Natural Samples Research Articles

Fiberoptic Laser-Induced Breakdown Spectroscopy System for in situ Measurement in a Hazardous Environment

We report the development of a novel fiberoptic laser-induced breakdown spectroscopy (FOLIBS) system for material characterization applications. The system utilizes a 1000-µm optical fiber to transport the laser beam to remote locations for elemental analysis via laser-induced breakdown spectroscopy. Air-fiber interface damage, internal damage, and air breakdown issues during laser-fiber coupling are identified and corresponding solutions are presented. For evaluating the physical properties of the plasma generated by the FOLIBS system, spectroscopic characterization is carried out using a titanium sample. The spectral features collected from a natural uranium sample are also presented. Hence, this rapid, remote, and flexible measurement technique is promising for in situ measurements in hazardous environments in nuclear energy, nuclear safeguards, and nonproliferation applications.

Sample preservation methods for nitrous oxide concentration and isotope ratio measurements in aquatic environments

AbstractThe nitrogen (N) and oxygen (O) stable isotope analysis of dissolved nitrous oxide (N2O) can provide important constraints on the sources and cycling of N2O in aquatic environments. The isotopic composition of aqueous N2O, both in field (natural abundance) or experimental (15N‐labeling) samples, however, may be altered by abiotic reactions involving nitrite () or hydroxylamine (NH2OH) and microbial activity during sample storage, if samples are not adequately preserved. Here we tested five different preservatives, mercuric chloride (HgCl2), copper sulfate (CuSO4), zinc chloride (ZnCl2), hydrochloric acid (HCl) mixed with sulfamic acid (SFA), and sodium hydroxide (NaOH), for fixing natural water samples from an estuary and a lake with different concentrations over a range of different storage times for N2O analyses. ZnCl2 and CuSO4 decreased the pH, and led to abiotic N2O production from , shifting the N2O isotopic composition significantly. Removal of with a mixture of SFA and HCl did not always prevent the alteration of the original N and O isotope composition of N2O, confirming the requirement for complete removal, and underscoring the biasing effects of at very low pH, even at trace levels. At low background, HgCl2 preservation led to robust and accurate results. However, in light of its toxicity and bioaccumulation potential in food webs, this fixative should be avoided. The addition of NaOH reduced abiotic side reactions involving , and yielded the most reliable and reproducible results for N2O concentration and isotope ratio measurements across tested settings and storage times.

Population dynamics of the Lyme disease bacterium, Borrelia burgdorferi, during rapid range expansion in New York State.

Recent changes in climate and human land-use have resulted in alterations of the geographic range of many species, including human pathogens. Geographic range expansion and population growth of human pathogens increase human disease risk. Relatively little empirical work has investigated the impact of range changes on within-population variability, a contributor to both colonization success and adaptive potential, during the precise time in which populations are colonized. This is likely due to the difficulties of collecting appropriate natural samples during the dynamic phase of migration and colonization. We systematically collected blacklegged ticks (Ixodes scapularis) across New York State (NY), USA, between 2006 and 2019, a time period coinciding with a rapid range expansion of ticks and their associated pathogens including Borrelia burgdorferi, the etiological agent of Lyme disease. These samples provide a unique opportunity to investigate the population dynamics of human pathogens as they expand into novel territory. We observed that founder effects were short-lived, as gene flow from long-established populations brought almost all B. burgdorferi lineages to newly colonized populations within just a few years of colonization. By 7 years post-colonization, B. burgdorferi lineage frequency distributions were indistinguishable from long-established sites, indicating that local B. burgdorferi populations experience similar selective pressures despite geographic separation. The B. burgdorferi lineage dynamics elucidate the processes underlying the range expansion and demonstrate that migration into, and selection within, newly colonized sites operate on different time scales.

Cryogenic and Dissolution DNP NMR on γ-Irradiated Organic Molecules.

