Coal-rock gas:Concept,connotation and classification criteria

In recent years,great breakthroughs have been made in the exploration and development of natural gas in deep coal-rock reservoirs in Junggar,Ordos and other basins in China.In view of the inconsistency between the industrial and academic circles on this new type of unconventional natural gas,this paper defines the concept of"coal-rock gas"on the basis of previous studies,and systematically analyzes its characteristics of occurrence state,transport and storage form,differential accumulation,and development law.Coal-rock gas,geologically unlike coalbed methane in the traditional sense,occurs in both free and adsorbed states,with free state in abundance.It is generated and stored in the same set of rocks through short distance migration,occasionally with the accumulation from other sources.Moreover,coal rock develops cleat fractures,and the free gas accumulates differentially.The coal-rock gas reservoirs deeper than 2000 m are high in pressure,temperature,gas content,gas saturation,and free-gas content.In terms of development,similar to shale gas and tight gas,coal-rock gas can be exploited by natural formation energy after the reservoirs connectivity is improved artificially,that is,the adsorbed gas is desorbed due to pressure drop after the high-potential free gas is recovered,so that the free gas and adsorbed gas are produced in succession for a long term without water drainage for pressure drop.According to buried depth,coal rank,pressure coefficient,reserves scale,reserves abundance and gas well production,the classification criteria and reserves/resources estimation method of coal-rock gas are presented.It is preliminarily estimated that the coal-rock gas in place deeper than 2000 m in China exceeds 30×10^(12)m^(3),indicating an important strategic resource for the country.The Ordos,Sichuan,Junggar and Bohai Bay basins are favorable areas for large-scale enrichment of coal-rock gas.The paper summarizes the technical and management challenges and points out the research directions,laying a foundation for the management,exploration,and development of coal-rock gas in China.

Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences.Neural-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this context.NCDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity.To this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention values.Two distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’s.Additionally,a trainingmethodology is proposed to guarantee that the training problem is sufficiently presented.Two classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the proposedmodel.The proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS COCO.The obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

Characterization of Some Typical Soils of the Miombo Woodland Ecosystem of Kitonga Forest Reserve, Iringa, Tanzania： Physico-Chemical Properties and Classification

Despite the fact that miombo woodland soils have significant implications in global climate change processes, few studies have been done to characterize and classify the soils of the miombo woodland ecosystem of Tanzania. The current study was carried out to map and classify soils of Kitonga Forest Reserve, which is a typical miombo woodland ecosystem, in order to generate relevant information for their use and management. A representative study area of 52 km2 was selected and mapped at a scale of 1：50,000 on the basis of relief. Ten representative soil profiles were excavated and described using standard methods. Soil samples were taken from genetic soil horizons and analyzed in the laboratory for physico-chemical characteristics using standard methods. Using field and laboratory analytical data, the soils were classified according to the FAO-World Reference Base （FAO-WRB） for Soil Resources system as Cambisols, Leptosols and Fluvisols. In the USDA-NRCS Soil Taxonomy system the soils were classified as Inceptisols and Entisols. Topographical features played an important role in soil formation. The different soil types differed in physico-chemical properties, hence exhibit differences in their potentials, constraints and need specific management strategies. Texture varied from sandy to different loams; pH from 5.1 to 5.9; organic carbon from 0.9 g/kg to 20 g/kg; and CEC from 3 cmol/（＋）kg to 24 cmol/（＋）kg. Sustainable management of miombo woodlands ecosystem soils requires reduced deforestation and reduced land degradation.

Application of Artificial Neural Networks to the Classification of Coal Reserve Assets

Using the classification results by the fuzzy clustering models as the basis for choosing the choosing patterns, a feed forward networks model for classification is given. Remarkable success was achieved in training the networks to learn the patterns and in classifying the coal reserve assets. The results show that the neural network approach for classification has some advantages such as stability and reliability.

