variance. The confirmatory factor analysis also showed that the factor loadings of the scale ranged from 0.46 to 0.96. NNFI, CFI, and IFI were found to be > 0.90, and RMSEA was found to be < 0.09. The CBSFC-CC was found to be a good multidimensional instrument for evaluating the burden on family caregivers of pediatric cancer patients. It can be used in clinical practice and research. This tool can be considered to tailor interventions aimed at improving caregiver outcomes.
Long-term Hearing Preservation and Speech Perception Performance Outcomes With the Slim Modiolar Electrode

Objective: Describe audiologic outcomes in hearing preservation (HP) cochlear implant candidates using a slim modiolar electrode (SME). Study Design: Retrospective. Setting: Tertiary referral center. Patients: Two hundred three adult cochlear implant patients with preoperative low-frequency pure-tone average (LFPTA) ≤ 80 dB HL that received the SME. Intervention: Implantation with a SME electrode. Main Outcome Measures: Primary outcome was postoperative HP, defined as LFPTA ≤80 dB HL. HP status was analyzed at “early” (activation or 3 mo) and “long-term” (6 or 12 mo) time frames using the patient's worst audiogram. Speech perception tests were compared between HP and non-HP cohorts. Results: Of the 203 HP candidates, the tip fold-over rate was 7.4%. The mean shifts in LFPTA at the “early” and “long-term” time points were 25.9 ± 16.2 dB HL and 29.6 ± 16.9 dB HL, respectively. Of 117 patients with preoperative LFPTA ≤60 dB HL, the early and long-term mean LFPTA shifts were 19.5 ± 12.3 dB HL and 32.6 ± 17.2 dB HL, respectively; early and long-term HP rates were 61.1% and 50.8%, respectively. For patients with preoperative LFPTA ≤80 dB HL, early and long-term HP rates were 45.5% and 43.7%, respectively. No significant difference was observed in postoperative speech perception performance (CNC, AzBio, HINT) at 3, 6, or 12 months between HP versus non-HP groups. Conclusions: HP is feasible using the SME. While electroacoustic stimulation was not studied in this cohort, HP provided no clear advantage in speech perception abilities in this group of patients. The current reporting standard of what constitutes HP candidacy (preoperative LFPTA ≤80 dB HL) should be reconsidered.
Characterization of sand fly breeding sites in district Malakand, Khyber Pakhtunkhwa, Pakistan, and evaluation of risk factors for cutaneous leishmaniasis in the region

Present study was carried to determine the sand fly species composition, breeding sites ecology, seasonal abundance, and spatial distribution in district Malakand, Khyber Pakhtunkhwa, Pakistan. In addition, risk factors associated with cutaneous leishmaniasis (CL) were also evaluated. Survey of indoor and outdoor habitats was carried out using sticky traps in 31 villages of Dargai and Batkhela tehsils of Malakand. Soil from habitats of adult and immature sand flies was analysed. Questionnaire‐based household survey was also performed in these villages to assess risk factors associated with CL. Soil samples from selected CL positive households were analysed for its contents. Additionally, clinicoepidemiological data from local health centres was examined for the year 2019. Total of 3,140 sand flies belonging to 18 species were collected. Phlebotomus sergenti was the most abundant species (38.16%). Its abundance had a strong positive correlation with mean monthly relative humidity and negative correlation with average temperature. Phlebotomus sergenti and Phlebotomus papatasi were abundant at an elevation ranging from 320 to 1,120 m above sea level and in agricultural lands near human settlements. Flight height preference apparatus collected maximum sand flies at 30 cm (1ft) above the ground and all species associated negatively with height. Soil analysis from habitats of adult and immature flies showed that highest mean number of adults and immatures were recorded from silt loam which carried highest concentrations of K2O, Mg, Ca, and Zn. Number of immature sand flies correlated moderately (r = .7, p < .05) with K2O soil concentrations. There was significant similarity between organic matter contents in soil samples from positive breeding sites and CL households (Wilcoxon rank‐sum test, p = .1976). In multivariate analysis model for CL risk factors, age (26–35 and >35 years), knowledge of leishmaniasis, living in a middle and upper class, preachers visit to villages, and assumption that Afghan refugees are more prone to CL were significant. CL patient's archived data from health centres showed that majority of patients had lesions on face and hands. Patient's influx was highest in February and March.
Investigation of US Cyclospora cayetanensis outbreaks in 2019 and evaluation of an improved Cyclospora genotyping system against 2019 cyclosporiasis outbreak clusters

