THE ROLE OF GENETICS IN DISEASE DIAGNOSIS AND TREATMENT MITOCHONDRIAL RESPIRATORY CHAIN DYSREGULATION IN GENOMIC MEDICINE

Although mitochondrial DNA respiration circuit abnormalities are among the most common metabolic diseases to manifest in children, identification can be difficult due to their medical variability. Given the multisystem nature of the condition and its diverse and generalized manifestations, making a final diagnosis often takes a long time. Within this summary, they give an in-depth account of the physical signs of adolescent Mitochondrial Respiratory Chain Disorders (MRCDs),analyze the available diagnostics and treatment possibilities, and emphasize current developments in this field of study. During the discovery of fresh biomarkers and the development of next generation sequencing (NGS) technology, extensive research over the years has considerably enhanced the regularity that precise diagnoses are produced. Given the intricate nature of mitochondrial DNA biology and its double genomic investments, Sequencing has made significant progress in identifying the genetic basis of Mitochondrial Respiratory Chain Disorders (MRCDs). Research studies have been created employing a variety of various methods of therapy in an effort to shift the goal on therapy that is mainly curative to possibly having a positive impact on the natural course of the trouble. That's because there is gained a greater awareness of the underlying causes of this category of ailments.

Application of Deep Learning Technique for the Detection of Coronary Heart Disease (CHD)

A blockage or cessation of blood flow to our heart muscle is a sign of Coronary Heart Disease (CHD), which is caused by an accumulation of fatty substances in the coronary arteries known as atheroma. The principal blood channels that deliver blood to the heart are known as coronary arteries. In this paper, the possible application of deep learning technique is explored for the detection of Coronary Heart Disease. In this study, the methodology includes infrastructure creation for the detection of CHD using supervised and unsupervised learning. The result shows the proceeding to manage the data set and process the data there, user input is appropriately gathered. The embedded system that conducts data translation is linked to the controller sensor, which regulates the CHD pulse system. The study concludes that the diagnostic accuracy of the deep learning model using Decision Tree (DT) and Naive Bayes (NB) Classification is effective and error-free. DT and NB Classification is an effective way of detecting CHD by the deep learning model. The future potential of this paper is the development of models for heart disease diagnoses in patients around the world.

Potential drug-drug interactions among hospitalised TB patients

BACKGROUND: Hospitalised TB patients are at heightened risk for developing drug-drug interactions (DDIs) due to overlapping CYP450 enzyme and/or drug transporter biotransformation of anti-TB drugs and co-medications given for treating TB-associated comorbidities. We aimed to compare the occurrence, characterisation and determinants of database identified potential DDIs (pDDIs) associated with first-line anti-TB drugs and other co-medications using a subscription and free access drug information database.METHOD: This was a single-centre retrospective study to assess pDDIs between first-line anti-TB drugs and other medications for comorbidities among hospitalised TB patients using IBM Micromedex^® and Drugs.com.RESULTS: On multivariate regression analysis, hospitalised TB patients with comorbidities such as diabetes mellitus, HIV infection and hypertension, longer hospitalisation, and patients administered with more than seven drugs during their hospital stay were associated with increased risk for the occurrence of pDDIs. Significant discrepancies were observed in the detection and severity of pDDIs between IBM Micromedex and Drugs.com.CONCLUSION: We recommend using free access drug information database to a subscription drug information database in drug interaction screening protocols in clinics for enhanced identification of pDDIs and reducing monetary burden in resource-limited settings.

P-381 A novel natural cycle proliferative phase/progesterone replacement(NC/PR) protocol for endometrial preparation for frozen-thawed embryo transfer(FET) Can it improve pregnancy outcomes : a prospective comparative study

Study question

Can a novel protocol for endometrial preparation- NC/PR-FET, lead to better pregnancy outcomes than artificial cycle (AC)-FET, with similar ease of cycle monitoring and scheduling?

Summary answer

NC/PR-FET protocol gave comparable clinical pregnancy rates (CPR), significantly less early miscarriage rates (MR) and significantly more live birth rates (LBR) compared to AC-FET protocol.

What is known already

AC-FET is the most common protocol for endometrial preparation due to flexibility, easy monitoring, and high pregnancy rates albeit with evidence of higher obstetrical risks e.g., miscarriage, pre-eclampsia versus NC-FET. This risk could be due to suboptimal hormonal administration in AC-FET leading to impaired endometrial maturation, predecidualization, and altered endometrial function. Studies indicate, estradiol may have a stronger negative effect(versus progesterone) on the endometrial transcriptome. Hence, we studied a novel protocol with NC-estrogen (but with progesterone similar to AC-FET), for better endometrial maturation, predecidualization along with simple monitoring and flexible ET scheduling. Previously, a proof-of-concept case-series has been reported.

Study design, size, duration

The prospective comparative study was conducted at a tertiary center (Jan2019-Dec2021) with 126 patients (both autologous [AO] and donor oocytes [DO]):79 (AC-FET) and 47 (NC/PR-FET) with similar ICSI, vitrification, ET protocols. 2 or 3 Grade A cleavage stage embryos were transferred. CPR, implantation rates(IR), early MR (≤13 weeks, miscarriages/clinical pregnancies) and LBR (≥28 weeks, LBs/ETs) were compared. Serum progesterone was studied on day 5 post-ET. Antenatal care and most deliveries were at the same institute.

Participants/materials, setting, methods

Patients were non-smokers with normal hysteroscopy. In AC-FET, oral estradiol (6-8mg) was initiated from day 1-2 (for 10-17 days). In both groups, when endometrium ≥7mm and S.progesterone<1ng/ml, intramuscular progesterone was initiated(daily till day 4 post-ET, then every 3rd day till week 8). ET was scheduled on day 4 of intramuscular progesterone, and concurrently, vaginal progesterone was added (till week 10). Chi-square, t-tests, and multiple regression was used for statistical analysis and 2-tailed P <.05 indicated statistical significance.

Main results and the role of chance

Mean age and BMI were comparable in NC/PR-FET and AC-FET groups: 34.48±4.69 years vs. 33.58 ± 5.62 years (P =.35) and 26.27 ± 3.62 kg/m2 vs. 27.34 ± 4.61kg/m2 (P =.18) respectively. Similarly, the source of oocytes (%AO) and % singletons were also comparable in NC/PR-FET and AC-FET groups: 57.4% vs. 46.8% (P = .25), and 78.1% vs. 75% (P= .75) respectively. There were 3 ectopic pregnancies overall. The CPRs and IRs were comparable: 68.09% (NC/PR-FET) vs. 60.76% (AC-FET) (P=.41) and 32.30% (NC/PR-FET) vs. 28.96% (AC-FET) (P=.45) respectively.

The early MR was significantly more in AC-FET vs. NC/PR-FET: 29.17% vs. 6.25% (P =.012). Consequently, LBRs were significantly more in NC/PR-FET vs. AC-FET: 59.57% vs. 39.24% (P =.028). After adjusting for age, BMI, AO/DO, this difference remained significant (AOR 2.66; 95% CI 1.21-5.81; P =.014)

Mean serum progesterone was comparable in all patients in both groups: 38.50 ± 13.6ng/ml (NC/PR-FET) vs. 40.98 ± 28.64 ng/ml (AC-FET) (P =.57). Serum progesterone levels were also comparable in patients who were pregnant in both groups: 38.51 ± 14.79 (NC/PR-FET) vs. 42.25 ± 32.63 (AC-FET) (P =.53).Also, in the AC-FET group serum progesterone was comparable in patients who aborted,44.23 ± 31.60 vs. those who didn’t, 41.48 ± 33.44 (P =.79).

