Timing Of Events Research Articles

A review of the contributions of forensic archaeology and anthropology to the process of disaster victim identification.

Forensic archaeology and anthropology have developed significantly over past decades and now provide considerable assistance to the investigation process of disaster victim recovery and identification. In what are often chaotic death and crime scenes, the formal process of utilizing archaeological methods can bring control, order, and ensure systematic search. Procedures assist in defining scene extent, locating victims and evidence, rule out areas for consideration, and provide standardized recording and quality assurance through dedicated use of standardized forms (pro formas). Combined archaeological and anthropological search methods maximize opportunities to recovery the missing through identifying remains, mapping distributions, and providing accounting of victims at the scene. Anthropological assistance in examinations contributes to individual assessment, resolving commingling and fragmentation issues, and utilizing DNA sampling methods and matching data to reassociate and account for the missing. Utilization of archaeology, anthropology, and DNA matching data provides scope to review crime scene recovery and determine requirements and potential for further survey and retrieval. Adopting the most suitable methods for a particular context can maximize recovery, efficiency, and resource use. Case studies demonstrate the utility of archaeological methods in a range of scenarios. They exemplify the success of multidisciplinary analysis in providing evidence of the sequence of events, the timing of events, the impact of taphonomic processes, the location and accounting of victims, and the demonstration of systematic scene search. The considerations provided in this article, utilizing archaeology and anthropology processes, may assist investigators in planning and implementing responses to mass fatalities.

Geoclimatic drivers of diversification in the largest arid and semi-arid environment of the Neotropics: Perspectives from phylogeography.

The South American Dry Diagonal, also called the Diagonal of Open Formations, is a large region of seasonally dry vegetation extending from northeastern Brazil to northern Argentina, comprising the Caatinga, Cerrado, and Chaco subregions. A growing body of phylogeography literature has determined that a complex history of climatic changes coupled with more ancient geological events has produced a diverse and endemic-rich Dry Diagonal biota. However, the exact drivers are still under investigation, and their relative strengths and effects are controversial. Pleistocene climatic fluctuations structured lineages via vegetation shifts, refugium formation, and corridors between the Amazon and Atlantic forests. In some taxa, older geological events, such as the reconfiguration of the São Francisco River, uplift of the Central Brazilian Plateau, or the Miocene inundation of the Chaco by marine incursions, were more important. Here, we review the Dry Diagonal phylogeography literature, discussing each hypothesized driver of diversification and assessing degree of support. Few studies statistically test these hypotheses, with most support drawn from associating encountered phylogeographic patterns such as population structure with the timing of ancient geoclimatic events. Across statistical studies, most hypotheses are well supported, with the exception of the Pleistocene Arc Hypothesis. However, taxonomic and regional biases persist, such as a proportional overabundance of herpetofauna studies, and the under-representation of Chaco studies. Overall, both Pleistocene climate change and Neogene geological events shaped the evolution of the Dry Diagonal biota, though the precise effects are regionally and taxonomically varied. We encourage further use of model-based analyses to test evolutionary scenarios, as well as interdisciplinary collaborations to progress the field beyond its current focus on the traditional set of geoclimatic hypotheses.

Localization for surface microseismic monitoring based on arrival time correction and VFOM

Abstract Unconventional resources have emerged as the primary source to meet the escalating demand for energy consumption, with hydraulic fracturing standing out as an effective means of boosting production. The utilization of microseismic monitoring is crucial for acquiring real-time or semi-real-time extension information of the fracture network to guide the fracturing process. The precise positioning of microseismic events is a fundamental aspect of microseismic monitoring. Traditional methods relying on (relative) arrival time significantly impact positioning accuracy due to picking errors. While waveform-based methods offer high accuracy, they require precise velocity models and are time-consuming. To overcome challenges associated with arrival time pickup and velocity accuracy, we introduce a virtual field optimization method (VFOM) based on arrival time correction. Initially, an equivalent velocity model is established, and the arrival time difference resulting from the model transformation of the master event is calculated to correct the observed arrival time of the target event. Subsequently, we match detector pairs, establish hyperboloids based on the corrected arrival time difference, and employ the intersection point of all hyperboloids as the positioning result. After that, we use the location results of the master event to enhance the accuracy of the target event. Finally, we apply the proposed method to both synthetic test and field datasets, demonstrating a significant improvement in the positioning accuracy and stability provided by the novel method. The robustness against arrival time error renders it a suitable choice for surface monitoring applications where signal quality is compromised. Furthermore, the simplified velocity model significantly diminishes the computational requirements in the positioning process, enhancing its efficiency, and consequently holds vast potential for application in real-time monitoring.

