Dozens Of Samples Research Articles

Ultradense Array of On-Chip Sensors for High-Throughput Electrochemical Analyses.

High-throughput sensors are valuable tools for enabling massive, fast, and accurate diagnostics. To yield this type of electrochemical device in a simple and low-cost way, high-density arrays of vertical gold thin-film microelectrode-based sensors are demonstrated, leading to the rapid and serial interrogation of dozens of samples (10 μL droplets). Based on 16 working ultramicroelectrodes (UMEs) and 3 quasi-reference electrodes (QREs), a total of 48 sensors were engineered in a 3D crossbar arrangement that devised a low number of conductive lines. By exploiting this design, a compact chip (75 × 35 mm) can enable performing 16 sequential analyses without intersensor interferences by dropping one sample per UME finger. In practice, the electrical connection to the sensors was achieved by simply switching the contact among WE adjacent fingers. Importantly, a short analysis time was ensured by interrogating the UMEs with chronoamperometry or square wave voltammetry using a low-cost and hand-held one-channel potentiostat. As a proof of concept, the detection of Staphylococcus aureus in 15 samples was performed within 14 min (20 min incubation and 225 s reading). Additionally, the implementation of peptide-tethered immunosensors in these chips allowed the screening of COVID-19 from patient serum samples with 100% accuracy. Our experiments also revealed that dispensing additional droplets on the array (in certain patterns) results in the overestimation of the faradaic current signals, a phenomenon referred to as crosstalk. To address this interference, a set of analyses was conducted to design a corrective strategy that boosted the testing capacity by allowing using all on-chip sensors to address subsequent analyses (i.e., 48 samples simultaneously dispensed on the chip). This strategy only required grounding the unused rows of QRE and can be broadly adopted to develop high-throughput UME-based sensors. In practice, we could analyze 48 droplets (with [Fe(CN)6]4-) within ∼8 min using amperometry.

Critical current stability of 2G REBCO tape for space-flight HTS leads

High reliability is an essential requirement for all spaceflight hardware. Like the Hitomi x-ray observatory, its follow-on mission XRISM uses 2G REBCO tapes as current leads for the superconducting magnets that are a key component of the Adiabatic Demagnetization Refrigerator (ADR) that cools the detector array. While the Hitomi Soft X-ray Spectrometer (SXS) worked flawlessly in orbit, during its development there were indications that the critical current of its specialized REBCO tapes could degrade over time when exposed to normal-humidity air. To demonstrate that the updates to the XRISM HTS lead assemblies had mitigated this risk, a series of tests were carried out to measure the stability of Ic of dozens of samples over a period greater than the flight assemblies were exposed to air during integration and test. The test rig allowed not only the measurement of the sample Ic , but also the localization of the voltage rise as the current approached Ic . We will discuss the trends in the critical current of the samples, as well as localization of lower Ic regions.

EasyAmplicon: An easy-to-use, open-source, reproducible, and community-based pipeline for amplicon data analysis in microbiome research.

It is difficult for beginners to learn and use amplicon analysis software because there are so many software tools to choose from, and all of them need multiple steps of operation. Herein, we provide a cross-platform, open-source, and community-supported analysis pipeline EasyAmplicon. EasyAmplicon has most of the modules needed for an amplicon analysis, including data quality control, merging of paired-end reads, dereplication, clustering or denoising, chimera detection, generation of feature tables, taxonomic diversity analysis, compositional analysis, biomarker discovery, and publication-quality visualization. EasyAmplicon includes more than 30 cross-platform modules and R packages commonly used in the field. All steps of the pipeline are integrated into RStudio, which reduces learning costs, keeps the flexibility of the analysis process, and facilitates personalized analysis. The pipeline is maintained and updated by the authors and editors of WeChat official account "Meta-genome."Our team will regularly release the latest tutorials both in Chinese and English, read the feedback from users, and provide help to them in the WeChat account and GitHub. The pipeline can be deployed on various platforms, and the installation time is less than half an hour. On an ordinary laptop, the whole analysis process for dozens of samples can be completed within 3 h. The pipeline is available at GitHub (https://github.com/YongxinLiu/EasyAmplicon) and Gitee (https://gitee.com/YongxinLiu/EasyAmplicon).

