bluuebunny/crossref_metadata_2025 huggingface dataset:
METABOLIC PATHWAY MODIFICATION FOR PRODUCTION OF XYLITOL FROM GLUCOSE IN ESCHERICHIA COLI
Authors: Abdullah Noradilin , Md Illias Rosli , Kheng Oon Low , Jaafar Nardiah Rizwana , Mohamad Sukri Norhamiza , Abdul Rahman Roshanida
Published: April 2022

Glucose is a cheap and readily available substrate for production of large-scale chemicals. Synthesis of xylitol, a high demand chemical in global market is currently done by using xylose, which contributes to its high operational cost. Studies on production of xylitol from glucose have explored several approaches, from sequential fermentation to multiple and single gene expression. Xylitol-5-phosphate dehydrogenase (XPDH), is an enzyme that enables conversion of glucose to xylitol in a single step fermentation. This study explores conversion of xylitol from glucose in E. coli by the expression of xpdh from Clostridium difficile with modifications in metabolic pathways to enhance xylitol production. The xpdh gene was carried by pACYC-Duet-1 expression vector and induced by the addition of IPTG. Initial screening of E. coli expressing xpdh (NA116) was done by shake-flask fermentation for 24 hours and its metabolites were analyzed by HPLC. NA116 was able to produce 0.273 g/L xylitol from 4.33 g/L consumed glucose in 24 hours. Further metabolic pathway modification to eliminate competing pathways yielded four mutants, NA207 (∆rpiA), NA208 (∆rpiB), NA209 (∆pgi) and NA211 (∆rpi∆Apgi). Screening of mutants for xylitol production showed that highest xylitol production from glucose was achieved by NA211 with almost double the amount of the original strain, 0.585 g/L. This showed successful xylitol conversion from glucose in a single fermentation in E. coli with improved yield through metabolic pathway modification.
Improving the Electrochemical Performance of Lithium-Rich Cathode Materials by Vanadium and Titanium Co-Doping Using Solution Combustion Synthesis
Authors: Paknahad Pouyan , Abasi Ali Akbar , Glenn Michael , Ghorbanzadeh Milad
Published: April 2022

A lithium-rich layered oxide cathode material (Li1.2[Mn0.54Ni0.13Co0.13]O2) was synthesized using solution combustion synthesis, and the electrochemical effects of co-doping with vanadium and titanium were studied. Materials analysis showed that layered oxides with an α-NaFeO2 structure, spherical morphology, and particle size distribution between 0.4 and 1 µm were synthesized. Electrochemical experiments (charge–discharge tests) also indicated that the presence of vanadium in the cathode structure increases its specific capacity to 257.6 mAh/g and improves rate capability and cyclic performance. Increasing the amount of titanium in the cathode improves cyclability; however, it also decreases specific capacity. The latter occurs because titanium is not electrochemically active, meaning it does not participate in redox reactions. Also, increasing the amount of titanium beyond 0.02 mol leads to a considerable decrease in the rate capability.
Mechanism of stress-induced attacks in an episodic neurologic disorder
Authors: Snell Heather D. , Vitenzon Ariel , Tara Esra , Chen Chris , Tindi Jaafar , Jordan Bryen A. , Khodakhah Kamran
Published: April 2022

Stress is the most common trigger among episodic neurologic disorders. In episodic ataxia type 2 (EA2), physical or emotional stress causes episodes of severe motor dysfunction that manifest as ataxia and dystonia. We used the
tottering
(tg/tg) mouse, a faithful animal model of EA2, to dissect the mechanisms underlying stress-induced motor attacks. We find that in response to acute stress, activation of α
1
-adrenergic receptors (α1-Rs) on Purkinje cells by norepinephrine leads to their erratic firing and consequently motor attacks. We show that norepinephrine induces erratic firing of Purkinje cells by disrupting their spontaneous intrinsic pacemaking via a casein kinase 2 (CK2)–dependent signaling pathway, which likely reduces the activity of calcium-dependent potassium channels. Moreover, we report that disruption of this signaling cascade at a number of nodes prevents stress-induced attacks in the
tottering
mouse. Together, our results suggest that norepinephrine and CK2 are required for the initiation of stress-induced attacks in EA2 and provide previously unidentified targets for therapeutic intervention.
Single metal-organic framework–embedded nanopit arrays: A new way to control neural stem cell differentiation
Authors: Cho Yeon-Woo , Jee Seohyeon , Suhito Intan Rosalina , Lee Jeong-Hyeon , Park Chun Gwon , Choi Kyung Min , Kim Tae-Hyung
Published: April 2022

Stable and continuous supply of essential biomolecules is critical to mimic in vivo microenvironments wherein spontaneous generation of various cell types occurs. Here, we report a new platform that enables highly efficient neuronal cell generation of neural stem cells using single metal-organic framework (MOF) nanoparticle–embedded nanopit arrays (SMENA). By optimizing the physical parameters of homogeneous periodic nanopatterns, each nanopit can confine single nMOFs (UiO-67) that are specifically designed for long-term storage and release of retinoic acid (RA). The SMENA platform successfully inhibited physical interaction with cells, which contributed to remarkable stability of the nMOF (RA⊂UiO-67) structure without inducing nanoparticle-mediated toxicity issues. Owing to the continuous and long-term supply of RA, the neural stem cells showed enhanced mRNA expressions of various neurogenesis-related activities. The developed SMENA platform can be applied to other stem cell sources and differentiation lineages and is therefore useful for various stem cell–based regenerative therapies.
Mechanism of RNA polymerase I selection by transcription factor UAF
Authors: Baudin Florence , Murciano Brice , Fung Herman K. H. , Fromm Simon A. , Mattei Simone , Mahamid Julia , Müller Christoph W.
Published: April 2022

