Interspecific association between two primatespecies in an urban park / Associação interespecífica entre duas espécies de primatas em um parque urbano

As with Amazonian primates, mixed associations between species in the Atlantic Forest are also influenced by ecological factors. However, Atlantic Forest primates may face additional challenges, such as isolation pressures and fragmentation of forest habitats, which may increase the frequency of these arrangements. The main of this work is to report a sympatry with possible interaction between individuals of two species of primates of the Pitheciidae and Callitrichidae families: Callicebus nigrifrons (Spix 1823) and Callithrix aurita (É. Geoffroy Saint-Hilaire 1812) in an urban park in the south of the state of Minas Gerais. Individuals were observed interacting during foraging and displacement. The association of individuals of the two species can be explained by the low quality of the forest fragment, as it can increases the chances of obtaining food resources and configures a dilution strategy against predator attacks.

Assim como ocorre com os primatas amazônicos, as associações mistas entre espécies na Mata Atlântica também são influenciadas por fatores ecológicos. No entanto, os primatas da Mata Atlântica podem enfrentar desafios adicionais, como pressões de isolamento e fragmentação de habitats florestais, que podem aumentar a frequência desses arranjos. O objetivo deste trabalho é apresentar um relato de simpatia com possível interação entre indivíduos de duas espécies de primatas das famílias Pitheciidae e Callitrichidae: Callicebus nigrifrons (Spix 1823) e Callithrix Resumo aurita (É. Geoffroy Saint-Hilaire 1812) em um parque urbano no sul do estado de Minas Gerais. Foram observados indivíduos interagindo durante o forrageio e deslocamento. A associação de indivíduos das duas espécies pode ser explicada devido à baixa qualidade do fragmento florestal, pois pode aumentar as chances de obter recursos alimentares e configura uma estratégia de diluição de contra-ataques de predadores.

Primeros registros del tigrillo Leopardus pardalis (Carnivora, Felidae) en el Santuario Histórico de Machupicchu, Cusco, Perú / First records of ocelot Leopardus pardalis (Carnivora, Felidae) from Machupicchu Historical Sanctuary, Cusco, Peru

Resumen Se reporta por primera vez la presencia del tigrillo u ocelote (Leopardus pardalis), en el Santuario Histórico de Machupicchu, mediante el uso de cámaras trampa. Se obtuvieron 21 registros fotográficos independientes en cuatro sectores de bosque montano entre junio de 2018 y marzo del 2020. El rango altitudinal registrado para esta especie dentro del Santuario comprende desde 2193 hasta 3414 metros de altitud, lo que incluye el segundo registro a mayor elevación en el Perú. Los registros indican un patrón de actividad catemeral, es decir sin ninguna preferencia de actividad horaria entre el día y la noche, y sugieren que la presencia de tigrillo en algunas áreas a más de 3000 m puede ser más común de lo que se pensaba, con ejemplares presentes todo el año.

Abstract We report for the first time the presence of the ocelot (Leopardus pardalis), in the Machupicchu Historic Sanctuary, using camera traps. Twenty-one independent photographic records were obtained in four montane forest sectors between June 2018 and March 2020. The altitudinal range recorded for this species within the Sanctuary ranges from 2193 to 3414 m of altitude, which includes the second highest elevation record in Peru. The records indicate a pattern of cathemeral activity, with no time preference between day and night, and suggest that the presence of ocelots in some areas above 3000 m may be more common than previously thought, with specimens present year-round.

Predictive model for extubation delay undergoing non-emergency major surgery based on random forest algorithm / 临床麻醉学杂志

Objective To construct and validate a clinical prediction model for delayed extubation undergoing non-emergency major surgery based on the random forest algorithm.Methods Clinical data of 7 528 patients undergoing non-emergency major surgery under general anesthesia from January 2018 to De-cember 2022 were retrospectively collected.The patients were divided into two groups according to whether extubation was performed within 2 hours after surgery:non-delayed extubation group(≤2 hours)and de-layed extubation group(＞2 hours).All the patients were randomly divided into a training set and a valida-tion set in a ratio of 7 ∶ 3.The predictive factors for delayed extubation after surgery were screened through LASSO regression and Logistic regression.The random forest model was established and verified by random forest algorithm.Results There were 123 patients(1.6%)experienced delayed extubation after surgery.ASA physical status,department,intraoperative use of flurbiprofen ester,dexmedetomidine,glucocorticoid,hypocalcemia,severe anemia,intraoperative blood transfusion,and airway spasm were identified as inde-pendent predictive factors for delayed extubation.The area under curve(AUC)value of the random forest prediction model in the validation set was0.751(95%CI0.742-0.778),and the sensitivity was98.1%,and the specificity was 41.9%.Conclusion The predictive model of delayed extubation undergoing non-e-mergency major surgery based on random forest algorithm has a good predictive value,which may be helpful to prevent delayed extubation undergoing non-emergency major surgery.

