Miracle-dz/UniFAR-FTUs huggingface dataset:
Diabetic Cardiomyopathy: Impact of Biological Sex on Disease Development and Molecular Signatures

Diabetic cardiomyopathy refers to a unique set of heart-specific pathological variables induced by hyperglycemia and insulin resistance. Given that cardiovascular disease (CVD) is the leading cause of death in the world, and type 2 diabetes incidence continues to rise, understanding the complex interplay between these two morbidities and developing novel therapeutic strategies is vital. Two hallmark characteristics specific to diabetic cardiomyopathy are diastolic dysfunction and cardiac structural mal-adaptations, arising from cardiac cellular responses to the complex toxicity induced by hyperglycemia with or without hyperinsulinemia. While type 2 diabetes is more prevalent in men compared to women, cardiovascular risk is higher in diabetic women than in diabetic men, suggesting that diabetic women take a steeper path to cardiomyopathy and heart failure. Accumulating evidence from randomized clinical trials indicate that although pre-menopausal women have lower risk of CVDs, compared to age-matched men, this advantage is lost in diabetic pre-menopausal women, which suggests estrogen availability does not protect from increased cardiovascular risk. Notably, few human studies have assessed molecular and cellular mechanisms regarding similarities and differences in the progression of diabetic cardiomyopathy in men versus women. Additionally, most pre-clinical rodent studies fail to include female animals, leaving a void in available data to truly understand the impact of biological sex differences in diabetes-induced dysfunction of cardiovascular cells. Elegant reviews in the past have discussed in detail the roles of estrogen-mediated signaling in cardiovascular protection, sex differences associated with telomerase activity in the heart, and cardiac responses to exercise. In this review, we focus on the emerging cellular and molecular markers that define sex differences in diabetic cardiomyopathy based on the recent clinical and pre-clinical evidence. We also discuss miR-208a, MED13, and AT2R, which may provide new therapeutic targets with hopes to develop novel treatment paradigms to treat diabetic cardiomyopathy uniquely between men and women.
PHYSICAL PHASE TRANSITIONS OF SECONDARY ORGANIC AEROSOL PARTICLES

Aerosols – fine particulate matter in air – are not only important for human health but also for atmospheric processes such as cloud formation and scattering of the sunlight in the atmosphere. Natural forests are a significant source for particulate matter: they emit gaseous compounds that are transformed by chemical reactions into molecules that form secondary organic aerosols (SOA). The SOA particles are formed from thousands of different organic molecules in air but yet, only a minor fraction of the compounds in the SOA particles have been identified. While the impacting mechanisms of the particles’ physical and chemical properties on Earth’s radiation budget are well-known and accepted, the significances of these effects remain to be quantified. This thesis focuses on investigating the physical properties of SOA particles: specifically, the phase state and hygroscopicity. The link between the physical properties and chemical composition of the SOA particles is investigated by applying novel instrumentation in both, laboratory and field studies. Hygroscopicity of atmospheric SOA particles has been widely studied previously but there has not been a method to quantify the viscosity nor diffusion limitations of the SOA particles. So far it has been assumed that atmospheric SOA is either in solid or liquid form in the atmosphere. Our findings which are based on particle bounce measurements suggest that the water uptake of SOA resembles that of solid surfaces until the humidity is high enough for dissolution to occur. This changes the general understanding of their interaction with water vapour directly affecting their ability to scatter sunlight. In terms of interaction of the SOA particles with organic vapors, we demonstrate that at atmospherically relevant RH, the viscosity of the -pinene SOA particles, which are considered to be representative for biogenic SOA in monoterpene dominated environments, has a minor effect on the evaporation of the particles. The findings presented in this thesis increase the understanding of one of the most relevant atmospheric processes involving organic dominated aerosols. We also demonstrate that the difference in SOA hygroscopic behaviour in suband supersaturated conditions can lead to clear effects in the direct aerosol forcing – highlighting the need to correct description of the process in atmospheric models. Obtaining closure across the full RH range is therefore a critical issue for accurate climate modelling.
Is the PCM Ready for ACTORs and Multicore CPUs? - A Use Case-based Evaluation

