bluuebunny/crossref_metadata_2025_split huggingface dataset:
Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition
Authors: Pat Narun , Wang Yue , Bartonicek Adam , Candia Julián , Stringaris Argyris 
Published: June 2022

AbstractDespite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based functional magnetic resonance imaging (fMRI). Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n = 3,989). We compared 9 predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and ordinary least squares (OLS) multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than nonlinear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects, and Friedman’s H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.
The brain network underlying attentional blink predicts symptoms of attention deficit hyperactivity disorder in children
Authors: Zhang Dai , Zhang Ruotong , Zhou Liqin , Zhou Ke , Chang Chunqi 
Published: June 2022

AbstractAttention deficit hyperactivity disorder (ADHD) is a chronic neuropsychiatric disease that can markedly impair educational, social, and occupational function throughout life. Behavioral deficits may provide clues to the underlying neurological impairments. Children with ADHD exhibit a larger attentional blink (AB) deficit in rapid serial visual presentation (RSVP) tasks than typically developing children, so we examined whether brain connectivity in the neural network associated with AB can predict ADHD symptoms and thus serve as potential biomarkers of the underlying neuropathology. We first employed a connectome-based predictive model analysis of adult resting-state functional magnetic resonance imaging data to identify a distributed brain network for AB. The summed functional connectivity (FC) strength within the AB network reliably predicted individual differences in AB magnitude measured by a classical dual-target RSVP task. Furthermore, the summed FC strength within the AB network predicted individual differences in ADHD Rating Scale scores from an independent dataset of pediatric patients. Our findings suggest that the individual AB network could serve as an applicable neuroimaging-based biomarker of AB deficit and ADHD symptoms.
Substitution rate heterogeneity across hexanucleotide contexts in noncoding chloroplast DNA
Authors: Morton Brian R 
Published: June 2022

Abstract
Substitutions between closely related noncoding chloroplast DNA sequences are studied with respect to the composition of the 3 bases on each side of the substitution, that is the hexanucleotide context. There is about 100-fold variation in rate, among the contexts, particularly on substitutions of A and T. Rate heterogeneity of transitions differs from that of transversions, resulting in a more than 200-fold variation in the transitions: transversion bias. The data are consistent with a CpG effect, and it is shown that both the A + T content and the arrangement of purines/pyrimidines along the same DNA strand are correlated with rate variation. Expected equilibrium A + T content ranges from 36.4% to 82.8% across contexts, while G–C skew ranges from −77.4 to 72.2 and A–T skew ranges from −63.9 to 68.2. The predicted equilibria are associated with specific features of the content of the hexanucleotide context, and also show close agreement with the observed context-dependent compositions. Finally, by controlling for the content of nucleotides closer to the substitution site, it is shown that both the third and fourth nucleotide removed on each side of the substitution directly influence substitution dynamics at that site. Overall, the results demonstrate that noncoding sites in different contexts are evolving along very different evolutionary trajectories and that substitution dynamics are far more complex than typically assumed. This has important implications for a number of types of sequence analysis, particularly analyses of natural selection, and the context-dependent substitution matrices developed here can be applied in future analyses.
On-site alert-level earthquake early warning using machine-learning-based prediction equations
Authors: Song Jindong , Zhu Jingbao , Wang Yuan , Li Shanyou 
Published: June 2022

