Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer's Disease

Background Alzheimer's disease (AD) is a major public health concern, and there is an urgent need to better understand its complex biology and develop effective therapies. AD progression can be tracked in patients through validated imaging and spinal fluid biomarkers of pathology and neuronal loss. We still, however, lack a coherent quantitative model that explains how these biomarkers interact and evolve over time. Such a model could potentially help identify the major drivers of disease in individual patients and simulate response to therapy prior to entry in clinical trials. A current theory of AD biomarker progression, known as the dynamic biomarker cascade model, hypothesizes AD biomarkers evolve in a sequential but temporally overlapping manner. A computational model incorporating assumptions about the underlying biology of this theory and its variations would be useful to test and refine its accuracy with longitudinal biomarker data from clinical trials. Methods We implemented a causal model to simulate time-dependent biomarker data under the descriptive assumptions of the dynamic biomarker cascade theory. We modeled pathologic biomarkers (beta-amyloid and tau), neuronal loss biomarkers, and cognitive impairment as nonlinear first-order ordinary differential equations (ODEs) to include amyloid-dependent and nondependent neurodegenerative cascades. We tested the feasibility of the model by adjusting its parameters to simulate three specific natural history scenarios in early-onset autosomal dominant AD and late-onset AD and determine whether computed biomarker trajectories agreed with current assumptions of AD biomarker progression. We also simulated the effects of antiamyloid therapy in late-onset AD. Results The computational model of early-onset AD demonstrated the initial appearance of amyloid, followed by biomarkers of tau and neurodegeneration and the onset of cognitive decline based on cognitive reserve, as predicted by the prior literature. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid or nonamyloid-related tauopathy, depending on the magnitude of comorbid pathology, and also closely matched the biomarker cascades predicted by the prior literature. Forward simulation of antiamyloid therapy in symptomatic late-onset AD failed to demonstrate any slowing in progression of cognitive decline, consistent with prior failed clinical trials in symptomatic patients. Conclusions We have developed and computationally implemented a mathematical causal model of the dynamic biomarker cascade theory in AD. We demonstrate the feasibility of this model by simulating biomarker evolution and cognitive decline in early- and late-onset natural history scenarios, as well as in a treatment scenario targeted at core AD pathology. Models resulting from this causal approach can be further developed and refined using patient data from longitudinal biomarker studies and may in the future play a key role in personalizing approaches to treatment.
Privacy Nudging in Search: Investigating Potential Impacts

From their impacts to potential threats, privacy and misinformation are a recurring top news story. Social media platforms (e.g. Facebook) and information retrieval (IR) systems (e.g. Google), are now in the public spotlight to address these issues. Our research investigates an approach, known as Nudging, applied to the domain of IR, as a potential means to minimize impacts and threats surrounding both matters. We perform our study in the space of health search for two reasons. First, encounters with misinformation in this space have potentially grave outcomes. Second, there are many potential threats to personal privacy as a result of the data collected during a search task. Adopting methods and a corpus from previous work as the foundation, our study asked users to determine the effectiveness of a treatment for 10 medical conditions. Users performed the tasks on 4 variants of a search engine results page (SERP) and a control, with 3 of the SERP's being a Nudge (re-ranking, filtering and a visual cue) intended to reduce impacts to privacy with minimal impact to search result quality. The aim of our work is to determine the Nudge that is least impactful to good decision making while simultaneously increasing privacy protection. We find privacy impacts are significantly reduced for the re-ranking and filtering strategies, with no significant impacts on quality of decision making.
GraphOne

