bluuebunny/crossref_metadata_2025 huggingface dataset:
Convex hulls in hamming space enable efficient search for similarity and clustering of genomic sequences
Authors: Campo David S. , Khudyakov Yury
Published: December 2020

Background

In molecular epidemiology, comparison of intra-host viral variants among infected persons is frequently used for tracing transmissions in human population and detecting viral infection outbreaks. Application of Ultra-Deep Sequencing (UDS) immensely increases the sensitivity of transmission detection but brings considerable computational challenges when comparing all pairs of sequences. We developed a new population comparison method based on convex hulls in hamming space. We applied this method to a large set of UDS samples obtained from unrelated cases infected with hepatitis C virus (HCV) and compared its performance with three previously published methods.

Results

The convex hull in hamming space is a data structure that provides information on: (1) average hamming distance within the set, (2) average hamming distance between two sets; (3) closeness centrality of each sequence; and (4) lower and upper bound of all the pairwise distances among the members of two sets. This filtering strategy rapidly and correctly removes 96.2% of all pairwise HCV sample comparisons, outperforming all previous methods. The convex hull distance (CHD) algorithm showed variable performance depending on sequence heterogeneity of the studied populations in real and simulated datasets, suggesting the possibility of using clustering methods to improve the performance. To address this issue, we developed a new clustering algorithm, k-hulls, that reduces heterogeneity of the convex hull. This efficient algorithm is an extension of the k-means algorithm and can be used with any type of categorical data. It is 6.8-times more accurate than k-mode, a previously developed clustering algorithm for categorical data.

Conclusions

CHD is a fast and efficient filtering strategy for massively reducing the computational burden of pairwise comparison among large samples of sequences, and thus, aiding the calculation of transmission links among infected individuals using threshold-based methods. In addition, the convex hull efficiently obtains important summary metrics for intra-host viral populations.
Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy
Authors: Sherafat Elham , Force Jordan , Măndoiu Ion I.
Published: December 2020

Background

Personalized cancer vaccines are emerging as one of the most promising approaches to immunotherapy of advanced cancers. However, only a small proportion of the neoepitopes generated by somatic DNA mutations in cancer cells lead to tumor rejection. Since it is impractical to experimentally assess all candidate neoepitopes prior to vaccination, developing accurate methods for predicting tumor-rejection mediating neoepitopes (TRMNs) is critical for enabling routine clinical use of cancer vaccines.

Results

In this paper we introduce Positive-unlabeled Learning using AuTOml (PLATO), a general semi-supervised approach to improving accuracy of model-based classifiers. PLATO generates a set of high confidence positive calls by applying a stringent filter to model-based predictions, then rescores remaining candidates by using positive-unlabeled learning. To achieve robust performance on clinical samples with large patient-to-patient variation, PLATO further integrates AutoML hyper-parameter tuning, classification threshold selection based on spies, and support for bootstrapping.

Conclusions

Experimental results on real datasets demonstrate that PLATO has improved performance compared to model-based approaches for two key steps in TRMN prediction, namely somatic variant calling from exome sequencing data and peptide identification from MS/MS data.
Analysis of associations between emotions and activities of drug users and their addiction recovery tendencies from social media posts using structural equation modeling
Authors: Jha Deeptanshu , Singh Rahul
Published: December 2020

Background

Addiction to drugs and alcohol constitutes one of the significant factors underlying the decline in life expectancy in the US. Several context-specific reasons influence drug use and recovery. In particular emotional distress, physical pain, relationships, and self-development efforts are known to be some of the factors associated with addiction recovery. Unfortunately, many of these factors are not directly observable and quantifying, and assessing their impact can be difficult. Based on social media posts of users engaged in substance use and recovery on the forum Reddit, we employed two psycholinguistic tools, Linguistic Inquiry and Word Count and Empath and activities of substance users on various Reddit sub-forums to analyze behavior underlining addiction recovery and relapse. We then employed a statistical analysis technique called structural equation modeling to assess the effects of these latent factors on recovery and relapse.

Results

We found that both emotional distress and physical pain significantly influence addiction recovery behavior. Self-development activities and social relationships of the substance users were also found to enable recovery. Furthermore, within the context of self-development activities, those that were related to influencing the mental and physical well-being of substance users were found to be positively associated with addiction recovery. We also determined that lack of social activities and physical exercise can enable a relapse. Moreover, geography, especially life in rural areas, appears to have a greater correlation with addiction relapse.

Conclusions

The paper describes how observable variables can be extracted from social media and then be used to model important latent constructs that impact addiction recovery and relapse. We also report factors that impact self-induced addiction recovery and relapse. To the best of our knowledge, this paper represents the first use of structural equation modeling of social media data with the goal of analyzing factors influencing addiction recovery.
Correction to: Low birthweight, prematurity, and intrauterine growth restriction: results from the baseline data of the first indigenous birth cohort in Brazil (Guarani Birth Cohort)
Authors: Barreto Carla Tatiana Garcia , Tavares Felipe Guimarães , Theme-Filha Mariza , Farias Yasmin Nascimento , de Nazaré Pantoja Lídia , Cardoso Andrey Moreira
Published: December 2020

An amendment to this paper has been published and can be accessed via the original article.
Cerebral oxygenation in 45-degree trendelenburg position for robot-assisted radical prostatectomy: a single-center, open, controlled pilot study
Authors: Wiesinger Clemens , Schoeb Dominik Stefan , Stockhammer Mathias , Mirtezani Emir , Mitterschiffthaler Lukas , Wagner Helga , Knotzer Johann , Pauer Walter
Published: December 2020

Background

Within the last decade, robotically-assisted laparoscopic prostatectomy (RALP) has become the standard for treating localized prostate cancer, causing a revival of the 45° Trendelenburg position. In this pilot study we investigated effects of Trendelenburg position on hemodynamics and cerebral oxygenation in patients undergoing RALP.

