bluuebunny/crossref_metadata_2025 huggingface dataset:
021 Experience from a SARS-CoV2 neurology clinic at the national hospital of neurology and neurosurgery
Authors: Jacinto Simeona , Berry Alex , Levee Viva , Heightman Melissa , Hillman Toby , Iodice Valeria , Cipolotti Lisa , Manji Hadi , Zandi Michael S , Namara Patricia Mc
Published: August 2022

Introduction

The profile of neurological disorders post-SARS-CoV-2 remains unclear. We describe obser- vations from a SARS-CoV-2 neurology clinic established to meet demand.

Methods

A retrospective case-note review of consecutive patients attending a SARS-CoV2 neurology clinic over a 6-month period was conducted. Demographics, clinical features, medications and inves- tigation results were collected.

Results

There were 122 patients included (78 female: 44 male, median age 44 years); all met WHO criteria for probable or confirmed SARS-CoV2 infection and 15.6% were hospitalised. Most frequently reported symptoms included fatigue (82.0%), cognitive disturbance (63.9%) and headache (63.9%). Attentional deficits were the most common observation on formal neuropsychometry and were associated with fatigue, sleep-impairment, migraine and anxiety symptoms (p<0.05). Majority of neuroimaging was reas- suring; only 3 patients had findings attributable to SARS-CoV2. Neurophysiology results were significant in 2 of 25 tests. Autonomic testing revealed vasodepressor syncope or postural tachycardia syndrome in 7 of 16 cases. Median follow-up was 186.5 days (range 25-552) and 59.8% reported improved symptoms.

Conclusion

The majority of patients had normal brain imaging and showed improvement. Further research is required to look into the biological mechanisms underlying neurological symptoms in SARS-CoV2 including in those not hospitalised for their initial illness.
Parallel Session 2: Neurodegeneration| Wed 18 May, 1115 – 1230|2 Degraded speech comprehension is a ‘real-world audiogram’ for dementia
Authors: Johnson Jeremy , Jiang Jessica , Benhamou Elia , Requena-Komuro Mai-Carmen , Sivasathiaseelan Harri , Nelson Annabel , Rohrer Jonathon , Bamiou Doris-Eva , Hardy Chris , Warren Jason
Published: August 2022

UCL

The recently recognised association between hearing impairment and dementia has important clinical and public health implications but remains poorly understood. In daily life, the most important sound we hear is speech: making sense of spoken messages that are ‘degraded’ by noise is fundamental to suc- cessful communication and depends on neural computations in the auditory brain that are vulnerable to neurodegenerative pathologies. Measuring degraded speech perception might therefore index both real-world hearing function and the underlying disease process in dementia.

We administered tests of degraded speech and pure tone audiometry, before correlating these measures with questionnaire data on real-world hearing ability in healthy control participants and patients with Alzheimer’s disease (AD), frontotemporal dementia (FTD) and the primary progressive aphasias (PPA). Audiometric performance was modulated by auditory cognitive performance and correlated poorly with real-world hearing ability. Degraded speech tests were able to stratify syndromic groups and showed significantly better correlation with real-world hearing measures.

Our findings suggest that auditory brain function is a critical determinant of daily life communication in people with dementia: tests of auditory cognition may constitute a ‘real-word audiogram’ for these diseases, adding value to standard audiometry and potentially detecting neurodegenerative patholo- gies earlier.
026 Croydon neurology: our DGH specialist nurse delivered lumbar puncture (LP) service
Authors: Schon Fred , Nitkunan Arani , Boodhoo Ajay , Bagtas Creselda
Published: August 2022

Demand for LPs in neurology is increasing in diagnostics, therapeutics and in assisting inexperienced junior doctors in acute medicine, necessitating good DGH based services.

Specialist nurses with an interest in multiple sclerosis or headaches seem obvious choices for this role.

We report our 30 months experience (May 2019-December 2021) of 138 LPs (initial 20 under training super- vision), average 4/month, range 0-7.

Main putative diagnoses were MS 88 and idiopathic intracranial hypertension (IIH) 34. Failure rate was 18/138 – 8/34 in IIH patients and 10/104 in the others.

Follow up phone calls to over 90% of patients revealed only 4 prominent post LP headaches none of whom needed a blood patch.

The delay from request to LP being carried out was 3 days to 3 weeks, average 2 weeks.

UK neurological services are increasingly moving from regional centres to DGHs. Little attention has been given to the training and delivery of DGH LP services. GIRFT recently suggested DGHs consider estab- lishing such a service. Our experience shows a dedicated specialist nurse supported by an interested Consultant can provide an excellent service and that the demand in a typical DGH is sufficient to justify establishing such a service.
028 Croydon neurology diagnostic coding of outpatients – the first year of experience
Authors: Nitkunan Arani , Collis Dominick , Harivardhan Keshav
Published: August 2022