Neurophysiological recordings from parietal areas of macaque brain during an instructed-delay reaching task

Facilitating data sharing in scientific research, especially in the domain of animal studies, holds immense value, particularly in mitigating distress and enhancing the efficiency of data collection. This study unveils a meticulously curated collection of neural activity data extracted from six electrophysiological datasets recorded from three parietal areas (V6A, PEc, PE) of two Macaca fascicularis during an instructed-delay foveated reaching task. This valuable resource is now accessible to the public, featuring spike timestamps, behavioural event timings and supplementary metadata, all presented alongside a comprehensive description of the encompassing structure. To enhance accessibility, data are stored as HDF5 files, a convenient format due to its flexible structure and the capability to attach diverse information to each hierarchical sub-level. To guarantee ready-to-use datasets, we also provide some MATLAB and Python code examples, enabling users to quickly familiarize themselves with the data structure.

Trajectories of Diabetes-Related Sequelae for Identifying Transition Probabilities, and Optimal Timepoints for Prevention of Ulceration, Infection, and Amputation

To reduce diabetes-related complications and to avoid futile procedures, foot and ankle surgeons need to understand the relative timings of catastrophic events, their incidence, and probabilities of transitions between disease states in diabetes in different patient populations. For this study, we tracked medical events (including an initial diagnosis of diabetes, ulcer, wound care, osteomyelitis, amputation, and reamputation, in order of severity) and the time between each such event in patients with diabetes, stratifying by sex, race, and ethnicity. We found that the longest average duration between the different lower extremity states was a diagnosis of diabetes to the occurrence of ulcer at 1137 days (38 months). The average durations of amputation to reamputation, osteomyelitis, wound care, and ulcer were 18, 49, 23, and 18 days, respectively. The length of each disease transition for females was greater, while those of the Hispanic population were shorter than in the total cohort. This knowledge may permit surgeons to time and tailor treatments to their patients, and help patients to address, delay, or avoid complications.

Changing phenology of benthic primary producers in inland waters: Current knowledge and future directions

AbstractBenthic primary producers (BPP) in inland waters, including aquatic macrophytes and periphyton, are foundational habitats that are highly sensitive to multiple human drivers of environmental change. However, long‐term seasonal monitoring of BPP is limited, leaving us with little information on the cause, directionality, and consequences of the potential shifts in timing of BPP life cycle events. Here, we review the literature on the phenological changes of BPP and show that BPP respond primarily to temperature, but also to other interactive drivers related to climate change and eutrophication. In addition, we present four rare case studies where BPP display strong and earlier shifts in event timing associated with increasing temperature and discuss potential impacts of these changes on ecosystem functioning. Given the responsive nature of BPP to multiple human drivers, we provide suggestions on how to improve basic monitoring to better understand the future impact of phenological changes of this critical habitat.

Complete phylogeny of Micrathena spiders suggests multiple dispersal events among Neotropical rainforests, islands and landmasses, and indicates that Andean orogeny promotes speciation.

The Neotropical region is the most diverse on the planet, largely owing to its mosaic of tropical rainforests. Multiple tectonic and climatic processes have been hypothesized to contribute to generating this diversity, including Andean orogeny, the closure of the Isthmus of Panama, the GAARlandia land bridge and historical connections among currently isolated forests. Micrathena spiders are diverse and widespread in the region, and thus a complete phylogeny of this genus allows the testing of hypotheses at multiple scales. We estimated a complete, dated phylogeny using morphological data for 117 Micrathena species and molecular data of up to five genes for a subset of 79 species. Employing event-based approaches and biogeographic stochastic mapping while considering phylogenetic uncertainty, we estimated ancestral distributions, the timing and direction of dispersal events and diversification rates among areas. The phylogeny is generally robust, with uncertainty in the position of some of the species lacking sequences. Micrathena started diversifying around 25 Ma. Andean cloud forests show the highest in-situ speciation, while the Amazon is the major dispersal source for adjacent areas. The Dry Diagonal generated few species and is a sink of diversity. Species exchange between Central and South America involved approximately 23 dispersal events and started ~20 Ma, which is consistent with a Miocene age for the Isthmus of Panama closure. We inferred four dispersal events from Central America to the Antilles in the last 20 Myr, indicating the spiders did not reach the islands through the GAARlandia land bridge. We identified important species exchange routes among the Amazon, Andean cloud forests and Atlantic forests during the Plio-Pleistocene. Sampling all species of the genus was fundamental to the conclusions above, especially in identifying the Andean forests as the area that generated the majority of species. This highlights the importance of complete taxonomic sampling in biogeographic studies.