Limitations, reasons for caution

This was a single center study. Due to limited sample size, we could not control for many potential confounders (e.g., multiple pregnancies). For the same reason, we could not compare obstetrical risks as a study outcome.

Wider implications of the findings

This study shows significantly better pregnancy outcomes with a novel NC/PR-FET protocol vs. AC-FET. This needs to be validated in larger studies. Since suboptimal hormone administration in AC-FET could be causal factor for pre-eclampsia, larger studies can test if the NC/PR-FET protocol can lead to better obstetrical outcomes.

Trial registration number

not applicable

Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study

BACKGROUND

Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces.

OBJECTIVE

We designed a strategy to gamify the collection and labeling of child emotion-enriched images to boost the performance of automatic child emotion recognition models to a level closer to what will be needed for digital health care approaches.

METHODS

We leveraged our prototype therapeutic smartphone game, GuessWhat, which was designed in large part for children with developmental and behavioral conditions, to gamify the secure collection of video data of children expressing a variety of emotions prompted by the game. Independently, we created a secure web interface to gamify the human labeling effort, called HollywoodSquares, tailored for use by any qualified labeler. We gathered and labeled 2155 videos, 39,968 emotion frames, and 106,001 labels on all images. With this drastically expanded pediatric emotion-centric database (>30 times larger than existing public pediatric emotion data sets), we trained a convolutional neural network (CNN) computer vision classifier of happy, sad, surprised, fearful, angry, disgust, and neutral expressions evoked by children.

RESULTS

The classifier achieved a 66.9% balanced accuracy and 67.4% F1-score on the entirety of the Child Affective Facial Expression (CAFE) as well as a 79.1% balanced accuracy and 78% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels. This performance is at least 10% higher than all previously developed classifiers evaluated against CAFE, the best of which reached a 56% balanced accuracy even when combining "anger" and "disgust" into a single class.

CONCLUSIONS

This work validates that mobile games designed for pediatric therapies can generate high volumes of domain-relevant data sets to train state-of-the-art classifiers to perform tasks helpful to precision health efforts.

Identification of Social Engagement Indicators Associated With Autism Spectrum Disorder Using a Game-Based Mobile App: Comparative Study of Gaze Fixation and Visual Scanning Methods

BACKGROUND

Autism spectrum disorder (ASD) is a widespread neurodevelopmental condition with a range of potential causes and symptoms. Standard diagnostic mechanisms for ASD, which involve lengthy parent questionnaires and clinical observation, often result in long waiting times for results. Recent advances in computer vision and mobile technology hold potential for speeding up the diagnostic process by enabling computational analysis of behavioral and social impairments from home videos. Such techniques can improve objectivity and contribute quantitatively to the diagnostic process.

OBJECTIVE

In this work, we evaluate whether home videos collected from a game-based mobile app can be used to provide diagnostic insights into ASD. To the best of our knowledge, this is the first study attempting to identify potential social indicators of ASD from mobile phone videos without the use of eye-tracking hardware, manual annotations, and structured scenarios or clinical environments.

METHODS

Here, we used a mobile health app to collect over 11 hours of video footage depicting 95 children engaged in gameplay in a natural home environment. We used automated data set annotations to analyze two social indicators that have previously been shown to differ between children with ASD and their neurotypical (NT) peers: (1) gaze fixation patterns, which represent regions of an individual's visual focus and (2) visual scanning methods, which refer to the ways in which individuals scan their surrounding environment. We compared the gaze fixation and visual scanning methods used by children during a 90-second gameplay video to identify statistically significant differences between the 2 cohorts; we then trained a long short-term memory (LSTM) neural network to determine if gaze indicators could be predictive of ASD.

RESULTS

Our results show that gaze fixation patterns differ between the 2 cohorts; specifically, we could identify 1 statistically significant region of fixation (P<.001). In addition, we also demonstrate that there are unique visual scanning patterns that exist for individuals with ASD when compared to NT children (P<.001). A deep learning model trained on coarse gaze fixation annotations demonstrates mild predictive power in identifying ASD.

CONCLUSIONS

Ultimately, our study demonstrates that heterogeneous video data sets collected from mobile devices hold potential for quantifying visual patterns and providing insights into ASD. We show the importance of automated labeling techniques in generating large-scale data sets while simultaneously preserving the privacy of participants, and we demonstrate that specific social engagement indicators associated with ASD can be identified and characterized using such data.

Improved detection of disease-associated gut microbes using 16S sequence-based biomarkers

Background

Sequencing partial 16S rRNA genes is a cost effective method for quantifying the microbial composition of an environment, such as the human gut. However, downstream analysis relies on binning reads into microbial groups by either considering each unique sequence as a different microbe, querying a database to get taxonomic labels from sequences, or clustering similar sequences together. However, these approaches do not fully capture evolutionary relationships between microbes, limiting the ability to identify differentially abundant groups of microbes between a diseased and control cohort. We present sequence-based biomarkers (SBBs), an aggregation method that groups and aggregates microbes using single variants and combinations of variants within their 16S sequences. We compare SBBs against other existing aggregation methods (OTU clustering and Microphenoor DiTaxa features) in several benchmarking tasks: biomarker discovery via permutation test, biomarker discovery via linear discriminant analysis, and phenotype prediction power. We demonstrate the SBBs perform on-par or better than the state-of-the-art methods in biomarker discovery and phenotype prediction.

Results

On two independent datasets, SBBs identify differentially abundant groups of microbes with similar or higher statistical significance than existing methods in both a permutation-test-based analysis and using linear discriminant analysis effect size. . By grouping microbes by SBB, we can identify several differentially abundant microbial groups (FDR <.1) between children with autism and neurotypical controls in a set of 115 discordant siblings. Porphyromonadaceae, Ruminococcaceae, and an unnamed species of Blastocystis were significantly enriched in autism, while Veillonellaceae was significantly depleted. Likewise, aggregating microbes by SBB on a dataset of obese and lean twins, we find several significantly differentially abundant microbial groups (FDR<.1). We observed Megasphaera andSutterellaceae highly enriched in obesity, and Phocaeicola significantly depleted. SBBs also perform on bar with or better than existing aggregation methods as features in a phenotype prediction model, predicting the autism phenotype with an ROC-AUC score of .64 and the obesity phenotype with an ROC-AUC score of .84.

Conclusions

SBBs provide a powerful method for aggregating microbes to perform differential abundance analysis as well as phenotype prediction. Our source code can be freely downloaded from http://github.com/briannachrisman/16s_biomarkers.

Prevalence of TEM, SHV, and CTX-M Beta-Lactamase genes in the urinary isolates of a tertiary care hospital

Introduction:

Extended-spectrum beta-lactamases (ESBLs) are the major cause of resistance to beta-lactam antibiotics such as penicillins, cephalosporins, and monobactams. They are derived from the narrow-spectrum beta-lactamases (TEM-1, TEM-2, or SHV-1) by mutations that alter the amino acid configuration around the enzyme active site.

Aim:

To determine the prevalence of ESBL (bla_TEM, bla_CTX-M, and bla_SHV) genes among the members of Enterobacteriaceae.