Diagnostic coding of inpatients is essential for funding however coding for outpatients has only just been recognised with a recommendation from GIRFT (Getting It Right First Time – a UK programme to improve quality of care). At Croydon University Hospital, which serves a population of about 350,000, 5 Neurology consultants started coding outpatients in June 2020 during the first wave of the pandemic. 12 months of data were collected to determine the percentage of outpatients that were coded. Over 12 months, 5697 patients were booked into outpatients, of which 565 did not attend (9.9%), 2338 (41%) were first appointments and 2794 (49%) were follow-ups. In the first month, 10.3% of patients were coded and by the twelfth, 74.2% of patients were coded (with a peak of 77.4% in the tenth). This proves that it is possible to code outpatients in a Neurology department at a district general hospital. This data will help us improve services for the community served and hopefully encourage the wider Neurology community to document diagnoses in an extractable manner.
029 Botulinum toxin injections into the urethral sphincter for managing urinary retention in Fowler’s syndrome
Authors: Ananthavarathan Piriyankan , Malladi Prasad , Pakzad Mahreen , Simeoni Sara , Panicker Jalesh
Published: August 2022

Background

Fowler’s Syndrome typically presents as chronic urinary retention in young women, charac- terised by poor urethral sphincter relaxation. Standard treatment sacral neuromodulation is invasive and may not necessarily be appropriate for all patients.

Aims

To evaluate efficacy and safety of botulinum toxin injections into the urethral sphincter in women with Fowler’s syndrome

Results

17 women (mean age 36.9±12.4 years) diagnosed with Fowler’s Syndrome (abnormal urethral sphincter EMG and/or elevated urethral pressure profile) unsuitable for sacral neuromodulation received 100U botulinum toxin injected EMG-guided into the external urethral sphincter between 2020-21. 48 injec- tions were administered: 9 patients received >1 injection (median 4 injections/patient, median interval 3.50 months).

12 women (71%) reported improvements following 37 injections (77%): 62% reported reduced need for self-catheterisation, 32% became catheter-free, and 8% reported improved urinary flow. Mean IPSS score (n=6) improved from 20.5 to 12.83, while Patient Perception of Intensity of Urgency score significantly improved from 0.87 to 2.03 (p<0.05). Only mild side effects were reported: transient stress incontinence (n=3), short-lasting pain and bleeding (n=3), increased urgency incontinence (n=2).

Conclusion

Minimally-invasive urethral sphincter botulinum toxin injections significantly improve urinary symptoms among women with Fowler’s syndrome and is well tolerated.
037 Apparent homozygous c.830G>A GNE myopathy in non-jewish Iraqi
Authors: Sim Nicholas Keyi , Ismail Azzam , Busby Mark , Cord Spilker
Published: August 2022

GNE myopathy is an adult-onset autosomal recessive distal myopathy uniquely characterised by quadri- ceps sparing. It was previously described in Japanese patients and a large ethnic cluster was identified in Jews originating from Iran. Since then, it is recognised to have a worldwide prevalence with multiple pathogenic variants. While compound heterozygous c.830G>A GNE myopathy has previously been reported, homozygous disease has only previously been reported in a non-Jewish Iranian cohort and the Indian subcontinent. Here we report a case of apparent homozygous c.830G>A GNE myopathy in a non-Jewish Iraqi patient.
040 Diagnosing primary brain tumours a retrospective audit at the Queen Elizabeth Hospital, Birmingham
Authors: Munasinghe Chathura , Parylo Jacek , Hayton Tom , Ranasinghe Thamala
Published: August 2022

Introduction

Primary brain tumours (PBTs) are rare, but they can have devastating impacts. Early diagnosis and treatment of PBTs can lead to improved patient outcomes. This audit analysed the proportion of PBT patients referred from primary care by the urgent 2-week wait pathway to the neuro-oncology multi-dis- ciplinary team (MDT) at the Queen Elizabeth Hospital, Birmingham (QEHB).

Methods

Neuro-oncology MDT data at the QEHB was reviewed retrospectively. A random number generator was used to select patients until a sample size of 50 was achieved. Inclusion criteria were patients presenting with a PBT. Exclusion criteria included metastatic disease and where a clear referral pathway could not be established.

Results

Of the 50 patients analyzed, 45 (90%) were secondary care referrals, and 5 (10%) were primary care referrals, via the 2-week wait pathway. Of the secondary care referrals, 26 patients were diagnosed in A&E. The most common presentations were seizure (12 patients) and headache (10 patients).

Discussion

These results indicate that few patients are diagnosed through primary care. Most diagnoses occurred in A&E departments. This data could warrant a shift in guidance for primary care practitioners, from the 2-week wait pathway, to a direct referral to A&E for earlier diagnosis.
047 Clinicopathological characteristics impacting on survival in sporadic CJD: insights from an international autopsy-confirmed series
Authors: Watson Neil , Hermann Peter , Ladogana Anna , Green Alison , Summers David , Pocchiari Maurizio , Zerr Inga , Brandel Jean-Philippe , Pal Suvankar ,   et al
Published: August 2022

Introduction

Sporadic CJD (sCJD) is universally fatal. While median survival is 5 months, there is consider- able heterogeneity with some surviving weeks while others survive several months or years. We sought to evaluate characteristics influencing disease duration, and the performance of latest diagnostic criteria incorporating cortical ribboning on MRI brain and the RT-QuIC assay.