Preribosomal RNA is selectively transcribed by RNA polymerase (Pol) I in eukaryotes. The yeast transcription factor upstream activating factor (UAF) represses Pol II transcription and mediates Pol I preinitiation complex (PIC) formation at the 35
S
ribosomal RNA gene. To visualize the molecular intermediates toward PIC formation, we determined the structure of UAF in complex with native promoter DNA and transcription factor TATA-box-binding protein (TBP). We found that UAF recognizes DNA using a hexameric histone-like scaffold with markedly different interactions compared with the nucleosome and the histone-fold-rich transcription factor IID (TFIID). In parallel, UAF positions TBP for Core Factor binding, which leads to Pol I recruitment, while sequestering it from DNA and Pol II/III–specific transcription factors. Our work thus reveals the structural basis of RNA Pol selection by a transcription factor.
A DNA molecular printer capable of programmable positioning and patterning in two dimensions
Authors: Benson Erik , Marzo Rafael Carrascosa , Bath Jonathan , Turberfield Andrew J.
Published: April 2022

Nanoscale manipulation and patterning usually require costly and sensitive top-down techniques such as those used in scanning probe microscopies or in semiconductor lithography. DNA nanotechnology enables exploration of bottom-up fabrication and has previously been used to design self-assembling components capable of linear and rotary motion. In this work, we combine three independently controllable DNA origami linear actuators to create a nanoscale robotic printer. The two-axis positioning mechanism comprises a moveable gantry, running on parallel rails, threading a mobile sleeve. We show that the device is capable of reversibly positioning a write head over a canvas through the addition of signaling oligonucleotides. We demonstrate “write” functionality by using the head to catalyze a local DNA strand–exchange reaction, selectively modifying pixels on a canvas. This work demonstrates the power of DNA nanotechnology for creating nanoscale robotic components and could find application in surface manufacturing, biophysical studies, and templated chemistry.
Whole-Genome Sequencing and Virulome Analysis of Escherichia coli Isolated from New Zealand Environments of Contrasting Observed Land Use
Authors: Cookson Adrian L. , Marshall Jonathan C. , Biggs Patrick J. , Rogers Lynn E. , Collis Rose M. , Devane Megan , Stott Rebecca , Wilkinson David A. , Kamke Janine , Brightwell Gale
Published: April 2022

This study takes a systematic sampling approach to assess the public health risk of
Escherichia coli
recovered from freshwater sites within forest and farmland. The New Zealand landscape is dominated by livestock farming, and previous work has demonstrated that “recreational exposure to water” is a risk factor for human infection by Shiga toxin-producing
Escherichia coli
(STEC).
Engineering Isopropanol Dehydrogenase for Efficient Regeneration of Nicotinamide Cofactors
Authors: Jia Qiao , Zheng Yu-Cong , Li Hai-Peng , Qian Xiao-Long , Zhang Zhi-Jun , Xu Jian-He
Published: April 2022

Oxidoreductases represent one group of the most important biocatalysts for synthesis of various chiral synthons. However, their practical application was hindered by the expensive nicotinamide cofactors used.
Genome Sequence of Carbapenemase-Producing Enterobacter cloacae 0102-4P-1 Harboring the IncC-Type Plasmid with a Multidrug Resistance Site Encoding
bla
NDM-1
, Isolated from Commercially Imported Shrimp
Authors: Nakayama Tatsuya , Yamaguchi Takahiro , Yamamoto Shiori , Jinnai Michio , Kumeda Yuko , Hase Atsushi
Published: April 2022

A carbapenem-resistant
Enterobacter cloacae
0102-4P-1 strain was isolated from commercially imported shrimp in Japan. Here, we present a draft genome sequence. The complete plasmid sequence was also determined by hybrid assembly sequencing using Oxford Nanopore and Illumina methods. The assembled whole genome and plasmid were 5,164,033 bp and 162,852 bp long, respectively.
SP1 Using kotter’s change model to reduce paediatric prescribing errors
Authors: Trivedi Ashifa , Ajitsaria Richa , Bate Tristan The
Published: April 2022

Aim

We describe a quality improvement project to reduce the paediatric prescribing error rate on a paediatric ward in a district general hospital. The project is called STAMP (Safe Treatment and Administration of Medicine in Paediatrics) and was started in 2016. In 2017 the local error rate was 5.2% (average monthly error rate) and decreased to 3.8% in 2019. Despite these earlier interventions the error rate has since not improved and in 2020 the error rate increased to 4.4%. Using Kotter’s Change Management model1 our aim was to decrease the prescribing error rate by 20% (from 4.4% to a target of 3.5%) by August 2021.

