Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy.

To investigate circuit mechanisms underlying locomotor behavior, we used serial-section electron microscopy (EM) to acquire a synapse-resolution dataset containing the ventral nerve cord (VNC) of an adult female Drosophila melanogaster. To generate this dataset, we developed GridTape, a technology that combines automated serial-section collection with automated high-throughput transmission EM. Using this dataset, we studied neuronal networks that control leg and wing movements by reconstructing all 507 motor neurons that control the limbs. We show that a specific class of leg sensory neurons synapses directly onto motor neurons with the largest-caliber axons on both sides of the body, representing a unique pathway for fast limb control. We provide open access to the dataset and reconstructions registered to a standard atlas to permit matching of cells between EM and light microscopy data. We also provide GridTape instrumentation designs and software to make large-scale EM more accessible and affordable to the scientific community.

Single-cell multiplex chromatin and RNA interactions in ageing human brain.

Dynamically organized chromatin complexes often involve multiplex chromatin interactions and sometimes chromatin-associated RNA1-3. Chromatin complex compositions change during cellular differentiation and ageing, and are expected to be highly heterogeneous among terminally differentiated single cells4-7. Here we introduce the multinucleic acid interaction mapping in single cells (MUSIC) technique for concurrent profiling of multiplex chromatin interactions, gene expression and RNA-chromatin associations within individual nuclei. When applied to 14 human frontal cortex samples from older donors, MUSIC delineated diverse cortical cell types and states. We observed that nuclei exhibiting fewer short-range chromatin interactions were correlated with both an 'older' transcriptomic signature and Alzheimer's disease pathology. Furthermore, the cell type exhibiting chromatin contacts between cis expression quantitative trait loci and a promoter tends to be that in which these cis expression quantitative trait loci specifically affect the expression of their target gene. In addition, female cortical cells exhibit highly heterogeneous interactions between XIST non-coding RNA and chromosome X, along with diverse spatial organizations of the X chromosomes. MUSIC presents a potent tool for exploration of chromatin architecture and transcription at cellular resolution in complex tissues.

Structured cerebellar connectivity supports resilient pattern separation.

The cerebellum is thought to help detect and correct errors between intended and executed commands1,2 and is critical for social behaviours, cognition and emotion3-6. Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise7. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network's first layer8-13. However, maximizing encoding capacity reduces the resilience to noise7. To understand how neuronal circuits address this fundamental trade-off, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest that these redundant, non-random connectivity motifs increase the resilience to noise at a negligible cost to the overall encoding capacity. This work reveals how neuronal network structure can support a trade-off between encoding capacity and redundancy, unveiling principles of biological network architecture with implications for the design of artificial neural networks.

Revealing protein-protein interactions at the transcriptome scale by sequencing.

We describe PROPER-seq (protein-protein interaction sequencing) to map protein-protein interactions (PPIs) en masse. PROPER-seq first converts transcriptomes of input cells into RNA-barcoded protein libraries, in which all interacting protein pairs are captured through nucleotide barcode ligation, recorded as chimeric DNA sequences, and decoded at once by sequencing and mapping. We applied PROPER-seq to human embryonic kidney cells, T lymphocytes, and endothelial cells and identified 210,518 human PPIs (collected in the PROPER v.1.0 database). Among these, 1,365 and 2,480 PPIs are supported by published co-immunoprecipitation (coIP) and affinity purification-mass spectrometry (AP-MS) data, 17,638 PPIs are predicted by the prePPI algorithm without previous experimental validation, and 100 PPIs overlap human synthetic lethal gene pairs. In addition, four previously uncharacterized interaction partners with poly(ADP-ribose) polymerase 1 (PARP1) (a critical protein in DNA repair) known as XPO1, MATR3, IPO5, and LEO1 are validated in vivo. PROPER-seq presents a time-effective technology to map PPIs at the transcriptome scale, and PROPER v.1.0 provides a rich resource for studying PPIs.

Local shape descriptors for neuron segmentation.

We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.

S100b treatment overcomes RAGE signaling deficits in myoblasts on advanced glycation end-product cross-linked collagen and promotes myogenic differentiation.

