Twin Pregnancy: Delivery and Complications

The aim of this research is to find out the incidence of twin pregnancies, the time and mode of delivery, as well as delivery related complications. The study is retro- and prospective and it covers a period of 20 years ranging from 1994 to 2014. In order to evaluate the incidence of twin pregnancy, the study includes a total of 73785 births with 1791 twins from them. Detailed analysis, regarding the delivery mode and complications was made for 562 twin pregnancies. The number of twin pregnancies in 1994 was 44 and in 2014 was 151. The incidence of twins increased from 1.18% at the beginning of the observed period to 3.79% at the end of the period. In 1994, 62% of women had vaginal delivery and only 38% Caesarean section. In 2014, 82% of the twins were delivered by Caesarean section and only 18% had vaginal delivery. Gestational age at the time of delivery in dichorionic diamniotic twins was 36+4 w.g., in monochorionic biamniotic twins - 35+0 w.g. and in monochorionic monoamniotic twins - 34+6 w.g. Results show that natal complications are more common in twin pregnancies, compared to singleton pregnancies. There is a significant increase in the incidence of twin pregnancy

Reasoning about Ambiguous Definite Descriptions

Natural language reasoning plays an increasingly important role in improving language models' ability to solve complex language understanding tasks. An interesting use case for reasoning is the resolution of context-dependent ambiguity. But no resources exist to evaluate how well Large Language Models can use explicit reasoning to resolve ambiguity in language. We propose to use ambiguous definite descriptions for this purpose and create and publish the first benchmark dataset consisting of such phrases. Our method includes all information required to resolve the ambiguity in the prompt, which means a model does not require anything but reasoning to do well. We find this to be a challenging task for recent LLMs. Code and data available at: https://github.com/sfschouten/exploiting-ambiguityComment: EMNLP 2023 Finding

Decentralized SGD with Asynchronous, Local and Quantized Updates

The ability to scale distributed optimization to large node counts has been one of the main enablers of recent progress in machine learning. To this end, several techniques have been explored, such as asynchronous, decentralized, or quantized communication--which significantly reduce the cost of synchronization, and the ability for nodes to perform several local model updates before communicating--which reduces the frequency of synchronization. In this paper, we show that these techniques, which have so far been considered independently, can be jointly leveraged to minimize distribution cost for training neural network models via stochastic gradient descent (SGD). We consider a setting with minimal coordination: we have a large number of nodes on a communication graph, each with a local subset of data, performing independent SGD updates onto their local models. After some number of local updates, each node chooses an interaction partner uniformly at random from its neighbors, and averages a possibly quantized version of its local model with the neighbor's model. Our first contribution is in proving that, even under such a relaxed setting, SGD can still be guaranteed to converge under standard assumptions. The proof is based on a new connection with parallel load-balancing processes, and improves existing techniques by jointly handling decentralization, asynchrony, quantization, and local updates, and by bounding their impact. On the practical side, we implement variants of our algorithm and deploy them onto distributed environments, and show that they can successfully converge and scale for large-scale image classification and translation tasks, matching or even slightly improving the accuracy of previous methods

Developing of Distributed Virtual Laboratories for Smart Sensor System Design Based on Multi-Dimensional Access Method

In the article it is considered preconditions and main principles of creation of virtual laboratories for computer-aided design, as tools for interdisciplinary researches. Virtual laboratory, what are offered, is worth to be used on the stage of the requirements specification or EFT-stage, because it gives the possibility of fast estimating of the project realization, certain characteristics and, as a result, expected benefit of its applications. Using of these technologies already increase automation level of design stages of new devices for different purposes. Proposed computer technology gives possibility to specialists from such scientific fields, as chemistry, biology, biochemistry, physics etc, to check possibility of device creating on the basis of developed sensors. It lets to reduce terms and costs of designing of computer devices and systems on the early stages of designing, for example on the stage of requirements specification or EFT-stage. An important feature of this project is using the advanced multi-dimensional access method for organizing the information base of the Virtual laboratory