Method

The paediatric pharmacy team collected weekly error data from December 2016 to August 2021. Reynolds et al2 report that hospital doctors are often unaware of their errors. As a result of this Reynolds et al developed a questionnaire to better understand the attitudes and perceptions of prescribers with regards to prescribing errors. Permission was asked if we could use the questionnaire locally and this was given to us.

The questionnaire was emailed to all paediatric prescribers, including non-medical prescribers, in September 2020. 97% (30/31) of respondents felt that the number of prescribing errors can be reduced. However, 65% (20/31) of prescribers said that they feel they do not make a significant number of prescribing errors. The results of the questionnaire helped us to better understand why prescribing errors happen and changed the way errors were fedback to prescribers. Interventions included paediatric pharmacy teaching at induction, reporting of weekly error rate, ‘spot the error’ quizzes and individual feedback given to prescribers. Prescribers were asked if they were happy to receive feedback via email if we were not able to give face to face feedback.

Results

The project has been able to achieve a reduction in error rate to an average of 2.1% (from September 2020 – July 2021) compared to an average error rate of 4.4% the previous year. The aim was to reduce the error rate to 3.5% or below and we have been able to achieve this constantly since October 2020. There have been four weeks where there have been no errors on the ward.

Conclusion

This project demonstrates that Kotter’s model can successfully be used in healthcare to reduce prescribing errors. Our most successful interventions have been paediatric pharmacy teaching at induction and emailing individual feedback to prescribers.

References

Kotter JP. Leading change. Boston, MA: Harvard Business Review Press; 2012.

Reynolds M, Jheeta S, Benn J, et al. Improving feedback on junior doctors’ prescribing errors: mixed-methods evaluation of a quality improvement project. BMJ Quality & Safety 2017;26:240-247.
COVID-19-related healthcare impacts: an uncontrolled, segmented time-series analysis of tuberculosis diagnosis services in Mozambique, 2017–2020
Authors: Manhiça Ivan , Augusto Orvalho , Sherr Kenneth , Cowan James , Cuco Rosa Marlene , Agostinho Sãozinha , Macuacua Bachir C. , Ramiro Isaías , Carimo Naziat , Matsinhe Maria Benigna , Gloyd Stephen , Chicumbe Sergio , Machava Raimundo , Tembe Stélio , Fernandes Quinhas
Published: April 2022

Introduction

Currently, COVID-19 dominates the public health agenda and poses a permanent threat, leading to health systems’ exhaustion and unprecedented service disruption. Primary healthcare services, including tuberculosis services, are at increased risk of facing severe disruptions, particularly in low-income and middle-income countries. Indeed, corroborating model-based forecasts, there is increasing evidence of the COVID-19 pandemic’s negative impact on tuberculosis case detection.

Methods

Applying a segmented time-series analysis, we assessed the effects of COVID-19-related measures on tuberculosis diagnosis service across districts in Mozambique. Ministry health information system data were used from the first quarter of 2017 to the end of 2020. The model, performed under the Bayesian premises, was estimated as a negative binomial with random effects for districts and provinces.

Results

A total of 154 districts were followed for 16 consecutive quarters. Together, these districts reported 96 182 cases of all forms of tuberculosis in 2020. At baseline (first quarter of 2017), Mozambique had an estimated incidence rate of 283 (95% CI 200 to 406) tuberculosis cases per 100 000 people and this increased at a 5% annual rate through the end of 2019. We estimated that 17 147 new tuberculosis cases were potentially missed 9 months after COVID-19 onset, resulting in a 15.1% (95% CI 5.9 to 24.0) relative loss in 2020. The greatest impact was observed in the southern region at 40.0% (95% CI 30.1 to 49.0) and among men at 15% (95% CI 4.0 to 25.0). The incidence of pulmonary tuberculosis increased at an average rate of 6.6% annually; however, an abrupt drop (15%) was also observed immediately after COVID-19 onset in March 2020.

Conclusion

The most significant impact of the state of emergency was observed between April and June 2020, the quarter after COVID-19 onset. Encouragingly, by the end of 2020, clear signs of health system recovery were visible despite the initial shock.
Feasibility, efficacy and safety of exercise stress echocardiography during the COVID-19 pandemic
Authors: Hampson Reinette , Botrous Christina , Chahal Navtej , Senior Roxy
Published: April 2022

Objective

To assess the feasibility, efficacy and safety of performing exercise stress echocardiography (ESE) for the assessment of myocardial ischaemia during the COVID-19 pandemic.

Methods and results

Baseline data were collected prospectively on 740 consecutive patients (mean age 61.4 years, 56.8% males), referred for a stress echocardiogram (SE), who underwent ESE between July 2020 (immediate post lockdown) and January 2021 according to national safety guidelines, in addition to patients wearing masks during ESE. Retrospective analysis was performed on follow-up data for outcomes. Propensity score matching was used to compare workload achieved during ESE pre-COVID-19, in 768 consecutive patients who underwent ESE between May 2014 and May 2015. Of the 725 (97.9%) diagnostic tests obtained, 69 (9.3%) demonstrated significant inducible ischaemia (≥3 segments) with no serious adverse events. Of the 61 patients who underwent coronary angiography, 51 (83%) demonstrated flow-limiting coronary artery disease. During a mean follow-up period of 4.6 months, one first-cardiac event was recorded.

Compliance with mask-wearing throughout ESE was seen in 98.7% of patients. Of the 17 healthcare professionals performing ESE, none contracted COVID-19 during this period. SE service performance increased to 96.8% of prepandemic levels (100%) from 26.6% at the start of July 2020 to the end of December 2020.

Propensity-matched data showed no significant difference in exercise workload between patients undergoing ESE during and prepandemic.

Conclusion

Performing ESE during the COVID-19 pandemic, with safety measures in place, is feasible, efficacious and safe. It impacted on the time patients were waiting to undergo a diagnostic test and yielded appropriate outcomes.

Service evaluation authorisation of research capability number

SE20/059.
Species-dependent differences in the inhibition of various potassium currents and in their effects on repolarization in cardiac ventricular muscle
Authors: Árpádffy-Lovas Tamás , Mohammed Aiman Saleh A. , Naveed Muhammad , Koncz István , Baláti Beáta , Bitay Miklós , Jost Norbert , Nagy Norbert , Baczkó István , Virág László , Varró András
Published: April 2022

Even though rodents are accessible model animals, their electrophysiological properties are deeply different from those of humans, making the translation of rat studies to humans rather difficult. We compared the mechanisms of ventricular repolarization in various animal models to those of humans by measuring cardiac ventricular action potentials from ventricular papillary muscle preparations using conventional microelectrodes and applying selective inhibitors of various potassium transmembrane ion currents. Inhibition of the IK1 current (10 µmol/L barium chloride) significantly prolonged rat ventricular repolarization, but only slightly prolonged it in dogs, and did not affect it in humans. On the contrary, IKr inhibition (50 nmol/L dofetilide) significantly prolonged repolarization in humans, rabbits, and dogs, but not in rats. Inhibition of the IKur current (1 µmol/L XEN-D0101) only prolonged rat ventricular repolarization and had no effect in humans or dogs. Inhibition of the IKs (500 nmol/L HMR-1556) and Ito currents (100 µmol/L chromanol-293B) elicited similar effects in all investigated species. We conclude that dog ventricular preparations have the strongest translational value and rat ventricular preparations have the weakest translational value in cardiac electrophysiological experiments.
Massive scalar field in de Sitter spacetime: a two-loop calculation and a comparison with the stochastic approach
Authors: Kamenshchik Alexander Yu. , Starobinsky Alexei A. , Vardanyan Tereza
Published: April 2022

We examine long-wavelength correlation functions of massive scalar fields in de Sitter spacetime. For the theory with a quartic self-interaction, the two-point function is calculated up to two loops. Comparing our results with the Hartree–Fock approximation and with the stochastic approach shows that the former resums only the cactus type diagrams, whereas the latter contains the sunset diagram as well and produces the correct result. We also demonstrate that the long-wavelength expectation value of the commutator of two fields is equal to zero both for spacelike and timelike separated points.
Wormhole in the Milky Way galaxy with global monopole charge
Authors: Das Priyam , Kalam Mehedi
Published: April 2022

Wormholes are tunnels or short-cuts in space-time, and their existence is very important for human civilization to express the vastness of space and time. So, it is necessary to analyze our own Milky Way galaxy if it can harbour any wormhole. This work is dedicated to the existence of wormhole geometry(at least theoretically) in the bulge and halo of the Milky Way Galaxy. The structure and existence of wormholes are verified in both the bulge and the halo region of the Milky Way galaxy (MWG). Different dark matter profiles like pseudo-isothermal, NFW and Universal Rotational Curve (URC) are analyzed to harbour these cosmic tunnels. Three kinds of redshift functions are used for each dark matter profile with the global monopole charge to cover all the possibilities of MWG supporting wormhole geometry.
A Study on Portfolio Selection Based on Fuzzy Linear Programming
Authors: Akbaş Serkan , Erbay Dalkiliç Türkan , Gül Aksoy Tuğba
Published: April 2022

Portfolio management is the allocation of funds in the hands of the investor to ensure minimum risk and maximum profitability among the existing securities. In the finance sector related to portfolio management, many approaches, theories, and models have been developed. In this study, a new model is proposed. The basic objective of this model is to minimize the risk of portfolio while maximizing expected return of the portfolio. The proposed model is based on the Mean Absolute Deviation Model (MAD) proposed by Konno-Yamazaki. Considering the uncertainty of expected returns in the Mean Absolute Deviation Model, the problem was remodeled with fuzzy logic approach. In the application section, the proposed model has been applied to two different indexes, namely Istanbul Stock Exchange (ISE-30) and Standard & Poor’s 500 (S&P 500). In application, 64-month return movements of stocks traded in both indexes between 01.01.2016-30.04.2021 were used. In order to examine the effectiveness of the model, firstly, monthly return data for both indexes were modeled with the Mean-Absolute Deviation method and the Mean Variance method. Then, the same data were modeled with the proposed method. In the conclusion section of the study, the results obtained from each method were compared.
Interactive Mining of Probabilistic Frequent Patterns in Uncertain Databases
Authors: Lin Ming-Yen , Fu Cheng-Tai , Hsueh Sue-Chen
Published: April 2022