Ground level utility of Access, Watch, Reserve classification: Insights from a tertiary care center in North India

BACKGROUND The overuse and misuse of antimicrobials contribute significantly to antimicrobial resistance(AMR),which is a global public health concern.India has particularly high rates of AMR,posing a threat to effective treatment.The World Health Or-ganization(WHO)Access,Watch,Reserve(AWaRe)classification system was introduced to address this issue and guide appropriate antibiotic prescribing.However,there is a lack of studies examining the prescribing patterns of antimi-crobials using the AWaRe classification,especially in North India.Therefore,this study aimed to assess the prescribing patterns of antimicrobials using the WHO AWaRe classification in a tertiary care centre in North India.Ophthalmology,Obstetrics and Gynecology).Metronidazole and ceftriaxone were the most prescribed antibiotics.According to the AWaRe classification,57.61%of antibiotics fell under the Access category,38.27%in Watch,and 4.11%in Reserve.Most Access antibiotics were prescribed within the Medicine department,and the same department also exhibited a higher frequency of Watch antibiotics prescriptions.The questionnaire survey showed that only a third of participants were aware of the AWaRe classification,and there was a lack of knowledge regarding AMR and the potential impact of AWaRe usage.RESULTS The research was carried out in accordance with the methodology presented in Figure 1.A total of n=123 patients were enrolled in this study,with each of them receiving antibiotic prescriptions.The majority of these prescriptions were issued to inpatients(75.4%),and both the Medicine and Surgical departments were equally represented,accounting for 49.6%and 50.4%,respectively.Among the healthcare providers responsible for prescribing antibiotics,72%were Junior Residents,18.7%were Senior Residents,and 9.3%were Consultants.These findings have been summarized in Table 1.The prescriptions included 27 different antibiotics,with metronidazole being the most prescribed(19%)followed by ceftriaxone(17%).The mean number of antibiotics used per patient was 1.84±0.83.The mean duration of antibiotics prescribed was 6.63±3.83 days.The maximum number of antibiotics prescribed per patient was five.According to the AWaRe classification,57.61%of antibiotics fell under the Access,38.27%in Watch,and 4.11%in Reserve categories,suggesting appropriate antibiotic selection according to these criteria.The distribution of antibiotics prescribed according to the WHO AWaRe categories is presented in Figure 2.The difference in prescribing frequencies amongst departments can be noted.Most of the antibiotics prescribed in the Access category were from the Medicine department(75.4%),followed by Surgery(24.6%).For Watch antibiotics,Medicine had a higher proportion(63.4%)compared to Surgery(36.6%).In terms of seniority,Junior Residents prescribed the highest number of antibiotics for both Access and Watch categories in Medicine and Surgery departments.Senior residents and Consultants prescribed a lower number of antibiotics in all categories and departments.Only a few antibiotics were prescribed in the Reserve category,with most prescriptions being from the Medicine department.The study also evaluated the Knowledge and Awareness of Healthcare professionals towards the WHO AWaRe classi-fication through a questionnaire survey.A total of 93 participants responded to the survey.Among them,most parti-cipants were Junior Residents(69.9%),followed by Senior Residents(25.8%)and Faculty(4.3%).When enquired if they knew about the WHO AWaRe classification only 33.3%of the participants responded positively.Of those who were aware of the AWaRe classification,the most common source of information was the internet(31.2%),followed by the antimicrobial policy of their institution(15.1%)as seen in Table 2.The survey results on the knowledge and awareness of AMR among healthcare professionals are also presented in Tables 3 and 4.Out of the 93 participants,68(73.1%)agreed that the emergence of AMR is inevitable,while only 13(14.0%)disagreed that AWaRe usage will result in the inability to treat serious infections.Additionally,58(62.4%)agreed that it will lead to lengthier hospital stays,43(46.2%)agreed that the success of chemotherapy and major surgery will be hampered,and the majority also agreed that its use will lead to increased cost of treatment and increased mortality rates.Regarding the utilization of AWaRe in the hospital summarized in Tables 4 and 5,35.5%of the participants agreed that it should be used,while only 2.2%disagreed.Additionally,34.4%agreed that AWaRe reduces adverse effects of inappro-priate prescription.However,37.6%of the participants considered that AWaRe threatens a clinician's autonomy and 30.1%thought that its use can delay treatment.Additionally,the DDD of each drug was also evaluated.The usage of various antimicrobial drugs in a hospital setting,along with their daily doses and DDD according to the WHO's Anatomical Therapeutic Chemical classification system was calculated.Some of the important findings include high usage rates of ceftriaxone and metronidazole,and relatively low usage rates of drugs like colistin and clindamycin.Additionally,some drugs had wider ranges than others.Comparison of WHO defined DDD with Daily Drug dose(Mean)in the studied prescriptions is represented in the Clustered Bar chart in Figure 3.Finally,the Mean Daily Drug Dose for prescribed drugs was compared with WHO defined DDD for each drug using a Student’s T test.The mean daily drug dose of amoxy/clav was significantly higher than the WHO DDD(1.8 vs 1.50,P=0.014),while the mean daily drug dose of metronidazole and doxycycline were significantly lower than the WHO DDD(P<0.001 and P=0.008,respectively).The mean daily drug dose of piperacillin/tazobactam,amikacin,clindamycin,and levofloxacin did not show significant differences compared to the WHO DDD(P>0.05).CONCLUSION This research indicates an appropriate proportion of prescriptions falling under the Access category(57.61%),suggesting appropriate antibiotic selection,a significant proportion also belongs to the Watch category(38.27%),emphasizing the need for greater caution to prevent the escalation of AMR.There is a moderate level of awareness among healthcare professionals about AMR and the steps being taken to tackle it,highlighting the gap in implementation of policies and need for more steps to be taken in spreading the knowledge about the subject.However,there is a significant difference between the WHO DDD and the prescribed daily dose in the analysed prescriptions suggesting overuse and underuse of antibiotics.

