bluuebunny/crossref_metadata_2025 huggingface dataset:
Sonographic Evaluation of Pulmonary Interstitial Edema in Patient With Preeclampsia
Authors: Gokkus Halil , Cosgun Zeliha , Cosgun Mehmet , Ekici Mustafa Ayhan , Kalaycioglu Oya
Published: September 2021

The aim of this study was to sonographically detect pulmonary edema, which is a major problem in pregnant women with preeclampsia, in the interstitial phase. We evaluated 41 preeclampsia patients and 21 control subjects prospectively. In the preeclampsia group, 26 patients had severe features, whereas the other 15 patients had none. To detect early fluid loading in lungs, sonographic B lines were counted from the intercostal space by using ultrasonography, and left ventricular loading findings were examined for corporation by using transthoracic echocardiography both before and after birth. In severe preeclampsia, the number of B lines before and after birth is statistically significant compared with the other groups. In addition, the total number of B lines calculated at 24 hours after delivery was significantly lower than that calculated before delivery (P < 0.018). In terms of prenatal E values, a statistically significant difference was found between all groups (P < 0.001). A strong positive and statistically significant relationship was found between B lines and prenatal E/e′ (r = 0.768; P < 0.001). The overall accuracy rate of the prenatal E/e′ and E value for estimation of the B line number classification is 0.791 (95% confidence interval, 0.674–0.908; P < 0.001) and 0.829 (95% confidence interval, 0.722–0.936; P < 0.001), respectively. Pulmonary edema is a serious complication in patients with severe preeclampsia and may be detected interstitially in some patients, even if it does not occur clinically.
Pulsatility and Resistance Indices of Uterine and Ovarian Arteries in Anovulatory Women With Polycystic Ovary Syndrome
Authors: Doğulu Fehime Funda , Aydoğmuş Süheyla , Gözüküçük Murat
Published: September 2021

We aimed to compare the ovarian and uterine artery blood flow of anovulatory patients with polycystic ovary syndrome (PCOS) with those of ovulatory women throughout the menstrual cycle using color Doppler ultrasound. Seventy-one women with PCOS, who were admitted to the infertility outpatient clinic of a training and research hospital, were included in the study. The patients were divided into 2 groups as anovulatory (group 1, n = 23) and ovulatory (group 2, n = 37). Each patient was followed up throughout her menstrual cycle and included in either group 1 or group 2. Anovulatory cycles were determined by consecutive ultrasound examinations, and the progesterone value was measured in the luteal period. Eleven patients were excluded from the study because they did not continue their follow-up. The uterine and ovarian artery pulsatility and resistance indices of all patients in both groups were evaluated 3 times throughout a menstrual cycle: 7th to 10th day, 13th to 17th day, and 21st to 25th day. It was observed that the uterine artery resistances of the patients with anovulatory cycles remained relatively high throughout the menstrual cycle compared with ovulatory cycles. Both pulsatility and resistance indices of uterine and ovarian arteries were significantly higher in anovulatory cycles compared with ovulatory cycles at all evaluation times throughout the menstrual cycle (P < 0.05). Ovarian artery resistance in anovulatory patients did not significantly change during the menstrual cycle. Anovulatory patients with PCOS have higher uterine and ovarian artery resistance than ovulatory artery resistance, and the former shows a significant decrease throughout the cycle.
Brain Data in Pediatric Disorders of Consciousness: Special Considerations
Authors: Ismail Fatima Y. , Saleem Ghazala T. , Ljubisavljevic Milos R.
Published: September 2021

The diagnosis and management of disorders of consciousness in children continue to present a clinical, research, and ethical challenge. Though the practice guidelines for diagnosis and management of disorders of consciousness in adults are supported by decades of empirical and pragmatic evidence, similar guidelines for infants and children are lacking. The maturing conscious experience and the limited behavioral repertoire to report consciousness in this age group restrict extrapolation from the adult literature. Equally challenging is the process of heightened structural and functional neuroplasticity in the developing brain, which adds a layer of complexity to the investigation of the neural correlates of consciousness in infants and children. This review discusses the clinical assessment of pediatric disorders of consciousness and delineates the diagnostic and prognostic utility of neurophysiological and neuroimaging correlates of consciousness. The potential relevance of these correlates for the developing brain based on existing theoretical models of consciousness in adults is outlined.
Mapping the Unconscious Brain: Insights From Advanced Neuroimaging
Authors: Qureshi Abid Y. , Stevens Robert D.
Published: September 2021

