Universality in odd-even harmonic generation and application in terahertz waveform sampling

Odd-even harmonics emitted from a laser-target system imprint rich, subtle information characterizing the system's dynamical asymmetry, which is desirable to decipher. In this Letter, we discover a simple universal relation between the odd-even harmonics and the asymmetry of the THz-assisted laser-atomic system -- atoms in a fundamental mid-IR laser pulse combined with a THz laser. First, we demonstrate numerically and then analytically formulize the harmonic even-to-odd ratio as a function of the THz electric field, the source of the system's asymmetry. Notably, we suggest a scaling that makes the obtained rule universal, independent of the parameters of both the fundamental pulse and atomic target. This universality facilitates us to propose a general pump-probe scheme for THz waveform sampling from the even-to-odd ratio, measurable within a conventional compact setup

Effects of riverbed incision on the hydrology of the Vietnamese Mekong Delta

The hydrogeomorphology of the Vietnamese Mekong Delta (VMD) has been significantly altered by natural and anthropogenic drivers. In this study, the spatiotemporal changes of the flow regime were examined by analysing the long-term daily, monthly, annual and extreme discharges and water levels from 1980 to 2018, supported by further investigation of the long-term annual sediment load (from the 1960s to 2015), river bathymetric data (in 1998, 2014 and 2017) and daily salinity concentration (from the 1990s to 2015) using various statistical methods and a coupled numerical model. Then, the effects of riverbed incision on the hydrology were investigated. The results show that the dry season discharge (i.e., in March–June) of the Tien River increased by up to 23% from the predam period (1980–1992) to the postdam period (1993–2018) but that the dry season water level at My Thuan decreased by up to −46%. The annual mean and monthly water levels in June at Tan Chau and in January and June–October at My Thuan in the Tien River decreased statistically, even though the respective discharges increased significantly. These decreased water levels instead of the increased discharges were attributed to the accelerated riverbed incision upstream from My Thuan, which increased by more than three times, from a mean rate of −0.16 m/year (−16.7 Mm³/year) in 1998–2014 to −0.5 m/year (−52.5 Mm³/year) in 2014–2017. This accelerated riverbed incision was likely caused by the reduction in the sediment load of the VMD (from 166.7 Mt/year in the predam period to 57.6 Mt/year in the postdam period) and increase in sand mining (from 3.9 Mm3 in 2012 to 13.43 Mm3 in 2018). Collectively, the decreased dry season water level in the Tien River is likely one of the main causes of the enhanced salinity intrusion

Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam

Groundwater salinization is considered as a major environmental problem in worldwide coastal areas, influencing ecosystems and human health. However, an accurate prediction of salinity concentration in groundwater remains a challenge due to the complexity of groundwater salinization processes and its influencing factors. In this study, we evaluate state-of-the-art machine learning (ML) algorithms for predicting groundwater salinity and identify its influencing factors. We conducted a study for the coastal multi-layer aquifers of the Mekong River Delta (Vietnam), using a geodatabase of 216 groundwater samples and 14 conditioning factors. We compared the predictive performances of different ML techniques, i.e., the Random Forest Regression (RFR), the Extreme Gradient Boosting Regression (XGBR), the CatBoost Regression (CBR), and the Light Gradient Boosting Regression (LGBR) models. The model performance was assessed by using root-mean-square error (RMSE), coefficient of determination (R2), the Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results show that the CBR model has the highest performance with both training (R2 = 0.999, RMSE = 29.90) and testing datasets (R2 = 0.84, RMSE = 205.96, AIC = 720.60, and BIC = 751.04). Ten of the 14 influencing factors, including the distance to saline sources, the depth of screen well, the groundwater level, the vertical hydraulic conductivity, the operation time, the well density, the extraction capacity, the thickness of the aquitard, the distance to fault, and the horizontal hydraulic conductivity are the most important factors for groundwater salinity prediction. The results provide insights for policymakers in proposing remediation and management strategies for groundwater salinity issues in the context of excessive groundwater exploitation in coastal lowland regions. Since the human-induced influencing factors have significantly influenced groundwater salinization, urgent actions should be taken into consideration to ensure sustainable groundwater management in the coastal areas of the Mekong River Delta

Respiratory virus laboratory pandemic planning an surveillance in central Viet Nam, 2008-2010

Introduction: Laboratory capacity is needed in central Viet Nam to provide early warning to public health authorities of respiratory outbreaks of importance to human health, for example the outbreak of influenza A(H1N1) pandemic in 2009. Polymerase chain reaction (PCR) procedures established as part of a capacity-building process were used to conduct prospective respiratory surveillance in a region where few previous studies have been undertaken.Methods: Between October 2008 and September 2010, nose and throat swabs from adults and children (approximately 20 per week) presenting with an acute respiratory illness to the Ninh Hoa General Hospital were collected. Same-day PCR testing and result reporting for 13 respiratory viruses were carried out by locally trained scientists.Results: Of 2144 surveillance samples tested, 1235 (57.6%) were positive for at least one virus. The most common were influenza A strains (17.9%), with pandemic influenza A(H1N1) 2009 and seasonal H3N2 strain accounting for 52% and 43% of these, respectively. Other virus detections included: rhinovirus (12.4%), enterovirus (8.9%), influenza B (8.3%), adenovirus (5.3%), parainfluenza (4.7%), respiratory syncytial virus (RSV) (3.9%), human coronavirus (3.0%) and human metapneumovirus (0.3%). The detection rate was greatest in the 0–5 year age group. Viral co-infections were identified in 148 (6.9%) cases.Discussion: The outbreak in 2009 of the influenza A(H1N1) pandemic strain provided a practical test of the laboratory’s pandemic plan. This study shows that the availability of appropriate equipment and molecular-based testing can contribute to important individual and public health outcomes in geographical locations susceptible to emerging infections

Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening