Miracle-dz/UniFAR-FTUs huggingface dataset:
Imaging Enterobacterales infections in patients using pathogen-specific positron emission tomography

18F-FDS PET/CT can detect and localize infections due to Enterobacterales in patients and noninvasively monitor response to antibiotic treatment. Pathogen-specific PET Enterobacterales infections can affect diverse locations within the body, and multidrug-resistant strains are difficult to diagnose and treat. Ordonez et al. used 18F-FDS as a bacteria-specific imaging agent to detect and monitor infections in patients. Positron emission tomography/computerized tomography (PET/CT) imaging showed selective uptake of the tracer in Enterobacterales infections as opposed to other types of inflammation or cancer. Signal was reduced in sites of drug-susceptible infections in patients after treatment with antibiotics. The authors also showed that the imaging agent could differentiate bacterial infection from SARS-CoV-2 in a hamster model, supporting the use of 18F-FDS for bacteria-specific imaging. Enterobacterales represent the largest group of bacterial pathogens in humans and are responsible for severe, deep-seated infections, often resulting in sepsis or death. They are also a prominent cause of multidrug-resistant (MDR) infections, and some species are recognized as biothreat pathogens. Tools for noninvasive, whole-body analysis that can localize a pathogen with specificity are needed, but no such technology currently exists. We previously demonstrated that positron emission tomography (PET) with 2-deoxy-2-[18F]fluoro-d-sorbitol (18F-FDS) can selectively detect Enterobacterales infections in murine models. Here, we demonstrate that uptake of 18F-FDS by bacteria occurs via a metabolically conserved sorbitol-specific pathway with rapid in vitro 18F-FDS uptake noted in clinical strains, including MDR isolates. Whole-body 18F-FDS PET/computerized tomography (CT) in 26 prospectively enrolled patients with either microbiologically confirmed Enterobacterales infection or other pathologies demonstrated that 18F-FDS PET/CT was safe, could rapidly detect and localize Enterobacterales infections due to drug-susceptible or MDR strains, and differentiated them from sterile inflammation or cancerous lesions. Repeat imaging in the same patients monitored antibiotic efficacy with decreases in PET signal correlating with clinical improvement. To facilitate the use of 18F-FDS, we developed a self-contained, solid-phase cartridge to rapidly (<10 min) formulate ready-to-use 18F-FDS from commercially available 2-deoxy-2-[18F]fluoro-d-glucose (18F-FDG) at room temperature. In a hamster model, 18F-FDS PET/CT also differentiated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia from secondary Klebsiella pneumoniae pneumonia—a leading cause of complications in hospitalized patients with COVID-19. These data support 18F-FDS as an innovative and readily available, pathogen-specific PET technology with clinical applications.
Practice Patterns of Spine Surgeons Regarding Osteoporosis: An International Survey

ABSTRACT Background: Osteoporosis (OP) represents a great challenge for the spine surgeon. Despite having effective pharmacological treatments for OP and surgical technical innovations, the awareness of spine surgeons regarding OP seems low. The purpose of this research was to assess practice patterns on the diagnosis and treatment of spine surgeons regarding OP. Methods: An electronic survey of ten multiple-choice questions was administered to members of the European Association of Neurosurgical Societies (EANS). The survey asked about the specialty, the workplace, and practice patterns and attitudes regarding OP and spine fusion surgery, pseudoarthrosis, and vertebral compression fractures (VCF). Results: A total of 122 surgeons completed the survey. In patients with suspected OP, 31.4% of surgeons would refer the patient to the OP specialist before surgery and 21.5% chose to perform the surgery without additional studies. A 66.4% of respondents would modify the surgical strategy in the case of OP. The most popular surgical techniques elected were cemented augmented screws (77.9%) and long-segment instrumentation (45.1%). Regarding pseudoarthrosis, 29.5% of surgeons opted to refer to the OP specialist, and 23.8% didn't consider any additional studies Concerning VCF management, 41.32% of respondents would refer the patient for treatment of OP, and the most common therapeutic strategy was conservatively treatment for 4 to 6 weeks and vertebroplasty or kyphoplasty if no improvement (55.74%). There was a higher proportion of surgeons that would not consider preoperative studies or referring patients with suspected OP for spine surgery (χ2 = 4.48, P = .03) and pseudoarthrosis (χ2 = 9.5, P = .002) compared to VCF. Conclusions: There was a greater awareness regarding optimizing OP management in VCF compared to patients with suspected OP for spine arthrodesis or pseudoarthrosis. There still opportunities for improvement for the timely diagnosis and treatment of OP in spine surgery patients.
Gram-Negative Surgical Site Infections After 989 Spinal Fusion Procedures: Associated Factors and the Role of Gram-Negative Prophylactic Antibiotic Coverage