Changes in lumbosacral sagittal plane parameters of L5/S1 disc herniation reabsorption / 中国组织工程研究

BACKGROUND:Previous studies have shown the correlation between lumbosacral sagittal plane parameters and natural absorption of lumbar disc herniation.However,the lumbosacral sagittal plane parameters included lumbar lordosis angle,lumbosacral joint angle,sacral inclination angle and many other parameters.The effects of each parameter on the natural absorption of the herniated disc were different.In addition,there are few studies on the reabsorption of a specific segment of intervertebral disc herniation at present,and most of the measured data are obtained from digital radiography or CT,while the correlation between lumbosacral sagittal plane parameters measured from MRI and reabsorption after L5/S1 intervertebral disc herniation is rarely reported. OBJECTIVE:To study the corresponding changes of lumbar sagittal plane parameters after L5/S1 intervertebral disc herniation reabsorption and to screen out the lumbosacral sagittal plane parameters with the most significant changes during intervertebral disc reabsorption. METHODS:Totally 57 patients with lumbar disc herniation who had complete MRI image data were selected and met the diagnostic criteria for lumbar disc herniation and only received non-surgical treatment for reabsorption of L5/S1 protrusion segments.MRI measured the protrusion area of the maximum protrusion plane in the coronal plane,lumbosacral sagittal plane parameters[lumbar curvature index,lumbar lordosis(α),L5/S1 disc angle(β),intervertebral height measurement,lumbosacral joint angle,sacral platform angle,sacral inclination angle,and lower lumbar lordosis angle].Besides,lumbosacral sagittal plane parameters were ranked in the importance of variables by random forest model in R software,and then significant variables were fitted with multiple linear regression.The changes between parameters before and after treatment were analyzed and compared by paired sample t-test. RESULTS AND CONCLUSION:(1)A total of 57 patients with L5/S1 lumbar disc herniation were included in this study,and the symptoms and imaging features of the patients were significantly relieved to a large extent.(2)Before treatment,there were 4 cases of grade 1,29 cases of grade 2 and 24 cases of grade 3 according to the Classification of Michigan State University.After treatment,there were 48 cases of grade 1 and 9 cases of grade 2.(3)The random forest model suggested that intervertebral height,lumbar curve index,sacral inclination angle,and lower lumbar lordosis angle changed significantly in L5/S1 disc herniation reabsorption,and the order of their change significance was lumbar curve index>intervertebral space height>sacral inclination angle>lower lumbar lordosis angle.(4)Lumbar curve index,lumbar lordosis and sacral platform angle increased,with statistical significance(P<0.05).There were no significant differences in disc angle,intervertebral height,lower lumbar lordosis angle,sacral inclination angle or lumbosacral joint angle(P>0.05).(5)Lumbar curvature index was the most significant parameter of the lumbosacral sagittal plane in herniated disc reabsorption.In addition,lumbar curve index,sacral inclination angle,and lower lumbar lordosis angle are commonly used clinically to describe the change of lumbar curvature,suggesting that L5/S1 disc herniation reabsorption is correlated with the change of lumbar curvature.It is indicated that in the treatment of lumbar disc herniation,a clinical cure can be achieved by improving or restoring the disordered lumbar curvature.

Key biomarkers for the diagnosis of intervertebral disc degeneration associated with oxidative stress:identification based on bioinformatics and machine learning / 中国组织工程研究

BACKGROUND:Oxidative stress is closely associated with the occurrence and progression of intervertebral disc degeneration,but its underlying mechanisms and effective treatment methods remain unclear. OBJECTIVE:To identify key genes associated with intervertebral disc degeneration accompanied by oxidative stress based on bioinformatics and three machine learning algorithms,as well as to conduct an immune infiltration analysis,followed by experimental validation. METHODS:Gene expression profiles related to intervertebral disc degeneration were obtained from the GEO database and oxidative stress-related genes obtained from the GeneCards database.Differential analysis and weighted gene co-expression networks analysis were performed on the intervertebral disc degeneration dataset.The intersection of the two analyses and the intersection with the oxidative stress-related genes were taken to obtain candidate hub genes.Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses on the candidate hub genes were performed.Machine learning algorithms(LASSO regression,SVM-RFE,and random forest)were used to select the optimal feature genes and perform the receiver operator characteristic curve validation.Simultaneously,immune infiltration analysis was conducted.Nucleus pulposus samples from patients with cervical spondylosis who were treated at the Second Hospital of Shanxi Medical University from July to November 2023 were enrolled as the intervertebral disc degeneration group and nucleus pulposus samples from patients with cervical spinal cord injury as the control group.The relative expression of feature genes in the degenerated intervertebral disc was validated using qPCR method. RESULTS AND CONCLUSION:After differential gene analysis,424 differentially expressed genes were obtained.Weighted gene co-expression networks analysis yielded 5 087 genes,and 1 399 oxidative stress genes were identified,leading to the identification of 23 candidate hub genes.Gene ontology analysis revealed that these candidate hub genes are primarily involved in bacterial defense response,molecular response to bacteria,and other biological processes.In terms of cellular component,they are associated with secretion granule lumen and cytoplasmic vesicle lumen,among others.As for molecular function,they are related to endopeptidase activity and compound binding,including sulfur compounds.Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that these candidate hub genes are associated with neutrophil extracellular trap formation and the renin-angiotensin system pathway,among other signaling pathways.By applying three machine learning algorithms and conducting the receiver operator characteristic curve validation,two key genes,HSPA6 and PKD1,were determined.Immune infiltration analysis revealed a strong correlation between HSPA6 and activated dendritic cells(r=0.88,P＜0.001)as well as activated CD4+ T cells(r=-0.72,P＜0.01).Similarly,PKD1 showed close associations with effector memory CD8+ T cells(r=0.55,P＜0.05)and activated dendritic cells(r=-0.56,P＜0.05).qPCR experimental results indicated that the expression level of HSPA6 was lower in the intervertebral disc degeneration group compared with the control group(P＜0.000 1),while the expression level of PKD1 was higher in the intervertebral disc degeneration group(P＜0.000 1).These findings suggest that HSPA6 and PKD1 can serve as biomarkers for intervertebral disc degeneration accompanied by oxidative stress.Interventions targeting HSPA6 and PKD1 may hold promise for improving intervertebral disc degeneration.

Sensitivity factor analysis of asymmetric gait quality evaluation model based on random forest algorithm / 中国组织工程研究

BACKGROUND:The assessment of asymmetric gait quality plays a pivotal role in guiding rehabilitation training;however,the link between gait quality and kinematic-kinetic gait parameters remains ambiguous. OBJECTIVE:To formulate a machine-learning model for evaluating gait quality based on gait parameters,identify factors sensitive to gait quality from asymmetric gait parameters,investigate the relationship between gait indicators and gait quality,and provide guidance for asymmetric gait training and rehabilitation. METHODS:An asymmetric gait database was established through the creation of asymmetric conditions.Kinematic and kinetic data were collected from 8 young and 8 elderly subjects(all male,right dominant population)during gait tests.Gait quality for each test data set was assessed using symmetry indices,resulting in the creation of a gait parameter-gait quality dataset.Utilizing the Random Forest algorithm,a gait quality evaluation model was developed and key quality parameter factors were identified through differential analysis.This model was iteratively refined.The model's performance was evaluated through 10-fold cross-validation,and its effectiveness was verified using the cross-validation dataset. RESULTS AND CONCLUSION:(1)A gradient test was designed to categorize gait quality into optimal,suboptimal,intermediate,and poor groups,with 759,329,133,and 125 instances,respectively.(2)The application of the Random Forest algorithm in gait quality assessment was explored.A relationship model was established between gait indicators and gait quality,yielding a predictive model accuracy of 95.99%.(3)The 13 main parameters significantly influencing asymmetric gait quality were identified through the Random Forest model's feature importance ranking.(4)An analysis of gait quality sensitivity factors using the 13 important parameters led to the identification of five key sensitivity indexes.The Random Forest model utilizing these sensitivity factors achieved a predictive accuracy of 94.20%.

Effects of forest therapy on human physical and mental health: A meta-analysis / 环境与职业医学

Background With urbanization and residential space expansion, ecological environment and human health issues have become hot social topics. Forest health, as a way of seeking health in nature, has begun to receive public attention in the context of the gradually increasing sub-healthy population and various psychological and physical diseases at a young age. Objective To systematically evaluate the effects of forest therapy on selected physical and mental health indicators. Methods Relevant research literature was retrieved from domestic and international databases (China National Knowledge Infrastructure, Wanfang Database, China Biomedical Literature Service System, Web of Science, ScienceDirect, PubMed, Embase, and Cochrane Library), with a time range from database establishment to January 31, 2023. Relevant data were extracted for meta-analysis to explore the relationship between forest therapy and selected psychological and physiological indicators. Results A total of 85 articles were included, and the meta-analysis results showed that better scores of Profile of Mood States, Positive and Negative Affect Scale, Beck Depression Inventory, and State Trait Anxiety Scale were found in the forest group than those in the urban group (P<0.05); the levels of systolic blood pressure, diastolic blood pressure, heart rate, sympathetic nerve indicator [ln (LF/HF)], salivary cortisol, and serum inflammatory factors were lower in the forest group than in the urban group, while parasympathetic nerve indicator [ln (HF)] level was higher in the forest group than in the urban group (P<0.05). The results of subgroup analysis showed that the changes in heart rate (SMD=−1.62, 95%CI: −2.41, −0.82), ln (HF) (SMD=1.29, 95%CI: 0.73, 1.85), ln (LF/HF) (SMD=−1.49, 95%CI: −2.13, −0.86), and salivary cortisol (SMD=−0.53, 95%CI: −0.81, −0.25) were more significant when the duration of forest therapy was ≤ 0.5 h, the recovery effect on emotional state was better in the >0.5~3 h group (such as tension SMD=−2.40, 95%CI: −3.21, 1.59), and the reduction effects on systolic blood pressure (SMD=−0.53, 95%CI: −1.03, −0.03) and diastolic blood pressure (SMD=−0.42, 95%CI: −0.88, 0.04) were better in the >3 h group. Seated meditation showed better recovery effects on multiple indicators of Profile of Mood States (such as fatigue SMD=−2.26, 95%CI: −3.07, −1.45), while walking showed better recovery effects on physiological indicators such as blood pressure (systolic blood pressure SMD=−0.57, 95%CI: −1.07, −0.06; diastolic blood pressure SMD=−0.72, 95%CI: −1.36, −0.07) and heart rate (SMD=−1.51, 95%CI: −2.38, -0.64). Except for blood pressure, the health benefits of forest therapy in the younger age group were generally better than those in the middle-aged and elderly group. Conclusion Relaxed and comfortable psychological feeling is reported when practicing forest therapy; it can lower blood pressure and heart rate, regulate the autonomic nervous system; it can also reduce the release of stress hormones and lower serum levels of inflammatory factors, exerting an auxiliary recovery effect on cardiovascular and immune system disorders. At the same time, the therapy duration, form, and age of the subjects have a certain impact on the effects of forest therapy practice.