SUMMARYTo rapidly and accurately provide alerts at target sites near the epicentre, we develop an on-site alert-level earthquake early warning (EEW) strategy involving P-wave signals and machine-learning-based prediction equations. These prediction equations are established for magnitude estimation and peak ground velocity (PGV) prediction accounting for multiple feature inputs and the support vector machine (SVM). These prediction equations are called SVM-M model for estimating magnitude and SVM-PGV model for predicting PGV, respectively. According to comparison between the predicted magnitude and PGV values with the predicted threshold values (M = 5.7 and PGV = 9.12 cm s–1, respectively), different alert level (0, 1, 2, 3) is issued at the different recording site when the predicted magnitude or PGV values exceed the given threshold values. Alert level 3 means that both the predicted magnitude and the predicted PGV exceed a given threshold, and there may be serious damage in this recording site. We apply the method to three destructive earthquake events (M ≥ 6.5) occurred in Japan, and our results indicate that with regard to the performance of SVM-PGV model for predicting PGV, at 3 s after P-wave arrival, the percentage of successful alarms (SAs) for these three events is higher than 95, 73 and 94 per cent, respectively, and the percentage of false alarms approaches 0. Additionally, with regard to the performance of SVM-M model for estimating magnitude, at 3 s after P-wave arrival, the percentage of SAs for these three events exceeds 95 per cent, and the percentage of missed alarms approaches 0. Moreover, almost all stations in the areas PGV ≥ 16 cm s–1 (IMM ≥ VII) near the epicentre issue alert level 3. The proposed method provides potential applications in EEW system.
A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation
Authors: Stafford Imogen S , Gosink Mark M , Mossotto Enrico , Ennis Sarah , Hauben Manfred 
Published: June 2022

AbstractBackgroundInflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time.MethodsOn May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure (“machine learning” OR “artificial intelligence”) AND (“Crohn* Disease” OR “Ulcerative Colitis” OR “Inflammatory Bowel Disease”). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research.ResultsSeventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation.DiscussionAvailability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.
Tofacitinib Response in Ulcerative Colitis (TOUR): Early Response After Initiation of Tofacitinib Therapy in a Real-world Setting
Authors: Long Millie D , Afzali Anita , Fischer Monika , Hudesman David , Abdalla Maisa , McCabe Robert , Cohen Benjamin L , Ungaro Ryan C , Harlan Will , Hanson John , Konijeti Gauree , Polyak Steven , Ritter Timothy , Salzberg Bruce , Seminerio Jennifer , English Emily , Zhang Xian , Sharma Puza P , Herfarth Hans H 
Published: June 2022

AbstractBackgroundTofacitinib is an oral, small-molecule JAK inhibitor for the treatment of ulcerative colitis (UC). Using a novel electronic reporting tool, we aimed to prospectively describe the onset of tofacitinib efficacy during induction therapy in a real-world study.MethodsPatient-reported outcome data (PROs) including the simple clinical colitis activity index (SCCAI), PRO Measurement Identification Systems (PROMIS) measures, and adverse events were collected daily for the first 14 days and at day 28 and 56. Paired t tests and P for trend were utilized to compare changes in SCCAI over time. Bivariate analyses and logistic regression models were performed to describe response (SCCAI &lt;5) and remission (SCCAI ≤2) by clinical factors.ResultsOf all included patients (n = 96), 67% had failed ≥2 biologics, and 61.5% were on concomitant steroids. Starting at day 3, PROs showed significant and persistent decline of the mean SCCAI (−1.1, P &lt; 000.1) including significantly lower SCCAI subscores for stool frequency (−0.3; P &lt; .003), bleeding (−0.3; P &lt; .0002) and urgency (−0.2; P &lt; .001). Steroid-free remission at day 14, 28, and 56 was achieved in 25%, 30.2%, and 29.2% of patients, respectively. Neither prior biologics nor endoscopic severity were independently predictive of response or remission in multivariate models. Numeric improvements in all PROMIS measures (anxiety, depression, social satisfaction) were seen through day 56. Rates of discontinuation due to adverse events were low.ConclusionsIn this prospective real-world study, tofacitinib resulted in a rapid and persistent improvement in UC disease activity PROs. The safety findings were consistent with the established safety profile of tofacitinib.
Innovative Earth-Friendly UV-Spectrophotometric Technique for In Vitro Dissolution Testing of Miconazole Nitrate and Nystatin in Their Vaginal Suppositories: Greenness Assessment
Authors: Mohamed Ahmed R , Abolmagd Ebrahim , Badrawy Mohamed , Nour Israa M 
Published: June 2022

Abstract

Background
The combined mixture of miconazole nitrate (MIC) and nystatin (NYS) has proven its efficiency as a prodigious remedy to cure women’s frequent infections: vaginal mycosis and vaginal candidiasis.