Diagnostic classification of endosonography for differentiating colorectal ulcerative diseases: A new statistical method

AIM To establish a classification method for differential diagnosis of colorectal ulcerative diseases, especially Crohn's disease(CD), primary intestinal lymphoma(PIL) and intestinal tuberculosis(ITB).METHODS We searched the in-patient medical record database for confirmed cases of CD, PIL and ITB from 2008 to 2015 at our center, collected data on endoscopic ultrasound(EUS) from randomly-chosen patients who formed the training set, conducted univariate logistic regression analysis to summarize EUS features of CD, PIL and ITB, and created a diagnostic classification method. All cases found to have colorectal ulcers using EUS were obtained from the endoscopy database and formed the test set. We then removed the cases which were easily diagnosed, and the remaining cases formed the perplexing test set. We re-diagnosed the cases in the three sets using the classification method, determined EUS diagnostic accuracies, and adjusted the classification accordingly. Finally, the re-diagnosing and accuracy-calculating steps were repeated.RESULTS In total, 272 CD, 60 PIL and 39 ITB cases were diagnosed from 2008 to 2015 based on the in-patient database, and 200 CD, 30 PIL and 20 ITB cases were randomly chosen to form the training set. The EUS features were summarized as follows: CD: Thickened submucosa with a slightly high echo level and visible layer; PIL: Absent layer and diffuse hypoechoic mass; and ITB: Thickened mucosa with a high or slightly high echo level and visible layer. The test set consisted of 77 CD, 30 PIL, 23 ITB and 140 cases of other diseases obtained from the endoscopy database. Seventy-four cases were excluded to form the perplexing test set. After adjustment of the classification, EUS diagnostic accuracies for CD, PIL and ITB were 83.6%(209/250), 97.2%(243/250) and 85.6%(214/250) in the training set, were 89.3%(241/270), 97.8%(264/270) and 84.1%(227/270) in the test set, and were 86.7%(170/196), 98.0%(192/196) and 85.2%(167/196) in the perplexing set, respectively.CONCLUSION The EUS features of CD, PIL and ITB are different. The diagnostic classification method is reliable in the differential diagnosis of colorectal ulcerative diseases.