Recent advances in neuroimaging have been a preeminent factor in the scientific effort to unravel mechanisms of conscious awareness and the pathophysiology of disorders of consciousness. In the first part of this review, we selectively discuss operational models of consciousness, the biophysical signal that is measured using different imaging modalities, and knowledge on disorders of consciousness that has been gleaned with each neuroimaging modality. Techniques considered include diffusion-weighted imaging, diffusion tensor imaging, different types of nuclear medicine imaging, functional MRI, magnetoencephalography, and the combined transcranial magnetic stimulation-electroencephalography approach. In the second part of this article, we provide an overview of how advanced neuroimaging can be leveraged to support neurological prognostication, the use of machine learning to process high-dimensional imaging data, potential applications in clinical practice, and future directions.
Recent advances in understanding neural correlates of anxiety disorders in children and adolescents
Authors: Zugman Andre , Winkler Anderson M. , Pine Daniel S.
Published: September 2021

Purpose of review

Anxiety disorders are some of the most common psychiatric diagnoses in children and adolescents, but attempts to improve outcome prediction and treatment have stalled. This review highlights recent findings on neural indices related to fear and anxiety that provide novel directions for attempts to create such improvements.

Recent findings

Stimuli capable of provoking fear engage many brain regions, including the amygdala, medial prefrontal cortex, hippocampus, and bed nucleus of the stria terminalis. Studies in rodents suggest that sustained, low-level threats are particularly likely to engage the bed nucleus of the stria terminalis, which appears to malfunction in anxiety disorders. However, anxiety disorders, like most mental illnesses, appear less likely to arise from alterations in isolated brain regions than in distributed brain circuitry. Findings from large-scale studies of brain connectivity may reveal signs of such broadly distributed dysfunction, though available studies report small effect sizes. Finally, we review novel approaches with promise for using such large-scale data to detect clinically relevant, broadly distributed circuitry dysfunction.

Recent work maps neural circuitry related to fear and anxiety. This circuitry may malfunction in anxiety disorders. Integrating findings from animal studies, big datasets, and novel analytical approaches may generate clinically relevant insights based on this recent work.
A systematic review of instruments for the assessment of eating disorders among adults
Authors: Schaefer Lauren M. , Crosby Ross D. , Machado Paulo P.P.
Published: September 2021

Purpose of review

The availability of psychometrically sound assessment instruments for assessing eating disorder symptomatology is crucial for both clinical practice and research. The purpose of the current review is to provide the reader with a list of psychometrically validated assessments for adults that are available within the field of eating disorders. Eating disorder interviews and self-report questionnaires were identified using online literature searches, reviewing previous review articles, and via research and/or clinical experience of the authors. The focus of the review was on (1) standard assessments that were frequently used in eating disorder research (such as the Eating Disorder Examination and Eating Attitudes Test), and (2) newer assessments that were developed over the past 5 years. Information compiled on each instrument included the purpose of the assessment, scores that can be derived, psychometric information, translations in other languages, and availability for use in research and clinical settings.

Recent findings

Several recent trends in assessment instruments were identified including updates based upon Diagnostic and Statistical Manual criteria, briefer assessments, assessments for specific populations, and assessment of specific clinical features observed in people with eating disorders.

The current review provides eating disorder clinicians and researchers a guide for making informed decisions about the selection of eating disorder assessments.
Eating disorders, pregnancy and the postnatal period: a review of the recent literature
Authors: Bye Amanda , Martini Maria Giulia , Micali Nadia
Published: September 2021

Purpose of review

Eating disorders (ED) are severe psychiatric disorders that affect women in reproductive age. The purpose of this review is to provide an up-to-date overview of the impact of maternal ED on pregnancy and the postnatal period. The clinical implications for identification and management of maternal ED are also discussed.

Recent findings

In the last 2 years, 15 articles focused on the impact of maternal ED in pregnancy and postpartum. Findings from this review indicate that around 15% of pregnant women are likely to have had an ED at some point in their lifetime, and about 5% have an ED in pregnancy. Although ED symptoms tend to decrease during pregnancy, remission is often only temporary with symptoms typically resurfacing in the postnatal period. Women with ED are prone to psychiatric comorbidities such as depression and anxiety during the perinatal period, with up to a third of women with ED reporting postnatal depression in clinical studies and prevalence ranging between 40% and 66% in general population samples. Furthermore, recent findings continue to highlight that current and prior history of maternal ED are associated with a heightened risk of adverse pregnancy and birth outcomes, most notably preterm birth and adverse birth weight outcomes.

These findings continue to emphasise the clinical importance of early identification and response to maternal ED to mitigate potentially adverse maternal and infant outcomes.
Whole-genome-based phylogenomic analysis of the Belgian 2016–2017 influenza A(H3N2) outbreak season allows improved surveillance
Authors: Van Poelvoorde Laura A. E. , Bogaerts Bert , Fu Qiang , De Keersmaecker Sigrid C. J. , Thomas Isabelle , Van Goethem Nina , Van Gucht Steven , Winand Raf , Saelens Xavier , Roosens Nancy , Vanneste Kevin
Published: September 2021

