Miracle-dz/UniFAR-FTUs huggingface dataset:
regional differences in the risk of insomnia symptoms among patients from general hospital outpatient clinics

Background: Region-specific differences in the prevalence of insomnia symptoms in outpatient clinics in China have received little systematic study. This study was conducted preliminarily to examine region-specific differences in the risk of insomnia symptoms in Chinese outpatients. Method: In total, 4,399 adult outpatients (urban vs rural residents: 1,768 vs 2,631) who completed three questions focusing on insomnia symptoms were included. Their sociodemographic and clinical information were collected with standardized questionnaires. Results: The prevalence of self-reported insomnia symptoms in urban residents (23.4%) was more frequent than the prevalence in rural residents (21.2%). The estimated prevalence of insomnia symptoms was significantly lower in rural than urban residents after adjusting for the potential confounders ( P = 0.015). Similarly, more urban (22.9%) than rural (13.4%) residents with insomnia symptoms had significantly higher treatment rates ( χ 2 = 14.9, P , 0.001). Multiple regression analyses showed that depressive symptoms, old age, and low education level were the most common risk factors for insomnia symptoms in both urban and rural residents. Conclusion: Our findings show that the prevalence of insomnia symptoms was relatively lower in rural than urban residents. Longitudinal studies are warranted to confirm the current findings.
On some problems related to 2-level polytopes

In this thesis we investigate a number of problems related to 2-level polytopes, in particular regarding their combinatorial structure and extension complexity. 2-level polytopes have been introduced as a generalization of stable set polytopes of perfect graphs, and despite their apparently simple structure, are at the center of many open problems: these include connection with communication complexity and the separation between linear and semidefinite programming. The extension complexity of a polytope P is a measure of the complexity of representing P : it is the smallest size of an extended formulation of P , which in turn is a linear description of a polyhedron that projects down to P . In the first chapter we introduce the main concepts that will be used through the thesis and we motivate our interest in 2-level polytopes. In the second chapter we examine several classes of 2-level polytopes arising in combinatorial settings and we prove a relation between the number of vertices and facets of such polytopes, which is conjectured to hold for all 2-level polytopes. The proofs are obtained through an improved understanding of the combinatorial structure of such polytopes, which in some cases leads to results of independent interest. In the third chapter we study the extension complexity of a restricted class of 2-level polytopes, the stable set polytopes of bipartite graphs, for which we obtain improved lower and upper bounds. In the fourth chapter we study slack matrices of 2-level polytopes, important combinatorial objects related to extension complexity, defining operations on them and giving algorithms for the following recognition problem: given a matrix, determine whether it is a slack matrix of some special class of 2-level polytopes. In the fifth chapter we address the problem of explicitly obtaining small size extended formulations whose existence is guaranteed by communication protocols. In particular we give an output-efficient algorithm to write down extended formulations for the stable set polytope of perfect graphs, making a well known result by Yannakakis constructive, and we extend this to all deterministic protocols. We then conclude the thesis outlining the main open questions that stem from our work.
Isolation , Identification and Cytotoxic Activity of Triterpenes and Flavonoids from Green Walnut ( Juglans regia L . ) Pericarps

Three -amyrine type triterpenes (1-3) in addition to oleanolic acid (4) and six known flavonoids, namely apigenin (5) and its 7-O--D-glucuronide (9), 5,6,4-trihydroxy-7,3-dimethoxy-flavone (6), cirsilineol (7), sudachitin (8) and cilicione-b (10) were isolated from the dichloromethane extract of the pericarps of Juglans regia. The structures of the compounds were established on the basis of spectroscopic analyses (UV, IR, HRESIMS and 1D & 2D NMR). Compounds 2, 3 and 6-10 were isolated from J. regia for the first time. To the best of our knowledge, compound 3 is a new natural product. All isolated compounds, as well as the dichloromethane extract were evaluated for their cytotoxic activity against the human cancer cell lines, MCF-7, HCT-116, HeLa, Κ562, Raji and THP-1. Compounds (1) and (5) showed the most potent anticancer activity against the cancer cells assessed. Interestingly, the concentration of compound (5) that was cytotoxic to cancer cells did not induce human peripheral blood mononuclear cell apoptosis. These results suggest that the structure of compound (5) may eventually serve as a lead for developing novel anticancer drugs with limited side effects against normal cells.
Performance of Comprehensive Risk Adjustment for the Prediction of In-Hospital Events Using Administrative Healthcare Data: The Queralt Indices

