From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling.

The behaviors and skills of models in many geosciences (e.g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration. A well-calibrated model can reasonably propagate information from observations to unobserved variables via model physics, but traditional calibration is highly inefficient and results in non-unique solutions. Here we propose a novel differentiable parameter learning (dPL) framework that efficiently learns a global mapping between inputs (and optionally responses) and parameters. Crucially, dPL exhibits beneficial scaling curves not previously demonstrated to geoscientists: as training data increases, dPL achieves better performance, more physical coherence, and better generalizability (across space and uncalibrated variables), all with orders-of-magnitude lower computational cost. We demonstrate examples that learned from soil moisture and streamflow, where dPL drastically outperformed existing evolutionary and regionalization methods, or required only ~12.5% of the training data to achieve similar performance. The generic scheme promotes the integration of deep learning and process-based models, without mandating reimplementation.

From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?

Dissolved oxygen (DO) reflects river metabolic pulses and is an essential water quality measure. Our capabilities of forecasting DO however remain elusive. Water quality data, specifically DO data here, often have large gaps and sparse areal and temporal coverage. Earth surface and hydrometeorology data, on the other hand, have become largely available. Here we ask: can a Long Short-Term Memory (LSTM) model learn about river DO dynamics from sparse DO and intensive (daily) hydrometeorology data? We used CAMELS-chem, a new data set with DO concentrations from 236 minimally disturbed watersheds across the U.S. The model generally learns the theory of DO solubility and captures its decreasing trend with increasing water temperature. It exhibits the potential of predicting DO in "chemically ungauged basins", defined as basins without any measurements of DO and broadly water quality in general. The model however misses some DO peaks and troughs when in-stream biogeochemical processes become important. Surprisingly, the model does not perform better where more data are available. Instead, it performs better in basins with low variations of streamflow and DO, high runoff-ratio (>0.45), and winter precipitation peaks. Results here suggest that more data collections at DO peaks and troughs and in sparsely monitored areas are essential to overcome the issue of data scarcity, an outstanding challenge in the water quality community.

Explore the relationship between fish community and environmental factors by machine learning techniques.

In the face of multiple habitat alterations originating from both natural and anthropogenic factors, the fast-changing environments pose significant challenges for maintaining ecosystem integrity. Machine learning is a powerful tool for modeling complex non-linear systems through exploratory data analysis. This study aims at exploring a machine learning-based approach to relate environmental factors with fish community for achieving sustainable riverine ecosystem management. A large number of datasets upon a wide variety of eco-environmental variables including river flow, water quality, and species composition were collected at various monitoring stations along the Xindian River of Taiwan during 2005 and 2012. Then the complicated relationship and scientific essences of these heterogonous datasets are extracted using machine learning techniques to have a more holistic consideration in searching a guiding reference useful for maintaining river-ecosystem integrity. We evaluate and select critical environmental variables by the analysis of variance (ANOVA) and the Gamma test (GT), and then we apply the adaptive network-based fuzzy inference system (ANFIS) for an estimation of fish bio-diversity using the Shannon Index (SI). The results show that the correlation between model estimation and the biodiversity index is higher than 0.75. The GT results demonstrate that biochemical oxygen demand (BOD), water temperature, total phosphorus (TP), and nitrate-nitrogen (NO3-N) are important variables for biodiversity modeling. The ANFIS results further indicate lower BOD, higher TP, and larger habitat (flow regimes) would generally provide a more suitable environment for the survival of fish species. The proposed methodology not only possesses a robust estimation capacity but also can explore the impacts of environmental variables on fish biodiversity. This study also demonstrates that machine learning is a promising avenue toward sustainable environmental management in river-ecosystem integrity.

Signals of stream fish homogenization revealed by AI-based clusters.

Risks of stream fish homogenization are attributable to multiple variables operating at various spatial and temporal scales. However, understanding the mechanisms of homogenization requires not only watershed-scale, but also exhaustive fish community structure shifts representing detailed local functional relationships essential to homogenization potentials. Here, we demonstrate the idea of applying AI-based clusters to reveal nonlinear responses of homogenization risks among heterogeneous hydro-chemo-bio variables in space and time. Results found that species introduction, dam isolation, and the potential of climate-mediated disruptions in hydrologic cycles producing degradation in water quality triggered shifts of community assembly and resulting structures producing detrimental conditions for endemic fishes. The AI-based clustering approach suggests that endemic species conservation should focus on alleviation of low flows, control of species introduction, limiting generalist expansion, and enhancing the hydrological connectivity fragmented by dams. Likewise, it can be applied in other geographical and environmental settings for finding homogenization mitigation strategies.