Advanced glycation end-products (AGEs) stochastically accrue in skeletal muscle and on collagen over an individual's lifespan, stiffening the muscle and modifying the stem cell (MuSC) microenvironment while promoting proinflammatory, antiregenerative signaling via the receptor for advanced glycation end-products (RAGEs). In the present study, a novel in vitro model was developed of this phenomenon by cross linking a 3-D collagen scaffold with AGEs and investigating how myoblasts responded to such an environment. Briefly, collagen scaffolds were incubated with d-ribose (0, 25, 40, 100, or 250 mM) for 5 days at 37°C. C2C12 immortalized mouse myoblasts were grown on the scaffolds for 6 days in growth conditions for proliferation, and 12 days for differentiation and fusion. Human primary myoblasts were also used to confirm the C2C12 data. AGEs aberrantly extended the DNA production stage of C2C12s (but not in human primary myoblasts) which is known to delay differentiation in myogenesis, and this effect was prevented by RAGE inhibition. Furthermore, the differentiation and fusion of myoblasts were disrupted by AGEs, which were associated with reductions in integrins and suppression of RAGE. The addition of S100b (RAGE agonist) recovered the differentiation and fusion of myoblasts, and the addition of RAGE inhibitors (FPS-ZM1 and Azeliragon) inhibited the differentiation and fusion of myoblasts. Our results provide novel insights into the role of the AGE-RAGE axis in skeletal muscle aging, and future work is warranted on the potential application of S100b as a proregenerative factor in aged skeletal muscle.NEW & NOTEWORTHY Collagen cross-linked by advanced glycation end-products (AGEs) induced myoblast proliferation but prevented differentiation, myotube formation, and RAGE upregulation. RAGE inhibition occluded AGE-induced myoblast proliferation, while the delivery of S100b, a RAGE ligand, recovered fusion deficits.

Confounder Adjustment Using the Disease Risk Score: A Proposal for Weighting Methods.

Propensity score analysis is a common approach to addressing confounding in nonrandomized studies. Its implementation, however, requires important assumptions (e.g., positivity). The disease risk score (DRS) is an alternative confounding score that can relax some of these assumptions. Like the propensity score, the DRS summarizes multiple confounders into a single score, on which conditioning by matching allows the estimation of causal effects. However, matching relies on arbitrary choices for pruning out data (e.g., matching ratio, algorithm, and caliper width) and may be computationally demanding. Alternatively, weighting methods, common in propensity score analysis, are easy to implement and may entail fewer choices, yet none have been developed for the DRS. Here we present 2 weighting approaches: One derives directly from inverse probability weighting; the other, named target distribution weighting, relates to importance sampling. We empirically show that inverse probability weighting and target distribution weighting display performance comparable to matching techniques in terms of bias but outperform them in terms of efficiency (mean squared error) and computational speed (up to >870 times faster in an illustrative study). We illustrate implementation of the methods in 2 case studies where we investigate placebo treatments for multiple sclerosis and administration of aspirin in stroke patients.

Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set.

We develop an automatic method for synaptic partner identification in insect brains and use it to predict synaptic partners in a whole-brain electron microscopy dataset of the fruit fly. The predictions can be used to infer a connectivity graph with high accuracy, thus allowing fast identification of neural pathways. To facilitate circuit reconstruction using our results, we develop CIRCUITMAP, a user interface add-on for the circuit annotation tool CATMAID.

Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring.

Predicting the drug-target interaction is crucial for drug discovery as well as drug repurposing. Machine learning is commonly used in drug-target affinity (DTA) problem. However, the machine learning model faces the cold-start problem where the model performance drops when predicting the interaction of a novel drug or target. Previous works try to solve the cold start problem by learning the drug or target representation using unsupervised learning. While the drug or target representation can be learned in an unsupervised manner, it still lacks the interaction information, which is critical in drug-target interaction. To incorporate the interaction information into the drug and protein interaction, we proposed using transfer learning from chemical-chemical interaction (CCI) and protein-protein interaction (PPI) task to drug-target interaction task. The representation learned by CCI and PPI tasks can be transferred smoothly to the DTA task due to the similar nature of the tasks. The result on the DTA datasets shows that our proposed method has advantages compared to other pre-training methods in the DTA task.