Nuclear magnetic resonance (NMR) plays a central role in the elucidation of chemical structures but is often limited by low sensitivity. Dissolution dynamic nuclear polarization (dDNP) emerges as a transformative methodology for both solution-state NMR and metabolic NMR imaging, which could overcome this limitation. Typically, dDNP relies on combining a stable radical with the analyte within a uniform glass under cryogenic conditions. The electron polarization is then transferred through microwave irradiation to the nuclei. The present study explores the use of radicals introduced via γ-irradiation, as bearers of the electron spins that will enhance 1H or 13C nuclides. 1H solid-state NMR spectra of γ-irradiated powders at 1-5 K revealed, upon microwave irradiation, signal enhancements that, in general, were higher than those achieved through conventional glass-based DNP. Transfer of these samples to a solution-state NMR spectrometer via a rapid dissolution driven by a superheated water provided significant enhancements of solution-state 1H NMR signals. Enhancements of 13C signals in the γ-irradiated solids were more modest, as a combined consequence of a low radical concentration and of the dilute concentration of 13C in the natural abundant samples examined. Nevertheless, ca. 700-800-fold enhancements in 13C solution NMR spectra of certain sites recorded at 11.7 T could still be achieved. A total disappearance of the radicals upon performing a dDNP-like aqueous dissolution and a high stability of the samples were found. Overall, the study showcases the advantages and limitations of γ-irradiated radicals as candidates for advancing spectroscopic dDNP-enhanced NMR.

The δ18O contrast between bulk goethite and water extracted from it does not reliably reveal goethite growth temperature

Goethite (α-FeOOH) contains two structurally-distinct oxygen sites that can potentially be independently interrogated for isotopic composition by analyzing bulk goethite δ18O (δ18Obulk) and the δ18O of water extracted from it (δ18Oextracted). The isotopic contrast between these two components may form the basis of a paleothermometer (Miller et al., 2020). We sought to better understand the behavior of goethite undergoing vacuum dehydroxylation, the key step in this potential paleothermometric technique. A refined method for making these measurements applied to a suite of natural and synthetic goethites revealed that δ18Oextracted is extremely sensitive to extraction temperature and to details of the vacuum environment in which the goethite is dehydroxylated. This sensitivity likely arises from the fundamental mechanism by which evolved water is removed from goethite in vacuum, via nm-wide pores penetrating nascent hematite surrounding dehydroxylating goethite. These pores may provide favorable conditions for back reaction that scrambles the oxygen isotopic composition, and also for kinetic mass fractionation. The character of these pores is known to vary with goethite composition, crystallinity, and extraction temperature. We identified additional sample-specific factors that affect δ18Oextracted. For example, some synthetic goethites undergo dehydroxylation at surprisingly low temperature (<90 °C) in vacuum, under conditions usually used to remove nonstoichiometric water. In addition, some of the synthetic goethites have spectroscopic characteristics suggesting a range of OH bonding environments that complicate the idea that goethite has just two structurally (and potentially isotopically) distinct oxygen sites. Instead, in fine grained or poorly crystalline goethite there may be a continuum of physisorbed water, chemisorbed water, and stoichiometric water that is simultaneously released during vacuum extraction. After investigating a wide range of extraction conditions, we determined that rapid heating to 255 ± 5 °C is optimal for achieving high degrees of water extraction with the least apparent back reaction. When applied to synthetic goethites grown between 5 °C and 70 °C, we found no compelling correlation between synthesis temperature and the contrast between δ18Obulk and δ18Oextracted. Analyses of two natural samples, known to have grown at ∼5 °C and ∼ 25 °C, revealed no significant difference in measured isotopic contrast. Together these observations suggest that goethite has no resolvable formation temperature information encoded in the bulk and extracted water oxygen isotope ratios as interrogated by the method we explored. If there is temperature-dependent isotopic contrast between the two oxygen sites in goethite, the use of the dehydroxylation method appears unable to reliably reveal it.

