julilili42/papers huggingface dataset:
Heart Rate Variability Covaries with Amygdala Functional Connectivity During Voluntary Emotion Regulation
2022-05-06

Abstract The Neurovisceral Integration Model posits that shared neural networks support the effective regulation of emotions and heart rate, with heart rate variability (HRV) serving as an objective, peripheral index of prefrontal inhibitory control. Prior neuroimaging studies have predominantly examined both HRV and associated neural functional connectivity at rest, as opposed to contexts that require active emotion regulation. The present study sought to extend upon previous resting-state functional connectivity findings, examining HRV and corresponding amygdala functional connectivity during a cognitive reappraisal task. Seventy adults (52 old and 18 young adults, 18-84 years, 51% male) received instructions to cognitively reappraise negative and neutral affective images during functional MRI scanning. HRV measures were derived from a finger pulse signal throughout the scan. During the task, young adults exhibited a significant inverse association between HRV and amygdala-medial prefrontal cortex (mPFC) functional connectivity, in which higher HRV was correlated with weaker amygdala-mPFC coupling, whereas old adults displayed a slight positive, albeit non-significant correlation. Furthermore, voxelwise whole-brain functional connectivity analyses showed that higher HRV was linked to weaker right amygdala-posterior cingulate cortex connectivity across old and young adults, and in old adults, higher HRV positively correlated with stronger right amygdala – right ventrolateral prefrontal cortex connectivity. Collectively, these findings highlight the importance of assessing HRV and neural functional connectivity during active regulatory contexts to further identify neural concomitants of HRV and adaptive emotion regulation.
Identification of viral dose and administration time in simulated phage therapy occurrences
2022-05-06

Abstract The rise in multidrug-resistant bacteria has sprung a renewed interest in applying phages as antibacterial, a procedure Western practitioners eventually abandoned due to several downfalls, including poor understanding of the dynamics between phages and bacteria. A successful phage therapy needs to account for the loss of infective virions and the multiplication of the hosts. The parameters critical inoculation size ( V F ) and failure threshold time ( T F ) have been introduced to assure that the viral dose ( v φ ) and administration time ( t φ ) would lead to an effective treatment. The problem with the definition of V F and T F is that they are non-linear equations with two unknowns; thus, their solution is cumbersome and not unique. The current study used machine learning in the form of a decision tree algorithm to determine ranges for the viral dose and administration times required to achieve an effective phage therapy. Within these ranges, a Pareto optimal solution of a multi-criterial optimization problem (MCOP) provides values leading to effective treatment. The algorithm was tested on a series of microbial consortia that described allochthonous invasions (the outgrowing of a species at high cell density by another species initially present at low concentration) to inhibit the growth of the invading species. The present study also introduced the concept of ‘mediated phage therapy’, where targeting a booster bacteria might decrease the virulence of a pathogen immune to phagial infection. The results demonstrated that the MCOP could provide pairs of v φ and t φ that could effectively wipe out the bacterial target from the considered micro-environment. In summary, the present work introduced a novel method for investigating the phage/bacteria interaction that could help increase the effectiveness of phage therapy. Author summary Phage therapy is a treatment that can help fight infections with bacteria resistant to antibiotics. However, several phage therapy application have failed, possibly because phages were administered at the wrong time or in insufficient amounts. The present study implemented a machine learning protocol to correctly calculate the administration time and viral load to obtain effective phage therapy. Four simulated microbial consortia, including one case where the pathogen was not directly a phage’s host, were employed to prove the procedure’s concept. The results demonstrated that the procedure is suitable to help the microbiologists to instantiate an effective phage therapy and clear infections.
The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices
2022-05-06

Integrated solar water splitting devices that produce hydrogen without the use of power inverters operate outdoors and are hence exposed to varying weather conditions. As a result, they might sometimes work at non- optimal operation points below or above the maximum power point of the photovoltaic component, which would directly translate into efficiency losses. Up until now, however, no common parameter describing and quantifying this and other real-life operating related losses (e.g. spectral mismatch) exists in the community. Therefore, the annual- hydrogen-yield-climatic-response (AHYCR) ratio is introduced as a figure of merit to evaluate the outdoor performance of integrated solar water splitting devices. This value is defined as the ratio between the real annual hydrogen yield and the theoretical yield assuming the solar-to-hydrogen device efficiency at standard conditions. This parameter is derived for an exemplary system based on state-of-the-art AlGaAs//Si dual-junction solar cells and an anion exchange membrane electrolyzer using hourly resolved climate data from a location in southern California and from reanalysis data of Antarctica. This work will help to evaluate, compare and optimize the climatic response of solar water splitting devices in different climate zones.
Fusing spheroids to aligned μ-tissues in a heart-on-chip featuring oxygen sensing and electrical pacing capabilities
2022-05-07

Over the last decade, Organ-on-Chip (OoC) emerged as a promising technology for advanced <i>in vitro</i> models, recapitulating key physiological cues. OoC approaches tailored for cardiac tissue engineering resulted in a variety of platforms, some of which integrate stimulation or probing capabilities. Due to manual handling processes, however, a large-scale standardized and robust tissue generation, applicable in an industrial setting, is still out of reach. Here, we present a novel cell injection and tissue generation concept relying on spheroids, which can be produced in large quantities and uniform size from induced pluripotent stem cell-derived human cardiomyocytes. Hydrostatic flow transports and accumulates spheroids in dogbone-shaped tissue chambers, which subsequently fuse and form aligned, contracting cardiac muscle fibers. Furthermore, we demonstrate electrical stimulation capabilities by utilizing fluidic media connectors as electrodes and provide the blueprint of a low-cost, open-source, scriptable pulse generator. We report on a novel integration strategy of optical O<sub>2</sub> sensor spots into resin-based microfluidic systems, enabling <i>in situ</i> determination of O<sub>2</sub> partial pressures. Finally, a proof-of-concept demonstrating electrical stimulation combined with <i>in situ</i> monitoring of metabolic activity in cardiac tissues is provided. The developed system thus opens the door for advanced OoCs integrating biophysical stimulation as well as probing capabilities and serves as a blueprint for the facile and robust generation of high density microtissues in microfluidic modules amenable to scaling-up and automation.
Concepts and clinical aspects of active implants for the treatment of bone fractures
2022-05-07

Nonunion is a complication of long bone fractures that leads to disability, morbidity and high costs. Early detection is difficult and treatment through external stimulation and revision surgery is often a lengthy process. Therefore, alternative diagnostic and therapeutic options are currently being explored, including the use of external and internal sensors. Apart from monitoring fracture stiffness and displacement directly at the fracture site, it would be desirable if an implant could also vary its stiffness and apply an intervention to promote healing, if needed. This could be achieved either by a predetermined protocol, by remote control, or even by processing data and triggering the intervention itself (self-regulated 'intelligent' or 'smart' implant). So-called active or smart materials like shape memory alloys (SMA) have opened up opportunities to build active implants. For example, implants could stimulate fracture healing by active shortening and lengthening via SMA actuator wires; by emitting pulses, waves, or electromagnetic fields. However, it remains undefined which modes of application, forces, frequencies, force directions, time durations and periods, or other stimuli such implants should ideally deliver for the best result. The present paper reviews the literature on active implants and interventions for nonunion, discusses possible mechanisms of active implants and points out where further research and development are needed to build an active implant that applies the most ideal intervention. STATEMENT OF SIGNIFICANCE: Early detection of delays during fracture healing and timely intervention are difficult due to limitations of the current diagnostic strategies. New diagnostic options are under evaluation, including the use of external and internal sensors. In addition, it would be desirable if an implant could actively facilitate healing ('Intelligent' or 'smart' implant). Implants could stimulate fracture healing via active shortening and lengthening; by emitting pulses, waves, or electromagnetic fields. No such implants exist to date, but new composite materials and alloys have opened up opportunities to build such active implants, and several groups across the globe are currently working on their development. The present paper is the first review on this topic to date.
Implicit measures of alcohol approach and drinking identity in alcohol use disorder: A preregistered double‐blind randomized trial with cathodal transcranial direct current stimulation (tDCS)
2022-05-07