There is a growing need to perform a diverse set of real-time analytics (batch and stream analytics) on evolving graphs to deliver the values of big data to users. The key requirement from such applications is to have a data store to support their diverse data access efficiently, while concurrently ingesting fine-grained updates at a high velocity. Unfortunately, current graph systems, either graph databases or analytics engines, are not designed to achieve high performance for both operations; rather, they excel in one area that keeps a private data store in a specialized way to favor their operations only. To address this challenge, we have designed and developed GraphOne, a graph data store that abstracts the graph data store away from the specialized systems to solve the fundamental research problems associated with the data store design. It combines two complementary graph storage formats (edge list and adjacency list) and uses dual versioning to decouple graph computations from updates. Importantly, it presents a new data abstraction, GraphView, to enable data access at two different granularities of data ingestions (called data visibility) for concurrent execution of diverse classes of real-time graph analytics with only a small data duplication. Experimental results show that GraphOne is able to deliver 11.40× and 5.36× average speedup in ingestion rate against LLAMA and Stinger, the two state-of-the-art dynamic graph systems, respectively. Further, they achieve an average speedup of 8.75× and 4.14× against LLAMA and 12.80× and 3.18× against Stinger for BFS and PageRank analytics (batch version), respectively. GraphOne also gains over 2,000× speedup against Kickstarter, a state-of-the-art stream analytics engine in ingesting the streaming edges and performing streaming BFS when treating first half as a base snapshot and rest as streaming edge in a synthetic graph. GraphOne also achieves an ingestion rate of two to three orders of magnitude higher than graph databases. Finally, we demonstrate that it is possible to run concurrent stream analytics from the same data store.
Design Tradeoffs for SSD Reliability

Flash memory-based SSDs are popular across a wide range of data storage markets, while the underlying storage medium—flash memory—is becoming increasingly unreliable. As a result, modern SSDs employ a number of indevice reliability enhancement techniques, but none of them offers a one size fits all solution when considering the multidimensional requirements for SSDs: performance, reliability, and lifetime. In this paper, we examine the design tradeoffs of existing reliability enhancement techniques such as data re-read, intra-SSD redundancy, and data scrubbing. We observe that an uncoordinated use of these techniques adversely affects the performance of the SSD, and careful management of the techniques is necessary for a graceful performance degradation while maintaining a high reliability standard. To that end, we propose a holistic reliability management scheme that selectively employs redundancy, conditionally re-reads, judiciously selects data to scrub. We demonstrate the effectiveness of our scheme by evaluating it across a set of I/O workloads and SSDs wear states.
A Pyrrhic Victory: The PMN Response to Ocular Bacterial Infections

Some tissues of the eye are susceptible to damage due to their exposure to the outside environment and inability to regenerate. Immune privilege, although beneficial to the eye in terms of homeostasis and protection, can be harmful when breached or when an aberrant response occurs in the face of challenge. In this review, we highlight the role of the PMN (polymorphonuclear leukocyte) in different bacterial ocular infections that invade the immune privileged eye at the anterior and posterior segments: keratitis, conjunctivitis, uveitis, and endophthalmitis. Interestingly, the PMN response from the host seems to be necessary for pathogen clearance in ocular disease, but the inflammatory response can also be detrimental to vision retention. This “Pyrrhic Victory” scenario is explored in each type of ocular infection, with details on PMN recruitment and response at the site of ocular infection. In addition, we emphasize the differences in PMN responses between each ocular disease and its most common corresponding bacterial pathogen. The in vitro and animal models used to identify PMN responses, such as recruitment, phagocytosis, degranulation, and NETosis, are also outlined in each ocular infection. This detailed study of the ocular acute immune response to infection could provide novel therapeutic strategies for blinding diseases, provide more general information on ocular PMN responses, and reveal areas of bacterial ocular infection research that lack PMN response studies.
Editor’s Presentation: ‘It takes a village to reduce cardiovascular risk’