Conditional score approaches to errors-in-variables competing risks data in discrete time.

Analysis of competing risks data has been an important topic in survival analysis due to the need to account for the dependence among the competing events. Also, event times are often recorded on discrete time scales, rendering the models tailored for discrete-time nature useful in the practice of survival analysis. In this work, we focus on regression analysis with discrete-time competing risks data, and consider the errors-in-variables issue where the covariates are prone to measurement errors. Viewing the true covariate value as a parameter, we develop the conditional score methods for various discrete-time competing risks models, including the cause-specific and subdistribution hazards models that have been popular in competing risks data analysis. The proposed estimators can be implemented by efficient computation algorithms, and the associated large sample theories can be simply obtained. Simulation results show satisfactory finite sample performances, and the application with the competing risks data from the scleroderma lung study reveals the utility of the proposed methods.

The use of cetuximab for oral cancer and adverse events with different treatment regimens: A single‐center retrospective study over an 11‐year period

AbstractBackgroundThe aim of this retrospective study was to assess the use and safety of cetuximab (C‐mab) monotherapy or combination therapy with various regimens for treating oral cancer in our hospital.MethodsThe study comprised 53 patients diagnosed with oral cancer who underwent chemotherapy with C‐mab between January 2013 and December 2022 at the Oral Cancer Center, Tokyo Dental College. In total, 68 regimens were analyzed. The cumulative rate was determined by analyzing the timing and incidence of adverse events (AEs) with each regimen using a Kaplan–Meier curve.ResultsThe treatment regimens administered included the following: induction C‐mab + paclitaxel + carboplatin (ICT‐PCE, n = 7), C‐mab + radiotherapy (RT) (n = 22), C‐mab + cisplatin + fluorouracil (n = 7), C‐mab + paclitaxel + carboplatin (n = 1), C‐mab + paclitaxel (n = 16), C‐mab maintenance (n = 11), and four other cases. The total number of doses administered was 895.ConclusionsThis retrospective study investigated the effectiveness of treatment regimens containing C‐mab for oral cancer and adverse reactions to these regimens. C‐mab is a crucial component of oral cancer treatment when used in multidrug therapy. C‐mab containing regimens may result in direct or indirect AEs. Ongoing, rigorous monitoring is essential to mitigate these events. Partnering with other departments can help to limit their severity.

Consistent time allocation fraction to vegetation green-up versus senescence across northern ecosystems despite recent climate change

Extended growing season lengths under climatic warming suggest increased time for plant growth. However, research has focused on climatic impacts to the timing or duration of distinct phenological events. Comparatively little is known about impacts to the relative time allocation to distinct phenological events, for example, the proportion of time dedicated to leaf growth versus senescence. We use multiple satellite and ground-based observations to show that, despite recent climate change during 2001 to 2020, the ratio of time allocated to vegetation green-up over senescence has remained stable [1.27 (± 0.92)] across more than 83% of northern ecosystems. This stability is independent of changes in growing season lengths and is caused by widespread positive relationships among vegetation phenological events; longer vegetation green-up results in longer vegetation senescence. These empirical observations were also partly reproduced by 13 dynamic global vegetation models. Our work demonstrates an intrinsic biotic control to vegetation phenology that could explain the timing of vegetation senescence under climate change.

Estimation of the time of zolpidem intake and differentiation between consumption and external contamination using MALDI-MSI for investigations on single hair samples