Methodology:

The present prospective study was carried out from January 2015 to June 2015 in the Department of Microbiology and Molecular Medicine of a Teaching Tertiary Care Hospital. A total of 526 urine samples were studied. Seventy-eight isolates were subjected to polymerase chain reaction for detection of ESBL genes.

Results:

In our study, ESBL genes were detected among 18 (45%) phenotypically confirmed ESBL producers and 20 (52.5%) phenotypically confirmed non-ESBL producers. The gene that predominated was bla_TEM (48.7%), followed by bla_CTX-M (7.6%) and bla_SHV (5.1%).

Conclusion:

Definitive identification of ESBL genes is only possible by molecular detection methods. Phenotypic tests need to be evaluated periodically as their performance may change with the introduction of new enzymes.

P–406 Placental histopathology is different in specific subsets of ICSI singleton pregnancies with programmed cycles : a prospective study

Study question

Does embryo vitrification or donor oocytes (DO) alter the histopathology of the placenta in ICSI singleton pregnancies with similar endometrial preparation?

Summary answer

Placentas from programmed cycles had significantly more immune/idiopathic-inflammation with vitrified-thawed embryos versus fresh transfer and significantly more maternal vascular-malperfusion(MVM) in DO versus autologous oocyte(AO) pregnancies.

What is known already

DO pregnancies and frozen embryo transfer(FET) pregnancies with programmed cycles are associated with hypertensive complications. As these complications are linked with abnormal placentation, comparing the placental histopathology in these pregnancies may point to a causative association.

Studies of placental histopathology in DO in comparison to AO pregnancies show a dysregulated immune process and vasculopathy. The hormonal milieu during implantation remains an important confounder.

Placental histopathology in fresh/ frozen cycles has recently shown variable results. To isolate the effect of embryo vitrification on placental histopathology, the donor oocyte model can provide valuable data, which till now is scarcely available.

Study design, size, duration

A prospective cohort study conducted in a tertiary center from 2018–2020.

Placental histopathology, pregnancy-outcomes were studied in 116 ICSI singleton pregnancies≥28 weeks.

Group1-Pregnancies with DO, by FET(n = 32) and freshET(n = 34) were compared to study the effect of embryo-vitrification.

Group2-Pregnancies by DO FET(n = 32) were compared to AO FET(n = 50) to study the effect of DO.

All patients had ICSI, cleavage embryo-transfer, programmed cycles and delivered at the same institute.

The placentas were examined by pathologists (blinded to the ET type).

Participants/materials, setting, methods

116 singleton pregnancies were followed for hypertensive disorders of pregnancy (HDP), preterm delivery(PTD&lt;37weeks) and low birth-weight (LBW&lt;2.5kg).

Placentas were examined for cord mal-insertions

Placental histopathology lesions were classified into 4 groups according to ‘Amsterdam criteria’ infectious-inflammatory, immune/ idiopathic-inflammatory, MVM, fetal vascular malperfusion (FVM).

Chi-square and t-tests were used to compare outcomes across groups. Adjusted odds ratio were calculated using logistic regression. Statistical significance set at P &lt;.05, two-tailed.

Main results and the role of chance

No patient had a history of chronic hypertension/smoking. Group 1 Patients conceived by DO, with FET and freshET were comparable with regards to age (34.1 vs 36.4years, P=.07),BMI(26.7 vs 27.1 kg/m2,P=.6),nulliparity(81%vs82%,P=.9) HDP(25%vs29.4%,P=0.69),birth-weight(2.48 vs 2.47kg,P=.93) LBW(31.3%vs41.2%,P=.41)respectively

PTD was significantly less in donor FET versus donor freshET (6.3%vs47.1%P=.0002) Placental weight and cord mal-insertions were comparable for FET vs freshET (466 vs 486gms P=.03 12.5%vs23.5% P=.25)respectively. Amongst the placental histopathology lesions, immune/ idiopathic-inflammatory lesions were significantly more in the FET vs freshET group (37.5% vs 11.8%,P=.02)The other lesions were comparable infectious-inflammatory(6.3%vs17.6%,P=.16), MVM(75%vs58.8%, P=.16),FVM(18.8%vs17.6%,P=.9)

Group 2 Patients conceived by DO compared to AO by FET were significantly older and had a higher BMI (34.1vs31.7years,P=.02 ,26.7vs25.5 kg/m2,P=.002) respectively.

Nulliparity was comparable(81%vs92%,P=.15) Birth weight was significantly less in DO vs AO(2.4vs2.7kg,P=.02) HDP and LBW were significantly more in DO vs AO(25%vs8% ,P=.03, 31.3%vs 8%,P=.007),respectively. PTD was comparable(6.3%vs8.0%,P=.77). Placental weight was significantly less in DO vs AO (466 vs 513gms,P=.03) cord mal-insertions were comparable(12.5% vs 24%,P=.2) The MVM lesions were significantly more in the DO group compared to AO(75% vs 40%,P=.002)

The difference remained after adjusting for age/BMI/HDP (AOR 4.31;95% CI 1.24–14.8;P=.02). The rest of placental lesions were comparable in DO vs AO, infectious-inflammatory lesions(6.3%vs16%,P=.19) immune/idiopathic-inflammatory lesions(37.5%vs28%,P=.37) FVM(18.8% vs 12%,P=.4)respectively.

Limitations, reasons for caution

These findings are based on a small number of patients. The results observed need to be confirmed using a larger study sample.

Wider implications of the findings: Placentas in pregnancies by embryo-vitrification, in a DO-model, had significantly more immune/idiopathic-inflammation, the cause/significance of this needs to be explored. Placentas in DO-pregnancies had significantly more MVM-lesions and increased risk of HDP, emphasizing the clinical/histopathological link of DO with HDP and the need for counselling/preventive strategies for HDP in DO-pregnancie.

Trial registration number

Not applicable

Contact Force Mediated Rapid Deposition of Colloidal Microspheres Flowing over Microstructured Barriers

Deposition of particles while flowing past constrictions is a ubiquitous phenomenon observed in diverse systems. Some common examples are jamming of salt crystals near the orifice of salt shakers, clogging of filter systems, gridlock in vehicular traffic, etc. Our work investigates the deposition events of colloidal microspheres flowing over microstructured barriers in microfluidic devices. The interplay of DLVO, contact, and hydrodynamic forces in facilitating rapid deposition of microspheres is discussed. Noticeably, a decrease in the electrostatic repulsion among microspheres leads to linear chain formations, whereas an increase in roughness results in rapid deposition.

A maximum flow-based network approach for identification of stable noncoding biomarkers associated with the multigenic neurological condition, autism

Background

Machine learning approaches for predicting disease risk from high-dimensional whole genome sequence (WGS) data often result in unstable models that can be difficult to interpret, limiting the identification of putative sets of biomarkers. Here, we design and validate a graph-based methodology based on maximum flow, which leverages the presence of linkage disequilibrium (LD) to identify stable sets of variants associated with complex multigenic disorders.

Results

We apply our method to a previously published logistic regression model trained to identify variants in simple repeat sequences associated with autism spectrum disorder (ASD); this L₁-regularized model exhibits high predictive accuracy yet demonstrates great variability in the features selected from over 230,000 possible variants. In order to improve model stability, we extract the variants assigned non-zero weights in each of 5 cross-validation folds and then assemble the five sets of features into a flow network subject to LD constraints. The maximum flow formulation allowed us to identify 55 variants, which we show to be more stable than the features identified by the original classifier.