Research on machine learning-based activity prediction models for KRAS inhibitors / 中国药科大学学报

@#Abstract: Kirsten rat sarcoma viral oncogene homolog (KRAS) gene is one of the most commonly mutated oncogenes. It has been found that KRAS inhibitors have the potential therapeutic effect on cancer patients with this gene mutation. In this study, machine learning was applied to develop a QSAR(quantitative structure-activity relationship) model for KRAS small molecule inhibitors. A total of 1857data points of IC50 and SMILES(simplified molecular input line entry system) for KRAS inhibitors were collected from three databases: ChEMBL, BindingDB, and PubChem. And nine different classifiers were constructed using three different feature screening methods combined with three machine learning models, namely, random forest, support vector machine, and extreme gradient boosting machine. The results showed that the SVM model combined with mutual information feature selection exhibited the best performance: AUCtest=0.912, ACCtest=0.859, F1test=0.890. Moreover, it also demonstrated good predictive performance on the external validation set(AUCExt=0.944, RecallExt=0.856, FPRExt=0.111). This study provides a new technical route for KRAS inhibitor screening in natural product databases using artificial intelligence methods.

Catheter-associated infection and influencing factors in anti-tumor chemo-therapy treated patients after indwelling peripherally inserted central ca-theter:analysis based on random forest model / 中国感染控制杂志

Objective To analyze the influencing factors for catheter-associated infection(CAI)in chemotherapy treated patients after indwelling peripherally inserted central catheter(PICC)based on a random forest model.Methods 400 tumor patients who received chemotherapy and PICC were selected and divided into the training set(n=300)and the test set(n=100)in a 3∶1 ratio through computer-generated random number.Patients in the training set were subdivided into the non-infection group and the infection group based on the occurrence of infec-tion.Clinical data from two groups of patients were compared.Influencing factors for the occurrence of CAI after PICC were analyzed with multivariate logistic regression model and the integrated classification algorithm of random forest model,and the predictive performance of the two methods was compared.Results Among 300 chemotherapy treated patients in the training set,32 cases(10.67％)experienced CAI.Compared with the non-infection group,patients in the infection group had more single punctures for catheterization,longer PICC retention time,larger pro-portion of catheter movement,larger proportion of complication with diabetes,higher frequency of dressing chan-ges,lower white blood cell count and immune function(all P＜0.05).PICC retention time,catheter movement,complication with diabetes,dressing change frequency,white blood cell(WBC)and immune function were inde-pendent influencing factors for CAI after PICC(all P＜0.05).The random forest model showed that ranking by the importance of different influencing factors was as following:PICC retention time,catheter movement,complication with diabetes,WBC,dressing change frequency and immune function.The integrated classification algorithm of random forest model for predicting the occurrence of CAI in chemotherapy treated patients showed that the area un-der the receiver operating characteristic(ROC)curve(AUC)was 0.872,which had better prediction performance compared with the logistic regression model(AUC=0.791).Conclusion PICC retention time,catheter movement,complicated with diabetes,dressing change frequency,WBC level and immune function are independent influencing factors for CAI in chemotherapy treated patients.The integrated classification algorithm of random forest model can be used to predict CAI in chemotherapy treated patients,and its prediction performance is better than that of the logistic regression model.

Development of a prediction model of surgical site infection after posterior lumbar interbody fusion in patients based on random forest algorithm / 中国脊柱脊髓杂志

Objectives:To analyze the risk factors related to infection after posterior lumbar interbody fusion(PLIF)by random forest algorithm and develop a prediction model,providing a certain reference for clinical prevention of surgical site infection(SSI)after PLIF.Methods:A retrospective study was conducted on the masked data of patients hospitalized for PLIF in the spinal surgery department of some third-level grade A hospitals in Beijing municipality and Hebei Province from June 2019 to June 2021 provided by Beijing Zhongwei Cloud Medical Data Analysis and Application Technology Research Institute through data processing and analysis.The classification data were analyzed and compared between SSI group and non-SSI group to obtain variables that significantly impacted the postoperative infection.SPSS Modeler 20 system was used as the tool for model development,and random forest algorithm was applied to analyze,obtaining the patient characteristics of postoperative infection,namely the infection model.Results:A total of 8,764 patients were included in study,and 373 patients were diagnosed with SSI,with an incidence rate of 4.4％(95％CI:2.2％to 6.5％).After statistical analysis,six variables,including obesity,ASA Ⅲ and above,prolonged operative time,chronic heart disease,diabetes and renal dysfunction,were independently associated with SSI.Classification with a random forest model yielded a high accuracy of 90.6％.The characteristics of patients prone to infection after PLIF(two models of infection)was:[(BMI=1)and(SD=1)and(ASA=1)and(RI=1)]or[(BMI=0)and(SD=1)and(DM=1)and(RI=1)].Conclusions:The random forest algorithm applied in this study could obtain an average accuracy of 90.6％,and two infection models were obtained as:(1)Patients with obesity,renal insufficiency,ASA grade Ⅲ or above,and operative time≥3h;(2)Patients who are not obese,but with diabetes,renal insufficiency,and the operative time ≥3h.

Study on the Risk Prediction Models of Overweight and Obesity in Medical Students / 中国卫生统计

Objective To construct logistic regression,random forest and SVM models to predict the influencing factors of overweight and obesity in medical students,and the prediction performance of the three models was compared,so as to obtain the optimal model for the risk assessment of overweight and obesity.Methods Participants included 1 866 medical students from a city in Hebei Province from May to December 2020.The relevant data of overweight and obesity screening were collected through self-test questionnaire;three models of logistic regression,random forest and SVM are constructed by python.Results The test set showed that the accuracy of logistic regression,random forest and SVM models were 96.26%,98.66%and 98.13%respectively;the specificity were 99.77%,100%and 99.00%,respectively;and the AUC were 0.88,0.99 and 0.88 respectively.Random forest is the optimal prediction model;according to the random forest model results,subjective well-being,negative events and students'economic status are more than 10%of weight in the model.Conclusion Subjective well-being,negative events and students'economic status are the main factors affecting the incidence of overweight and obesity in medical students;the prediction performance of random forest model was better than logistic regression model and SVM model.