Seasonal influenza epidemics are associated with high mortality and morbidity in the human population. Influenza surveillance is critical for providing information to national influenza programmes and for making vaccine composition predictions. Vaccination prevents viral infections, but rapid influenza evolution results in emerging mutants that differ antigenically from vaccine strains. Current influenza surveillance relies on Sanger sequencing of the haemagglutinin (HA) gene. Its classification according to World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC) guidelines is based on combining certain genotypic amino acid mutations and phylogenetic analysis. Next-generation sequencing technologies enable a shift to whole-genome sequencing (WGS) for influenza surveillance, but this requires laboratory workflow adaptations and advanced bioinformatics workflows. In this study, 253 influenza A(H3N2) positive clinical specimens from the 2016–2017 Belgian season underwent WGS using the Illumina MiSeq system. HA-based classification according to WHO/ECDC guidelines did not allow classification of all samples. A new approach, considering the whole genome, was investigated based on using powerful phylogenomic tools includingbeastand Nextstrain, which substantially improved phylogenetic classification. Moreover, Bayesian inference viabeastfacilitated reassortment detection by both manual inspection and computational methods, detecting intra-subtype reassortants at an estimated rate of 15 %. Real-time analysis (i.e. as an outbreak is ongoing) via Nextstrain allowed positioning of the Belgian isolates into the globally circulating context. Finally, integration of patient data with phylogenetic groups and reassortment status allowed detection of several associations that would have been missed when solely considering HA, such as hospitalized patients being more likely to be infected with A(H3N2) reassortants, and the possibility to link several phylogenetic groups to disease severity indicators could be relevant for epidemiological monitoring. Our study demonstrates that WGS offers multiple advantages for influenza monitoring in (inter)national influenza surveillance, and proposes an improved methodology. This allows leveraging all information contained in influenza genomes, and allows for more accurate genetic characterization and reassortment detection.
The Trend in the Sales of Menopausal Hormone and Other Osteoporosis Medications in South Korea from 2016 to 2019
Authors: Lee Eun Sil , Kwon Sogene , Park Hyoung Moo
Published: September 2021

Background: Increasing geriatric population, osteoporosis prevalence, and interest in bone health are key contributing factors for the growth of osteoporosis medication markets. Thus, this study evaluated changes in the menopausal hormone and other osteoporosis medication markets from 2016 to 2019.Methods: This study’s dataset was obtained from the International Marketing Services of IQVIA Inc. in South Korea. The sales of medications for osteoporosis treatment with menopausal hormones were evaluated for drug sales for osteoporosis treatment from 2016 to 2019.Results: The results showed that the tissue-selective estrogen complex (TSEC) sales had increased annually while the estrogen-progesterone therapy (EPT) sales had decreased. Excluding menopausal hormones, bisphosphonates were the most widely sold medications for osteoporosis treatment. Among the bisphosphonate medications, sales of ibandronate and zoledronate increased annually, while alendronate and risedronate decreased. Teriparatide also showed increasing sales. A rapid rise was noted in the sales of denosumab.Conclusions: While the sales of TSEC, injectable bisphosphonates, and denosumab have increased annually, the sales of EPT, estrogen therapy, oral bisphosphonates have not increased, as reflected in hormone therapy and osteoporosis medication market trends. This study showed the recent trends in hormone therapy and the osteoporosis medication market from 2016 to 2019 in South Korea.
Cross-Calibration of Bone Mineral Densities and Body Composition between GE Lunar Prodigy and Osteosys Primus
Authors: Ha Yong-Chan , Yoo Jun-Il
Published: September 2021

Background: The aim of this study was to investigate the correlation between bone mineral density (BMD) and body composition measured by the Osteosys Primus® and the GE Lunar Prodigy® and to calculate the conversion rate between the 2 devices.Methods: The 40 subjects were men and women in aged 20 to 29 years old. All participants were scanned twice on both the Osteosys Primus (OsteoSys) and the GE Lunar Prodigy (GE Healthcare) DXA systems using the manufacturers’ standard scanning and positioning protocols.Results: Compared to the GE Lunar device, the mean Osteosys fat mass was overestimated to be 12.1% (1,776.9 g) in the whole body, 5.1% (163.9 g) in gynoid, and 6.7% (87.2 g) in android. Compared with the GE Lunar device, the mean BMDs of the Osteosys Primus were underestimated to be 2.3% (0.023 g/cm2) in the whole body and 3.1% (0.035 g/cm2) in L1-4. Compared with the GE Lunar device, the mean lean mass derived by the Osteosys Primus were underestimated to 2.3% (1,045.3 g) in the total body, 3.8% (179.4 g) in arms, and 7.7% (1,104.8 g) in legs, respectively. There were a strong correlation of BMD and body composition between both groups.Conclusions: Linear correction equations were developed to ensure comparability of BMD and muscle mass between the Osteosys Primus and the GE Lunar Prodigy. Importantly, use of equations from previous studies would have increased the discrepancy between the Osteosys Primus and the GE Lunar Prodigy.
Deep immune profiling reveals early-stage and highly coordinated immune responses in mild COVID-19 patients
Authors: Capelle Christophe M. , Cire Séverine , Domingues Olivia , Ernens Isabelle , Hedin Fanny , Fischer Aurélie , Snoeck Chantal , Ammerlaan Wim , Konstantinou Maria , Grzyb Kamil , Skupin Alex , Carty Cara L. , Hilger Christiane , Gilson Georges , Celebic Aljosa , Sol Antonio Del , Kaplan Ian M. , Betsou Fay , Abdelrahman Tamir , Cosma Antonio , Vaillant Michel , Fagherazzi Guy , Ollert Markus , Hefeng Feng Q.
Published: September 2021