Methods

501 autopsy-confirmed cases from surveillance centres in the UK, France, Germany and Italy were stratified into short (<75 days), typical (75-222 days) and long (>222 days) survival groups. We evaluated clinical features, investigation results, and diagnostic criteria classification.

Results

Short survival was associated with male sex (p=0.02) and older age (p=0.004). Prion protein gene codon 129 polymorphism status greatly influenced duration (p=<0.001, with longest survival in methionine- valine heterozygotes). Extrapyramidal features were most frequent in long survivors (p=0.04). EEG and 14-3-3 were most sensitive with short survival (p<0.001), RT-QuIC with typical survival (p=0.03) and MRI sen- sitivity did not vary (p=0.4). Previous diagnostic criteria lacked sensitivity in long survivors (p=0.001) while updated criteria were equivalent between groups (p=0.19): sensitivity for long survivors increased 27.7%.

Conclusions

This study demonstrates important factors influencing survival and associated phenotypes. The latest diagnostic criteria for sCJD have significantly enhanced diagnosis in long survivors.
049 Hippocampal place cell dysfunction in an APP knock-in mouse model of Alzheimer’s disease
Authors: Wood Ruth , Burton Stephen , Katsouri Loukia , Greenaway Borgel , Chan Dennis , O’Keefe John
Published: August 2022

The hippocampus is one of the first cortical regions to exhibit Alzheimer’s disease (AD) pathology. The spatially-related firing of hippocampal place cells provides the cellular basis for spatial memory, and although this is impaired relatively early in AD few studies have examined the effects of AD pathology on place cell firing.

An in-depth characterisation of hippocampal pyramidal cell activity was undertaken in an APP knock-in model. Electrophysiological recordings from the left CA1 subregion of four 15-month-old, freely moving, APPNL-G-F mice and four wild-type littermate controls yielded data from 270 cells. Following recordings amyloid β plaque burden was quantified in fixed sections of APPNL-G-F brain tissue stained with Thioflavin-S.

Significantly fewer APPNL-G-F pyramidal cells exhibited spatial firing, and APPNL-G-F place cells showed deficits in rate coding and temporal coding of spatial information. APPNL-G-F place cells had a lower spatial information content, larger place fields, reduced phase-locking to the theta rhythm of the local field potential, and a reduction in theta phase precession. A positive correlation was identified between amyloid β plaque burden and pyramidal cell spatial information.

The results provide initial support for the hypothesis that AD pathology disrupts hippocampal function which manifests as altered place cell activity and spatial behaviour.
050 Feasibility of an automated assessment to measure cognition and mood in the acute stroke setting
Authors: Bell Simon , Harkness Kirsty , Roman Madalina , Gardner Jon , Richards Emma , Howe Jo , Sikaonga Mary , Mirheidari Bahman , Christensen Heidi , Blackburn Daniel
Published: August 2022

Introduction

Over 50% of stroke survivors have cognitive impairment. National guidelines promote early cognitive testing however, current pen-and-paper based tests are not always appropriate, typically take place in hospital and are time costly for busy clinicians.

This project aimed to create an easy-to-use cognitive assessment tool specifically designed for the needs of stroke survivors. We used a computerised doctor utilising automatic speech recognition and machine learning.

Methods

Patients are approached if they pass the eligibility criteria of having recent acute stroke/TIA, and do not have pre-existing condition i.e dementia, severe aphasia Participants could speak to the digital doctor on the ward or at home via a web-version.

Results

Recruitment started on 8th December 2020; We have screened 614 people assessed for suspected acute stroke/TIA at Sheffield Teaching Hospitals. Of those we have recruited 71 participants (13 with TIA) Mean NIHSS of 4.5 and mean MoCA of 24.6. We will present initial results of factors affecting participant recruitment. We will also compare the mood and anxiety screening scores used in this study to those collected via the SNAPP database.

Discussion

Screening was adapted due to Covid pandemic and utilising remote consent and participa- tion allowed the project to continue.
The effect of the speed limit zone on the dissipation energy: A synchronized traffic flow model
Authors: Karakhi A. , Lakouari N. , Khallouk A. , Bentaleb K. , Marzoug R. , Ez-Zahraouy H.
Published: August 2022

The speed limit signs can be used to enhance traffic safety, nevertheless, the large differences between the speed of vehicles provoked by the speed limit zone (SLZ) may increase the dissipation of energy caused by the deceleration of vehicles. In this paper, the effect of the SLZ on traffic flow and energy dissipation is investigated based on a cellular automaton model that considers the three traffic phases (free flow, synchronized flow and wide moving jams). It is found that the throughput and the energy dissipation depend strongly on the length of the SLZ as well as on the speed adopted inside the SLZ. The microscopic effect of the SLZ is also studied. The dependence of the density and the length of platoons induced by the SLZ were evaluated. The characteristic of the traffic flow can be also evaluated with the energy dissipation. For high densities, the effect of the SLZ becomes seldom on the flow, however, the energy dissipation shows salient features, which can be used as an indicator of traffic phases.
Multi-scale regression based on detrending moving average and its application to seismic data
Authors: Qin Jiaxin , Lin Min
Published: August 2022

