Miracle-dz/UniFAR-FTUs huggingface dataset:
Type of Drain in Chronic Subdural Hematoma—A Systematic Review and Meta-Analysis

Background: Chronic subdural hematoma (cSDH) is one of the most common neurosurgical diseases, while burr-hole drainage is the most frequently used surgical treatment. Strong evidence exists that subdural drain (SDD) placement reduces recurrence rates. However, the insertion of a subperiosteal drain (SPD) was shown to lead to similar recurrence rates and less complications than SDD. The aim of this study is to provide a systematic review of the literature and conduct a meta-analysis of studies comparing SPD with SDD after burr-hole drainage of cSDH. Methods: Pubmed and Embase databases were searched using a systematic search strategy to identify studies on drain location up to December 2019. Two independent researchers assessed the studies for inclusion and quality. Primary outcome measure was recurrence, while secondary outcome measures were drain misplacement, morbidity, mortality, and clinical outcome. Besides randomized controlled trials (RCT), we included non-randomized prospective cohort studies, as well as retrospective cohort studies. A fixed effects model was used if low heterogeneity (I2 < 50%) was present, otherwise a random effects model was used. Results: Following removal of duplicates, we screened 1109 articles of which 10 articles were included in our qualitative and quantitative analyses. One study was an RCT, three were non-randomized prospective cohort studies, and the remaining articles were retrospective cohort studies or subgroup analysis. In these 10 articles, 1,553 patients were treated with SPD and 1782 patients with SDD. Comparing the recurrence rate of cSDH a significant difference was found between SPD and SDD insertion (11.9 and 12.3%; RR 0.8, 95% CI 0.67–0.97, I2 = 0%, z = −2.27, p = 0.02). SPD had significantly lower rates of drain misplacement and parenchymal injuries (1.2 and 7.8%; RR 0.17, 95% CI 0.07–0.42, I2 = 0%, z = −3.4, p = 0.0001), as well as morbidity (6.4 and 8.2%; RR 0.65, 95% CI 0.5–0.84, I2 = 44.5%, z = −3.32, p =0.0009). Mortality rates (5.0 and 4.6%; RR 0.83, 95% CI 0.6–1.14, I2 = 0%, z = −1.2, p = 0.25) and favorable clinical outcome (89.6 and 88.9%; RR 1.1, 95% CI 0.89–1.24, I2 = 54.2%, t = 0.98, p = 0.40) were comparable in both groups. Conclusion: The insertion of SPD after burr-hole drainage of cSDH showed lower rates of recurrence, drain misplacements and parenchymal injuries, as well as overall morbidity, while clinical outcome and mortality were comparable to SDD. Therefore, the insertion of SPD after surgical drainage of cSDH should be encouraged.
A Framework for Determining Population-Level Vulnerability to Climate: Evidence for Growth Hysteresis in Chamaecyparis thyoides Along Its Contiguous Latitudinal Distribution

The impact of ecological and climatological factors on individual organisms over time and space is inherently complex and creates substantial uncertainty about how climate change will influence the global biosphere. To understand some of this complexity, we investigated the factors influencing individual growth of Chamaecyparis thyoides over 61 years within 18 populations across the ca 1500 km and 11 degrees of latitude. We then applied a vulnerability framework to understand how the variability of tree growth response to climate varies between populations and regions across our network. Surprisingly, we found the growth of trees in the central portion of our network responded more synchronously to warming and drought than trees in the southern end of our network, suggesting greater vulnerability in the central populations with continued warming. Our analyses and framework approach revealed substantial complexity in growth responses to climate within and between populations. We found potential resiliency within all populations, but higher inter-population than intra-population variability in response to climate. We found that latitude was an important proxy for the growth response to temperatures during the non-growing season and spring, but that ecosystem structure can modify the growth response and vulnerability to drought during the summer. The range of growth responses to warming is greater in the southern populations than in more northernly populations. This asymmetrical distribution of growth response across our study network provides evidence for a kind of ecological hysteresis, more southerly populations could be more resilient with warming. Despite the fact that this species primarily lives in wetlands, we found drought stress to be an important constraint on growth. Our study and analyses help to explain the disparities between forecasts of how climatic change might impact tree species and ecosystems over space.
Calsequestrins New Calcium Store Markers of Adult Zebrafish Cerebellum and Optic Tectum

Calcium stores in neurons are heterogeneous in compartmentalization and molecular composition. Danio rerio (zebrafish) is an animal model with a simply folded cerebellum similar in cellular organization to that of mammals. The aim of the study was to identify new endoplasmic reticulum (ER) calcium store markers in zebrafish adult brain with emphasis on cerebellum and optic tectum. By quantitative polymerase chain reaction, we found three RNA transcripts coding for the intra-ER calcium binding protein calsequestrin: casq1a, casq1b, and casq2. In brain homogenates, two isoforms were detected by mass spectrometry and western blotting. Fractionation experiments of whole brain revealed that Casq1a and Casq2 were enriched in a heavy fraction containing ER microsomes and synaptic membranes. By in situ hybridization, we found the heterogeneous expression of casq1a and casq2 mRNA to be compatible with the cellular localization of calsequestrins investigated by immunofluorescence. Casq1 was expressed in neurogenic differentiation 1 expressing the granule cells of the cerebellum and the periventricular zone of the optic tectum. Casq2 was concentrated in parvalbumin expressing Purkinje cells. At a subcellular level, Casq1 was restricted to granular cell bodies, and Casq2 was localized in cell bodies, dendrites, and axons. Data are discussed in relation to the differential cellular and subcellular distribution of other cerebellum calcium store markers and are evaluated with respect to the putative relevance of calsequestrins in the neuron-specific functional activity.
SCORE

Technology scaling and program/erase cycling result in an increasing bit error rate in NAND flash storage. Some solid state drives (SSDs) adopt overlong error correction codes (ECCs), whose redundancy size exceeds the spare area limit of flash pages, to protect user data for improved reliability and lifetime. However, the read performance is significantly degraded, because a logical data page and its ECC redundancy are stored in two flash pages. In this article, we find that caching ECCs has a large potential to reduce flash reads by achieving higher hit rates, compared to caching data. Then, we propose a novel scheme to efficiently cache overlong ECCs, called SCORE, to improve the SSD performance. Exceeding ECC redundancy (called ECC residues) of logically consecutive data pages are grouped into ECC pages. SCORE partitions RAM to cache both data pages and ECC pages in a workload-adaptive manner. Finally, we verify SCORE using extensive trace-driven simulations. The results show that SCORE obtains high ECC hit rates without sacrificing data hit rates, thus improving the read performance by an average of 22% under various workloads, compared to the state-of-the-art schemes.
POSTER: Recovering Performance for Vector-based Machine Learning on Managed Runtime

The explosive growth of data and the demand for learning useful information from such data have stimulated a crossdisciplinary area named big learning, which leverages a cluster of commodity machines to run machine learning applications on large datasets. Those applications are usually implemented on high-level programming frameworks [1–4], which are running atop managed runtimes. Since the applications usually involve many iterations of computation over input data, the frameworks will cache them for performance consideration. In this work, we investigate a set of vectorbased machine learning applications in Spark MLlib [3] and find that cached datasets have a strong impact on overall efficiency. To confirm the problem, we design and implement a new data structure in place of original vectors and re-implement some vector-based benchmarks on it. Our evaluation shows that the newly written code can reach as much as 4.31x speedup compared with original one. Given the observation, we provide CADANA, a CAched DAtasets ANAlyzer to automatically optimize the cached datasets. This tool will conduct code analysis to find out the usage of cached datasets and refine their abstractions and layouts. The analysis contains three compiler-like optimization techniques: redundant fields elimination, data batching, and layout transformation. We also provide two analysis engines to make CADANA adaptive to various workloads.
TDDFS