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces

Drug Utilisation Patterns of Alternatives to Ranitidine-Containing Medicines in Patients Treated with Ranitidine:A Network Analysis of Data from Six European National Databases

Introduction: Ranitidine, a histamine H2-receptor antagonist (H2RA), is indicated in the management of gastric acid-related disorders. In 2020, the European Medicines Agency (EMA) recommended suspension of all ranitidine-containing medicines in the European Union (EU) due to the presence of N-nitrosodimethylamine (NDMA) impurities, which were considered to be carcinogenic. The aim of this study was to investigate the impact of regulatory intervention on use patterns of ranitidine-containing medicines and their therapeutic alternatives. Objectives: The aim was to study drug utilisation patterns of ranitidine and report discernible trends in treatment discontinuation and switching to alternative medications. Methods: This retrospective, population-based cohort study was conducted using primary care records from six European countries between 2017 and 2023. To explore drug utilisation patterns, we calculated (1) incident use of ranitidine, other H2RAs, and other alternative drugs for the treatment of gastric ulcer and/or gastric bleeding; (2) ranitidine discontinuation; and (3) switching from ranitidine to alternative drugs (H2RAs, proton-pump inhibitors [PPIs], and other medicinal products for acid-related disorders). Results: During the study period, 385,273 new ranitidine users were observed, with most users being female and aged 18–74 years. Ranitidine was the most commonly prescribed H2RA in the pre-referral period (September 2017–August 2019), with incidence rates between 0.8 and 9.0/1000 person years (PY). A steep decline to 0.3–3.8/1000 PY was observed in the referral period (September 2019–March 2020), eventually dropping to 0.0–0.4/1000 PY in the post-referral period (April 2020–March 2022). Switching from ranitidine to alternative drugs increased in the post-referral period, with the majority of patients switching to PPIs. Discontinuation of ranitidine use ranged from 270 to 380/1000 users in 2017 and decreased over time. Conclusions:Ranitidine was commonly used prior to referral, but it was subsequently discontinued and replaced primarily with PPIs.</p

Evidence of SARS-CoV-2 bacteriophage potential in human gut microbiota

Background: In previous studies we have shown that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replicates in vitro in bacterial growth medium, that the viral replication follows bacterial growth, and it is influenced by the administration of specific antibiotics. These observations are compatible with a 'bacteriophage-like' behaviour of SARS-CoV-2. Methods: We have further elaborated on these unusual findings and here we present the results of three different supplementary experiments: (1) an electron-microscope analysis of samples of bacteria obtained from a faecal sample of a subject positive to SARS-CoV-2; (2) mass spectrometric analysis of these cultures to assess the eventual de novo synthesis of SARS-CoV-2 spike protein; (3) sequencing of SARS-CoV-2 collected from plaques obtained from two different gut microbial bacteria inoculated with supernatant from faecal microbiota of an individual positive to SARS-CoV-2. Results: Immuno-labelling with Anti-SARS-CoV-2 nucleocapsid protein antibody confirmed presence of SARS-CoV-2 both outside and inside bacteria. De novo synthesis of SARS-CoV-2 spike protein was observed, as evidence that SARS-CoV-2 RNA is translated in the bacterial cultures. In addition, phage-like plaques were spotted on faecal bacteria cultures after inoculation with supernatant from faecal microbiota of an individual positive to SARS-CoV-2. Bioinformatic analyses on the reads obtained by sequencing RNA extracted from the plaques revealed nucleic acid polymorphisms, suggesting different replication environment in the two bacterial cultures. Conclusions: Based on these results we conclude that, in addition to its well-documented interactions with eukaryotic cells, SARS-CoV-2 may act as a bacteriophage when interacting with at least two bacterial species known to be present in the human microbiota. If the hypothesis proposed, i.e., that under certain conditions SARS-CoV-2 may multiply at the expense of human gut bacteria, is further substantiated, it would drastically change the model of acting and infecting of SARS-CoV-2, and most likely that of other human pathogenic viruses