Many modern applications such as sensor networks produce probabilistic data. These data are collected into an uncertain database. To interpret uncertainty and to mine frequent patterns in an uncertain database, all possible certain databases are considered, which generates an exponential number of combinations and makes the mining problem highly complicated. In practice, mining is interactive, which makes the discovery of frequent itemsets in an uncertain database even more challenging. The objective of interactive mining is to shorten the time that is required to obtain the desired patterns in the iterated lengthy mining process. The time-consuming mining process in an uncertain database is exacerbated by repeated processing if the mining is performed from scratch. Therefore, we propose an interactive mining algorithm called iDIP to solve this problem. The iDIP algorithm adopts an approximation mechanism to mine the patterns and prunes candidates by using the existing patterns. Comprehensive experiments using both real and synthetic datasets show that iDIP outperforms the well-known re-mining-based MB algorithm for 4.3 times faster on average. In addition, iDIP has good linear scalability.
Online Learning of Parameters for Modeling User Preference Based on Bayesian Network
Authors: Kan Yirong , Yue Kun , Wu Hao , Fu Xiaodong , Sun Zhengbao
Published: April 2022

By analyzing users’ behavior data for personalized services, most state-of-the-art methods for user preference modeling are often based on batch-mode machine learning algorithms, where all rating data are assumed to be available throughout the training process. However, data in the real world often arrives sequentially and user preference may change dynamically. The real-time characteristics of rating data make the algorithms for preference modeling challenging to suit real-world online applications. By the user preference model (UPM) based on Bayesian network with a latent variable (BNLV), uncertain relationships among relevant attributes of users, objects and ratings could be represented, in which user preference is represented by the latent variable. In this paper, we propose an online approach for parameter learning of UPM. Specifically, we first extend the classic Voting EM algorithm by using Bayesian estimation in terms of the situation with latent variables. Consequently, we propose the algorithm for learning parameters of UPM from few and sequentially-changing rating data to reflect the gradually changing preferences. Finally, we test the effectiveness of our proposed algorithm by conducting experiments on various datasets. Experimental results demonstrate the superiority of our method in various measurements.
Market Exit and Minimax Regret
Authors: Umbhauer Gisèle
Published: April 2022

This paper shows how minimax regret sheds new light on an old economic topic, market-exit games. It focuses on wars of attrition, namely overcrowded duopoly markets where the strategic variable is the exit time. The only symmetric Nash equilibrium (NE) of the game studied is a mixed-strategy equilibrium that leads to a null expected payoff, i.e., the payoff a firm gets when it immediately exits the market. This result is not convincing, both from a behavioral and from a strategic viewpoint. The minimax regret approach that builds upon opposite regrets — exiting the market too late and exiting the market too early — is more convincing and ensures that both firms obtain a strictly positive expected payoff.

Highlights Minimax regret behavior sheds new light on the optimal exit-times in overcrowded markets. Minimax regret behavior provides a new behavioral content to mixed strategies. In overcrowded duopolies, if the firms’ aim consists in minimizing regrets, then they get better payoffs than in the mixed-strategy NE.
Material Design with Topology Optimization Based on the Neural Network
Authors: Li Bin , Guo Hongwei , Zhuang Xiaoying
Published: April 2022

This paper describes a neural network (NN)-based topology optimization approach for designing microstructures. The design variables are the NN weights and biases used to describe the density field, which is independent of element meshes. The number of design variables and gray elements is reduced substantially, and no filtering is necessary. Three numerical examples are provided to demonstrate the efficacy of the proposed method, namely, maximum shear modulus, maximum bulk modulus, and negative Poisson’s ratio.
Leveraging Computational Storage for Power-Efficient Distributed Data Analytics
Authors: HeydariGorji Ali , Rezaei Siavash , Torabzadehkashi Mahdi , Bobarshad Hossein , Alves Vladimir , Chou Pai H.
Published: April 2022

This article presents a family of computational storage drives (CSDs) and demonstrates their performance and power improvements due to in-storage processing (ISP) when running big data analytics applications. CSDs are an emerging class of solid state drives that are capable of running user code while minimizing data transfer time and energy. Applications that can benefit from in situ processing include distributed training, distributed inferencing, and databases. To achieve the full advantage of the proposed ISP architecture, we propose software solutions for workload balancing before and at runtime for training and inferencing applications. Other applications such as sharding-based databases can readily take advantage of our ISP structure without additional tooling. Experimental results on different capacity and form factors of CSDs show up to 3.1× speedup in processing while reducing the energy consumption and data transfer by up to 67% and 68%, respectively, compared to regular enterprise solid state drives.
Text Implicates Prosodic Ambiguity: A Corpus for Intention Identification of the Korean Spoken Language
Authors: Cho Won Ik , Kim Nam Soo
Published: April 2022

Phonetic features are indispensable in understanding the spoken language. Especially in Korean, which is wh-in-situ and head-final, the addressee of spoken language sometimes finds it hard to discern the speaker’s original intention if not provided with the sentence prosody. However, acoustic information may not be guaranteed for all spoken language processing, due to the difficulty of managing and computing speech data. This article suggests a corpus that aims to distinguish utterances with ambiguous intention from clear-cut ones, utilizing the prosodic ambiguity of the text input. In detail, the resulting classification system decides whether the given text input is one of fragment, statement, question, command, rhetorical question/command, or indecisive, taking into account the intonation-dependency of the text. Based on an intuitive understanding of the Korean language engaged in the data annotation, we construct a corpus with seven intention categories, train classification systems, and validate the utility of our dataset with quantitative and qualitative analyses.
Static and Dynamic Isolated Indian and Russian Sign Language Recognition with Spatial and Temporal Feature Detection Using Hybrid Neural Network
Authors: Rajalakshmi E. , Elakkiya R. , Prikhodko Alexey L. , Grif M. G. , Bakaev Maxim A. , Saini Jatinderkumar R. , Kotecha Ketan , Subramaniyaswamy V.
Published: April 2022

