Automating Object Transformations for Dynamic Software Updating via Online Execution Synthesis

Dynamic software updating (DSU) is a technique to upgrade a running software system on the fly without stopping the system. During updating, the runtime state of the modified components of the system needs to be properly transformed into a new state, so that the modified components can still correctly interact with the rest of the system. However, the transformation is non-trivial to realize due to the gap between the low-level implementations of two versions of a program. This paper presents AOTES, a novel approach to automating object transformations for dynamic updating of Java programs. AOTES bridges the gap by abstracting the old state of an object to a history of method invocations, and re-invoking the new version of all methods in the history to get the desired new state. AOTES requires no instrumentation to record any data and thus has no overhead during normal execution. We propose and implement a novel technique that can synthesize an equivalent history of method invocations based on the current object state only. We evaluated AOTES on software updates taken from Apache Commons Collections, Tomcat, FTP Server and SSHD Server. Experimental results show that AOTES successfully handled 51 of 61 object transformations of 21 updated classes, while two state-of-the-art approaches only handled 11 and 6 of 61, respectively

Photocatalytic conversion of lignin into aromatic monomers with adsorbents of radical species from water dissociation

Photocatalytic conversion of lignin into aromatic monomers by cleaving interunit C–O bonds is a promising approach to reduce reliance on fossil fuel resource. However, industrial implementation is hindered by insufficient reaction efficiency and selectivity for target monomers. This study addresses these challenges by applying adsorbent-mediated catalysis and developing a quaternary Cd xZn 1–xIn 2S 4 photocatalyst. This catalyst utilizes radical species from photocatalytic water dissociation to promote the cleavage of aryl ether C–O bonds in lignin model compounds, achieving nearly complete conversion with over 90% selectivity for target aromatic monomers. This is a significant improvement over the 50% or less conversion and selectivity performed by water-free photocatalytic system. The study shows that H∗ and ∗OH from water dissociation can modify the catalytic microenvironment and reaction kinetics, increasing the energy barrier for side reactions and enhancing hydrogen transfer efficiency. Therefore, the target C–O cleavages can be remarkably promoted while the oxy-dehydrogenation side reactions can be impeded.</p

Genome-wide identification and analysis of the IQM gene family in soybean

IQM, a plant-specific calmodulin-binding protein, plays multiple roles in plant growth and development. Although a comprehensive analysis has been carried out on the IQM family genes in Arabidopsis and rice, the number and functions of IQM genes in other species have not been explored. In this study, we identified 15 members of the soybean (Glycine max) IQM gene family using BLASTP tools. These members were distributed on 12 soybean chromosomes and constitute six pairs caused by fragment duplication events. According to phylogeny, the 15 genes were divided into three subfamilies (I, II, and III), and members of the same subfamily had similar gene and protein structures. Yeast two-hybrid experiments revealed that the IQ motif is critical for the binding of GmIQM proteins to GmCaM, and its function is conserved in soybean, Arabidopsis, and rice. Based on real-time PCR, the soybean IQM genes were strongly induced by PEG and NaCl, suggesting their important biological functions in abiotic stress responses. Overall, this genome-wide analysis of the soybean IQM gene family lays a solid theoretical foundation for further research on the functions of GmIQM genes and could serve as a reference for the improvement and breeding of soybean stress resistance traits

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK

A Meta-analysis of Gene Expression Signatures of Blood Pressure and Hypertension

Genome-wide association studies (GWAS) have uncovered numerous genetic variants (SNPs) that are associated with blood pressure (BP). Genetic variants may lead to BP changes by acting on intermediate molecular phenotypes such as coded protein sequence or gene expression, which in turn affect BP variability. Therefore, characterizing genes whose expression is associated with BP may reveal cellular processes involved in BP regulation and uncover how transcripts mediate genetic and environmental effects on BP variability. A meta-analysis of results from six studies of global gene expression profiles of BP and hypertension in whole blood was performed in 7017 individuals who were not receiving antihypertensive drug treatment. We identified 34 genes that were differentially expressed in relation to BP (Bonferroni-corrected p&lt;0.05). Among these genes, FOS and PTGS2 have been previously reported to be involved in BP-related processes; the others are novel. The top BP signature genes in aggregate explain 5%–9% of inter-individual variance in BP. Of note, rs3184504 in SH2B3, which was also reported in GWAS to be associated with BP, was found to be a trans regulator of the expression of 6 of the transcripts we found to be associated with BP (FOS, MYADM, PP1R15A, TAGAP, S100A10, and FGBP2). Gene set enrichment analysis suggested that the BP-related global gene expression changes include genes involved in inflammatory response and apoptosis pathways. Our study provides new insights into molecular mechanisms underlying BP regulation, and suggests novel transcriptomic markers for the treatment and prevention of hypertension

Multi-tissue integrative analysis of personal epigenomes

Evaluating the impact of genetic variants on transcriptional regulation is a central goal in biological science that has been constrained by reliance on a single reference genome. To address this, we constructed phased, diploid genomes for four cadaveric donors (using long-read sequencing) and systematically charted noncoding regulatory elements and transcriptional activity across more than 25 tissues from these donors. Integrative analysis revealed over a million variants with allele-specific activity, coordinated, locus-scale allelic imbalances, and structural variants impacting proximal chromatin structure. We relate the personal genome analysis to the ENCODE encyclopedia, annotating allele- and tissue-specific elements that are strongly enriched for variants impacting expression and disease phenotypes. These experimental and statistical approaches, and the corresponding EN-TEx resource, provide a framework for personalized functional genomics

CARE: cache guided deterministic replay for concurrent Java programs

Deterministic replay tools help programmers debug concurrent pro-grams. However, for long-running programs, a replay tool may generate huge log of shared memory access dependences. In this paper, we present CARE, an application-level deterministic record and replay technique to reduce the log size. The key idea of CARE is logging read-write dependences only at per-thread value predic-tion cache misses. This strategy records only a subset of all exact read-write dependences, and reduces synchronizations protecting memory reads in the instrumented code. Realizing that such record strategy provides only value-deterministic replay, CARE also adopts variable grouping and action prioritization heuristics to synthesize sequentially consistent executions at replay in linear time. We implemented CARE in Java and experimentally evaluated it with recognized benchmarks. Results showed that CARE successfully resolved all missing read-write dependences, producing sequentially consistent replay for all benchmarks. CARE exhibited 1.7–40× (median 3.4×) smaller runtime overhead, and 1.1–309 × (median 7.0×) smaller log size against state-of-the-art technique LEAP

The Nucleation Period of Self-Stabilized Precipitation Polymerization

Self-stabilized precipitation (2SP) polymerization provides a new way for the high-value utilization of substantial olefin fractions in the petrochemical industry. However, the fundamental mechanism of particle nucleation and growth remains poorly understood. In this work, we present a general picture of 2SP polymerization, and the nucleation process is modeled by a dynamic population balance equation. The nucleation period involves primary particle aggregation and reduction in the particle number density prior to the emergence of a colloidally stable steady-state particle number density in 2SP polymerization. The model simulations exhibited reasonable agreement with the experimental conversion, particle diameter, and particle number density over the course of the reaction. This investigation not only advances the understanding of 2SP polymerization but also contributes insights into the broader realm of heterogeneous polymerization mechanisms