Cryoglobulinemic Vasculitis in Disguise: Cryofibrinogenemia as Variant of Monoclonal Gammopathy of Renal Significance.

Monoclonal gammopathy with cryoactivity (ie, cryoglobulins) that causes glomerulonephritis is considered within the spectrum of monoclonal gammopathy of renal significance. Cryofibrinogenemia (cryoactivity of coagulation factors) is very rarely associated with glomerulonephritis. We present a 39-year-old woman with a relapsing nephrotic syndrome. Laboratory investigation detected cryofibrinogen; the precipitate consisted of fibrinogen and a monoclonal immunoglobulin (M-protein; IgG-λ), and the latter was also detected in serum (4g/L). Initial conventional immunosuppressive therapy resulted in temporary renal remission. In view of the M-protein, subsequent therapy consisted of bortezomib/dexamethasone and high-dose melphalan followed by autologous hematopoietic stem cell transplantation, and resulted in a very good partial hematological response and temporary renal remission. However, after hematological and renal relapse, we performed unique experiments to clarify the role of the M-protein. Mixing patient serum with donor plasma resulted in cryoactivity, composed of M-protein+fibrinogen. Patient plasma deprived of M-protein did not have cryoactivity. Therefore, cryoactivity was dependent on the M-protein. We started lenalidomide, which resulted in very good partial hematological and renal remission. Thus, cryofibrinogenemia can be the consequence of an M-protein, which we suggest should be defined as monoclonal gammopathy of renal significance.

Pros and cons of artificial intelligence implementation in diagnostic pathology.

The rapid introduction of digital pathology has greatly facilitated development of artificial intelligence (AI) models in pathology that have shown great promise in assisting morphological diagnostics and quantitation of therapeutic targets. We are now at a tipping point where companies have started to bring algorithms to the market, and questions arise whether the pathology community is ready to implement AI in routine workflow. However, concerns also arise about the use of AI in pathology. This article reviews the pros and cons of introducing AI in diagnostic pathology.

Application of immunocapture and nanoflow LC-MS/MS to overcome the barriers to the quantitation of oxytocin in plasma of women in third stage labour.

AIMS: Postpartum haemorrhage (PPH) is the leading cause of maternal mortality worldwide. To prevent PPH, the WHO recommends administration of oxytocin (OT) immediately after birth, i.e. during the third stage of labour (TSL). Previous studies demonstrate that methods to quantify OT in biological matrices, e.g. enzyme-linked immunosorbent assays (ELISA), radioimmunoassays (RIA) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) lack the specificity and/or sensitivity to accurately quantify OT in plasma from women administered OT during TSL. This is due to increased metabolic clearance of OT in late-stage pregnancy and at the time of childbirth, resulting in extremely low OT plasma concentrations. This study describes the development of an ultra-sensitive bioanalytical method that overcomes the issues previously reported and enables accurate pharmacokinetic analyses of exogenously administered OT in TSL. METHODS: A selective and sensitive assay to quantify OT in TSL plasma was developed. Immunoprecipitation (IP) was applied to selectively extract OT from the TSL plasma, thereby generating clean extracts compatible with nanoflow LC (nLC). nLC-MS/MS was chosen for its high sensitivity and ability to differentiate between OT and potentially co-captured OT-like immunoreactive products. RESULTS: The presented methodology is accurate and precise, with a good linear fit between 100-10 000 fg mL-1 OT. TSL plasma samples from a clinical phase 1 study (NCT02999100) were analysed successfully, enabling OT quantification down to 100 fg mL-1. CONCLUSIONS: The presented IP-nLC-MS/MS method succeeded in overcoming the sensitivity challenge related to the assay of OT in TSL plasma and thereby revealing the PK profiles of OT in TSL plasma clinical study samples.

Cellular senescence in kidney biopsies is associated with tubular dysfunction and predicts CKD progression in childhood cancer patients with karyomegalic interstitial nephropathy.