Background: Accurate risk adjustment is crucial for healthcare management and benchmarking. Purpose: We aimed to compare the performance of classic comorbidity functions (Charlson ’ s and Elixhauser ’ s), of the All Patients Re ﬁ ned Diagnosis Related Groups (APR-DRG), and of the Queralt Indices, a family of novel, comprehensive comorbidity indices for the prediction of key clinical outcomes in hospitalized patients. Material and Methods: We conducted an observational, retrospective cohort study using administrative healthcare data from 156,459 hospital discharges in Catalonia (Spain) during 2018. Study outcomes were in-hospital death, long hospital stay, and intensive care unit (ICU) stay. We evaluated the performance of the following indices: Charlson ’ s and Elixhauser ’ s functions, Queralt ’ s Index for secondary hospital discharge diagnoses (Queralt DxS), the overall Queralt ’ s Index, which includes pre-existing comorbidities, in-hospital complications, and principal discharge diagnosis (Queralt Dx), and the APR-DRG. Discriminative ability was evaluated using the area under the curve (AUC), and measures of goodness of ﬁ t were also computed. Subgroup analyses were conducted by principal discharge diagnosis, by age, and type of admission. Results: Queralt DxS provided relevant risk adjustment information in a larger number of patients compared to Charlson ’ s and Elixhauser ’ s functions, and outperformed both for the prediction of the 3 study outcomes. Queralt Dx also outperformed Charlson ’ s and Elixhauser ’ s indices, and yielded superior predictive ability and goodness of ﬁ t compared to APR-DRG (AUC for in-hospital death 0.95 for Queralt Dx, 0.77 – 0.93 for all other indices; for ICU stay 0.84 for Queralt Dx, 0.73 – 0.83 for all other indices). The performance of Queralt DxS was at least as good as that of the APR-DRG in most principal discharge diagnosis subgroups. Conclusion: Our ﬁ ndings suggest that risk adjustment should go beyond pre-existing comorbidities and include principal discharge diagnoses and in-hospital complications. Validation of comprehensive risk adjustment tools such as the Queralt indices in other settings is needed. DxS includes pre-existing comorbidities. policy, and preventative measures to promote good health and improve morbidity and mortality in the population. The journal welcomes submitted papers covering original research, basic science, clinical & epidemiological studies, reviews and evaluations, guidelines, expert opinion and commentary, case reports and extended reports. The manuscript management system is completely online and includes a very quick and fair peer-review system, which is all easy to use. Visit http://www.dovepress. com/testimonials.php to read real quotes from published authors.
Efficient GCN Deployment with Spiking Property on Spatial-Temporal Neuromorphic Chips

Spiking Graph Convolutional Networks (SGCNs) are an emerging class of neural networks that operate on graph-structured data and leverage the advantages of both spiking neural networks and GCNs, making them ideal for graph representation learning tasks. However, deploying SGCNs on neuromorphic chips is challenging due to complex spiking dynamics, structured message computation, and limited chip resources, impeding their applications on terminal devices. In this work, we present an algorithm mapping strategy using a contemporary neuromorphic chip to efficiently deploy SGCNs and maximize chip resource utilization. Our mapping scheme leverages the spatio-temporal features of the chip and partitions computation graphs into small sub-graphs, enabling the reuse of computation resources in a temporal and spatial manner by a flexible scheme setting and corresponding scheduling. Simulation experiments show that our approach achieves favorable results on citation benchmark datasets by significantly reducing up to 79.8% memory usage and more than 60% computational requirements of SGCN deployment.
Structural inhibition of dynamin-mediated membrane fission by endophilin