Abstract Alcohol use disorder (AUD) is a severe and widespread mental disorder with a huge negative impact on the social, economic and health dimensions. The identification of risk factors for the development of AUD and for relapse in existing AUD are crucial for prevention and treatment approaches. Alcohol‐related implicit associations have been shown to contribute to drinking and might partially explain sudden relapses. The aims of this study are to investigate implicit associations in abstinent AUD patients and to test whether cathodal transcranial direct current stimulation (tDCS) modulates implicit associations. We measured performance in two alcohol‐related implicit association tests (IATs) and two control tasks (flower‐insect IAT, Stroop task) in 27 abstinent AUD patients with 31.5 ( SD = 36) days of abstinence on average. During the execution of the tasks, we applied 1 mA cathodal or sham tDCS over the left dorsolateral prefrontal cortex (dlPFC) in a sham‐controlled within‐subject design. Results show an implicit bias of alcohol avoidance and implicit nondrinking identity for abstinent AUD patients. Cathodal tDCS modulated neither alcohol‐related implicit associations nor the control tasks. This study complements knowledge about implicit alcohol‐related association in AUD patients and shows no effect of a neuromodulatory intervention to alter implicit associations with the present parameters.
Evaluation of signal analysis algorithms for ipsilateral motor-evoked potentials induced by transcranial magnetic stimulation
2022-05-07

Abstract Objective . Evaluating ipsilateral motor-evoked potentials (iMEP) induced by transcranial magnetic stimulation is challenging. In healthy adults, isometric contraction is necessary to facilitate iMEP induction; therefore, the signal may be masked by the concurrent muscle activity. Signal analysis algorithms for iMEP evaluation need to be benchmarked and evaluated. Approach . An open analysis toolbox for iMEP evaluation was implemented on the basis of 11 previously reported algorithms, which were all threshold based, and a new template-based method based on data-driven signal decomposition. The reliability and validity of these algorithms were evaluated with a dataset of 4244 iMEP from 55 healthy adults. Main results. iMEP estimation varies drastically between algorithms. Several algorithms exhibit high reliability, but some appear to be influenced by background activity of muscle preactivation. Especially in healthy subjects, template-based approaches might be more valid than threshold-based ones. Measurement of iMEP persistence requires algorithms that reject some trials as MEP negative. The stricter the algorithms reject trials, the less reliable they generally are. Our evaluation identifies an optimally strict and reliable algorithm. Significance. We show different benchmarks and propose application for different use cases.
Parents’ role construction for educational involvement and socioeconomic status: Relations to early adolescents’ academic self-concepts and achievement in reading, writing, and mathematics
2022-05-07

Parents play a critical role for their children’s academic development. However, little is known about the link between parental role construction for educational involvement (i.e., parents’ beliefs of whether they should be involved in their child’s education) and educational outcomes in early adolescence. This study therefore investigated relations between parental role construction and early adolescents’ reading, writing, and mathematics self-concepts and achievement. In addition, it was tested whether these effects vary depending on socio-economic status (SES). Relying on data from two measurement points in the first year of secondary school (413 adolescents, 50.5% girls, and their parents), results from longitudinal structural equation models revealed positive relations between parental role construction and initial writing achievement and self-concept. A stronger relation between role construction and adolescents’ mathematics achievement at both measurement points emerged in the presence of higher SES. This study contributes to the current knowledge on links between parental beliefs and educational outcomes in early adolescence and refines these insights by showing that SES moderates some of the effects of parental role construction.
The semi-automatic classification of an open-ended question on panel survey motivation and its application in attrition analysis
2022-05-07