The Sign Language Recognition system intends to recognize the Sign language used by the hearing and vocally impaired populace. The interpretation of isolated sign language from static and dynamic gestures is a difficult study field in machine vision. Managing quick hand movement, facial expression, illumination variations, signer variation, and background complexity are amongst the most serious challenges in this arena. While deep learning-based models have been used to accomplish the entirety of the field's state-of-the-art outcomes, the previous issues have not been fully addressed. To overcome these issues, we propose a Hybrid Neural Network Architecture for the recognition of Isolated Indian and Russian Sign Language. In the case of static gesture recognition, the proposed framework deals with the 3D Convolution Net with an atrous convolution mechanism for spatial feature extraction. For dynamic gesture recognition, the proposed framework is an integration of semantic spatial multi-cue feature detection, extraction, and Temporal-Sequential feature extraction. The semantic spatial multi-cue feature detection and extraction module help in the generation of feature maps for Full-frame, pose, face, and hand. For face and hand detection, GradCam and Camshift algorithm have been used. The temporal and sequential module consists of a modified auto-encoder with a GELU activation function for abstract high-level feature extraction and a hybrid attention layer. The hybrid attention layer is an integration of segmentation and spatial attention mechanism. The proposed work also involves creating a novel multi-signer, single, and double-handed Isolated Sign representation dataset for Indian and Russian Sign Language. The experimentation was done on the novel dataset created. The accuracy obtained for Static Isolated Sign Recognition was 99.76%, and the accuracy obtained for Dynamic Isolated Sign Recognition was 99.85%. We have also compared the performance of our proposed work with other baseline models with benchmark datasets, and our proposed work proved to have better performance in terms of Accuracy metrics.
A Practical Shared Optical Cache With Hybrid MWSR/R-SWMR NoC for Multicore Processors
Authors: Han Haiyang , Alexoudi Theoni , Vagionas Chris , Pleros Nikos , Hardavellas Nikos
Published: April 2022

Conventional electronic memory hierarchies are intrinsically limited in their ability to overcome the memory wall due to scaling constraints. Optical caches and interconnects can mitigate these constraints, and enable processors to reach performance and energy efficiency unattainable by purely electronic means. However, the promised benefits cannot be realized through a simple replacement process; to reach its full potential, the architecture needs to be holistically redesigned. This article proposes Pho$, an opto-electronic memory hierarchy architecture for multicores. Pho$ replaces conventional core-private electronic caches with a large shared optical L1 built with optical SRAMs. The shared optical cache is supported by Pho$Net, a novel hybrid MWSR/R-SWMR optical NoC that provides low-latency and high-bandwidth communication between the electronic cores and the shared optical L1 at low optical loss. Pho$Net’s unique network arbitration protocol seamlessly co-arbitrates the request and reply sub-networks and facilitates cache requests and replies that optimize for the common case of cache hits. Through Pho$ we solve the problems that render previous designs impractical. Our results show that Pho$ achieves on average 1.41× performance speedup (3.89× max) and 31% lower energy-delay product (90% max) against conventional designs. Moreover, the Pho$Net optical NoC for core-cache communication consumes 70% less power compared to directly applying previously proposed optical NoC architectures.
Discriminative Visual Similarity Search with Semantically Cycle-consistent Hashing Networks
Authors: Zhang Zheng , Wang Jianning , Zhu Lei , Lu Guangming
Published: April 2022

Deep hashing has great potential in large-scale visual similarity search due to its preferable efficiency in storage and computation. Technically, deep hashing for visual similarity search inherits the powerful representation capability of deep neural networks, and it encodes visual features into compact binary codes by preserving representative semantic visual features. Works in this field mainly focus on building the relationship between the visual and objective hash spaces, while they seldom study the triadic cross-domain semantic knowledge transfer among visual, semantic, and hashing spaces, leading to a serious semantic ignorance problem during space transformation. In this article, we propose a novel deep tripartite semantically interactive hashing framework, dubbed Semantically Cycle-consistent Hashing Networks (SCHNs), for discriminative hash code learning. Particularly, we construct a flexible semantic space and a transitive latent space, in conjunction with the visual space, to jointly deduce the privileged discriminative hash space. Specifically, a new semantic space is conceived to strengthen the flexibility and completeness of categories in the semantic feature inference phase. At the same time, a transitive latent space is formulated to explore and uncover the shared semantic interactivity embedded in visual and semantic features. Moreover, to further ensure semantic consistency across multiple spaces, we propose to build a cyclic adversarial learning module to preserve and keep their semantic concurrence during space transformation. Notably, our SCHN, for the first time, establishes the cyclic principle of deep semantic-preserving hashing by adaptive semantic parsing across different spaces in a single-modal visual similarity search. In addition, the entire learning framework is jointly optimized in an end-to-end manner. Extensive experiments performed on diverse large-scale datasets evidence the superiority of our method against other state-of-the-art deep hashing algorithms. The source codes of this article are available at https://github.com/JalinWang/SCHN.
The Book Review Column
Authors: Green Frederic
Published: April 2022