Diagnostic Value of Texture Analysis Based on Quantitative Susceptibility Mapping in Parkinson's Disease / 中国医学影像学杂志

Purpose To explore the value of texture analysis in the diagnosis and course evaluation of Parkinson's disease(PD)by analyzing the texture features of gray matter nuclei and white matter on quantitative susceptibility mapping(QSM)sequences.Materials and Methods A total of 30 PD patients and 22 normal controls from July 2019 to November 2020 in Jiangyin People's Hospital were prospectively enrolled to perform enhanced gradient echo T2* weighted angiography(ESWAN)sequence scanning.All QSM images were obtained through post-processing.Region of interest was manually obtained,including bilateral caudate heads,globus pallidus,putamen,substantia nigra,red nucleus,cerebellar dentate nucleus and white matter at the center of the semicircle.The texture features of the region of interest were extracted.After dimension reduction and screening,a set of optimal texture parameters were obtained,and a random forest prediction model was constructed.The diagnostic efficiency of the model was analyzed and evaluated and the reliability of the model was evaluated.The correlation between the selected texture features and the clinical scale of PD patients was analyzed.Results A group(n=5)of the best texture feature parameters were obtained from QSM map.The area under curve range of independent prediction PD was 0.697-0.823,the area under curve of random forest model was 0.910,and the accuracy of cross validation was 0.888.Texture feature(r4_wavelet_LLL_firstorder_Energy)of PD group was negatively correlated with the scores of the mini mental state examination(r=-0.470,P=0.011).Conclusion The texture analysis based on QSM has a high diagnostic value for PD,and the texture features of the left putamen have a certain correlation with the cognitive function of PD patients.

Establishment of prognostic model for severe primary graft dysfunction in patients with idiopathic pulmonary fibrosis after lung transplantation / 器官移植

Objective To explore the establishment of a prognostic model based on machine learning algorithm to predict primary graft dysfunction (PGD) in patients with idiopathic pulmonary fibrosis (IPF) after lung transplantation. Methods Clinical data of 226 IPF patients who underwent lung transplantation were retrospectively analyzed. All patients were randomly divided into the training and test sets at a ratio of 7:3. Using regularized logistic regression, random forest, support vector machine and artificial neural network, the prognostic model was established through variable screening, model establishment and model optimization. The performance of this prognostic model was assessed by the area under the receiver operating characteristic curve (AUC), positive predictive value, negative predictive value and accuracy. Results Sixteen key features were selected for model establishment. The AUC of the four prognostic models all exceeded 0.7. DeLong and McNemar tests found no significant difference in the performance among different models (both P>0.05). Conclusions Based on four machine learning algorithms, the prognostic model for grade 3 PGD after lung transplantation is preliminarily established. The overall prediction performance of each model is similar, which may predict the risk of grade 3 PGD in IPF patients after lung transplantation.

Application of machine learning method for survival analysis / 预防医学

Abstract@#Survival analysis has been widely used in the field of medical research. The Cox proportional hazard model is commonly used, but its practical application is limited. Machine learning method can compensate for the shortcomings of the Cox proportional hazard model in terms of nonlinear data processing and prediction accuracy. This article reviewed the advance of machine learning methods represented by neural networks, within the field of survival analysis, and highlighted the principles and benefits of three machine learning methods that DeepSurv, Deep-Hit and random survival forest, providing methodological insights for the analysis of complex survival data.

Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan / Previsão de produção florestal em diferentes cenários de mudanças climáticas usando modelos inteligentes de dados no Paquistão

Abstract This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (KRR) models were developed and evaluated using yield data of two species (Blue pine and Silver fir) as an objective variable and climate data (temperature, humidity, rainfall and wind speed) as predictive variables. Prediction accuracy of both the models were assessed by means of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (r), relative root mean squared error (RRMSE), Legates-McCabe's (LM), Willmott's index (WI) and Nash-Sutcliffe (NSE) metrics. Overall, the RF model outperformed the KRR model due to its higher accuracy in forecasting of forest yield. The study strongly recommends that RF model should be applied in other regions of the country for prediction of forest growth and yield, which may help in the management and future planning of forest productivity in Pakistan.

Resumo Este estudo teve como objetivo desenvolver e avaliar modelos baseados em dados para previsão da produção florestal em diferentes cenários de mudanças climáticas na divisão florestal Gallies do distrito de Abbottabad, Paquistão. Os modelos Random Forest (RF) e Kernel Ridge Regression (KRR) foram desenvolvidos e avaliados usando dados de produção de duas espécies (pinheiro-azul e abeto-prateado) como uma variável objetiva e dados climáticos (temperatura, umidade, precipitação e velocidade do vento) como preditivos variáveis. A precisão da previsão de ambos os modelos foi avaliada por meio de erro quadrático médio (RMSE), erro absoluto médio (MAE), coeficiente de correlação (r), erro quadrático médio relativo (RRMSE), Legates-McCabe's (LM), índice de Willmott (WI) e métricas Nash-Sutcliffe (NSE). No geral, o modelo RF superou o modelo KRR devido à sua maior precisão na previsão do rendimento florestal. O estudo recomenda fortemente que o modelo RF seja aplicado em outras regiões do país para previsão do crescimento e produtividade florestal, o que pode ajudar no manejo e planejamento futuro da produtividade florestal no Paquistão.

Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Abstract This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (KRR) models were developed and evaluated using yield data of two species (Blue pine and Silver fir) as an objective variable and climate data (temperature, humidity, rainfall and wind speed) as predictive variables. Prediction accuracy of both the models were assessed by means of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (r), relative root mean squared error (RRMSE), Legates-McCabes (LM), Willmotts index (WI) and Nash-Sutcliffe (NSE) metrics. Overall, the RF model outperformed the KRR model due to its higher accuracy in forecasting of forest yield. The study strongly recommends that RF model should be applied in other regions of the country for prediction of forest growth and yield, which may help in the management and future planning of forest productivity in Pakistan.

Resumo Este estudo teve como objetivo desenvolver e avaliar modelos baseados em dados para previsão da produção florestal em diferentes cenários de mudanças climáticas na divisão florestal Gallies do distrito de Abbottabad, Paquistão. Os modelos Random Forest (RF) e Kernel Ridge Regression (KRR) foram desenvolvidos e avaliados usando dados de produção de duas espécies (pinheiro-azul e abeto-prateado) como uma variável objetiva e dados climáticos (temperatura, umidade, precipitação e velocidade do vento) como preditivos variáveis. A precisão da previsão de ambos os modelos foi avaliada por meio de erro quadrático médio (RMSE), erro absoluto médio (MAE), coeficiente de correlação (r), erro quadrático médio relativo (RRMSE), Legates-McCabes (LM), índice de Willmott (WI) e métricas Nash-Sutcliffe (NSE). No geral, o modelo RF superou o modelo KRR devido à sua maior precisão na previsão do rendimento florestal. O estudo recomenda fortemente que o modelo RF seja aplicado em outras regiões do país para previsão do crescimento e produtividade florestal, o que pode ajudar no manejo e planejamento futuro da produtividade florestal no Paquistão.

Riverine forest as a significant habitat to harbor a wide range of bird species

Abstract Riverine forests are unique and highly significant ecosystems that are globally important for diverse and threatened avian species. Apart from being a cradle of life, it also serves as a gene pool that harbors a variety of flora and fauna species (repeated below). Despite the fact, this fragile ecosystem harbored avian assemblages; it is now disappearing daily as a result of human activity. Determining habitat productivity using bird species is critical for conservation and better management in the future. Multiple surveys were conducted over a 15-month period, from January to March 2019, using the distance sampling point count method. A total of 250 point count stations were fixed systematically at 300 m intervals. In total, 9929 bird individuals were recorded, representing 57 species and 34 families. Out of 57 bird species, two were vulnerable, one was data deficient, one was nearly threatened, and the remaining 53 species were of least concern. The Eurasian Collard Dove Streptopelia decaocto (14.641 ± 2.532/ha), White-eared Bulbul Pycnonotus leucotis (13.398 ± 4.342/ha) and Common Babbler Turdoides caudata (10.244 ± 2.345/ha) were the three first plenteous species having higher densities. However, the densities of three species, i.e., Lesser Whitethroat Sylvia curruca, Gray Heron Ardea cinerea and Pallas Fish Eagle Haliaeetus leucoryphus, were not analyzed due to the small sample size. The findings of diversity indices revealed that riverine forest has harbored the diverse avian species that are uniformly dispersed across the forest. Moreover, recording the ten foraging guilds indicated that riverine forest is rich in food resources. In addition, the floristic structure importance value index results indicated that riverine forest is diverse and rich in flora, i.e. trees, shrubs, weeds and grass, making it an attractive and productive habitat for bird species.

Resumo As florestas ribeirinhas são ecossistemas únicos e altamente significativos que são globalmente importantes para diversas espécies de aves ameaçadas de extinção. Além de serem o berço da vida, também servem como um conjunto genético que abriga uma variedade de espécies da flora e da fauna. Apesar disso, esse frágil ecossistema abrigava um conjunto de aves, mas agora está desaparecendo diariamente como resultado da atividade humana. Determinar a produtividade do hábitat usando espécies de pássaros é fundamental para a conservação e melhor gestão no futuro. Vários levantamentos foram realizados ao longo de um período de 15 meses, de janeiro de 2018 a março de 2019, por meio do método de contagem de pontos de amostragem de distância. Foram fixadas sistematicamente 250 estações de contagem de pontos em intervalos de 300 m. No total, foram registrados 9.929 indivíduos de aves, representando 57 espécies e 34 famílias. Das 57 espécies de aves, duas eram vulneráveis, uma tinha dados insuficientes, uma estava quase ameaçada e as 53 espécies restantes eram as menos preocupantes. O: Pomba de colar euroasiática - Streptopelia decaocto (14.641 ± 2.532/ha), o Bulbul de orelha branca - Pycnonotus leucotis (13.398 ± 4.342/ha) e Tagarela comum - Turdoides caudata (10.244 ± 2.345/ha) foram as três primeiras espécies abundantes com maiores densidades. No entanto, as densidades de três espécies, Papa-amoras-cinzento (Sylvia curruca), Garça-real-europeia (Ardea cinerea) e Águia-pescadora de Pallas (Haliaeetus leucoryphus), não foram analisadas por causa do pequeno tamanho da amostra. Os resultados dos índices de diversidade revelaram que a floresta ribeirinha abrigou diversas espécies de aves que estão uniformemente dispersas pela floresta. Além disso, o registro das dez guildas de forrageamento indicou que a floresta ribeirinha é rica em recursos alimentares. Além disso, os resultados do índice de valor de importância da estrutura florística indicaram que a floresta ribeirinha é variada e rica em flora, ou seja, árvores, arbustos, ervas daninhas e grama, tornando-a um hábitat atraente e produtivo para espécies de aves.

