Miracle-dz/UniFAR-FTUs huggingface dataset:
Early parenting behaviour is associated with complex attention outcomes in middle to late childhood in children born very preterm

ABSTRACT Attention deficits are common in children born very preterm (VP), especially for children with higher social risk. The aim of this study was to examine the association between parenting behavior and attention in children born VP, and whether this association is influenced by familial social risk. Two hundred and twenty-four children born <30 weeks’ gestation and/or with a birth weight <1250 g were recruited at birth. At 2 years, social risk was calculated and parenting behaviors were observed during a parent-child interaction task, with children’s attention skills assessed at 7 and 13 years using standardized assessments. Higher levels of sensitive parenting at 2 years were positively associated with divided attention at age 7 years, and higher levels of intrusive parenting were negatively associated with divided attention at 13 years. Children born VP with higher social risk were more positively influenced by sensitive parenting behavior for sustained attention at 7 years, selective attention at 13 years, and divided attention at 7 and 13 years than children born VP with lower social risk. Additionally, children born VP with higher social risk were more negatively influenced by intrusive parenting for sustained attention outcomes at 7 years than those with lower social risk. In summary, the evidence for a contribution of early parenting to attention outcomes in children born VP was stronger for more complex attention (divided attention) compared with basic attention domains. Our findings also suggest that early parenting behavior has a particular influence on children born VP from socially disadvantaged environments for attention outcomes.
A Randomized, Single-Blind Clinical Trial Comparing Robotic-Assisted Fluoroscopic-Guided with Ultrasound-Guided Renal Access for Percutaneous Nephrolithotomy

Purpose: We conducted a randomized, single-blind clinical trial comparing the surgical outcomes of robotic-assisted fluoroscopic-guided (RAF group) and ultrasound-guided (US group) renal access in mini-percutaneous nephrolithotomy (PCNL). Materials and Methods: We recruited patients who underwent mini-PCNL with ureteroscopic assistance for large renal stones between January 2020 and May 2021. Block randomization was performed using online software. Automated needle target with x-ray was used for fluoroscopic-guided renal access in the RAF group. PCNL was performed by residents using a pneumatic lithotripsy system with 16.5Fr/17.5Fr tracts. The primary outcome was single puncture success, and the secondary outcomes were stone-free rate, complication rate, parameters measured during renal access and fluoroscopy time. Results: In total, 71 patients (35 in US group, 36 in RAF group) were enrolled. No difference was seen in the single puncture success rate between the US and RAF groups (34.3% and 50.0%, p=0.2). In 14.3% cases in the US group vs no cases in the RAF group, the resident was unable to obtain access due to difficult targeting (p=0.025). The mean number of needle punctures was significantly fewer, and the median duration of needle puncture was shorter in the RAF group (1.83 vs 2.51 times, p=0.025; 5.5 vs 8.0 minutes, p=0.049, respectively). The stone-free rate at 3 months after surgery was 83.3% and 70.6% in the RAF and US groups, respectively (p=0.26). Multivariate analysis revealed that RAF guidance reduced the mean number of needle punctures by 0.73 times (p=0.021). Conclusions: RAF renal access in mini-PCNL may have further potential applications in this field.
A deep residual inception network with channel attention modules for multi-label cardiac abnormality detection from reduced-lead ECG

Objective. Most arrhythmias due to cardiovascular diseases alter the heart’s electrical activity, resulting in morphological alterations in electrocardiogram (ECG) recordings. ECG acquisition is a low-cost, non-invasive process and is commonly used for continuous monitoring as a diagnostic tool for cardiac abnormality identification. Our objective is to diagnose twenty-nine cardiac abnormalities and sinus rhythm using varied lead ECG signals. Approach. This work proposes a deep residual inception network with channel attention mechanism (RINCA) for twenty-nine cardiac arrhythmia classification along with normal ECG from multi-label ECG signal with different lead combinations. The RINCA architecture employing the inception-based convolutional neural network backbone uses residual skip connections with the channel attention mechanism. The inception model facilitates efficient computation and prevents overfitting while exploring deeper networks through dimensionality reduction and stacked 1-dimensional convolutions. The residual skip connections alleviate the vanishing gradient problem. The attention modules selectively leverage the temporally significant segments in a sequence and predominant channels for multi-lead ECG signals, contributing to the decision-making. Main results. Exhaustive experimental evaluation on the large-scale ‘PhysioNet/Computing in Cardiology Challenge (2021)’ dataset demonstrates RINCA's efficacy. On the hidden test data set, RINCA achieves the challenge metric score of 0.55, 0.51, 0.53, 0.51, and 0.53 (ranked 2nd, 5th, 4th, 5th and 4th) for the twelve-lead, six-lead, four-lead, three-lead, and two-lead combination cases, respectively. Significance. The proposed RINCA model is more robust against varied sampling frequency, recording time, and data with heterogeneous demographics than the existing art. The explainability analysis shows RINCA's potential in clinical interpretations.
Making sense of the delegitimation experiences of people suffering from indoor air problems in their homes

