Spatial-temporal change of river thermal environment and anthropogenic impact in China.

River water temperature is important and closely related to river ecosystem, concerning fishery industry, human health, and the land-sea exchange of nutrients, especially for great powers with a good deal of heat emission from once-through cooling systems of thermal power plants. However, the changes in river water temperature under the joint action of climate change and human activity such as the heat emission have not been well investigated for rising powers, hampering environmental policy making for sustainable development. Therefore, we have taken advantage of a recently-developed land surface model including river water temperature calculation with anthropogenic thermal discharge and zonal statistics to quantitatively make out the river water temperature variation and the man-made influence over the past thirty years (1981-2010) in China for the first time. Results show that the estimated water temperature in major rivers is generally close to the observation with the r2 of 0.83, though the underestimation exists in some rivers. The river water in the Pearl River Basin was the warmest with the mean temperature of 19.2 °C and the others in order were in the Southeast Basin, the Huaihe River Basin, the Yangtze River Basin, the Haihe River Basin, the Yellow River Basin, the Southwest Basin, the Song-Liao River Basin, and the Continental Basin, ranging from 16.7 °C to 6.3 °C. The Huaihe River Basin had the fastest mean increase rate of ca. 0.27 °C decade-1, while the slowest mean increase rate of ca. 0.13 °C decade-1 existed in the Pearl River Basin. At the subbasin scale, a meridionally-distributed hot spot zone (along the 115°E) of the increasing water temperature has been identified, where the trends ranged from 0.2 °C decade-1 to 0.5 °C decade-1. Air temperature exerted a major control on the spatial pattern of climatological water temperature, while both air temperature and downwelling solar flux played a leading role in the distribution of water temperature change trends. Although anthropogenic thermal emission heated the rivers locally, the impacts in the Song-Liao River, the Haihe River, the Huaihe River, and the middle and lower reaches of Yellow River and Yangtze River had been raised up to ca. 4.5 °C, when comparing with those controlled by climate change only. In general, these results show the acceptable level of river water temperature simulation in the land surface model, and could provide a scientific reference for the assessment on riverine thermal environment under the climate change and social impact in China.

Mitochondrial genome data provide insights into the phylogenetic relationships within Triplophysadalaica (Kessler, 1876) (Cypriniformes, Nemacheilidae).

Due to the detrimental effect of formaldehyde on DNA, ethanol has replaced formalin as the primary preservative for animal specimens. However, short-term formalin fixation of specimens might be applied during field collection. In an increasing number of studies, DNA extraction and sequencing have been successfully conducted from formalin-fixed specimens. Here the DNA from five specimens of Triplophysadalaica (Kessler, 1876) were extracted and performed high-throughput sequencing. Four of the specimens underwent short-term fixation with formalin and were subsequently transferred to ethanol. One was continuously stored in ethanol. No significant difference of DNA quality and amount were observed among these samples. Followed by assembly and annotation, five mitochondrial genomes ranging in length from 16,569 to 16,572 bp were obtained. Additionally, previously published data of other individuals or species were included to perform phylogenetic analyses. In the reconstructed trees, all eight individuals of T.dalaica form a monophyletic group within the Triplophysa branch. The group is divided into three clades: (1) samples from the Yellow River, (2) those from the Yangtze River, and (3) those from the Haihe River, and the Lake Dali Nur. This study sheds initial light on the phylogeographic relationships among different populations of T.dalaica, and will support the research about its evolutionary history in the future.

[Effects of Land Use Types on Water Quality at Different Buffer Scales: Tianjin Section of the Haihe River Basin as an Example].

