Fast peak error correction algorithms for proteoform identification using top-down tandem mass spectra

MOTIVATION

Proteoform identification is an important problem in proteomics. The main task is to find a modified protein that best fits the input spectrum. To overcome the combinatorial explosion of possible proteoforms, the proteoform mass graph and spectrum mass graph are used to represent the protein database and the spectrum, respectively. The problem becomes finding an optimal alignment between the proteoform mass graph and the spectrum mass graph. Peak error correction is an important issue for computing an optimal alignment between the two input mass graphs.

RESULTS

We propose a faster algorithm for the error correction alignment of spectrum mass graph and proteoform mass graph problem and produce a program package TopMGFast. The newly designed algorithms require less space and running time so that we are able to compute global optimal alignments for the two input mass graphs in a reasonable time. For the local alignment version, experiments show that the running time of the new algorithm is reduced by 2.5 times. For the global alignment version, experiments show that the maximum mass errors between any pair of matched nodes in the alignments obtained by our method are within a small range as designed, while the alignments produced by the state-of-the-art method, TopMG, have very large maximum mass errors for many cases. The obtained alignment sizes are roughly the same for both TopMG and TopMGFast. Of course, TopMGFast needs more running time than TopMG. Therefore, our new algorithm can obtain more reliable global alignments within a reasonable time. This is the first time that global optimal error correction alignments can be obtained using real datasets.

AVAILABILITY AND IMPLEMENTATION

The source code of the algorithm is available at https://github.com/Zeirdo/TopMGFast.

Elevated risk of multimorbidity post-COVID-19 infection: protective effect of vaccination

BACKGROUND

It is unclear how the coronavirus disease 2019 (Covid-19) pandemic has affected multimorbidity incidence among those with one pre-existing chronic condition, as well as how vaccination could modify this association.

AIM

To examine the association of Covid-19 infection with multimorbidity incidence among people with one pre-existing chronic condition, including those with prior vaccination.

DESIGN

Nested case-control study.

METHODS

We conducted a territory-wide nested case-control study with incidence density sampling using Hong Kong electronic health records from public healthcare facilities and mandatory Covid-19 reports. People with one listed chronic condition (based on a list of 30) who developed multimorbidity during 1 January 2020-15 November 2022 were selected as case participants and randomly matched with up to 10 people of the same age, sex and with the same first chronic condition without having developed multimorbidity at that point. Conditional logistic regression was used to estimate adjusted odds ratios (aORs) of multimorbidity.

RESULTS

In total, 127 744 case participants were matched with 1 230 636 control participants. Adjusted analysis showed that there were 28%-increased odds of multimorbidity following Covid-19 [confidence interval (CI) 22% to 36%] but only 3% (non-significant) with prior full vaccination with BNT162b2 or CoronaVac (95% CI -2% to 7%). Similar associations were observed in men, women, older people aged 65 or more, and people aged 64 or younger.

CONCLUSIONS

We found a significantly elevated risk of multimorbidity following a Covid-19 episode among people with one pre-existing chronic condition. Full vaccination significantly reduced this risk increase.

Metallic Micro-Nano Network-Based Soft Transparent Electrodes: Materials, Processes, and Applications

Soft transparent electrodes (TEs) have received tremendous interest from academia and industry due to the rapid development of lightweight, transparent soft electronics. Metallic micro-nano networks (MMNNs) are a class of promising soft TEs that exhibit excellent optical and electrical properties, including low sheet resistance and high optical transmittance, as well as superior mechanical properties such as softness, robustness, and desirable stability. They are genuinely interesting alternatives to conventional conductive metal oxides, which are expensive to fabricate and have limited flexibility on soft surfaces. This review summarizes state-of-the-art research developments in MMNN-based soft TEs in terms of performance specifications, fabrication methods, and application areas. The review describes the implementation of MMNN-based soft TEs in optoelectronics, bioelectronics, tactile sensors, energy storage devices, and other applications. Finally, it presents a perspective on the technical difficulties and potential future possibilities for MMNN-based TE development.

Travel restrictions cannot prevent the introduction of new COVID variants

We predict the arrival time of a hypothetically new variant emerging from China for each country/region to examine the effectiveness of travel restrictions in preventing the importation of new variants of SARS-COV-2. Results show that travel restrictions are ineffective in delaying the arrival of the virus in the post-pandemic era.