Methods

We enrolled 58 patients undergoing RALP and 22 patients undergoing robot-assisted partial nephrectomy (RAPN) (control group) in our study. Demographic patient data and intraoperative parameters including cerebral oxygenation and cerebral hemodynamics were recorded for all patients. Cerebral function was also assessed pre- and postoperatively via the Mini Mental Status (MMS) exam. Changes in parameters during surgery were modelled by a mixed effects model; changes in the MMS result were evaluated using the Wilcoxon signed rank test.

Results

Preoperative assessment of patient characteristics, standard blood values and vital parameters revealed no difference between the two groups.

Conclusions

Applying a 45° Trendelenburg position causes no difference in postoperative brain function, and does not alter cerebral oxygenation during a surgical procedure lasting up to 5 h. Further studies in larger patient cohorts will have to confirm these findings.

Trial registration

German Clinical Trial Registry; DRKS00005094; Registered 12th December 2013—Retrospectively registered;https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00005094.
Surgical suture course for dental students with the Peyton-4-step approach versus the PDCA cycle using video assisted self-monitoring
Authors: Leitmann A. , Reinert Siegmar , Weise Hannes
Published: December 2020

Background

In this prospective study the Peyton 4-step approach of demonstration–deconstruction–comprehension (verbalization by the learner), and performance by the learner was compared to the PDCA cycle/Deming-Circle (Plan–Do–Check (video assisted self-monitoring)–Act) as a teaching method for surgical suturing and nodes with end performance as the primary objective.

Methods

Students of the third clinical semester in dental medicine were randomly selected to one of the two teaching methods. They completed a first course during the third clinical semester and a subsequent course during the fourth clinical semester. The focus was on learning surgical suturing techniques. Before the course started a questionnaire was handed out to both groups to evaluate their initial level of performance. Each course ended with a practical test to review the content of the course. The evaluation followed standardized parameters. Some of the test tasks in test one were repeated in test two to measure a horizontal as well as vertical difference in performance level.

Results

53 students (Peyton: n = 28/18 female, 10 male; PDCA: n = 25/14 female, 11 male) have completed both courses. The evaluation of the subjective questionnaires showed that the members of the PDCA-groups achieved a higher subjective increase in performance. The objective results also indicated higher learning success in the PDCA-groups compared to the Peyton-Group.

Discussion/Conclusion

This study demonstrated significant learning success for both groups in their own self-assessment as well as in the results of the practical exercises. Subsequently, the superiority of the PDCA cycle could be shown for almost all criteria for surgical suturing techniques. Several studies prioritize the teaching of practical skills according to Peyton and consider step 3 (“comprehension”) to be the essential factor. The PDCA cycle, which has its origins in industrial quality management, and its success can be understood from the perspective of learning theory in terms of Jean Piaget’s model of equilibration. The necessity of active reflection on the learning content through practice constitutes the key element for transfer into long-term memory.
An interpretable risk prediction model for healthcare with pattern attention
Authors: Kamal Sundreen Asad , Yin Changchang , Qian Buyue , Zhang Ping
Published: December 2020

Background

The availability of massive amount of data enables the possibility of clinical predictive tasks. Deep learning methods have achieved promising performance on the tasks. However, most existing methods suffer from three limitations: (1) There are lots of missing value for real value events, many methods impute the missing value and then train their models based on the imputed values, which may introduce imputation bias. The models’ performance is highly dependent on the imputation accuracy. (2) Lots of existing studies just take Boolean value medical events (e.g. diagnosis code) as inputs, but ignore real value medical events (e.g., lab tests and vital signs), which are more important for acute disease (e.g., sepsis) and mortality prediction. (3) Existing interpretable models can illustrate which medical events are conducive to the output results, but are not able to give contributions of patterns among medical events.

Methods

In this study, we propose a novel interpretablePatternAttention model withValueEmbedding (PAVE) to predict the risks of certain diseases. PAVE takes the embedding of various medical events, their values and the corresponding occurring time as inputs, leverage self-attention mechanism to attend to meaningful patterns among medical events for risk prediction tasks. Because only the observed values are embedded into vectors, we don’t need to impute the missing values and thus avoids the imputations bias. Moreover, the self-attention mechanism is helpful for the model interpretability, which means the proposed model can output which patterns cause high risks.

Results

We conduct sepsis onset prediction and mortality prediction experiments on a publicly available dataset MIMIC-III and our proprietary EHR dataset. The experimental results show that PAVE outperforms existing models. Moreover, by analyzing the self-attention weights, our model outputs meaningful medical event patterns related to mortality.

