Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System

Quantum dot-based multiphoton fluorescent pipettes for targeted neuronal electrophysiology

Targeting visually identified neurons for electrophysiological recording is a fundamental neuroscience technique; however, its potential is hampered by poor visualization of pipette tips in deep brain tissue. We describe quantum dot-coated glass pipettes that provide strong two-photon contrast at deeper penetration depths than those achievable with current methods. We demonstrated the pipettes' utility in targeted patch-clamp recording experiments and single-cell electroporation of identified rat and mouse neurons in vitro and in vivo

F3B: A Low-Overhead Blockchain Architecture with Per-Transaction Front-Running Protection

Front-running attacks, which benefit from advanced knowledge of pending transactions, have proliferated in the blockchain space since the emergence of decentralized finance. Front-running causes devastating losses to honest participants and continues to endanger the fairness of the ecosystem. We present Flash Freezing Flash Boys (F3B), a blockchain architecture that addresses front-running attacks by using threshold cryptography. In F3B, a user generates a symmetric key to encrypt their transaction, and once the underlying consensus layer has finalized the transaction, a decentralized secret-management committee reveals this key. F3B mitigates front-running attacks because, before the consensus group finalizes it, an adversary can no longer read the content of a transaction, thus preventing the adversary from benefiting from advanced knowledge of pending transactions. Unlike other mitigation systems, F3B properly ensures that all unfinalized transactions, even with significant delays, remain private by adopting per-transaction protection. Furthermore, F3B addresses front-running at the execution layer; thus, our solution is agnostic to the underlying consensus algorithm and compatible with existing smart contracts. We evaluated F3B on Ethereum with a modified execution layer and found only a negligible (0.026%) increase in transaction latency, specifically due to running threshold decryption with a 128-member secret-management committee after a transaction is finalized; this indicates that F3B is both practical and low-cost

QuePaxa: Escaping the tyranny of timeouts in consensus

Leader-based consensus algorithms are fast and efficient under normal conditions, but lack robustness to adverse conditions due to their reliance on timeouts for liveness. We present QuePaxa, the first protocol offering state-of-the-art normal-case efficiency without depending on timeouts. QuePaxa uses a novel randomized asynchronous consensus core to tolerate adverse conditions such as denial-of-service (DoS) attacks, while a one-round-trip fast path preserves the normal-case efficiency of Multi-Paxos or Raft. By allowing simultaneous proposers without destructive interference, and using short hedging delays instead of conservative timeouts to limit redundant effort, QuePaxa permits rapid recovery after leader failure without risking costly view changes due to false timeouts. By treating leader choice and hedging delay as a multi-armed-bandit optimization, QuePaxa achieves responsiveness to prevalent conditions, and can choose the best leader even if the current one has not failed. Experiments with a prototype confirm that QuePaxa achieves normal-case LAN and WAN performance of 584k and 250k cmd/sec in throughput, respectively, comparable to Multi-Paxos. Under conditions such as DoS attacks, misconfigurations, or slow leaders that severely impact existing protocols, we find that QuePaxa remains live with median latency under 380ms in WAN experiments

Presentation Mode of Glycans Affect Recognition of Human Serum anti-Neu5Gc IgG Antibodies

Recognition of carbohydrates by antibodies can be affected by antigen composition and density. This had been investigated in a variety of controllable multivalent systems using synthetic carbohydrate antigens, yet such effects on anticarbohydrate antibodies in circulating human serum have not been fully addressed thus far. All humans develop a polyclonal and diverse response against carbohydrates containing a nonhuman sialic acid form, N-glycolylneuraminic acid (Neu5Gc). This red meat-derived monosaccharide is incorporated into a diverse collection of human glycans resulting in circulating anti-Neu5Gc antibodies in human sera. Such antibodies can cause exacerbation of diseases mediated by chronic inflammation such as cancer and atherosclerosis. We aimed to evaluate how different presentation modes of Neu5Gc-glycans can affect the detection of anti-Neu5Gc IgGs in human serum. Here, we compare serum IgG recognition of Neu5Gc-containing glycoproteins, glycopeptides, and synthetic glycans. First, Neu5Gc-positive or Neu5Gc-deficient mouse strains were used to generate glycopeptides from serum glycoproteins. Then we developed a reproducible ELISA to screen human sera against Neu5Gc-positive glycopeptides for detection of human serum anti-Neu5Gc IgGs. Finally, we evaluated ELISA screens against glycopeptides in comparison with glycoproteins, as well as against elaborated arrays displaying synthetic Neu5Gc-glycans. Our results demonstrate that the presentation mode and diversity of Neu5Gc-glycans are critical for detection of the full collection of human serum anti-Neu5Gc IgGs