It is important to explore the relationship between land use types and water quality to improve the surface water environment. Based on monthly water quality monitoring data from 16 nationally controlled surface water quality monitoring stations in Tianjin and land use data in 2021, GIS spatial analysis and mathematical and statistical methods were used to study the influence of land use types on surface water quality in buffer zones at different scales. The results showed that:① the land use types in the study area were mainly construction land, farmland, and water areas, which had significant effects on river water quality. Except for water temperature (WT) and pH, the farmland, construction land, and water areas were negatively correlated with each water quality indicator; forest land and grassland were positively correlated with dissolved oxygen (DO) and total nitrogen (TN) and negatively correlated with other water quality indicators. ② The water quality indicators showed obvious spatial differences in different seasons. The pH, DO and TN concentrations were higher in the dry season, whereas the permanganate index, ammonia nitrogen (NH4+-N), and total phosphorus (TP) concentrations were higher in the rainy season. ③ The results of the RDA analysis showed that the 800 m buffer zone land use had the greatest explanatory power for water quality changes in the dry season (50.4%), whereas the 3 000 m buffer zone land use could explain the water quality changes in the rainy season to the greatest extent (49.6%); from the average explanation rate of the dry and rainy seasons, the 3 000 m buffer zone was the best impact scale (50.0%) on water quality indicators in Tianjin. ④ The partial least squares regression (PLSR) analysis showed that the most important variables affecting surface water quality changes were construction land, farmland, and water areas. The predictive ability of the PLSR model of most water quality indicators was stronger in the dry season than that in the rainy season. In the dry season, all water quality indicators, except WT and pH, were most influenced by farmland. In the rainy season, construction land had the greatest influence on WT and NH4+-N concentrations, and the most important influencing factor for the remaining water quality indicators was still farmland. This study showed that the rational planning of land use types within 3 000 m of rivers or lakes was beneficial to improving the water quality of surface water.

Simulating behavior of perfluorooctane sulfonate (PFOS) in the mainstream of a river system with sluice regulations.

Perfluorooctane sulfonate (PFOS) is a persistent, anionic and ubiquitous contaminant that undergoes long-range transport within the environment. Its behavior has attracted wide-range academic and regulatory attention. In this article, a mass balance model was employed to simulate PFOS concentrations in the mainstream of Haihe River water system, encompassing sluices and artificial rivers. The dynamic simulation of PFOS concentrations in both sediment and freshwater took into account fluctuations in PFOS emissions, water levels and water discharge. Furthermore, the study delved into exploring the impacts of sluices and artificial rivers on the behavior of PFOS. The simulated concentrations of PFOS in steady state agreed with the measured concentrations in surveys carried out in Nov. 2019, July 2020, Oct. 2020, and June 2021. Every year, approximately 24 kg PFOS was discharged into the Bohai Sea with Chaobai New River being the largest contributor for 44 %. Moreover, the transport of PFOS in the original rivers is likely to be restricted by sluices and replaced by artificial rivers. Monte Carlo analysis showed that model predictions of PFOS concentrations in sediment were subject to greater uncertainty than those in freshwater as the former is impacted by more parameters, such as density of sediment. This study provides a scientific basis for the local government to manage and control PFOS.

Machine learning model of imipenem-resistant Klebsiella pneumoniae based on MALDI-TOF-MS platform: An observational study.

Background and Aim: Machine learning is an important branch and supporting technology of artificial intelligence, we established four machine learning model for the drug sensitivity of Klebsiella pneumoniae to imipenem based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and compared their diagnostic effect. Methods: The data of MALDI-TOF-MS and imipenem sensitivity of 174 cases of K. pneumoniae isolated from clinical specimens in the laboratory of microbiology department of Tianjin Haihe Hospital from 2019 January to 2020 December were collected. The mass spectrometry and imipenem sensitivity of 70 cases of imipenem-sensitive and 70 resistant cases were randomly selected to establish the training set model, 17 cases of sensitive and 17 cases of resistant cases were randomly selected to establish the test set model. Mass spectral peak data were subjected to orthogonal partial least squares discriminant analysis (OPLS-DA), the training set data model was established by machine learning least absolute shrinkage and selection operator (LASSO) algorithm, logistic regression (LR) algorithm, support vector machines (SVM) algorithm, neural network (NN) algorithm, the area under the curve (AUC) and confusion matrix of training set and test set model were calculated and selected by Grid search and 3-fold Cross-validation respectively, the accuracy of the prediction model was verified by test set confusion matrix. Results: The R²Y and Q² of OPLS-DA were 0.546 and 0.0178. The AUC of the best training set and test set models were 0.9726 and 0.9100, 1.0000 and 0.8581, 0.8462 and 0.6263, 1.0000 and 0.7180 evaluated by LASSO, LR, SVM and NN model respectively. The accuracy of the LASSO, LR, SVM and NN model were 87%, 79%, 62%, and 68% in test set, respectively. Conclusion: The LASSO prediction model of K. pneumoniae sensitivity to imipenem established in this study has a high accuracy rate and has potential clinical decision support ability.

Occurrence, spatiotemporal distribution patterns,partitioning and risk assessments of multiple pesticide residues in typical estuarine water environments in eastern China.

The low terrain and the prosperous agriculture in the east of China, have caused the accumulation of pesticide residues in the estuaries. Therefore, this study analyzed the spatiotemporal distribution and partition tendency of 106 pesticides based on their abundance, frequencies, and concentrations in the aquatic environment of 16 river estuaries in 7 major basins in the eastern China by using solid-phase extraction (SPE) with high-performance liquid chromatography tandem mass spectrometry (HPLC‒MS/MS) and gas chromatography tandem mass spectrometry (GC‒MS/MS). In addition, potential risk of multiple pesticides was also evaluated. The results showed that herbicides were the dominant pesticide type, while triazines were the predominate substance group of pesticide. In addition, triadimenol, vinclozolin, diethylatrazine, prometryn, thiamethoxam, atrazine, and metalachlor were the major pesticides in the water, while prometryn, metalachlor, and atrazine were the main pesticides in the sediment. The average total concentration of pesticide was 751.15 ng/L in the dry season, 651.17 ng/L in the wet season, and 617.37 ng/L in the normal season, respectively. The estuaries of the Huai River Basin, the Yangtze River Basin, the Hai River Basin, and the Yellow River Basin have been affected by the low pollution treatment efficiency, weak infrastructure, and agricultural/non-agricultural activities in eastern China, resulting in relatively serious pesticide pollution. The estuaries of Huaihe River, Yangtze River, Xiaoqing River, and Luanhe River had large pesticide abundance and comparatively severe pesticide pollution, while the estuaries of Tuhai River and Haihe River had heavy pesticide contamination in the sediment, which might be induced by historical sedimentary factors. The log KOC values showed that except for thioketone, other pesticides were relatively stable due to the adsorption by sediment. The ecological risk assessment results indicated that insecticides had a high risk. Teenagers were the most severely affected by the noncarcinogenic risk of pesticides, while adults were mostly affected by the carcinogenic risk of pesticides. Therefore, pesticide hazards in the water environment of estuaries in eastern China needs to be further close supervision.

Anthropogenic original DOM is a critical factor affecting LNA bacterial community assembly.

We investigated the geographical and environmental distance-decay relationships for both of the two bacteria in the Haihe River, Tianjin, China. HNA bacteria exhibited a stronger geographical variation-dependent pattern while LNA bacteria exhibited a stronger environmental variation-dependent pattern. Variance partition analysis (VPA), Mantel test, and partial mantel test validated the discrepant impacts of geographical distance and environmental factors on their two communities. The heterogeneous selection dominated community assembly of LNA bacteria demonstrates their greater sensitivity to environmental conditions. As the deterministic environmental factor, anthropogenic original dissolved organic matter (DOM) functions exclusively on LNA bacteria, and it is the critical factor leading to the discrepant biogeographical patterns of LNA and HNA bacteria. LNA bacteria interact with HNA bacteria and mediate the DOM driving total bacteria assembly. The LNA keystone taxa, Pseudomonas, Rheinheimera, Candidatus Aquiluna, and hgcl clade are capable to compete with HNA bacteria for anthropogenic original DOM, and are potential indicators of anthropogenic pollution. Our research reveals the non-negligible effect of the LNA bacteria in regulating the ecological response of total bacteria.

Cerebral venous thrombosis (CVT) complicating tubercular meningitis.

BACKGROUND: Tuberculous meningitis (TBM), complicated with cerebral venous thrombosis (CVT), has been sparsely reported and needs to be investigated further. METHODS: Among those with tuberculous meningitis in Haihe Hospital, Tianjin University, 3 patients with venous sinus thrombosis were identified retrospectively. "Tuberculous meningitis" and "cerebral venous thrombosis" were used as keywords, and the retrieved literature was summarized and analyzed. Our data were combined with previously reported case data to describe this new condition. RESULTS: Among 28 patients with a median onset age of 31 years for TBM, 17 were females. The manifestations were fever, headache, and seizure. Magnetic resonance imaging (MRI) venography showed that the most common site of venous sinus thrombosis involved superior sagittal sinus, left transverse sinus, left sigmoid sinus, cavernous sinus, and straight sinus. The abnormalities found on MRI include hydrocephalus, exudates, hemorrhage, meningeal enhancement, infarction, and tuberculoma. In the acute phase, all patients received standard anti-TB treatment, and 14/28 patients received anticoagulant treatment. The mortality rate of these patients was 17.9%, and 21/28 (75%) became functionally independent. CONCLUSIONS: CVT is one of the rare complications of TMB and must be considered a differential diagnosis in patients with TBM who show poor clinical features and/or develop new neurological signs.

Gumarontinib in patients with non-small-cell lung cancer harbouring MET exon 14 skipping mutations: a multicentre, single-arm, open-label, phase 1b/2 trial.

Background: Approximately 3-4% of patients with non-small-cell lung cancer (NSCLC) have MET exon 14 (METex14) skipping mutations. We report primary results from the phase 2 stage of a phase 1b/2 study of gumarontinib, a selective, potent, oral MET inhibitor, in patients with METex14 skipping mutation-positive (METex14-positive) NSCLC. Methods: The single-arm, multicentre, open-label, phase 2 stage of the GLORY study was conducted at 42 centres across China and Japan. Adults with locally advanced or metastatic METex14-positive NSCLC received oral gumarontinib 300 mg once daily in continuous 21-day cycles until disease progression, intolerable toxicity, or withdrawal of consent. Eligible patients had failed one or two prior lines of therapy (not including a MET inhibitor), were ineligible for/refused chemotherapy, and had no genetic alterations targetable with standard therapies. The primary endpoint was objective response rate in patients with a valid baseline tumour assessment, by blinded independent review. The study was registered at ClinicalTrials.gov (NCT04270591). Findings: Between Aug 2, 2019 and Apr 28, 2021, 84 patients were enrolled and received gumarontinib (median follow-up 13.5 months [IQR 8.7-17.1]), at data cut-off (Apr 28, 2022) five patients whose METex14 status could not be confirmed by a central laboratory were excluded from the efficacy analysis. The objective response rate was 66% (95% CI 54-76) overall (n = 79), 71% (95% CI 55-83) in treatment-naïve patients (n = 44), and 60% (95% CI 42-76) in previously-treated patients (n = 35). The most common treatment-related adverse events (any grade) were oedema (67/84 patients, 80%) and hypoalbuminuria (32/84, 38%). Grade ≥3 treatment-emergent adverse events occurred in 45 (54%) patients. Treatment-related adverse events leading to permanent discontinuation occurred in 8% (7/84) of patients. Interpretation: Gumarontinib monotherapy had durable antitumour activity with manageable toxicity in patients with locally advanced or metastatic METex14-positive NSCLC when used in first line or later. Funding: Haihe Biopharma Co., Ltd. Supported in part by grants from the National Science and Technology Major Project of China for "Clinical Research of Gumarontinib, a highly selective MET inhibitor" (2018ZX09711002-011-003); the National Natural Science Foundation of China (82030045 to S.L. and 82172633 to YF.Y); Shanghai Municipal Science & Technology Commission Research Project (19411950500 to S.L.); Shanghai Shenkang Action Plan (16CR3005A to S.L.) and Shanghai Chest Hospital Project of Collaborative Innovation (YJXT20190105 to S.L.).

Heavy metals in influent and effluent from 146 drinking water treatment plants across China: Occurrence, explanatory factors, probabilistic health risk, and removal efficiency.