Conclusions

PAVE learns effective medical event representation by incorporating the values and occurring time, which can improve the risk prediction performance. Moreover, the presented self-attention mechanism can not only capture patients’ health state information, but also output the contributions of various medical event patterns, which pave the way for interpretable clinical risk predictions.

Availability

The code for this paper is available at:https://github.com/yinchangchang/PAVE.
SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest
Authors: Rao Gaurav , Choudhury Salimur , Lingras Pawan , Savage David , Mago Vijay
Published: December 2020

Background

When an Out-of-Hospital Cardiac Arrest (OHCA) incident is reported to emergency services, the 911 agent dispatches Emergency Medical Services to the location and activates responder network system (RNS), if the option is available. The RNS notifies all the registered users in the vicinity of the cardiac arrest patient by sending alerts to their mobile devices, which contains the location of the emergency. The main objective of this research is to find the best match between the user who could support the OHCA patient.

Methods

For performing matching among the user and the AEDs, we used Bipartite Matching and Integer Linear Programming. However, these approaches take a longer processing time; therefore, a new method Preprocessed Integer Linear Programming is proposed that solves the problem faster than the other two techniques.

Results

The average processing time for the experimentation data was   1850 s using Bipartite matching,   32 s using the Integer Linear Programming and  2 s when using the Preprocessed Integer Linear Programming method. The proposed algorithm performs matching among users and AEDs faster than the existing matching algorithm and thus allowing it to be used in the real world.

Conclusion:

This research proposes an efficient algorithm that will allow matching of users with AED in real-time during cardiac emergency. Implementation of this system can help in reducing the time to resuscitate the patient.
Design of a companion bioinformatic tool to detect the emergence and geographical distribution of SARS-CoV-2 Spike protein genetic variants
Authors: Massacci Alice , Sperandio Eleonora , D’Ambrosio Lorenzo , Maffei Mariano , Palombo Fabio , Aurisicchio Luigi , Ciliberto Gennaro , Pallocca Matteo
Published: December 2020

Background

Tracking the genetic variability of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is a crucial challenge. Mainly to identify target sequences in order to generate robust vaccines and neutralizing monoclonal antibodies, but also to track viral genetic temporal and geographic evolution and to mine for variants associated with reduced or increased disease severity. Several online tools and bioinformatic phylogenetic analyses have been released, but the main interest lies in the Spike protein, which is the pivotal element of current vaccine design, and in the Receptor Binding Domain, that accounts for most of the neutralizing the antibody activity.

Methods

Here, we present an open-source bioinformatic protocol, and a web portal focused on SARS-CoV-2 single mutations and minimal consensus sequence building as a companion vaccine design tool. Furthermore, we provide immunogenomic analyses to understand the impact of the most frequent RBD variations.

Results

Results on the whole GISAID sequence dataset at the time of the writing (October 2020) reveals an emerging mutation, S477N, located on the central part of the Spike protein Receptor Binding Domain, the Receptor Binding Motif. Immunogenomic analyses revealed some variation in mutated epitope MHC compatibility, T-cell recognition, and B-cell epitope probability for most frequent human HLAs.

Conclusions

This work provides a framework able to track down SARS-CoV-2 genomic variability.
Workshop Review: Seismic multiples discussed at SEG/KOC workshop
Authors: El-Emam Adel , Al-Abri Said
Published: December 2020

SEG and Kuwait Oil Company (KOC) organized a successful workshop titled “Seismic multiples — The challenges and the way forward.” It was held 3–5 December 2019 in Kuwait City, Kuwait. The workshop had good participation, with more than 90 experts in attendance from a variety of companies and universities. Thirty-six presentations were shared over a span of 12 sessions.
Workshop Preview: Data Analytics and Machine Learning Hackathon 2021: A deep dive into the open-source data challenge for E&P
Authors: Di Haibin , Kaul Anisha , Truelove Leigh , Li Weichang , Hu Wenyi , Abubakar Aria
Published: December 2020

We present a data challenge as part of the hackathon planned for the August 2021 SEG Research Workshop on Data Analytics and Machine Learning for Exploration and Production. The hackathon aims to provide hands-on machine learning experience for beginners and advanced practitioners, using a relatively well-defined problem and a carefully curated data set. The seismic data are from New Zealand's Taranaki Basin. The labels for a subset of the data have been generated by an experienced geologist. The objective of the challenge is to develop innovative machine learning solutions to identify key horizons.
Geophysics Bright Spots
Authors: Behura Jyoti
Published: December 2020

Welcome to the latest installment of Geophysics Bright Spots. Below is an article that the editors found interesting in the latest issue of Geophysics. Although this list contains only one article, you will find many other interesting articles in the issue.
Reviews
Authors: Taylor Patrick
Published: December 2020

Shale: Subsurface Science and Engineering, by Thomas Dewers, Jason Heath, and Marcelo Sánchez, ISBN 978-1-119-06668-2, 2019, American Geophysical Union and Wiley, 320 p.
Second law of thermodynamics in nonminimally coupled gravity
Authors: Azevedo R. P. L. , Avelino P. P.
Published: December 2020

