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Additional file 2: Tabls S2. The gene number and DAVID enrichment score in each module.

Additional file 3: Fig. S1. Module preservation between even partitionings of datasets.

Additional file 1: Tabls S1. The number of genes in each module.

Additional file 1: Kaplan-Meier curve of module (a)M6, (b)M16, (c)G1, (d)G12,(e)W13 and (f)Q7.

Knowledge of the situation of the topography of a plain in a country is an important issue for planning rural fitting out and investigate geology layers and hydrology state. This study is based on the analysis of the topography leading to explore the physical state of the environment by using different structures. The methodology used was consisted to perform statistical analysis by linear regression, to study density and structural dynamics as well a characterization of the ground by performing the 3D. As results, the analysis procedure over contour lines and TIN structures showed significant dynamics of the physical surface. The dynamics of these oscillations of ground gave place to characterize the geological, hydrological and topographical state of the region. In the geological concept, the 3D mode of contour lines structure has showed the determination of the thicknesses of the outcrop layers. However, the TIN structure clearly defined the level of degradation of environment as well the draining axes constituting the hydrological network.

Water management in agricultural areas requires knowledge of flow directions as well as lost water recuperation areas. The Inner Delta of Niger threatened by desertification, despite its great agricultural potential, loses an enormous quantity of runoff water every year. In this paper, the study focuses on the detection of flow directions and areas susceptible to be flooded. For this purpose, two new approaches have been applied, namely “geometrical interval and profile interpolation” to identify flow directions and flood zones. The flow directions and flood zones of the inner Delta of Niger were obtained from SRTM image (Shuttle Radar Topography Mission) using ArcMap software. The obtained results from the surfaces classification technique “geometrical Interval” have permitted to identify the lowest areas between 258 and 264 m altitude occupying 58% of the total area. Thereafter, the areas favorable for profile tracing are identified through the stretched colour tone. The flow paths leading to these flood zones are detected by the “longitudinal profiles” technique showing downward slope profiles. The whole area dominated by rocky outcrops and by small scattered hills, is endowed of lowlands including a large slit of southwest to northeast direction.

The problem that the underwater robot is winded by fishing nets is the most concerned. At present, there are still no effective methods which can detect fishing nets in real time to avoid obstacles. As fishing net images taken with traditional detection technique are blurry and have low definition, it is hard to identify fishing nets. In view of this phenomenon, we proposed a new underwater detection method in this paper and used underwater laser scanning system to collect clear fishing net images. Due to the high cost of data acquisition and the single amount of experiment data, we took advantage of a deep generative network to amplify original data. In fishing nets detection network, we used a residual network as new base net to deepen the network layers, then combined the method of regression with some region suggestion to detect fishing nets, which can improve the accuracy of fishing nets detection without affecting the real-time performance. Finally, the contrast experiment of laser fishing net images is carried out to verify the effectiveness of the proposed method.