Heavy metals (HMs) in drinking water have drawn worldwide attention due to their risks to public health; however, a systematic assessment of the occurrence of HMs in drinking water treatment plants (DWTPs) at a large geographical scale across China and the removal efficiency, human health risks, and the correlation with environmental factors have yet to be established. Therefore, this study characterised the occurrence patterns of nine conventional dissolved HMs in the influent and effluent water samples from 146 typical DWTPs in seven major river basins across China (which consist of the Yangtze River, the Yellow River, the Songhua River, the Pearl River, the Huaihe River, the Liaohe River and the Haihe River) for the first time and removal efficiency, probabilistic health risks, and the correlation with water quality. According to the findings, a total of eight HMs (beryllium (Be), antimony (Sb), barium (Ba), molybdenum (Mo), nickel (Ni), vanadium (V), cobalt (Co) and titanium (Ti)) were detected, with detection frequencies in influent and effluent water ranging from 2.90 (Mo) to 99.30% (Ba) and 1.40 (Ti) to 97.90% (Ba), respectively. The average concentration range was 0.41 (Be)- 77.36 (Sb) µg/L. Among them, Sb (exceeding standard rate 8%), Ba (2.89%), Ni (21.43%), and V (1.33%) were exceeded the national standard (GB5749-2022). By combining Spearman's results and redundancy analysis, our results revealed a close correlation among pH, turbidity (TURB), potassium permanganate index (CODMn), and total nitrogen (TN) along with the concentration and composition of HMs. In addition, the concentration of HMs in finished water was strongly affected by the concentration of HMs in raw water, as evidenced by the fact that HMs in surface water poses a risk to the quality of finished water. Metal concentration was the primary factor in assessing the health risk of a single metal, and the carcinogenic risk of Ba, Mo, Ni, and Sb should be paid attention to. In DWTPs, the removal efficiencies of various HMs also vary greatly, with an average removal rate ranging from 16.30% to 95.64%. In summary, our findings provide insights into the water quality and health risks caused by HMs in drinking water.

Groundwater vulnerability assessment using AHP-DRASTIC-GALDIT comprehensive model: a case study of Binhai New Area, Tianjin, China.

Binhai New Area (BHNA), as one of the most economically and industrially regions in the Haihe River Basin, China, is seriously affected by seawater intrusion and groundwater over-exploitation. Groundwater vulnerability assessment (GVA) is an effective tool to protect the groundwater resources from being polluted. In this study, vertical and horizontal groundwater conditional factors were first assessed separately by two different models. The AHP-DRASTIC model was used to evaluate the intrinsic groundwater vulnerability and the AHP-GALDIT model was used to evaluate the specific groundwater vulnerability to seawater intrusion. Then, a GIS-based overlaying approach was used to get the comprehensive shallow groundwater vulnerability. The results of the comprehensive model showed that the vulnerability areas of very low, low, medium, and high account for 1.37%, 11.36%, 60.56%, and 26.71%, respectively. Finally, to effectively manage the groundwater in the study area, two remediation areas, two control areas, and one protected area were determined based on the comprehensive groundwater vulnerability maps. This study can not only promote the development of rational exploitation of shallow groundwater and prevention of groundwater pollution in BHNA but also provide a framework for future research in the GVA on the coast.

Impacts of vegetation restoration on water resources and carbon sequestration in the mountainous area of Haihe River basin, China.

The mountainous region of the Haihe River basin (MHRB) plays an important role in the water resource supply of its nearby mega-cities, including Beijing and Tianjin, and large areas of cropland. With the implementation of afforestation projects in recent decades, vegetation and carbon (C) uptake have greatly increased in the MHRB. In addition, the annual runoff has significantly declined, threatening regional water security. The trade-off relationship between water yield and C uptake in the MHRB remains unknown. This study employed a biogeochemical model (Biome-BGC) to simulate the natural vegetation dynamics and gross primary productivity (GPP) during 1982-2019 driven by climate forcing. A distributed hydrological model (geomorphology-based hydrological model, GBHM) was adopted to assess the impact of vegetation restoration on the hydrological processes. The results indicated that the leaf area index in the MHRB increased significantly (P < 0.01) during 1982-2019, which led to evapotranspiration increase and runoff (R) reduction. Under the influence of vegetation restoration, both the GPP and the water use efficiency (WUE) increased significantly in the MHRB during 2000-2019, however, the improvement of WUE decreased with the aridity index increasing. Our results showed that vegetation restoration can improve C sequestration efficiency in the MHRB and that the trade-off between water yield and C sequestration should be considered in planning ecological projects to achieve C neutrality.

Comparative analysis of water quality prediction performance based on LSTM in the Haihe River Basin, China.

