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Abstract Precipitation is a critical part of the global hydrological cycle that determines the distribution of water resources. It is also an essential meteorological variable used as input for hydroclimatic models and projections. However, precipitation data frequently lack complete series, especially at daily and sub‐daily precipitation stations, which are usually large, bulky, and complex. To address this, gap filling is commonly used to produce complete hydrometeorological data series without missing values. Several gap‐filling methods have been developed and improved. This study seeks to fill the gaps of 201 daily precipitation time series in Central Italy by localizing the approach used to generate the Serially Complete dataset for the Planet Earth (SC‐Earth). This method combines the outcome of 15 strategies based on four various gap‐filling techniques (quantile mapping, spatial interpolation, machine learning, and multi‐strategy merging). These strategies employ the daily dataset of the neighbouring stations and the matched ERA5 data to estimate missing values at the target stations. Both raw data and the final serially complete station datasets (SCDs) underwent comprehensive quality control. Many accuracy indicators have been utilized to evaluate the performance of the strategies' estimations and the final SCD, such as Correlation Coefficient (CC), Root mean square error (RMSE), Relative bias (Bias %), and Kling‐Gupta efficiency (KGE″). Multi‐strategy merging strategy based on the Modified Kling‐Gupta efficiency (MS1) shows the highest performance as an individual precipitation gap‐filling strategy. However, the machine learning strategy using random forest (ML3) has the most outstanding share in the final estimates among all other strategies. In the end, the temporal–spatial performance of the final SCD is promising and depends on the pattern of the missing values (MV%). The mean values of KGE″, CC, variability (α), and bias term (β) are 0.9, 0.93, 1.064, and 4.98 × 10−7, respectively.

BACKGROUND: Published studies have demonstrated inconclusive relationships between serum lipid levels and mortality after cancer. METHODS: The primary objective was to evaluate the relationship between fasting lipid levels and mortality after cancer. Data were obtained on baseline lipids and outcomes after cancer from 1263 postmenopausal women diagnosed with 13 obesity-related cancers who were part of the Womens Health Initiative (WHI) lipid biomarkers cohort. Obesity-related cancers included incident invasive cancers of the breast, colorectum, endometrium, esophagus (adenocarcinoma), kidney, liver, gallbladder, pancreas, ovaries, small intestine, thyroid, stomach, as well as multiple myeloma. Baseline lipid measurements included high-density lipoprotein (HDL)-cholesterol, low-density lipoprotein (LDL)-cholesterol, and non-HDL-cholesterol. Outcomes were all cause, cancer-specific, and CVD mortality. Multivariable Cox proportional hazards models were used to measure associations between lipid levels and mortality (all cause, cancer, and CVD) after a cancer diagnosis, with lipids analyzed as continuous variables. RESULTS: Among women with obesity-related cancer, there were 707 deaths, of which 379 (54%) were due to cancer and 113 (16%) were due to CVD. Mean time from blood draw to cancer diagnosis was 5.1 years (range: 0.05-10 years). LDL-C values above the 95th percentile were associated with higher risk of all-cause mortality (p

Proton pump inhibitors (PPIs) have off-target activity on fatty acid synthase (FASN), a critical enzyme in energy balance and cancer growth. We evaluated risk of common obesity-related cancers: breast, colorectal (CRC), and endometrial, with use of PPI and histamine-2 receptor antagonists (H2RA) in 124,931 postmenopausal women enrolled in the Womens Health Initiative. Incident cancer cases were physician-adjudicated. Cox proportional hazards models were used to estimate multivariable hazard ratios (HR) and 95% confidence intervals (CI) for cancer incidence after year 3. There were 7956 PPI ever users and 9398 H2RA only users. Ever use of either PPI or H2RA was not associated with risk of breast cancer (n = 9186) nor risk of endometrial cancer (n = 1231). The risk of CRC (n = 2280) was significantly lower in PPI users (HR = 0.75, 95% CI = 0.61-0.92), but not in H2RA users (HR = 1.13, 95% CI = 0.97-1.31). The association of PPI use with CRC was apparent regardless of BMI or NSAID use, and was stronger with longer PPI duration (p = 0.006) and potency (p = 0.005). The findings that PPI use, but not H2RA use, demonstrate an inverse dose-response relationship with risk of CRC is consistent with preclinical data showing FASN inhibition prevents colon cancer progression and supports a role of PPI in CRC prevention.