Handschin and colleagues reported real-world prospective cross sectional data about 1003 primary care hypertensive patients, aiming at evaluating the cardiovascular risk profile of hypertensive patients according to the 2013 European Society of Hypertension/ European Society of Cardiology Guidelines. It suggests that optimal blood pressure control (therapeutic targets) was achieved in only one-third of patients and that nearly 45% of hypertensive patients had a high or very high overall cardiovascular risk, corresponding to a 10-year risk for cardiovascular morbidity and mortality of greater than 20%. Moreover, a high proportion of patients (78.5%) had more than three cardiovascular risk factors, diabetes mellitus, and subclinical or end-organ damage. The findings of this study are important because they are a wake-up call for the prevention community to involve the family and the local community health worker to help high-risk populations better manage their hypertension and other risk factors. Clearly the lack of success in achieving optimal targets reflected by this study suggests that self-care is not sufficient to manage blood pressure and accompanying cardiovascular risk factors. In this regard a multidisciplinary team-based approach remains essential in order to provide comprehensive successful prevention programmes (holistic care). ‘It takes a village to reduce cardiovascular risk! ’.
Reaping the performance of fast NVM storage with uDepot

Many applications require low-latency key-value storage, a requirement that is typically satisfied using key-value stores backed by DRAM. Recently, however, storage devices built on novel NVM technologies offer unprecedented performance compared to conventional SSDs. A key-value store that could deliver the performance of these devices would offer many opportunities to accelerate applications and reduce costs. Nevertheless, existing key-value stores, built for slower SSDs or HDDs, cannot fully exploit such devices. In this paper, we present uDepot, a key-value store built bottom-up to deliver the performance of fast NVM blockbased devices. uDepot is carefully crafted to avoid inefficiencies, uses a two-level indexing structure that dynamically adjusts its DRAM footprint to match the inserted items, and employs a novel task-based IO run-time system to maximize performance, enabling applications to use fast NVM devices at their full potential. As an embedded store, uDepot’s performance nearly matches the raw performance of fast NVM devices both in terms of throughput and latency, while being scalable across multiple devices and cores. As a server, uDepot significantly outperforms state-of-the-art stores that target SSDs under the YCSB benchmark. Finally, using a Memcache service on top of uDepot we demonstrate that data services built on NVM storage devices can offer equivalent performance to their DRAM-based counterparts at a much lower cost. Indeed, using uDepot we have built a cloud Memcache service that is currently available as an experimental offering in the public cloud.
The Bimodal Logic of Commuting Difference Operators Is Decidable

In this paper, we show that the bimodal logic of two commuting diﬀerence operators [ Diﬀ , Diﬀ ] is decidable (in N2ExpTime ), despite lacking the ﬁnite model property and thereby remaining impervious to standard ﬁltration techniques. The proof of decidability involves an exponential-time reduction from the satisﬁability problem of the commutator [ Diﬀ , Diﬀ ] to that of the product logic Diﬀ × Diﬀ , via an intermediate quasimodel construction. To the best of the author’s knowledge, this marks the ﬁrst example of a (non-trivial) reduction between a commutator [ L h , L v ] and its respective product logic L h × L v , where the two logics do not coincide. By adapting this same technique, we are able to establish the ﬁnite model property for [ S5 , Diﬀ ], without recourse to standard ﬁltration techniques that break down for logics that are not Horn-axiomatizable, such as that of the diﬀerence operator.
From Bubbles to Lists: Designing Clustering for Due Diligence

In due diligence, lawyers are tasked with reviewing a large set of legal documents to identify documents and portions thereof that may be problematic for a merger or acquisition. In an effort to aid users to review more efficiently, we sought to determine how document-level clustering may help users of a due diligence system during their workflow. Following an iterative design methodology, we conducted several user studies with different versions of a document-level clustering feature consisting of three distinct phases and 27 users. We found that the interface should adapt to a user's understanding of what "similar documents" means so that trust can be established in the feature. Furthermore, the ability to negotiate with the underlying algorithm is facilitated by the establishment of trust. Finally, while the usage of this feature may be influenced by a user's role, it remains primarily a project management tool.
SLM-DB: Single-Level Key-Value Store with Persistent Memory

