Miracle-dz/UniFAR-FTUs huggingface dataset:
Resuscitation fluids as drugs: targeting the endothelial glycocalyx

Abstract Fluid resuscitation is an essential intervention in critically ill patients, and its ultimate goal is to restore tissue perfusion. Critical illnesses are often accompanied by glycocalyx degradation caused by inflammatory reactions, hypoperfusion, shock, and so forth, leading to disturbed microcirculatory perfusion and organ dysfunction. Therefore, maintaining or even restoring the glycocalyx integrity may be of high priority in the therapeutic strategy. Like drugs, however, different resuscitation fluids may have beneficial or harmful effects on the integrity of the glycocalyx. The purpose of this article is to review the effects of different resuscitation fluids on the glycocalyx. Many animal studies have shown that normal saline might be associated with glycocalyx degradation, but clinical studies have not confirmed this finding. Hydroxyethyl starch (HES), rather than other synthetic colloids, may restore the glycocalyx. However, the use of HES also leads to serious adverse events such as acute kidney injury and bleeding tendencies. Some studies have suggested that albumin may restore the glycocalyx, whereas others have suggested that balanced crystalloids might aggravate glycocalyx degradation. Notably, most studies did not correct the effects of the infusion rate or fluid volume; therefore, the results of using balanced crystalloids remain unclear. Moreover, mainly animal studies have suggested that plasma may protect and restore glycocalyx integrity, and this still requires confirmation by high-quality clinical studies.
Deep learning-based extended field of view computed tomography image reconstruction: influence of network design on image estimation outside the scan field of view

The problem of data truncation in Computed Tomography (CT) is caused by the missing data when the patient exceeds the Scan Field of View (SFOV) of a CT scanner. The reconstruction of a truncated scan produces severe truncation artifacts both inside and outside the SFOV. We have employed a deep learning-based approach to extend the field of view and suppress truncation artifacts. Thereby, our aim is to generate a good estimate of the real patient data and not to provide a perfect and diagnostic image even in regions beyond the SFOV of the CT scanner. This estimate could then be used as an input to higher order reconstruction algorithms [1]. To evaluate the influence of the network structure and layout on the results, three convolutional neural networks (CNNs), in particular a general CNN called ConvNet, an autoencoder, and the U-Net architecture have been investigated in this paper. Additionally, the impact of L1, L2, structural dissimilarity and perceptual loss functions on the neural network’s learning have been assessed and evaluated. The evaluation of data set comprising 12 truncated test patients demonstrated that the U-Net in combination with the structural dissimilarity loss showed the best performance in terms of image restoration in regions beyond the SFOV of the CT scanner. Moreover, this network produced the best mean absolute error, L1, L2, and structural dissimilarity evaluation measures on the test set compared to other applied networks. Therefore, it is possible to achieve truncation artifact removal using deep learning techniques.
The Middle Meningeal Artery: Branches, Dangerous Anastomoses, and Implications in Neurosurgery and Neuroendovascular Surgery

The middle meningeal artery (MMA) has always been the workhorse corridor for devascularization of dural-based intracranial lesions and, more recently, has been established as a target for the endovascular management of chronic subdural hematomas. The MMA anatomy is complex and deceitful, and its territory of irrigation (including cranial nerves) is poorly understood. Furthermore, MMA variations and anastomoses are more frequent than expected, which may predispose to procedure-related morbidity. A literature search was conducted in electronic databases per PRISMA guidelines for studies describing normal and abnormal MMA anatomy including variations in MMA origin and dangerous anastomoses. Our institutional case series of greater than 100 MMA embolizations for management of chronic subdural hematomas were reviewed for abnormal MMA anatomy, and clinically relevant case examples are presented. In this article, we provide a comprehensive review of the MMA to provide a better understanding and appreciation of this artery, including pearls and pitfalls, that we hope will aid the neurosurgeon and neurointerventionalist in safely tackling these lesions.
Educational Attainment, Race, and Ethnicity as Predictors for Ideal Cardiovascular Health: From the National Health and Nutrition Examination Survey