Reproduction number of SARS-CoV-2 Omicron variants, China, December 2022-January 2023

China adjusted the zero-COVID strategy in late 2022, triggering an unprecedented Omicron wave. We estimated the time-varying reproduction numbers of 32 provincial-level administrative divisions from December 2022 to January 2023. We found that the pooled estimate of initial reproduction numbers is 4.74 (95% confidence interval: 4.41, 5.07).

Optimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China

BACKGROUND

Long-lasting nonpharmaceutical interventions (NPIs) suppressed the infection of COVID-19 but came at a substantial economic cost and the elevated risk of the outbreak of respiratory infectious diseases (RIDs) following the pandemic. Policymakers need data-driven evidence to guide the relaxation with adaptive NPIs that consider the risk of both COVID-19 and other RIDs outbreaks, as well as the available healthcare resources.

METHODS

Combining the COVID-19 data of the sixth wave in Hong Kong between May 31, 2022 and August 28, 2022, 6-year epidemic data of other RIDs (2014-2019), and the healthcare resources data, we constructed compartment models to predict the epidemic curves of RIDs after the COVID-19-targeted NPIs. A deep reinforcement learning (DRL) model was developed to learn the optimal adaptive NPIs strategies to mitigate the outbreak of RIDs after COVID-19-targeted NPIs are lifted with minimal health and economic cost. The performance was validated by simulations of 1000 days starting August 29, 2022. We also extended the model to Beijing context.

FINDINGS

Without any NPIs, Hong Kong experienced a major COVID-19 resurgence far exceeding the hospital bed capacity. Simulation results showed that the proposed DRL-based adaptive NPIs successfully suppressed the outbreak of COVID-19 and other RIDs to lower than capacity. DRL carefully controlled the epidemic curve to be close to the full capacity so that herd immunity can be reached in a relatively short period with minimal cost. DRL derived more stringent adaptive NPIs in Beijing.

INTERPRETATION

DRL is a feasible method to identify the optimal adaptive NPIs that lead to minimal health and economic cost by facilitating gradual herd immunity of COVID-19 and mitigating the other RIDs outbreaks without overwhelming the hospitals. The insights can be extended to other countries/regions.

Cell competition for neuron-derived trophic factor controls the turnover and lifespan of microglia

Microglia are brain-resident macrophages capable of long-term maintenance through self-renewal. Yet the mechanism governing the turnover and lifespan of microglia remains unknown. In zebrafish, microglia arise from two sources, rostral blood island (RBI) and aorta-gonad-mesonephros (AGM). The RBI-derived microglia are born early but have a short lifespan and diminish in adulthood, while the AGM-derived microglia emerge later and are capable of long-term maintenance in adulthood. Here, we show that the attenuation of RBI microglia is due to their less competitiveness for neuron-derived interleukin-34 (Il34) caused by age-dependent decline of colony-stimulating factor-1 receptor a (csf1ra). Alterations of Il34/Csf1ra levels and removal of AGM microglia revamp the proportion and lifespan of RBI microglia. The csf1ra/CSF1R expression in zebrafish AGM-derived microglia and murine adult microglia also undergo age-dependent decline, leading to the elimination of aged microglia. Our study reveals cell competition as a general mechanism controlling the turnover and lifespan of microglia.

Touch IoT enabled by wireless self-sensing and haptic-reproducing electronic skin

Tactile sensations are mainly transmitted to each other by physical touch. Wireless touch perception could be a revolution for us to interact with the world. Here, we report a wireless self-sensing and haptic-reproducing electronic skin (e-skin) to realize noncontact touch communications. A flexible self-sensing actuator was developed to provide an integrated function in both tactile sensing and haptic feedback. When this e-skin was dynamically pressed, the actuator generated an induced voltage as tactile information. Via wireless communication, another e-skin could receive this tactile data and run a synchronized haptic reproduction. Thus, touch could be wirelessly conveyed in bidirections between two users as a touch intercom. Furthermore, this e-skin could be connected with various smart devices to form a touch internet of things where one-to-one and one-to-multiple touch delivery could be realized. This wireless touch presents huge potentials in remote touch video, medical care/assistance, education, and many other applications.

Roles of acyl-CoA-binding proteins in plant reproduction

Acyl-CoA-binding proteins (ACBPs) constitute a well-conserved family of proteins in eukaryotes that are important in stress responses and development. Past studies have shown that ACBPs are involved in maintaining, transporting and protecting acyl-CoA esters during lipid biosynthesis in plants, mammals, and yeast. ACBPs show differential expression and various binding affinities for acyl-CoA esters. Hence, ACBPs can play a crucial part in maintaining lipid homeostasis. This review summarizes the functions of ACBPs during the stages of reproduction in plants and other organisms. A comprehensive understanding on the roles of ACBPs during plant reproduction may lead to opportunities in crop improvement in agriculture.