BackgroundHigher physical activity levels are associated with lower breast cancer-specific mortality. In addition, the metabolic syndrome is associated with higher breast cancer-specific mortality. Whether the physical activity association with breast cancer mortality is modified by number of metabolic syndrome components (cardiometabolic risk factors) in postmenopausal women with early-stage breast cancer remains unknown.MethodsCardiovascular risk factors included high waist circumference, hypertension, high cholesterol, and diabetes. Breast cancers were verified by medical record review. Mortality finding were enhanced by serial National Death Index queries. Cox proportional hazards regression models were used to estimate associations between baseline physical activity and subsequent breast cancer-specific and overall mortality following breast cancer diagnosis in Women's Health Initiative participants. These associations were examined after stratifying by cardiometabolic risk factor group.ResultsAmong 161,308 Women's Health Initiative (WHI) participants, 8543 breast cancers occurred after 9.5 years (median) follow-up in women, additionally with information on cardiometabolic risk factors and physical activity at entry. In multi-variable analyses, as measured from cancer diagnosis, higher physical activity levels were associated with lower all-cause mortality risk (hazard ratio [HR] 0.86, 95% confidence interval [CI] 0.78-0.95, trend P

This study was aimed at evaluation of two diagnostic techniques and the cultural isolation of Staphylococcus aureus for the testing of subclinical mastitis in lactating cows. A total of 144 milk samples from 36 cows were examined. Ten (10) ml milk samples were aseptically collected from each quarter into labelled sterile universal bottles. The California Mastitis Test (CMT) and the Methylene Blue Reduction Test (MBRT) were carried out on each sample, before cultural isolation (gold standard test) was conducted. Forty eight milk samples were CMT-positive and 60 samples were MBRT-positive, while the gold standard (S. aureus isolation) had 31 positives. The 89.5 % samples of CMT-positive were correctly identified by the test culture. The proportions of samples that tested negative for Subclinical Mastitis (SCM) that did not have the disease, which is a negative predictive value, are 97.9 % and 92.9 % for CMT and MBRT, respectively. The highest total resistance of S. aureus to antibiotics was detected for gentamycin and chloramphenicol (100 %), followed by streptomycin and amoxicillin at 80.6 % and 74.1 %, respectively. The control and monitoring of subclinical mastitis in lactating cows are of great importance to human health. A good management system constitutes the priority in controlling subclinical mastitis in lactating cows.

Flood early warning systems (FEWS) are effective means for saving human lives from the devastating impacts of extreme hydrological events. FEWS relies on hydrologic monitoring networks that are typically expensive and challenging to design. This issue is particularly relevant when identifying the most cost-efficient number, type, and positioning of the sensors for FEWS that may be used to take decisions and alert the population at flood risk.In this study, we focus on a widely recognized FEWS solution to analyze hydrological monitoring and forecasting performances expressed as discharge in various cross-sections of a drainage network. We propose and test a novel framework that aims to maximize FEWS performances while minimizing the number of sections that need instrumentation and suggesting optimal sensor placement to enhance forecasting accuracy. In the selected case study, we demonstrate through feature importance measure that only four sub-basins can achieve the same forecasting performance as the potential twenty-six cross-sections of the local hydrologic monitoring network. The operational dashboard resulting from our proposed framework can assist decision-makers in maximizing the performance and wider adoption of flood early warning systems across geographic and socio-economic scales.

Abstract Accurate modeling of snow depth (SD) processes is critical for understanding global energy balance changes, affecting climate change mitigation strategies. This study evaluates the Coupled Model Intercomparison Project Phase 6 (CMIP6) model performance in simulating daily SD across Canada. We assess CMIP6 outputs against observed data, focusing on daily SD averages, snow cover durations, and rates of accumulation and depletion, alongside annual SD peaks for 11 major Canadian catchments. Our findings reveal that CMIP6 simulations generally overestimate daily SD by 57.7% and extend snow cover duration by 30.5 days on average. While three models (CESM2, UKESM1‐0‐LL and MIROC6) notably align with observed annual SD peaks, simulation biases suggest the need for enhanced model parameterization to accurately capture snow physics, particularly in regions with permanent snow cover and complex terrains. This analysis underscores the necessity of refining CMIP6 simulations and incorporating detailed geographical data for better SD predictions.