In the present work we show that the second law of thermodynamics does not generally hold if the matter and gravitational fields are nonminimally coupled. We demonstrate this result by explicitly computing the evolution of the entropy of the matter fields in the case of a closed homogeneous and isotropic universe filled with dust and radiation, showing that, in this case, the sign of the entropy variation is determined by the evolution of the universe. The preservation of the second law of thermodynamics in these modified theories would require its generalization to account for a gravitational entropy contribution.
Giant deformations and soft-inflation in LCE balloons
Authors: Giudici Andrea , Biggins John S.
Published: December 2020

We propose that ballooning can be controlled, enriched and amplified by using rubbery networks of aligned molecular rods known as liquid crystal elastomers (LCEs). Firstly, LCEs are promising artificial muscles, showing large spontaneous deformations in response to heat and light. In LCE balloons, spontaneous deformations can trigger classic ballooning, either as phase separation (at constant volume) or a volume jump (at constant pressure), resulting in greatly magnified actuation strains. Secondly, even at constant temperature, LCEs have unusual mechanics augmented by soft modes of deformation in which the nematic director rotates within the elastomer. These soft modes enrich the mechanics of LCE balloons, which can also “balloon” between rotated and unrotated states, either during the classic instability, or as a separate pre-cursor, leading to successive instabilities during inflation.
Subwavelength acoustic focusing within multi-breadth bands with a window-shape metasurface
Authors: Gao Feng , Xie Jianlan , Peng Yuchen , Yan Bei , Liu Exian , Peng Peng , Li Hang , Jiang Jiapei , Liu Jianjun
Published: December 2020

The traditional phase gradient acoustic focusing metasurfaces based on the generalized Snell law (GSL) can manipulate acoustic waves freely. However, their applications are limited largely because of their narrow bandwidth, large volume, and single function. To resolve these problems, a kind of window-shape acoustic focusing metasurface (WSAFM) is proposed, the well performed focusing can be obtained by the flexibly varied longitudinal length of two handles inside the basic structure unit (BSU). The results demonstrate that, within multi-breadth bands (5000 Hz –6500 Hz, 7100 Hz –7600 Hz, 12100 Hz –13600 Hz), the WSAFM can realize the near-zero–sidelobe (NZS) subwavelength focusing, the variation of focus position, and the breakthrough of diffraction limit at multi-frequency. More importantly, when the non-Hermitian parameters (non-Hermitian system) are introduced to the WSAFM, the focus intensity can be enhanced without varying other parameters. The WSAFM proposed in this paper shows promising potentials in biomedical ultrasound imaging and the design of acoustic devices with multi-breadth bands.
Association of Cognitive Function and Retinal Neural and Vascular Structure in Type 1 Diabetes
Authors: Fickweiler Ward , Wolfson Emily A , Paniagua Samantha M , Yu Marc Gregory , Adam Atif , Bahnam Vanessa , Sampani Konstantina , Wu I-Hsien , Musen Gail , Aiello Lloyd P , Shah Hetal , Sun Jennifer K , King George L
Published: December 2020

Context

Cognitive dysfunction is a growing and understudied public health issue in the aging type 1 diabetic population and is difficult and time-consuming to diagnose. Studies in long duration type 1 diabetes have reported the presence of proliferative diabetic retinopathy was associated with cognitive dysfunction.

Objective

This study assessed whether structural and vascular abnormalities of the retina, representing an extension of the central nervous system, are associated with cognitive impairment and other complications of type 1 diabetes.

Methods

An observational cross-sectional study of individuals with 50 or more years of type 1 diabetes (Joslin Medalist Study) was conducted at a university hospital in the United States. The study included 129 participants with complete cognitive testing. Validated cognitive testing measures included psychomotor speed, and immediate, and delayed memory. Optical coherence tomography (OCT) and OCT angiography (OCTA) were performed to obtain neural retinal layer thicknesses and vascular density for superficial (SCP) and deep retinal capillary plexus (DCP). Multivariable modeling was adjusted for potential confounders associated with outcomes in unadjusted analyses.

Results

Decreased vessel density of the SCP and DCP was associated with worse delayed memory (DCP: P = .002) and dominant hand psychomotor speed (SCP: P = .01). Thinning of the retinal outer nuclear layer was associated with worse psychomotor speed both in nondominant and dominant hands (P = .01 and P = .05, respectively). Outer plexiform layer thickness was associated with delayed memory (P = .04).

Conclusion

These findings suggest that noninvasive retinal imaging using OCT and OCTA may assist in estimating the risks for cognitive dysfunction in people with type 1 diabetes.
Development of a Prediction Score to Avoid Confirmatory Testing in Patients With Suspected Primary Aldosteronism
Authors: Burrello Jacopo , Amongero Martina , Buffolo Fabrizio , Sconfienza Elisa , Forestiero Vittorio , Burrello Alessio , Adolf Christian , Handgriff Laura , Reincke Martin , Veglio Franco , Williams Tracy Ann , Monticone Silvia , Mulatero Paolo
Published: December 2020

Context

The diagnostic work-up of primary aldosteronism (PA) includes screening and confirmation steps. Case confirmation is time-consuming, expensive, and there is no consensus on tests and thresholds to be used. Diagnostic algorithms to avoid confirmatory testing may be useful for the management of patients with PA.