Abstract Cyclosporiasis is an illness characterised by watery diarrhoea caused by the food-borne parasite Cyclospora cayetanensis. The increase in annual US cyclosporiasis cases led public health agencies to develop genotyping tools that aid outbreak investigations. A team at the Centers for Disease Control and Prevention (CDC) developed a system based on deep amplicon sequencing and machine learning, for detecting genetically-related clusters of cyclosporiasis to aid epidemiologic investigations. An evaluation of this system during 2018 supported its robustness, indicating that it possessed sufficient utility to warrant further evaluation. However, the earliest version of CDC's system had some limitations from a bioinformatics standpoint. Namely, reliance on proprietary software, the inability to detect novel haplotypes and absence of a strategy to select an appropriate number of discrete genetic clusters would limit the system's future deployment potential. We recently introduced several improvements that address these limitations and the aim of this study was to reassess the system's performance to ensure that the changes introduced had no observable negative impacts. Comparison of epidemiologically-defined cyclosporiasis clusters from 2019 to analogous genetic clusters detected using CDC's improved system reaffirmed its excellent sensitivity (90%) and specificity (99%), and confirmed its high discriminatory power. This C. cayetanensis genotyping system is robust and with ongoing improvement will form the basis of a US-wide C. cayetanensis genotyping network for clinical specimens.
Characterizing Tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Abstract. Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties, but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parametrized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a Remotely Piloted Aircraft System (RPAS). Distributions of snow specific area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than Cambridge Bay (CB) where TVC is at a lower latitude with a sub-arctic shrub tundra compared to CB which is a graminoid tundra. DHF were fitted with a gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. Snow depth simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas > 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE) grid 2.0 enhanced resolution at 37 GHz.
Novel Speckle Tracking Analysis Showed Excellent Reproducibility for Size and Shape of the Fetal Heart and Good Reproducibility for Strain and Fractional Shortening

Introduction: This study aimed to evaluate reproducibility and agreement of fetal cardiac shape and deformation using FetalHQ. Methods: Fifty normal fetuses at 20–38 weeks of gestation were evaluated. Two operators independently selected an optimal cardiac cycle using FetalHQ®™software for speckle tracking analysis. Intra- and interobserver correlation coefficient and limits of agreement for cardiac shape and deformation were estimated. Results: Global cardiac markers: high correlation (r = 0.98) and agreement (mean difference, standard deviation [MD, SD] 5.07, 75.8) for ventricular area; moderate correlation (r = 0.78) and agreement (MD, SD: 0.016, 0.08) for global sphericity index (SI) and for left ventricle (LV) global strain (r = 0.65; MD, SD: −4.48, 11.9); and low but still significant correlation (r = 0.58) and agreement (MD, SD: −3.77, 12.27) for right ventricle (RV) global strain. For individual ventricular parameters: high correlation for LV ([median r; range] 0.98; 0.93–0.99) and RV (r = 0.98; 0.97–1.0) SI, and for LV (r = 0.92: 0.56–0.99) and RV (r = 0.96; 0.67–0.99) end diastolic diameters; moderate correlation for LV fractional shortening (r = 0.53; 0.87–0.98); and no significant correlation for RV fractional shortening (r = 0.36; 0.32–0.97). Inter- and intraobserver correlation and agreement were similar for all evaluated parameters. Conclusion: Speckle tracking analysis of the fetal heart provides reliable estimations of global and LV shape and deformation. Low correlation in the RV can be related to anatomical structures such as the moderator band.
Modeling Concentrated Cross-Attention for Neural Machine Translation with Gaussian Mixture Model

Cross-attention is an important component of neural machine translation (NMT), which is always realized by dot-product attention in previous methods. However, dot-product attention only considers the pair-wise correlation between words, resulting in dispersion when dealing with long sentences and neglect of source neighboring relationships. Inspired by linguistics, the above issues are caused by ignoring a type of cross-attention, called concentrated attention, which focuses on several central words and then spreads around them. In this work, we apply Gaussian Mixture Model (GMM) to model the concentrated attention in cross-attention. Experiments and analyses we conducted on three datasets show that the proposed method outperforms the baseline and has significant improvement on alignment quality, N-gram accuracy, and long sentence translation.
Reinforcement Learning for Load-balanced Parallel Particle Tracing