Conclusion

Our method allows for the creation of machine learning models that can identify predictive variants. Our results help pave the way towards biomarker-based diagnosis methods for complex genetic disorders.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00262-x.

Estimating sequencing error rates using families

BACKGROUND

As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample.

RESULTS

We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method's versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites.

CONCLUSION

Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.

Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection

Standard medical diagnosis of mental health conditions requires licensed experts who are increasingly outnumbered by those at risk, limiting reach. We test the hypothesis that a trustworthy crowd of non-experts can efficiently annotate behavioral features needed for accurate machine learning detection of the common childhood developmental disorder Autism Spectrum Disorder (ASD) for children under 8 years old. We implement a novel process for identifying and certifying a trustworthy distributed workforce for video feature extraction, selecting a workforce of 102 workers from a pool of 1,107. Two previously validated ASD logistic regression classifiers, evaluated against parent-reported diagnoses, were used to assess the accuracy of the trusted crowd's ratings of unstructured home videos. A representative balanced sample (N = 50 videos) of videos were evaluated with and without face box and pitch shift privacy alterations, with AUROC and AUPRC scores > 0.98. With both privacy-preserving modifications, sensitivity is preserved (96.0%) while maintaining specificity (80.0%) and accuracy (88.0%) at levels comparable to prior classification methods without alterations. We find that machine learning classification from features extracted by a certified nonexpert crowd achieves high performance for ASD detection from natural home videos of the child at risk and maintains high sensitivity when privacy-preserving mechanisms are applied. These results suggest that privacy-safeguarded crowdsourced analysis of short home videos can help enable rapid and mobile machine-learning detection of developmental delays in children.

Indels in SARS-CoV-2 occur at template-switching hotspots

The evolutionary dynamics of SARS-CoV-2 have been carefully monitored since the COVID-19 pandemic began in December 2019. However, analysis has focused primarily on single nucleotide polymorphisms and largely ignored the role of insertions and deletions (indels) as well as recombination in SARS-CoV-2 evolution. Using sequences from the GISAID database, we catalogue over 100 insertions and deletions in the SARS-CoV-2 consensus sequences. We hypothesize that these indels are artifacts of recombination events between SARS-CoV-2 replicates whereby RNA-dependent RNA polymerase (RdRp) re-associates with a homologous template at a different loci ("imperfect homologous recombination"). We provide several independent pieces of evidence that suggest this. (1) The indels from the GISAID consensus sequences are clustered at specific regions of the genome. (2) These regions are also enriched for 5' and 3' breakpoints in the transcription regulatory site (TRS) independent transcriptome, presumably sites of RNA-dependent RNA polymerase (RdRp) template-switching. (3) Within raw reads, these indel hotspots have cases of both high intra-host heterogeneity and intra-host homogeneity, suggesting that these indels are both consequences of de novo recombination events within a host and artifacts of previous recombination. We briefly analyze the indels in the context of RNA secondary structure, noting that indels preferentially occur in "arms" and loop structures of the predicted folded RNA, suggesting that secondary structure may be a mechanism for TRS-independent template-switching in SARS-CoV-2 or other coronaviruses. These insights into the relationship between structural variation and recombination in SARS-CoV-2 can improve our reconstructions of the SARS-CoV-2 evolutionary history as well as our understanding of the process of RdRp template-switching in RNA viruses.

Selection of trustworthy crowd workers for telemedical diagnosis of pediatric autism spectrum disorder

Crowd-powered telemedicine has the potential to revolutionize healthcare, especially during times that require remote access to care. However, sharing private health data with strangers from around the world is not compatible with data privacy standards, requiring a stringent filtration process to recruit reliable and trustworthy workers who can go through the proper training and security steps. The key challenge, then, is to identify capable, trustworthy, and reliable workers through high-fidelity evaluation tasks without exposing any sensitive patient data during the evaluation process. We contribute a set of experimentally validated metrics for assessing the trustworthiness and reliability of crowd workers tasked with providing behavioral feature tags to unstructured videos of children with autism and matched neurotypical controls. The workers are blinded to diagnosis and blinded to the goal of using the features to diagnose autism. These behavioral labels are fed as input to a previously validated binary logistic regression classifier for detecting autism cases using categorical feature vectors. While the metrics do not incorporate any ground truth labels of child diagnosis, linear regression using the 3 correlative metrics as input can predict the mean probability of the correct class of each worker with a mean average error of 7.51% for performance on the same set of videos and 10.93% for performance on a distinct balanced video set with different children. These results indicate that crowd workers can be recruited for performance based largely on behavioral metrics on a crowdsourced task, enabling an affordable way to filter crowd workforces into a trustworthy and reliable diagnostic workforce.

Feature replacement methods enable reliable home video analysis for machine learning detection of autism

Autism Spectrum Disorder is a neuropsychiatric condition affecting 53 million children worldwide and for which early diagnosis is critical to the outcome of behavior therapies. Machine learning applied to features manually extracted from readily accessible videos (e.g., from smartphones) has the potential to scale this diagnostic process. However, nearly unavoidable variability in video quality can lead to missing features that degrade algorithm performance. To manage this uncertainty, we evaluated the impact of missing values and feature imputation methods on two previously published autism detection classifiers, trained on standard-of-care instrument scoresheets and tested on ratings of 140 children videos from YouTube. We compare the baseline method of listwise deletion to classic univariate and multivariate techniques. We also introduce a feature replacement method that, based on a score, selects a feature from an expanded dataset to fill-in the missing value. The replacement feature selected can be identical for all records (general) or automatically adjusted to the record considered (dynamic). Our results show that general and dynamic feature replacement methods achieve a higher performance than classic univariate and multivariate methods, supporting the hypothesis that algorithmic management can maintain the fidelity of video-based diagnostics in the face of missing values and variable video quality.

Swimming statistics of cargo-loaded single bacteria

Burgeoning interest in the area of bacteria-powered micro robotic systems prompted us to study the dynamics of cargo transport by single bacteria. In this paper, we have studied the swimming behaviour of oil-droplets attached as cargo to the cell bodies of single bacteria. The oil-droplet loaded bacteria exhibit super-diffusive motion which is characterised by a high degree of directional persistence. Interestingly, bacteria could navigate with cargo size as large as 8 μm resulting in an increased rotational drag of more than 2 orders when compared to the free bacteria. The directional change of cargo loaded bacterial trajectories seems to be enhanced by steric hindrance from other oil-droplets present in the environment.