Using evolutionary algorithms and machine learning to explore sequence space for the discovery of antimicrobial peptides

We present a proof-of-concept methodology for efficiently optimizing a chemical trait by using an artificial evolutionary workflow. We demonstrate this by optimizing the efficacy of antimicrobial peptides (AMPs). In particular, we used a closed-loop approach that combines a genetic algorithm, machine learning, and in vitro evaluation to improve the antimicrobial activity of peptides against Escherichia coli. Starting with a 13-mer natural AMP, we identified 44 highly potent peptides, achieving up to a ca. 160-fold increase in antimicrobial activity within just three rounds of experiments. During these experiments, the conformation of the peptides selected was changed from a random coil to an α-helical form. This strategy not only establishes the potential of in vitro molecule evolution using an algorithmic genetic system but also accelerates the discovery of antimicrobial peptides and other functional molecules within a relatively small number of experiments, allowing the exploration of broad sequence and structural space

Open Humans:A platform for participant-centered research and personal data exploration

Background Many aspects of our lives are now digitized and connected to the internet. As a result, individuals are now creating and collecting more personal data than ever before. This offers an unprecedented chance for human-participant research ranging from the social sciences to precision medicine. With this potential wealth of data comes practical problems (e.g., how to merge data streams from various sources), as well as ethical problems (e.g., how best to balance risks and benefits when enabling personal data sharing by individuals). Results To begin to address these problems in real time, we present Open Humans, a community-based platform that enables personal data collections across data streams, giving individuals more personal data access and control of sharing authorizations, and enabling academic research as well as patient-led projects. We showcase data streams that Open Humans combines (e.g., personal genetic data, wearable activity monitors, GPS location records, and continuous glucose monitor data), along with use cases of how the data facilitate various projects. Conclusions Open Humans highlights how a community-centric ecosystem can be used to aggregate personal data from various sources, as well as how these data can be used by academic and citizen scientists through practical, iterative approaches to sharing that strive to balance considerations with participant autonomy, inclusion, and privacy.publishedVersio

Characterization of immunogenic Neu5Gc in bioprosthetic heart valves

Background: The two common sialic acids (Sias) in mammals are N-acetylneuraminic acid (Neu5Ac) and its hydroxylated form N-glycolylneuraminic acid (Neu5Gc). Unlike most mammals, humans cannot synthesize Neu5Gc that is considered foreign and recognized by circulating antibodies. Thus, Neu5Gc is a potential xenogenic carbohydrate antigen in bioprosthetic heart valves (BHV) that tend to deteriorate in time within human patients. Methods: We investigated Neu5Gc expression in non-engineered animal-derived cardiac tissues and in clinically used commercial BHV, and evaluated Neu5Gc immunogenicity on BHV through recognition by human anti-Neu5Gc IgG. Results: Neu5Gc was detected by immunohistochemistry in porcine aortic valves and in porcine and bovine pericardium. Qualitative analysis of Sia linkages revealed Siaa2-3> Siaa2-6 on porcine/bovine pericardium while the opposite in porcine aortic/pulmonary valve cusps. Similarly, six commercial BHV containing either porcine aortic valve or porcine/bovine/equine pericardium revealed Siaa2-3> Siaa2-6 expression. Quantitative analysis of Sia by HPLC showed porcine/bovine pericardium express 4-fold higher Neu5Gc levels compared to the porcine aortic/pulmonary valves, with Neu5Ac at 6-fold over Neu5Gc. Likewise, Neu5Gc was expressed on commercial BHV (186.3 +/- 16.9 pmol Sia/mu g protein), with Neu5Ac at 8-fold over Neu5Gc. Affinity-purified human anti-Neu5Gc IgG showing high specificity toward Neu5Gc-glycans (with no binding to Neu5Ac-glycans) on a glycan microarray, strongly bound to all tested commercial BHV, demonstrating Neu5Gc immune recognition in cardiac xenografts. Conclusions: We conclusively demonstrated Neu5Gc expression in native cardiac tissues, as well as in six commercial BHV. These Neu5Gc xeno-antigens were recognized by human anti-Neu5Gc IgG, supporting their immunogenicity. Altogether, these findings suggest BHV-Neu5Gc/anti-Neu5Gc may play a role in valve deterioration warranting further investigation