Exploring synergistic benefits of Water-Food-Energy Nexus through multi-objective reservoir optimization schemes.

This study proposed a holistic three-fold scheme that synergistically optimizes the benefits of the Water-Food-Energy (WFE) Nexus by integrating the short/long-term joint operation of a multi-objective reservoir with irrigation ponds in response to urbanization. The three-fold scheme was implemented step by step: (1) optimizing short-term (daily scale) reservoir operation for maximizing hydropower output and final reservoir storage during typhoon seasons; (2) simulating long-term (ten-day scale) water shortage rates in consideration of the availability of irrigation ponds for both agricultural and public sectors during non-typhoon seasons; and (3) promoting the synergistic benefits of the WFE Nexus in a year-round perspective by integrating the short-term optimization and long-term simulation of reservoir operations. The pivotal Shihmen Reservoir and 745 irrigation ponds located in Taoyuan City of Taiwan together with the surrounding urban areas formed the study case. The results indicated that the optimal short-term reservoir operation obtained from the non-dominated sorting genetic algorithm II (NSGA-II) could largely increase hydropower output but just slightly affected water supply. The simulation results of the reservoir coupled with irrigation ponds indicated that such joint operation could significantly reduce agricultural and public water shortage rates by 22.2% and 23.7% in average, respectively, as compared to those of reservoir operation excluding irrigation ponds. The results of year-round short/long-term joint operation showed that water shortage rates could be reduced by 10% at most, the food production rate could be increased by up to 47%, and the hydropower benefit could increase up to 9.33 million USD per year, respectively, in a wet year. Consequently, the proposed methodology could be a viable approach to promoting the synergistic benefits of the WFE Nexus, and the results provided unique insights for stakeholders and policymakers to pursue sustainable urban development plans.

A data-mining framework for exploring the multi-relation between fish species and water quality through self-organizing map.

The steep slopes of rivers can easily lead to large variations in river water quality during typhoon seasons in Taiwan, which may poses significant impacts on riverine eco-hydrological environments. This study aims to investigate the relationship between fish communities and water quality by using artificial neural networks (ANNs) for comprehending the upstream eco-hydrological system in northern Taiwan. We collected a total of 276 heterogeneous datasets with 8 water quality parameters and 25 fish species from 10 sampling sites. The self-organizing feature map (SOM) was used to cluster, analyze and visualize the heterogeneous datasets. Furthermore, the structuring index (SI) was adopted to determine the relative importance of each input variable of the SOM and identify the indicator factors. The clustering results showed that the SOM could suitably reflect the spatial characteristics of fishery sampling sites. Besides, the patterns of water quality parameters and fish species could be distinguishably (visually) classified into three eco-water quality groups: 1) typical upstream freshwater fishes that depended the most on dissolved oxygen (DO); 2) typical middle-lower reach riverine freshwater fishes that depended the most on total phosphorus (TP) and ammonia nitrogen; and 3) low lands or pond (reservoirs) freshwater fishes that depended the most on water temperature, suspended solids and chemical oxygen demand. According to the results of the SI, the representative indicators of water quality parameters and fish species consisted of DO, TP and Onychostoma barbatulum. This grouping result suggested that the methodology can be used as a guiding reference to comprehensively relate ecology to water quality. Our methods offer a cost-effective alternative to more traditional methods for identifying key water quality factors relating to fish species. In addition, visualizing the constructed topological maps of the SOM could produce detailed inter-relation between water quality and the fish species of stream habitat units.

Assessing the natural and anthropogenic influences on basin-wide fish species richness.

Theory predicts that the number of fish species increases with river size in natural free-flowing rivers, but the relationship is lost under intensive exploitation of water resources associated with dams and/or landscape developments. In this paper, we aim to identify orthomorphic issues that disrupt theoretical species patterns based on a multi-year, basin-wide assessment in the Danshuei River Watershed of Taiwan. We hypothesize that multiple human-induced modifications fragment habitat areas leading to decreases of local fish species richness. We integrally relate natural and anthropogenic influences on fish species richness by a multiple linear regression model that is driven by a combination of factors including river network structure controls, water quality alterations of habitat, and disruption of channel connectivity with major discontinuities in habitat caused by dams. We found that stream order is a major forcing factor representing natural influence on fish species richness. In addition to stream order, we identified dams, dissolved oxygen deficiency (DO), and excessive total phosphorus (TP) as major anthropogenic influences on the richness of fish species. Our results showed that anthropogenic influences were operating at various spatial scales that inherently regulate the physical, chemical, and biological condition of fish habitats. Moreover, our probability-based risk assessment revealed causes of species richness reduction and opportunities for mitigation. Risks of species richness reduction caused by dams were determined by the position of dams and the contribution of tributaries in the drainage network. Risks associated with TP and DO were higher in human-activity-intensified downstream reaches. Our methodology provides a structural framework for assessing changes in basin-wide fish species richness under the mixed natural and human-modified river network and habitat conditions. Based on our analysis results, we recommend that a focus on landscape and riverine habitats and maintaining long-term monitoring programs are crucial for effective watershed management and river conservation plans.