With the rapid increase in the amount of data produced and the development of new types of storage devices, storage tiering continues to be a popular way to achieve a good tradeoff between performance and cost-effectiveness. In a basic two-tier storage system, a storage tier with higher performance and typically higher cost (the fast tier) is used to store frequently-accessed (active) data while a large amount of less-active data are stored in the lower-performance and low-cost tier (the slow tier). Data are migrated between these two tiers according to their activity. In this article, we propose a Tier-aware Data Deduplication-based File System, called TDDFS, which can operate efficiently on top of a two-tier storage environment. Specifically, to achieve better performance, nearly all file operations are performed in the fast tier. To achieve higher cost-effectiveness, files are migrated from the fast tier to the slow tier if they are no longer active, and this migration is done with data deduplication. The distinctiveness of our design is that it maintains the non-redundant (unique) chunks produced by data deduplication in both tiers if possible. When a file is reloaded (called a reloaded file) from the slow tier to the fast tier, if some data chunks of the file already exist in the fast tier, then the data migration of these chunks from the slow tier can be avoided. Our evaluation shows that TDDFS achieves close to the best overall performance among various file-tiering designs for two-tier storage systems.
Poker

We introduce Poker, a permutation-based approach for vectorizing multiple queries over B+-trees. Our key insight is to combine vector loads and path-encoding-based permutations to alleviate memory latency while keeping the number of key comparisons needed for a query to a minimum. Implemented as a C++ template library, Poker represents a general-purpose solution for vectorizing the queries over indexing trees on multi-core processors equipped with SIMD units. For a set of five representative benchmarks evaluated with 24 configurations each, Poker outperforms the state of the art by 2.11x with one single thread and 2.28x with eight threads on an Intel Broadwell processor that supports 256-bit AVX2, on average. In addition, strip-mining queries will further improve Poker’s performance by 1.21x (with one single thread) and 1.31x (with eight threads), on average.
KiWi

We present KiWi, the first atomic KV-map to efficiently support simultaneous large scans and real-time access. The key to achieving this is treating scans as first class citizens and organizing the data structure around them. KiWi provides wait-free scans, whereas its put operations are lightweight and lock-free. It optimizes memory management jointly with data structure access. We implement KiWi and compare it to state-of-the-art solutions. Compared to other KV-maps providing atomic scans, KiWi performs either long scans or concurrent puts an order of magnitude faster. Its scans are twice as fast as non-atomic ones implemented via iterators in the Java skiplist.
Complex Dynamics in a Minimal Model of Protection-Based Mutualism

This paper presents the first five variable model of mutualism motivated by the interaction between ants and homopterans. In this mutualism, homopterans benefit both directly through increased feeding rates and indirectly through predator protection. Results of our analyses show oscillatory, complex, and chaotic dynamic behavior. In addition, we show that intraspecies interactions are crucial for closing trophic levels and stabilizing the dynamic system from potential “chaotic” behavior.
Computing Tropical Prevarieties With Satisfiability Modulo Theories (SMT) Solvers

A novel way to use SMT (Satisfiability Modulo Theories) solvers to compute the tropical prevariety (resp. equilibrium) of a polynomial system is presented. The new method is benchmarked against a naive approach that uses purely polyhedral methods. It turns out that the SMT approach is faster than the polyhedral approach for models that would otherwise take more than one minute to compute, in many cases by a factor of 60 or more, and in the worst case is only slower by a factor of two. Furthermore, the new approach is an anytime algorithm, thus offering a way to compute parts of the solution when the polyhedral approach is infeasible.
Is Artificial Intelligence the Most Reliable Way to Predict Mortality After Liver Transplantation