Dynamin, which mediates membrane fission during endocytosis, binds endophilin and other members of the Bin-Amphiphysin-Rvs (BAR) protein family. How endophilin influences endocytic membrane fission is still unclear. Here, we show that dynamin-mediated membrane fission is potently inhibited in vitro when an excess of endophilin co-assembles with dynamin around membrane tubules. We further show by electron microscopy that endophilin intercalates between turns of the dynamin helix and impairs fission by preventing trans interactions between dynamin rungs that are thought to play critical roles in membrane constriction. In living cells, overexpression of endophilin delayed both fission and transferrin uptake. Together, our observations suggest that while endophilin helps shape endocytic tubules and recruit dynamin to endocytic sites, it can also block membrane fission when present in excess by inhibiting inter-dynamin interactions. The sequence of recruitment and the relative stoichiometry of the two proteins may be critical to regulated endocytic fission.
Synaptic Normalisation for On-Chip Learning in Analog CMOS Spiking Neural Networks

Spiking Neural Networks (SNNs) are becoming increasingly popular for their application in Edge Artificial Intelligence (Edge-AI) due to their sparse and low-latency computation. Among these networks, analog hardware SNNs are chosen for their ability to emulate complex dynamics in neurons and synapses, especially in integrated Metal Oxide Semiconductor (MOS) technology. They can form memories of external stimuli by modulating the strength of synaptic weights. In this context, binary weights are a common hardware design choice, due to their ease to program and store. The use of binary weights in SNNs worsens the bias introduced by the coding level of input stimuli (i.e. fraction of active input nodes), where the network activity is highly correlated to the number of excited neurons. In this paper, we present a Complementary Metal Oxide Semiconductor (CMOS) solution for the coding level bias, by proposing a novel circuit that employs synaptic normalisation at the neuron level. This circuit modifies the gain of the neuron depending on its input weights, with a small footprint and therefore high scalability.
Identifying content-invariant neural signatures of perceptual vividness

Some conscious experiences are more vivid than others. Although perceptual vividness is a key component of human consciousness, how variation in this magnitude property is registered by the human brain is unknown. A striking feature of magnitudes in other psychological domains, such as number or reward, is the existence of neural magnitude codes that are independent from sensory contents. To test whether perceptual vividness similarly covaries with neural codes that are invariant to sensory content, we reanalysed existing MEG and fMRI data from two distinct studies, quantifying perceptual vividness via subjective ratings of awareness and visibility. Using representational similarity and decoding analyses, we find evidence for the existence of content-invariant neural codes for perceptual vividness distributed across visual, parietal, and frontal cortices. Our findings are consistent with a hypothesis that the subjective vividness of conscious experience is supported by neural signatures similar to magnitude codes in other cognitive domains. Significance Statement The vividness of conscious experience varies across different stimuli and contexts. Despite being a fundamental feature of conscious awareness, exactly how perceptual vividness is encoded in the human brain remains unclear. Neural codes underpinning magnitude in reward and numerosity domains have been shown to be unchanging as stimulus identity varies. Here, we test whether components of neural activity covarying with the magnitude of perceptual vividness are similarly independent of perceptual content in analyses of MEG and fMRI data. We find dynamic, content-invariant neural signatures of vividness in visual, parietal, and frontal cortices. Our findings introduce the surprising notion that neural signatures of conscious experience might follow similar coding principles to those found for magnitude properties of entirely different cognitive domains.
MiDAS 5: Global diversity of bacteria and archaea in anaerobic digesters