Background Educational attainment is protective for cardiovascular health (CVH), but the benefits of education may not persist across racial and ethnic groups. Our objective was to determine whether the association between educational attainment and ideal CVH differs by race and ethnicity in a nationally representative sample. Methods and Results Using the National Health and Nutrition Examination Survey, we determined the distribution of ideal CVH, measured by Life’s Simple 7, across levels of educational attainment. We used multivariable ordinal logistic regression to assess the association between educational attainment (less than high school, high school graduate, some college, college graduate) and Life’s Simple 7 category (ideal, intermediate, poor), by race and ethnicity (Asian, Black, Hispanic, White). Covariates were age, sex, history of cardiovascular disease, health insurance, access to health care, and income–poverty ratio. Of 7771 National Health and Nutrition Examination Survey participants with complete data, as level of educational attainment increased, the criteria for ideal health were more often met for most metrics. After adjustment for covariates, effect of education was attenuated but remained significant (P<0.01). Those with at least a college degree had 4.12 times the odds of having an ideal Life’s Simple 7 compared with less than high school (95% CI, 2.70–5.08). Among all racial and ethnic groups, as level of educational attainment increased, so did Life’s Simple 7. The magnitude of the association between education and CVH varied by race and ethnicity (interaction P<0.01). Conclusions Our findings demonstrate that educational attainment has distinct associations with ideal CVH that differs by race and ethnicity. This work demonstrates the need to elucidate barriers preventing individuals from racial and ethnic minority groups from achieving equitable CVH.
Patients' Perspective of the Impacts of Group Psychoeducation for Bipolar Disorder

Abstract Little is known regarding the mechanisms involved in the clinical improvement of patients with bipolar disorder (BD) after group psychoeducation. We aimed at investigating these mechanisms by focusing on their subjective experience. Thirteen patients with BD aged 35.54 (SD, 12.06) were recruited. Interviews were analyzed using thematic analysis. Four high-order themes were identified: a) relationship among patients, b) effect of the facilitation style, c) program-related factors, and d) subjective impacts. “Relationships among patients” included a lower-ordered theme evoked by all participants, that is, “shared experiences. ” Shared experiences included acknowledging that BD has a neurobiological substrate and that its manifestations are similar in BD; the social support and empowering message of those who have managed to exert control over the illness were also highlighted. Our results shed some light on the mechanisms underlying the effectiveness of group psychoeducation. The shared experience of patients seems to play an important role, probably through destigmatization.
How do recorded mental health recovery narratives create connection and improve hopefulness?

Abstract Background Mental health recovery narratives are an active ingredient of recovery-oriented interventions such as peer support. Recovery narratives can create connection and hope, but there is limited evidence on the predictors of impact. Aims The aim of this study was to identify characteristics of the narrator, narrative content and participant which predict the short-term impact of recovery narratives on participants. Method Independent studies were conducted in an experimental (n = 40) and a clinical setting (n = 13). In both studies, participants with mental health problems received recorded recovery narratives and rated impact on hopefulness and connection. Predictive characteristics were identified using multi-level modelling. Results The experimental study found that narratives portraying a narrator as living well with mental health problems that is intermediate between no and full recovery, generated higher self-rated levels of hopefulness. Participants from ethnic minority backgrounds had lower levels of connection with narrators compared to participants from a white background, potentially due to reduced visibility of a narrator’s diversity characteristics. Conclusions Narratives describing partial but not complete recovery and matching on ethnicity may lead to a higher impact. Having access to narratives portraying a range of narrator characteristics to maximise the possibility of a beneficial impact on connection and hopefulness.
Chronic altered light–dark cycle differentially affects hippocampal CA1 and DG neuronal arborization in diurnal and nocturnal rodents

ABSTRACT The hippocampus, an extension of the temporal part of the cerebral cortex, plays a crucial role in learning and memory. Structural and functional complexity within the hippocampus is greatly affected by a variety of external environmental stimuli including alteration in the light–dark (LD) cycle. The effect of altered LD cycle in learning and memory associated cognitive impairment has been reported in rodents. However, a comparative study of underlying neuronal changes between nocturnal and diurnal species is not well explored. The objective of the present study was to explore the morphological changes in hippocampal CA1 and DG neurons in response to prolonged constant condition viz. constant light (LL) and constant darkness (DD) in diurnal squirrels and nocturnal mice. Animals (n = 5/group) were placed in chronocubicle under 12:12 h LD, LL and DD. After four weeks, brain tissues were collected and processed for Golgi-Cox staining to analyze morphological changes in CA1 and DG neurons. The total and basal dendritic length, basal dendrite number, branch end, the diameter of apical dendrite and spine density were analyzed. The results showed a significant reduction in structural complexity of CA1 and DG neurons of squirrels exposed to prolonged constant darkness, whereas mice showed a significant increase as compared to LD. However, a significantly reduced neuronal complexity was observed in both squirrels and mice exposed to prolonged constant light. The results obtained were further confirmed by Sholl analysis of CA1 and DG neurons. The present study suggests that prolonged constant light may cause adverse effects on the neuronal complexity of both diurnal and nocturnal animals, but constant darkness may cause adverse effects mainly to the diurnal animals.
Chinese consensus on prevention of colorectal neoplasia (2021, Shanghai)