Combination of automated sample preparation and micro-flow LC–MS for high-throughput plasma proteomics

BackgroundNon-invasive detection of blood-based markers is a critical clinical need. Plasma has become the main sample type for clinical proteomics research because it is easy to obtain and contains measurable protein biomarkers that can reveal disease-related physiological and pathological changes. Many efforts have been made to improve the depth of its identification, while there is an increasing need to improve the throughput and reproducibility of plasma proteomics analysis in order to adapt to the clinical large-scale sample analysis.MethodsWe have developed and optimized a robust plasma analysis workflow that combines an automated sample preparation platform with a micro-flow LC–MS-based detection method. The stability and reproducibility of the workflow were systematically evaluated and the workflow was applied to a proof-of-concept plasma proteome study of 30 colon cancer patients from three age groups.ResultsThis workflow can analyze dozens of samples simultaneously with high reproducibility. Without protein depletion and prefractionation, more than 300 protein groups can be identified in a single analysis with micro-flow LC–MS system on a Orbitrap Exploris 240 mass spectrometer, including quantification of 35 FDA approved disease markers. The quantitative precision of the entire workflow was acceptable with median CV of 9%. The preliminary proteomic analysis of colon cancer plasma from different age groups could be well separated with identification of potential colon cancer-related biomarkers.ConclusionsThis workflow is suitable for the analysis of large-scale clinical plasma samples with its simple and time-saving operation, and the results demonstrate the feasibility of discovering significantly changed plasma proteins and distinguishing different patient groups.

DeepNCI: DFT Noncovalent Interaction Correction with Transferable Multimodal Three-Dimensional Convolutional Neural Networks.

A multimodal deep learning model, DeepNCI, is proposed for improving noncovalent interactions (NCIs) calculated via density functional theory (DFT). DeepNCI is composed of a three-dimensional convolutional neural network (3D CNN) for abstracting critical and comprehensive features from 3D electron density, and a neural network for modeling one-dimensional quantum chemical properties. By merging features from two networks, DeepNCI is able to reduce the root-mean-square error of DFT-calculated NCI from 1.19 kcal/mol to ∼0.2 kcal/mol for a NCI molecular database (>1000 molecules). The representativeness of the joint features can be visualized by t-distributed stochastic neighbor embedding (t-SNE), where they can distinguish categorized NCI systems quite well. Therefore, the fused model performs better than its component networks. In addition, the 3D CNN takes electron density as inputs that are in the same range, despite the size of molecular systems, so it can promote model applicability and transferability. To clarify the applicability of DeepNCI, an application domain (AD) has been defined with merged features using the K-nearest-neighbor method. The calculations for external test sets are shown that AD can properly monitor the reliability for a prediction. The model transferability is tested with a small database of homolysis bond dissociation energy including only dozens of samples. With NCI database pretrained parameters, the same or better performance than the reported results is achieved by transfer learning. This suggests that the DeepNCI model is transferable and it may transfer to other relative tasks, which possibly can resolve some small sampling problems. The source code of DeepNCI can be freely accessed at https://github.com/wenzelee/DeepNCI.

Emergent mesoscopic quantum vortex and Planckian dissipation in the strange metal phase

A major puzzle of condensed-matter physics is the physics behind the linear-in-temperature law of resistivity in many exotic metallic systems, including cuprates, pnictides, and heavy fermions. In this work, we propose, based on a symmetry-breaking analysis, that the strange metal phase is a novel emergent mesoscopic quantum state, beyond Landau’s quasiparticle excitation, which is composed of fluctuating vortices. The model predicts, in a straightforward way, the local magnetic field with a correlation time determined by the Coulomb potential, validated by observations of dynamic muon spin relaxation rates in both 3d cuprates and 5d iridate without fitting parameter. Furthermore, the model resolves the underlying quantum mechanism of the Planckian dissipation in terms of carrier scattering by fluctuating vortex, which predicts a scattering rate proportional to the vortex density, thus deriving both linear-in temperature and linear-in field laws, with a universal scattering coefficient validated by data of several dozens of samples for cuprates and iron pnictides. These findings offer a new phenomenology for non-Fermi liquid in strongly correlated materials.