ABSTRACT Purpose Little is known about the delegitimation experiences of people who associate their health problems with the indoor air quality of their homes (i.e., indoor air sufferers). From other contexts, it is known that people suffering from contested illnesses frequently report delegitimation from authorities and laypersons. Therefore, we analysed delegitimation experiences among indoor air sufferers, focusing on how they explain why others delegitimize them. Method Two types of qualitative data—semi-structured interviews with eight people and essays written by 28 people—were subjected to a thematic analysis. Results Thematic analysis revealed three themes: 1) lack of understanding; 2) others’ lack of morality; and 3) social discrimination and inequality. Conclusion This study demonstrates that indoor air sufferers are vulnerable as individuals and as a group, and suggests that authorities working with people suffering from indoor air problems in homes must pay more attention to sufferers’ ability/willingness to trust people and the system responsible for their care.
Dynamics of waste-to-energy incineration R&D collaboration networks: a social network analysis based on patent data

Abstract The municipal solid waste in China is increasing at an unprecedented pace due to the upgrading urbanization and improving living standard. The waste-to-energy (WTE) incineration technology offers a viable and potentially profitable technical solution to this booming phenomenon. The highly complex and systematic nature of WTE technologies have led to the emergence of WTE innovation network in the past decade. This study applies complex network theory as well as social network analysis to investigate the social network structure evolution of WTE technologies in terms of global network properties, major actors’ centrality and collaboration relationships. Based on the data-set covering joint filing patents pertaining to waste heat over the period 2006–2015, this study finds that the overall WTE innovation network exhibits the properties of scale-free network with minority organizations linking to abundant organizations. A large fraction of major actors represent the first-movers in the WTE research and still control the technological trajectories. The collaboration relationships have moved toward higher level of multidisciplinary and larger technological distance. Overall, the evolution of WTE innovation network features a young state of industry with high regime dependence. Finally, some suggestions are proposed to shed light on benign development of WTE innovation network.
Revictimisation of Women in Non-Urban Areas: A Scoping Review

Literature widely acknowledges that women who experience Child Sex Abuse (CSA) have a higher risk of experiencing sexual revictimisation later in life, yet less is known about experiences of revictimisation in non-urban areas. The aim of this review is to examine what is known internationally regarding revictimisation of non-urban women, and to provide future research, practice and policy recommendations. A total of 2414 articles were identified through a comprehensive search across five broad health sciences and humanities databases; 11 articles met inclusion criteria and were included in this review. This review found a general lack of qualitative revictimisation studies, and limited research focusing on non-urban women. While existing studies included non-urban research samples, few articles (n = 3) explored how non-urban location contextualises revictimisation experiences. Most peer-reviewed articles identified within this paper (n = 7) examined intimate partner violence (IPV) revictimisation, highlighting a significant lack of research on sexual revictimisation within non-urban settings. Findings from the review indicate that experiences of violence in childhood and adulthood are frequent among non-urban women, and that experiencing child abuse is associated with a heightened likelihood of poor mental health and IPV in adulthood. The review also found that non-urban women faced significant structural disadvantage including low levels of employment and income, limited-service sector resources, unsafe family environments and exposure to community violence. Additional qualitative research is needed to better understand the experiences and needs of non-urban revictimised women, particularly within an Australian context.
Practice Management Strategies for Imaging Facilities Facing an Acute Iodinated Contrast Media Shortage.