Applications of zinc stable isotope analysis in environmental and biological systems: a review

Zinc (Zn) is an essential trace element that is involved in both biotic and abiotic processes in Earth's surface environments. Over the last 20 years, advances in mass spectrometry instrumentation have enabled unprecedented high-precision Zn isotope abundance ratio determinations in natural and anthropogenic samples. Supported by a solid theoretical background inherited from the traditional non-metal stable isotopes (C, N, H, O, S), the understanding of the controlling factors of Zn isotope fractionation has rapidly evolved. In this article, we review the main applications and findings of Zn isotopes in a variety of scientific domains, including metal contamination, magmatic differentiation, plant uptake, weathering, global climate change, dietary and trophic chains, and biomedicine. The analytical aspects of Zn isotope determination in solid and water samples and the principles and mechanisms of Zn isotope fractionation are also reviewed. This work aims to provide a general yet in-depth panorama of Zn isotope chemistry and to demonstrate the versatility and potential of this isotope system for studying the biological, geological and chemical cycling of Zn.

Impacts of CO2-CH4 Mixed Gas on Property of Formation Oil from the Bohai Oilfield

Mechanism analysis and technical scheme optimization on CO2 displacement and CO2 storage are based on the high-pressure physical properties of CO2-added formation oil. Oil and natural gas samples from the BZ25-1 block in the Bohai oilfield were used to conduct high-pressure physical property experiments to explore the impacts of CO2-CH4 mixed gas on the properties of formation oil. After injecting different amounts of mixed gas, the saturated pressure was measured by constant mass expansion test, the viscosity was measured by falling ball method, the expansion coefficient was measured by gas injection expansion test, and the gas–oil ratio and volume coefficient were obtained by single degassing test. The results show that with gas injection, the saturation pressure and dissolved gas–oil ratio of formation oil increase, the volume coefficient and expansion factor go up, while the oil viscosity reduces. With the increase in gas addition, the properties of formation oil continue to improve, but the increase in improvement becomes flat. With the increase in pressure, the amount of dissolved gas in the formation oil will also increase. High-purity CO2 is more helpful to change the properties of formation oil, while the gas mixed with CH4 is more beneficial to elevate the formation energy. For the BZ 25-1 block, the gas injection amount of about 80 mol% is appropriate and the CO2 purity of 60% can well balance the oil properties improvement and the formation pressure elevation.

Percutaneous Internal Ring Suturing for Inguinal Hernia Repair in Children: Retrospective Cohort of 714 Patients with Minimum 3-Year Follow-Up.

Background and Objectives: Despite numerous described techniques, laparoscopy has yet to replace open surgery as the gold standard for inguinal hernia (IH) repair in children. This may be due to many variables, including the lack of long-term follow-up and concern for increased recurrence. In this study, we present our long-term follow-up data on children undergoing percutaneous internal ring suturing (PIRS) for IH repair. Materials and Methods: This retrospective cohort study included children who underwent PIRS for IH between May 2013 and May 2021 at three tertiary care institutions, with at least three years of follow-up. Age at surgery, side of IH, presence of contralateral patent processus vaginalis, surgical and anesthesia time, and complications were noted. Parents were contacted to enquire about long-term complications, such as recurrence. Results: Long-term follow-up (average 6.9 ± 2.3 years) was available for 714 patients. For unilateral and bilateral procedures, the average surgical time was 13.6 ± 5.4 and 19.9 ± 3.0, and the average anesthesia time was 27.7 ± 12.9 and 33.9 ± 14.1 min, respectively. Complications were seen in 0.84% of patients and 1.2% of procedures, and recurrence was observed in 0.98% of patients and 0.78% of procedures. Conclusions: Our study, with a nearly 7-year follow-up, provides substantial evidence that PIRS is a safe and effective technique for IH repair in children, with low recurrence and complication rates. Despite the study's retrospective nature and limited sample size, it contributes valuable data supporting the use of PIRS in pediatric IH repair.