We investigate the statistical properties of multi-scale regression model based on detrending moving average (DMA). The performance of the multi-scale regression estimator based on DMA is evaluated by varying the length, distribution and structure for different position parameters. Using different position parameters for the detrending windows in simulation, we find that the variance of the estimated regression coefficients for position parameter [Formula: see text] is the smallest. By changing series length, distribution and structure, the estimated regression coefficients are stably near the theoretical values. The method is applied to analyze the dependence of inter-earthquakes time (IET) on inter-earthquakes distances (IED) between consecutive earthquakes in the California region. Results suggest that the cross-correlation between the IET and IED series is statistically significant. Scale-dependent statistic of estimated DMA multi-scale regression coefficient demonstrates significant dependence between IET and IED series.
A new hybrid CRWENO-MWENOZ-adaptive moving mesh method for cavitating two-phase compressible fluid flow simulation
Authors: Shafaei Mohammad Hadi , Alishahi Mohammad Mehdi , Emdad Homayoun
Published: August 2022

In this paper, a high-order hybrid method for solving compressible two-phase fluid flow, including cavitation, is presented. In this regard, assuming pressure and temperature equilibrium, mass and heat transfer between the different phases are modeled. In this work, the CRMWENOZ method, which is a new combination of compact and weighted essentially non-oscillatory (WENO) methods and is more accurate than conventional methods, is presented. The new high-order hybrid method aims to predict the density and the pressure discontinuities in two-phase flow by combining the CRMWENOZ high-order method and an adaptive moving mesh technique. For this purpose, the adaptive moving mesh partial differential equation (MMPDE) method would also improve the accuracy of results by concentrating on the grid nodes in high-gradient regions for transient flows. Applying the CRMWENOZ method alongside MMPDE and using the fifth-order Radau method for time discretization lead to a substantial improvement in the accuracy of simulation, particularly near the liquid–gas interfaces. The accuracy of the proposed hybrid method was compared to other studies’ predictions of one-dimensional (1D) expansion and shock tube problems containing two-phase flow with and without cavitation. The results showed that the hybrid method presented was more accurate than the usual two-phase flow methods while using a reasonable amount of computer resources.
Space clusters of magnetic currents in modified U(1) gauge model: Geometrical approach
Authors: Mostovoy S. D. , Pavlovsky O. V.
Published: August 2022

The aim of this work is to study the spatial arrangement of magnetic currents in lattice compact electrodynamics with additional magnetic number chemical potential introduced. The behavior of the magnetic currents’ correlator on the plane of model parameters indicates the presence of several phases. By considering the mutual arrangement of microscopic currents in 4D space, two methods of geometric description of the magnetic currents’ properties are formulated. An analysis of the trends in the behavior of their characteristics shows a systematic change under the magnetic number chemical potential’s increase.
Artificial Intelligence-Aided Computation for Green’s Function of the Spherical Layered Earth
Authors: Pan Zhuohong , Wang Baoquan , Liu Jianben , Wang Yan , Tian Yang , Wang Haocheng
Published: August 2022

Currently, there are considerable challenges associated with the theoretical derivation and numerical calculation of Green’s function for spherical layered earths. In order to study the coupling effect between wide-area interconnected systems and the earth, this earth model is widely used. Artificial intelligence methods have been applied in order to overcome these challenges. First, Green’s function in the form of infinite Legendre series is derived using intelligent symbolic operations, and the weight function of Legendre series is recursively computed by means of a recursive algorithm. It is on this basis that the intelligent complex image method has been developed for solving spherical layered Green’s functions, which transforms the summation of infinite series of Green’s functions into the superposition of complex image potentials. Additionally, the upper error limit of the algorithm has been determined, and examples have been provided to demonstrate the accuracy of the algorithm. A solution based on multiple precision algorithms has been proposed to resolve the numerical singularity problem and the extremely slow convergence of the spherical layered Green’s function at the earth’s scale. It has been demonstrated that the intelligent complex image method has advantages in terms of computing speed and accuracy. A spherical layered Green’s function at the earth scale is presented in this paper as an effective and intelligent solution.
Macroscopic–microscopic calculations of the ground state properties of Z = 120 isotopes and their α-decay chains
Authors: Seif W. M. , Abdulghany A. R. , Nasr Amira
Published: August 2022