EVALUATION OF INTERNATIONAL CLASSIFICATION CRITERIA (2002) FOR PRIMARY SJGREN'S SYNDROME IN CHINESE PATIENTS

Objective To evaluate the sensitivity and specificity of international classification criteria （2002） for primary Sjogren＇s syndrome （pSS） and the role of lower lip biopsy in diagnosis of pSS in Chinese patients. Mothoda Patients who were diagnosed by the experts/rheumatologists as pSS during 1990-2002 from the Department of Rheumatology, Peking Union Medical College Hospital were retrospectively collected as experimental group. Patients who were diagnosed as non-pSS connective tissue diseases or non-connective tissue diseases served as control group. Those with a history of head-neck radiation, hepatitis C virus infection, AIDS, lymphoma, sarcoidosis, graft versus host disease （GVHD）, and anti-acetylcholine drug use were exempted. Both groups were required to complete questionnaires about symptoms such as dry eyes and dry mouth, and complete the objective tests of keratoconjunctivitis and xerostomia including Schirmer test, corneal staining, unstimulated salivary flow, sialography, lower lip biopsy, and antinuclear antibodies （including anti-SSA/SSB antibodies） test. Results A total of 330 pSS patients were included in experimental group and 185 non-pSS patients in control group. The mean age of both groups matched （47.8 ± 10.9 years vs. 46.2±13.6 years, P 〉 0.05）. The sensitivities of the criteria in pSS patients with lower lip biopsy and in pSS patients without lower lip biopsy were 89.2% and 87.2%, respectively; the overall sensitivity was 88.5%. The specificity was 97.3%. A total of 11.3% pSS patients with negative anti-SSA/SSB antibodies were diagnosed as pSS by lower lip biopsy. Coadwion The international classification criteria （2002） for pSS is feasible in Chinese patients. It has high sensitivity and specificity, and may serve as diagnosis criteria in routine clinical practice.

Performance of the Montreal classification for inflammatory bowel diseases

AIM: To validate the Montreal classification system for Crohn&#x02019;s disease (CD) and ulcerative colitis (UC) within the Netherlands.

Phylogeny,character evolution,and classification of Selaginellaceae(lycophytes)

Selaginella is the largest and most taxonomically complex genus in lycophytes.The fact that over 750 species are currently treated in a single genus makes Selaginellales/Selaginellaceae unique in pteridophytes.Here we assembled a dataset of six existing and newly sampled plastid and nuclear loci with a total of 684 accessions(74%increase of the earlier largest sampling)representing ca.300 species to infer a new phylogeny.The evolution of 10 morphological characters is studied in the new phylogenetic context.Our major results include:(1)the nuclear and plastid phylogenies are congruent with each other and combined analysis well resolved and strongly supported the relationships of all but two major clades;(2)the Sinensis group is resolved as sister to S.subg.Pulviniella with strong support in two of the three analyses;(3)most morphological characters are highly homoplasious but some characters alone or combinations of characters well define the major clades in the family;and(4)an infrafamilial classification of Selaginellaceae is proposed and the currently defined Selaginella s.l.is split into seven subfamilies(corresponding to the current six subgenera t the Sinensis group)and 19 genera(the major diagnosable clades)with nine new species-poor genera.We support the conservation of Selaginella with a new type,S.flabellata,to minimize nomenclatural instability.We provide a key to subfamilies and genera,images illustrating their morphology,their morphological and geographical synopses,a list of constituent species,and necessary new combinations.This new classification will hopefully facilitate communication,promote further studies,and help conservation.

Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease.Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long.With an attempt to avoid discomfort to participants in performing long physical tasks for data recording,this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory(LSTM)neural networks.Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture,fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.

Classification and 3-D distribution of upper layer water masses in the northern South China Sea

Using the fuzzy cluster analysis and the temperature-salinity(T-S) similarity number analysis of cruise conductivity-temperature-depth(CTD) data in the upper layer(0–300 m) of the northern South China Sea(NSCS), we classify the upper layer water of the NSCS into six water masses: diluted water(D), surface water(SS),the SCS subsurface water mass(U_S), the Pacific Ocean subsurface water mass(U_P), surface-subsurface mixed water(SU) and subsurface-intermediate mixed water(UI). A new stacked stereogram is used to illustrate the water mass distribution, and to examine the source and the distribution of U_P, combining with the sea surface height data and geostrophic current field. The results show that water mass U_P exists in all four seasons with the maximum range in spring and the minimum range in summer. In spring and winter, the U_P intrudes into the Luzon Strait and the southwest of Taiwan Island via the northern Luzon Strait in the form of nonlinear Rossby eddies, and forms a high temperature and high salinity zone east of the Dongsha Islands. In summer, the U_P is sporadically distributed in the study area. In autumn, the U_P is located in the upper 200 m layer east of Hainan Island.