ABSTRACT Background: To identify, analyze, and report the patient- and procedure-related factors associated with surgical site infection (SSI) after spinal fusion (SF) surgery. Methods: We included any SSI-SF from January 2013 to September 2015. A total of 989 spine surgeries that required instrumentation were performed. Results: Twenty-four out of 989 (2.43%) patients presented with SSI. More than half of the SSI cases (54%) got infected with either exclusively gram-negative bacteria or a combination of gram-negative and gram-positive bacteria; 9.1% of the surgeries involved the sacral spine (90 out of 989 patients). SSI in long constructs (more than 3 levels) was performed in 66.7% compared with 33.3% with short constructs; 87.5 % of the reported SSI (21 patients) were done through a posterior approach. Of patients who had SSI, 87.5% received prophylactic antibiotics, 92% were operated on during the daytime shift, 50% required blood transfusion, and 79% required surgical debridement. Four patients out of 24 patients died (17%) due to unrelated SSI complications. Conclusions: The overall incidence of gram-negative infections after long SFs remains low in our study population. Despite this low overall incidence, our results demonstrate a relative higher incidence of gram-negative SSIs in surgeries involving more than 3 spinal levels and for all those involving the sacral spine. We propose that there may be a potential benefit of gram-negative prophylactic antibiotic coverage in patients falling in either 1 of these categories. Further multivariate analysis and/or randomized studies may be necessary to confirm our results. Level of Evidence: 3.
Metabolic Alterations in Older Women With Low Bone Mineral Density Supplemented With Lactobacillus reuteri

Osteoporosis and its associated fractures are highly prevalent in older women. Recent studies have shown that gut microbiota play important roles in regulating bone metabolism. A previous randomized controlled trial (RCT) found that supplementation with Lactobacillus reuteri ATCC PTA 6475 (L.reuteri) led to substantially reduced bone loss in older women with low BMD. However, the total metabolic effects of L. reuteri supplementation on older women are still not clear. In this study, a post hoc analysis (not predefined) of serum metabolomic profiles of older women from the previous RCT was performed to investigate the metabolic dynamics over 1 year and to evaluate the effects of L. reuteri supplementation on human metabolism. Distinct segregation of the L. reuteri and placebo groups in response to the treatment was revealed by partial least squares‐discriminant analysis. Although no individual metabolite was differentially and significantly associated with treatment after correction for multiple testing, 97 metabolites responded differentially at any one time point between L. reuteri and placebo groups (variable importance in projection score >1 and p value <0.05). These metabolites were involved in multiple processes, including amino acid, peptide, and lipid metabolism. Butyrylcarnitine was particularly increased at all investigated time points in the L. reuteri group compared with placebo, indicating that the effects of L. reuteri on bone loss are mediated through butyrate signaling. Furthermore, the metabolomic profiles in a case (low BMD) and control population (high BMD) of elderly women were analyzed to confirm the associations between BMD and the identified metabolites regulated by L. reuteri supplementation. The amino acids, especially branched‐chain amino acids, showed association with L. reuteri treatment and with low BMD in older women, and may serve as potential therapeutic targets. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
Increase in C‐Reactive Protein Predicts Increase in Rate of Bone Mineral Density Loss: The Study of Women's Health Across the Nation