One of the substantial successes achieved in nuclear physics in the last two decades was the synthesis of dozens of isotopes of new elements up to [Formula: see text]Og, which closed the seventh row of the periodic table and inspired the ambition of adding more elements. This work aims to study extensively the ground state structure and decay properties of proposed [Formula: see text] isotopes. We employed Macroscopic–microscopic scheme based on the Skyrme energy density functional, the Woods–Saxon single-particle potential and Strutinsky’s method to find the structure properties, in a multidimensional deformation space. The same nucleon–nucleon potential is used to find the [Formula: see text]-decay half-life, within the Preformed Cluster Model. For 45 [Formula: see text]120 isotopes, the total energy surfaces, ground state masses, binding energy, deformations and fissionability are determined. The [Formula: see text]-values of the different competing decays and [Formula: see text]-decay half-lives are investigated based on the obtained structure. The results indicate the [Formula: see text]120 isotopes to be the most bound among the studied isotopes. The smallest fission barriers are indicated for the [Formula: see text]120 isotopes. The [Formula: see text]120 isotopes showed the longer half-lives against [Formula: see text]-decay, with an order of [Formula: see text]. The longest half-lives are estimated for the [Formula: see text]120 ([Formula: see text] and [Formula: see text]120 ([Formula: see text] isotopes. The longest full decay chain ending with the stable [Formula: see text]Pb nucleus is anticipated for the [Formula: see text]120 isotope. The shortest decay chain of [Formula: see text]120 terminates after three [Formula: see text]-decays by the spontaneous fission of [Formula: see text]Fl.
Compressed data separation via unconstrained l1-split analysis
Authors: Gu Mingyang , Li Song , Lin Junhong
Published: August 2022

We investigate the problems of reconstruction signals’ distinct subcomponents, that are approximately sparse in terms of morphologically different dictionaries, from a few noisy linear measurements. We show that under common assumptions on a restricted isometric property adapted to the composed dictionary and a mutual coherence condition between the two different dictionaries, the two distinct subcomponents can be stably and approximately recovered via the unconstrained [Formula: see text]-split analysis, with an error bound that is in general unimprovable for the Gaussian noises.
Using Boltzmann Plots Method to Calculate Plasma Parameters Generated from a Magnesium Target Using Optical Emission Spectroscopy Technique
Authors: Shehab Maryam M.
Published: August 2022

The laser-generated plasma was characterized using laser-induced breakdown spectroscopy after fundamental radiation from a Nd:YAG laser with an energy range of 300–500 mJ was focused onto a magnesium target. Using magnesium emission lines and Boltzmann plots, the plasma parameters were computed based on experimental data. The plasma electron temperature varied between (2.36–3.12) eV as a function of laser energy, while the density of electrons varied between (1.02–1.49[Formula: see text] cm3at ([Formula: see text] nm). These values grew as laser irradiation increased, albeit at varied rates depending on the wavelength.
Lipid droplets imaging with three-photon microscopy
Authors: He Mubin , Park Hojeong , Niu Guangle , Xia Qiming , Zhang Hequn , Tang Ben Zhong , Qian Jun
Published: August 2022

Lipid droplets (LDs) participate in many physiological processes, the abnormality of which will cause chronic diseases and pathologies such as diabetes and obesity. It is crucial to monitor the distribution of LDs at high spatial resolution and large depth. Herein, we carried three-photon imaging of LDs in fat liver. Owing to the large three-photon absorption cross-section of the luminogen named NAP-CF3 ([Formula: see text][Formula: see text]cm6 s2), three-photon fluorescence fat liver imaging reached the largest depth of 80[Formula: see text][Formula: see text]m. Fat liver diagnosis was successfully carried out with excellent performance, providing great potential for LDs-associated pathologies research.
On general Sombor index of graphs
Authors: Phanjoubam Chinglensana , Mawiong Sainkupar Mn , Buhphang Ardeline M.
Published: August 2022

In this paper, we extend the recently introduced vertex-degree-based topological index, the Sombor index, and we call it general Sombor index. The general Sombor index generalizes both the forgotten index and the Sombor index. We present the bounds in terms of other important graph parameters for general Sombor index. We also explore the Nordhaus–Gaddum-type result for the general Sombor index. We present further the relations between general Sombor index and other generalized indices: general Randić index and general sum-connectivity index.
Some properties of certain automorphism groups of finite p-groups
Authors: Soleimani Rasoul
Published: August 2022

Let [Formula: see text] be a group and let [Formula: see text] denote the absolute center of [Formula: see text]. An automorphism [Formula: see text] of [Formula: see text] is called an absolute central automorphism if [Formula: see text] for each [Formula: see text]. An automorphism [Formula: see text] of [Formula: see text] is called a central automorphism if [Formula: see text] for each [Formula: see text]. Also, let [Formula: see text] be an autonilpotent finite [Formula: see text]-group of class [Formula: see text], where [Formula: see text]. We call an automorphism [Formula: see text] of [Formula: see text] is an [Formula: see text]th autoclass-preserving if for all [Formula: see text], there exists an element [Formula: see text] such that [Formula: see text], where [Formula: see text] is the [Formula: see text]th autocommutator subgroup of [Formula: see text]. In this paper, first, we characterize finite [Formula: see text]-group [Formula: see text] of class [Formula: see text] such that every central automorphism is absolute central. We also obtain a necessary and sufficient condition for an autonilpotent finite [Formula: see text]-group of class [Formula: see text] such that each absolute central automorphism is an [Formula: see text]th autoclass-preserving.
Beyond Natural Proofs: Hardness Magnification and Locality
Authors: Chen Lijie , Hirahara Shuichi , Oliveira Igor Carboni , Pich Ján , Rajgopal Ninad , Santhanam Rahul
Published: August 2022

Hardness magnification reduces major complexity separations (such as
EXP
⊈
NC
1
) to proving lower bounds for some natural problem
Q
against weak circuit models. Several recent works [
11
,
13
,
14
,
40
,
42
,
43
,
46
] have established results of this form. In the most intriguing cases, the required lower bound is known for problems that appear to be significantly easier than
Q
, while
Q
itself is susceptible to lower bounds, but these are not yet sufficient for magnification.

