julilili42/papers huggingface dataset:
Intestinal interstitial fluid isolation provides novel insight into the human host-microbiome interface
2024-01-11

Aims The gastrointestinal (GI) tract is composed of distinct subregions which exhibit segment-specific differences in microbial colonization and (patho)physiological characteristics. Gut microbes can be collectively considered as an active endocrine organ. Microbes produce metabolites, which can be taken up by the host and can actively communicate with the immune cells in the gut lamina propria with consequences for cardiovascular health. Variation in bacterial load and composition along the GI tract may influence the mucosal microenvironment and thus be reflected its interstitial fluid (IF). Characterization of the segment-specific microenvironment is challenging and largely unexplored because of lack of available tools. Method and Results Here, we developed methods, namely tissue centrifugation and elution, to collect IF from the mucosa of different intestinal segments. These methods were first validated in rats and mice, and the tissue elution method was subsequently translated for use in humans. These new methods allowed us to quantify microbiota-derived metabolites, mucosa-derived cytokines, and proteins at their site-of-action. Quantification of short-chain fatty acids showed enrichment in the colonic IF. Metabolite and cytokine analyses revealed differential abundances within segments, often significantly increased compared to plasma, and proteomics revealed that proteins annotated to the extracellular phase were site-specifically identifiable in IF and were differentially expressed when compared to matched serum, all suggesting local synthesis. Conclusion Collection of IF from defined segments and the direct measurement of mediators at the site-of-action in rodents and humans bypasses the limitations of indirect analysis of fecal samples or serum, providing direct insight into this understudied compartment.
Causal inference of diachronic semantic maps from cross-linguistic synchronic polysemy data
2024-01-11

Semantic maps are used in lexical typology to summarize cross-linguistic implicational universals of co-expression between meanings in a domain. They are defined as networks which, using as few links as possible, connect the meanings so that every isolectic set (i.e., set of meanings that can be expressed by the same word in some language) forms a connected component. Due to the close connection between synchronic polysemies and semantic change, semantic maps are often interpreted diachronically as encoding potential pathways of semantic extension. While semantic maps are traditionally generated by hand, there have been attempts to automate this complex and non-deterministic process. I explore the problem from a new algorithmic angle by casting it in the framework of causal discovery, a field which explores the possibility of automatically inferring causal structures from observational data. I show that a standard causal inference algorithm can be used to reduce cross-linguistic polysemy data into minimal network structures which explain the observed polysemies. If the algorithm makes its link deletion decisions on the basis of the connected component criterion, the skeleton of the resulting causal structure is a synchronic semantic map. The arrows which are added to some links in the second stage can be interpreted as expressing the main tendencies of semantic extension. Much of the existing literature on semantic maps implicitly assumes that the data from the languages under analysis is correct and complete, whereas in reality, semantic map research is riddled by data quality and sparseness problems. To quantify the uncertainty inherent in the inferred diachronic semantic maps, I rely on bootstrapping on the language level to model the uncertainty caused by the given language sample, as well as on random link processing orders to explore the space of possible semantic maps for a given input. The maps inferred from the samples are then summarized into a consensus network where every link and arrow receives a confidence value. In experiments on cross-linguistic polysemy data of varying shapes, the resulting confidence values are found to mostly agree with previously published results, though challenges in directionality inference remain.
Making clinical trials a public norm for health decisions in sub-Saharan Africa
2024-01-11

A clinical trial is intrinsically a collaborative undertaking. The complex steps, including identifying the biological mechanisms, discovering products, preclinical studies, and the clinical development from phase 1 to 3 clinical trials allowing market authorisation of a product, are unlikely to be feasible for a single institution or a country alone. Collaboration is therefore necessary to establish and maintain the research and innovation that is a prerequisite to tackle health threats, irrespective of the socioeconomic status of the countries (1).
Predicting individual long-term prognosis of spatial neglect based on acute stroke patient data
2024-01-11