As the most water shortage and water polluted area in China, the water quality prediction is of utmost needed and important in Haihe River Basin for its water resource management. The long short-term memory (LSTM) has been a widely used tool for water quality forecast in recent years. The performance and adaptability of LSTM for water quality prediction of different indicators needs to be discussed before it adopted in a specific basin. However, literature contains very few studies on the comparative analysis of the various prediction accuracy of different water quality indicators and the causes, especially in Haihe River Basin. In this study, LSTM was employed to predict biochemical oxygen demand (BOD), permanganate index (CODMn), dissolved oxygen (DO), ammonia nitrogen (NH3-N), total phosphorus (TP), hydrogen ion concentration (pH), and chemical oxygen demand digested by potassium dichromate (CODCr). According to results under 24 different input conditions, it is demonstrated that LSTMs present better predicting on BOD, CODMn, CODCr, and TP (median Nash-Sutcliffe efficiency reaching 0.766, 0.835, 0.837, and 0.711, respectively) than NH3-N, DO, and pH (median Nash-Sutcliffe efficiency of 0.638, 0.625, and 0.229, respectively). Besides, the performance of LSTM to predict water quality is linearly related to the maximum value of temporal autocorrelation and cross-correlation coefficients of water quality indicators calculated by maximal information coefficient with the coefficients of determination of 0.79 to approximately 0.80. This study would provide new knowledge and support for the practical application and improvement of the LSTM in water quality prediction.

Practice and study on the establishment of transformation-oriented scientific research program in medical field: Taking a medical new research & development institution of Haihe Laboratory of Cell Ecosystem as an example / 中华医学科研管理杂志

Objective:To explore the innovation mode of independent transformation-oriented science and technology research program approval by medical new research & development (R&D) institution.Methods:Through analyzing the program layout, funds, review experts, undertaking units, chief experts and interdisciplinarity to summarize the experiences of the independent transformation-oriented municipal program approval by Haihe laboratory of Cell Ecosystem in 2022.Results:As a new medical R&D institution, which vigorously constructed by Tianjin, Haihe laboratory of Cell Ecosystem has carried out the practice of the independent transformation-oriented municipal program through the measures of layout of full-chain transformation, conducting transformation-oriented review, gathering high-level research talents, and emphasizing interdisciplinarity.Conclusions:The experiences of Haihe laboratory of Cell Ecosystem make significance for medical new R&D institutions to explore and cultivate scientific research program with transformation potential and to promote the transformation of scientific and technological achievements, which are powerful factors for new R&D institutions to play a role of pilot and provide important support to scientific and technological innovation and transformation.

Comparison of the diagnostic effect on different machine learning predictive model of imipenem sensitivity of Klebsiella pneumoniae based on MALDI-TOF MS / 中华检验医学杂志

Objective:Machine learning is not only an important branch of artificial intelligence, but also supporting technologies for bioinformatics analysis. In the presence work, four machine-learning-predictive model for the drug-sensitivity of Klebsiella pneumoniae to imipenem were established based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and the diagnostic effect of these methods was exmained. Methods:A retrospective study was performed and the data of MALDI-TOF-MS and imipenem sensitivity of a total number of 684 cases Klebsiella pneumoniae isolated from clinical specimens in the laboratory of microbiology department of Tianjin Haihe Hospital from 2019 January to 2020 December were collected. The mass spectrometry and imipenem sensitivity data of 70 cases identified as imipenem-sensitive and 70 resistant cases were simple randomly selected to establish the training set model; whereas 30 cases of sensitive and 30 cases of resistant cases were randomly selected to establish the test set model. Mass spectral peak data were subjected to Orthogonal Partial least squares Discriminant Analysis (OPLS-DA). The training set data model was established by machine learning least absolute shrinkage and selection operator (LASSO) algorithm, Logistic Regression (LR) algorithm, Support vector machines (SVM) algorithm, neural network (NN) algorithm. The area under the curve (AUC) and confusion matrix of training set and test set model were calculated and selected by Grid search and 10-fold Cross-validation respectively, the accuracy of the prediction model was verified by test set confusion matrix. Results:The R2Y and Q2 of OPLS-DA were 0.546 3 and 0.017 8. The AUC of the best training set and test set models were 1.000 0 and 0.858 1, 1.000 0 and 0.820 1, 0.940 8 and 0.756 1, 1.000 0 and 0.697 2 evaluated by LASSO, LR, SVM and NN model respectively. The accuracy of the model were 99% (69/70), 100% (70/70), 91% (64/70) and 100% (70/70) for prediction of drug resistance, 100% (70/70), 100% (70/70), 90% (63/70) and 100% (70/70) for drug sensitivity prediction, the correct rate were 99% (139/140), 100% (140/140), 91% (127/140) and 100% (140/140) in training set, the test set showed that the accuracy were 93% (28/30), 87% (26/30), 60% (18/30) and 60% (18/30) for prediction of drug resistance, 100% (30/30), 80% (24/30), 93% (28/30) and 67% (20/30) for drug sensitivity prediction, the correct rate were 97% (58/60), 83% (50/60), 77% (46/60) and 63% (38/60) by LASSO, LR, SVM and NN model respectively.Conclusion:The LASSO prediction model of Klebsiella pneumoniae sensitivity to imipenem established in this study has a high accuracy rate and has potential clinical decision support ability.