While immunopathology has been widely studied in severe COVID-19 patients, immunoprotective factors in non-hospitalized patients have remained largely elusive. We systematically analyzed 484 peripheral immune cell signatures, various serological parameters and TCR repertoire in a longitudinal cohort of 63 mild and 15 hospitalized patients versus 14 asymptomatic and 26 control individuals. Within three days following PCR diagnosis, we observed coordinated responses of CD4 and CD8 T cells, various antigen presenting cells and antibody-secreting cells in mild, but not hospitalized COVID-19 patients. This early-stage SARS-CoV-2-specific response was predominantly characterized by substantially expanded clonotypes of CD4 and less of CD8 T cells. The early-stage responses of T cells and dendritic cells were highly predictive for later seroconversion and protective antibody levels after three weeks in mild non-hospitalized, but not in hospitalized patients. Our systemic analysis provides the first full picture and early-stage trajectory of highly coordinated immune responses in mild COVID-19 patients.
Estimating salt consumption in 49 low- and middle-income countries: Development, validation and application of a machine learning model
Authors: Guzman-Vilca Wilmer Cristobal , Castillo-Cara Manuel , Carrillo-Larco Rodrigo M.
Published: September 2021

Background

Global targets to reduce salt intake have been proposed but their monitoring is challenged by the lack of population-based data on salt consumption. We developed a machine learning (ML) model to predict salt consumption based on simple predictors, and applied this model to national surveys in low- and middle-income countries (LMICs).

Methods

Pooled analysis of WHO STEPS surveys. We used 19 surveys with spot urine samples for the ML model derivation and validation; we developed a supervised ML regression model based on: sex, age, weight, height, systolic and diastolic blood pressure. We applied the ML model to 49 new STEPS surveys to quantify the mean salt consumption in the population.

Results

The pooled dataset in which we developed the ML model included 45,152 people. Overall, there were no substantial differences between the observed (8.1 g/day (95% CI: 8.0-8.2 g/day)) and ML-predicted (8.1 g/day (95% CI: 8.1-8.2 g/day)) mean salt intake (p= 0.065). The pooled dataset where we applied the ML model included 157,699 people; the overall predicted mean salt consumption was 8.1 g/day (95% CI: 8.1-8.2 g/day). The countries with the highest predicted mean salt intake were in Western Pacific. The lowest predicted intake was found in Africa. The country-specific predicted mean salt intake was within reasonable difference from the best available evidence.

Conclusions

A ML model based on readily available predictors estimated daily salt consumption with good accuracy. This model could be used to predict mean salt consumption in the general population where urine samples are not available.

Funding

Wellcome Trust (214185/Z/18/Z)
Differential regulation of mouse hippocampal gene expression sex differences by chromosomal content and gonadal sex
Authors: Ocañas Sarah R. , Ansere Victor A. , Tooley Kyla B. , Hadad Niran , Chucair-Elliott Ana J. , Stanford David R. , Rice Shannon , Wronowski Benjamin , Pham Kevin D. , Hoffman Jessica M. , Austad Steven N. , Stout Michael B. , Freeman Willard M.
Published: September 2021

Common neurological disorders, like Alzheimer’s disease (AD), multiple sclerosis (MS), and autism, display profound sex differences in prevalence and clinical presentation. However, sex differences in the brain with health and disease are often overlooked in experimental models. Sex effects originate, either directly or indirectly, from hormonal or sex chromosomal mechanisms. To delineate the contributions of genetic sex (XX v. XY) versus gonadal sex (ovaries v. testes) to the epigenomic regulation of hippocampal sex differences, we use the Four Core Genotypes (FCG) mouse model to uncouple chromosomal and gonadal sex. Transcriptomic and epigenomic analyses of ∼12-month-old FCG mice hippocampi, revealed genomic context-specific regulatory effects of genotypic and gonadal sex on X- and autosome-encoded gene expression and DNA modification patterns. X-chromosomal epigenomic patterns, classically associated with X-inactivation, were established almost entirely by genotypic sex, independent of gonadal sex. Differences in X-chromosome methylation were primarily localized to gene regulatory regions including promoters, CpG islands, CTCF binding sites, and active/poised chromatin, with an inverse relationship between methylation and gene expression. Whereas, autosomal gene expression demonstrated regulation by both genotypic and gonadal sex, particularly in immune processes. These data demonstrate an important regulatory role of sex chromosomes, independent of gonadal sex, on sex-biased hippocampal transcriptomic and epigenomic profiles. Future studies will need to further interrogate specific CNS cell types, identify the mechanisms by which sex chromosomes regulate autosomes, and differentiate organizational from activational hormonal effects.
Simultaneous Dissection of Grain Carotenoid Levels and Kernel Color in Biparental Maize Populations with Yellow-to-Orange Grain
Authors: LaPorte Mary-Francis , Vachev Mishi , Fenn Matthew , Diepenbrock Christine
Published: September 2021