Visible-Light Spectroscopy and Rock Magnetic Analyses of Iron Oxides in Mixed-Mineral Assemblages

Iron oxide assemblages are central to many pursuits, ranging from Mars exploration to environmental remediation. Oxides and oxyhydroxides of iron both carry the special properties of color and magnetism. In this paper, we use visible-light spectroscopy and rock magnetic data collected at varying temperatures (~77–973 K) to analyze the concentrations and identities of iron oxides found in natural hematite-dominated samples that were obtained from a scientific drill core of Late Triassic red beds in the American Southwest. Our results suggest that hematite colorization of Earth materials varies from red to blue/purple as crystal size increases. Second-derivative analysis of the collected visible-light spectra allows this variation to be measured through the characteristic wavelength band position. Magnetic coercivity data indicate “hardness” differences that also may suggest smaller grain sizes are associated with redder colors. Yellowish maghemite and goethite have overlapping characteristic wavelength band positions that make it challenging to distinguish their contributions to mixed assemblages from visible-light data alone. Remanent magnetizations acquired at ~77 K and room temperature suggest the presence of hematite and a low-coercivity phase that may be maghemite and/or oxidized magnetite. However, we interpret this phase as maghemite in order to explain the changes in iron oxide concentrations indicated by visible-light intensities near ~425 nm and because the thermal demagnetization data suggest that goethite is absent from the samples. Future research that increases the resolution of hematite, maghemite, and goethite detection in experimental and natural samples will provide opportunities to refine the study of past climates and constrain soil iron availability under future changes in global moisture and temperature. Multimethod datasets improve understanding of environmental conditions that cause iron oxides assemblages to shift in phase dominance, grain size, and crystallinity.

Helium Geochemical Characteristics and Favorable Zones in the Tarim Basin: Implications for Helium Exploration

Helium is an irreplaceable ore resource for many applications, such as nuclear magnetic resonance, aviation, semiconductors, and nuclear energy. Extracting helium in a free state from natural gas is currently the only economical approach at the industrial level. In this study, we compiled geochemical data of 719 natural gas samples from 36 oil and gas fields in the Tarim basin that include experimental results and previously reported data. Helium is of primarily crustal origin in the Tarim Basin according to helium isotope characteristics (not exceeding 0.1 Ra), except in the Ake gas field that has not more than 7% of mantle helium. Helium concentrations in diverse tectonic units vary considerably. Oil-type gas, on the whole, has a higher helium concentration relative to coal-type gas. Abundant helium flux, a favorable fault system between the source-reservoir system, no strong charging of gaseous hydrocarbons, and the good sealing capacity are important factors that control the formation of helium-rich gas fields. Considering both the helium concentration and natural gas reserves, helium-rich gas fields located in the Southwest Depression and Tabei Uplift can be regarded as the major favorable zones of further deployment for helium extraction.

Prediction of hydroxyl radical exposure during ozonation using different machine learning methods with ozone decay kinetic parameters

The abatement of micropollutants by ozonation can be accurately calculated by measuring the exposures of molecular ozone (O3) and hydroxyl radical (•OH) (i.e., ∫[O3]dt and ∫[•OH]dt). In the actual ozonation process, ∫[O3]dt values can be calculated by monitoring the O3 decay during the process. However, calculating ∫[•OH]dt is challenging in the field, which necessitates developing models to predict ∫[•OH]dt from measurable parameters. This study demonstrates the development of machine learning models to predict ∫[•OH]dt (the output variable) from five basic input variables (pH, dissolved organic carbon concentration, alkalinity, temperature, and O3 dose) and two optional ones (∫[O3]dt and instantaneous ozone demand, IOD). To develop the models, four different machine learning methods (random forest, support vector regression, artificial neural network, and Gaussian process regression) were employed using the input and output variables measured (or determined) in 130 different natural water samples. The results indicated that incorporating ∫[O3]dt as an input variable significantly improved the accuracy of prediction models, increasing overall R2 by 0.01−0.09, depending on the machine learning method. This suggests that ∫[O3]dt plays a crucial role as a key variable reflecting the •OH-yielding characteristics of dissolved organic matter. Conversely, IOD had a minimal impact on the accuracy of the prediction models. Generally, machine-learning-based prediction models outperformed those based on the response surface methodology developed as a control. Notably, models utilizing the Gaussian process regression algorithm demonstrated the highest coefficients of determination (overall R2 = 0.91−0.95) among the prediction models.