Objective

Development and validation of diagnostic models to confirm or exclude PA diagnosis in patients with a positive screening test.

Design, Patients, and Setting

We evaluated 1024 patients who underwent confirmatory testing for PA. The diagnostic models were developed in a training cohort (n = 522), and then tested on an internal validation cohort (n = 174) and on an independent external prospective cohort (n = 328).

Main Outcome Measure

Different diagnostic models and a 16-point score were developed by machine learning and regression analysis to discriminate patients with a confirmed diagnosis of PA.

Results

Male sex, antihypertensive medication, plasma renin activity, aldosterone, potassium levels, and the presence of organ damage were associated with a confirmed diagnosis of PA. Machine learning-based models displayed an accuracy of 72.9%–83.9%. The Primary Aldosteronism Confirmatory Testing (PACT) score correctly classified 84.1% at training and 83.9% or 81.1% at internal and external validation, respectively. A flow chart employing the PACT score to select patients for confirmatory testing correctly managed all patients and resulted in a 22.8% reduction in the number of confirmatory tests.

Conclusions

The integration of diagnostic modeling algorithms in clinical practice may improve the management of patients with PA by circumventing unnecessary confirmatory testing.
Giraffes and hominins: reductionist model predictions of compressive loads at the spine base for erect exponents of the animal kingdom
Authors: Günther Michael , Mörl Falk
Published: December 2020

In humans, compressive stress on intervertebral discs is commonly deployed as a measurand for assessing the loads that act within the spine. Examining this physical quantity is crucially beneficial: the intradiscal pressure can be directly measured in vivo in humans, and is immediately related to compressive stress. Hence, measured intradiscal pressure data are utterly useful for validating such biomechanical animal models that have the spine incorporated, and can, thus, compute compressive stress values. Here, we utilise human intradiscal pressure data to verify the predictions of a reductionist spine model, which has in fact only one joint degree of freedom. We calculate the pulling force of one lumped anatomical structure that acts past this (intervertebral) joint at the base of the spine—lumbar in hominins, cervical in giraffes—to compensate the torque that is induced by the weight of all masses located cranially to the base. Given morphometric estimates of the human and australopith trunks, respectively, and the giraffe's neck, as well as the respective structures’ lever arms and disc areas, we predict, for all three species, the compressive stress on the intervertebral disc at the spine base, while systematically varying the angular orientation of the species’ spinal columns with respect to gravity. The comparison between these species demonstrates that hominin everyday compressive disc stresses are lower than such in big quadrupedal animals. Within each species, erecting the spine from being bent forward by, for example, thirty degrees to fully upright posture reduces the compressive disc stress roughly to a third. We conclude that erecting the spine immediately allows to carry extra loads of the order of body weight, and yet the compressive disc stress is lower than in a moderately forward-bent posture with none extra load.
FIB/SEM-based analysis of Borrelia intracellular processing by human macrophages
Authors: Klose Matthias , Scheungrab Maximilian , Luckner Manja , Wanner Gerhard , Linder Stefan
Published: December 2020

Borrelia burgdorferi is the causative agent of Lyme disease, a multisystemic disorder affecting primarily skin, joints and nervous system. Successful internalization and intracellular processing of borreliae by immune cells like macrophages is decisive for the outcome of a respective infection. Here, we use for the first time focused ion beam scanning electron microscopy tomography (FIB/SEM tomography) to visualize the interaction of borreliae with primary human macrophages with high resolution. We report that interaction between macrophages and the elongated and highly motile borreliae can lead to formation of membrane tunnels that extend deeper into the host cytoplasm than the actual phagosome, most probably as a result of partial extrication of captured borreliae. We also show that membrane tubulation at borreliae-containing phagosomes, a process suggested earlier as a mechanism leading to phagosome compaction, but hard to visualize in live cell imaging, is apparently a frequent phenomenon. Finally, we demonstrate that the endoplasmic reticulum (ER) forms multiple STIM1-positive contact sites with both membrane tunnels and phagosome tubulations, confirming the important role of the ER during uptake and intracellular processing of borreliae.
Defining endogenous TACC3–chTOG–clathrin–GTSE1 interactions at the mitotic spindle using induced relocalization
Authors: Ryan Ellis L. , Shelford James , Massam-Wu Teresa , Bayliss Richard , Royle Stephen J.
Published: December 2020

A multiprotein complex containing TACC3, clathrin and other proteins has been implicated in mitotic spindle stability. To disrupt this complex in an anti-cancer context, we need to understand its composition and how it interacts with microtubules. Induced relocalization of proteins in cells is a powerful way to analyze protein–protein interactions and, additionally, monitor where and when these interactions occur. We used CRISPR/Cas9 gene editing to add tandem FKBP–GFP tags to each complex member. The relocalization of endogenous tagged protein from the mitotic spindle to mitochondria and assessment of the effect on other proteins allowed us to establish that TACC3 and clathrin are core complex members and that chTOG (also known as CKAP5) and GTSE1 are ancillary to the complex, binding respectively to TACC3 and clathrin, but not each other. We also show that PIK3C2A, a clathrin-binding protein that was proposed to stabilize the TACC3–chTOG–clathrin–GTSE1 complex during mitosis, is not a member of the complex. This work establishes that targeting the TACC3–clathrin interface or their microtubule-binding sites are the two strategies most likely to disrupt spindle stability mediated by this multiprotein complex.
Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth
Authors: Nordhaus William D.
Published: December 2020