Karyomegalic interstitial nephropathy (KIN) has been reported as an incidental finding in patients with childhood cancer treated with ifosfamide. It is defined by the presence of tubular epithelial cells (TECs) with enlarged, irregular, and hyperchromatic nuclei. Cellular senescence has been proposed to be involved in kidney fibrosis in hereditary KIN patients. We report that KIN could be diagnosed 7-32 months after childhood cancer diagnosis in 6/6 consecutive patients biopsied for progressive chronic kidney disease (CKD) of unknown cause between 2018 and 2021. The morphometry of nuclear size distribution and markers for DNA damage (γH2AX), cell-cycle arrest (p21+, Ki67-), and nuclear lamina decay (loss of lamin B1), identified karyomegaly and senescence features in TECs. Polyploidy was assessed by chromosome fluorescence in situ hybridization (FISH). In all six patients the number of p21-positive TECs far exceeded the typically small numbers of truly karyomegalic cells, and p21-positive TECs contained less lysozyme, testifying to defective resorption, which explains the consistently observed low-molecular-weight (LMW) proteinuria. In addition, polyploidy of TEC was observed to correlate with loss of lysozyme staining. Importantly, in the five patients with the largest nuclei, the percentage of p21-positive TECs tightly correlated with estimated glomerular filtration rate loss between biopsy and last follow-up (R2 = 0.93, p < 0.01). We conclude that cellular senescence is associated with tubular dysfunction and predicts CKD progression in childhood cancer patients with KIN and appears to be a prevalent cause of otherwise unexplained CKD and LMW proteinuria in children treated with DNA-damaging and cell stress-inducing therapy including ifosfamide. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Exploring the mechanism of graphene-oxide reduction by hydrazine in a multi-epoxide environment with DFT calculations.

Reduction mechanisms between hydrazine and a multi-epoxide arrangement were investigated on a finite-sized graphene-oxide model with density functional theory. Three multistep reaction pathways were explored to examine different graphene-oxide (GO) deoxygenation scenarios. Epoxides sharing the same hexagonal ring show the typical one-by-one elimination of the oxygen functional groups through two protonation steps and the formation of cis-diazine and water. Nevertheless, the migration of one of the epoxy groups to an out-of-ring position has to precede the reduction. When a hexagonal ring separates two epoxy groups, forming a partially reduced surface with two hydroxyl groups is energetically favoured. This reduction product is so stable that it may remain on the surface after the termination of the reduction process. If further deoxygenation occurs, it can lead to surface fragmentation due to the ring opening of the remaining epoxides. The formation of nitrogen-containing functional groups at the edge of the graphene-oxide flake is also considered, and their surface presence is evaluated based on their thermodynamic stabilities.

Improving biomedical Named Entity Recognition with additional external contexts.

OBJECTIVE: Biomedical Named Entity Recognition (bio NER) is the task of recognizing named entities in biomedical texts. This paper introduces a new model that addresses bio NER by considering additional external contexts. Different from prior methods that mainly use original input sequences for sequence labeling, the model takes into account additional contexts to enhance the representation of entities in the original sequences, since additional contexts can provide enhanced information for the concept explanation of biomedical entities. METHODS: To exploit an additional context, given an original input sequence, the model first retrieves the relevant sentences from PubMed and then ranks the retrieved sentences to form the contexts. It next combines the context with the original input sequence to form a new enhanced sequence. The original and new enhanced sequences are fed into PubMedBERT for learning feature representation. To obtain more fine-grained features, the model stacks a BiLSTM layer on top of PubMedBERT. The final named entity label prediction is done by using a CRF layer. The model is jointly trained in an end-to-end manner to take advantage of the additional context for NER of the original sequence. RESULTS: Experimental results on six biomedical datasets show that the proposed model achieves promising performance compared to strong baselines and confirms the contribution of additional contexts for bio NER. CONCLUSION: The promising results confirm three important points. First, the additional context from PubMed helps to improve the quality of the recognition of biomedical entities. Second, PubMed is more appropriate than the Google search engine for providing relevant information of bio NER. Finally, more relevant sentences from the context are more beneficial than irrelevant ones to provide enhanced information for the original input sequences. The model is flexible to integrate any additional context types for the NER task.

A review of non-thermal plasma -catalysis: The mutual influence and sources of synergetic effect for boosting volatile organic compounds removal.