This work presents a highly sensitive monolithic optical receiver in $\mathrm {0.35~\mu m}$ CMOS. The integrator-based frontend employs periodic photocurrent integration on a parasitic capacitance in combination with an ultra-low-capacitance PIN photodiode. This results in a small effective integration capacitance, and thus a high input-charge-to-output-voltage conversion ratio. Interfering parasitic phenomena that had to be dealt with in the frontend are clock-feedthrough, reset settling time and mismatch. The PIN-diode itself was investigated in terms of spectral responsivity (0.32 A/W at 642 nm), capacitance (3.2 fF for $|V_{\mathrm {bias}}|\gt {\mathrm {4~V}}$ ) and bandwidth (1.7 GHz for $|V_{\mathrm {bias}}|\gt {\mathrm {10~V}}$ ). The BER of the receiver was characterized using correlated double sampling in post-processing. The resulting sensitivity of −52.32 dBm leads to a distance of 18.36 dB to the quantum limit at 100 Mb/s, 642 nm and $\mathrm {BER}=2\cdot 10^{-3}$ .

This paper presents a highly-sensitive monolithic optoelectronic receiver in $\mathbf{180\, {nm}}$ CMOS. An integrating front-end with noise matching via an negative Miller capacitance is proposed, to reduce the power penalty imposed by large PIN photodiodes (PDs). Three new multi-dot PIN PDs are integrated with the front-end. At a wavelength of $\mathbf{642\, {nm}}$ and reverse bias of $\mathbf {8}\,\mathbf {V}$, their responsivity (capacitance) is $\mathbf {0.38\, {A/W}}$ ($\mathbf{29\, {fF}}$), $\mathbf {0.36\, {A/W}}$ ($\mathbf{33\, {fF}}$), and $\mathbf {0.43\, {A/W}}$ ($\mathbf{123\, {fF}}$), respectively. Compared to our previous integrating PIN receivers, the light-sensitive area is up to 30 times larger. At a supply voltage of $\mathbf {1.8\, {V}}$, wavelength of $\mathbf{642\, {nm}}$, bit rate of $\mathbf {20\, {Mbit/s}}$, and reference ${\mathbf{BER}=2\cdot 10^{-3}}$, the prototype receiver achieves a sensitivity of $\mathbf {-55.4\, {dBm}}$ for the first PD, $\mathbf {-56.5\, {dBm}}$ for the second PD, and $\mathbf {-53.4\, {dBm}}$ for the third PD. The best sensitivity equals a distance of only $\mathbf {21.2\, {dB}}$ to the quantum limit.

BackgroundBreast cancer (BC) survivors experience an increased burden of long-term comorbidities, including heart failure (HF). However, there is limited understanding of the risk for the development of HF subtypes, such as HF with preserved ejection fraction (HFpEF), in BC survivors.ObjectivesThis study sought to estimate the incidence of HFpEF and HF with reduced ejection fraction (HFrEF) in postmenopausal BC survivors and to identify lifestyle and cardiovascular risk factors associated with HF subtypes.MethodsWithin the Women's Health Initiative, participants with an adjudicated diagnosis of invasive BC were followed to determine the incidence of hospitalized HF, for which adjudication procedures determined left ventricular ejection fraction. We calculated cumulative incidences of HF, HFpEF, and HFrEF. We estimated HRs for risk factors in relation to HF, HFpEF, and HFrEF using Cox proportional hazards survival models.ResultsIn 2,272 BC survivors (28.6% Black and 64.9% White), the cumulative incidences of hospitalized HFpEF and HFrEF were 6.68% and 3.96%, respectively, over a median of 7.2 years (IQR: 3.6-12.3 years). For HFpEF, prior myocardial infarction (HR: 2.83; 95% CI: 1.28-6.28), greater waist circumference (HR: 1.99; 95% CI: 1.14-3.49), and smoking history (HR: 1.65; 95% CI: 1.01-2.67) were the strongest risk factors in multivariable models. With the exception of waist circumference, similar patterns were observed for HFrEF, although none were significant. In relation to those without HF, the risk of overall mortality in BC survivors with hospitalized HFpEF was 5.65 (95% CI: 4.11-7.76), and in those with hospitalized HFrEF, it was 3.77 (95% CI: 2.51-5.66).ConclusionsIn this population of older, racially diverse BC survivors, the incidence of HFpEF, as defined by HF hospitalizations, was higher than HFrEF. HF was also associated with an increased mortality risk. Risk factors for HF were largely similar to the general population with the exception of prior myocardial infarction for HFpEF. Notably, both waist circumference and smoking represent potentially modifiable factors.