Variations in soil physico-chemical properties, soil stocks, and soil stoichiometry under different soil layers, the major forest region Liupan Mountains of Northwest China

Abstract Liupan Mountains are an important region in China in the context of forest cover and vegetation due to huge afforestation and plantation practices, which brought changes in soil physio-chemical properties, soil stocks, and soil stoichiometries are rarely been understood. The study aims to explore the distribution of soil nutrients at 1-m soil depth in the plantation forest region. The soil samples at five depth increments (0-20, 20-40, 40-60, 60-80, and 80-100 cm) were collected and analyzed for different soil physio-chemical characteristics. The results showed a significant variation in soil bulk density (BD), soil porosity, pH, cation exchange capacity (CEC), and electric conductivity (EC) values. More soil BD (1.41 g cm-3) and pH (6.97) were noticed in the deep soil layer (80-100 cm), while the highest values of porosity (60.6%), EC (0.09 mS cm-1), and CEC (32.9 c mol kg-1) were reflected in the uppermost soil layer (0-20 cm). Similarly, the highest contents of soil organic carbon (SOC), total phosphorus (TP), available phosphorus (AP), total nitrogen (TN), and available potassium (AK) were calculated in the surface soil layer (0-20 cm). With increasing soil depth increment a decreasing trend in the SOC and other nutrient concentration were found, whereas the soil total potassium (TK) produced a negative correlation with soil layer depth. The entire results produced the distribution of SOCs and TNs (stocks) at various soil depths in forestland patterns were 020cm > 2040cm > 4060cm 6080cm 80100 cm. Furthermore, the stoichiometric ratios of C, N, and P, the C/P, and N/P ratios showed maximum values (66.49 and 5.46) in 0-20 cm and lowest values (23.78 and 1.91) in 80-100 cm soil layer depth. Though the C/N ratio was statistically similar across the whole soil profile (0-100 cm). These results highlighted that the soil depth increments might largely be attributed to fluctuations in soil physio-chemical properties, soil stocks, and soil stoichiometries. Further study is needed to draw more conclusions on nutrient dynamics, soil stocks, and soil stoichiometry in these forests.

Resumo As montanhas de Liupan são uma região importante na China no contexto de cobertura florestal e vegetação devido às enormes práticas de florestamento e plantação, que trouxeram mudanças nas propriedades físico-químicas do solo, e estoques e estequiometrias do solo raramente são compreendidos. O estudo visa explorar a distribuição de nutrientes do solo a 1 m de profundidade do solo na região da floresta plantada. As amostras de solo em cinco incrementos de profundidade (0-20, 20-40, 40-60, 60-80 e 80-100 cm) foram coletadas e analisadas para diferentes características físico-químicas do solo. Os resultados mostraram uma variação significativa nos valores de densidade do solo (BD), porosidade do solo, pH, capacidade de troca catiônica (CEC) e condutividade elétrica (CE). Mais DB do solo (1,41 g cm-3) e pH (6,97) do solo foram observados na camada profunda do solo (80-100 cm), enquanto os maiores valores de porosidade (60,6%), CE (0,09 mS cm-1) e CEC (32,9 c mol kg-1) foram refletidos na camada superior do solo (0-20 cm). Da mesma forma, os maiores teores de carbono orgânico do solo (SOC), fósforo total (TP), fósforo disponível (AP), nitrogênio total (TN) e potássio disponível (AK) foram calculados na camada superficial do solo (0-20 cm). Com o aumento do incremento da profundidade do solo, uma tendência decrescente no SOC e na concentração de outros nutrientes foi encontrada, enquanto o potássio total do solo (TK) produziu uma correlação negativa com a profundidade da camada do solo. Todos os resultados produziram a distribuição de SOCs e TNs (estoques) em várias profundidades de solo em padrões de floresta 0 20cm> 20 40cm> 40 60cm 60 80cm 80 100 cm. Além disso, as relações estequiométricas de C, N e P, as relações C / P e N / P, apresentaram valores máximos (66,49 e 5,46) em 0-20 cm, e valores mais baixos (23,78 e 1,91) em solo de 80-100 cm profundidade da camada. Embora a relação C / N fosse estatisticamente semelhante em todo o perfil do solo (0-100 cm). Esses resultados destacaram que os incrementos de profundidade do solo podem ser amplamente atribuídos a flutuações nas propriedades físico-químicas do solo, estoques e estequiometrias do solo. Mais estudos são necessários para tirar conclusões adicionais sobre a dinâmica dos nutrientes, estoques de solo e estequiometria do solo nessas florestas.

Tree species composition, growing stock and biomass carbon dynamics of the major timber species in Hindu Kush regions of Pakistan

Abstract Using inventory data, this study evaluates the species composition, growing stock volume (GSV), and biomass carbon (BMC) of the five major timber species in the sub-tropical, and temperate/sub-alpine regions of Pakistan. It was found that the stem density varies between 50 and 221 trees ha -1, with a mean of 142 trees ha-1 (13.68 million trees for entire forest area). Among the species, Pinus wallichiana showed a high species composition (27.80%) followed by Picea smithiana (24.64%). The GSV was found in the range of 67.81 to 425.94 m3 ha-1, with a total GSV value of 20.68 million m3 for the entire region. Similarly, The BMC ranged from 27.04 to 169.86 Mg ha-1, with a mean BMC value of 86.80 Mg ha-1. The total amount of stored carbon was found at 8.69 million tons for a total of 95842 ha of commercially managed forest. Furthermore, the correlation analysis between the basal area (BA) and GSV and BMC showed that BA is the best predictor of GSV and BMC. The findings provide insights to the policy makers and forest managers regarding the sustainable commercial forest management as well as forest carbon management in the recent global carbon management for climate change mitigation.