Most patients with colorectal cancer (CRC) were diagnosed in advanced stage and the prognosis is poor. Therefore, early detection and prevention of CRC are very important. As with other cancers, there is also the tertiary prevention for CRC. The primary prevention is etiological prevention, which is mainly the treatment of adenoma or inflammation for preventing the development into cancer. The secondary prevention is the early diagnosis and early treatment for avoiding progressing to advanced cancer. The tertiary prevention belongs to the broad category of prevention, mainly for advanced CRC, through surgical treatment and postoperative adjuvant chemotherapy, radiotherapy, targeted therapy, immunotherapy for preventing tumor recurrence or metastasis. This consensus is based on the recent domestic and international consensus guidelines and the latest progress of international researches in the past five years. This consensus opinion seminar was hosted by the Chinese Society of Gastroenterology and Cancer Collaboration Group of Chinese Society of Gastroenterology, and was organized by the Division of Gastroenterology and Hepatology & Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University. The consensus opinion contains 60 statement clauses, the standard and basis of the evidence‐based medicine grade and voting grade of the statement strictly complied with the relevant international regulations and practice.
Workplace trauma and chronic stressor exposure among direct service providers working with people experiencing homelessness

Abstract Background Service providers working with people experiencing homelessness can be directly and indirectly exposed to trauma and other chronic stressors in their work. The types of individuals that are most at-risk of problematic outcomes from traumatic event exposure are unknown. Aim This study examined exposure to and effects of workplace traumas and stressors among service providers working with people experiencing homelessness in Canada. Methods A cross-sectional survey was completed by 701 direct service providers working in the homeless service, supportive housing, and harm reduction sectors. Descriptive statistics, hierarchical multiple regression, and double moderation models were used in the analysis. Results Employment in homeless service settings, service provision to single adults, and more time in direct contact with service users were each positively associated with the frequency of exposure to critical events and chronic stressors. Younger age, lived experience of behavioural health problems, more frequent exposure to chronic stressors, and less social support from coworkers were significantly correlated with post-traumatic stress and general psychological distress. Emotional support from supervision moderated the relationship between direct exposure to workplace critical events and post-traumatic stress. Conclusions More trauma-informed psychosocial supports tailored to the needs of direct service providers working with people experiencing homelessness are needed.
Involvement of the dorsal and ventral attention networks in visual attention span

Visual attention span (VAS), which refers to the window size of multielement parallel processing in a short time, plays an important role in higher‐level cognition (e.g., reading) as required by encoding large amounts of information input. However, it is still a matter of debate about the underlying neural mechanism of VAS. In the present study, a modified visual 1‐back task was designed by using nonverbal stimuli and nonverbal responses, in which possible influences of target presence and position were considered to identify more pure VAS processing. A task‐driven functional magnetic resonance imaging (fMRI) experiment was then performed, and 30 healthy adults participated in this study. Results of confirmatory and exploratory analyses consistently revealed that both dorsal attention network (DAN) and ventral attention network (VAN) were significantly activated during this visual simultaneous processing. In particular, more significant activation in the left superior parietal lobule (LSPL), as compared to that in the bilateral inferior frontal gyrus (IFGs), suggested a greater involvement of DAN in VAS‐related processing in contrast to VAN. In addition, it was also found that the activation in temporoparietal junctions (TPJs) were suppressed during multielement processing only in the target‐absent condition. The current results suggested the recruitment of LSPL in covert attentional shifts and top‐down control of VAS resources distribution during the rapid visual simultaneous processing, as well as the involvement of bilateral IFGs (especially RIFG) in both VAS processing and inhibitory control. The present findings might bring some enlightenments for diagnosis of the atypicality of attentional disorders and reading difficulties.
“Late-stage” deforestation enhances storm trends in coastal West Africa