Maize enriched in provitamin A carotenoids could be key in combatting vitamin A deficiency in human populations relying on maize as a food staple. Consumer studies indicate that orange maize may be regarded as novel and preferred. This study identifies genes of relevance for grain carotenoid concentrations and kernel color, through simultaneous dissection of these traits in 10 families of the U.S. maize nested association mapping population that have yellow to orange grain. Quantitative trait loci (QTL) were identified via joint-linkage analysis, with phenotypic variation explained for individual kernel color QTL ranging from 2.4 to 17.5%. These QTL were cross-analyzed with significant marker-trait associations in a genome-wide association study that utilized ∼27 million variants. Nine genes were identified: four encoding activities upstream of the core carotenoid pathway, one at the pathway branchpoint, three within the α- or β-pathway branches, and one encoding a carotenoid cleavage dioxygenase. Of these, three exhibited significant pleiotropy between kernel color and one or more carotenoid traits. Kernel color exhibited moderate positive correlations with β-branch and total carotenoids and negligible correlations with α-branch carotenoids. These findings can be leveraged to simultaneously achieve desirable kernel color phenotypes and increase concentrations of provitamin A and other priority carotenoids.
Substantia nigra degradation results in widespread changes in medial zona incerta afferent and efferent connectomics
Authors: Kim Linda H. , Chomiak Taylor , Tran Michelle A. , Tam Stephanie , McPherson Claire , Eaton Shane E. A. , Ou Young , Kiss Zelma H. T. , Whelan Patrick J.
Published: September 2021

Parkinson’s disease (PD) is a complex disease affecting many facets of movement, especially gait abnormalities such as shuffling and freezing of gait. The nigrostriatal pathways of the basal ganglia are traditionally targeted by existing therapies; however, other pathways may be more relevant to gait, such as the pedunculopontine nucleus and the zona incerta (ZI). The A13 nucleus may be such a target as it has emerged as an area of interest in dopamine motor function. Yet, this area remains understudied compared to other dopamine nuclei, especially in animal models of PD. In 6-OHDA mice, we found a reduction in locomotion in the open field and gait dysfunction during treadmill tests. Medial ZI dopamine cells, containing the A13 nucleus, were preserved following 6-OHDA, in contrast to a marked reduction in substantia nigra pars compacta (SNc) neurons. There was extensive remodelling of the A13 afferent and efferent connectome following nigrostriatal lesions. Afferent input patterns displayed a marked reduction in cross-correlation across brain regions in 6-OHDA mice, while efferent projections showed an increase. In a human PD patient with advanced gait dysfunction we found that the A13 nucleus was preserved, suggesting that remodelling could also occur in humans. This work points to the A13 region as a potential therapeutic target in PD.

Significance Statement

Recently it was found that the medial zona incerta projects to the cuneiform nucleus suggesting a parallel dopaminergic projection onto motor regions. Here we investigated the connectome of the A13 region and examined the afferent and efferent projections in normal mice and mice with a unilateral Parkinsonian mouse model. We found that the connectome was reconfigured following nigrostriatal degeneration. This work provides a comprehensive insight into the plasticity in a dopaminergic-rich area of the zona incerta in PD injury models.
A network-based approach to integrate nutrient environment in the prediction of synthetic lethality in cancer metabolism
Authors: Apaolaza Iñigo , José-Eneriz Edurne San , Valcarcel Luis V. , Agirre Xabier , Prósper Felipe , Planes Francisco J.
Published: September 2021

Synthetic Lethality (SL) is a promising concept in cancer research. A number of computational methods have been developed to predict SL in cancer metabolism, among which our network-based computational approach, based on genetic Minimal Cut Sets (gMCSs), can be found. A major challenge of these approaches to SL is to systematically consider tumor environment, which is particularly relevant in cancer metabolism. Here, we propose a novel definition of SL for cancer metabolism that integrates genetic interactions and nutrient availability in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase inhibition in different environments. Finally, our novel approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies.
Framework estimation of stochastic gene activation using transcription average level
Authors: Chen Liang , Lin Genghong , Jiao Feng
Published: September 2021

