Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimorbidity and Physical and Cognitive Function: Performance of a New Multimorbidity-Weighted Index

Background Multimorbidity is an important health outcome but is difficult to quantify. We recently developed a multimorbidity-weighted index (MWI) and herein assess its performance in an independent nationally-representative cohort. Methods Health and Retirement Study (HRS) participants completed an interview on physician-diagnosed chronic conditions and physical functioning. We determined the relationship of chronic conditions on physical functioning and validated these weights with the original, independently-derived MWI. We then determined the association between MWI with physical functioning, grip strength, gait speed, basic and instrumental activities of daily living (ADL/IADL) limitations, and the modified Telephone Interview for Cognitive Status (TICS-m) in adjusted models. Results Among 20,509 adults, associations between chronic conditions and physical functioning varied several-fold. MWI values based on weightings in the HRS and original cohorts correlated strongly (Pearson's r = .92) and had high classification agreement (κ statistic = .80, p < .0001). Participants in the highest versus lowest MWI quartiles had weaker grip strength (-2.91 kg, 95% confidence interval [CI]: -3.51, -2.30), slower gait speed (-0.29 m/s, 95% CI: -0.35, -0.23), more ADL (0.79, 95% CI: 0.71, 0.87) and IADL (0.49, 95% CI: 0.44, 0.55) limitations, and lower TICS-m (-0.59, 95% CI: -0.77, -0.41) (all p < .001). We observed monotonic graded relationships for all outcomes with increasing MWI quartiles. Conclusion A multimorbidity index weighted to physical functioning performed nearly identically in a nationally-representative cohort as it did in its development cohorts, confirming broad generalizability. MWI was strongly associated with subjective and objective physical and cognitive performance. Thus, MWI serves as a valid patient-centered measure of multimorbidity, an important construct in research and clinical practice.
Revisiting Deep Intrinsic Image Decompositions

While invaluable for many computer vision applications, decomposing a natural image into intrinsic reflectance and shading layers represents a challenging, underdetermined inverse problem. As opposed to strict reliance on conventional optimization or filtering solutions with strong prior assumptions, deep learning based approaches have also been proposed to compute intrinsic image decompositions when granted access to sufficient labeled training data. The downside is that current data sources are quite limited, and broadly speaking fall into one of two categories: either dense fully-labeled images in synthetic/narrow settings, or weakly-labeled data from relatively diverse natural scenes. In contrast to many previous learning-based approaches, which are often tailored to the structure of a particular dataset (and may not work well on others), we adopt core network structures that universally reflect loose prior knowledge regarding the intrinsic image formation process and can be largely shared across datasets. We then apply flexibly supervised loss layers that are customized for each source of ground truth labels. The resulting deep architecture achieves state-of-the-art results on all of the major intrinsic image benchmarks, and runs considerably faster than most at test time.
White Matter Diffusion of Major Fiber Tracts Implicated in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder found to have widespread alterations in the function and synchrony of brain regions. These differences may underlie alterations in microstructural organization, such as in white matter pathways. To investigate the diffusion of major white matter tracts, the current study examined multiple indices of white matter diffusion in 42 children and adults with ASD and 44 typically developing (TD) age- and IQ-matched peers using diffusion tensor imaging. Diffusivity measures were compared between groups for the following tracts: bilateral cingulum bundle, corpus callosum, inferior longitudinal fasciculus, superior longitudinal fasciculus, and uncinate fasciculus. Results indicate a significant reduction in fractional anisotropy (FA) for the left superior longitudinal fasciculus (LSLF) in ASD children and adults compared with TD peers. A significant increase in radial diffusivity for ASD participants was also found in the same cluster along the LSLF. In addition, a significant positive correlation emerged for all subjects between FA for the LSLF and age, with FA increasing with age. These findings point to a significant alteration in long-distance white matter connectivity in children and adults with ASD, potentially underscoring the relationship between alterations in white matter diffusion and the ASD phenotype. These results also suggest that the white matter alterations in autism may be subtle and related to the developmental trajectory.
The association between polymorphisms of genes related to inflammation and recurrent pregnancy loss

Abstract Aim: Recurrent pregnancy loss (RPL) occurs in 1–2% of pregnant women and about 50% of RPL cases are unexplained. Previous studies have shown that genetic variation in immune response genes can contribute to the risk in pregnancy maintenance during pregnancy. The aim of the present study was to evaluate the relationship between RPL and genes those have previously been associated with an inflammatory process on 107 RPL cases and 187 healthy controls. Methods: In this work, the single-nucleotide polymorphisms was examined by utilizing the direct sequencing and the Sequenom MassARRAY system. Results: The FAU rs769440 G allele had higher frequencies in patients with RPL (p = .019). No association was observed between other polymorphisms and RPL. Conclusion: The results showed an association between FAU rs769440 polymorphism and RPL in Chinese Han population.
Analyzing Implicit Aspects and Aspect Dependent Sentiment Polarity for Aspect-based Sentiment Analysis on Informal Turkish Texts