Precision Telemedicine through Crowdsourced Machine Learning: Testing Variability of Crowd Workers for Video-Based Autism Feature Recognition

Mobilized telemedicine is becoming a key, and even necessary, facet of both precision health and precision medicine. In this study, we evaluate the capability and potential of a crowd of virtual workers-defined as vetted members of popular crowdsourcing platforms-to aid in the task of diagnosing autism. We evaluate workers when crowdsourcing the task of providing categorical ordinal behavioral ratings to unstructured public YouTube videos of children with autism and neurotypical controls. To evaluate emerging patterns that are consistent across independent crowds, we target workers from distinct geographic loci on two crowdsourcing platforms: an international group of workers on Amazon Mechanical Turk (MTurk) (N = 15) and Microworkers from Bangladesh (N = 56), Kenya (N = 23), and the Philippines (N = 25). We feed worker responses as input to a validated diagnostic machine learning classifier trained on clinician-filled electronic health records. We find that regardless of crowd platform or targeted country, workers vary in the average confidence of the correct diagnosis predicted by the classifier. The best worker responses produce a mean probability of the correct class above 80% and over one standard deviation above 50%, accuracy and variability on par with experts according to prior studies. There is a weak correlation between mean time spent on task and mean performance (r = 0.358, p = 0.005). These results demonstrate that while the crowd can produce accurate diagnoses, there are intrinsic differences in crowdworker ability to rate behavioral features. We propose a novel strategy for recruitment of crowdsourced workers to ensure high quality diagnostic evaluations of autism, and potentially many other pediatric behavioral health conditions. Our approach represents a viable step in the direction of crowd-based approaches for more scalable and affordable precision medicine.

Game theoretic centrality: a novel approach to prioritize disease candidate genes by combining biological networks with the Shapley value

Background

Complex human health conditions with etiological heterogeneity like Autism Spectrum Disorder (ASD) often pose a challenge for traditional genome-wide association study approaches in defining a clear genotype to phenotype model. Coalitional game theory (CGT) is an exciting method that can consider the combinatorial effect of groups of variants working in concert to produce a phenotype. CGT has been applied to associate likely-gene-disrupting variants encoded from whole genome sequence data to ASD; however, this previous approach cannot take into account for prior biological knowledge. Here we extend CGT to incorporate a priori knowledge from biological networks through a game theoretic centrality measure based on Shapley value to rank genes by their relevance–the individual gene’s synergistic influence in a gene-to-gene interaction network. Game theoretic centrality extends the notion of Shapley value to the evaluation of a gene’s contribution to the overall connectivity of its corresponding node in a biological network.

Results

We implemented and applied game theoretic centrality to rank genes on whole genomes from 756 multiplex autism families. Top ranking genes with the highest game theoretic centrality in both the weighted and unweighted approaches were enriched for pathways previously associated with autism, including pathways of the immune system. Four of the selected genes HLA-A, HLA-B, HLA-G, and HLA-DRB1–have also been implicated in ASD and further support the link between ASD and the human leukocyte antigen complex.

Conclusions

Game theoretic centrality can prioritize influential, disease-associated genes within biological networks, and assist in the decoding of polygenic associations to complex disorders like autism.

Data-Driven Diagnostics and the Potential of Mobile Artificial Intelligence for Digital Therapeutic Phenotyping in Computational Psychiatry

Data science and digital technologies have the potential to transform diagnostic classification. Digital technologies enable the collection of big data, and advances in machine learning and artificial intelligence enable scalable, rapid, and automated classification of medical conditions. In this review, we summarize and categorize various data-driven methods for diagnostic classification. In particular, we focus on autism as an example of a challenging disorder due to its highly heterogeneous nature. We begin by describing the frontier of data science methods for the neuropsychiatry of autism. We discuss early signs of autism as defined by existing pen-and-paper-based diagnostic instruments and describe data-driven feature selection techniques for determining the behaviors that are most salient for distinguishing children with autism from neurologically typical children. We then describe data-driven detection techniques, particularly computer vision and eye tracking, that provide a means of quantifying behavioral differences between cases and controls. We also describe methods of preserving the privacy of collected videos and prior efforts of incorporating humans in the diagnostic loop. Finally, we summarize existing digital therapeutic interventions that allow for data capture and longitudinal outcome tracking as the diagnosis moves along a positive trajectory. Digital phenotyping of autism is paving the way for quantitative psychiatry more broadly and will set the stage for more scalable, accessible, and precise diagnostic techniques in the field.

Dataset for the reporting of urinary tract carcinoma-biopsy and transurethral resection specimen: recommendations from the International Collaboration on Cancer Reporting (ICCR)

The International Collaboration on Cancer Reporting (ICCR) is an alliance of major pathology organisations in Australasia, Canada, Europe, United Kingdom, and United States of America that develops internationally standardised, evidence-based datasets for the pathology reporting of cancer specimens. This dataset was developed by a multidisciplinary panel of international experts based on previously published ICCR guidelines for the production of cancer datasets. It is composed of Required (core) and Recommended (noncore) elements identified on the basis of literature review and expert consensus. The document also includes an explanatory commentary explaining the rationale behind the categorization of individual data items and provides guidance on how these should be collected and reported. The dataset includes nine required and six recommended elements for the reporting of cancers of the urinary tract in biopsy and transurethral resection (TUR) specimens. The required elements include specimen site, operative procedure, histological tumor type, subtype/variant of urothelial carcinoma, tumor grade, extent of invasion, status of muscularis propria, noninvasive carcinoma, and lymphovascular invasion (LVI). The recommended elements include clinical information, block identification key, extent of T1 disease, associated epithelial lesions, coexistent pathology, and ancillary studies. The dataset provides a structured template for globally harmonized collection of pathology data required for management of patients diagnosed with cancer of the urinary tract in biopsy and TUR specimens. It is expected that this will facilitate international collaboration, reduce duplication of effort in updating current national/institutional datasets, and be particularly useful for countries that have not developed their own datasets.

Feature Selection and Dimension Reduction of Social Autism Data

Autism Spectrum Disorder (ASD) is a complex neuropsychiatric condition with a highly heterogeneous phenotype. Following the work of Duda et al., which uses a reduced feature set from the Social Responsiveness Scale, Second Edition (SRS) to distinguish ASD from ADHD, we performed item-level question selection on answers to the SRS to determine whether ASD can be distinguished from non-ASD using a similarly small subset of questions. To explore feature redundancies between the SRS questions, we performed filter, wrapper, and embedded feature selection analyses. To explore the linearity of the SRS-related ASD phenotype, we then compressed the 65-question SRS into low-dimension representations using PCA, t-SNE, and a denoising autoencoder. We measured the performance of a multilayer perceptron (MLP) classifier with the top-ranking questions as input. Classification using only the top-rated question resulted in an AUC of over 92% for SRS-derived diagnoses and an AUC of over 83% for dataset-specific diagnoses. High redundancy of features have implications towards replacing the social behaviors that are targeted in behavioral diagnostics and interventions, where digital quantification of certain features may be obfuscated due to privacy concerns. We similarly evaluated the performance of an MLP classifier trained on the low-dimension representations of the SRS, finding that the denoising autoencoder achieved slightly higher performance than the PCA and t-SNE representations.

Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks

Background

Obtaining a diagnosis of neuropsychiatric disorders such as autism requires long waiting times that can exceed a year and can be prohibitively expensive. Crowdsourcing approaches may provide a scalable alternative that can accelerate general access to care and permit underserved populations to obtain an accurate diagnosis.

Objective

We aimed to perform a series of studies to explore whether paid crowd workers on Amazon Mechanical Turk (AMT) and citizen crowd workers on a public website shared on social media can provide accurate online detection of autism, conducted via crowdsourced ratings of short home video clips.

Methods

Three online studies were performed: (1) a paid crowdsourcing task on AMT (N=54) where crowd workers were asked to classify 10 short video clips of children as “Autism” or “Not autism,” (2) a more complex paid crowdsourcing task (N=27) with only those raters who correctly rated ≥8 of the 10 videos during the first study, and (3) a public unpaid study (N=115) identical to the first study.