This longitudinal cohort study's aim was to detect whether larger increases in C‐reactive protein (CRP) predict greater amounts of subsequent bone loss in women transitioning from premenopause to postmenopause. Participants were initially 42 to 52 years of age and premenopausal or early perimenopausal. The sample included 1431 women who were not using hormone therapy and whose CRP values were not consistent with acute inflammation. Individual fixed effects (IFE) models estimated the association of log2 CRP with subsequent bone mineral density (BMD) decline rate, adjusted for menopause transition (MT) stage (1: premenopausal or early perimenopausal; 2: late perimenopausal or early postmenopausal; or 3: late postmenopausal), body mass index, diabetes, smoking, alcohol, bone active medications, and anti‐inflammatory medications. BMD decline at both the lumbar spine (LS) and femoral neck (FN) was faster for observations made in MT stage 2 than that during other stages (all p < .001). In adjusted IFE models, MT stage modified the relation between increase in CRP and BMD decline rate (interaction p values <.05). Each within‐woman doubling of CRP was associated with a 0.09% faster yearly decline in FN BMD in MT stages 1 (p = .006) and 3 (p = .03), and 0.10% faster decline in LS BMD in MT stage 3 only (p = .007). Within‐woman increases in CRP in premenopause and early perimenopause and in late postmenopause predict faster BMD decline in the next ~2 years, but the magnitude of CRP's effect is small. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
Introduction to neural network‐based question answering over knowledge graphs

Question answering has emerged as an intuitive way of querying structured data sources and has attracted significant advancements over the years. A large body of recent work on question answering over knowledge graphs (KGQA) employs neural network‐based systems. In this article, we provide an overview of these neural network‐based methods for KGQA. We introduce readers to the formalism and the challenges of the task, different paradigms and approaches, discuss notable advancements, and outline the emerging trends in the field. Through this article, we aim to provide newcomers to the field with a suitable entry point to semantic parsing for KGQA, and ease their process of making informed decisions while creating their own QA systems.
Time series analysis via network science: Concepts and algorithms

There is nowadays a constant flux of data being generated and collected in all types of real world systems. These data sets are often indexed by time, space, or both requiring appropriate approaches to analyze the data. In univariate settings, time series analysis is a mature field. However, in multivariate contexts, time series analysis still presents many limitations. In order to address these issues, the last decade has brought approaches based on network science. These methods involve transforming an initial time series data set into one or more networks, which can be analyzed in depth to provide insight into the original time series. This review provides a comprehensive overview of existing mapping methods for transforming time series into networks for a wide audience of researchers and practitioners in machine learning, data mining, and time series. Our main contribution is a structured review of existing methodologies, identifying their main characteristics, and their differences. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified way and language. We first describe the case of univariate time series, which can be mapped to single layer networks, and we divide the current mappings based on the underlying concept: visibility, transition, and proximity. We then proceed with multivariate time series discussing both single layer and multiple layer approaches. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic.
Can Organizational Spirituality Contribute to Knowledge Management?