Significance Tropical deforestation tends to increase regional air temperatures, but its impacts on rainfall are more complex. The conventional picture, based largely on studies over Amazonia, is that storm frequency can increase over small, deforested areas but is reduced when the landscape is predominantly deforested. This study examines Southern West Africa, a coastal region that has little remaining intact forest. Here, the ongoing patchy deforestation increases the frequency of afternoon storms locally. Deforestation appears especially effective at triggering storms near the coast, where temperature-sensitive sea breezes dominate, and rapidly urbanizing populations are vulnerable to flood risk. Our results emphasize dynamical processes over moisture limitation and are highly relevant for many tropical deforestation hotspots, which, unlike Amazonia, are located near the coastline. Deforestation affects local and regional hydroclimate through changes in heating and moistening of the atmosphere. In the tropics, deforestation leads to warming, but its impact on rainfall is more complex, as it depends on spatial scale and synoptic forcing. Most studies have focused on Amazonia, highlighting that forest edges locally enhance convective rainfall, whereas rainfall decreases over drier, more extensive, deforested regions. Here, we examine Southern West Africa (SWA), an example of “late-stage” deforestation, ongoing since 1900 within a 300-km coastal belt. From three decades of satellite data, we demonstrate that the upward trend in convective activity is strongly modulated by deforestation patterns. The frequency of afternoon storms is enhanced over and downstream of deforested patches on length scales from 16 to 196 km, with greater increases for larger patches. The results are consistent with the triggering of storms by mesoscale circulations due to landscape heterogeneity. Near the coast, where sea breeze convection dominates the diurnal cycle, storm frequency has doubled in deforested areas, attributable to enhanced land–sea thermal contrast. These areas include fast-growing cities such as Freetown and Monrovia, where enhanced storm frequency coincides with high vulnerability to flash flooding. The proximity of the ocean likely explains why ongoing deforestation across SWA continues to increase storminess, as it favors the impact of mesoscale dynamics over moisture availability. The coastal location of deforestation in SWA is typical of many tropical deforestation hotspots, and the processes highlighted here are likely to be of wider global relevance.
Multi Document Reading Comprehension

Reading Comprehension (RC) is a task of answering a question from a given passage or a set of passages. In the case of multiple passages, the task is to find the best possible answer to the question. Recent trials and experiments in the field of Natural Language Processing (NLP) have proved that machines can be provided with the ability to not only process the text in the passage and understand its meaning to answer the question from the passage, but also can surpass the Human Performance on many datasets such as Standford's Question Answering Dataset (SQuAD). This paper presents a study on Reading Comprehension and its evolution in Natural Language Processing over the past few decades. We shall also study how the task of Single Document Reading Comprehension acts as a building block for our Multi-Document Reading Comprehension System. In the latter half of the paper, we'll be studying about a recently proposed model for Multi-Document Reading Comprehension - RE3QA that is comprised of a Reader, Retriever, and a Re-ranker based network to fetch the best possible answer from a given set of passages.
On the Use of RBF Interpolation for Flux Reconstruction

Flux reconstruction provides a framework for solving partial differential equations in which functions are discontinuously approximated within elements. Typically, this is done by using polynomials. Here, the use of radial basis functions as a methods for underlying functional approximation is explored in one dimension, using both analytical and numerical methods. At some mesh densities, RBF flux reconstruction is found to outperform polynomial flux reconstruction, and this range of mesh densities becomes finer as the width of the RBF interpolator is increased. A method which avoids the poor conditioning of flat RBFs is used to test a wide range of basis shapes, and at very small values, the polynomial behaviour is recovered. Changing the location of the solution points is found to have an effect similar to that in polynomial FR, with the Gauss--Legendre points being the most effective. Altering the location of the functional centres is found to have only a very small effect on performance. Similar behaviours are determined for the non-linear Burgers' equation.
Optimizing Memory in Reservoir Computers

A reservoir computer is a way of using a high dimensional dynamical system for computation. One way to construct a reservoir computer is by connecting a set of nonlinear nodes into a network. Because the network creates feedback between nodes, the reservoir computer has memory. If the reservoir computer is to respond to an input signal in a consistent way (a necessary condition for computation), the memory must be fading; that is, the influence of the initial conditions fades over time. How long this memory lasts is important for determining how well the reservoir computer can solve a particular problem. In this paper, I describe ways to vary the length of the fading memory in reservoir computers. Tuning the memory can be important to achieve optimal results in some problems; too much or too little memory degrades the accuracy of the computation.
Deep Probabilistic Graph Matching

Most previous learning-based graph matching algorithms solve the \textit{quadratic assignment problem} (QAP) by dropping one or more of the matching constraints and adopting a relaxed assignment solver to obtain sub-optimal correspondences. Such relaxation may actually weaken the original graph matching problem, and in turn hurt the matching performance. In this paper we propose a deep learning-based graph matching framework that works for the original QAP without compromising on the matching constraints. In particular, we design an affinity-assignment prediction network to jointly learn the pairwise affinity and estimate the node assignments, and we then develop a differentiable solver inspired by the probabilistic perspective of the pairwise affinities. Aiming to obtain better matching results, the probabilistic solver refines the estimated assignments in an iterative manner to impose both discrete and one-to-one matching constraints. The proposed method is evaluated on three popularly tested benchmarks (Pascal VOC, Willow Object and SPair-71k), and it outperforms all previous state-of-the-arts on all benchmarks.