Meta Learning for Low-Resource Molecular Optimization.

The goal of molecular optimization (MO) is to discover molecules that acquire improved pharmaceutical properties over a known starting molecule. Despite many recent successes of new approaches for MO, these methods were typically developed for particular properties with rich annotated training examples. Thus, these approaches are difficult to implement in real scenes where only a small amount of pharmaceutical data is usually available due to the expense and significant effort required for the data collection. Here, we propose a new approach, Meta-MO, for molecular optimization with a handful of training samples based on the well-recognized first-order meta-learning algorithms. By using a set of meta tasks with rich training samples, Meta-MO trains a meta model through the meta-learning optimization and adapts the learned model to new low-resource MO tasks. Meta-MO was shown to consistently outperform several pretraining and multitask training procedures, providing an average improvement in the success rate of 4.3% on a large-scale bioactivity data set with diverse target variations. We also observed that Meta-MO resulted in the best performing models across fine-tuning sets with only dozens of samples. To the best of our knowledge, this is the first study to apply meta learning to MO tasks. More importantly, such a strategy could be further extended to many low-resource scenarios in real-world drug design.

Experimental methods in chemical engineering: Barrier properties

AbstractUp to half of food spoils or goes to waste; packaging is one element that can extend shelf life and reduce the landfill burden. However, plastic packaging contributes to landfills and microparticles in the environment and, as a consequence, society has mandated industry and academics to identify sustainable materials to replace petroleum derived plastics. Oxygen, water, and CO2 permeability are among the physico‐chemical properties we measure to identify the suitability of new polymer formulations. Other application of gas permeability include petroleum engineering, carbon capture, water purification, and biological systems. Here we concentrate on the basic concepts of gas transport through polymeric film as well as the effect of structural and environmental parameters. We then describe common instrumentation and data they produce with a specific focus on reference standards. To identify the major research areas, we compiled 4271 articles indexed by Web of Science since 2017 with film and polymer as keywords. The VOSViewer software tool classified the 100 most frequent keywords from these articles into six clusters: nanofilteration, thin film composites, and reverse osmosis; nanocomposites, morphology, and polyvinyl alcohol; mechanical, barrier, and physicochemical properties; permeability, membranes, and transport properties; chitosan and antimicrobial and antioxidant properties; and, films, nanoparticles, and drug delivery. Barrier property research will continue to focus on developing biobased polymers and analyzers capable of measuring multiple compounds simultaneously with dozens of samples while minimizing time.

The Continental Intercalaire groundwaters of the Tidikelt (In-Salah region, Algeria). Hydrochemical and isotopic features

ABSTRACT The Continental Intercalaire (CI) groundwaters of the Tidikelt (In-Salah region) are characterized by a high salinity and ion concentrations higher than the maximum standards for drinkability. The total dissolved solids range from 850 to 3390 mg L–1 for conductivities ranging from 1470 to 6780 μS cm–1. Their chemical facies is dominated by Cl–, SO4 2– and Na+, respectively. Alkali ions, Cl– and SO4 2– are acquired through the dissolution of Halite (NaCl) and Gypsum (CaSO4, 2H2O). CI waters have depleted δ values for 18O and 2H, corresponding to a cold end-member. This is an indication of a very homogeneous aquifer which is similar to what was observed for the CI in the eastern sub-basin (Great Oriental Erg) and for palaeowaters elsewhere in the Middle-East and Libya. Tritium analyses show that these waters are all very weakly tritiated, which is a testimony of the non-renewed character for these waters. Both δ 13C and 14C measured on more than a dozen of samples also show that CI groundwaters are old, with ages comprised between 19,000 and 35,000 years with an average δ 13C of –10 ‰. This means that these waters are derived from old precipitation whose features were totally different from the very scarce prevailing ones.