Tungsten oxide‐iodide/poly‐2‐aminobenzenethiol nanocomposite with iodine intercalation as a promising electrode for potentiometric sensing of Pb2+ ions in water

AbstractTungsten oxide‐iodide/poly‐2‐aminobenzenethiol nanocomposite (WO2I2/P2ABT) is created through the introduction of iodine into polymer chains, where iodine serves as an oxidizing agent during the synthesis process. With a highly porous structure, the sensing capabilities of WO2I2/P2ABT for detecting Pb2+ ions are successfully demonstrated, revealing a Nernstian slope of 26.2 mV/decade. This detection is accomplished through a simple potentiometric technique, employing a simple two‐electrode cell setup. To further validate its performance, cyclic voltammetry is conducted using a three‐electrode system, revealing a remarkable sensitivity of 7.2 × 10−5 A M−1 for Pb2+ ions. The nanocomposite sensor's selectivity is rigorously examined by subjecting it to testing in the presence of 0.01 M interfering ions. The results unequivocally demonstrate that the sensor remains unresponsive to these interfering ions, underscoring its remarkable selectivity for Pb2+ ions. Moreover, the sensor's behavior is evaluated under real‐world conditions using natural samples, where, no indications of interference from other ions are observed. This is estimated by the absence of cyclic peaks in the voltammogram, indicating the sensor's unique ability to selectively detect Pb2+ ions without being perturbed by other ions that may be naturally occurring in the samples. These findings emphasize the nanocomposite sensor's potential for a wide array of applications in environmental monitoring and analytical chemistry. Its extraordinary combination of high sensitivity, impeccable selectivity, and robust performance in practical scenarios establishes it as an invaluable tool for detecting Pb2+ ions across various contexts.

Benchmarking long-read sequencing strategies for obtaining ASV-resolved rRNA operons from environmental microeukaryotes.

The use of short-read metabarcoding for classifying microeukaryotes is challenged by the lack of comprehensive 18S rRNA reference databases. While recent advances in high-throughput long-read sequencing provide the potential to greatly increase the phylogenetic coverage of these databases, the performance of different sequencing technologies and subsequent bioinformatics processing remain to be evaluated, primarily because of the absence of well-defined eukaryotic mock communities. To address this challenge, we created a eukaryotic rRNA operon clone-library and turned it into a precisely defined synthetic eukaryotic mock community. This mock community was then used to evaluate the performance of three long-read sequencing strategies (PacBio circular consensus sequencing and two Nanopore approaches using unique molecular identifiers) and three tools for resolving amplicons sequence variants (ASVs) (USEARCH, VSEARCH, and DADA2). We investigated the sensitivity of the sequencing techniques based on the number of detected mock taxa, and the accuracy of the different ASV-calling tools with a specific focus on the presence of chimera among the final rRNA operon ASVs. Based on our findings, we provide recommendations and best practice protocols for how to cost-effectively obtain essentially error-free rRNA operons in high-throughput. An agricultural soil sample was used to demonstrate that the sequencing and bioinformatic results from the mock community also translates to highly diverse natural samples, which enables us to identify previously undescribed microeukaryotic lineages.

Water–salt migration and deformation characteristics in gravelly sulfate saline soil under the effect of localized fine sand accumulation