In this study, we demonstrate how supervised learning can extract interpretable survey motivation measurements from a large number of responses to an open-ended question. We manually coded a subsample of 5,000 responses to an open-ended question on survey motivation from the GESIS Panel (25,000 responses in total); we utilized supervised machine learning to classify the remaining responses. We can demonstrate that the responses on survey motivation in the GESIS Panel are particularly well suited for automated classification, since they are mostly one-dimensional. The evaluation of the test set also indicates excellent overall performance. We present the pre-processing steps and methods we used for our data, and by discussing other popular options that might be more suitable in other cases, we also generalize beyond our use case. We also discuss various minor problems, such as a necessary spelling correction. Finally, we can showcase the analytic potential of the resulting categorization of panelists' motivation through an event history analysis of panel dropout. The analytical results allow a close look at respondents' motivations: they span a wide range, from the urge to help to interest in questions or the incentive and the wish to influence those in power through their participation. We conclude our paper by discussing the re-usability of the hand-coded responses for other surveys, including similar open questions to the GESIS Panel question.
Human genetic factors associated with pneumonia risk, a cue for COVID-19 susceptibility
2022-05-08

Pneumonia, an acute respiratory tract infection, is one of the major causes of mortality worldwide. Depending on the site of acquisition, pneumonia can be community acquired pneumonia (CAP) or nosocomial pneumonia (NP). The risk of pneumonia, is partially driven by host genetics. CYP1A1 is a widely studied pulmonary CYP family gene primarily expressed in peripheral airway epithelium. The CYP1A1 genetic variants, included in this study, alter the gene activity and are known to contribute in lung inflammation, which may cause pneumonia pathogenesis. In this study, we performed a meta-analysis to establish the possible contribution of CYP1A1 gene, and its three variants (rs2606345, rs1048943 and rs4646903) towards the genetic etiology of pneumonia risk. Using PRISMA guidelines, we systematically reviewed and meta-analysed case-control studies, evaluating risk of pneumonia in patients carrying the risk alleles of CYP1A1 variants. Heterogeneity across the studies was evaluated using I2 statistics. Based on heterogeneity, a random-effect (using maximum likelihood) or fixed-effect (using inverse variance) model was applied to estimate the effect size. Pooled odds ratio (OR) was calculated to estimate the overall effect of the risk allele association with pneumonia susceptibility. Egger's regression test and funnel plot were used to assess publication bias. Subgroup analysis was performed based on pneumonia type (CAP and NP), population, as well as age group. A total of ten articles were identified as eligible studies, which included 3049 cases and 2249 healthy controls. The meta-analysis findings revealed CYP1A1 variants, rs2606345 [T vs G; OR = 1.12 (0.75-1.50); p = 0.02; I2 = 84.89%], and rs1048943 [G vs T; OR = 1.19 (0.76-1.61); p = 0.02; I2 = 0.00%] as risk markers whereas rs4646903 showed no statistical significance for susceptibility to pneumonia. On subgroup analysis, both the genetic variants showed significant association with CAP but not with NP. We additionally performed a spatial analysis to identify the key factors possibly explaining the variability across countries in the prevalence of the coronavirus disease 2019 (COVID-19), a viral pneumonia. We observed a significant association between the risk allele of rs2606345 and rs1048943, with a higher COVID-19 prevalence worldwide, providing us important links in understanding the variability in COVID-19 prevalence.
Using mobile money data and call detail records to explore the risks of urban migration in Tanzania
2022-05-08