Introduction: Graft allocation in Liver Transplantation (LT) should be based on the greatest survival benefit to the patients awaiting transplantation. This study developed a predictive model to determine recipient mortality 1 year after LT. Materials and Methods: We developed Artificial Neural Network (ANN) and Logistic Regression (LR) models and compared their results with the Balance of Risk (BAR), Survival Outcomes Following Transplantation (SOFT), and Model for End-stage Liver Disease (MELD) scores. The Development Group used to create the predictive models included 1235 valid cases, while 200 consecutive transplant patients since January 2009 were included in the Generalization Group for internal validation. Results: The area under the curve (AUC) of the ANN model (0.82) was higher than that of the LR model (0.68). For the Generalization Group, the MELD, SOFT, and BAR scores had AUCs of 0.56, 0.57, and 0.62, respectively. The ANN model had a significantly higher AUC than that of each score (MELD, p=0.005; SOFT, p=0.009; BAR, p=0.02). Conclusion: ANN model was superior to the LR model and other scales currently used to predict mortality during the first year after LT and to match a particular graft with potential recipients.
A Modified Skip-Gram Algorithm for Extracting Drug-Drug Interactions from AERS Reports

Drug-drug interactions (DDIs) are one of the indispensable factors leading to adverse event reactions. Considering the unique structure of AERS (Food and Drug Administration Adverse Event Reporting System (FDA AERS)) reports, we changed the scope of the window value in the original skip-gram algorithm, then propose a language concept representation model and extract features of drug name and reaction information from large-scale AERS reports. The validation of our scheme was tested and verified by comparing with vectors originated from the cooccurrence matrix in tenfold cross-validation. In the verification of description enrichment of the DrugBank DDI database, accuracy was calculated for measurement. The average area under the receiver operating characteristic curve of logistic regression classifiers based on the proposed language model is 6% higher than that of the cooccurrence matrix. At the same time, the average accuracy in five severe adverse event classes is 88%. These results indicate that our language model can be useful for extracting drug and reaction features from large-scale AERS reports.
COVID-19 mobile positioning surveillance and contact tracing, and patient privacy

BACKGROUND: The coronavirus disease pandemic is the biggest global economic and health challenge of the century Its effect and impact are still evolving with deaths estimated to reach 40 million if not checked One effective and complementary strategy to slow the spread and reduce the impact is to trace primary and secondary contacts using technology OBJECTIVE: The objective of this paper is to survey strategies for digital contact tracing for COVID-19 pandemic and to present how using mobile positioning data conforms with Nigeria's data privacy regulations METHODS: We conducted an exploratory review of current measures for COVID-19 contact tracing globally We then analyzed how countries are using mobile positioning data technology in handling the COVID 19 pandemic spread We made recommendations for how Nigeria can adopt this approach in context of Nigeria's Data protection Regulation (NDPR) RESULTS: Despite the potentials, digital contact tracing always comes in conflict with patient data privacy regulations We found that Nigeria's response complies with the NDPR, and that it is possible to leverage telecommunications call detail registry (CDR) to complement current strategies within the NDPR regulation CONCLUSIONS: Our study show that mobile position data contact tracing is important for epidemic control as long as it conforms to relevant data privacy regulation Implementation guideline will limit data misuse
Multi-Task Joint Learning Model for Segmenting and Classifying Tongue Images Using a Deep Neural Network

Automatic tongue image segmentation and tongue image classification are two crucial tongue characterization tasks in traditional Chinese medicine (TCM). Due to the complexity of tongue segmentation and fine-grained traits of tongue image classification, both tasks are challenging. Fortunately, from the perspective of computer vision, these two tasks are highly interrelated, making them compatible with the idea of Multi-Task Joint learning (MTL). By sharing the underlying parameters and adding two different task loss functions, an MTL method for segmenting and classifying tongue images is proposed in this paper. Moreover, two state-of-the-art deep neural network variants (UNET and Discriminative Filter Learning (DFL)) are fused into the MTL to perform these two tasks. To the best of our knowledge, our method is the first attempt to manage both tasks simultaneously with MTL. We conducted extensive experiments with the proposed method. The experimental results show that our joint method outperforms the existing tongue characterization methods. Besides, visualizations and ablation studies are provided to aid in understanding our approach, which suggest that our method is highly consistent with human perception.
Daycare attendance and asthma control, Asthma Call-back Survey 2012–2014