In this work, we provide more examples of this phenomenon and investigate the prospects of proving new lower bounds using this approach. In particular, we consider the following essential questions associated with the hardness magnification program:

–

Does hardness magnification avoid the natural proofs barrier of Razborov and Rudich
[
51
]
?

–

Can we adapt known lower-bound techniques to establish the desired lower bound for
Q
?

We establish that some instantiations of hardness magnification overcome the natural proofs barrier in the following sense: slightly superlinear-size circuit lower bounds for certain versions of the minimum circuit-size problem imply the non-existence of natural proofs. As the non-existence of natural proofs implies the non-existence of efficient learning algorithms, we show that certain magnification theorems not only imply strong worst-case circuit lower bounds but also rule out the existence of efficient learning algorithms.

Hardness magnification might sidestep natural proofs, but we identify a source of difficulty when trying to adapt existing lower-bound techniques to prove strong lower bounds via magnification. This is captured by a
locality barrier
: existing magnification theorems
unconditionally
show that the problems
Q
considered above admit highly efficient circuits extended with small fan-in oracle gates, while lower-bound techniques against weak circuit models quite often easily extend to circuits containing such oracles. This explains why direct adaptations of certain lower bounds are unlikely to yield strong complexity separations via hardness magnification.
Versatile Control of Fluid-directed Solid Objects Using Multi-task Reinforcement Learning
Authors: Ren Bo , Ye Xiaohan , Pan Zherong , Zhang Taiyuan
Published: August 2022

We propose a learning-based controller for high-dimensional dynamic systems with coupled fluid and solid objects. The dynamic behaviors of such systems can vary across different simulators and the control tasks subject to changing requirements from users. Our controller features high versatility and can adapt to changing dynamic behaviors and multiple tasks without re-training, which is achieved by combining two training strategies. We use meta-reinforcement learning to inform the controller of changing simulation parameters. We further design a novel task representation, which allows the controller to adapt to continually changing tasks via hindsight experience replay. We highlight the robustness and generality of our controller on a row of dynamic-rich tasks, including scooping up solid balls from a water pool, in-air ball acrobatics using fluid spouts, and zero-shot transferring to unseen simulators and constitutive models. In all the scenarios, our controller consistently outperforms the plain multi-task reinforcement-learning baseline.
Augmented Paths and Reodesics for Topologically-Stable Matching
Authors: Sahillioğlu Yusuf , Horsman Devin
Published: August 2022

We propose a fully-automatic method that computes from scratch point-to-point dense correspondences between isometric shapes under topological noise. While relying on pairwise distance preservation constraints is common and generally sufficient to handle isometric deformations, presence of topological noise needs further actions that we present as our main contributions. First, instead of comparing distances over two paths on two input surfaces, we cast fuzzy votes at the path endpoints based on topologically-robust heat diffusion from path vertices. Second, we make the matching even more stable to topological noise by introducing the so-called reodesics, which are locally shortest geodesics that go through robust matches. In addition to the five standard datasets for isometric shape correspondence with and without topological noise, we employ and release a sixth one geared specifically towards topological noise evaluation with ground-truth information. We demonstrate our qualitative and quantitative advantages over seven recent state-of-the-art methods on these six datasets.
Context-aware Pseudo-true Video Interpolation at 6G Edge
Authors: Li Ran , Wei Wei , Hao Peinan , Su Jian , Sun Fengyuan
Published: August 2022

In the 6G network, lots of edge devices facilitate the low-latency transmission of video. However, with limited processing and storage capabilities, the edge devices cannot afford to reconstruct the vast amount of video data. On the condition of edge computing in the 6G network, this article fuses a self-similarity-based context feature into Frame Rate Up-Conversion (FRUC) to generate the pseudo-true video sequences at high frame rate, and its core is the extraction of the context layer for each video frame. First, we extract the patch centered at each pixel and use the self-similarity descriptor to generate the correlation surface. Then, the expectation or skewness of the correlation surface in statistics is computed to represent its context feature. By attaching an expectation or a skewness to each pixel, the context layer is constructed and added to the video frame as a new channel. According to the context layer, we predict the motion vector field of the absent frame by using the bidirectional context match and finally produce the interpolated frame. From the experimental results, it can be seen that by deploying the proposed FRUC algorithm on edge devices, the output pseudo-true video sequences have satisfying objective and subjective qualities.
Recent Advances in Fully Dynamic Graph Algorithms – A Quick Reference Guide
Authors: Hanauer Kathrin , Henzinger Monika , Schulz Christian
Published: August 2022