Eukaryotic microbial distribution pattern and its potential effects on fisheries in the fish reserves of Qiantang River in breeding season

To examine the eukaryotic biodiversity of aquatic ecosystems in the Qiantang River,China,eukaryotic microbes in the river were investigated using 18S rRNA gene sequencing during the breeding season(July to August 2018).Four distinct distribution patterns(1.Jiande;2.Tonglu and Fuyang;3.Jiubao;4.Yanguan)of the microbial community and their potential effects on fishery activities were observed.Results show lower abundances of Dinophyta and Fungi and higher abundances of Cryptophyta and Chlorophyta in Tonglu and Fuyang than those in the other three sections.In addition,the reserves(Tonglu and Fuyang)destabilized the original eukaryotic microbial co-occurrence network.Among all the environmental factors measured,nitrogen(nitrite,nitrate,ammonium),water temperature and total chlorophyll a acted as major driving factors that controlled the eukaryotic microbial distribution.Furthermore,the existence of some algae(e.g.,Chrysophyceae,Cryptophytes,and Chlorophyceae)and fungi(e.g.,Rhizophydium)in Tonglu and Fuyang was beneficial to juvenile fish growth and water quality,although some detrimental species(e.g.,Aphanomyces)needed attention.This study provides further insights into the sustainable protection and utilization of rivers.

An Efficient 3D CNN Framework with Attention Mechanisms for Alzheimer’s Disease Classification

Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous system.Neurodegenerative disorders,such as Alzheimer’s,Huntington’s,Parkinson’s,amyotrophic lateral sclerosis,multiple system atrophy,and multiple sclerosis,are characterized by progressive deterioration of brain function,resulting in symptoms such as memory impairment,movement difficulties,and cognitive decline.Early diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the diseases.Magnetic resonance imaging(MRI)is widely used by neurologists for diagnosing brain abnormalities.The majority of the research in this field focuses on processing the 2D images extracted from the 3D MRI volumetric scans for disease diagnosis.This might result in losing the volumetric information obtained from the whole brain MRI.To address this problem,a novel 3D-CNN architecture with an attention mechanism is proposed to classify whole-brain MRI images for Alzheimer’s disease(AD)detection.The 3D-CNN model uses channel and spatial attention mechanisms to extract relevant features and improve accuracy in identifying brain dysfunctions by focusing on specific regions of the brain.The pipeline takes pre-processed MRI volumetric scans as input,and the 3D-CNN model leverages both channel and spatial attention mechanisms to extract precise feature representations of the input MRI volume for accurate classification.The present study utilizes the publicly available Alzheimer’s disease Neuroimaging Initiative(ADNI)dataset,which has three image classes:Mild Cognitive Impairment(MCI),Cognitive Normal(CN),and AD affected.The proposed approach achieves an overall accuracy of 79%when classifying three classes and an average accuracy of 87%when identifying AD and the other two classes.The findings reveal that 3D-CNN models with an attention mechanism exhibit significantly higher classification performance compared to other models,highlighting the potential of deep learning algorithms to aid in the early detection and prediction of AD.

Parkinson’s Disease Classification Using Random Forest Kerb Feature Selection

Parkinson’s disease(PD)is a neurodegenerative disease cause by a deficiency of dopamine.Investigators have identified the voice as the underlying symptom of PD.Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection.Machine learning(ML)models have recently helped to solve problems in the classification of chronic diseases.This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system.It includes PD classification models of Random forest,decision Tree,neural network,logistic regression and support vector machine.The feature selection is made by RF mean-decrease in accuracy and mean-decrease in Gini techniques.Random forest kerb feature selection(RFKFS)selects only 17 features from 754 attributes.The proposed technique uses validation metrics to assess the performance of ML models.The results of the RF model with feature selection performed well among all other models with high accuracy score of 96.56%and a precision of 88.02%,a sensitivity of 98.26%,a specificity of 96.06%.The respective validation score has an Non polynomial vector(NPV)of 99.47%,a Geometric Mean(GM)of 97.15%,a Youden’s index(YI)of 94.32%,and a Matthews’s correlation method(MCC)90.84%.The proposed model is also more robust than other models.It was also realised that using the RFKFS approach in the PD results in an effective and high-performing medical classifier.