Abstract One of the most pressing questions after a stroke is whether an individual patient will recover in the long-term. Previous studies demonstrated that spatial neglect – a common behavioral deficit after right hemispheric stroke – is a strong predictor for poor performance on a wide range of everyday tasks and for resistance to rehabilitation. The possibility of predicting long-term prognosis of spatial neglect is therefore of great relevance. The aim of the present study was to test the prognostic value of different imaging and non-imaging features from right hemispheric stroke patients: individual demographics (age, sex), initial neglect severity, and acute lesion information (size, location). Patients’ behavior was tested twice in the acute and the chronic phases of stroke and prediction models were built using machine learning-based algorithms with repeated nested cross-validation and feature selection. Model performances indicate that demographic information seemed less beneficial. The best variable combination comprised individual neglect severity in the acute phase of stroke, together with lesion location and size. The latter were based on individual lesion overlaps with a previously proposed chronic neglect region-of-interest (ROI) that covers anterior parts of the superior and middle temporal gyri and the basal ganglia. These variables achieved a remarkably high level of accuracy by explaining 66% of the total variance of neglect patients, making them promising features in the prediction of individual outcome prognosis.
Synthesis of Tridecacene by Multistep Single-Molecule Manipulation
2024-01-12

Acenes represent a unique class of polycyclic aromatic hydrocarbons that have fascinated chemists and physicists due to their exceptional potential for use in organic electronics. While recent advances in on-surface synthesis have resulted in higher acenes up to dodecacene, a comprehensive understanding of their fundamental properties necessitates their expansion toward even longer homologues. Here, we demonstrate the on-surface synthesis of tridecacene via atom-manipulation-induced conformational preparation and dissociation of a trietheno-bridged precursor on a Au(111) surface. The generated tridecacene has been investigated by scanning tunneling microscopy and spectroscopy (STM/STS), combined with first-principles calculations. We observe that the STS transport gap (1.09 eV) shrinks again following the gap reopening of dodecacene (1.4 eV). Spin-polarized density functional theory calculations confirm an antiferromagnetic open-shell ground-state electronic configuration for tridecacene in the gas phase. Interestingly, tridecacene's open-shell character is significantly reduced upon interaction with the Au(111) surface despite being only physisorbed. The interaction with the surface leads to a lowering of the magnetization of tridecacene, a reduced gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), compared to the gas phase, and a reduced relative energy to the nonmagnetic state, making it nearly isoenergetic. These observations show qualitatively that the influence of the Au(111) substrate on the properties of long acenes is significant, which is important for interpreting the measured STS transport gaps. Our work contributes to a fundamental understanding of the electronic properties of long acenes, confirming a nonmonotonous length-dependent HOMO-LUMO gap, and to the development of multistep tip-assisted synthesis of elusive compounds.
Different inflammatory signatures based on CSF biomarkers relate to preserved or diminished brain structure and cognition
2024-01-12

Abstract Neuroinflammation is a hallmark of Alzheimer’s disease (AD) and both positive and negative associations of individual inflammation-related markers with brain structure and cognitive function have been described. We aimed to identify inflammatory signatures of CSF immune-related markers that relate to changes of brain structure and cognition across the clinical spectrum ranging from normal aging to AD. A panel of 16 inflammatory markers, Aβ42/40 and p-tau181 were measured in CSF at baseline in the DZNE DELCODE cohort ( n = 295); a longitudinal observational study focusing on at-risk stages of AD. Volumetric maps of gray and white matter (GM/WM; n = 261) and white matter hyperintensities (WMHs, n = 249) were derived from baseline MRIs. Cognitive decline ( n = 204) and the rate of change in GM volume was measured in subjects with at least 3 visits ( n = 175). A principal component analysis on the CSF markers revealed four inflammatory components (PCs). Of these, the first component PC1 (highly loading on sTyro3, sAXL, sTREM2, YKL-40, and C1q) was associated with older age and higher p-tau levels, but with less pathological Aβ when controlling for p-tau. PC2 (highly loading on CRP, IL-18, complement factor F/H and C4) was related to male gender, higher body mass index and greater vascular risk. PC1 levels, adjusted for AD markers, were related to higher GM and WM volumes, less WMHs, better baseline memory, and to slower atrophy rates in AD-related areas and less cognitive decline. In contrast, PC2 related to less GM and WM volumes and worse memory at baseline. Similar inflammatory signatures and associations were identified in the independent F.ACE cohort. Our data suggest that there are beneficial and detrimental signatures of inflammatory CSF biomarkers. While higher levels of TAM receptors (sTyro/sAXL) or sTREM2 might reflect a protective glia response to degeneration related to phagocytic clearance, other markers might rather reflect proinflammatory states that have detrimental impact on brain integrity.
Phase Angle of Bioelectrical Impedance Analysis as an Indicator for Diabetic Polyneuropathy in Type 2 Diabetes Mellitus
2024-01-12