The water-salt migration law and deformation characteristics of coarse-grained saline soils have been extensively studied and illustrated. However, owing to the influence of the chemical composition and physical properties of the soils, coarse-grained soils are prone to localized soil absorption during mixing and compaction. This type of working condition of the existing localized fine sand accumulation layers is seldom discussed in the literature. In this study, water-salt migration and deformation of natural gradation specimens and specimens with localized fine sand accumulation layers in natural gradation were monitored and detected for the field fill conditions in an airport embankment project using self-designed test equipment based on nine freeze–thaw cycle physical simulation tests at environmental temperatures ranging from −30 °C to 25 °C. Under the freeze–thaw cycle, compared with the natural gradation, the specimens with localized fine sand accumulation layers had a higher influence on water and salt migration, which indicates that the depth range of drastic changes in water and salt increased by 80% and 84%, respectively. The cumulative deformation curves under the effects of natural gradation and localized fine sand accumulation exhibited similar trends. The difference between the deformation of the natural samples and samples with localized fine sand accumulation layers was 16% when the salt content of the upper part of the roadbed was 0.3%. In addition, the cumulative vertical settlement deformation of the specimens decreased with an increase in the salt content of the upper part of the roadbed and gradually transformed into vertical uplift deformation. The results of this study provide a basis for the selection of materials for airport roadbed backfill and their application in construction in seasonally frozen areas.

Quantitative radiation dosimetry by measuring thermoluminescence of resistors in electronic personal dosimeters

A study on retrospective dosimetry was performed using electronic personal dosimeters (EPDs) for reconstructing doses received by radiographic testing workers. The dosimetric properties of the thermoluminescence (TL) peak in the temperature range of 100–200 °C were investigated by measuring the TL of the resistors extracted from the EPDs in a darkroom environment. Results showed that this TL peak exhibited optimal dosimetric properties, with a minimum detectable dose as low as 13 mGy. To calculate the absorbed dose using the resistors, a simplified single aliquot regenerative (SAR) dose method using the TL peak was employed. The zero dose of the commercial EPD (model CLOVER) was determined to be 58 ± 72 mGy through the random selection of six EPDs. Additionally, a dose overestimation correction factor for compensating rapid sensitivity changes after TL measurement of the natural sample was calculated as 1.73 ± 0.09. Furthermore, it was observed that the TL signal faded exponentially to approximately 60% over a period of 12 weeks. Subsequently, retrospective dosimetry was performed by irradiating EPDs with a standard gamma ray dose of 1 Gy. The radiation exposure dose calculated from the TL peak of the resistors was found to be approximately 10% lower. These findings showed that the retrospective dosimetry with EPD can be utilized for accurately estimating the radiation exposure dose.

Hydraulic barrier properties of soil beneath Egbedi active municipal solid waste dumpsite

This study researches the potential of soil underlying an active municipal solid waste dumpsite as compacted clay liner in municipal solid waste repository structure. Identification of soil samples and their chemical constituents was conducted. Ex-situ tests were conducted in the laboratory to obtain three important hydraulic barrier parameters: coefficient of hydraulic permeability (k), volumetric shrinkage strain (VSS) and shear strength (UCS) of the natural soil samples employing three compaction techniques namely; British Standard Light (BSL), West African Standard (WAS) and British Standard Heavy (BSH). Soil specimens were also prepared with varying moulding moisture contents −2%, +2 and + 4 % of optimum moisture content (OMC). The microstructure of the natural specimen was investigated via scanning electron microscopy. The natural samples were identified as; A-2-7(0), A-2-7(2) and A-7-5(5) for sample A, B and C respectively according to AASHTO classification while all samples are classified as SC (Clayey Sand) according to USCS classification. The moulding moisture content that satisfy the k and VSS conditions of k ≤ 1.0 × 10−7 cm/s and VSS ≤ 4 % falls between 9.63 – 17.68 % and 9.63–12.75 % respectively, but the tested specimens fall short of strength condition. This study revealed that the natural soil samples fail to fulfil the design parameter requirements for strength in its application as liner/cover in the active waste dumpsite, therefore further investigation to ameliorate the strength property of the soil samples using additives or microbial induced calcite precipitation (MICP) technique is recommended.