This column consists of two reviews that only Bill Gasarch could write. Well. . . I suppose it wouldn't be impossible for someone else to review the first, and not even less wrong to write the second, but it's a treat to have an all-Gasarch program!
SIGACT News Complexity Theory Column 112
Authors: Hemaspaandra Lane A.
Published: April 2022

As I write this (in January), it is -7F (and -17F with the wind chill factor) here in Western New York, and looking at the pictures of this guest article's authors, I'm thinking that (from the outdoor pictures) Sweden and Switzerland are looking like pretty toasty places! (Robert's picture seems to be taken inside, but I see that currently the temperature is -15F in Montreal, so one can't blame him: brrrrrr! For completeness, Lund is currently 34F and Lausanne is currently 38F, but the time-zone di erence is helping them a bit... and also, for all I know those might be summer snaps.)
Guest Column
Authors: de Rezende S.F. , Göös M. , Robere R.
Published: April 2022

We survey lower-bound results in complexity theory that have been obtained via newfound interconnections between propositional proof complexity, boolean circuit complexity, and query/communication complexity. We advocate for the theory of total search problems (TFNP) as a unifying language for these connections and discuss how this perspective suggests a whole programme for further research.
Managing Retirement Incomes
Authors: Banks James , Crawford Rowena
Published: April 2022

In this article we discuss the state of the literature relating to the decumulation of retirement wealth and the management of retirement incomes. On the one hand, life-cycle models that allow for strong bequest motives and for the effects of medical expense risks have been shown to be able to rationalize retirees’ wealth, income, and consumption trajectories. On the other, studies of individual asset choices and portfolio decisions seem to suggest low levels of financial literacy and engagement as well as non-negligible consequences of age-related cognitive decline on financial decision making. We argue that future work should try to reconcile these two sets of conflicting findings into a coherent and holistic evidence base to inform policy, because issues concerning the management of retirement incomes, and insurance against different risks in retirement more generally, will become increasingly important for future cohorts of retirees.
Obtaining Complete Human Proteomes
Authors: Martinez-Val Ana , Guzmán Ulises H. , Olsen Jesper V.
Published: April 2022

Proteins are the molecular effectors of the information encoded in the genome. Proteomics aims at understanding the molecular functions of proteins in their biological context. In contrast to transcriptomics and genomics, the study of proteomes provides deeper insight into the dynamic regulatory layers encoded at the protein level, such as posttranslational modifications, subcellular localization, cell signaling, and protein–protein interactions. Currently, mass spectrometry (MS)–based proteomics is the technology of choice for studying proteomes at a system-wide scale, contributing to clinical biomarker discovery and fundamental molecular biology. MS technologies are continuously being developed to fulfill the requirements of speed, resolution, and quantitative accuracy, enabling the acquisition of comprehensive proteomes. In this review, we present how MS technology and acquisition methods have evolved to meet the requirements of cutting-edge proteomics research, which is describing the human proteome and its dynamic posttranslational modifications with unprecedented depth. Finally, we provide a perspective on studying proteomes at single-cell resolution.
Human Cerebellar Development and Transcriptomics: Implications for Neurodevelopmental Disorders
Authors: Haldipur Parthiv , Millen Kathleen J. , Aldinger Kimberly A.
Published: April 2022

Developmental abnormalities of the cerebellum are among the most recognized structural brain malformations in human prenatal imaging. Yet reliable information regarding their cause in humans is sparse, and few outcome studies are available to inform prognosis. We know very little about human cerebellar development, in stark contrast to the wealth of knowledge from decades of research on cerebellar developmental biology of model organisms, especially mice. Recent studies show that multiple aspects of human cerebellar development significantly differ from mice and even rhesus macaques, a nonhuman primate. These discoveries challenge many current mouse-centric models of normal human cerebellar development and models regarding the pathogenesis of several neurodevelopmental phenotypes affecting the cerebellum, including Dandy-Walker malformation and medulloblastoma. Since we cannot model what we do not know, additional normative and pathological human developmental data are essential, and new models are needed.
In vitro cell cultures of Brunner’s glands from male mouse to study GLP-1 receptor function
Authors: Voetmann Louise M. , Underwood Christina R. , Rolin Bidda , Hansen Axel K. , Kirk Rikke K. , Pyke Charles , Knudsen Lotte B. , Frederiksen Klaus S.
Published: April 2022