The web provides a suitable media for users to post comments on different topics. In most of such content, authors express different opinions on different features or aspects of the topic. In aspect based sentiment analysis, it is analyzed as to for which aspect which opinion is expressed. Once aspects are available, the next important step is to match aspects with correct sentiments. In this work, we investigate enhancements for two cases in matching step: extracting implicit aspects, and sentiment words whose polarity depends on the aspect. The techniques are applied on Turkish informal texts collected from a products forum. Experimental evaluation shows that additional steps applied for these cases improve the accuracy of aspect based sentiment analysis.
Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

Sentiment analysis (SA) techniques are applied to assess aspects of language that are used to express feelings, evaluations and opinions in areas such as customer sentiment extraction. Most studies have focused on SA techniques for widely used languages such as English, but less attention has been paid to Arabic, particularly the Saudi dialect. Most Arabic SA studies have built systems using supervised approaches that are domain dependent; hence, they achieve low performance when applied to a new domain different from the learning domain, and they require manually labelled training data, which are usually difficult to obtain. In this article, we propose a novel lexicon-based algorithm for Saudi dialect SA that features domain independence. We created an annotated Saudi dialect dataset and built a large-scale lexicon for the Saudi dialect. Then, we developed our weighted lexicon-based algorithm. The proposed algorithm mines the associations between polarity and non-polarity words for the dataset and then weights these words based on their associations. During algorithm development, we also proposed novel rules for handling some linguistic features such as negation and supplication. Several experiments were performed to evaluate the performance of the proposed algorithm.
Spliceosomal components protect embryonic neurons from R-loop-mediated DNA damage and apoptosis

ABSTRACT RNA splicing factors are essential for the viability of all eukaryotic cells; however, in metazoans some cell types are exquisitely sensitive to disruption of splicing factors. Neuronal cells represent one such cell type, and defects in RNA splicing factors can lead to neurodegenerative diseases. The basis for this tissue selectivity is not well understood owing to difficulties in analyzing the consequences of splicing factor defects in whole-animal systems. Here, we use zebrafish mutants to show that loss of spliceosomal components, including splicing factor 3b, subunit 1 (sf3b1), causes increased DNA double-strand breaks and apoptosis in embryonic neurons. Moreover, these mutants show a concomitant accumulation of R-loops, which are non-canonical nucleic acid structures that promote genomic instability. Dampening R-loop formation by conditional induction of ribonuclease H1 in sf3b1 mutants reduced neuronal DNA damage and apoptosis. These findings show that splicing factor dysfunction leads to R-loop accumulation and DNA damage that sensitizes embryonic neurons to apoptosis. Our results suggest that diseases associated with splicing factor mutations could be susceptible to treatments that modulate R-loop levels. Summary: Loss of RNA splicing factors causes R-loop accumulation and DNA damage in embryonic neurons, sensitizing them to radiation-induced cell death. These findings suggest that diseased cells with mutations in splicing factors are vulnerable to radiotherapy.
PhosphoLipid transfer protein (PLTP) exerts a direct pro-inflammatory effect on rheumatoid arthritis (RA) fibroblasts-like-synoviocytes (FLS) independently of its lipid transfer activity

Rheumatoid arthritis (RA) is a chronic inflammatory rheumatic disease with modification of lipids profile and an increased risk of cardiovascular events related to inflammation. Plasma phospholipid transfer protein (PLTP) exerts a lipid transfer activity through its active form. PLTP can also bind to receptors such as ATP-binding cassette transporter A1 (ABCA1). In addition to its role in lipoprotein metabolism and atherosclerosis, the latest advances came in support of a complex role of PLTP in the regulation of the inflammatory response, both with pro-inflammatory or anti-inflammatory properties. The aim of the present study was to decipher the role of PLTP in joint inflammation and to assess its relevance in the context of RA. PLTP expression was examined by western-blot and by immunochemistry. ABCA1 expression was analyzed by flow cytometry. Lipid transfer activity of PLTP and pro-inflammatory cytokines were measured in sera and synovial fluid (SF) from RA patients and controls (healthy subjects or osteoarthritis patients [OA]). FLS were treated with both lipid-transfer active form and inactive form of recombinant human PLTP. IL-8, IL-6, VEGF and MMP3 produced by FLS were assessed by ELISA, and proliferation by measuring 3H-Thymidine incorporation. RA synovial tissues showed higher PLTP staining than OA and PLTP protein levels were also significantly higher in RA-FLS. In addition, RA, unlike OA patients, displayed elevated levels of PLTP activity in SF, which correlated with pro-inflammatory cytokines. Both lipid-transfer active and inactive forms of PLTP significantly increased the production of cytokines and proliferation of FLS. ABCA1 was expressed on RAFLS and PLTP activated STAT3 pathway. To conclude, PLTP is highly expressed in the joints of RA patients and may directly trigger inflammation and FLS proliferation, independently of its lipid transfer activity. These results suggest a pro-inflammatory role for PLTP in RA.
Trifunctional lipid probes for comprehensive studies of single lipid species in living cells