This paper investigates how to leverage emerging byte-addressable persistent memory (PM) to enhance the performance of key-value (KV) stores. We present a novel KV store, the Single-Level Merge DB (SLM-DB), which takes advantage of both the B+-tree index and the Log-Structured Merge Trees (LSM-tree) approach by making the best use of fast persistent memory. Our proposed SLM-DB achieves high read performance as well as high write performance with low write ampliﬁcation and near-optimal read ampli-ﬁcation. In SLM-DB, we exploit persistent memory to maintain a B+-tree index and adopt an LSM-tree approach to stage inserted KV pairs in a PM resident memory buffer. SLM-DB has a single-level organization of KV pairs on disks and performs selective compaction for the KV pairs, collecting garbage and keeping the KV pairs sorted sufﬁ-ciently for range query operations. Our extensive experimental study demonstrates that, in our default setup, compared to LevelDB, SLM-DB provides 1.07 - 1.96 and 1.56 - 2.22 times higher read and write throughput, respectively, as well as comparable range query performance.
Sequencing and Functional Annotation of the Whole Genome of Shiraia bambusicola

Shiraia bambusicola is a rare medicinal fungus found in China that causes bamboo plants to decay and die with severe infection. Hypocrellin, its main active ingredient, is widely used in several fields, such as medicine, agriculture, and food industry. In this study, to clarify the genomic components, taxonomic status, pathogenic genes, secondary metabolite synthesis pathways, and regulatory mechanisms of S. bambusicola, whole-genome sequencing, assembly, and functional annotation were performed using high-throughput sequencing and bioinformatics approaches. It was observed that S. bambusicola has 33 Mb genome size, 48.89% GC content, 333 scaffolds, 2590 contigs, 10,703 genes, 82 tRNAs, and 21 rRNAs. The total length of the repeat sequence is 2,151,640 bp. The annotation of 5945 proteins was obtained from InterProScan hits based on the Gene Ontology database. Phylogenetic analysis showed that S. bambusicola belongs to Shiraiaceae, a new family of Pleosporales. It was speculated that there are more than two species or genus in Shiraiaceae. According to the annotation, 777 secreted proteins were associated with virulence or detoxification, including 777 predicted by the PHI database, 776 by the CAZY and Fungal CytochromeP450 database, and 441 by the Proteases database. The 252 genes associated with the secondary metabolism of S. bambusicola were screened and enriched into 28 pathways, among which the terpenoids, staurosporine, aflatoxin, and folate synthesis pathways have not been reported in S. bambusicola. The T1PKS was the main gene cluster among the 28 secondary metabolite synthesis gene clusters in S. bambusicola. The analysis of the T3PKS gene cluster related to the synthesis of hypocrellin showed that there was some similarity between S. bambusicola and 10 other species of fungi; however, the similarity was very low wherein the highest similarity was 17%. The genomic information of S. bambusicola obtained in this study was valuable to understand its genetic function and pathogenicity. The genomic information revealed that several enzyme genes and secreted proteins might be related to their host interactions and pathogenicity. The annotation and analysis of its secondary metabolite synthesis genes and gene clusters will be an important reference for future studies on the biosynthesis and regulation mechanism of the secondary metabolites, contributing to the discovery of new metabolites and accelerating drug development and application.
A Recursively Enumerable Kripke Complete First-Order Logic Not Complete with Respect to a First-Order Definable Class of Frames

It is well-known that every quantified modal logic complete with respect to a firstorder definable class of Kripke frames is recursively enumerable. Numerous examples are also known of “natural” quantified modal logics complete with respect to a class of frames defined by an essentially second-order condition which are not recursively enumerable. It is not, however, known if these examples are instances of a pattern, i.e., whether every recursively enumerable, Kripke complete quantified modal logic can be characterized by a first-order definable class of frames. While the question remains open for normal logics, we show that, in the context of quasi-normal logics, this is not so, by exhibiting an example of a recursively enumerable, Kripke complete quasi-normal logic that is not complete with respect to any first-order definable class of (pointed) frames.
Pattern-based Topic Modelling for Query Expansion