In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms. However, these theoretical results have received very little attention from the practical perspective. Few of the algorithms are implemented and tested on real datasets, and their practical potential is far from understood. Here, we present a quick reference guide to recent engineering and theory results in the area of fully dynamic graph algorithms.
Regression Discontinuity Designs
Authors: Cattaneo Matias D. , Titiunik Rocío
Published: August 2022

The regression discontinuity (RD) design is one of the most widely used nonexperimental methods for causal inference and program evaluation. Over the last two decades, statistical and econometric methods for RD analysis have expanded and matured, and there is now a large number of methodological results for RD identification, estimation, inference, and validation. We offer a curated review of this methodological literature organized around the two most popular frameworks for the analysis and interpretation of RD designs: the continuity framework and the local randomization framework. For each framework, we discuss three main topics: ( a) designs and parameters, focusing on different types of RD settings and treatment effects of interest; ( b) estimation and inference, presenting the most popular methods based on local polynomial regression and methods for the analysis of experiments, as well as refinements, extensions, and alternatives; and ( c) validation and falsification, summarizing an array of mostly empirical approaches to support the validity of RD designs in practice.
Turning Attention Inside Out: How Working Memory Serves Behavior
Authors: van Ede Freek , Nobre Anna C.
Published: August 2022

Flexible behavior requires guidance not only by sensations that are available immediately but also by relevant mental contents carried forward through working memory. Therefore, selective-attention functions that modulate the contents of working memory to guide behavior (inside-out) are just as important as those operating on sensory signals to generate internal contents (outside-in). We review the burgeoning literature on selective attention in the inside-out direction and underscore its functional, flexible, and future-focused nature. We discuss in turn the purpose (why), targets (what), sources (when), and mechanisms (how) of selective attention inside working memory, using visual working memory as a model. We show how the study of internal selective attention brings new insights concerning the core cognitive processes of attention and working memory and how considering selective attention and working memory together paves the way for a rich and integrated understanding of how mind serves behavior.
The ebb and flow of cardiac lymphatics: a tidal wave of new discoveries
Authors: Harris Natalie R. , Bálint László , Dy Danielle M. , Nielsen Natalie R. , Méndez Hernán G. , Aghajanian Amir , Caron Kathleen M.
Published: August 2022

The heart is imbued with a vast lymphatic network that is responsible for fluid homeostasis and immune cell trafficking. Disturbances in the forces that regulate microvascular fluid movement can result in myocardial edema, which has profibrotic and proinflammatory consequences and contributes to cardiovascular dysfunction. This review explores the complex relationship between cardiac lymphatics, myocardial edema, and cardiac disease. It covers the revised paradigm of microvascular forces and fluid movement around the capillary as well as the arsenal of preclinical tools and animal models used to model myocardial edema and cardiac disease. Clinical studies of myocardial edema and their prognostic significance are examined in parallel to the recent elegant animal studies discerning the pathophysiological role and therapeutic potential of cardiac lymphatics in different cardiovascular disease models. This review highlights the outstanding questions of interest to both basic scientists and clinicians regarding the roles of cardiac lymphatics in health and disease.
Visual Attention and Motion Estimation-Based Video Retargeting for Medical Data Security
Authors: Liu Qingfang , Kang Baosheng , Hua Qiaozhi , Wen Zheng , Li Haipeng
Published: August 2022

Medical data security is an important guarantee for intelligent medical system. Medical video data can help doctors understand the patients’ condition. Medical video retargeting can greatly reduce the storage capacity of data on the premise of preserving the original content information as much as possible. The smaller volume of medical data can reduce the execution time of data encryption and threat detection algorithm and improve the performance of medical data security methods. The existing methods mainly focus on the temporal pixel relationship and foreground motion between adjacent frames, but these methods ignore the user’s attention to the video content and the impact of background movement on retargeting, resulting in serious deformation of important content and area. To solve the above problems, this paper proposes an innovative video retargeting method, which is based on visual attention and motion estimation. Firstly, the visual attention map is obtained from eye tracking data, by K-means clustering method and Euclidean distance factor equation. Secondly, the motion estimation map is generated from both the foreground and background displacements, which are calculated based on the feature points and salient object positions between adjacent frames. Then, the visual attention map, the motion estimation map, and gradient map are fused to the importance map. Finally, video retargeting is performed by mesh deformation based on the importance map. Experiment on open datasets shows that the proposed method can protect important area and has a better effect on salient object flutter suppression.
The Role of Gap Junctions in the Generation of Smooth Muscle Cells from Bone Marrow Mesenchymal Stem Cells
Authors: Wang Ying , Yang Boping , Hu Pan , Lu Shentao , Lei Li , Liu Lubin
Published: August 2022