Significance Some lipids such as sphingosine and diacylglycerol are potent signaling effectors. However, comprehensive investigations of their bioactive actions are often hampered by a lack of tools that can be used in living cells. Here, we present chemically modified lipids that allow investigation of acute lipid signaling, lipid metabolism, lipid−protein interactions, and lipid localization by using a single probe for each target lipid. Equipped with a caging group, the lipid probe is biologically inactive, until activated by a flash of light. A second photoreaction cross-links the probe to protein interactors that may subsequently be analyzed by mass spectrometry or fluorescence/electron microscopy. We envision that this versatile design will be central to unraveling complex lipid signaling networks. Lipid-mediated signaling events regulate many cellular processes. Investigations of the complex underlying mechanisms are difficult because several different methods need to be used under varying conditions. Here we introduce multifunctional lipid derivatives to study lipid metabolism, lipid−protein interactions, and intracellular lipid localization with a single tool per target lipid. The probes are equipped with two photoreactive groups to allow photoliberation (uncaging) and photo–cross-linking in a sequential manner, as well as a click-handle for subsequent functionalization. We demonstrate the versatility of the design for the signaling lipids sphingosine and diacylglycerol; uncaging of the probe for these two species triggered calcium signaling and intracellular protein translocation events, respectively. We performed proteomic screens to map the lipid-interacting proteome for both lipids. Finally, we visualized a sphingosine transport deficiency in patient-derived Niemann−Pick disease type C fibroblasts by fluorescence as well as correlative light and electron microscopy, pointing toward the diagnostic potential of such tools. We envision that this type of probe will become important for analyzing and ultimately understanding lipid signaling events in a comprehensive manner.
QuickEdit: Editing Text & Translations by Crossing Words Out

We propose a framework for computer-assisted text editing. It applies to translation post-editing and to paraphrasing. Our proposal relies on very simple interactions: a human editor modifies a sentence by marking tokens they would like the system to change. Our model then generates a new sentence which reformulates the initial sentence by avoiding marked words. The approach builds upon neural sequence-to-sequence modeling and introduces a neural network which takes as input a sentence along with change markers. Our model is trained on translation bitext by simulating post-edits. We demonstrate the advantage of our approach for translation post-editing through simulated post-edits. We also evaluate our model for paraphrasing through a user study.
Promoting Secure Email Communication and Authentication

Nowadays, the possibility to communicate securely is crucial for users in the private as well as in the business context. However, to do so they have to face problems regarding mismatching mental models of encryption and bad usability not only concerning the encryption, but also the authentication process. To solve this problem, we evaluate users’ perception on encryption and authentication schemes in order to (1) derive a process, which is more in line with their expectations and (2) use authentication schemes which provide security but also achieve a high acceptance rate from users. We plan to integrate our findings into a prototypical software in order to evaluate users’ acceptance for our technical approach.
Iterative inversion of deformation vector fields with feedback control

Purpose Often, the inverse deformation vector field (DVF) is needed together with the corresponding forward DVF in four‐dimesional (4D) reconstruction and dose calculation, adaptive radiation therapy, and simultaneous deformable registration. This study aims at improving both accuracy and efficiency of iterative algorithms for DVF inversion, and advancing our understanding of divergence and latency conditions. Method We introduce a framework of fixed‐point iteration algorithms with active feedback control for DVF inversion. Based on rigorous convergence analysis, we design control mechanisms for modulating the inverse consistency (IC) residual of the current iterate, to be used as feedback into the next iterate. The control is designed adaptively to the input DVF with the objective to enlarge the convergence area and expedite convergence. Three particular settings of feedback control are introduced: constant value over the domain throughout the iteration; alternating values between iteration steps; and spatially variant values. We also introduce three spectral measures of the displacement Jacobian for characterizing a DVF. These measures reveal the critical role of what we term the nontranslational displacement component (NTDC) of the DVF. We carry out inversion experiments with an analytical DVF pair, and with DVFs associated with thoracic CT images of six patients at end of expiration and end of inspiration. Results The NTDC‐adaptive iterations are shown to attain a larger convergence region at a faster pace compared to previous nonadaptive DVF inversion iteration algorithms. By our numerical experiments, alternating control yields smaller IC residuals and inversion errors than constant control. Spatially variant control renders smaller residuals and errors by at least an order of magnitude, compared to other schemes, in no more than 10 steps. Inversion results also show remarkable quantitative agreement with analysis‐based predictions. Conclusion Our analysis captures properties of DVF data associated with clinical CT images, and provides new understanding of iterative DVF inversion algorithms with a simple residual feedback control. Adaptive control is necessary and highly effective in the presence of nonsmall NTDCs. The adaptive iterations or the spectral measures, or both, may potentially be incorporated into deformable image registration methods.
Nose radius and cutting speed effects during milling of AISI 304 material