Electronic skin as wireless human-machine interfaces for robotic VR

The coronavirus pandemic has highlighted the importance of developing intelligent robotics to prevent infectious disease spread. Human-machine interfaces (HMIs) give a chance of interactions between users and robotics, which play a significant role in teleoperating robotics. Conventional HMIs are based on bulky, rigid, and expensive machines, which mainly focus on robots/machines control, but lack of adequate feedbacks to users, which limit their applications in conducting complicated tasks. Therefore, developing closed-loop HMIs with both accurate sensing and feedback functions is extremely important. Here, we present a closed-loop HMI system based on skin-integrated electronics, whose electronics compliantly interface with the whole body for wireless motion capturing and haptic feedback via Bluetooth, Wireless Fidelity (Wi-Fi), and Internet. The integration of visual and haptic VR via skin-integrated electronics together into a closed-loop HMI for robotic VR demonstrates great potentials in noncontact collection of bio samples, nursing infectious disease patients and many others.

Photocatalytic Air Purification Using Functional Polymeric Carbon Nitrides

The techniques for the production of the environment have received attention because of the increasing air pollution, which results in a negative impact on the living environment of mankind. Over the decades, burgeoning interest in polymeric carbon nitride (PCN) based photocatalysts for heterogeneous catalysis of air pollutants has been witnessed, which is improved by harvesting visible light, layered/defective structures, functional groups, suitable/adjustable band positions, and existing Lewis basic sites. PCN-based photocatalytic air purification can reduce the negative impacts of the emission of air pollutants and convert the undesirable and harmful materials into value-added or nontoxic, or low-toxic chemicals. However, based on previous reports, the systematic summary and analysis of PCN-based photocatalysts in the catalytic elimination of air pollutants have not been reported. The research progress of functional PCN-based composite materials as photocatalysts for the removal of air pollutants is reviewed here. The working mechanisms of each enhancement modification are elucidated and discussed on structures (nanostructure, molecular structue, and composite) regarding their effects on light-absorption/utilization, reactant adsorption, intermediate/product desorption, charge kinetics, and reactive oxygen species production. Perspectives related to further challenges and directions as well as design strategies of PCN-based photocatalysts in the heterogeneous catalysis of air pollutants are also provided.

Crystal structure of parallel G-quadruplex formed by the two-repeat ALS- and FTD-related GGGGCC sequence

The hexanucleotide repeat expansion, GGGGCC (G4C2), within the first intron of the C9orf72 gene is known to be the most common genetic cause of both amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). The G4C2 repeat expansions, either DNA or RNA, are able to form G-quadruplexes which induce toxicity leading to ALS/FTD. Herein, we report a novel crystal structure of d(G4C2)2 that self-associates to form an eight-layer parallel tetrameric G-quadruplex. Two d(G4C2)2 associate together as a parallel dimeric G-quadruplex which folds into a tetramer via 5'-to-5' arrangements. Each dimer consists of four G-tetrads connected by two CC propeller loops. Especially, the 3'-end cytosines protrude out and form C·C+•C·C+/ C·C•C·C+ quadruple base pair or C•C·C+ triple base pair stacking on the dimeric block. Our work sheds light on the G-quadruplexes adopted by d(G4C2) and yields the invaluable structural details for the development of small molecules to tackle neurodegenerative diseases, ALS and FTD.

Structural basis of DNA replication origin recognition by human Orc6 protein binding with DNA

The six-subunit origin recognition complex (ORC), a DNA replication initiator, defines the localization of the origins of replication in eukaryotes. The Orc6 subunit is the smallest and the least conserved among ORC subunits. It is required for DNA replication and essential for viability in all species. Orc6 in metazoans carries a structural homology with transcription factor TFIIB and can bind DNA on its own. Here, we report a solution structure of the full-length human Orc6 (HsOrc6) alone and in a complex with DNA. We further showed that human Orc6 is composed of three independent domains: N-terminal, middle and C-terminal (HsOrc6-N, HsOrc6-M and HsOrc6-C). We also identified a distinct DNA-binding domain of human Orc6, named as HsOrc6-DBD. The detailed analysis of the structure revealed novel amino acid clusters important for the interaction with DNA. Alterations of these amino acids abolish DNA-binding ability of Orc6 and result in reduced levels of DNA replication. We propose that Orc6 is a DNA-binding subunit of human/metazoan ORC and may play roles in targeting, positioning and assembling the functional ORC at the origins.