Abstract Society demands more sustainable business. Increasing organizational spirituality can be one manner of accomplishing this humanized strategy, and knowledge management is an efficient method to diffuse high-level values through the company. Spirituality has a vital role in organizational theory and practice. Nevertheless, this also places a heavy burden on practitioners. To address this association, we conducted a systematic literature review, systematizing and categorizing the results to answer the research question, “can organizational spirituality contribute to knowledge management? ”, and propose a future investigation research agenda. We utilized the Web of Science and Scopus database. We submitted the articles to VOSviewer software version 1.6. 16 for building, displaying, and exploring a bibliometric map supported by network data. The beginning was about wisdom, followed by a major focus on knowledge fields after increasing attention to spirituality. VOSviewer provided a network with two clusters, namely, spirituality dynamics and knowledge dynamics. Spirituality and knowledge labels have connections in both clusters. Wisdom and organizational wisdom are isolated from the other terms. Based on the articles analyzed, organizational spirituality can indeed assist knowledge management. One should ponder, however, that there are still few empirical studies with non-generalizable results. Considering the mysticism and excess of non-scientific articles (and scarcity of scientific ones), we recommend a change in approaching it. Traditional and positivist methodologies are not the most suitable; consequently, innovative and mixed methods ought to be used, providing the research’s scientific nature. It is crucial to look at the insertion of spirituality in management with a scientific and critical eye.
Cis-regulatory elements in conserved non-coding sequences of nuclear receptor genes indicate for crosstalk between endocrine systems

Abstract Nuclear receptors (NRs) are ligand-activated transcription factors that regulate gene expression when bound to specific DNA sequences. Crosstalk between steroid NR systems has been studied for understanding the development of hormone-driven cancers but not to an extent at a genetic level. This study aimed to investigate crosstalk between steroid NRs in conserved intron and exon sequences, with a focus on steroid NRs involved in prostate cancer etiology. For this purpose, we evaluated conserved intron and exon sequences among all 49 members of the NR Superfamily (NRS) and their relevance as regulatory sequences and NR-binding sequences. Sequence conservation was found to be higher in the first intron (35%), when compared with downstream introns. Seventy-nine percent of the conserved regions in the NRS contained putative transcription factor binding sites (TFBS) and a large fraction of these sequences contained splicing sites (SS). Analysis of transcription factors binding to putative intronic and exonic TFBS revealed that 5 and 16%, respectively, were NRs. The present study suggests crosstalk between steroid NRs, e.g., vitamin D, estrogen, progesterone, and retinoic acid endocrine systems, through cis-regulatory elements in conserved sequences of introns and exons. This investigation gives evidence for crosstalk between steroid hormones and contributes to novel targets for steroid NR regulation.
Influence Decompositions For Neural Network Attribution

Methods of neural network attribution have emerged out of a necessity for explanation and accountability in the predictions of black-box neural models. Most approaches use a variation of sensitivity analysis, where individual input variables are perturbed and the downstream eﬀects on some output metric are measured. We demonstrate that a number of critical functional properties are not revealed when only considering lower-order perturbations. Motivated by these shortcomings, we propose a general framework for decomposing the orders of inﬂuence that a collection of input variables has on an output classiﬁcation. These orders are based on the cardinality of input subsets which are perturbed to yield a change in classiﬁcation. This decomposition can be naturally applied to attribute which input variables rely on higher-order coordination to impact the classiﬁcation decision. We demonstrate that our approach correctly iden-tiﬁes higher-order attribution on a number of synthetic examples. Additionally, we show-case the diﬀerences between attribution in our approach and existing approaches on benchmark networks for MNIST and ImageNet.
A Vehicle Reidentification Algorithm Based on Double-Channel Symmetrical CNN

It has become a challenging research topic to accurately identify the vehicles in the past from the mass monitoring data. ,e challenge is that the vehicle in the image has a large attitude, angle of view, light, and other changes, and these complex changes will seriously affect the vehicle recognition performance. In recent years, the convolutional neural network (CNN) has achieved great success in the field of vehicle reidentification. However, due to the small amount of vehicle annotation in the dataset of vehicle reidentification, the existing CNNmodel is not fully utilized in the training process, which affects the ability to identify the deep learning model. In order to solve the above problems, a double-channel symmetric CNN vehicle recognition algorithm is proposed by improving the network structure. In this method, two samples are taken as input at the same time, in which each sample has complementary characteristics. In this case, with limited training samples, the combination of inputs will be more diversified, and the training process of the CNNmodel will be more abundant. Experiments show that the recognition accuracy of the proposed algorithm is better than other existing methods, which further verifies the effectiveness of the proposed algorithm in this study.
Multi-scale deep learning and optimal combination ensemble approach for AQI forecasting using big data with meteorological conditions