ABSTRACT Not only milling parameters, but also cutting tool properties affect the machining performance. Therefore, in the current work, the effect of nose radius and cutting speed on the wear, force, surface roughness and chip morphology in down and up milling of AISI 304 stainless steel was investigated. Machining experiments were conducted with cutting tools with radii of 0.4, 0.8 and 1.2 mm at various cutting speeds in both down and up milling. Experimental results showed that the main tool failure mechanisms and modes were adhesion, abrasion, chipping and fracture during milling with various nose radii. Cutting forces dropped with the increment in nose radius regardless of the cutting speed and milling direction, except for up milling at 100 m/min. From the experimental results, it was found that roughness diminished with increase in both nose radius and speed. Surface roughness and the resultant forces during up milling were found to be lower than that during down milling. It was observed that the increment in nose radius increased the edge serration in chip morphology.
Next-generation sequencing analysis of a cluster of hepatitis C virus infections in a haematology and oncology center

Molecular characterization of early hepatitis C virus (HCV) infection remains rare. Ten out of 78 patients of a hematology/oncology center were found to be HCV RNA positive two to four months after hospitalization. Only two of the ten patients were anti-HCV positive. HCV hypervariable region 1 (HVR1) was amplified in seven patients (including one anti-HCV positive) and analyzed by next generation sequencing (NGS). Genetic variants were reconstructed by Shorah and an empirically established 0.5% variant frequency cut-off was implemented. These sequences were compared by phylogenetic and diversity analyses. Ten unrelated blood donors with newly acquired HCV infection detected at the time of donation (HCV RNA positive and anti-HCV negative) served as controls. One to seven HVR1 variants were found in each patient. Sequences intermixed phylogenetically with no evidence of clustering in individual patients. These sequences were more similar to each other (similarity 95.4% to 100.0%) than to those of controls (similarity 64.8% to 82.6%). An identical predominant variant was present in four patients, whereas other closely related variants dominated in the remaining three patients. In five patients the HCV population was limited to a single variant or one predominant variant and minor variants of less than 10% frequency. In conclusion, NGS analysis of a cluster of HCV infections acquired in the hospital setting revealed the presence of low diversity, very closely related variants in all patients, suggesting an early-stage infection with the same virus. NGS combined with phylogenetic analysis and classical epidemiological analysis could help in tracking of HCV outbreaks.
A Provably Correct Algorithm for Deep Learning that Actually Works

We describe a layer-by-layer algorithm for training deep convolutional networks, where each step involves gradient updates for a two layer network followed by a simple clustering algorithm. Our algorithm stems from a deep generative model that generates mages level by level, where lower resolution images correspond to latent semantic classes. We analyze the convergence rate of our algorithm assuming that the data is indeed generated according to this model (as well as additional assumptions). While we do not pretend to claim that the assumptions are realistic for natural images, we do believe that they capture some true properties of real data. Furthermore, we show that our algorithm actually works in practice (on the CIFAR dataset), achieving results in the same ballpark as that of vanilla convolutional neural networks that are being trained by stochastic gradient descent. Finally, our proof techniques may be of independent interest.
Validation of linear damage rules using random loading

Fatigue damage is a parameter which plays an important role in lifetime and reliability predictions of randomly loaded structures. In this paper, four different forms of the Miner linear accumulation damage rule are examined. They are used to compare observed experimental damage and the results of numerical simulations for both uniaxial and multiaxial fatigue. First, critical fatigue damage values for all forms of the Miner accumulation damage rule are calculated for uniaxial cases. Based on the results presented in this paper, it has been established that for high strength materials the critical damage value is around 0.3. These results are then applied to the multiaxial cases. The multiaxiality is taken into account using either the signed von Mises or critical plane method. Finally, the fatigue fracture surface has been calculated and compared to the experimental pattern of fatigue fracture. The results show that the fatigue fracture surface determined by the numerical simulation is comparable to the fatigue fracture observed experimentally.