As droughts have widespread social and ecological impacts, it is critical to develop long-term adaptation and mitigation strategies to reduce drought vulnerability. Climate models are important in quantifying drought changes. Here, we assess the ability of 285 CMIP6 historical simulations, from 17 models, to reproduce drought duration and severity in three observational data sets using the Standardized Precipitation Index (SPI). We used summary statistics beyond the mean and standard deviation, and devised a novel probabilistic framework, based on the Hellinger distance, to quantify the difference between observed and simulated drought characteristics. Results show that many simulations have less than ±10% error in reproducing the observed drought summary statistics. The hypothesis that simulations and observations are described by the same distribution cannot be rejected for more than 80% of the grids based on our H distance framework. No single model stood out as demonstrating consistently better performance over large regions of the globe. The variance in drought statistics among the simulations is higher in the tropics compared to other latitudinal zones. Though the models capture the characteristics of dry spells well, there is considerable bias in low precipitation values. Good model performance in terms of SPI does not imply good performance in simulating low precipitation. Our study emphasizes the need to probabilistically evaluate climate model simulations in order to both pinpoint model weaknesses and identify a subset of best-performing models that are useful for impact assessments.

Abstract Agriculture is directly related to food security as it determines the global food supply. Research in agriculture to predict crop productivity and losses helps avoid high food demand with little supply and price spikes. Here, we review ten crop models and one intercomparison project used for simulating crop growth and productivity under various impacts from soil–crop–atmosphere interactions. The review outlines food security and production assessments using numerical models for maize, wheat, and rice production. A summary of reviewed studies shows the following: (1) model ensembles provide smaller modeling errors compared to single models, (2) single models show better results when coupled with other types of models, (3) the ten reviewed crop models had improvements over the years and can accurately predict crop growth and yield for most of the locations, management conditions, and genotypes tested, (4) APSIM and DSSAT are fast and reliable in assessing broader output variables, (5) AquaCrop is indicated to investigate water footprint, quality and use efficiency in rainfed and irrigated systems, (6) all models assess nitrogen dynamics and use efficiency efficiently, excluding AquaCrop and WOFOST, (7) JULES specifies in evaluating food security vulnerability, (8) ORYZA is the main crop model used to evaluate paddy rice production, (9) grain filling is usually assessed with APSIM, DAISY, and DSSAT, and (10) the ten crop models can be used as tools to evaluate food production, availability, and security.

Abstract Extreme rainfall events are expected to intensify with global warming, posing significant challenges to both human and natural environments. Despite the importance of such assessments, they are unevenly widespread across the globe. Here, using bias corrected climate simulations of the latest phase of the Coupled Model Intercomparison Project (CMIP6), we provide a comprehensive assessment on how different rainfall events are expected to change across Brazil. Specifically, (a) we explored the projected changes in both intensity and frequency of rainfall events belonging to the right‐tail of the rainfall distribution using a non‐parametric approach, and (b) quantified how rainfall events associate with different return periods are expected to intensify, using a parametric approach. We found that extreme rainfall events will become more frequent and intense by the end of the century, with averaged projected changes for rainfall exceeding the historical rainfall quantile q0.99 of nearly 100% and 10% on frequency and intensity, respectively. Non‐extreme rainfall events, in contrast, are expected to be less frequent, aligning with the compensation hypothesis. For instance, Brazilian 100‐year rainfall are anticipated to intensify, on average, 17% and 31% under the moderate and the highest CMIP6 emission scenarios, respectively. Finally, our findings suggest that frequency, rather than intensity, dictates the projected changes of rainfall. We believe that the evidence gathered here will certainly contribute to not only an improved understanding of Brazilian rainfall events but also to a better comprehension of the different rainfall properties, their interplay and how the different ways of assessing them may affect climate studies.