Objective
A novel green spectrophotometric technique, namely the Fourier self-deconvolution method (FSD), was employed for the quantitative determination of MIC and NYS in their pure and pharmaceutical forms without prior separation. Moreover, the proposed technique was first employed to study the dissolution profile of the cited drugs in their pharmaceutical formulation according to FDA recommendations without excipient interference.

Method
The FSD method is based on a simple mathematical manipulation of zero-order spectra of the cited drugs, which suffered from severe overlapping, so zero-order spectra of the cited drugs were deconvoluted using the Fourier wavelet function in spectrophotometer software. The deconvoluted amplitudes for MIC and NYS were measured at 255 nm and 320 nm, respectively.

Results
Linearity ranges were 70–700 µg/mL for MIC and 1–25 µg/mL for NYS. The greenness of the proposed technique was assessed using two assessment tools, namely eco-scale scoring and green analytical procedure index (GAPI), revealing the excellent greenness of this technique. The proposed technique was validated consistent with ICH guidelines and statistically compared to the reported method with no significant differences between them.

Conclusions
The proposed technique has advantages of being simple, time-saving, and noting need any modification to be suitable for quantitative analysis of MIC and NYS in both pharmaceutical and laboratory mixtures.

Highlights
An innovative FSD method was developed for quantitative analysis of MIC and NYS in their synthetic and pharmaceutical mixtures and applied for in vitro dissolution testing of their pharmaceutical mixture, producing satisfactory results.
A longitudinal investigation of the effects of age, dietary fiber type and level, and injectable antimicrobials on the fecal microbiome and antimicrobial resistance of finisher pigs
Authors: Gaire Tara N , Noyes Noelle R , Scott H Morgan , Ericsson Aaron C , Dunmire Kara , Tokach Mike D , Paulk Chad B , Vinasco Javier , Roenne Boyd , Nagaraja T G , Volkova Victoriya V 
Published: June 2022

AbstractAge and diet are among the factors that influence the community composition of the fecal microbiome. Additionally, antimicrobial use can alter the composition of bacterial communities. An 86-d study with finisher pigs aimed to evaluate age-related dynamics (day 98 to 177 of age), effects of types and levels of dietary fiber, and injectable antimicrobials on the fecal microbiome and antimicrobial resistance (AMR) was conducted. A total of 287 pigs, housed in 36 pens, with 7 to 8 pigs per pen, fed a corn grain and soybean meal-based basal diet, formulated to contain 8.7% neutral detergent fiber (NDF), were randomly assigned to one of three treatments: 1) basal diet with no supplement, 2) basal diet supplemented with 20% distillers dried grains with solubles (DDGS) formulated to contain 13.6% NDF, or 3) basal diet supplemented with 14.5% sugar beet pulp (SBP) formulated to contain 13.6% NDF. Five finisher pigs from each treatment group were selected randomly, and fecal samples were collected on days 98, 110, 144, and 177 of age. In addition, fecal samples were collected from pigs that were injected intramuscularly ceftiofur hydrochloride or penicillin G on days 1 and 3 along with pen-mate-untreated controls on day 1. Fecal samples were subjected to 16S rRNA amplicon-based microbiome analysis and culture methods to quantify the abundance of total AMR coliforms and enterococci populations. The alpha-diversity, such as species richness, increased with age, and the overall bacterial composition changed with age (P =0.001) and diet (P = 0.001). Diet-associated shifts in the specific bacterial taxa were observed. The richness, diversity, and evenness of bacterial taxa did not differ between pigs that were injected with ceftiofur vs. their untreated pen mates or by dietary treatments but differed in pigs that received penicillin G injection. Both antimicrobial treatments contributed to changes in the overall fecal bacterial composition at the genus level. Collectively, the data demonstrate that both age and the diet (control vs. DDGS-, control vs. SBP-, or DDGS- vs. SBP-based diets) were associated with the overall bacterial community composition, and the impact of age on variations in fecal microbiome composition was greater than the diet. Antibiotic treatment had minimal effect on bacterial diversity and relative abundance of taxa. Furthermore, diets and antimicrobial treatment had minimal impact on the overall counts of AMR coliforms and enterococci populations in feces.
Autumn roost-site selection by male hoary bats (Lasiurus cinereus) in northern California
Authors: Salganek Skye , Weller Theodore J , Szewczak Joseph M 
Published: June 2022