Estimation of drug ingestion time (event time) and distinguishing between drug ingestion and external contamination are important for interpreting hair analysis results in forensics practice. Here, we present a matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) method for in situ analysis of intact hair. We applied a longitudinal cutting method for a single hair to analysis authentic hair samples from a victim of a drug-facilitated sexual assault (DFSA) case and zolpidem-soaked hair. MALDI-MSI showed that zolpidem-positive segments distributed at 4−6 mm or 6−8 mm from the root in three single hairs of a DFSA victim collected 25 days after the event, at concentrations ranging from 0.1 to 5.7 pg mm−1, in agreement with the results from segmental analysis using liquid chromatography tandem mass spectrometry (LC-MS/MS). The estimation of drug intake time was about 20–30 days before sampling, which was consistent with the known time of drug intake. This MALDI-MS method allows imaging analysis of trace substances in a single hair and can realize the intuitive reflection of drug taking time. In addition, zolpidem applied by soaking was mainly distributed on both sides of the longitudinal hair shaft, whereas ingested zolpidem was found only in the middle of the hair shaft of the DFSA victim. The MALDI-MS images of unwashed and washed hair suggested that the amount of externally applied drug was decreased by washing, it was still present on surface layer (cuticle) sides although. Visualization using MALDI-MSI could therefore distinguish between drug ingestion and contamination by reflecting the distribution and deposition site of the drug in hair.

Personalized risk assessment of new onset depression in long-term cancer survivors.

e13798 Background: Advances in cancer detection and treatment have improved long-term survival. It is critical to understand the impact that experiencing cancer has on psychological health, given that the prevalence of depression in long-term survivors is higher than in the general population. Our aim was to develop a risk prediction model to identify long-term (5-year) survivors at high risk of developing depression. Methods: We conducted a retrospective study of the SEER-Medicare database beneficiaries with localized breast, prostate, colon, or rectal cancer diagnosed between 2003-2011 who had survived at least 5 years since diagnosis. We required Part D coverage in the breast cohort to account for adjuvant endocrine therapy. Our primary outcome was the cumulative incidence of depression between 5-10 years after diagnosis, which was defined using a claims-based algorithm as a single or recurrent episode of major depressive disorder, recent episode of bipolar I disorder, or dysthymic disorder. For each cancer site, we constructed restricted mean survival time models, a non-parametric method that yields time ratios; a time ratio (TR) < 1 indicates shorter mean time to event (TTE). Results: Our cohort consisted of 80,579 survivors. The cumulative incidence of depression occurring between 5-10 years after diagnosis was 16.9% for breast cancer, 7.8% for prostate cancer, 10.6% for colon cancer, and 8.6% for rectal cancer. The cumulative incidence of new onset depression increased over time and was 7.2% vs. 13.9% among survivors diagnosed before and after 2008, respectively. Compared with patients age < 70 at diagnosis, patients aged 75 and older exhibited 5-10% shorter mean TTE across breast, prostate, and colon cancer. There was no association between Non-Hispanic (NH) Black race or Hispanic ethnicity and depression compared with NH White survivors. Asian survivors were at lower risk of developing depression. Disease specific factors, including stage, grade, hormone receptor status (breast), or PSA (prostate), did not predict depression in long-term survivors. Receipt of treatment in the 3-5 years after initial diagnosis was associated with moderately greater risk for all survivors, with the shortest mean TTE among breast cancer survivors (TR=0.91, 95% CI=0.85-0.97). Across all cohorts, the greatest predictor of depression was a prior history of depression. The incidence of depression in long-term survivors was 42% in patients with a prior history of depression vs. 8% among survivors without a prior history of depression. Conclusions: Diagnoses of depression increased in cancer patients since 2008 and were associated with prior history of depression, advanced age, census tract poverty, and continued treatment 3 years after diagnosis. Treatment type and disease specific factors were not strong predictors of depression.

Dynamic machine learning model to forecast patient availability for clinical trials.