Abstract Context Due to the heterogenous clinical symptoms and deficits, the diagnosis of diabetic polyneuropathy (DPN) is still difficult in clinical routines, leading to increased morbidity and mortality. Objective We studied the correlation of phase angle (PhA) of bioelectrical impedance analysis (BIA) with clinical, laboratory, and physical markers of DPN to evaluate PhA as a possible diagnostic method for DPN. Materials and methods In this cross-sectional observational study as part of the Heidelberg Study on Diabetes and Complications, we examined 104 healthy individuals and 205 patients with type 2 diabetes mellitus (T2D), among which 63 had DPN. The PhA was calculated from multifrequency BIA. Nerve conduction studies, quantitative sensory testing (QST) and diffusion-weighted magnetic resonance neurography to determine fractional anisotropy (FA) reflecting peripheral nerve integrity were performed. Results T2D patients with DPN had lower PhA values (5.71 ± 0.10) compared to T2D patients without DPN (6.07 ± 0.08, P = .007, + 6.1%) and healthy controls (6.18 ± 0.08, P &amp;lt; .001, + 7.9%). Confounder-adjusted analyses showed correlations of the PhA with conduction velocities and amplitudes of the peroneal (β=.28; β=.31, P &amp;lt; .001) and tibial nerves (β=.28; β=.32, P &amp;lt; .001), Z-scores of QST (thermal detection β=.30, P &amp;lt; .05) and the FA (β=.60, P &amp;lt; .001). Receiver-operating characteristic analysis showed similar performance of PhA in comparison to the mentioned diagnostic methods. Conclusion The study shows that PhA is, in comparison to other test systems used, at least an equally good and much easier to handle investigator-independent marker for detection of DPN.
Main soil microbial groups assessed by phospholipid fatty acid analysis of temperate alley agroforestry systems on crop- and grassland
2024-01-12

The phospholipid fatty acid (PLFA) composition of soils was analysed at three poplar-based silvo-arable systems and at one willow-based silvo-grassland alley agroforestry system in Central Germany. The objective was to analyse tree row effects on the PLFA composition of main fungal and main bacterial groups. The fungal groups were BAM (Basidiomycota + Ascomycota + Mucoromycota) and AMF (Arbuscular Mycorrhizal Fungi). The bacterial groups were Gram-negative, Firmicutes, and Actinobacteria. The total PLFA content varied between 53 and 170 nmol g−1 soil. Total PLFA and microbial biomass carbon (MBC) showed a strong linear relationship, which resulted in a mean MBC/total PLFA ratio of 4.2 μg nmol−1. AMF contributed on average 4 mol% and the fungal BAM group 10 % to total PLFA. Gram-negative bacteria contributed on average 37 mol%, Firmicutes 23 mol%, and Actinobacteria 6 mol% to total PLFA. The presence of poplar or willow trees increased the mean total PLFA content in comparison with the alleyways by 30 %. Especially the mean contribution of fungal PLFA to total PLFA showed a significant +7.0 mol% increase in the tree row compared with the alleyways, exclusively caused by the BAM group (+7.6 mol%), whereas the contribution of the AMF PLFA linearly decreased from the middle of the alleyway to the tree row at all sites. Within the alleyways, the Gram-negative/Firmicutes PLFA ratio showed a significant decline from the 1 m up to the 24 m distance samples at sites Dornburg and Forst. Despite a decrease of AMF in tree rows, agroforestry tree rows led to a rapid increase in fungi, most likely due to the promotion of ecto-mycorrhizal fungi.
Large‐Scale Screening: Phenotypic and Mutational Spectrum in Isolated and Combined Dystonia Genes
2024-01-12