BACKGROUND: Cardiometabolic abnormalities are a leading cause of death among women, including women with cancer. METHODS: This study examined the association between prediagnosis cardiovascular health and total and cause-specific mortality among 12,076 postmenopausal women who developed local- or regional-stage invasive cancer in the Womens Health Initiative (WHI). Cardiovascular risk factors included waist circumference, hypertension, high cholesterol, and type 2 diabetes. Obesity-related cancers included breast cancer, colorectal cancer, endometrial cancer, kidney cancer, pancreatic cancer, ovarian cancer, stomach cancer, liver cancer, and non-Hodgkin lymphoma. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) adjusted for important predictors of survival. RESULTS: After a median follow-up of 10.0 years from the date of the cancer diagnosis, there were 3607 total deaths, with 1546 (43%) due to cancer. Most participants (62.9%) had 1 or 2 cardiometabolic risk factors, and 8.1% had 3 or 4. In adjusted models, women with 3 to 4 risk factors (vs none) had a higher risk of all-cause mortality (HR, 1.99; 95% CI, 1.73-2.30), death due to cardiovascular disease (CVD) (HR, 4.01; 95% CI, 2.88-5.57), cancer-specific mortality (HR, 1.37; 95% CI, 1.1-1.72), and other-cause mortality (HR, 2.14; 95% CI, 1.70-2.69). A higher waist circumference was associated with greater all-cause mortality (HR, 1.17; 95% CI, 1.06-1.30) and cancer-specific mortality (HR, 1.22; 95% CI, 1.04-1.42). CONCLUSIONS: Among postmenopausal women diagnosed with cancer in the WHI, cardiometabolic risk factors before the cancer diagnosis were associated with greater all-cause, CVD, cancer-specific, and other-cause mortality. These results raise hypotheses regarding potential clinical intervention strategies targeting cardiometabolic abnormalities that require future prospective studies for confirmation. LAY SUMMARY: This study uses information from the Womens Health Initiative (WHI) to find out whether cardiac risk factors are related to a greater risk of dying among older women with cancer. The WHI is the largest study of medical problems faced by older women in this country. The results show that women who have 3 or 4 risk factors are more likely to die of any cause, heart disease, or cancer in comparison with women with no risk factors. It is concluded that interventions to help to lower the burden of cardiac risk factors can have an important impact on survivorship among women with cancer.

OBJECTIVES: Lung cancer is the leading cause of cancer mortality in both men and women in the United States. COPD is associated with lung cancer independently of cigarette smoking, but remains understudied in women. Utilizing data from the Womens Health Initiative Observational Study (WHI-OS), this report investigates the association between COPD and development of lung cancer, with a focus on ethnicity and cancer subtype. MATERIALS AND METHODS: The WHI-OS, part of the larger Womens Health Initiative (WHI), is comprised of postmenopausal women between ages 50 and 79 years old at enrollment. Self-administered questionnaires were utilized to gather baseline demographic, socioeconomic, and behavioral information from participants. For this analysis, COPD status was determined at study entry (baseline) and on annual survey (incident). Information on the primary outcome of interest, diagnosis of lung cancer, was also collected annually. RESULTS AND CONCLUSION: Of the 92,789 women examined, 1,536 developed lung cancer. Overall, women with COPD were 1.64 times more likely to develop lung cancer than those without COPD, after adjusting for smoking status and intensity, ethnicity, education, body mass index, and income (HR = 1.64, 95 % CI: 1.43, 1.89). The relationship between COPD and lung cancer was not found to be significantly different between ethnic groups (p-value = 0.697). The associations between COPD and lung cancer was similar across subtypes (HR range 1.31-2.16), after adjusting for smoking status and intensity. COPD increases risk of lung cancer in women, thus they may benefit from more intensive surveillance compared to similar women without COPD.