Multicore CPUs have been common for years. However, developing parallel software is still an issue. To ease the development, software developers can use a range of frameworks and approaches, e.g., OpenMP, MPI or ACTOR. These approaches have an enormous impact on the performance of the software. Thus, Software Performance Engineering (SPE) needs to consider the impact of the parallelization approaches to deliver reliable results. In this paper, we evaluate the capability of the Palladio Component Model (PCM) based on the use case of a bank transaction example with a realization following the ACTOR approach. We observed that the accuracy of the performance predictions is unsatisfying, the modeling is challenging, and the characteristics of the ACTOR approach cannot be modeled. In future we need to consider additional attributes or properties to enrich the PCM as well to include concepts like active resources, message passing, and automatization concepts.
The links between prenatal stress and offspring development and psychopathology: disentangling environmental and inherited influences

Background Exposure to prenatal stress is associated with later adverse health and adjustment outcomes. This is generally presumed to arise through early environmentally mediated programming effects on the foetus. However, associations could arise through factors that influence mothers' characteristics and behaviour during pregnancy which are inherited by offspring. Method A ‘prenatal cross-fostering’ design where pregnant mothers are related or unrelated to their child as a result of in vitro fertilization (IVF) was used to disentangle maternally inherited and environmental influences. If links between prenatal stress and offspring outcome are environmental, association should be observed in unrelated as well as related mother–child pairs. Offspring birth weight and gestational age as well as mental health were the outcomes assessed. Results Associations between prenatal stress and offspring birth weight, gestational age and antisocial behaviour were seen in both related and unrelated mother–offspring pairs, consistent with there being environmental links. The association between prenatal stress and offspring anxiety in related and unrelated groups appeared to be due to current maternal anxiety/depression rather than prenatal stress. In contrast, the link between prenatal stress and offspring attention deficit hyperactivity disorder was only present in related mother–offspring pairs and therefore was attributable to inherited factors. Conclusions Genetically informative designs can be helpful in testing whether inherited factors contribute to the association between environmental risk factors and health outcomes. These results suggest that associations between prenatal stress and offspring outcomes could arise from inherited factors and post-natal environmental factors in addition to causal prenatal risk effects.
A Novel Pilot Decontamination Method for Massive MIMO Systems Using Social Spider Optimization Algorithm

—The promising capacity and data rate brought by massive MIMO communication systems lead to consider it as a very prominent fifth generation mobile technology. Pilot contamination is identified as the most significant impairment that limits the exploitation of its optimum capacity. It is caused by the re-use of the same, or at least non-orthogonal pilot sequences, among different cells that degrade the performance of channel estimation. In order to mitigate this issue, we propose a partial re-use of pilot sequences among users in the adjacent cells, which are close to the base station (BS) and also possesses minimum movement velocity. A novel multi factor social spider optimization algorithm (MSSOA), which imitates the cooperative behavior of social spiders, is used for selecting the eligible users for this pilot reuse in the neighboring cells. The algorithm tries to maximize the lowest SINR value for every user in the cell. The performance of the proposed method is evaluated through simulations and it is confirmed that the proposed method gives a better bit error rate (BER), sum-rate, and normalized mean square error (NMSE), over the conventional pilot assignment schemes.
Radial‐Artery or Saphenous‐Vein Grafts in Coronary‐Artery Bypass Surgery