Abstract Background Pathogenic variants in several genes have been linked to genetic forms of isolated or combined dystonia. The phenotypic and genetic spectrum and the frequency of pathogenic variants in these genes have not yet been fully elucidated, neither in patients with dystonia nor with other, sometimes co‐occurring movement disorders such as Parkinson's disease (PD). Objectives To screen &gt;2000 patients with dystonia or PD for rare variants in known dystonia‐causing genes. Methods We screened 1207 dystonia patients from Germany (DysTract consortium), Spain, and South Korea, and 1036 PD patients from Germany for pathogenic variants using a next‐generation sequencing gene panel. The impact on DNA methylation of KMT2B variants was evaluated by analyzing the gene's characteristic episignature. Results We identified 171 carriers (109 with dystonia [9.0%]; 62 with PD [6.0%]) of 131 rare variants (minor allele frequency &lt;0.005). A total of 52 patients (48 dystonia [4.0%]; four PD [0.4%, all with GCH1 variants]) carried 33 different (likely) pathogenic variants, of which 17 were not previously reported. Pathogenic biallelic variants in PRKRA were not found. Episignature analysis of 48 KMT2B variants revealed that only two of these should be considered (likely) pathogenic. Conclusion This study confirms pathogenic variants in GCH1 , GNAL , KMT2B , SGCE , THAP1 , and TOR1A as relevant causes in dystonia and expands the mutational spectrum. Of note, likely pathogenic variants only in GCH1 were also found among PD patients. For DYT‐KMT2B, the recently described episignature served as a reliable readout to determine the functional effect of newly identified variants. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Lanthanide Amide Complexes Supported by the Bis‐tris(pyrazolyl)borate Ligand Environment
2024-01-12

Abstract Synthesis of primary lanthanide amides in the bis ‐hydrotris(1‐pyrazolyl)borate ligand environment has been achieved. Salt metathesis of [Dy(Tp) 2 (OTf)] 1‐Dy (OTf=CF 3 SO 3 ) with K(N′′) (N′′=N(SiMe 3 ) 2 ) in toluene yielded the [ bis (silyl)]amide [Dy(Tp) 2 (N′′)] 2‐Dy . Complexes 1‐Ln and 2‐Ln were both used to access primary lanthanide amides, where either metathesis of 1‐Ln with K(NHAr CF3 ) (Ar CF3 =C 6 H 3 (CF 3 ) 2 ‐3,5) or protonolysis of 2‐Ln with H 2 NAr CF3 in toluene yielded [Ln(Tp) 2 (NHAr CF3 )] 3‐Ln (Ln=Y, Dy). The synthesis of parent amides was also attempted, but the metathesis of 1‐Y with NaNH 2 yielded complicated reaction mixtures, but from which the dimeric parent amide [{Y(Tp) 2 ( μ ‐NH 2 )} 2 ] 4‐Y and an ‘ate’ ‐salt [{Y(Tp) 2 ( μ 2 ‐OTf)( μ 3 ‐OTf)Na(THF) 2 } 2 ] 5‐Y were isolated. Full characterisation data are presented for all complexes, including the structure determination.
Crystallization of the Fundamental Tetracyanamidometallates Li4[Si(CN2)4] and Li4[Ge(CN2)4] from Polymeric Li‐T‐(NCN)‐I Precursors (T=Si, Ge)
2024-01-12

Abstract The tetracyanamidometallates Li 4 [ T (CN 2 ) 4 ] with T =Si, Ge were prepared by solid‐state metathesis (SSM) reactions between T I 4 and Li 2 (CN 2 ). The new compounds Li 4 [ T (CN 2 ) 4 ] are obtained as crystalline phases in high yields and represent the most basic examples of the family of tetracyanamidometallates. The formation of these compounds, via SiI 4 and Li 2 (CN 2 ), is analyzed by means of differential scanning calorimetry (DSC). The [Si(CN 2 ) 4 ] 4− ion in the structure of Li 4 [Si(CN 2 ) 4 ] can be considered an analogue to the [SiO 4 ] 4− ion in Li 4 SiO 4 . The crystal structures of Li 4 [ T (CN 2 ) 4 ] were solved and refined isotypically on the basis of single‐crystal X‐ray diffraction data in the space group C 2/ c , and further characterized by infrared and solid‐state NMR techniques.
The Martens–Mumford theorem and the Green–Lazarsfeld secant conjecture
2024-01-12