Results

For Study 1, the mean score of the participants who completed all questions was 7.50/10 (SD 1.46). When only analyzing the workers who scored ≥8/10 (n=27/54), there was a weak negative correlation between the time spent rating the videos and the sensitivity (ρ=–0.44, P=.02). For Study 2, the mean score of the participants rating new videos was 6.76/10 (SD 0.59). The average deviation between the crowdsourced answers and gold standard ratings provided by two expert clinical research coordinators was 0.56, with an SD of 0.51 (maximum possible SD is 3). All paid crowd workers who scored 8/10 in Study 1 either expressed enjoyment in performing the task in Study 2 or provided no negative comments. For Study 3, the mean score of the participants who completed all questions was 6.67/10 (SD 1.61). There were weak correlations between age and score (r=0.22, P=.014), age and sensitivity (r=–0.19, P=.04), number of family members with autism and sensitivity (r=–0.195, P=.04), and number of family members with autism and precision (r=–0.203, P=.03). A two-tailed t test between the scores of the paid workers in Study 1 and the unpaid workers in Study 3 showed a significant difference (P<.001).

Conclusions

Many paid crowd workers on AMT enjoyed answering screening questions from videos, suggesting higher intrinsic motivation to make quality assessments. Paid crowdsourcing provides promising screening assessments of pediatric autism with an average deviation <20% from professional gold standard raters, which is potentially a clinically informative estimate for parents. Parents of children with autism likely overfit their intuition to their own affected child. This work provides preliminary demographic data on raters who may have higher ability to recognize and measure features of autism across its wide range of phenotypic manifestations.

Coalitional Game Theory Facilitates Identification of Non-Coding Variants Associated With Autism

Studies on autism spectrum disorder (ASD) have amassed substantial evidence for the role of genetics in the disease's phenotypic manifestation. A large number of coding and non-coding variants with low penetrance likely act in a combinatorial manner to explain the variable forms of ASD. However, many of these combined interactions, both additive and epistatic, remain undefined. Coalitional game theory (CGT) is an approach that seeks to identify players (individual genetic variants or genes) who tend to improve the performance-association to a disease phenotype of interest-of any coalition (subset of co-occurring genetic variants) they join. This method has been previously applied to boost biologically informative signal from gene expression data and exome sequencing data but remains to be explored in the context of cooperativity among non-coding genomic regions. We describe our extension of previous work, highlighting non-coding chromosomal regions relevant to ASD using CGT on alteration data of 4595 fully sequenced genomes from 756 multiplex families. Genomes were encoded into binary matrices for three types of non-coding regions previously implicated in ASD and separated into ASD (case) and unaffected (control) samples. A player metric, the Shapley value, enabled determination of individual variant contributions in both sets of cohorts. A total of 30 non-coding positions were found to have significantly elevated player scores and likely represent significant contributors to the genetic coordination underlying ASD. Cross-study analyses revealed that a subset of mutated non-coding regions (all of which are in human accelerated regions (HARs)) and related genes are involved in biological pathways or behavioral outcomes known to be affected in autism, suggesting the importance of single nucleotide polymorphisms (SNPs) within HARs in ASD. These findings support the use of CGT in identifying hidden yet influential non-coding players from large-scale genomic data, to better understand the precise underpinnings of complex neurodevelopmental disorders such as autism.

Outgroup Machine Learning Approach Identifies Single Nucleotide Variants in Noncoding DNA Associated with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a heritable neurodevelopmental disorder affecting 1 in 59 children. While noncoding genetic variation has been shown to play a major role in many complex disorders, the contribution of these regions to ASD susceptibility remains unclear. Genetic analyses of ASD typically use unaffected family members as controls; however, we hypothesize that this method does not effectively elevate variant signal in the noncoding region due to family members having subclinical phenotypes arising from common genetic mechanisms. In this study, we use a separate, unrelated outgroup of individuals with progressive supranuclear palsy (PSP), a neurodegenerative condition with no known etiological overlap with ASD, as a control population. We use whole genome sequencing data from a large cohort of 2182 children with ASD and 379 controls with PSP, sequenced at the same facility with the same machines and variant calling pipeline, in order to investigate the role of noncoding variation in the ASD phenotype. We analyze seven major types of noncoding variants: microRNAs, human accelerated regions, hypersensitive sites, transcription factor binding sites, DNA repeat sequences, simple repeat sequences, and CpG islands. After identifying and removing batch effects between the two groups, we trained an ℓ1-regularized logistic regression classifier to predict ASD status from each set of variants. The classifier trained on simple repeat sequences performed well on a held-out test set (AUC-ROC = 0.960); this classifier was also able to differentiate ASD cases from controls when applied to a completely independent dataset (AUC-ROC = 0.960). This suggests that variation in simple repeat regions is predictive of the ASD phenotype and may contribute to ASD risk. Our results show the importance of the noncoding region and the utility of independent control groups in effectively linking genetic variation to disease phenotype for complex disorders.

Measurements of the associated production of a Z boson and b jets in pp collisions at s = 8 TeV

Measurements of the associated production of a Z boson with at least one jet originating from a b quark in proton-proton collisions ats = 8 TeV are presented. Differential cross sections are measured with data collected by the CMS experiment corresponding to an integrated luminosity of 19.8fb - 1 . Z bosons are reconstructed through their decays to electrons and muons. Cross sections are measured as a function of observables characterizing the kinematics of the b jet and the Z boson. Ratios of differential cross sections for the associated production with at least one b jet to the associated production with any jet are also presented. The production of a Z boson with at least two b jets is investigated, and differential cross sections are measured for the dijet system. Results are compared to theoretical predictions, testing two different flavour schemes for the choice of initial-state partons.

Study of Jet Quenching with Z+jet Correlations in Pb-Pb and pp Collisions at sqrt[s]_{NN}=5.02 TeV

The production of jets in association with Z bosons, reconstructed via the μ^{+}μ^{-} and e^{+}e^{-} decay channels, is studied in pp and, for the first time, in Pb-Pb collisions. Both data samples were collected by the CMS experiment at the LHC, at a nucleon-nucleon center-of-mass energy of 5.02 TeV. The Pb-Pb collisions were analyzed in the 0%-30% centrality range. The back-to-back azimuthal alignment was studied in both pp and Pb-Pb collisions for Z bosons with transverse momentum p_{T}^{Z}>60  GeV/c and a recoiling jet with p_{T}^{jet}>30  GeV/c. The p_{T} imbalance x_{jZ}=p_{T}^{jet}/p_{T}^{Z}, as well as the average number of jet partners per Z, R_{jZ}, was studied in intervals of p_{T}^{Z}. The R_{jZ} is found to be smaller in Pb-Pb than in pp collisions, which suggests that in Pb-Pb collisions a larger fraction of partons associated with the Z bosons fall below the 30  GeV/c p_{T}^{jet} threshold because they lose energy.