This review is aimed at researchers in air pollution control seeking to understand the latest advancements in volatile organic compound (VOC) removal. Implementing of plasma-catalysis technology for the removal of volatile organic compounds (VOCs) led to a significant boost in terms of degradation yield and mineralization rate with low by-product formation. The plasma-catalysis combination can be used in two distinct ways: (I) the catalyst is positioned downstream of the plasma discharge, known as the "post plasma catalysis configuration" (PPC), and (II) the catalyst is located in the plasma zone and exposed directly to the discharge, called "in plasma catalysis configuration" (IPC). Coupling these two technologies, especially for VOCs elimination has attracted the interest of many researchers in recent years. The term "synergy" is widely reported in their works and associated with the positive effect of the plasma catalysis combination. This review paper investigates the state of the art of newly published papers about catalysis, photocatalysis, non-thermal plasma, and their combination for VOC removal application. The focus is on understanding different synergy sources operating mutually between plasma and catalysis discussed and classified into two main parts: the effect of the plasma discharge on the catalyst and the effect of the catalyst on plasma discharge. This approach has the potential for application in air purification systems for industrial processes or indoor environments.

Review on personal protective equipment: Emerging concerns in micro(nano)plastic pollution and strategies for addressing environmental challenges.

The COVID-19 pandemic was caused by the SARS-CoV-2 virus, marking one of the most catastrophic global health crises of the 21st century. Throughout this period, widespread use and improper disposal of personal protective equipment (PPE) emerged as a pressing environmental issue, significantly impacting various life forms. During the COVID-19 pandemic, there was a high rate of PEP disposal. An alarming 1.6 × 106 tons of plastic waste each day has been generated since the onset of the outbreak, predominantly from the inadequate disposal of PPE. The mismanagement and subsequent degradation of discarded PPE significantly contribute to increased non-biodegradable micro(nano)plastic (MNP) waste. This pollution has had profound adverse effects on terrestrial, marine, and aquatic ecosystems, which have been extensively of concern recently. Accumulated MNPs within aquatic organisms could serve as a potential route for human exposure when consuming seafood. This review presents a novel aspect concerning the pollution caused by MNPs, particularly remarking on their role during the pandemic and their detrimental effects on human health. These microplastic particles, through the process of fragmentation, transform into nanoparticles, persisting in the environment and posing potential hazards. The prevalence of MNP from PPE, notably masks, raises concerns about their plausible health risks, warranting global attention and comprehensive exploration. Conducting a comprehensive evaluation of the long-term effects of these processes and implementing effective management strategies is essential.

Comparative study of two indoor microbial volatile pollutants, 2-Methyl-1-butanol and 3-Methyl-1-butanol, on growth and antioxidant system of rice (Oryza sativa) seedlings.

2-Methyl-1-butanol (2MB) and 3-Methyl-1-butanol (3MB) are microbial volatile organic compounds (VOCs) and found in indoor air. Here, we applied rice as a bioindicator to investigate the effects of these indoor microbial volatile pollutants. A remarkable decrease in germination percentage, shoot and root elongation, as well as lateral root numbers were observed in 3MB. Furthermore, ROS production increased by 2MB and 3MB, suggesting that pentanol isomers could induce cytotoxicity in rice seedlings. The enhancement of peroxidase (POD) and catalase (CAT) activity provided evidence that pentanol isomers activated the enzymatic antioxidant scavenging systems, with a more significant effect observed in 3MB. Furthermore, 3MB induced higher activity levels of glutathione (GSH), oxidized glutathione (GSSG), and the GSH/GSSG ratio in rice compared to the levels induced by 2MB. Additionally, qRT-PCR analysis showed more up-regulation in the expression of glutaredoxins (GRXs), peroxiredoxins (PRXs), thioredoxins (TRXs), and glutathione S-transferases (GSTUs) genes in 3MB. Taking the impacts of pentanol isomers together, the present study suggests that 3MB exhibits more cytotoxic than 2MB, as such has critical effects on germination and the early seedling stage of rice. Our results provide molecular insights into how isomeric indoor microbial volatile pollutants affect plant growth through airborne signals.