Faced with the rapid update of nonlinear and irregular big data from the environmental monitoring system, both the public and managers urgently need reliable methods to predict possible air pollutions in the future. Therefore, a multi-scale deep learning (MDL) and optimal combination ensemble (OCE) approach for hourly air quality index (AQI) forecasting is proposed in this paper, named MDL-OCE model. Before normal modeling, all original data are preprocessed through missing data filling and outlier testing to ensure smooth computation. Due to the complexity of such big data, slope-based ensemble empirical mode decomposition (EEMD) is adopted to decompose the time series of AQI and meteorological conditions into a finite number of simple intrinsic mode function (IMF) components and one residue component. Then, to unify the number of components of different variables, the fine-to-coarse (FC) technique is used to reconstruct all components into high frequency component (HF), low frequency component (LF), and trend component (TC). For purpose of extracting the underlying relationship between AQI and meteorological conditions, the three components are respectively trained and predicted by different deep learning architectures (stacked sparse autoencoder (SSAE)) with a multilayer perceptron (MLP). The corresponding forecasting results of three components are merged by OCE method to better achieve the ultimate AQI forecasting outputs. The empirical results clearly demonstrate that our proposed MDL-OCE model outperforms other advanced benchmark models in terms of forecasting performances in all cases.
Designing Transportable Experiments Under S-admissability

We consider the problem of designing a randomized experiment on a source population to estimate the Average Treatment Effect (ATE) on a target population. Under the covariate shift assumption, we design an unbiased importance-weighted estimator for the target population's ATE. To reduce the variance of our estimator, we design a covariate balance condition between the treatment and control group. Under the rerandomization scheme [Morgan et al., 2012], the experimenter repeatedly rejects a random assignment until it satisfies the condition. We show that when the sample size is large, for a rejection threshold $\alpha$, our balance condition achieves the optimal variance reduction.
Tracking Regret Bounds for Online Submodular Optimization

In this paper, we propose algorithms for on-line submodular optimization with tracking regret bounds. Online submodular optimization is a generic framework for sequential decision making used to select subsets. Existing algorithms for online submodular optimization have been shown to achieve small (static) regret , which means that the algorithm’s performance is comparable to the performance of a ﬁxed optimal action. Such algorithms, however, may perform poorly in an environment that changes over time. To overcome this problem, we apply a tracking-regret -analysis framework to online submodular optimization, one by which output is assessed through comparison with time-varying optimal sub-sets. We propose algorithms for submodular minimization, monotone submodular maximization under a size constraint, and unconstrained submodular maximization, and we show tracking regret bounds. In addition, we show that our tracking regret bound for sub-modular minimization is nearly tight.
The Dynamic SecYEG Translocon

The spatial and temporal coordination of protein transport is an essential cornerstone of the bacterial adaptation to different environmental conditions. By adjusting the protein composition of extra-cytosolic compartments, like the inner and outer membranes or the periplasmic space, protein transport mechanisms help shaping protein homeostasis in response to various metabolic cues. The universally conserved SecYEG translocon acts at the center of bacterial protein transport and mediates the translocation of newly synthesized proteins into and across the cytoplasmic membrane. The ability of the SecYEG translocon to transport an enormous variety of different substrates is in part determined by its ability to interact with multiple targeting factors, chaperones and accessory proteins. These interactions are crucial for the assisted passage of newly synthesized proteins from the cytosol into the different bacterial compartments. In this review, we summarize the current knowledge about SecYEG-mediated protein transport, primarily in the model organism Escherichia coli, and describe the dynamic interaction of the SecYEG translocon with its multiple partner proteins. We furthermore highlight how protein transport is regulated and explore recent developments in using the SecYEG translocon as an antimicrobial target.