An unanticipated but severe shortage in iodinated contrast media (ICM) is currently affecting imaging practices across the globe and is expected to persist through at least the end of June 2022. This supply shock may lead to healthcare systems experiencing an acute imaging crisis, as many affected facilities have contrast agent supplies that are anticipated to last only a week or two under normal operating conditions. To maximize the opportunity to continue to provide optimal care for patients with emergent or life-threatening imaging indications and thereby minimize the overall impact on patient care, practice leaders will need to quickly assess their contrast material inventories, prioritize examination indications, and reduce their expected short-term usage of ICM. This Clinical Perspective reviews ICM conservation techniques that we have deployed, or are considering deploying, depending on the severity and length of the supply shortage.
Household Air Pollution Concentrations after Liquefied Petroleum Gas Interventions in Rural Peru: Findings from a One-Year Randomized Controlled Trial Followed by a One-Year Pragmatic Crossover Trial

Background: Household air pollution (HAP) from biomass fuel combustion remains a leading environmental risk factor for morbidity worldwide. Objective: Measure the effect of liquefied petroleum gas (LPG) interventions on HAP exposures in Puno, Peru. Methods: We conducted a 1-y randomized controlled trial followed by a 1-y pragmatic crossover trial in 180 women age 25–64 y. During the first year, intervention participants received a free LPG stove, continuous fuel delivery, and regular behavioral messaging, whereas controls continued their biomass cooking practices. During the second year, control participants received a free LPG stove, regular behavioral messaging, and vouchers to obtain LPG tanks from a nearby distributor, whereas fuel distribution stopped for intervention participants. We collected 48-h kitchen area concentrations and personal exposures to fine particulate matter (PM) with aerodynamic diameter ≤2. 5μm (PM2.5), black carbon (BC), and carbon monoxide (CO) at baseline and 3-, 6-, 12-, 18-, and 24-months post randomization. Results: Baseline mean [±standard deviation (SD)] PM2.5 (kitchen area concentrations 1,220±1,010 vs. 1,190±880 μg/m3; personal exposure 126±214 vs. 104±100 μg/m3), CO (kitchen 53±49 vs. 50±41 ppm; personal 7±8 vs. 7±8 ppm), and BC (kitchen 180±120 vs. 210±150 μg/m3; personal 19±16 vs. 21±22 μg/m3) were similar between control and intervention participants. Intervention participants had consistently lower mean (±SD) concentrations at the 12-month visit for kitchen (41±59 μg/m3, 3±6 μg/m3, and 8±13 ppm) and personal exposures (26±34 μg/m3, 2±3 μg/m3, and 3±4 ppm) to PM2.5, BC, and CO when compared to controls during the first year. In the second year, we observed comparable HAP reductions among controls after the voucher-based intervention for LPG fuel was implemented (24-month visit PM2.5, BC, and CO kitchen mean concentrations of 34±74 μg/m3, 3±5 μg/m3, and 6±6 ppm and personal exposures of 17±15 μg/m3, 2±2 μg/m3, and 3±4 ppm, respectively), and average reductions were present among intervention participants even after free fuel distribution stopped (24-month visit PM2.5, BC, and CO kitchen mean concentrations of 561±1,251 μg/m3, 82±124 μg/m3, and 23±28 ppm and personal exposures of 35±38 μg/m3, 6±6 μg/m3, and 4±5 ppm, respectively). Discussion: Both home delivery and voucher-based provision of free LPG over a 1-y period, in combination with provision of a free LPG stove and longitudinal behavioral messaging, reduced HAP to levels below 24-h World Health Organization air quality guidelines. Moreover, the effects of the intervention on HAP persisted for a year after fuel delivery stopped. Such strategies could be applied in LPG programs to reduce HAP and potentially improve health. https://doi.org/10.1289/EHP10054
Immunological response of polysaccharide nanogel-incorporating PEG hydrogels in an in vivo diabetic model