Abstract Objective To examine the association between daycare attendance and asthma control among children aged 0 to 4 years with asthma Methods We analyzed 2012–2014 data from the Behavioral Risk Factor Surveillance System Asthma Call-back Survey on 388 children with asthma aged 0 to 4 years with information on daycare attendance in the past 12 months. We calculated weighted prevalence ratios to assess the association between daycare attendance and asthma control (categorized based on day-time and nighttime asthma symptoms, activity limitation, and short-acting beta agonist use). Adjusted models controlled for parent or guardian education, household income, race, sex, cost barriers to asthma care, long-term control medication use, and the number of other children in the child’s household. Results In this sample of children with asthma, representative of 520,400 children in 26 U.S. states, 34% attended daycare in the past 12 months. Only 32% of children who attended daycare in the past 12 months reported having an asthma action plan on file at the daycare they most recently attended. Presence of the asthma triggers of pets, mold, and smoking in a child’s daycare were reported to be uncommon. Prevalence of uncontrolled asthma was 44% in children who attended daycare in the past 12 months and 68% in children who did not. The adjusted prevalence ratio between daycare attendance and uncontrolled asthma was 0.96 (95% confidence interval 0.73, 1.25). Conclusions When adjusting for covariates, we observed no evidence of an association between daycare attendance in early life and uncontrolled asthma.
Investigating the complex story of one ditch—A multidisciplinary study of ditch infill provides insight into the spatial organisation within the oppidum of Bibracte (Burgundy, France)

Seemingly empty spaces in various archaeological settings have left many unanswered questions. This paper focuses on the appearance, maintenance and possible function of a large empty area situated at the summit plateau of the Iron Age oppidum Bibracte in France. Multidisciplinary research of the infill of the ditch that delimited this area in the 1st century BC has provided evidence on the primary function and the formation processes of the structure itself, and for the reconstruction of the appearance, maintenance and function of the area it enclosed. The results allow us to gain insight into a variety of topics, including the role of trees, hygiene measures and waste management strategies at this urbanised hilltop centre. This paper demonstrates that multi-proxy analyses provide detailed insight into the function of archaeological features in a local environmental context and the potential of such approaches in archaeology.
MiR‐370 accelerated cerebral ischemia reperfusion injury via targeting SIRT6 and regulating Nrf2/ARE signal pathway

Cerebral ischemia reperfusion (CIR) is one of the highly lethal diseases in the world. MicroRNA‐370 (miR‐370) exerts multiple functions in different diseases. However, further research is needed to investigate the potential role of miR‐370 in CIR injury. The in vivo middle cerebral artery occlusion (MCAO) rat model and in vitro oxygen‐glucose deprivation/reoxygenation (OGD/R) SH‐SY5Y cell model were successfully established to mimic CIR injury. The infarct sizes of brain tissues from rats were evaluated. The relationship between miR‐370 and silencing information regulatory protein 6 (SIRT6) was confirmed by luciferase activity assay. The cell viability and apoptosis were determined by CCK‐8 assay and terminal‐deoxynucleoitidyl transferase mediated nick end labeling staining. In this study, miR‐370 was upregulated in brain tissues of MCAO rats and knockdown of miR‐370 decreased cerebral infarction volume of MCAO rats and it alleviated CIR injury in vivo. The in vitro experiments indicated that knockdown of miR‐370 promoted cell viability and alleviated OGD/R‐induced SH‐SY5Y cell apoptosis. Additionally, the TargetScan predicted that SIRT6 was a target of miR‐370 and confirmed by luciferase activity assay. Moreover, miR‐370 inhibited SIRT6 expression and regulated Nrf2/ARE signal pathway, whereas overexpression of SIRT6 partly reversed the effect of miR‐370 on OGD/R‐induced SH‐SY5Y cell injury. Thus, we could conclude that miR‐370 accelerated CIR injury via targeting SIRT6 and regulating Nrf2/ARE signal pathway, which might provide novel therapeutic targets for CIR injury treatment.