P-227 Blastocyst rate prediction by artificial intelligence: evaluation of static oocyte images in patients and donors

Abstract Study question Can a deep learning tool accurately predict blastocyst development from single static oocyte images across diverse patient populations? Summary answer The automatic oocyte score was directly correlated with the blastocyst formation rate in conventional treatments using patient oocytes and in the egg donation program. What is known already The implementation of artificial intelligence (AI)-based algorithms has revolutionized gamete and embryo evaluation in assisted reproduction, providing a standardized basis for clinical decisions. Despite these advances, the implementation of AI algorithms in oocyte evaluation has faced unique challenges. This study explores the predictive ability of an AI algorithm on the actual blastocyst arrival rate from static oocyte images, focusing on finding differences or similarities in these predictions between patients and donors. Study design, size, duration This retrospective cohort study analyzed 1092 static images of oocytes before ICSI. The sample included 791 donor and 301 patient oocyte images. A deep learning algorithm was employed to assess oocyte scores, categorizing them into quartiles. The study explored the correlation between oocyte scores and the actual rate of blastocyst arrival for all oocytes. The rates were also compared between patients and donors, assessing an effective cut-off point to improve the accuracy of the predictions. Participants/materials, setting, methods The oocyte images analyzed were captured with cameras connected to microinjection microscopes immediately before the ICSI procedure. Oocyte images and oocyte age were used as input data for the blind analysis performed by the deep learning-based algorithm. The algorithm predicted blastocyst arrival probability for each oocyte, and these predictions were compared with actual outcomes. This evaluation assessed the algorithm’s performance across patient and donor populations, providing valuable insights into its predictive accuracy for blastocyst arrival. Main results and the role of chance Significant differences in blastocyst formation rates were observed between quartiles of scores assigned by the artificial intelligence (AI) algorithm. Oocytes classified in Quartile 1 showed a significantly lower blastocyst arrival rate (54.63±49.99, n = 216) compared to Quartiles 3 (71.88±45.06, n = 224) and 4 (66.82±47.19, n = 220)*. In general, each increased unit in the algorithm was significantly associated with an increased probability of blastocyst formation (OR = 1.10 [95% CI: 1.02-1.16])*. The algorithm successfully predicted the blastocyst arrival rate, with an area under the curve (AUC) of 0.60 (95% CI: 0.56-0.65). When assessing differences between patients and donors, both groups exhibited similar rates in the first quartile (59.68% and 52.60%, respectively) and the three last quartiles combined (67.55% and 66.02%, respectively). However, an effective cutoff point was identified at 2.8 (between first and second quartile), where both populations experienced a notable increase in blastocyst formation rates. (*p&amp;lt;.05) Limitations, reasons for caution This project is limited by its retrospective, single-center nature and small sample size. Consequently, it would be advisable to conduct a large-scale multicenter study to confirm the conclusions of this study. Wider implications of the findings This study highlights the efficiency of the artificial intelligence algorithm in predicting blastocyst arrival from oocyte images. Establishing a cutoff point in oocyte scoring could enhance the management of the egg donation program and provide individualized guidance to patients based on the quality of their gametes. Trial registration number not applicable

P-582 Analysis of assisted reproductive outcomes for gynecologic cancer survivors: a retrospective study