Effects of Different Types of Human Disturbance on Total and Nitrogen-Transforming Bacteria in Haihe River.

Haihe River is the largest water system in North China and is injected into the Bohai Sea in Tianjin City. In this study, different types of human disturbance (urban sewage, industrial pollution, ship disturbance) were selected from the upper reaches of Haihe river Tianjin section down to the estuary that connected with Bohai Sea for evaluation. By metagenomic sequencing, the effects of different types of disturbances on bacteria communities in Haihe sediments were studied, with a special focus on the function of nitrogen-cycling bacteria that were further analyzed through KEGG comparison. By analyzing the physical and chemical characteristics of sediments, results showed that human disturbance caused a large amount of nitrogen input into Haihe River, and different types of human disturbance led to distinct spatial heterogeneity in different sections of Haihe River. The bacteria community was dominated by Proteobacteria, followed by Chloroflexi, Bacteroidetes, Actinobacteria and Acidobacteria. The relative abundance of each phylum varied at different sites as a response to different types of human disturbances. In nitrogen cycling, microorganisms including nitrogen fixation and removal were detected at each site, which indicated the active potential for nitrogen transformation in Haihe River. In addition, a large number of metabolic pathways relating to human diseases were also revealed in urban and pollution sites by function potential, which provided an important basis for the indicative role of urban river ecosystem for public health security. In summary, by evaluating both the ecological role and function potential of bacteria in Haihe River under different types of human disturbance, the knowledge of microorganisms for healthy and disturbed river ecosystems has been broadened, which is also informative for further river management and bioremediation.

High cerebrospinal fluid lactate concentration at 48 h of hospital admission predicts poor outcomes in patients with tuberculous meningitis: A multicenter retrospective cohort study.

Objective: This study aimed to analyze the cerebrospinal fluid (CSF) parameters affecting the outcomes of patients with tuberculous meningitis (TBM). Methods: This is a multi-center, retrospective, cohort study involving 81 patients who were diagnosed with TBM and treated in Haihe Clinical College of Tianjin Medical University, Tianjin Medical University General Hospital, and General Hospital of Air Force PLA from January 2016 to December 2019. Baseline data, Glasgow Coma Scale (GCS) score, and clinical presentations of all patients were collected at admission. CSF samples were collected at 48 h, 1, 2, and 3 weeks after admission. CSF lactate, adenosine deaminase, chloride, protein, glucose levels and intracranial pressure were measured. After a follow-up of 16.14 ± 3.03 months, all patients were assessed using the modified Rankin Scale (mRS) and divided into good (mRS scores of 0-2 points) and poor outcome groups (mRS scores of 3-6 points). The differences in patients' baseline data, GCS score, clinical presentations, and levels of CSF parameters detected at 48 h, 1, 2, and 3 weeks after admission between two groups were compared. Statistically significant variables were added to the binary logistic regression model to identify the factors impacting the outcomes of patients with TBM. Receiver operating characteristic (ROC) curve was used to assess the predictive ability of the model. Results: The CSF lactate level exhibited a decreasing trend within 3 weeks of admission in the two groups. For the within-group comparison, statistically significant differences in the lactate level was found in both groups between four different time points. A binary logistic regression model revealed that CSF lactate level at 48 h after admission, age, and GSC score on admission were independently associated with the outcomes of patients with TBM. ROC curve analysis showed that the area under the ROC curve (AUC) was 0.786 for the CSF lactate level (48 h), 0.814 for GCS score, and 0.764 for age. Conclusion: High CSF lactate level at 48 h after admission is one of the important factors for poor outcomes in patients with TBM.

Clinical characteristics of 310 SARS-CoV-2 Omicron variant patients and comparison with Delta and Beta variant patients in China.