Abstract
Effective conservation and management of a species relies upon understanding its habitat throughout its full annual cycle. For hoary bats, Lasiurus cinereus, their ecology is largely known from summer with the exception that they move long distances during autumn and spring and maintain some level of activity year-round. Autumn is a critically important period for hoary bats as they complete long-distance migrations between summer and winter range and they mate, both of which are energetically expensive. We studied hoary bat roost selection during autumn in a migratory stopover area in redwood habitat in northwestern California. We hypothesized that hoary bats select day roosts during autumn migration that minimize energetic expenditures and improve mating opportunities. We located 24 day roost sites of male hoary bats and evaluated site-level roost selection by comparing roosts with 120 random locations. We used multiple logistic regression to compare remotely sensed habitat features derived from LiDAR and other spatial data. The three most important variables for distinguishing roost from random locations were distance to creek, roads, and meadow. We speculate that selection of roosts in proximity to open spaces and flyways facilitated social behaviors related to mating that are commonly observed in this area.
#32 Pediatric Febrile Neutropenia with Low Risk of Systemic Infection: When Is It Safe to Stop Antibiotics?
Authors: Alali Muayad 
Published: June 2022

Abstract

Background
There are no standard practices about antibiotics duration in unexplained stable febrile neutropenia (FN). Absolute neutrophile count (ANC) recovery has been used clinically to represent bone marrow recovery (BMR) but data about safe ANC threshold for antibiotic cessation (AC) and discharge is still unknown. Other markers should be considered. We hypothesized that absolute monocyte count (AMC), and absolute phagocyte count (APC= ANC + AMC+ bands) are more sensitive, and an earlier safe marker of AC compared with ANC

Method
A retrospective review was performed for FN episodes (FNEs) at a single institution (2009 and 2016) in pediatric oncology patients. Eligible FNEs who were a febrile for 24 hours and had no documented infectious source identified at AC and did not receive chemotherapy 10 days following AC. Ten-day post-AC primary end points (recurrent fever, readmission, blood stream infection BSI, microbiology documented infection MDI, and adverse outcomes) were assessed and compared among different BMR parameters (ANC vs AMC vs APC). Secondary end point compares length of stay, antibiotics free days and cost-effectiveness among different BMR markers.

Results
Eligible FNEs were 391 at time of AC in 235 patients. Three groups were compared based on ANC (cells/μL) at the time of AC : &lt; 200 in 102 (26%), 200-500 in 111 (28%), and &gt;500 /uL in 178 (46%) with an overall ten-day recurrent fever rate 7.4% (29/391). No significant differences in primary end point outcomes rates were identified among 3 ANC groups (11.7%,, 6.3% and 5.6% respectively, P=0.06) and readmission (10%,4.5%, 4%, respectively; P=0.05)(Table 1), however, there is a trend toward unfavorable outcomes in ANC&lt; 200 group compared with ANC&gt;200. In subset analysis in group of ANC&gt;200, favorable outcomes in (ANC&gt;200 with AMC&gt;100) than in the (ANC&gt;200 with AMC&lt;100) group. There was significant risk reduction in primary end points (75% in recurrent fever, 85% readmission, 87% in BSI, 88% in MDI and 87% in adverse events in (ANC&gt;200 with AMC&gt;100) compared with (ANC&gt;200 with AMC&lt;100/uL) group. More importantly, primary end points based on AMC&gt;100 /uL as BMR for AC, regardless ANC values, showed significant favorable outcomes compared AMC&lt;100 /uL. There is 80%, 88, 90%, 89%, and 93% risk reduction in end points recurrent fever, readmission BSI, MDI, and adverse outcomes respectively. Similar favorable results were seen when APC&gt;300 used as threshold for AC (Table 2, 3). Median of length of stay of FN was 3 days shorter using AMC &gt;100/uL for BMR compared with any threshold of ANC (P&lt; 0.01) and decrease overall FN cost stay (P&lt; 0.01).