Extensive perineal and pelvic defects reconstructed simultaneously using bilateral pedicled gracilis and rectus abdominis muscle flaps after en-bloc excision of locally invasive perineal mucinous adenocarcinoma 1.

We report a case of successful simultaneous reconstruction of pelvic and perineal defects using bilateral pedicled gracilis and inferiorly based rectus abdominis muscle flaps after en-bloc excision of the tumour and abdominoperineal resection of locally advanced invasive perineal mucinous adenocarcinoma originating from a chronic anal fistula.

Intracellular beta-tubulin/chaperonin containing TCP1-beta complex serves as a novel chemotherapeutic target against drug-resistant tumors.

In the present study, treatment of HEK-293 cells with the synthetic small molecule N-iodoacetyl-tryptophan (I-Trp) at submicromolar concentrations efficiently induced cell apoptosis as judged from the accumulation of sub-G(0) cells and intracellular DNA fragmentation. Activation of all intracellular caspases, except caspase-1, was detected in I-Trp-treated cells. Proteomic analysis revealed that beta-tubulin acted as a specific intracellular target of I-Trp. Protein fingerprinting analysis indicated that the Cys(354) residue in the peptide fragment TAVCDIPPR of beta-tubulin, which is located at the binding interface with chaperonin containing TCP1-beta (CCT-beta), was alkylated by I-Trp. Moreover, site-directed mutagenesis of Cys(354) (Cys-Ala) abolished the incorporation of I-Trp into beta-tubulin, suggesting Cys(354) is indeed the targeting site of I-Trp. Immunoprecipitation showed that the beta-tubulin/CCT-beta complex was constitutively formed but disrupted after treatment with I-Trp. Overexpression of the truncated beta-tubulin (T351-S364) or treatment with I-Trp or the synthetic peptide Myr-TAVCDIPPRG caused more severe cell apoptosis in multidrug-resistant MES-SA/Dx5 cancer cells due to higher levels of CCT-beta relative to wild-type MES-SA cancer cells. Silencing the expression of CCT-beta rendered MES-SA/Dx5 cells less sensitive to I-Trp-induced apoptotic cell death. These findings suggest that the beta-tubulin/CCT-beta complex may serve as an effective chemotherapeutic target for treating clinical tubulin-binding agent-resistant or CCT-beta-overexpressing tumors.

Stenotrophomonas maltophilia bacteremia in burn patients.

Stenotrophomonas maltophilia has been increasingly reported as a nosocomial opportunistic pathogen in debilitated patients, including burn patients. There is, however, only one published report in English that discusses S. maltophilia bacteremia in burns. We performed a retrospective chart review and statistical analysis of the incidence, the duration of hospital stays before a diagnosis of bacteremia, antimicrobial susceptibility, prognosis, and mortality risk factors in burn patients. From January 1996 to December 2004, 14 episodes of S. maltophilia bacteremia in 13 of 666 patients admitted to the burn center at our hospital were identified. The patients, nine males and four females, ranged in age from 1 to 76 years (mean: 42.9+/-24.4 years). Eleven injuries were from flame burns and two were from scald burns. The mean total burned surface area (TBSA) was 47+/-30.2% and mean prognostic burn index (PBI) was 81.7+/-31.3. The average annual incidence was 2.3 episodes per 1000 admissions, and no outbreak cluster was noticed. The mean hospital stay before bacteremia was 19.8+/-11.9 days. Most isolates were susceptible to ticarcillin-clavulanate (87.5%) and moxalactam (85.7%). The overall mortality was 30.7% (4/13) and correlated significantly with TBSA (P<0.01) and PBI (P<0.05). The incidence of S. maltophilia bacteremia was higher in hospitalized burn patients than in hospitalized non-burn patients. Different antimicrobial susceptibility patterns may exist, especially in different geographic regions. Awareness of the possibility of infection by this opportunistic pathogen and commencement of adequate antibiotics treatment, especially after 3 weeks of intensive care, should be incorporated into the strategy of treatment in major burn patients.