Although the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has spread, data on the clinical characteristics of infected patients are limited. In this study, the demographic, clinical characteristics, and laboratory data of 310 SARS-CoV-2 Omicron variant patients treated at Haihe Hospital of Tianjin were collected and analyzed. Information on these patients was compared to 96 patients with the Delta variant of concern (VOC) and 326 patients with the Beta VOC during the previous coronavirus disease 2019 (COVID-19) outbreak in Harbin. Of the 310 patients infected with the Omicron variant, the median age was 35 years. Most patients were clinically classified as mild (57.74%), and the most common symptoms were cough (48.71%), fever (39.35%), and sore throat (38.26%). The results for different vaccination groups in the Omicron group showed that the median of "SARS-CoV-2 specific IgG" after 2 or 3 doses of vaccination was higher than the unvaccinated group (all Ps â€‹< â€‹0.05). Older age was associated with a higher proportion of moderate cases and lower asymptomatic and mild cases based on clinical classifications. Compared to the Delta and Beta groups, the median age of the Omicron group was younger. The total number of asymptomatic patients and mild patients in the Omicron virus group was higher than the Delta and Beta groups (60.97% vs. 54.17% vs. 47.55%). This study presented the clinical characteristics of the first group of patients infected with the Omicron variant in Tianjin, China, and compared their clinical features with patients infected by the Delta and Beta variants, which would increase our understanding of the characteristics of SARS-CoV-2 Omicron variant.

Comprehensive climatic suitability evaluation of peanut in Huang-Huai-Hai region under the background of climate change.

The climate changes influence the growing suitability of peanut, an important oil crop. Climatic suitability evaluation in the Huang-Huai-Hai region, the main peanut producing region of China, which can optimize peanut planting structure and provide basis for increasing output. In this study, the temperature, precipitation, sunshine and comprehensive suitability models were established by using the climatic suitability function in different growth periods of peanut. In this study, the climate suitability function of peanut in different growth periods was used to establish the temperature, precipitation, sunshine and comprehensive suitability model. Combined with the meteorological data after Anusplin interpolation, the spatial distribution and chronological change of peanut climate suitability were analyzed. The results show that with climate change, the overall climate becomes warmer and drier and the temperature and precipitation suitability increase, but the sunshine suitability decreases. Based on the comprehensive suitability model, the suitability evaluation results are divided into four levels: the most suitable, suitable, sub-suitable and unsuitable. Among them, the most suitable peanut planting areas in the Huang-Huai-Hai region are concentrated in the west of the Haihe River Basin and the Huaihe River Basin. The data from the next 30 years show that both the most suitable and suitable areas have been expanded. Through the verification of yield correlation analysis and spatial distribution of disaster frequency, it can be seen that the evaluation results have high accuracy, which can be used to guide and optimize peanut production practices.

Identification of soil particle size distribution in different sedimentary environments at river basin scale by fractal dimension.

The geomorphology of river basin is complex, and its soil sedimentary characteristics are poorly defined. To study the spatial variability of soil structure in different sedimentary environments at the basin scale, 356 sets of soil samples were collected from five typical sedimentary environments in the Yellow River Basin and the Haihe River Basin, including the upper and lower reaches of the rivers, mountain-front plains, central alluvial plains and eastern coastal plains. The particle size distribution (PSD) of the soil samples was obtained using a laser particle size analyzer, and the fractal dimension (D) of the soil structure was derived by applying fractal theory. The PSD, D and the correlation between them were analyzed by  the Pearson correlation method for typical sedimentary environments in two basins. The results show that: (1) The main soil types in the typical geological environments in the basin are sand, loamy sand, sandy loam, silty loam, and silty soil. The soil particle size in the upper and lower reaches of the rivers was higher than that in the plain areas. (2) In the plane, The D value descended in different regions in the following order: the mountain-front plain > the eastern coastal plain > the upper Yellow River > the central alluvial plain > the lower Yellow River. In the vertical direction for both rivers, the D value showed a decreasing trend with increasing burial depth. (3) The model results showed a cubic polynomial correlation between D values and PSD, which was closely related to the non-uniformity of particle size during sorting and deposition. The soil PSD and fractal characteristics are effective tools for the quantitative evaluation of soil structure in various sedimentary environments in the basin.