One big problem with information retrieval (IR) is that the size of queries is usually short and the key-words in a query are very often ambiguous or inconsistent. Automatic query expansion is a widely recognized technique which is eﬀective to deal with this problem. However, many query expansions meth-ods require extra information such as explicit relevance feedback from users or pseudo relevance feed-back from retrieval results. In this paper, we propose an unsupervised query expansion method, called Topical Query Expansion (TQE), which does not require extra information. The proposed TQE method expands a given query based on the topical patterns which can create links among those more associated and semantic words in each topic. This model also discovers related topics that are related to the original query. Based on the expanded terms and related topics, we propose to rank the document relevance with diﬀerent ranking strategies. We conduct experiments on popularly used datasets, TREC datasets, to evaluate the proposed methods. The results demonstrate outstanding results against several state-of-the-art models.
Chinese Consumers’ Preferences for Attributes of Fresh Milk: A Best–Worst Approach

Fresh milk represents a major type of dairy product in China, while it remains at a low level in terms of consumption. Consumers’ preferences are a crucial factor determining consumers’ attitudes and behavior towards fresh milk consumption. However, little is known about what drives consumers’ preferences for fresh milk. This study intends to fill this gap based on a survey with a sample of 1248 respondents in ten cities in China. The best–worst scaling (BWS) method was employed to measure the importance of different attributes that consumers consider when selecting fresh milk. The BWS result indicates that safety certification, shelf-life, and nutrition were ranked as the most important attributes of fresh milk, whereas origin, purchasing location, and package were found to be the least preferred attributes. Furthermore, the study also explored heterogeneities by dividing all samples into a developed area and a less developed area, and a latent class model (LCM) was then applied to classify consumers based on their preferences and demographics in these two areas, respectively. In developed areas, consumers were divided into “Safety First” and “Taste Sensitive”, and Gender and Age were significant determinants of class membership. In less developed areas, consumers were classified as “Quality Sensitive” and “Brand Sensitive”, while Education, Pregnancy, and Health condition were significant determinants of class membership. The findings might be useful for the government in terms of encouraging dairy companies to implement safety management certification, and suggest that companies should design differentiated strategies in different areas.
Curcumin Ameliorates Benzo[a]pyrene-Induced DNA Damages in Stomach Tissues of Sprague-Dawley Rats

Benzo[a]pyrene (BaP) is a well-known carcinogen formed during the cooking process. Although BaP exposure has been implicated as one of the risk factors for lung cancer in animals and humans, there are only limited data on BaP-induced gastrointestinal cancer. Therefore, this study investigated the protective effects of curcumin on BaP-induced DNA damage in rat stomach tissues. BaP (20 mg/kg/day) and curcumin (50, 100, or 200 mg/kg) were administered daily to Sprague-Dawley rats by oral gavage over 30 days. Curcumin was pre-administered before BaP exposure. All rats were euthanized, and liver, kidney, and stomach tissues were removed at 24 h after the last treatment. We observed that aspartate aminotransferase (AST), alanine aminotransferase (ALT), and glucose levels were significantly reduced in rats treated with high dose co-administration of curcumin (200 mg/kg) compared to BaP alone. The expression levels of cytochrome P450 (CYP) 1A1 and CYP1B1 were significantly increased in the liver of rats treated with BaP. However, co-administration of curcumin (200 mg/kg) with BaP markedly reduced CYP1A1 expression in a dose-dependent manner. Furthermore, plasma levels of BaP-diolepoxide (BPDE) and BaP metabolites were significantly reduced by co-administration of curcumin (200 mg/kg). Additionally, co-administration of curcumin (200 mg/kg) with BaP significantly reduced the formation of BPDE-I-DNA and 8-hydroxydeoxy guanosine (8-OHdG) adducts in the liver, kidney, and stomach tissues. The inhibition of these adduct formations were more prominent in the stomach tissues than in the liver. Overall, our observations suggest that curcumin might inhibit BaP-induced gastrointestinal tumorigenesis and shows promise as a chemopreventive agent.