BACKGROUND The use of radial‐artery grafts for coronary‐artery bypass grafting (CABG) may result in better postoperative outcomes than the use of saphenous‐vein grafts. However, randomized, controlled trials comparing radial‐artery grafts and saphenous‐vein grafts have been individually underpowered to detect differences in clinical outcomes. We performed a patient‐level combined analysis of randomized, controlled trials to compare radial‐artery grafts and saphenous‐vein grafts for CABG. METHODS Six trials were identified. The primary outcome was a composite of death, myocardial infarction, or repeat revascularization. The secondary outcome was graft patency on follow‐up angiography. Mixed‐effects Cox regression models were used to estimate the treatment effect on the outcomes. RESULTS A total of 1036 patients were included in the analysis (534 patients with radial‐artery grafts and 502 patients with saphenous‐vein grafts). After a mean (±SD) follow‐up time of 60±30 months, the incidence of adverse cardiac events was significantly lower in association with radial‐artery grafts than with saphenous‐vein grafts (hazard ratio, 0.67; 95% confidence interval [CI], 0.49 to 0.90; P=0.01). At follow‐up angiography (mean follow‐up, 50±30 months), the use of radial‐artery grafts was also associated with a significantly lower risk of occlusion (hazard ratio, 0.44; 95% CI, 0.28 to 0.70; P<0.001). As compared with the use of saphenous‐vein grafts, the use of radial‐artery grafts was associated with a nominally lower incidence of myocardial infarction (hazard ratio, 0.72; 95% CI, 0.53 to 0.99; P=0.04) and a lower incidence of repeat revascularization (hazard ratio, 0.50; 95% CI, 0.40 to 0.63; P<0.001) but not a lower incidence of death from any cause (hazard ratio, 0.90; 95% CI, 0.59 to 1.41; P=0.68). CONCLUSIONS As compared with the use of saphenous‐vein grafts, the use of radial‐artery grafts for CABG resulted in a lower rate of adverse cardiac events and a higher rate of patency at 5 years of follow‐up. (Funded by Weill Cornell Medicine and others.)
Scientific Discovery as Link Prediction in Influence and Citation Graphs

We introduce a machine learning approach for the identification of “white spaces” in scientific knowledge. Our approach addresses this task as link prediction over a graph that contains over 2M influence statements such as “CTCF activates FOXA1”, which were automatically extracted using open-domain machine reading. We model this prediction task using graph-based features extracted from the above influence graph, as well as from a citation graph that captures scientific communities. We evaluated the proposed approach through backtesting. Although the data is heavily unbalanced (50 times more negative examples than positives), our approach predicts which influence links will be discovered in the “near future” with a F1 score of 27 points, and a mean average precision of 68%.
Isometry: A Path-Based Distributed Data Transfer System

Data transfers in parallel systems have a significant impact on the performance of applications. Most existing systems generally support only data transfers between memories with a direct hardware connection and have limited facilities for handling transformations to the data's layout in memory. As a result, to move data between memories that are not directly connected, higher levels of the software stack must explicitly divide a multi-hop transfer into a sequence of single-hop transfers and decide how and where to perform data layout conversions if needed. This approach results in inefficiencies, as the higher levels lack enough information to plan transfers as a whole, while the lower level that does the transfer sees only the individual single-hop requests. We present Isometry, a path-based distributed data transfer system. The Isometry path planner selects an efficient path for a transfer and submits it to the Isometry runtime, which is optimized for managing and coordinating the direct data transfers. The Isometry runtime automatically pipelines sequential direct transfers within a path and can incorporate flexible scheduling policies, such as prioritizing one transfer over another. Our evaluation shows that Isometry can speed up data transfers by up to 2.2x and reduce the completion time of high priority transfers by up to 95% compared to the baseline Realm data transfer system. We evaluate Isometry on three benchmarks and show that Isometry reduces transfer time by up to 80% and overall completion time by up to 60%.
Genetic and functional analyses identify DISC1 as a novel callosal agenesis candidate gene