Gene activation is usually a non-Markovian process that has been modeled as various frameworks that consist of multiple rate-limiting steps. Understanding the exact activation framework for a gene of interest is a central problem for single-cell studies. In this paper, we focus on the dynamical data of the average transcription level M (t), which is typically neglected when deciphering gene activation. Firstly, the smooth trend lines of M (t) data present rich, visually dynamic features. Secondly, tractable analysis of M (t) allows the establishment of bijections between M (t) dynamics and system parameter regions. Because of these two clear advantages, we can rule out frameworks that fail to capture M (t) features and we can further test potential competent frameworks by fitting M (t) data. We implemented this procedure to determine an exact activation framework for a large number of mouse fibroblast genes under tumor necrosis factor induction; the cross-talk between the signaling and basal pathways is crucial to trigger the first peak of M (t), while the following damped gentle M (t) oscillation is regulated by the multi-step basal pathway. Moreover, the fitted parameters for the mouse genes tested revealed two distinct regulation scenarios for transcription dynamics. Taken together, we were able to develop an efficient procedure for using traditional M (t) data to estimate the gene activation frameworks and system parameters. This procedure, together with sophisticated single-cell transcription data, may facilitate a more accurate understanding of stochastic gene activation.

Author Summary

It has been suggested that genes randomly transit between inactive and active states, with mRNA produced only when a gene is active. The gene activation process has been modeled as a framework of multiple rate-limiting steps listed sequentially, parallel, or in combination. The system step numbers and parameters can be predicted by computationally fitting sophisticated single-cell transcription data. However, current algorithms require a prior hypothetical framework of gene activation. We found that the prior estimation of the framework can be achieved using the traditional dynamical data of mRNA average level M (t) which present easily discriminated dynamical features. The theory regarding M (t) profiles allows us to confidently rule out other frameworks and to determine optimal frameworks by fitting M (t) data. We successfully applied this procedure to a large number of mouse fibroblast genes and confirmed that M (t) is capable of providing a reliable estimation of gene activation frameworks and system parameters.
Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data
Authors: Lotfollahi Mohammad , Dony Leander , Agarwala Harshita , Theis Fabian
Published: September 2021

Learning robust representations can help uncover underlying biological variation in scRNA-seq data. Disentangled representation learning is one approach to obtain such informative as well as interpretable representations. Here, we learn disentangled representations of scRNA-seq data usingβvariational autoencoder (β-VAE) and apply the model for out-of-distribution (OOD) prediction. We demonstrate accurate gene expression predictions for cell-types absent from training in a perturbation and a developmental dataset. We further show thatβ-VAE outperforms a state-of-the-art disentanglement method for scRNA-seq in OOD prediction while achieving better disentanglement performance.
A Generalizable Speech Emotion Recognition Model Reveals Depression and Remission
Authors: Hansen Lasse , Zhang Yan-Ping , Wolf Detlef , Sechidis Konstantinos , Ladegaard Nicolai , Fusaroli Riccardo
Published: September 2021

Objective

Affective disorders are associated with atypical voice patterns; however, automated voice analyses suffer from small sample sizes and untested generalizability on external data. We investigated a generalizable approach to aid clinical evaluation of depression and remission from voice using transfer learning: we train machine learning models on easily accessible non-clinical datasets and test them on novel clinical data in a different language.

Methods

A Mixture-of-Experts machine learning model was trained to infer happy/sad emotional state using three publicly available emotional speech corpora in German and US English. We examined the model’s predictive ability to classify the presence of depression on Danish speaking healthy controls (N = 42), patients with first-episode major depressive disorder (MDD) (N = 40), and the subset of the same patients who entered remission (N = 25) based on recorded clinical interviews. The model was evaluated on raw, de-noised, and speaker-diarized data.

Results

The model showed separation between healthy controls and depressed patients at the first visit, obtaining an AUC of 0.71. Further, speech from patients in remission was indistinguishable from that of the control group. Model predictions were stable throughout the interview, suggesting that 20-30 seconds of speech might be enough to accurately screen a patient. Background noise (but not speaker diarization) heavily impacted predictions.

Conclusion

A generalizable speech emotion recognition model can effectively reveal changes in speaker depressive states before and after remission in patients with MDD. Data collection settings and data cleaning are crucial when considering automated voice analysis for clinical purposes.

Significant outcomes

- Using a speech emotion recognition model trained on other languages, we predicted the presence of MDD with an AUC of 0.71.

- The speech emotion recognition model could accurately detect changes in voice after patients achieved remission from MDD.

- Preprocessing steps, particularly background noise removal, greatly influenced classification performance.

Limitations

- No data from non-remitters, meaning that changes to voice for that group could not be assessed.

- It is unclear how well the model would generalize beyond Germanic languages.