e13641 Background: Patient eligibility is a routine concept in clinical research, comprised of simple or complex set of inclusion or exclusion criteria applied in a specific sequence. Our methodology introduces the concept of ‘availability’ as a narrower term that is closer to the actual number that will be considered for a specific study and who may decide to participate. This specific research develops Machine Learning (ML) model that dynamically predicts likelihood of Multiple Myeloma progression and departure from targeted outcomes to sustain therapy and to indicate potential availability for clinical trials – to reduce screening efforts and improve screening precision of clinical research organizations. Methods: The cohort for training these models was drawn from the ConcertAI Oncology Research database, further enriched by key variables derived from unstructured data sources. Eligible patients for the analysis had a diagnosis of Multiple Myeloma made after Jan 1, 2020. Progressions were labelled using International Myeloma Working Group Criteria. Patients that did not satisfy the criteria were censored. Labels were assigned to patients after initial diagnosis of MM, and the duration between diagnosis to label was computed to build time to event (TTE) models. Over 2000 features were created to build this model, e.g., laboratory values, vitals, biomarkers, medications, ECOG, stage, etc. Variable creation preserved temporality of lab variables and vitals and noted presence of other variables. Several ML methods were employed to build the TTE models, with gradient boosting resulting in the highest c-index (0.8) based on 12 clinically relevant features. A temporal validation of the model was performed on a simulated index date. Results: Cohort used for training the model comprised of N = 4315 patients with average age at diagnosis of 72.3 years, and ~11% of the patients labelled as progressing. Temporal validation was performed on N = 1874 patients, with ~9% progression rate. The area under the curve (AUC) and weighted F1 score for this cohort was 87% and 89% respectively. Conclusions: Patient availability assessments are complex, not supported effectively by existing solutions and often require 100’s of hours from highly trained clinical personnel. An AI model predicting Multiple Myeloma progression built using a large-scale real-world dataset demonstrated 2-4x improvement in forecasting patient availability over other approaches. Integrating these solutions into clinical workflows can improve the effectiveness of research teams; enable more patients to have clinical trials as viable options at the right time; and allow care teams to have forward visibility to choices of standard-of-care and clinical trial alternatives in the best interest of patient outcomes. [Table: see text]

Deathbed experiences and meaning-making: Perspectives of family caregivers of patients who received cancer palliative care.

The deathbed symbolizes a time when the patient is dangerously ill and where death is imminent. The memories of family caregivers during this time can potentially shape their meaning-making of the patient's death and bereavement adjustment. We aimed to understand the experiences of family caregivers at the deathbed of patients receiving palliative care. We also examined caregiver's meaning-making that occurred after the patient's death. In this retrospective qualitative study, family caregivers of cancer patients who received palliative care in Singapore were recruited through purposive sampling. In-person, semi-structured interviews were individually conducted with study participants to understand their experiences from a caregiver's perspective before and after the death of the patient. Thematic content analysis method was conducted. A total of 25 bereaved family caregivers were interviewed, with spouses, adult children, and others comprising one-third each of the sample. Six themes emerged from caregivers' recollected experiences around the patient deathbed: Lasting image of the patient, A time of intense emotions, Healthcare providers prepare caregivers, A time for saying goodbye, Rituals provide comfort, and Impact on family ties. Four themes emerged surrounding post-loss meaning-making: An end to the pain and suffering, "Have I done enough?," Significance in the timing of events, and Gaining strength and personal growth. The deathbed is a salient time for family caregivers as they prepare for patient's death. There are opportunities to provide support to the family based on the study findings.

Lithologies and Chronologic Opportunities of Materials to Be Returned From the Artemis Exploration Zone

AbstractThe Artemis exploration zone is a geologically complex region likely to host some of the oldest and as‐yet‐unstudied materials on the Moon. We review six potential Artemis landing sites (001, 004, 007, 011, 102, and 105) within candidate Artemis III landing regions ”Connecting Ridge,“ “Peak Near Shackleton,” “Leibnitz Beta Plateau,” “de Gerlache Rim,” and “de Gerlache Rim 2.” Kaguya Spectral Profiler mineral data were used to determine the average lithological composition at each landing site. Potentially accessible geologic materials, their ages and significance, and appropriate application of radiometric chronometers are discussed in reference to return samples from each potential landing site. Chronological analyses of return samples from the Artemis exploration zone will enable the anchoring of the lunar impact flux curve, determine the absolute timing of pivotal events in lunar geologic history, and reveal the geological diversity of the differentiated lunar body.

A rapid assessment tool for systemic treatment outcomes in colorectal cancer with deep bidirectional transformers.