Resumo Usando dados de inventário, este estudo avaliou a composição de espécies, volume de estoque crescente (GSV) e carbono de biomassa (BMC) das cinco principais espécies madeireiras nas regiões subtropicais e temperadas/subalpinas do Paquistão. Constatou-se que a densidade do caule variou entre 50 e 221 árvores ha-1, com média de 142 árvores ha-1 (13,68 milhões de árvores para toda a área florestal). Entre as espécies, Pinus wallichiana apresentou alta composição de espécies (27,80%), seguida de Picea smithiana (24,64%). O GSV foi encontrado na faixa de 67,81 a 425,94 m3 ha-1, com um valor total de 20,68 milhões de m3 para toda a região. Da mesma forma, o BMC variou de 27,04 a 169,86 mg ha-1, com valor médio de 86,80 mg ha-1. A quantidade total de carbono armazenado foi de 8,69 milhões de toneladas para um total de 95.842 ha de floresta manejada comercialmente. Além disso, a análise de correlação entre área basal (BA), GSV e BMC mostrou que BA é o melhor preditor de GSV e BMC. As descobertas fornecem insights para os formuladores de políticas e gestores florestais sobre o manejo florestal comercial sustentável, bem como o manejo florestal de carbono no recente gerenciamento global de carbono para a mitigação das mudanças climáticas.

Riverine forest as a significant habitat to harbor a wide range of bird species / Floresta ribeirinha como hábitat significativo para abrigo de uma gama de espécies de aves

Riverine forests are unique and highly significant ecosystems that are globally important for diverse and threatened avian species. Apart from being a cradle of life, it also serves as a gene pool that harbors a variety of flora and fauna species (repeated below). Despite the fact, this fragile ecosystem harbored avian assemblages; it is now disappearing daily as a result of human activity. Determining habitat productivity using bird species is critical for conservation and better management in the future. Multiple surveys were conducted over a 15-month period, from January to March 2019, using the distance sampling point count method. A total of 250 point count stations were fixed systematically at 300 m intervals. In total, 9929 bird individuals were recorded, representing 57 species and 34 families. Out of 57 bird species, two were vulnerable, one was data deficient, one was nearly threatened, and the remaining 53 species were of least concern. The Eurasian Collard Dove - Streptopelia decaocto (14.641 ± 2.532/ha), White-eared Bulbul - Pycnonotus leucotis (13.398 ± 4.342/ha) and Common Babbler - Turdoides caudata (10.244 ± 2.345/ha) were the three first plenteous species having higher densities. However, the densities of three species, i.e., Lesser Whitethroat - Sylvia curruca, Gray Heron - Ardea cinerea and Pallas Fish Eagle - Haliaeetus leucoryphus, were not analyzed due to the small sample size. The findings of diversity indices revealed that riverine forest has harbored the diverse avian species that are uniformly dispersed across the forest. Moreover, recording the ten foraging guilds indicated that riverine forest is rich in food resources. In addition, the floristic structure importance value index results indicated that riverine forest is diverse and rich in flora, i.e. trees, shrubs, weeds and grass, making it an attractive and productive habitat for bird species.

As florestas ribeirinhas são ecossistemas únicos e altamente significativos que são globalmente importantes para diversas espécies de aves ameaçadas de extinção. Além de serem o berço da vida, também servem como um conjunto genético que abriga uma variedade de espécies da flora e da fauna. Apesar disso, esse frágil ecossistema abrigava um conjunto de aves, mas agora está desaparecendo diariamente como resultado da atividade humana. Determinar a produtividade do hábitat usando espécies de pássaros é fundamental para a conservação e melhor gestão no futuro. Vários levantamentos foram realizados ao longo de um período de 15 meses, de janeiro de 2018 a março de 2019, por meio do método de contagem de pontos de amostragem de distância. Foram fixadas sistematicamente 250 estações de contagem de pontos em intervalos de 300 m. No total, foram registrados 9.929 indivíduos de aves, representando 57 espécies e 34 famílias. Das 57 espécies de aves, duas eram vulneráveis, uma tinha dados insuficientes, uma estava quase ameaçada e as 53 espécies restantes eram as menos preocupantes. O: Pomba de colar euroasiática - Streptopelia decaocto (14.641 ± 2.532/ha), o Bulbul de orelha branca - Pycnonotus leucotis (13.398 ± 4.342/ha) e Tagarela comum - Turdoides caudata (10.244 ± 2.345/ha) foram as três primeiras espécies abundantes com maiores densidades. No entanto, as densidades de três espécies, Papa-amoras-cinzento (Sylvia curruca), Garça-real-europeia (Ardea cinerea) e Águia-pescadora de Pallas (Haliaeetus leucoryphus), não foram analisadas por causa do pequeno tamanho da amostra. Os resultados dos índices de diversidade revelaram que a floresta ribeirinha abrigou diversas espécies de aves que estão uniformemente dispersas pela floresta. Além disso, o registro das dez guildas de forrageamento indicou que a floresta ribeirinha é rica em recursos alimentares. Além disso, os resultados do índice de valor de importância da estrutura florística indicaram que a floresta ribeirinha é variada e rica em flora, ou seja, árvores, arbustos, ervas daninhas e grama, tornando-a um hábitat atraente e produtivo para espécies de aves.