Data availability statement

Due to the nature of the data (autobiographical interviews in a clinical population), the recordings of the participants cannot be shared publicly. The aggregated model predictions and code used to run the analyses is available athttps://github.com/HLasse/SERDepressionDetection.
Response outcome gates the effect of spontaneous cortical state fluctuations on perceptual decisions
Authors: Reato Davide , Steinfeld Raphael , Tacão-Monteiro André , Renart Alfonso
Published: September 2021

Sensory responses of cortical neurons are more discriminable when evoked on a base-line of desynchronized spontaneous activity, but cortical desynchronization has not generally been associated with more accurate perceptual decisions. Here we show that mice perform more accurate auditory judgements when activity in the auditory cortex is elevated and desynchronized before stimulus onset, but only if the previous trial was an error, and that this relationship is occluded if previous outcome is ignored. We confirmed that the outcome-dependent effect of brain state on performance is neither due to idiosyncratic associations between the slow components of either signal, nor to the existence of specific cortical states evident only after errors. Instead, errors appear to gate the effect of cortical state fluctuations on discrimination accuracy. Neither facial movements nor pupil size during the baseline were associated with accuracy, but they were predictive of measures of responsivity, such as the probability of not responding to the stimulus or of responding prematurely. These results suggest that the functional role of cortical state on behavior is dynamic and constantly regulated by performance monitoring systems.
HiCImpute: A Bayesian Hierarchical Model for Identifying Structural Zeros and Enhancing Single Cell Hi-C Data
Authors: Xie Qing , Han Chenggong , Jin Victor , Lin Shili
Published: September 2021

Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicate things further is the fact that not all zeros are created equal, as some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros), whereas others are indeed due to insufficient sequencing depth (sampling zeros), especially for loci that interact infrequently. Differentiating between structural zeros and sampling zeros is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of zeros has received little attention in the single cell Hi-C literature, where the issue of sparsity has been addressed mainly as a data quality improvement problem. To fill this gap, in this paper, we propose HiCImpute, a Bayesian hierarchy model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. HiCImpute takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available. Through an extensive set of analyses of synthetic and real data, we demonstrate the ability of HiCImpute for identifying structural zeros with high sensitivity, and for accurate imputation of dropout values in sampling zeros. Downstream analyses using data improved from HiCImpute yielded much more accurate clustering of cell types compared to using observed data or data improved by several comparison methods. Most significantly, HiCImpute-improved data has led to the identification of subtypes within each of the excitatory neuronal cells of L4 and L5 in the prefrontal cortex.
Kronos scRT: a uniform framework for single-cell replication timing analysis
Authors: Gnan Stefano , Josephides Joseph M. , Wu Xia , Spagnuolo Manuela , Saulebekova Dalila , Bohec Mylène , Dumont Marie , Baudrin Laura G. , Fachinetti Daniele , Baulande Sylvain , Chen Chun-Long
Published: September 2021

Mammalian genomes are replicated in a cell-type specific order and in coordination with transcription and chromatin organization. Although the field of replication is also entering the single-cell era, current studies require cell sorting, individual cell processing and have yielded a limited number (<100) of cells. Here, we have developed Kronos scRT (https://github.com/CL-CHEN-Lab/Kronos_scRT), a software for single-cell Replication Timing (scRT) analysis. Kronos scRT does not require a specific platform nor cell sorting, allowing the investigation of large datasets obtained from asynchronous cells. Analysis of published available data and droplet-based scWGS data generated in the current study, allows exploitation of scRT data from thousands of cells for different mouse and human cell lines. Our results demonstrate that, although most cells replicate within a close timing range for a given genomic region, replication can also occur stochastically throughout S phase. Altogether, Kronos scRT allows investigating the RT program at a single-cell resolution for both homogeneous and heterogeneous cell populations in a fast and comprehensive manner.
Sex differences in the reward value of familiar mates in prairie voles
Authors: Vahaba Daniel M. , Halstead Emily R. , Donaldson Zoe R. , Ahern Todd H. , Beery Annaliese K.
Published: September 2021

The rewarding properties of social interactions facilitate relationship formation and maintenance. Prairie voles are one of the few laboratory species that form selective relationships, manifested as “partner preferences” for familiar partners versus strangers. While both sexes exhibit strong partner preferences, this similarity in outward behavior likely results from sex-specific neurobiological mechanisms. We recently used operant conditioning to demonstrate that females work harder for access to a familiar versus unfamiliar conspecific of either sex, while males worked equally hard for access to any female, indicating a key sex difference in social motivation. As tests were performed with one social target at a time, males might have experienced a ceiling effect, and familiar females might be more relatively rewarding in a choice scenario. Here we performed a social choice operant task in which voles could repeatedly lever-press to gain temporary access to either the chamber containing their mate or one containing a novel opposite-sex vole. Females worked hardest to access their mate, while males pressed at similar rates for either female. Individual male behavior was heterogeneous, congruent with multiple mating strategies in the wild. Voles exhibited preferences for favorable over unfavorable environments in a non-social operant task, indicating that lack of social preference does not reflect lack of discrimination between chambers. Oxytocin receptor genotype at the intronic SNP NT213739 replicated a prior association with stranger-directed aggression within the test. These findings suggest that convergent preference behavior in male and female voles results from sex-divergent pathways, particularly in the realm of social motivation.
microRNAs as Indicators into the Causes and Consequences of Whole Genome Duplication Events
Authors: Peterson Kevin J. , Beavan Alan , Chabot Peter , McPeek Mark L. , Pisani Davide , Fromm Bastian , Simakov Oleg
Published: September 2021