e15567 Background: Retrospective investigations involving cancer treatment encounter the challenge of manually reviewing voluminous imaging reports to identify the best responses and the timing of disease progression. Here, we employed natural language processing, specifically deep bidirectional transformers, to automate the extraction of treatment response data. Methods: Data were sourced from the Yonsei Cancer Data Library database, focusing on patients with stage II-IV colorectal cancer at the Yonsei Cancer Center from Jan 1, 2010, to Dec 31, 2020. Imaging reports from 6,574 patients were gathered, amounting to 97,119 CT readings. Among these, 9,000 CT reports corresponding to 2,859 patients were randomly subjected to multilabel manual labeling by four radiology experts, based on the RECIST version 1.1 classification (CR/NED, PR, SD, PD). The pretrained BERT-base-uncased model was employed and fine-tuned for the downstream tasks of multilabel classification. 6,765 reports were used for training, while the remaining 1,000 reports were divided equally between the validation and test sets. Additionally, an algorithm for integrating and extracting treatment response data was also implemented. Clinical data were meticulously collected for 47 patients with stage IV early-onset colorectal cancer (EOCRC) receiving palliative first-line chemotherapy. The primary objective was to evaluate the model’s accuracy in predicting the timing of disease progression events within a ±30-day window. Secondary objectives focused on the model’s ability to identify the best response and its timing within a ±45-day window. Results: The evaluation of the classification accuracy across 1,000 individual CT reports, categorized as CR/NED (319), PR (86), SD (272), and PD (298), revealed accuracy with an AUROC of 0.956 and an F1 score of 0.823. All EOCRC patients were initially diagnosed with stage IV disease, and 45 out of 47 (95.7%) underwent metastasectomy either before initiating chemotherapy or during the first-line chemotherapy treatment. In clinical evaluation, the model demonstrated 72.3% (95% Confidence Interval [CI], 59.5-85.1) accuracy in predicting the day of disease progression within a ±30-day window. It achieved 55.3% (95% CI, 41.1-69.5) accuracy in predicting the best response category and its timing within ±45-days. Notably, the model was more accurate in predicting CR/NED at 72% compared to SD and PR, which had accuracy of 27.3% and 15.4%, respectively. Conclusions: Our model demonstrated high performance in classifying CT reports, which is expected to provide rapid insights into patient progress and improve clinical decision-making. However, the complexity involved in integrating and extracting data result in slight constraints in performance. This emphasizes the need for further research into multimodal large language models that can integrate a wider variety of clinical data.

Trends and Climate‐Sensitivity of Phenology in China's Natural and Planted Forests

AbstractForest is the largest carbon sink on land, and the timing of forest phenology profoundly influences carbon uptake. However, disparities in the timing and sensitivity of forest phenology, particularly between planted forests (PF) and natural forests (NF), have yet to be determined. In this study, we analyzed the phenological trends and the climate‐sensitivities of China's NF and PF from 2000 to 2022 using field survey data and satellite data. We found that the start of the growing season (start of the season (SOS)) in NF and PF advanced significantly by 0.07 and 0.08 days yr−1, while the end of the growing season (end of the season (EOS)) in both NF and PF delayed by 0.06 days yr−1. Moreover, precipitation and air temperature jointly influenced the timing of phenological events. The pre‐season precipitation delayed the EOS by 0.009 days mm−1 and 0.006 days mm−1 in NF and PF. Moreover, the SOS was more sensitive to precipitation in PF than in NF in arid areas (i.e., pre‐season precipitation <100 mm). Air temperature significantly advanced the SOS by 0.74 and 0.95 days °C−1 in NF and PF and delayed the EOS by 0.89 and 0.71 days °C−1 in NF and PF. Additionally, the sensitivities of SOS and EOS to air temperature significantly differed between NF and PF. Our findings demonstrate a significant phenological discrepancy between PF and NF. The results suggest that NF and PF responded differently to climate variability, implying forest origin should be considered in the projection of carbon sequestration capability under changing climate.

Utility of the NLR and the ratio of NLR after and before adverse events for identifying irAEs and bacterial infections in patients with cancer during PD-(L)1 inhibitors treatment.