Abstract Cell-based therapies hold significant advantages in comparison with the traditional drug-based or injection-based treatments. However, for long-term functional cellular implants, immune acceptance must be established. To accomplish the acceptance of the implanted cells, various biomaterial systems have been studied. Nanogels have shown great potential for modulation of cellular microenvironments, acting as a physical barrier between the immune system and the implant. However, internalization of nano-scale materials by implanted cells is not desirable and is yet to be overcome. In this study, we incorporated acrylate modified cholesterol-bearing pullulan (CHPOA) nanogels into poly (ethylene glycol) diacrylate (PEGDA) hydrogels through covalent crosslinking, where we used visible light-induced photopolymerization. We characterized morphology and swelling properties of CHPOA incorporated PEG composite hydrogels using FE-SEM and gravimetric analysis. Also, we investigated the biocompatibility properties of composite hydrogels in vivo, where we used both healthy and diabetic mice. We induced diabetes in mice using a low dose streptozotocin (STZ) injections and implanted composite hydrogels in both diabetic and healthy mice through subcutaneous route. Immune cell infiltration of the retrieved tissue was examined through histological analysis, where we observed minimum immune response levels of 0–2 rareness, according to ISO standard of biological evaluation of medical devices. Our observation suggests that the composite hydrogel developed here can be used to introduce nanostructured domains into bulk hydrogels and that this system has potential to be used as immunologically acceptable composite material in cellular therapy without internalization of nanoparticles.
Estimation of uncertainty using revised draft ISO 1996-2

The aim of this paper is to examine methods for the estimation of the uncertainty associated with environmental acoustic measurements. It is considered best practice that any measurement should be accompanied by a quantitative indication of its quality, that is, the uncertainty of the measurement. ISO 1996-2:2007 "Acoustics - Description, measurement and assessment of environmental noise - Part 2: Determination of environmental noise levels" presents guidelines on how to determine the measurement uncertainty associated with environmental acoustic measurements, although the presentation of this value is not yet mandatory. However, this international standard is to be revised (stage 90.92 at 17 June 2012). From discussion with members of the ISO Standard working group, the last working draft ISO 1996-2:2011(11-02-02 2nd working draft) follows the uncertainty calculation methodology recommended in IMAGINE documents and it states that the estimation of measurement uncertainty should be reported. This paper presents a method, based on the ISO working document, to perform this calculation as well as two worked examples (road traffic noise and railway traffic noise). In concluding, this paper presents a reflection on why an estimate of the uncertainty of the measurement is essential in environmental acoustics, comments on the approach currently being followed by the main European and International standards, and finally summarises methods to minimise the uncertainty associated with environmental noise level measurements.
Self-derivation of new knowledge through memory integration varies as a function of prior knowledge

ABSTRACT The current research was an investigation of the effects of prior, domain-relevant knowledge on self-derivation of new, integrated knowledge. Adults were presented with novel “stem” facts and tested for self-derivation of new knowledge through integration of the facts in memory. To examine the effects of prior knowledge on memory integration, we tested participants under three within-subject conditions, in which in advance of stem encoding, they were provided with either: (1) no domain relevant information (No Knowledge control), (2) generally relevant information about the domain (General), or (3) generally relevant information about the domain along with a stem fact that was directly necessary for self-derivation (General + Stem). Prior exposure to both General and General + Stem knowledge facilitated memory for the novel, explicitly-taught stem facts. Moreover, for both prior knowledge conditions, the amount of domain-relevant knowledge retained in memory was associated with trial-by-trial self-derivation success. Importantly, the type of prior knowledge modulated memory integration in different ways. Whereas General + Stem knowledge enhanced selective retrieval and integration of the stem facts, General knowledge supported learning of the individual stem facts, but not their integration with one another. Together, the findings indicate how malleable, domain-specific experience shapes encoding, integration, and flexible extension of new, related information.
Harnessing the power of excess statistical significance: Weighted and iterative least squares.

We introduce a new meta-analysis estimator, the weighted and iterated least squares (WILS), that greatly reduces publication selection bias (PSB) when selective reporting for statistical significance (SSS) is present. WILS is the simple weighted average that has smaller bias and rates of false positives than conventional meta-analysis estimators, the unrestricted weighted least squares (UWLS), and the weighted average of the adequately powered (WAAP) when there is SSS. As a simple weighted average, it is not vulnerable to violations in publication bias corrections models' assumptions too often seen in application. WILS is based on the novel idea of allowing excess statistical significance (ESS), which is a necessary condition of SSS, to identify when and how to reduce PSB. We show in comparisons with large-scale preregistered replications and in evidence-based simulations that the remaining bias is small. The routine application of WILS in the place of random effects would do much to reduce conventional meta-analysis's notable biases and high rates of false positives. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Designing Human-Centered Collective Intelligence

DESIGNING HUMAN-CENTERED COLLECTIVE INTELLIGENCE Ivor D. Addo Marquette University, 2016 Human-Centered Collective Intelligence (CI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, Zoei, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the Zoei framework to design collective intelligence.