Exocrine glands in the submucosa of the proximal duodenum secrete alkaline fluid containing mucus to protect the intestinal mucosa from acidic stomach contents. These glands, known as Brunner’s glands, express high glucagon-like peptide 1 receptor (GLP-1R) levels. Previous studies have suggested that activation of the GLP-1R induces expression of barrier protective genes in Brunner’s glands. Still, the lack of a viable in vitro culture of Brunner’s glands has hampered additional studies of the functional consequences of GLP-1R activation. In this study, we established a procedure to isolate and culture cells derived from murine Brunner’s glands. The isolated glandular cells retained functional GLP-1R expression in culture, making this in vitro system suitable for the study of GLP-1R activation. We found that cells derived from the Brunner’s glands of mice pretreated with semaglutide contained significantly more mucus compared with Brunner’s glands from vehicle-treated mice. Our data suggest a protective intestinal response upon semaglutide treatment, but further studies are required to leverage the full potential of cultured Brunner’s gland cells.
Estradiol deficiency reduces the satellite cell pool by impairing cell cycle progression
Authors: Larson Alexie A. , Shams Ahmed S. , McMillin Shawna L. , Sullivan Brian P. , Vue Cha , Roloff Zachery A. , Batchelor Eric , Kyba Michael , Lowe Dawn A.
Published: April 2022

The size of the satellite cell pool is reduced in estradiol (E2)-deficient female mice and humans. Here, we use a combination of in vivo and in vitro approaches to identify mechanisms, whereby E2 deficiency impairs satellite cell maintenance. By measuring satellite cell numbers in mice at several early time points postovariectomy (Ovx), we determine that satellite cell numbers decline by 33% between 10 and 14 days post-Ovx in tibialis anterior and gastrocnemius muscles. At 14 days post-Ovx, we demonstrate that satellite cells have a reduced propensity to transition from G0/G1 to S and G2/M phases, compared with cells from ovary-intact mice, associated with changes in two key satellite cell cycle regulators, ccna2 and p16INK4a. Further, freshly isolated satellite cells treated with E2 in vitro have 62% greater cell proliferation and require less time to complete the first division. Using clonal and differentiation assays, we measured 69% larger satellite cell colonies and enhanced satellite cell-derived myoblast differentiation with E2 treatment compared with vehicle-treated cells. Together, these results identify a novel mechanism for preservation of the satellite cell pool by E2 via promotion of satellite cell cycling.
Potential Applications of Halloysite Nanotubes as Drug Carriers: A Review
Authors: Ganapathy Dhanraj , Shanmugam Rajeshkumar , Pitchiah Sivaperumal , Murugan Preethika , Chinnathambi Arunachalam , Alharbi Sulaiman Ali , Durairaj Kaliannan , Sundramoorthy Ashok K.
Published: April 2022

Halloysite nanotubes (HNTs) are naturally occurring tubular clay nanomaterials that are made from multiple‐rolled aluminosilicate kaolin panels. The aluminol and siloxane groups on the surface of HNT facilitate the formation of hydrogen bonds with biomaterials on its surface. It is a cost‐effective nanomaterial that found applications in a variety of fields of science and technology. The biocompatible properties of HNT resulted in various applications such as in nanomedicine, biomedicine, tissue engineering, drug delivery, sequence delivery, cancer, stem cells isolation, bioimaging, and sensors. Due to its tubular form, it has played a vital role as drug delivery carriers, superior nanocarrier for numerous medicines, and biological agents with larger loading capacity and longer releasing kinetics. HNT has also been investigated and used extensively in targeted drug transfer applications with a variety of medicines. These studies provided positive outcomes which have led to versatile medicinal applications. Herein, we have highlighted the latest developments on HNT‐based drug carriers.
Image Semantic Space Segmentation Based on Cascaded Feature Fusion and Asymmetric Convolution Module
Authors: Li Xiaojuan , Ma Xingmin
Published: April 2022

With the rapid development of deep convolutional neural networks, the results of image semantic segmentation are remarkable, and the segmentation effect is greatly improved. The pooling layer of the convolutional neural network will reduce the resolution of the feature map, which makes the convolutional neural network lose a lot of spatial information while extracting semantic features. How to integrate semantic features with semantic information and spatial information will become an important factor to improve the performance of semantic segmentation. Firstly, this paper improves the global attention upsampling module and uses the improved global attention upsampling module to form a multiscale global attention up-mining module in a new connection way. The upsampling module of multiscale attention establishes the relationship between high-level features and lower-level features at a longer distance. Compared with PANet, the method proposed in this paper deepens the close relationship between semantic information and spatial information. Experiments show that the segmentation effect of the feature fusion method based on cascade is better than that of the feature fusion method based on weight. The segmentation effect of the two fusion methods is improved by 8.3% and 5.7% compared with the PANet on the PASCAL VOC 2012 dataset and by 4.5% and 3.6% on the Cityscapes dataset, respectively. The research results of this paper make the high-level semantic information and shallow feature information cooperate to improve the segmentation effect.
Research on Intelligent Target Tracking Algorithm Based on MDNet under Artificial Intelligence
Authors: Wang Yu
Published: April 2022

Target tracking is an important subject in computer vision technology, which has developed rapidly in recent ten years, and its application have become wider and wider. In this process, it has transferred from a simple experimental tracking environment to a complex real scene where more challenges need to be solved. The rapid development of deep learning has promoted the research progress of digital vision. Target tracking technology is an important foundation of digital vision research, which makes it develop from academia to industry. In this paper, a method of target tracking using MDNet is introduced. Starting with the attention mechanism, two attention mechanisms are added to extract and integrate the better features. Case partitioning is used to reduce the investment of tracking module and minimize the network size during tracking, and its result can be prevented from getting worse. Finally, the experiment is analyzed in detail.