Abstract Understanding what factors predict whether an urban migrant will end up in a deprived neighbourhood or not could help prevent the exploitation of vulnerable individuals. This study leveraged pseudonymized mobile money interactions combined with cell phone data to shed light on urban migration patterns and deprivation in Tanzania. Call detail records were used to identify individuals who migrated to Dar es Salaam, Tanzania’s largest city. A street survey of the city’s subwards was used to determine which individuals moved to more deprived areas. t -tests showed that people who settled in poorer neighbourhoods had less money coming into their mobile money account after they moved, but not before. A machine learning approach was then utilized to predict which migrants will move to poorer areas of the city, making them arguably more vulnerable to poverty, unemployment and exploitation. Features indicating the strength and location of people’s social connections in Dar es Salaam before they moved (‘pull factors’) were found to be most predictive, more so than traditional ‘push factors’ such as proxies for poverty in the migrant’s source region.
Histological findings of corneal tissue after failed phototherapeutic keratectomy in macular corneal dystrophy – a case report
2022-05-08

Abstract Background Macular corneal dystrophy is a rare inherited disease of the cornea leading to deposits mainly in the stroma. Affected patients suffer from progressive loss of visual acuity which should be treated with penetrating keratoplasty. This is the first case report describing the clinical and histopathological findings of corneal tissue after failed phototherapeutic keratectomy (PTK) in a patient with macular corneal dystrophy. Case presentation A 32-year-old man presented with visual impairment, blurred vision and increasing glare sensitivity in both eyes in 2014. All symptoms had existed for several years and had recently increased sharply. A corneal dystrophy was diagnosed and penetrating keratoplasty was recommended but the patient was hesitant to undergo surgery. In 2018, in contrast to current guidelines, a PTK was performed in both eyes in Turkey for unknown reasons. In May 2019, he presented again in our clinic. Best corrected visual acuity was markedly reduced in both eyes. Slit-lamp examination revealed multiple dense, poorly circumscribed grey-white patchy changes in the stroma accompanied by corneal opacity in both eyes. In February 2020, the patient decided to have penetrating keratoplasty performed at the University Eye Hospital in Tübingen. The explanted cornea was stained for acid mucopolysaccharides (AMP) and periodic acid–Schiff staining (PAS). The histopathological examination revealed destruction of Bowman’s layer and a subepithelial fibrosis band due to the PTK previously performed. The AMP staining demonstrated blue deposits typical of macular corneal dystrophy, mainly in the stroma but also in the endothelium. Interestingly, the acidic mucopolysaccharides were found increased in the PTK-induced subepithelial fibrosis band. The postoperative course after keratoplasty was favourable with a significant increase in visual acuity and a clear graft. Conclusions This report presents the first case of a histologically evident exacerbation of macular corneal dystrophy after PTK and emphasizes the relevance of thorough pre-interventional diagnosis and patient selection to consider other therapeutic approaches, such as penetrating keratoplasty.
The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)
2022-05-09

Substances of unknown or variable composition, complex reaction products, or biological materials (UVCBs) are over 70 000 "complex" chemical mixtures produced and used at significant levels worldwide. Due to their unknown or variable composition, applying chemical assessments originally developed for individual compounds to UVCBs is challenging, which impedes sound management of these substances. Across the analytical sciences, toxicology, cheminformatics, and regulatory practice, new approaches addressing specific aspects of UVCB assessment are being developed, albeit in a fragmented manner. This review attempts to convey the "big picture" of the state of the art in dealing with UVCBs by holistically examining UVCB characterization and chemical identity representation, as well as hazard, exposure, and risk assessment. Overall, information gaps on chemical identities underpin the fundamental challenges concerning UVCBs, and better reporting and substance characterization efforts are needed to support subsequent chemical assessments. To this end, an information level scheme for improved UVCB data collection and management within databases is proposed. The development of UVCB testing shows early progress, in line with three main methods: whole substance, known constituents, and fraction profiling. For toxicity assessment, one option is a whole-mixture testing approach. If the identities of (many) constituents are known, grouping, read across, and mixture toxicity modeling represent complementary approaches to overcome data gaps in toxicity assessment. This review highlights continued needs for concerted efforts from all stakeholders to ensure proper assessment and sound management of UVCBs.