What are the prospects for long-run economic growth? One prominent line of economic thinking is the trend toward stagnation. Stagnationism has a long history in economics, beginning prominently with Malthus and occasionally surfacing in different guises. Prominent themes here are the following: Will economic growth slow and perhaps even reverse under the weight of resource depletion? Will overpopulation and diminishing returns lower living standards? Will unchecked CO2 emissions lead to catastrophic changes in climate and human systems? Have we depleted the store of potential great inventions? Will the aging society lead to diminished innovativeness? (JEL D83, E25, O31, O32, O41, O47)
Sectoral Price Facts in a Sticky-Price Model
Authors: Carvalho Carlos , Lee Jae Won , Park Woong Yong
Published: December 2020

We develop a multisector sticky-price DSGE model that can endogenously deliver differential responses of prices to aggregate and sectoral shocks. Input-output production linkages and a (standard) monetary policy rule contribute to a slow response of prices to aggregate shocks. In turn, labor market segmentation at the sectoral level induces within-sector strategic substitutability in price-setting decisions, which helps the model deliver a fast response of prices to sector-specific shocks. We estimate the model using aggregate and sectoral price and quantity data for the United States and find that it accounts well for a range of sectoral price facts. (JEL E12, E21, E31, E32, E43, E52)
Disease-associated mutations in mitochondrial precursor tRNAs affect binding, m1R9 methylation, and tRNA processing by mtRNase P
Authors: Karasik Agnes , Wilhelm Catherine A. , Fierke Carol A. , Koutmos Markos
Published: December 2020

Mitochondrial diseases linked to mutations in mitochondrial (mt) tRNA sequences are common. However, the contributions of these tRNA mutations to the development of diseases is mostly unknown. Mutations may affect interactions with (mt)tRNA maturation enzymes or protein synthesis machinery leading to mitochondrial dysfunction. In human mitochondria, in most cases the first step of tRNA processing is the removal of the 5′ leader of precursor tRNAs (pre-tRNA) catalyzed by the three-component enzyme, mtRNase P. Additionally, one component of mtRNase P, mitochondrial RNase P protein 1 (MRPP1), catalyzes methylation of the R9 base in pre-tRNAs. Despite the central role of 5′ end processing in mitochondrial tRNA maturation, the link between mtRNase P and diseases is mostly unexplored. Here, we investigate how 11 different human disease-linked mutations in (mt)pre-tRNAIle, (mt)pre-tRNALeu(UUR), and (mt)pre-tRNAMet affect the activities of mtRNase P. We find that several mutations weaken the pre-tRNA binding affinity (KDs are approximately two- to sixfold higher than that of wild-type), while the majority of mutations decrease 5′ end processing and methylation activity catalyzed by mtRNase P (up to ∼55% and 90% reduction, respectively). Furthermore, all of the investigated mutations in (mt)pre-tRNALeu(UUR) alter the tRNA fold which contributes to the partial loss of function of mtRNase P. Overall, these results reveal an etiological link between early steps of (mt)tRNA-substrate processing and mitochondrial disease.
Envelope Theorems for Multistage Linear Stochastic Optimization
Authors: Terça Gonçalo , Wozabal David
Published: December 2020

Sensitivities for Stochastic Optimization
Possible Prophylactic and Therapeutic Foods for Prevention and Management of COVID-19- An Updated Review
Authors: Enani Sumia Mohammad
Published: December 2020

Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has resulted in an outbreak that is spreading globally. In the absence of a vaccine or effective treatment, improving the body's immune response to combat the virus, or, at least alleviate its health complications, becomes imperative. Potential prophylactic and therapeutic food interventions using black seed, garlic, honey, wasabi and high vit C foods have been proposed in various studies on previous coronaviruses, SARS-CoV and Middle East Respiratory Syndrome Coronavirus (MERS-CoV). Due to the high similarity in the three dimensional structure between SARS-CoV-2 and SARS-CoV, studies that reported antiviral action of certain foods against various viruses including SARS-CoV and MERS-CoV have been discussed in this short review.
Effect of Frozen Storage on Nutritional, Microbial and Sensorial Quality of Fish Balls and Fish Fingers Produced from Indian Mackerel
Authors: Alkuraieef Amal N. , Alsuhaibani Amnah M. , Alshawi Amal H. , Aljahani Amani H.
Published: December 2020