We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel particle tracing performance in distributed-memory systems. Our method combines three novel components: (1) a work donation algorithm, (2) a high-order workload estimation model, and (3) a communication cost model. First, we design an RL-based work donation algorithm. Our algorithm monitors workloads of processes and creates RL agents to donate data blocks and particles from high-workload processes to low-workload processes to minimize program execution time. The agents learn the donation strategy on the fly based on reward and cost functions designed to consider processes' workload changes and data transfer costs of donation actions. Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations. Third, we design a communication cost model that considers both block and particle data exchange costs, helping RL agents make effective decisions with minimized communication costs. We demonstrate that our algorithm adapts to different flow behaviors in large-scale fluid dynamics, ocean, and weather simulation data. Our algorithm improves parallel particle tracing performance in terms of parallel efficiency, load balance, and costs of I/O and communication for evaluations with up to 16,384 processors.
26Aluminum from Massive Binary Stars. II. Rotating Single Stars Up to Core Collapse and Their Impact on the Early Solar System

Radioactive nuclei were present in the early solar system (ESS), as inferred from analysis of meteorites. Many are produced in massive stars, either during their lives or their final explosions. In the first paper of this series (Brinkman et al. 2019), we focused on the production of 26Al in massive binaries. Here, we focus on the production of another two short-lived radioactive nuclei, 36Cl and 41Ca, and the comparison to the ESS data. We used the MESA stellar evolution code with an extended nuclear network and computed massive (10–80 M ⊙), rotating (with initial velocities of 150 and 300 km s−1) and nonrotating single stars at solar metallicity (Z = 0.014) up to the onset of core collapse. We present the wind yields for the radioactive isotopes 26Al, 36Cl, and 41Ca, and the stable isotopes 19F and 22Ne. In relation to the stable isotopes, we find that only the most massive models, ≥60 and ≥40 M ⊙ give positive 19F and 22Ne yields, respectively, depending on the initial rotation rate. In relation to the radioactive isotopes, we find that the ESS abundances of 26Al and 41Ca can be matched with by models with initial masses ≥40 M ⊙, while 36Cl is matched only by our most massive models, ≥60 M ⊙. 60Fe is not significantly produced by any wind model, as required by the observations. Therefore, massive star winds are a favored candidate for the origin of the very short-lived 26Al, 36Cl, and 41Ca in the ESS.
Evolving Architectures with Gradient Misalignment toward Low Adversarial Transferability

Deep neural network image classifiers are known to be susceptible, not only to adversarial examples created for them, but also to those created for others. This phenomenon poses a potential security risk in various black-box systems that rely on image classifiers. One of the observations on networks that have transferability of adversarial examples between them is the similarity of their architectures. Networks with high architectural similarity tend to share high transferability as well. Thus, in this study, we address this problem from a novel perspective by investigating the contribution of network architecture to transferability. Specifically, we propose an architecture searching framework that employs neuroevolution to evolve network architectures and gradient misalignment loss to encourage networks to converge into dissimilar functions after training. Our findings indicate that the proposed framework successfully discovers architectures that reduce transferability from four standard networks, including ResNet and VGG, while maintaining good accuracy on unperturbed images. In addition, the evolved networks trained with gradient misalignment exhibit significantly lower transferability than a standard network trained with gradient misalignment, which indicates that network architecture plays an important role in reducing transferability. We demonstrate that designing or exploring proper network architectures is a promising approach to tackle the transferability issue and train adversarially robust image classifiers.
Combinatorics of NC-Probability Spaces with Independent Constants

Unlike classical and free independence, the boolean and monotone notions of independence lack of the property of independent constants. In the scalar case, this leads to restrictions for the central limit theorems, as observed by F. Oravecz. We characterize the property of independent constants from a combinatorial point of view, based on cumulants and set partitions. This characterization also holds for the operator-valued extension. Our considerations lead rather directly to very mild variations of boolean and monotone cumulants, where constants are now independent. These alternative probability theories are closely related to the usual notions. Hence, an important part of the boolean/monotone probability theories can be imported directly. We describe some standard combinatorial aspects of these variations (and their cyclic versions), such as their Möbius functions, which feature well-known combinatorial integer sequences. The new notions with independent constants seem also more strongly related to the operator-valued extension of c-free probability.
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models

Recent works have shown that powerful pre-trained language models (PLM) can be fooled by small perturbations or intentional attacks. To solve this issue, various data augmentation techniques are proposed to improve the robustness of PLMs. However, it is still challenging to augment semantically relevant examples with sufficient diversity. In this work, we present Virtual Data Augmentation (VDA), a general framework for robustly fine-tuning PLMs. Based on the original token embeddings, we construct a multinomial mixture for augmenting virtual data embeddings, where a masked language model guarantees the semantic relevance and the Gaussian noise provides the augmentation diversity. Furthermore, a regularized training strategy is proposed to balance the two aspects. Extensive experiments on six datasets show that our approach is able to improve the robustness of PLMs and alleviate the performance degradation under adversarial attacks. Our codes and data are publicly available at bluehttps://github.com/RUCAIBox/VDA.