Background. Studies have shown that stem cell transplantation can improve smooth muscle cell (SMC) regeneration and remodelling. Gap junctions can enhance the cytoprotective effects of neighbouring cells. We investigated the effect of gap junctions on the differentiation of bone marrow mesenchymal stem cells (BMSCs) into SMCs. Materials and Methods. Rat BMSCs and SMCs were obtained from the bone marrow and bladder of Sprague-Dawley rats, respectively. Flow cytometry and multilineage differentiation were performed to assess the characteristics of these cells. BMSCs and SMCs were incubated together in cocultures in the presence and absence of heptanol, an uncoupler of gap junctions. Cocultures were divided into three groups consisting of a contact coculture, noncontact coculture, and contact coculture plus heptanol groups. The expression of BMSC-specific markers and the effect of gap junctions on the differentiation of BMSCs were evaluated by performing real-time reverse transcription-polymerase chain reaction, immunofluorescence analysis, and western blotting after cocultures. Results. CD90 and CD44 were markedly expressed, and CD31 and CD45 were weakly or not expressed in BMSCs. The cells also showed good osteogenic and adipogenic differentiation ability. Compared with the noncontact coculture group, the SMC markers such as α-SMA, calponin, and connexin43 increased in the contact coculture group. The effect of contact in the coculture group was significantly weakened by heptanol. Conclusions. The results suggested that gap junctions play an important role in the generation of SMCs from BMSCs. The formation of SMCs can potentially be used to repair the sphincter muscle of patients with stress urinary incontinence.
Service-Oriented Modeling for Blockchain-Enabled Supply Chain Quality Information Systems
Authors: Shi Yani , Ying Jiji , Shi Dongying , Yan Jiaqi
Published: August 2022

Quality management is one of the most critical issues in supply chain management. The rapid growth of information technologies, such as blockchain technology, has facilitated effective information systems development to support supply chain quality management. However, a significant challenge in developing blockchain-enabled supply chain quality information systems is how to deal with information asymmetry and the conflicting interests of supply chain partners. Taking a service-dominant view, this research proposes a Blockchain-Oriented Service Modeling (BOSM) approach for blockchain-enabled supply chain quality information systems. We provide a visual language for modeling the coordination and integration of business processes and domain knowledge at the knowledge level to facilitate the alignment of blockchain technology with supply chain quality management. The proposed approach bridges operational service computing with strategic service management in blockchain-enabled supply chain quality management and facilitates the communication between business people in supply chain management and software professionals in blockchain-based service computing. A case study on a dairy supply chain is presented to show advantages of the modeling framework under the service-dominant view, separating the cause of quality from the carrier of quality in the design of blockchain-enabled supply chain quality information systems.
Automatic Diagnosis of Mild Cognitive Impairment Based on Spectral, Functional Connectivity, and Nonlinear EEG-Based Features
Authors: Movahed Reza Akbari , Rezaeian Mohammadreza
Published: August 2022

Accurate and early diagnosis of mild cognitive impairment (MCI) is necessary to prevent the progress of Alzheimer’s and other kinds of dementia. Unfortunately, the symptoms of MCI are complicated and may often be misinterpreted as those associated with the normal ageing process. To address this issue, many studies have proposed application of machine learning techniques for early MCI diagnosis based on electroencephalography (EEG). In this study, a machine learning framework for MCI diagnosis is proposed in this study, which extracts spectral, functional connectivity, and nonlinear features from EEG signals. The sequential backward feature selection (SBFS) algorithm is used to select the best subset of features. Several classification models and different combinations of feature sets are measured to identify the best ones for the proposed framework. A dataset of 16 and 18 EEG data of normal and MCI subjects is used to validate the proposed system. Metrics including accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR) are evaluated using 10-fold crossvalidation. An average AC of 99.4%, SE of 98.8%, SP of 100%, F1 of 99.4%, and FDR of 0% have been provided by the best performance of the proposed framework using the linear support vector machine (LSVM) classifier and the combination of all feature sets. The acquired results confirm that the proposed framework provides an accurate and robust performance for recognizing MCI cases and outperforms previous approaches. Based on the obtained results, it is possible to be developed in order to use as a computer-aided diagnosis (CAD) tool for clinical purposes.
Investment Risk Assessment of EPC Power Transmission and Transformation Projects under New Energy Grid Connection Based on the Owner’s Perspective
Authors: Ding Yan , Zhang Xiaomei , Zhou Liqing , Xu Chuanbo
Published: August 2022

Power transmission and transformation projects (PTTPs) under new energy grid connections are different from ordinary engineering construction projects. With large investment amounts, various supporting facilities, and high safety and quality requirements, PTTPs are vulnerable to the volatility of new energy power generation, climate, geological conditions, geopolitical environment, technological changes, and other kinds of uncertain factors. Therefore, the investment risk of PTTPs under new energy grid connection is particularly considerable in the project management. For the owner of PTTPs, adopting the engineering-procurement-construction (EPC) mode is an effective attempt to solve the project construction problems faced by the owners. Thus, this paper deeply excavates the key risk points of PTTPs in the initial investment phase under the EPC mode and constructs the novel risk evaluation index system from the perspectives of economy, management, policy, society, and environment. An assessment model is established based on triangle fuzzy number-hesitant fuzzy linguistic term sets (HFLTS-TFNs), the entropy method, and the fuzzy comprehensive evaluation method, which is used to evaluate the investment risk of PTTPs to provide reference for power grid corporations to control project risks. Finally, a case study in the Hexi Corridor region, Gansu Province, China, is illustrated to demonstrate the rationality of the decision model and find management risks are the most vital risk factors for PTTPs under EPC mode.