Searches for pair production of third-generation squarks in [Formula: see text][Formula: see text] pp collisions

Searches are presented for direct production of top or bottom squark pairs in proton-proton collisions at the CERN LHC. Two searches, based on complementary techniques, are performed in all-jet final states that are characterized by a significant imbalance in transverse momentum. An additional search requires the presence of a charged lepton isolated from other activity in the event. The data were collected in 2015 at a centre-of-mass energy of 13[Formula: see text] with the CMS detector and correspond to an integrated luminosity of 2.3[Formula: see text]. No statistically significant excess of events is found beyond the expected contribution from standard model processes. Exclusion limits are set in the context of simplified models of top or bottom squark pair production. Models with top and bottom squark masses up to 830 and 890[Formula: see text], respectively, are probed for light neutralinos. For models with top squark masses of 675[Formula: see text], neutralino masses up to 260[Formula: see text] are excluded at 95% confidence level.

A search for new phenomena in pp collisions at [Formula: see text] in final states with missing transverse momentum and at least one jet using the [Formula: see text] variable

A search for new phenomena is performed in final states containing one or more jets and an imbalance in transverse momentum in pp collisions at a centre-of-mass energy of 13[Formula: see text]. The analysed data sample, recorded with the CMS detector at the CERN LHC, corresponds to an integrated luminosity of 2.3[Formula: see text]. Several kinematic variables are employed to suppress the dominant background, multijet production, as well as to discriminate between other standard model and new physics processes. The search provides sensitivity to a broad range of new-physics models that yield a stable weakly interacting massive particle. The number of observed candidate events is found to agree with the expected contributions from standard model processes, and the result is interpreted in the mass parameter space of fourteen simplified supersymmetric models that assume the pair production of gluinos or squarks and a range of decay modes. For models that assume gluino pair production, masses up to 1575 and 975[Formula: see text] are excluded for gluinos and neutralinos, respectively. For models involving the pair production of top squarks and compressed mass spectra, top squark masses up to 400[Formula: see text] are excluded.

Measurement of prompt and nonprompt [Formula: see text] production in [Formula: see text] and [Formula: see text] collisions at [Formula: see text]

This paper reports the measurement of [Formula: see text] meson production in proton-proton ([Formula: see text]) and proton-lead ([Formula: see text]) collisions at a center-of-mass energy per nucleon pair of [Formula: see text] by the CMS experiment at the LHC. The data samples used in the analysis correspond to integrated luminosities of 28[Formula: see text] and 35[Formula: see text] for [Formula: see text] and [Formula: see text] collisions, respectively. Prompt and nonprompt [Formula: see text] mesons, the latter produced in the decay of [Formula: see text] hadrons, are measured in their dimuon decay channels. Differential cross sections are measured in the transverse momentum range of [Formula: see text], and center-of-mass rapidity ranges of [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]). The nuclear modification factor, [Formula: see text], is measured as a function of both [Formula: see text] and [Formula: see text]. Small modifications to the [Formula: see text] cross sections are observed in [Formula: see text] relative to [Formula: see text] collisions. The ratio of [Formula: see text] production cross sections in [Formula: see text]-going and Pb-going directions, [Formula: see text], studied as functions of [Formula: see text] and [Formula: see text], shows a significant decrease for increasing transverse energy deposited at large pseudorapidities. These results, which cover a wide kinematic range, provide new insight on the role of cold nuclear matter effects on prompt and nonprompt [Formula: see text] production.

Relative Modification of Prompt ψ(2S) and J/ψ Yields from pp to PbPb Collisions at sqrt[s_{NN}]=5.02 TeV

The relative modification of the prompt ψ(2S) and J/ψ yields from pp to PbPb collisions, at the center-of-mass energy of 5.02 TeV per nucleon pair, is presented. The analysis is based on pp and PbPb data samples collected by the CMS experiment at the LHC in 2015, corresponding to integrated luminosities of 28.0  pb^{-1} and 464  μb^{-1}, respectively. The double ratio of measured yields of prompt charmonia reconstructed through their decays into muon pairs, (N_{ψ(2S)}/N_{J/ψ})_{PbPb}/(N_{ψ(2S)}/N_{J/ψ})_{pp}, is determined as a function of PbPb collision centrality and charmonium transverse momentum p_{T}, in two kinematic intervals: |y|<1.6 covering 6.5<p_{T}<30  GeV/c and 1.6<|y|<2.4 covering 3<p_{T}<30  GeV/c. The centrality-integrated double ratios are 0.36±0.08(stat)±0.05(syst) in the first interval and 0.24±0.22(stat)±0.09(syst) in the second. The double ratio is lower than unity in all the measured bins, suggesting that the ψ(2S) yield is more suppressed than the J/ψ yield in the explored phase space.

Cross-disorder comparative analysis of comorbid conditions reveals novel autism candidate genes

BACKGROUND

Numerous studies have highlighted the elevated degree of comorbidity associated with autism spectrum disorder (ASD). These comorbid conditions may add further impairments to individuals with autism and are substantially more prevalent compared to neurotypical populations. These high rates of comorbidity are not surprising taking into account the overlap of symptoms that ASD shares with other pathologies. From a research perspective, this suggests common molecular mechanisms involved in these conditions. Therefore, identifying crucial genes in the overlap between ASD and these comorbid disorders may help unravel the common biological processes involved and, ultimately, shed some light in the understanding of autism etiology.

RESULTS

In this work, we used a two-fold systems biology approach specially focused on biological processes and gene networks to conduct a comparative analysis of autism with 31 frequently comorbid disorders in order to define a multi-disorder subcomponent of ASD and predict new genes of potential relevance to ASD etiology. We validated our predictions by determining the significance of our candidate genes in high throughput transcriptome expression profiling studies. Using prior knowledge of disease-related biological processes and the interaction networks of the disorders related to autism, we identified a set of 19 genes not previously linked to ASD that were significantly differentially regulated in individuals with autism. In addition, these genes were of potential etiologic relevance to autism, given their enriched roles in neurological processes crucial for optimal brain development and function, learning and memory, cognition and social behavior.

CONCLUSIONS

Taken together, our approach represents a novel perspective of autism from the point of view of related comorbid disorders and proposes a model by which prior knowledge of interaction networks may enlighten and focus the genome-wide search for autism candidate genes to better define the genetic heterogeneity of ASD.

Suppression and azimuthal anisotropy of prompt and nonprompt [Formula: see text] production in PbPb collisions at [Formula: see text][Formula: see text]

The nuclear modification factor [Formula: see text] and the azimuthal anisotropy coefficient [Formula: see text] of prompt and nonprompt (i.e. those from decays of b hadrons) [Formula: see text] mesons, measured from PbPb and pp collisions at [Formula: see text] [Formula: see text] at the LHC, are reported. The results are presented in several event centrality intervals and several kinematic regions, for transverse momenta [Formula: see text] [Formula: see text] and rapidity [Formula: see text], extending down to [Formula: see text] [Formula: see text] in the [Formula: see text] range. The [Formula: see text] of prompt [Formula: see text] is found to be nonzero, but with no strong dependence on centrality, rapidity, or [Formula: see text] over the full kinematic range studied. The measured [Formula: see text] of nonprompt [Formula: see text] is consistent with zero. The [Formula: see text] of prompt [Formula: see text] exhibits a suppression that increases from peripheral to central collisions but does not vary strongly as a function of either y or [Formula: see text] in the fiducial range. The nonprompt [Formula: see text] [Formula: see text] shows a suppression which becomes stronger as rapidity or [Formula: see text] increases. The [Formula: see text] and [Formula: see text] of open and hidden charm, and of open charm and beauty, are compared.