Fish quality is important in the food industry. Studies on the nutritional, microbial and minerals in Indian mackerel fish are limited. Therefore, this study was carried out to assess the quality and production of fish products (balls and fingers). Additionally, the effect of frozen storage for six months on the microbial, nutritional and sensory evaluation of fish balls and fingers was studied. The obtained results showed that the estimated minerals (zinc, cadmium, chromium, copper, lead and mercury) contents in Indian mackerel muscles were lower than the maximum permissible limits for human consumption. The levels of total bacterial counts and total yeast counts in Indian mackerel purchased from three different stores varied. After 6 months of storage, the microbial content decreased in Indian mackerel fish balls and fingers to less than 2.0×102 CFU/g, which was due to the effect of freezing on the growth and activity of microorganisms, while the carbohydrate, fat and energy contents increased, and the ash, protein and moisture contents decreased; however, Indian mackerel fish fingers had elevated ash, carbohydrate, fat, protein and energy contents and a reduced moisture content after freezing. Sensory evaluation of Indian mackerel balls and fingers at the start and end of the storage period (6 months) revealed good scores for appearance, odor, texture, taste and acceptability. These results provide insights into the benefits of good-quality Indian mackerel fish in the fish product industry and their availability after storage for six months.
Grain Dimension, Nutrition and Nutraceutical Properties of Black and Red Varieties of Rice in India
Authors: Rajendran Mala , Ravi Chandran Keerthana
Published: December 2020

Traditional colored rice varieties in India are the source of carbohydrates, phytochemicals and minerals. They facilitate the growth of probiotics in intestine and protect human from many chronic diseases. The present study investigated the nutritional properties such as total sugars, digestible sugars, resistant sugars, hydrolysis index, glycemic index and total proteins of thirteen colored varieties of rice in India. Nutraceutical properties like anti diabetic and prebiotic activity were investigated by standard methods. Chak hao poreiton and mappillai samba grains were 6.3 mm in length. Lowest length of 5.1 mm was recorded in 60 m Kuruvai. Among the rice varieties, mappillai samba has high concentration of digestible starch of 91% and Chak hao poreiton had low concentration of 62%. Resistant starch was 38% in Chak hao poreiton and 8% in mappillai samba. Lowest glycemic index of 52 and 53 were recorded in karuthakar poha and Chak hao poreiton respectively. Anthocyanin extracted from Chak hao poreiton inhibited 24% of human pancreatic α-amylase activity. It significantly increased the probiotic number from 0.15 CFU/mL to 1.95 CFU/mL. The study revealed that the black rice variety, Chak hao poreiton was rich in resistant starch and exhibited low glycemic index. The anthocyanins from Chak hao poreiton possessed significant antidiabetic and prebiotic activity. Molecular docking studies revealed the interaction of anthocyanin with pancreatic α-amylase, β-glucosidase and GLUT1.
Consequential Effect of Gluten-Free Diet on Health-Related Quality of Life in Celiac Populace-A Meta-Analysis
Authors: Rustagi Shivani , Choudhary Saumya , Khan Sheeba , Jain Tanu
Published: December 2020

Globally, celiac disease (CD) affects around 1-2% of the population. Gluten elimination becomes the cornerstone treatment and is also being followed by non-celiac as a healthy dietary habit. However, there is lack of sustainable evidence to understand this view. Adherence to gluten-free diet (GFD) rendered a few to reduced quality of life. Hence, a meta-analysis has been performed to determine interaction of GFD and Health-related Quality of Life (HRQoL). NCBI/MEDLINE, PubMed, Cochrane Library, Google Scholar and Science Direct were combed from date of inception to October 30, 2018 for studies assessing the effect of GFD using validated questionnaires on HRQoL a) between healthy controls and celiac patients b) dietary adherence to GFD in celiac subjects. Random effect model was used for meta-analysis. Twenty-five studies comprising 5148 CD subjects fit in the inclusion criteria. GFD had moderate significant association with HRQoL, for PGWB odds ratio’s (OR) 0.613 [95% CI, 0.449-0.837], SF-36 Mental Component Score (MCS) 0.026 [95% CI, 0.011-0.060], Physical Component Score (PCS) 0.066 95% [CI, 0.032-0.138]. Partial adherence to GFD had lower quality of life when compared to strictly adherent patients for OR’s SF-36 MCS 5.080 [95% CI, 1.885- 13.692], PCS 3.204 [95% CI, 1.579- 6.503] and CDQoL 2.439 [95%CI (1.724- 3.450)]. The results implied moderate significant association between GFD and HRQoL and better compliance leads to favourable HRQoL.
Nutritional Composition, Textural and Sensory Properties of Ocimum Basilicum L. Seeds Incorporated Steamed Rice Cake
Authors: R Arivuchudar , P Nazni
Published: December 2020