The Green–Lazarsfeld secant conjecture predicts that the syzygies of a curve of sufficiently high degree are controlled by its special secants. We prove this conjecture for all curves of Clifford index at least two and not bielliptic and for all line bundles of a certain degree. Our proof is based on a classic result of Martens and Mumford on Brill–Noether varieties and on a simple vanishing criterion that comes from the interpretation of syzygies through symmetric products of curves.
No Evidence for an Association of HIV and Antiviral Treatment With Changes in Framingham Cardiovascular Risk Score in the Ndlovu Cohort Study
2024-01-12

Background HIV and antiretroviral therapy (ART) have been associated with increased cardiovascular disease (CVD) risk in high‐income countries. The authors studied the longitudinal association between HIV and ART and nonlaboratory Framingham Risk Score (FRS) in a middle‐income country. Methods and Results This longitudinal analysis of the NCS (Ndlovu Cohort Study), South Africa used baseline to 36‐month follow‐up data. Demographics, HIV, ART status, and cardiometabolic measures were obtained. FRS was used as a CVD risk measure. Through linear mixed models, FRS trends over time and the association with HIV were studied. Analysis included 1136 participants, with 609 (54%) having HIV, and 495 (81%) taking ART. At baseline, 9.8% of participants had a high FRS. People living with HIV (PLHIV) had a 3.2% lower FRS than HIV‐negative participants ( P &lt;0.001). FRS increased similarly for both groups over time. Other factors associated with FRS were secondary and higher education (ß value: −0.075, P &lt;0.001; ß value: −0.084, P &lt;0.001) and alcohol consumption (ß value: 0.011, P &lt;0.001). Conclusions CVD risk increased for all participants over 36 months, suggesting classic risk factors rather than HIV status or ART to be drivers of CVD risk. People living with HIV had a significantly lower FRS than their HIV‐negative counterparts, possibly related to HIV itself or a more frequent interaction with healthcare services. No association of HIV and ART with changes in FRS over 36 months was observed, suggesting the need for research using clinical endpoints to elucidate the effects of HIV and ART on CVD risk. Population‐based prevention of CVD risk factors in sub‐Saharan Africa is warranted, regardless of HIV status.
Privacy-Preserving Machine Learning (PPML)Inference for Clinically Actionable Models: How to Monetize ESSG Modelling Efforts?
2024-01-12

<title>Abstract</title> Purpose: Machine learning (ML) refers to algorithms (often models) that are learned directly from data, germane to past experience. As algorithms have constantly been evolving with the exponential increase of computing power and vastly generated data, privacy of algorithms as well as of data becomes extremely important due to regulations and IP rights. Therefore, it is vital to address privacy and security concerns of both data and model together with other performance metrics when commercializing machine learning models. Aim: Our motivation is to show that it is possible to deploy privacy-preserving machine learning inference methods to monetize the investment in ML models without disclosing them and patients’ data by providing a security analysis to define an appropriate query limit per user with ESSG’s adult spinal deformity dataset. Method: We implement a privacy-preserving tree-based machine learning inference and run two security scenarios (scenario A and scenario B) containing four parts with progressively increasing the number of synthetic data points, which are used to enhance the accuracy of the attacker’s substitute model. A target model is generated with particular operation site(s) in each scenario, and substitute models are built with nine-time threefold cross-validation using the XGBoost algorithm with the remaining sites’ data to assess the security of the target model. First, we create box plots of the test sets’ accuracy, sensitivity, precision, and F-score metrics to compare the substitute models’ performance with the target model. Second, we compare the gain values of the target and substitute models’ features. Third, we provide an in-depth analysis to check the inclusion of target model split points in substitute models with a heatmap. Finally, we compare the outputs of public and privacy-preserving models and report intermediate timing results. Results: The privacy-preserving XGBoost model results are identical to the original plaintext model in the aforementioned two scenarios in terms of prediction accuracy. The differences between performance metrics of best-performing substitute models and target models are 0.27, 0.18, 0.25, 0.26 for scenario A, and 0.04, 0, 0.04, and 0.03 for scenario B for accuracy, sensitivity, precision, and F-score, respectively. The differences between target model accuracy and the mean accuracy values of models in each scenario on the substitute models’ test dataset are 0.38 for scenario A and 0.14 for scenario B. Conclusion: Based on our findings, we conclude that machine learning models (i.e., our target models) may contribute to the advancement in the field of application where they are deployed. When the security of both the model and the user data is guaranteed, it is possible to monetize the modeling efforts of companies or organizations such as ESSG.