Measurement of the WZ production cross section in pp collisions at [Formula: see text] and 8[Formula: see text] and search for anomalous triple gauge couplings at [Formula: see text]

The WZ production cross section is measured by the CMS experiment at the CERN LHC in proton-proton collision data samples corresponding to integrated luminosities of 4.9[Formula: see text] collected at [Formula: see text], and 19.6[Formula: see text] at [Formula: see text]. The measurements are performed using the fully-leptonic WZ decay modes with electrons and muons in the final state. The measured cross sections for [Formula: see text] are [Formula: see text] [Formula: see text] and [Formula: see text] [Formula: see text]. Differential cross sections with respect to the [Formula: see text] boson [Formula: see text], the leading jet [Formula: see text], and the number of jets are obtained using the [Formula: see text] data. The results are consistent with standard model predictions and constraints on anomalous triple gauge couplings are obtained.

Observation of Charge-Dependent Azimuthal Correlations in p-Pb Collisions and Its Implication for the Search for the Chiral Magnetic Effect

Charge-dependent azimuthal particle correlations with respect to the second-order event plane in p-Pb and PbPb collisions at a nucleon-nucleon center-of-mass energy of 5.02 TeV have been studied with the CMS experiment at the LHC. The measurement is performed with a three-particle correlation technique, using two particles with the same or opposite charge within the pseudorapidity range |η|<2.4, and a third particle measured in the hadron forward calorimeters (4.4<|η|<5). The observed differences between the same and opposite sign correlations, as functions of multiplicity and η gap between the two charged particles, are of similar magnitude in p-Pb and PbPb collisions at the same multiplicities. These results pose a challenge for the interpretation of charge-dependent azimuthal correlations in heavy ion collisions in terms of the chiral magnetic effect.

Measurement of the [Formula: see text] production cross section using events in the [Formula: see text] final state in pp collisions at [Formula: see text]

The cross section of top quark-antiquark pair production in proton-proton collisions at [Formula: see text] is measured by the CMS experiment at the LHC, using data corresponding to an integrated luminosity of 2.2[Formula: see text]. The measurement is performed by analyzing events in which the final state includes one electron, one muon, and two or more jets, at least one of which is identified as originating from hadronization of a b quark. The measured cross section is [Formula: see text], in agreement with the expectation from the standard model.

Measurement of the production cross section of a W boson in association with two b jets in pp collisions at [Formula: see text]

The production cross section of a W boson in association with two b jets is measured using a sample of proton-proton collisions at [Formula: see text] collected by the CMS experiment at the CERN LHC. The data sample corresponds to an integrated luminosity of 19.8[Formula: see text]. The W bosons are reconstructed via their leptonic decays, [Formula: see text], where [Formula: see text] or [Formula: see text]. The fiducial region studied contains exactly one lepton with transverse momentum [Formula: see text] and pseudorapidity [Formula: see text], with exactly two b jets with [Formula: see text] and [Formula: see text] and no other jets with [Formula: see text] and [Formula: see text]. The cross section is measured to be [Formula: see text]+[Formula: see text], in agreement with standard model predictions.

Search for Dark Matter and Supersymmetry with a Compressed Mass Spectrum in the Vector Boson Fusion Topology in Proton-Proton Collisions at sqrt[s]=8 TeV

A first search for pair production of dark matter candidates through vector boson fusion in proton-proton collisions at sqrt[s]=8  TeV is performed with the CMS detector. The vector boson fusion topology enhances missing transverse momentum, providing a way to probe supersymmetry, even in the case of a compressed mass spectrum. The data sample corresponds to an integrated luminosity of 18.5  fb^{-1}, recorded by the CMS experiment. The observed dijet mass spectrum is consistent with the standard model expectation. In an effective field theory, dark matter masses are explored as a function of contact interaction strength. The most stringent limit on bottom squark production with mass below 315 GeV is also reported, assuming a 5 GeV mass difference with respect to the lightest neutralino.

Measurements of the [Formula: see text] production cross section in lepton+jets final states in pp collisions at 8 [Formula: see text] and ratio of 8 to 7 [Formula: see text] cross sections

A measurement of the top quark pair production ([Formula: see text]) cross section in proton-proton collisions at the centre-of-mass energy of 8[Formula: see text] is presented using data collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 19.6[Formula: see text]. This analysis is performed in the [Formula: see text] decay channels with one isolated, high transverse momentum electron or muon and at least four jets, at least one of which is required to be identified as originating from hadronization of a b quark. The calibration of the jet energy scale and the efficiency of b jet identification are determined from data. The measured [Formula: see text] cross section is [Formula: see text]. This measurement is compared with an analysis of 7[Formula: see text] data, corresponding to an integrated luminosity of 5.0[Formula: see text], to determine the ratio of 8[Formula: see text] to 7[Formula: see text] cross sections, which is found to be [Formula: see text]. The measurements are in agreement with QCD predictions up to next-to-next-to-leading order.

An Experience of Optimizing A Community-Based Micro-Insurance Model to Bridge the Gap between Treatment Cost and Ability to pay at BPKIHS

In the year 2001 the leaders of BPKIHS started a micro social insurance scheme; Social Health Insurance (SHI) for prospective research. It is a method of financing and managing health care using compulsory contributions from employers, employees and may be from the government. Household members from organized groups in catchment areas enrolled voluntarily. Photographed service cards were issued entitled for free IPD/OPD consultations, investigations and bed charges excluding CT scans and specialty treatment. Institute bore operation and medicine costs up to 10000 and 3500 respectively for IPD yearly. Premium was fixed after a research through focus group discussion in villages of Morang, Sunsari and Biratnagar. A flat rate premium of 15 and 50 NRS/adult/month for villagers, city dwellers and half for children was fixed. Marginalized community and handicapped paid 33% of premium, 33% by Institute and 33% by concerned VDC. As the client number increased from 2383 to 7392 in second and to 15779 in third year, Premium: Expenditure ratio moved from 225:222 to 198:391. Average cost sharing of premium to expenditure came to 226:332 showing a negative 3alance. This scheme completed its fourth year till this research in 2005 A.D. But it was closed due to deficit. As reinsured population occasionally concealed information by not incorporating all family members. They defaulted in subsequent year after utilizing the benefits of SHI and misused the card for uninsured ones. It may, in its optimized form, become a model to be widely adopted to bridge the gap between the cost of treatment and the ability to pay in developing countries.

Measurement of dijet azimuthal decorrelation in pp collisions at [Formula: see text]

A measurement of the decorrelation of azimuthal angles between the two jets with the largest transverse momenta is presented for seven regions of leading jet transverse momentum up to 2.2[Formula: see text]. The analysis is based on the proton-proton collision data collected with the CMS experiment at a centre-of-mass energy of 8[Formula: see text] corresponding to an integrated luminosity of 19.7[Formula: see text]. The dijet azimuthal decorrelation is caused by the radiation of additional jets and probes the dynamics of multijet production. The results are compared to fixed-order predictions of perturbative quantum chromodynamics (QCD), and to simulations using Monte Carlo event generators that include parton showers, hadronization, and multiparton interactions. Event generators with only two outgoing high transverse momentum partons fail to describe the measurement, even when supplemented with next-to-leading-order QCD corrections and parton showers. Much better agreement is achieved when at least three outgoing partons are complemented through either next-to-leading-order predictions or parton showers. This observation emphasizes the need to improve predictions for multijet production.