Abstract Study question Do women diagnosed with gynecologic cancer have worse pregnancy and neonatal outcomes of assisted reproductive technology (ART) ? Summary answer Decreased live birth rate was observed in female gynecologic cancer patients undergoing ART, particularly in endometrial cancers and cervical cancers. What is known already Gynecologic malignancies comprise approximately 13% of female cancer diagnoses, and oncological treatments such as surgery, chemotherapy, abdominal or pelvic radiotherapy, and hormonotherapy may induce infertility, premature ovarian failure, or early menopause, resulting in a loss of reproductive potential. ART has emerged as an important option for cancer patients undergoing treatment, but the reproductive outcomes of ART among cancer survivors remain poorly understood. Limited research exists regarding the impact of a history of gynecologic cancer on reproductive outcomes of ART. Study design, size, duration 136 women diagnosed with gynecologic cancer who underwent their first IVF/ICSI treatment between 2013 and 2021 were retrospectively enrolled in this study. 241 infertile women without any history of cancer were matched to the cancer group. Participants/materials, setting, methods Women diagnosed with gynecologic cancer who underwent their first IVF/ICSI treatment between 2013 and 2021 were included. Infertile women without any history of cancer were matched to the cancer group. The primary outcome was the cumulative live birth rate. A propensity score-based patient-matching approach was adopted to ensure comparability between individuals with and without specific cancer type. Main results and the role of chance A total of 136 patients with a history of gynecologic cancer and 241 healthy infertile controls were included in this study. Endometrial cancer constituted 50.70% of the cases and cervical cancer constituted 34.60% of the cases. The cancer group exhibited significant shorter duration of stimulation, lower levels of estradiol, lower number of retrieved oocytes, day-3 embryos, and blastocysts compared to the control group (P &amp;lt; 0.05). The cumulative live birth rate of the gynecologic cancer group was significantly lower than that of the control group (36.10% vs. 60.50%, P &amp;lt; 0.001). Maternal and neonatal complications did not significantly differ between the groups (P &amp;gt; 0.05). The endometrial cancer and cervical cancer groups showed significantly lower cumulative live birth rates than their matched controls (38.60% vs. 64.50%, P = 0.011 and 24.20% vs. 68.60%, P &amp;lt; 0.001, respectively). Limitations, reasons for caution Our study was limited by the retrospective design in nature and small sample size of its patient cohort. In addition, the effect of cancer treatments was not evaluated specifically in the current study due to the limited number of patients and possible inconsistency in surgical operations. Wider implications of the findings Our study provides data support for further exploration of the effects of endometrial cancer, cervical cancer, and other gynecologic cancers on fertility. Comprehensive ovarian function assessment, detailed counseling, individualized fertility guidance, and close follow-up after ART therapy are recommended for patients with gynecologic malignancy history. Trial registration number Not applicable

Life Detection on Icy Moons Using Flow Cytometry and Intrinsically Fluorescent Biomolecules.

In a previous experiment, we demonstrated the capability of flow cytometry as a potential life detection technology for icy moons using exogenous fluorescent stains (Wallace et al., 2023). In this companion experiment, we demonstrated the capability of flow cytometry to detect life using intrinsically fluorescent biomolecules in addition to exogenous stains. We used a method similar to our previous work to positively identify six classes of intrinsically fluorescent biomolecules: flavins, carotenoids, chlorophyll, tryptophan, NAD+, and NAD(P)H. We demonstrated the effectiveness of this method with six known organisms and known abiotic material and showed that the cytometer is easily able to distinguish the known organisms and the known abiotic material by using the intrinsic fluorescence of these six biomolecules. To simulate a life detection experiment on an icy moon lander, we used six natural samples with unknown biotic and abiotic content. We showed that flow cytometry can identify all six intrinsically fluorescent biomolecules and can separate the biotic material from the known abiotic material on scatter plots. The use of intrinsically fluorescent biomolecules in addition to exogenous stains will potentially cast a wider net for life detection on icy moons using flow cytometry.

Perturbation Augmentation for Adversarial Training with Diverse Attacks

Adversarial Training (AT) aims to alleviate the vulnerability of deep neural networks to adversarial perturbations. However, the AT techniques struggle to maintain the performance on natural samples while improving the deep model’s robustness. The absence of perturbation diversity in generated during the adversarial training degrades the generalizability of the robust models, causing overfitting to particular perturbations and a decrease in natural performance. This study proposes an adversarial training framework that augments adversarial directions from a single-step attack to address the trade-off between robustness and generalization. Inspired by feature scattering adversarial training, the proposed framework computes a principal adversarial direction with a single-step attack that finds a perturbation disrupting the inter-sample relationships in the mini-batch during adversarial training. The principal direction obtained at each iteration is augmented by sampling new adversarial directions within a region spanning 45 degrees from the principal adversarial direction. The proposed adversarial training approach does not require extra backpropagation steps in adversarial direction augmentation. Therefore, generalization of the robust model is improved without posing an additional burden on the feature scattering adversarial training. Experiments on CIFAR-10, CIFAR-100, SVHN, Tiny-ImageNet, and The German Traffic Sign Recognition Benchmark consistently improve the accuracy on adversarial with an almost pristine natural performance.