DTI and Structural MRI Classification in Alzheimer’s Disease

In this paper, we propose a fully automated method to individually classify patients with Alzheimer’s disease (AD) and elderly control subjects based on diffusion tensor (DTI) and anatomical magnetic resonance imaging (MRI). We propose a new multimodal measure that combines anatomical and diffusivity measures at the voxel level. Our approach relies on whole-brain parcellation into 73 anatomical regions and the extraction of multimodal characteristics in these regions. Discriminative features are identified using different feature selection (FS) methods and used in a Support Vector Machine (SVM) for individual classification. Fifteen AD patients and 16 elderly controls were discriminated using mean diffusivity alone, combination of mean diffusivity and fractional anisotropy, and multimodal measures in the 73 ROIs and the overall accuracy obtained was 65.2%, 68.6% and 72% respectively. Overall accuracy reached 99% in multimodal measures when relevant regions were selected.

Metallic Reserves of Submarine Manganese Nodules to be Estimated and Harnessed Prudently: Evidence from China's Jiaolong Manned Submersible

Since the discovery of oceanic manganese nodules during the expedition of the British ocean-going ship Challenger from 1872 to 1876, research and development for seabed manganese nodules have never ceased owing to the huge economic inducements. Manganese nodules are the black or dark brown, spherical or massive Mn-bearing ores, deposits of which are found on the sea bottom. The nodules are a mixture of silicate and insoluble potassium permanganates （also with sub-Ti, Fe and Na permanganates） that contain more than 30 kinds of metallic elements, among which those of greatest economic interest are Mn （27-30%）, Ni（1.25-1.5%）, Cu（1- 1.4%）, Co（0.2- 0.25 %）, Fe, Si, and AI, with minor amounts of Ca, Na, K, Ti, B, H and O.

China’s Price System Jeopardizes its Grain Reserves

2008 is a year of bumper harvest in summer grain across China. The failure of numerous state-owned grain depots to purchase grain in times of bumper harvest, however, directly threatens grain reserve security and state control over grain prices in the upcoming year. An important factor underpinning the difficulty of state grain depots to purchase grain is the unwillingness of farmers to sell grain due to the excess of the current market price over the government "protected price" aimed at preventing cheap grain from harming farmers. When grassroots grain depots find themselves in trouble, foreign capital stealthily moves in by taking advantage of this situation. To fulfill grain storage tasks and receive various state subsidies, some state-owned grain depots have no alternative but to surreptitiously raise the purchase price. By contrast, some not so courageous state-owned grain depots can only borrow money to finance the purchase of commodity grain at market prices and subsequently figure out a way to pay back such loans. Behind such distorted grain purchase behavior lies a rough and rugged history of grain price reform in China.

Clinical Observation and Proposed Classification of Vitiliginous Patches by a Wood’s Lamp

Although vitiligo lesion especially in static state is characterized as sharply demarcated and complete depigmented macule, we encounter patients who have various manners of hypopigmented lesions. We examined the 81 lesions using the newly released Wood’s lamp (Woody®) and investigated whether or not vitiliginous lesions could be uniformly classified under Wood’s lamp illumination and also this classification helped to estimate the tendency of repigmentation after treatment. As result, the vitiliginous lesions were categorized into 4 types on intra- and peri-lesions prior to treatment by using the Wood’s lamp. The inside and border of the lesions were classified as follows: clear white, faint, multi-dot, and perifollicular for the inside, and sharp, blunt, confetti, and trichrome for the border. Suggestive residual pigmentation was detected in 73.6% of patients at the first visit and repigmentation was observed in 67.9% of patients at least 3 months after treatment. Lesions with the “clear white” inside pattern showed significantly lower repigmentation frequency in 38.5% of patients compared to others. The borders with 4 enlarged lesions were composed of 3 of confetti-type and one of sharp-type. This preliminary study demonstrated that detailed observation with a Wood’s lamp could be the basis to classify vitiliginous lesions and might be useful for predicting not only disease progression but also repigmentation prior to treatment.