Conclusion
Our results suggest that AMC &gt; 100 /uL regardless of ANC/uL, or APC&gt; 300 or ANC&gt;200 with AMC&gt;100/uL all are a safe thresholds for empiric AC. Further prospective studies are needed to validate AMC as accurate surrogate for AC in FN children with cancer.
#40 An RSV live-attenuated vaccine candidate lacking G protein mucin domains is highly attenuated, immunogenic, and effective in RSV prevention in BALB/c mice
Authors: Perez Maria A 
Published: June 2022

Abstract

Background
RSV is a leading viral respiratory pathogen in infants. Live attenuated vaccines (LAV) show promise to reduce the disease burden of RSV disease. The RSV attachment protein (G protein) is heavily glycosylated with mucin domains that promote viral attachment. They may also aid in immune evasion through steric shielding of immunogenic epitopes. The objective of this study was to create and rescue RSV LAV candidates lacking the RSV G protein mucin domains with attenuated LAV replication and increased immunogenicity.

Method
LAV vaccine candidates “G155” and “G155-S” were generated from recombinant A2-line19F strain RSV by deletion of the G-protein mucin domains. G155-S additionally underwent deletion of the transmembrane domain, such that G155-S only expressed the secreted form of the G protein, while G155 expressed both transmembrane and secreted forms. Western blotting was used to characterize F and G protein expression in both vaccine candidates in comparison to wild-type (wt) A2-line19F. Enzyme-linked immunosorbent assays (ELISAs) were used to measure monoclonal antibody binding to LAV virus lysate. Viral growth kinetics were measured in HEp2 cells and in BALB/c mouse lungs by a fluorescent focus unit (FFU) assay. Immunogenicity was determined by measuring serum neutralizing antibody titers in BALB/c mice following a prime/boost regimen on days 0 and 28. Mice were challenged with wt RSV on day 60, and LAV efficacy was determined by measuring RSV lung viral loads by FFU assay and RT-PCR on day 4 post-RSV challenge.

Results
The vaccine candidates showed increased monoclonal antibody binding to surface F protein by ELISA, despite similar levels of F protein expression to wt, consistent with F epitope de-shielding. Both vaccine candidates were significantly attenuated in vitro and were undetectable in mouse lungs via FFU assay. By RT-PCR, LAV viral RNA was reduced 3-fold for G155-S, and 22-fold for G155 compared to wt RSV in mouse lungs. Both vaccine candidates elicited similar neutralizing antibody titers compared to wt A2-line19F on day 28 (G155 GMT 13, 95% CI 3, 53; G155-S GMT 10, 95% CI 2, 51; A2-line19F GMT 13, 95% CI 12, 15), and significant increases in titers were observed on day 59 (G155 GMT 440, 95% CI 5, 36057; G155-S 436, 95% CI 354, 537; A2-line19F 275, 95% CI 134, 567). Following RSV challenge, vaccinated mice showed no detectable lung virus by FFU assay and a reduction in lung viral RNA by RT-PCR of 560-fold for G155 and 604-fold for G155-S compared to RSV-challenged unvaccinated mice.

Conclusion
G155 and G155-S are highly attenuated RSV LAV candidates that protect against RSV challenge in mice and produce high levels of neutralizing antibodies comparable to wt RSV.