Anaerobic digestion represents a key biotechnology for the transformation of organic waste into renewable energy (biogas) and relies on complex microbial communities that work in concert to degrade the complex substrates into methane and carbon dioxide. Here, we sequenced more than half a million high-quality, full-length 16S rRNA gene sequences from 285 full-scale anaerobic digesters (ADs) across the world to expand our knowledge about diversity and function of the bacteria and archaea in ADs. The sequences were processed into full-length 16S rRNA amplicon sequence variants (FL-ASVs), which were added to the MiDAS 4 database for bacteria and archaea in wastewater treatment systems to create ‘MiDAS 5’. The expansion of the MiDAS database significantly increased the coverage for bacteria and archaea in ADs worldwide, leading to an improved rate of genus and species-level classification. Using MiDAS 5, we carried out an amplicon-based, global-scale microbial community profiling of the sampled ADs using three common sets of primers targeting different regions of the 16S rRNA gene in bacteria and/or archaea. We revealed how environmental conditions and biogeography shape the AD microbiota. We also identify core and conditionally rare or abundant taxa, encompassing less than 1000 genera and species. These represent 83-99% and 10-64% of the accumulated read abundance respectively, across samples depending on the amplicon primers used. Finally, we examined the global diversity of functional groups with known importance for the anaerobic digestion process. Our online global MiDAS Field Guide presents the data generated in this study and summarizes present knowledge about all taxa.
A fast comparative genome browser for diverse bacteria and archaea

Genome sequencing has revealed an incredible diversity of bacteria and archaea, but there are no fast and convenient tools for browsing across these genomes. It is cumbersome to view the prevalence of homologs for a protein of interest, or the gene neighborhoods of those homologs, across the diversity of the prokaryotes. We developed a web-based tool, fast.genomics, that uses two strategies to support fast browsing across the diversity of prokaryotes. First, the database of genomes is split up. The main database contains one representative from each of the 6,377 genera that have a high-quality genome, and additional databases for each taxonomic order contain up to 10 representatives of each species. Second, homologs of proteins of interest are identified quickly by using accelerated searches, usually in a few seconds. Once homologs are identified, fast.genomics can quickly show their prevalence across taxa, view their neighboring genes, or compare the prevalence of two different proteins. Fast.genomics is available at https://fast.genomics. lbl.gov. Importance Now that we have genome sequences for tens of thousands of species of bacteria and archaea, we would like to predict the functions of their proteins. One common strategy is comparative genomics: by considering which genomes contain similar proteins, and which proteins are often encoded near each other, we can often guess the proteins’ functions. But there was no good way to do these analyses quickly. We built a website that performs them in a few seconds. We used two strategies to speed up the key step, which is finding similar proteins. First, we split up the database of genomes into a main database with one representative for each genus, and sub-databases for each taxonomic order. Either way, searches against fewer genomes are much faster. Second, we use accelerated searches to find similar proteins, with only a slight loss of sensitivity.
Modelling a borehole subjected to fluid pressure

Fluid pressure inside a borehole produces hydraulic fracture and damage zones in the vicinity of the borehole. These fractures result from stress concentrations around the borehole. The results of numerical simulation of a borehole subjected to fluid pressure using a micromechanical damage model [2] are presented in this paper. It is observed that tensional concentrated stresses are generated around the bottom of the borehole by the applied fluid pressure. Furthermore, fractures develop from the corners of the bottom of the borehole.
Altered metabolic function induced by amyloid-beta oligomers and PSEN1-mutations in iPSC-derived astrocytes

Altered energy metabolism in Alzheimer’s disease (AD) is considered a major pathological hallmark implicated in the early stages of the disease process. Astrocytes play a central role in brain homeostasis and are increasingly implicated in multiple neurodegenerative diseases. We report that astrocytes differentiated from early onset familial Alzheimer’s disease (fAD) patients or control cells treated with Amyloid β oligomers exhibit significant changes in their metabolism including glucose uptake, glutamate uptake and lactate release, with increases in oxidative and glycolytic metabolism. Furthermore, we demonstrate evidence of gliosis in fAD astrocytes in addition to a change in metabolic pathways including glutamate, purines, arginine, and the citric acid cycle. Homeostatic responses to brain activity and cellular metabolism are central to normal brain function. However, altered brain metabolism and cellular stress present significant risk factors for the onset and progression of neurodegenerative disease. This study demonstrates that fAD derived astrocytes present multiple metabolic and disease associated phenotypes early in their development suggesting that chronic alterations in fAD patient early in life that present significant risk factors for disease progression in mid-life and suggest key targets for potential diagnostic features and therapeutic agents late onset dementia in midlife.
rTMS to right parietal cortex decreases the precision of spatial priors in perceptual decision making