e14620 Background: Identifying immune-related adverse events (irAEs) or bacterial infections in cancer patients undergoing treatment with PD-(L)1 inhibitors can be challenging. This study aimed to assess the effectiveness of the neutrophil-to-lymphocyte ratio (NLR) in distinguishing between irAEs and bacterial infections in cancer patients receiving PD-(L)1 inhibitors. Methods: We conducted a retrospective analysis of cancer patients who received at least one dose of PD-(L)1 inhibitors at Affiliated Cancer Hospital of Zhengzhou University from 2018 to 2023. We compared the changes in peripheral blood cell counts before and after the occurrence of adverse events, as well as the ratio of the NLR at the time of adverse events (post-NLR) to the NLR before adverse events (pre-NLR). Bacterial infections were diagnosed based on clear etiology and/or controlled adverse events through the use of antibiotics without systemic glucocorticoid administration during treatment. irAEs were diagnosed based on clinicolaboratory criteria, or the improvement of immune system disorders after withdrawal of PD-(L)1 inhibitors or systemic glucocorticoid administration, excluding bacterial infection. Data were collected from microbiology, radiography, serology, and physician notes. Results: Among the 4173 patients who were administered PD-(L)1 inhibitors, 217 individuals experienced a total of 249 irAEs, while 256 patients were diagnosed with 257 bacterial infections. When comparing pre-infection levels with those after bacterial infection, it was observed that the NLR increased significantly. Similarly, in the group experiencing irAEs, the NLR also showed a significant increase after the occurrence of the irAEs. Notably, the NLR was significantly higher in patients with bacterial infections compared to those with irAEs [11.08 (6.63, 19.29) vs 2.78 (1.92, 4.31), P < 0.001]. Furthermore, the post-NLR/pre-NLR ratio was higher in the bacterial infection group than in the irAEs group [2.86 (1.56, 6.20) vs 1.16 (0.82, 1.76), P < 0.001]. Appraising the potential of NLR and the post-NLR/pre-NLR ratio in identifying irAEs and bacterial infections in cancer patients, the receiver operating characteristic (ROC) curve analysis was performed. The area under the curve (AUC) was 0.91 for NLR and 0.81 for the post-NLR/pre-NLR ratio, respectively. Using a cut-off value of 4.56 for NLR and 1.96 for the post-NLR/pre-NLR ratio yielded the highest sensitivity and specificity. Conclusions: The NLR along with the post-NLR/pre-NLR ratio could serve as valuable diagnostic indicators for irAEs and bacterial infections in cancer patients undergoing treatment with PD-(L)1 inhibitors.

Ohio River basin snow ablation and the role of rain‐on‐snow

AbstractSnow cover ablation within the Ohio River Basin (ORB) plays an important role in regional hydroclimatology, while also representing a potential hazard during large and rapid events. Rain‐on‐snow ablation is a particular challenge, where runoff rates are typically enhanced due to the dual inputs of snowmelt and liquid precipitation. Here, we present a 40‐year climatology of snow ablation events frequency, intensity, and timing for the ORB using a 4‐km gridded snow‐water‐equivalent dataset, focusing on the relative proportion of events caused by rain‐on‐snow and changes over time. Spatial patterns of snow ablation frequency and intensity mirror that of seasonal snowfall totals, with higher (lower) values in the northern and eastern (southern) portions of the basin. Rain‐on‐snow events represent approximately 40% of all ablation events within the basin and result in approximately 24%–25% more snow‐water‐equivalent loss than non‐rain‐on‐snow events, plus an additional 3–12 mm of liquid precipitation per event on average. Peak frequency of ablation and rain‐on‐snow events occurs in late winter and early spring, similar to that of the surrounding region. Over time, the frequency of ablation and rain‐on‐snow events has decreased in the northern and eastern portions of the basin, in some cases by as much as 30%. Trends in event magnitudes were more isolated but decreased across portions of central IN, northern KY, eastern OH and northern WV. Additionally, the magnitude of precipitation during rain‐on‐snow events has increased across the region, extending from northern KY into western PA by over 100% in many cases. Broadly, we find tendencies towards fewer events with less snow loss but more liquid precipitation that suggest complicated impacts to the hydroclimatology warranting further investigation.

Seismic Swell Effect Correction Using GVF-Based Guideline

Swells in marine seismic data disrupt the continuity of the reflection events and make interpretation difficult. The swell effect removal process consists of sea-bottom detection and correction, which removes the swell effect by shifting the detected sea-bottom signal to the known or estimated seafloor location. The quality of correction depends on the accuracy of the sea-bottom detection. Since general sea-bottom signal detection techniques have been applied directly after pre-processing, many mispick scenarios exist due to the characteristics of the data, and additional processes are needed. This study proposes a method for accurately detecting sea-bottom reflection signals using the guideline. The errors are caused by mistaking other reflection events as sea-bottom signals. The guideline can improve detection accuracy by providing rough event times of sea-bottom reflection. The guideline was extracted from the gradient vector flow (GVF) technique, a segmentation method in image processing. In the GVF data, the seafloor signals are smoothed, and the energy is focused on the sea bottom. The threshold method detected sea-bottom signals in the range after the guideline. The detected signals were shifted to estimated seafloor locations to attenuate the swell effect. The GVF-based guideline (GVF-GL) method was applied to field data acquired by the airgun and chirp sources with different frequencies. The sea-bottom location was successfully detected, and the continuity of the sea-bottom and subsurface signals in the section was improved.