O ENCONTRO DE PIAGET COM O TESTE DE INTELIGÊNCIA DE CYRIL BURT: UMA REVISÃO NARRATIVA

de Cyril Burt na criação do método clínico de Jean Piaget, auxiliando na composição de conhecimentos deste assunto pouco estudado. Piaget estudou em Neuchâtel, Zurique e Paris antes de integrar o Instituto Jean-Jacques Rousseau em Genebra, no qual lançou seus primeiros livros, que o levaram a lugar de destaque na psicologia mundial. O presente trabalho faz uma comparação entre os dados disponíveis na literatura para recompor essa trajetória até o ponto decisivo de mudança no trabalho experimental de Piaget, que aconteceu em Paris, onde estudou o teste de inteligência de Cyril Burt. Conhecer os elementos da criação do Método Clínico é importante para compreender os fundamentos ontológicos e epistemológicos dos dados gerados por ele e suas consequências teóricas. Do contato com esse teste, Piaget sofreu três impactos determinantes para sua vida e obra. A primeira está ligada à tradução do teste, que ressaltou questões sobre parte/todo na linguagem das crianças. O segundo é a possibilidade de Piaget de observar dezenas de amostras do raciocínio explicado das crianças por elas mesmas, o que permitiu o ingresso das técnicas de observação da zoologia em sua psicologia. Por fim, as possibilidades de modificação e adaptação do teste, sua primeira experiência original em psicologia experimental, técnica que carregaria sua vida toda na constituição de seu Método Clínico.

Integrative Taxonomy Reveals Two New Species of Stalked Barnacle (Cirripedia, Thoracica) From Seamounts of the Western Pacific With a Review of Barnacles Distributed in Seamounts Worldwide

Dozens of samples of stalked barnacles were collected from deep-sea seamounts of the tropical western Pacific by remotely operated vehicles during two expeditions in 2017 and 2018. Integrative taxonomy indicates that they represent two new species belonging to the families Scalpellidae and Poecilasmatidae, respectively. In terms of morphology, Arcoscalpellum angularum sp. nov. is distinguished from congeneric species by the angular processes on the dorsum of its soft body and the absence of a caudal appendage, whereas Glyptelasma robustum sp. nov. differs from its congeners in its robust peduncle, semicircular concaved carina, and long filamentary appendages. The validity of the two new species is supported by genetic analyses inferred from COI gene sequences and geographic distribution. To date, very few seamounts in the oceans have been investigated for scientific purposes, and little is known about the barnacles that inhabit these seamounts. A literature search reveals about 112 barnacle species recorded in seamounts mainly in the eastern and western regions of the Pacific Ocean. Existing data are insufficient for research on species speciation and diffusion; still more credible data on the distribution of barnacles in seamounts should be collected.

Orithopsid crabs from the Lower Cretaceous Paja Formation in Boyacá (Colombia), and the earliest record of parasitic isopod traces in Raninoida

The Barremian–Aptian Paja Formation, as exposed in Villa de Leyva and nearby areas (Boyacá, Colombia), is one of the most fossiliferous Cretaceous units in northern South America, yet no fossil crabs had ever been recorded from this formation in the region. Here we report the first occurrence of crabs from the upper Aptian “Arcillolitas con nódulos huecos” Member of the Paja Formation in Boyacá, belonging to two species of orithopsid crabs: Bellcarcinus aptiensisLuque, 2014 and Planocarcinus olssoni (Rathbun, 1937). The newly collected material — represented by dozens of samples in small nodules — includes individuals preserving key rostral, orbital and pereonal traits previously unknown from the type material. Furthermore, a few specimens of B. aptiensis have swellings in their branchial regions, consistent with the deformation produced by parasitic isopods infesting the crab's gill chambers. To our knowledge, this is the first record of parasitic isopod traces in the family Orithopsidae, and the oldest evidence of isopod parasitism in raninoidan crabs. The new material here reported increases the number or marine arthropods known from the fossiliferous Paja Formation in Boyacá and provides valuable information for further palaeoecological reconstructions of these shallow-marine settings in northern South America during the Early Cretaceous.