Throughout life human observers make perceptual decisions under uncertainty guided by prior knowledge about the world’s causal structure and properties. According to Bayesian probability theory, optimal decision making relies on integrating prior knowledge with current sensory inputs, weighted by their relative precisions (i.e., inverse of uncertainty). Thus, representing uncertainty is essential for optimal decisions. Although extensive research suggests that human perceptual decision making follows Bayesian principles, the neural underpinnings of priors and their uncertainties remain elusive. In this five-day study, we employed psychophysics, Bayesian causal inference models, and offline low-frequency (i.e., 1Hz) transcranial magnetic stimulation (TMS) to probe the role of right intraparietal sulcus (IPS), a key region for spatial processing, in the integration of prior knowledge with auditory/visual inputs for spatial decisions. Model-free and Bayesian modelling analyses consistently showed a reduction in the precision of observers’ long-term spatial prior and in the influence of their previous spatial choices on their current spatial decisions for right IPS-TMS compared to sham-TMS. In contrast, the causal prior and the auditory/visual uncertainties remained unaffected. The results show that offline IPS-TMS can selectively reduce the precision or influence of observers’ long-term spatial prior and their short-term spatial expectations on perceptual decisions, without affecting their causal prior or sensory uncertainties (i.e., likelihood). Our findings provide causal evidence for the role of parietal cortex, situated at the top of the audiovisual spatial processing hierarchy, in encoding the uncertainty of spatial - but not causal - priors during perceptual decision-making. Significance statement Perceptual decisions under uncertainty are pervasive in everyday life. Substantial evidence suggests that humans perform perceptual decisions near-optimally. They combine sensory inputs with prior knowledge about the signals’ causal structure and locations weighted by their uncertainties. Yet, the neural underpinnings remain elusive. Combining psychophysics, Bayesian models, and offline low-frequency inhibitory transcranial magnetic stimulation (TMS), we provide causal evidence that the parietal cortex is crucial for encoding the uncertainty of spatial - but not causal - priors during perceptual decision-making. Intriguingly, observers relied less on their long-term and short-term prior spatial expectations after parietal-TMS, as compared to sham-TMS. These results provide important insights into the neural substrates of priors and their uncertainties as key ingredients for near-optimal decisions consistent with normative Bayesian principles.
Age and obesity-driven changes in the extracellular matrix of the primary tumor and metastatic site influence tumor invasion and metastatic outgrowth

Younger age and obesity increase the incidence and metastasis of triple-negative breast cancer (TNBC), an aggressive subtype of breast cancer. The extracellular matrix (ECM) promotes tumor invasion and metastasis. We characterized the effect of age and obesity on the ECM of mammary fat pads, lungs, and liver using a diet-induced obesity (DIO) model. At 4 week intervals, we either injected the mammary fat pads with allograft tumor cells to characterize tumor growth and metastasis or isolated the mammary fat pads and livers to characterize the ECM. Age had no effect on tumor growth but increased lung and liver metastasis after 16 weeks. Obesity increased tumor growth starting at 12 weeks, increased liver metastasis only at 4 weeks, and weight gain correlated to increased lung but not liver metastasis. Utilizing whole decellularized ECM coupled with proteomics, we found that early stages of obesity were sufficient to induce changes in the ECM composition and invasive potential of mammary fat pads with increased abundance of pro-invasive ECM proteins Collagen IV and Collagen VI. We identified cells of stromal vascular fraction and adipose stem and progenitor cells as primarily responsible for secreting Collagen IV and VI, not adipocytes. We characterized the changes in ECM in the lungs and liver, and determined that older age decreases the metastatic potential of lung and liver ECM while later-stage obesity increases the metastatic potential. These data implicate ECM changes in the primary tumor and metastatic microenvironment as mechanisms by which age and obesity contribute to breast cancer progression. Significance Younger age and obesity increase the incidence and metastasis of triple-negative breast cancer. Our data suggest that changes in the breast, lung and liver ECM are driving some of these effects.