The incidence of obesity and prediabetes is high, especially in the southern Indian states, Kerala and Tamilnadu, which will pave its way for other chronic metabolic diseases. The steamed rice cake or Idli, the staple diet, of South Indian population is high in glycaemic index. Despite, the diabetics from middle and high income population consume Idli for at least twice a day, for many ages. Hence, in this study, the notion of incorporating Ocimum basilicum seeds, has been considered, to ascertain if this can add value to the staple diet of major populace i.e Idli or steamed rice cake. The raw or unprocessed, roasted and steamed Ocimum basilicum seeds in a proportion of 5%, 10%, 15% and 20% of the black gram was added to the steamed rice cake batter prepared by standardised procedure and was steamed until doneness. All the variations of Ocimum basilicum seed incorporated Idlis were subjected to proximate, textural and sensory analysis.The proximate analysis results depicted that the composition of parameters viz. moisture, energy, protein, fibre and ash, of the Ocimum basilicum seed incorporated steamed rice cake showed significant difference (p<0.05) in all the variations, on comparison with control. The TPA results signify that the textural parameters of Idlis with 15% of roasted O.basilicum seeds (V7) and 20% of roasted O.basilicum seeds (V8) incorporation seem to be more acceptable. On organoleptic analysis, the mean score for overall acceptability of the 20% raw O.basilicum seeds incorporated variation and 20% roasted O.basilicum seeds incorporated variations of the steamed rice cake was maximum and showed acceptance index parallel to the control. Thus, it can be opined that the seeds of Ocimum basilicum can be used in steamed rice cake preparation as a means of value addition.
Inference Based on Type-II Hybrid Censored Data from a Pareto Distribution
Authors: Çetinkaya Çağatay
Published: December 2020

The Pareto distribution takes part in life-testing experiments as a finite range distribution. In this study, inference studies for the scale and shape parameters of the Pareto distribution under type-II hybrid censoring scheme are considered. The main reason for choosing this censoring scheme is its advantage of guaranteeing at least particular failures to be observed by the end of the experiment. Maximum likelihood and Bayes estimation methods are used with their approximate confidence intervals. Proposed estimation methods are compared numerically based on simulation studies. A numerical example is also used to illustrate the theoretical outcomes.
Bayesian Estimation of Transmuted Weibull Distribution under Different Loss Functions
Authors: Yousaf Rahila , Ali Sajid , Aslam Muhammad
Published: December 2020

In this article, we aim to estimate the parameters of the transmuted Weibull distribution (TWD) using Bayesian approach, as the Weibull distribution plays an important role in reliability engineering and life testing problems. Informative and non-informative priors under squared error loss function (SELF), precautionary loss function (PLF) and quadratic loss function (QLF) are assumed to estimate the scale, the shape and the transmuted parameter of the TWD. In addition to this, we also compute the Bayesian credible intervals (BCIs). To estimate parameters, we adopt Markov Chain Monte Carlo (MCMC) technique assuming uncensored and censored environments in terms of different sample sizes and censoring rates. The posterior risks, associated with each estimator are used to compare the performance of different estimators. Two real data sets are analyzed to illustrate the flexibility of the proposed distribution.
State Crime, Structural Violence and COVID-19
Authors: Gordon Neve , Green Penny
Published: December 2020

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Neural network enabled metasurface design for phase manipulation
Authors: Jiang Li , Li Xiaozhong , Wu Qingxin , Wang Lianhui , Gao Li
Published: December 2020

The phase of electromagnetic waves can be manipulated and tailored by artificial metasurfaces, which can lead to ultra-compact, high-performance metalens, holographic and imaging devices etc. Usually, nanostructured metasurfaces are associated with a large number of geometric parameters, and the multi-parameter optimization for phase design cannot be possibly achieved by conventional time-consuming simulations. Deep learning tools capable of acquiring the relationship between complex nanostructure geometry and electromagnetic responses are best suited for such challenging task. In this work, by innovations in the training methods, we demonstrate that deep neural network can handle six geometric parameters for accurately predicting the phase value, and for the first time, perform direct inverse design of metasurfaces for on-demand phase requirement. In order to satisfy the achromatic metalens design requirements, we also demonstrate simultaneous phase and group delay prediction for near-zero group delay dispersion. Our results suggest significantly improved design capability of complex metasurfaces with the aid of deep learning tools.
Fast simulation and design of the fiber probe with a fiber-based pupil filter for optical coherence tomography using the eigenmode expansion approach
Authors: Qiu Jianrong , Meng Jia , Liu Zhiyi , Han Tao , Ding Zhihua
Published: December 2020

Fiber probes for optical coherence tomography (OCT) recently employ a short section of step-index multimode fiber (SIMMF) to generate output beams with extended depth of focus (DOF). As the focusing region of the output beam is generally close to the probe end, it is not feasible to adopt the methods for bulk-optics with spatial pupil filters to the fiber probes with fiber-based filters. On the other hand, the applicable method of the beam propagation method (BPM) to the fiber probes is computationally inefficient to perform parameter scan and exhaustive search optimization. In this paper, we propose the method which analyzes the non-Gaussian beams from the fiber probes with fiber-based filters using the eigenmode expansion (EME) method. Furthermore, we confirm the power of this method in designing fiber-based filters with increased DOF gain and uniformly focusing by introducing more and higher-order fiber modes. These results using the EME method are in good agreement with that by the BPM, while the latter takes 1-2 orders more computation time. With higher-order fiber modes involved, a novel probe design with increased DOF gain and suppressed sidelobe is proposed. Our findings reveal that the fiber probes based on SIMMFs are able to achieve about four times DOF gain at maximum with uniformly focusing under acceptable modal dispersion. The EME method enables fast and accurate simulation of fiber probes based on SIMMFs, which is important in the design of high-performance fiber-based micro-imaging systems for biomedical applications.