DWS-MKL: Depth-width-scaling multiple kernel learning for data classification

Deep learning technologies have been rapidly developing recently. They have shown excellent performances in many fields. However, deep learning networks have weak adaptability to small sample sizes.Usually, deep learning networks require tens of thousands of samples to avoid overfitting. In contrast, the kernel method can deal with small sample sizes. In this paper, we use only dozens of samples to train a successful classifier. Deep multiple kernel learning (DMKL) has emerged in recent years. It combines the ideas of deep learning and multiple kernel learning. Unfortunately, the DMKL network with a fixed structure cannot adapt to data of different dimensions and sizes. Therefore, in this paper, we propose a novel depth-width-scaling multiple kernel learning (DWS-MKL) algorithm. It has the ability to adjust the architecture according to the input data. We optimize the estimation of leave-one-out error by using the span bound instead of the dual objective function. Finally, we choose a large number of data sets from the UCI benchmark data sets for classification tasks. We also evaluate DWS-MKL on the MNIST data set. The experimental results show that different frameworks have different performances on the same dataset. Our DWS-MKL algorithm obtains better classification results than the state-of-the-art kernel learning algorithms, as determined by a thorough comparison. The encouraging results demonstrate that our method has the potential to improve generalization performance of the model.

INrich, Rapid and Robust Method to Enrich N-Terminal Proteome in a Highly Multiplexed Platform.

The field of terminal proteomics is limited in that it is optimized for large-scale analysis via multistep processes involving liquid chromatography. Here, we present an integrated N-terminal peptide enrichment method (iNrich) that can handle as little as 25 μg of cell lysate via a single-stage encapsulated solid-phase extraction column. iNrich enables simple, rapid, and reproducible sample processing, treatment of a wide range of protein amounts (25 μg ∼ 1 mg), multiplexed parallel sample preparation, and in-stage sample prefractionation using a mixed-anion-exchange filter. We identified ∼5000 N-terminal peptides (Nt-peptides) from only 100 μg of human cell lysate including Nt-formyl peptides. Multiplexed sample preparation facilitated quantitative and robust enrichment of N-terminome with dozens of samples simultaneously. We further developed the method to incorporate isobaric tags such as a tandem mass tag (TMT) and used it to discover novel peptides during ER stress analysis. The iNrich facilitated high-throughput N-terminomics and degradomics at a low cost using commercially available reagents and apparatus, without requiring arduous procedures.

Fine-scale barriers to connectivity across a fragmented South-East Asian landscape in six songbird species.

Habitat fragmentation is a major extinction driver. Despite dramatically increasing fragmentation across the globe, its specific impacts on population connectivity across species with differing life histories remain difficult to characterize, let alone quantify. Here, we investigate patterns of population connectivity in six songbird species from Singapore, a highly fragmented tropical rainforest island. Using massive panels of genome‐wide single nucleotide polymorphisms across dozens of samples per species, we examined population genetic diversity, inbreeding, gene flow and connectivity among species along a spectrum of ecological specificities. We found a higher resilience to habitat fragmentation in edge‐tolerant and forest‐canopy species as compared to forest‐dependent understorey insectivores. The latter exhibited levels of genetic diversity up to three times lower in Singapore than in populations from contiguous forest elsewhere. Using dense genomic and geographic sampling, we identified individual barriers such as reservoirs that effectively minimize gene flow in sensitive understorey birds, revealing that terrestrial forest species may exhibit levels of sensitivity to fragmentation far greater than previously expected. This study provides a blueprint for conservation genomics at small scales with a view to identifying preferred locations for habitat corridors, flagging candidate populations for restocking with translocated individuals and improving the design of future reserves.

Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically.

IntroductionThe research of auxiliary diagnosis has always been one of the hotspots in the world. The implementation of auxiliary diagnosis support algorithm for medical text data faces challenges with interpretability and creditability. The improvement of clinical diagnostic techniques means not only the improvement of diagnostic accuracy but also the further study of diagnostic basis. Traditional research methods for diagnostic markers often require a large amount of time and economic costs. Research objects are often dozens of samples, and it is, therefore, difficult to synthesize large amounts of data. Therefore, the comprehensiveness and reliability of traditional methods have yet to be improved. Therefore, the establishment of a model that can automatically diagnose diseases and automatically provide a diagnostic basis at the same time has a positive effect on the improvement of medical diagnostic techniques.MethodsHere, we established an auxiliary diagnostic tool based on attention deep learning algorithm to diagnostic hyperlipemia and automatically predict the corresponding diagnostic markers using hematological parameters. In this paper, we not only demonstrated the ability of the proposed model to automatically diagnose diseases using text-based medical data, such as physiological parameters, but also demonstrated its ability to forecast disease diagnostic markers. Human physiological parameters are used as input to the model, and the doctor’s diagnosis results as an output. Through the attention layer, the degree of attention of the model to different physiological parameters can be obtained, that is, the model provides a diagnostic basis.ResultsIt achieved 94% ACC, 97.48% AUC, 96% sensitivity and 92% specificity with the test dataset. All the above samples are drawn from clinical practice. Moreover, the model predicted the diagnostic markers of hyperlipidemia by the attention mechanism, and the results were fully agreeable to the golden criteria.DiscussionThe auxiliary diagnosis system proposed in this paper not only achieves the accurate and robust performance, and can be used for the preliminary diagnosis of patients, but also showing its great potential to discover new diagnostic markers. Therefore, it not only can improve the efficiency of clinical diagnosis but also shorten the research period of researching a diagnosis basis to an extent. It has a positive significance to the development of the medical diagnosis level.

A new distributed feature selection technique for classifying gene expression data

Classification of gene expression data is a pivotal research area that plays a substantial role in diagnosis and prediction of diseases. Generally, feature selection is one of the extensively used techniques in data mining approaches, especially in classification. Gene expression data are usually composed of dozens of samples characterized by thousands of genes. This increases the dimensionality coupled with the existence of irrelevant and redundant features. Accordingly, the selection of informative genes (features) becomes difficult, which badly affects the gene classification accuracy. In this paper, we consider the feature selection for classifying gene expression microarray datasets. The goal is to detect the most possibly cancer-related genes in a distributed manner, which helps in effectively classifying the samples. Initially, the available huge amount of considered features are subdivided and distributed among several processors. Then, a new filter selection method based on a fuzzy inference system is applied to each subset of the dataset. Finally, all the resulted features are ranked, then a wrapper-based selection method is applied. Experimental results showed that our proposed feature selection technique performs better than other techniques since it produces lower time latency and improves classification performance.

BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing

Despite its widespread use, RNA-seq is still too laborious and expensive to replace RT-qPCR as the default gene expression analysis method. We present a novel approach, BRB-seq, which uses early multiplexing to produce 3′ cDNA libraries for dozens of samples, requiring just 2 hours of hands-on time. BRB-seq has a comparable performance to the standard TruSeq approach while showing greater tolerance for lower RNA quality and being up to 25 times cheaper. We anticipate that BRB-seq will transform basic laboratory practice given its capacity to generate genome-wide transcriptomic data at a similar cost as profiling four genes using RT-qPCR.

PIXUL-ChIP: integrated high-throughput sample preparation and analytical platform for epigenetic studies.