Whole genome duplications (WGDs) have long been considered the causal mechanism underlying the dramatic increase in vertebrate morphological complexity relative to invertebrates. This is due to the retention and neo-functionalization of paralogues generated during these events, evolving new regulatory circuits, and ultimately morphological novelty. Nonetheless, an alternative hypothesis suggests that behind the retention of most paralogues is not neo-functionalization, but instead the degree of the inter-connectivity of the intended gene product, as well as the mode of the WGD itself. Here, we explore both the causes and consequences of WGD by examining the distribution, expression, and molecular evolution of microRNAs (miRNAs) in both gnathostome vertebrates as well as chelicerate arthropods. We find that although the number of miRNA paralogues tracks the number of WGDs experienced within the lineage, few of these paralogues experienced changes to the seed sequence, and thus are functionally equivalent relative to their mRNA targets. Nonetheless, the paralogues generated by the gnathostome 2R allotetraploidization event are retained in higher numbers on one sub-genome relative the second, with the miRNAs found on the preferred set of paralogons showing both higher expression of mature miRNA transcripts and slower molecular evolution of the precursor miRNA sequences. Importantly, WGDs do not result in the creation of miRNA novelty, nor do WGDs correlate to increases in complexity. Instead, it is the number of miRNA seed sequences in the genome itself that not only better correlate to instances in complexification, but also mechanistically explain why complexity increases when new miRNA families are established.
Determination of hemicellulose, cellulose, holocellulose and lignin content using FTIR inCalycophyllum spruceanum(Benth.) K.Schum. andGuazuma crinitaLam.
Authors: Javier-Astete Rosario , Jimenez-Davalos Jorge , Zolla Gaston
Published: September 2021

Capirona (Calycophyllum spruceanum(Benth.) K. Schum.) and Bolaina (Guazuma crinitaLam.) are fast-growing Amazonian trees with increasing demand in timber industry. Therefore, it is necessary to determine the content of cellulose, hemicellulose, holocellulose and lignin in juvenile tress to accelerate forest breeding programs. The aim of this study was to identify chemical differences between apical and basal stem of Capirona and Bolaina to develop models for estimating the chemical composition using Fourier transform infrared (FTIR) spectra. FTIR-ATR spectra were obtained from 150 samples for each specie that were 1.8 year-old. The results showed significant differences between the apical and basal stem for each species in terms of cellulose, hemicellulose, holocellulose and lignin content. This variability was useful to build partial least squares (PLS) models from the FTIR spectra and they were evaluated by root mean squared error of predictions (RMSEP) and ratio of performance to deviation (RPD). Lignin content was efficiently predicted in Capirona (RMSEP = 0.48, RPD > 2) and Bolaina (RMSEP = 0.81, RPD > 2). In Capirona, the predictive power of cellulose, hemicellulose and holocellulose models (0.68 < RMSEP < 2.06, 1.60 < RPD < 1.96) were high enough to predict wood chemical composition. In Bolaina, model for cellulose attained an excellent predictive power (RMSEP = 1.82, RPD = 6.14) while models for hemicellulose and holocellulose attained a good predictive power (RPD > 2.0). This study showed that FTIR-ATR together with PLS is a reliable method to determine the wood chemical composition in juvenile trees of Capirona and Bolaina.
Redox-dependent Structure and Dynamics of Macrophage Migration Inhibitory Factor Reveal Sites of Latent Allostery
Authors: Skeens Erin , Gadzuk-Shea Meagan , Shah Dilip , Bhandari Vineet , Schweppe Devin K. , Berlow Rebecca B. , Lisi George P.
Published: September 2021

Macrophage migration inhibitory factor (MIF) is a multifunctional immunoregulatory protein that is a key player in the innate immune response. Given its overexpression at sites of inflammation in a wide range of diseases marked by increasingly oxidative cellular environment, a comprehensive structural understanding of how cellular redox conditions may impact the structure and function of MIF is necessary. We used solution NMR spectroscopy and mass spectrometry to investigate structural and dynamic signatures of MIF under varied solution redox conditions. Our results indicate that the MIF structure is modified and becomes increasingly dynamic in an oxidative environment, which may be a means to alter the MIF functional response in a redox-dependent manner. We identified latent allosteric sites within MIF that are redox-sensitive and mutational analysis reveals that loss of redox-responsive residues attenuates activation of the coreceptor CD74. Leveraging sites of redox-sensitivity therefore reveals an avenue to modulate MIF function in its “disease state” via structure-based drug design.