Agenesis of the corpus callosum (AgCC) is a congenital brain malformation that occurs in approximately 1:1,000–1:6,000 births. Several syndromes associated with AgCC have been traced to single gene mutations; however, the majority of AgCC causes remain unidentified. We investigated a mother and two children who all shared complete AgCC and a chromosomal deletion at 1q42. We fine mapped this deletion and show that it includes Disrupted‐in‐Schizophrenia 1 (DISC1), a gene implicated in schizophrenia and other psychiatric disorders. Furthermore, we report a de novo chromosomal deletion at 1q42.13 to q44, which includes DISC1, in another individual with AgCC. We resequenced DISC1 in a cohort of 144 well‐characterized AgCC individuals and identified 20 sequence changes, of which 4 are rare potentially pathogenic variants. Two of these variants were undetected in 768 control chromosomes. One of these is a splice site mutation at the 5′ boundary of exon 11 that dramatically reduces full‐length mRNA expression of DISC1, but not of shorter forms. We investigated the developmental expression of mouse DISC1 and find that it is highly expressed in the embryonic corpus callosum at a critical time for callosal formation. Taken together our results suggest a significant role for DISC1 in corpus callosum development. © 2011 Wiley‐Liss, Inc.
Assigning Tasks to Workers based on Historical Data: Online Task Assignment with Two-sided Arrivals

Efficient allocation of tasks to workers is a central problem in crowdsourcing. In this paper, we consider a special setting inspired from spatial crowdsourcing platforms where both workers and tasks arrive dynamically. Additionally, we assume all tasks are heterogeneous and each worker-task assignment brings a distinct reward. The natural challenge lies in how to incorporate the uncertainty in the arrivals from both workers and tasks into our online allocation policy such that the total expected rewards are maximized. To attack this challenge, we assume the arrival patterns of worker "types'' and task "types'' are not erratic and can be predicted from historical data. To be more specific, we consider a finite time horizon T and assume in each time-step, a single worker and task are sampled (i.e., "arrive'') from two respective distributions independently, and this sampling process repeats identically and independently for the entire T online time-steps. Our model, called Online Task Assignment with Two-Sided Arrival (OTA-TSA), is a significant generalization of the classical online task assignment where the set of tasks is assumed to be available offline. For the general version of OTA-TSA, we present an optimal non-adaptive algorithm which achieves an online competitive ratio of 0.295. For the special case of OTA-TSA where the reward is a function of just the worker type, we present an improved algorithm (which is adaptive) and achieves a competitive ratio of at least 0.343. On the hardness side, along with showing that the ratio obtained by our non-adaptive algorithm is the best possible among all non-adaptive algorithms, we further show that no (adaptive) algorithm can achieve a ratio better than $0.581 (unconditionally), even for the special case of OTA-TSA with homogenous tasks (i.e., all rewards are same). At the heart of our analysis lies a new technical tool (which is a refined notion of the birth-death process), called the two-stage birth-death process, which may be of independent interest. Finally, we perform numerical experiments on two real-world datasets obtained from crowdsourcing platforms to complement our theoretical results.
Tumor-derived thymic stromal lymphopoietin enhances lung metastasis through an alveolar macrophage-dependent mechanism

ABSTRACT It is well-recognized that macrophages, which arise from circulating precursors, enhance tumor progression in patients and animal models. However, less is known regarding the role of tissue-resident macrophages in metastasis. Moreover, the identification of tumor factors which influence macrophage function in the metastatic niche remains incomplete. Here, we investigated one such cytokine known as thymic stromal lymphopoietin (TSLP). Our rationale to focus on TSLP was based on two non-overlapping findings; first, TSLP exacerbates asthma in part by altering the lung macrophage response and, secondly, TSLP is produced by certain mouse and human tumor systems, although its role in neoplasia remains understudied. Thus, we tested the hypothesis that tumor-derived TSLP augments lung metastasis by rendering alveolar macrophages pro-tumorigenic. To test this hypothesis, we principally employed the 4T1 tumor model, which produces high levels of TSLP and metastasizes to the lung. TSLP loss-of-function significantly reduced spontaneous lung metastasis, as well as lung colonization. Moreover, similar outcomes were observed in both wild-type and immune-deficient hosts, suggesting that TSLP acted on innate immune cells such as macrophages. To test this notion, pharmacologic depletion of alveolar macrophages significantly reduced lung tumor growth of the TSLP-expressing, but not TSLP-deficient tumor population. In contrast, depleting macrophages originating from the circulation did not impact lung tumor growth. Lastly, TSLP increased the invasive and angiogenic gene expression profile of the alveolar macrophage population. Altogether, our study identified a novel TSLP-alveolar macrophage axis in lung metastasis, which offers new insights into mechanisms of metastasis and potential therapeutic targets.
ImVerde: Vertex-Diminished Random Walk for Learning Network Representation from Imbalanced Data