A canonical genealogy of artificial intelligence must include technologies and data being built with, for and from animals. Animal identification using forms of electronic monitoring and digital management began in the 1970s. Early data innovations comprised RFID tags and transponders that were followed by digital imaging and computer vision. Initially applied in the 1980s for agribusiness to identify meat products and to classify biosecurity data for animal health, yet computer vision is interlaced in subtler ways with commercial pattern recognition systems to monitor and track people in public spaces. As such this paper explores a set of managerial projects in Australian agriculture connected to computer vision and machine learning tools that contribute to dual-use. Herein, ‘the cattle crush’ is positioned as a pivotal space for animal bodies to be interrogated by AI imaging, digitization and data transformation with forms of computational and statistical analysis. By disentangling the kludge of numbering, imaging and classifying within precision agriculture the paper highlights a computational transference of techniques between species, institutional settings and domains that is relevant to regulatory considerations for AI development. The paper posits how a significant sector of data innovation – concerning uses on animals – may tend to evade some level of regulatory and ethical scrutiny afforded to human spaces and settings, and as such afford optimisation of these systems beyond our recognition.

Abstract We merge classical extreme value methods to extract high (high temperatures (HT)) and low (low temperatures (LT)) temperatures and form time series having at least one extreme value per year. Observed daily maximum and minimum temperature records are used from 4,797 quality‐controlled, global, surface stations over 1970–2019. We assess changes in the magnitude and frequency of extreme temperatures by introducing and applying novel methods that exploit the definition of stationarity. Analysis shows significant increasing (40.6% of the stations) and decreasing (41.1%) trends in the frequency of high and LT, respectively, and increasing trends in both high‐ and low‐temperature values (35.6% and 49.7%). Globally, HT and LT frequencies are increasing and decreasing, respectively, by 0.9% and 1.1% per year, relative to the expected frequencies under the assumption of stationarity. The global mean annual HT and LT magnitudes are increasing by 0.004 and 0.016°C/year compared to the expected ones under stationarity. The results indicate that the assumption of stationarity fails to explain the observed changes. The proposed methods are an alternative approach to classical extreme value methods and a useful tool to reveal changes in extremes in the era of earth‐system change.

BackgroundMost studies demonstrating an association between excess adiposity and postmenopausal breast cancer have used anthropometric measures, particularly body mass index (BMI). However, more direct body fat measures may more accurately determine the relationship between body fat distribution and breast cancer risk.MethodsCox proportional hazards regression models were created to examine the associations of dual-energy x-ray absorptiometry (DXA) body fat measures (at baseline and during follow-up) with breast cancer risk among 10 931 postmenopausal women from the Women's Health Initiative cohort. A total of 639 incident invasive breast cancer cases (including 484 estrogen receptor positive (ER+) cases) were ascertained after a median follow-up of 15.0 years.ResultsExcess whole body fat mass and trunk fat mass were positively associated with risk invasive breast cancer risk. These associations persisted even after additional adjustment for standard anthropometric measures. In time-dependent analyses, we observed that both whole body fat mass and trunk fat mass, in the highest versus lowest category, were associated with a doubling of risk of invasive breast cancer overall (HR: 2.17; 95% CI: 1.54-3.05 and 2.20; 1.55-3.14, respectively) and of ER+ breast cancer (2.05; 1.37-3.05 and 2.03; 1.34-3.07, respectively). The remaining DXA measures were also positively associated with breast cancer risk in baseline and time-dependent analyses.ConclusionThese findings suggest that DXA-derived body fat measures are positively associated with breast cancer risk after adjustment for BMI and other conventional breast cancer risk factors.

BackgroundObesity is a strong risk factor for endometrial cancer, but it is unclear whether metabolic syndrome (MetS) contributes to endometrial cancer risk over and above the contribution of obesity.MethodsWe examined the association of MetS and its components with risk of endometrial cancer in a sub-cohort of 24,210 women enrolled in the Women's Health Initiative cohort study. Two variants of the National Cholesterol Education Program Adult Treatment Panel III definition of the MetS were used: one including and one excluding waist circumference (WC). Cox proportional hazards models were used to estimate the association of the study exposures with disease risk.ResultsWhen WC was included in the definition, MetS showed an approximately two-fold increase in endometrial cancer risk (HR 2.20; 95% CI 1.61-3.02); however, when WC was excluded, MetS was no longer associated with risk. We also observed that women with hyperglycemia, dyslipidemia and hypertension, in combination, had almost a twofold increased risk of endometrial cancer, independent of WC (HR 1.94; 95% CI 1.09, 3.46). Glucose, and, in particular, WC and body mass index were also positively associated with risk.ConclusionsOur findings suggest that MetS may predict risk of endometrial cancer independent of obesity among women with the remaining four Mets components.