Chromatin immunoprecipitation (ChIP) is the most widely used approach for identification of genome-associated proteins and their modifications. We have previously introduced a microplate-based ChIP platform, Matrix ChIP, where the entire ChIP procedure is done on the same plate without sample transfers. Compared to conventional ChIP protocols, the Matrix ChIP assay is faster and has increased throughput. However, even with microplate ChIP assays, sample preparation and chromatin fragmentation (which is required to map genomic locations) remains a major bottleneck. We have developed a novel technology (termed ‘PIXUL’) utilizing an array of ultrasound transducers for simultaneous shearing of samples in standard 96-well microplates. We integrated PIXUL with Matrix ChIP (‘PIXUL-ChIP’), that allows for fast, reproducible, low-cost and high-throughput sample preparation and ChIP analysis of 96 samples (cell culture or tissues) in one day. Further, we demonstrated that chromatin prepared using PIXUL can be used in an existing ChIP-seq workflow. Thus, the high-throughput capacity of PIXUL-ChIP provides the means to carry out ChIP-qPCR or ChIP-seq experiments involving dozens of samples. Given the complexity of epigenetic processes, the use of PIXUL-ChIP will advance our understanding of these processes in health and disease, as well as facilitate screening of epigenetic drugs.

The cervix: is last century's paradigm more fitting than today's?

Predicting delivery has always been an obstetrical conundrum. Nobody understands why or how the cervix opens when it does. A debate about cervical structure and function has been continuing for a century, changing as imaging and staining techniques evolve, and the cervix has been seen variously as the primary driver or passive bystander in the process of parturition. Today's dogma is that the cervix is largely a downstream responder to signals that cause alterations to its collagen scaffolding, while other components - e.g. smooth muscle - play relatively minor roles. This was not always true. From the mid-1800s to mid-1900s, parturition was generally thought to be dictated by the cervix, perhaps via a specific region at the junction of the cervix and uterus. In the 1920s, a Viennese obstetrician obsessed with ‘the problems of the isthmus’ proposed that the terms isthmus and internal os not be used interchangeably, because the isthmus ‘lives its own life’ as opposed to being a simple circular borderline between the uterus and cervix (Frankl Br J Obstet Gynaecol 1933;40:397; see Figure 1). He detailed his work and that of others on the morphology of the isthmus, a ‘hollow space’ in the proximal cervix, bordered by superior ‘anatomic’ and inferior ‘histologic’ ostia. He described how, by the second trimester, the superior os disappears as it coincides with the ‘so-called contractional ring of the gravid uterus’ and the inferior os becomes the internal os. Twenty years later, a British pathologist obsessed with cervical muscle set out to counteract the growing theory that collagen alone determines cervical function (Hughesdon Br J Obstet Gynaecol 1952;59:763). He explored dozens of samples from fetuses, girls and women aged from 6 days to 91 years, many of whom were pregnant, defining epithelium, glands, muscle, collagen, etc. He found that cervical muscle bundles are evident by 180 days post-conception and persist throughout life, with temporal ‘elaborations’ (e.g. during pregnancy). He suggested that if contemporaneous investigators found muscle to be sparse or random, in contrast to collagen, it was because their processing and staining techniques had distorted the anatomy. He also noted that, even if the bundles were random, the fact that they were arranged in a ring meant they could act as a ‘compressor’. Decades later, a study in labouring women demonstrated that the cervix itself contracts, suggesting an active role in parturition (Olah et al. Br J Obstet Gynaecol 1994;101:341). Recently, interest in the proximal cervix has once again been reinvigorated. With diffusion tensor magnetic resonance imaging, Nott et al. identified a circumferential ‘occlusive structure’ in this region (BJOG 2018;125:812), and with immunohistochemistry techniques and functional organ baths, Vink et al. found a dense ring of smooth muscle cells that demonstrate integrated force generation upon exposure to oxytocin (AJOG 2016;215:478.e1). New techniques like these are furthering the debate about cervical structure and function. Perhaps such studies will finally bring us closer to predicting the timing of birth. None declared. Completed disclosure of interests form available to view online as supporting information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.