Imbalanced data widely exists in many high-impact applications. An example is in air traffic control, where we aim to identify the leading indicators for each type of accident cause from historical records. Among all three types of accident causes, historical records with 'personnel issues' are much more than the other two types ('aircraft issues' and 'environmental issues') combined. Thus, the resulting dataset is highly imbalanced, and can be naturally modeled as a network. Up until now, most existing work on imbalanced data analysis focused on the classification setting, and very little is devoted to learning the node representation from imbalanced networks. To address this problem, in this paper, we propose Vertex-Diminished Random Walk (VDRW) for imbalanced network analysis. The key idea is to encourage the random particle to walk within the same class by adjusting the transition probabilities each step. It resembles the existing Vertex Reinforced Random Walk in terms of the dynamic nature of the transition probabilities, as well as some convergence properties. However, it is more suitable for analyzing imbalanced networks as it leads to more separable node representations in the embedding space. Then, based on VDRW, we propose a semi-supervised network representation learning framework named ImVerde for imbalanced networks, in which context sampling uses VDRW and the label information to create node-context pairs, and balanced-batch sampling adopts a simple under-sampling method to balance these pairs in different classes. Experimental results demonstrate that ImVerde based on VDRW outperforms state-of-the-art algorithms for learning network representation from imbalanced data.
Different Timing Features in Brain Processing of Core and Moral Disgust Pictures: An Event-Related Potentials Study

Disgust, an emotion motivating withdrawal from offensive stimuli, protects us from the risk of biological pathogens and sociomoral violations. Homogeneity of its two types, namely, core and moral disgust has been under intensive debate. To examine the dynamic relationship between them, we recorded event-related potentials (ERPs) for core disgust, moral disgust and neutral pictures while participants performed a modified oddball task. ERP analysis revealed that N1 and P2 amplitudes were largest for the core disgust pictures, indicating automatic processing of the core disgust-evoking pictures. N2 amplitudes were higher for pictures evoking moral disgust relative to core disgust and neutral pictures, reflecting a violation of social norms. The core disgust pictures elicited larger P3 and late positive potential (LPP) amplitudes in comparison with the moral disgust pictures which, in turn, elicited larger P3 and LPP amplitudes when compared to the neutral pictures. Taken together, these findings indicated that core and moral disgust pictures elicited different neural activities at various stages of information processing, which provided supporting evidence for the heterogeneity of disgust.
The Future of Prosody: It's about Time

Prosody is usually defined in terms of the three distinct but interacting domains of pitch, intensity and duration patterning, or, more generally, as phonological and phonetic properties of 'suprasegmentals', speech segments which are larger than consonants and vowels. Rather than taking this approach, the concept of multiple time domains for prosody processing is taken up, and methods of time domain analysis are discussed: annotation mining with timing dispersion measures, time tree induction, oscillator models in phonology and phonetics, and finally the use of the Amplitude Envelope Modulation Spectrum (AEMS). While frequency demodulation (in the form of pitch tracking) is a central issue in prosodic analysis, in the present context it is amplitude envelope demodulation and frequency zones in the long time-domain spectra of the demodulated envelope which are focused. A generalised view is taken of oscillation as iteration in abstract prosodic models and as modulation and demodulation of a variety of rhythms in the speech signal.