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A reliable LULC model aims at predicting the spatial distribution of specific LULC classes for a later year by utilizing the trends from previous years thereby helps in appropriate LULC planning. The Ganga River Basin (GRB) has undergone significant LULC changes during past decades. The changes in LULC pattern was investigated for 1975 and 2010 to have better understanding of the conversion process and thereby predicting the future trend for 2045 using Dyna-CLUE (Conversion of Land-Use and its Effects) model. Four types of data were fed into the model i.e., (a) spatial policies and restrictions; (b) LULC type specific conversion settings; (c) LULC requirements (demands) and (d) location characteristics. The possible and impossible conversions among LULC classes were dealt with through Restriction and No Restriction area. The land conversion allocation was determined by establishing preference between LULC classes and the driving factors using binary logistic regression. Relative Operating Characteristic curves provided an overall value of 0.86 implying acceptability of regression results. The simulated result (with ‘no restriction’ area criteria) showed 578,296 km2 agriculture area in 2010 and 579,235 km2 for 2045, wherein 575,874 km2 (99.58%) of agriculture area could remain unchanged during 2010–2045; while with the restricted area, agriculture area of 577,675 km2 in 2010 and 578,516 km2 for 2045; whereas 576,242 km2 (99.75%) of agriculture area may remain unchanged during 2010–2045. Biophysical drivers namely altitude, slope, aspect, soil types, precipitation and temperature, emerged as major controlling factors for LULC change in GRB. Logistic regression analysis showed that population density is positively related with agriculture and expansion of settlements.

Deep neural network (DNN) architecture based models have high expressive power and learning capacity. However, they are essentially a black box method since it is not easy to mathematically formulate the functions that are learned within its many layers of representation. Realizing this, many researchers have started to design methods to exploit the drawbacks of deep learning based algorithms questioning their robustness and exposing their singularities. In this paper, we attempt to unravel three aspects related to the robustness of DNNs for face recognition: (i) assessing the impact of deep architectures for face recognition in terms of vulnerabilities to attacks, (ii) detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks; and (iii) making corrections to the processing pipeline to alleviate the problem. Our experimental evaluation using multiple open-source DNN-based face recognition networks, and three publicly available face databases demonstrates that the performance of deep learning based face recognition algorithms can suffer greatly in the presence of such distortions. We also evaluate the proposed approaches on four existing quasi-imperceptible distortions: DeepFool, Universal adversarial perturbations, $$l_2$$ , and Elastic-Net (EAD). The proposed method is able to detect both types of attacks with very high accuracy by suitably designing a classifier using the response of the hidden layers in the network. Finally, we present effective countermeasures to mitigate the impact of adversarial attacks and improve the overall robustness of DNN-based face recognition.

Alternative splicing of pre-mRNA increases proteomic diversity, a crucial mechanism in defining tissue identity. We demonstrate differentially spliced interleukin (IL)-7 in distinct anatomic areas in the adult, in developing human brains and in normal human neuronal progenitor (NHNP) cells. IL-7c (c, the canonical form spanning all six exons) or its variants IL-7 delta 5, delta 4 or delta 4/5 were cloned and expressed as recombinant proteins. IL-7 and splice variants were able to shift the differentiation of NHNP cells as compared with the diluent control (P0.01) defined by anti-beta (III)-tubulin and glial fibrillary acidic protein expression, with different degrees (IL-7cdelta 4/5IL-7 delta 5); IL-7 delta 4 exhibited a significantly weaker potency. Differentiation was confirmed by transcriptome analysis of IL-7c-stimulated neural NHNP cells, resulting in 58 differentially expressed genes; some of these are involved in neural differentiation, for example, the developmentally regulated transcription factor krüppel-like factor 12, musashi 2, a translational regulator of cell fate or the sonic hedgehog receptor patch 1. This suggests that IL-7 influences neural development at a molecular level by participating in human brain architecture through glia cell formation: a paradigm that alternative splicing in cytokines, for example, for IL-7, has a physiological role in human organ development and progenitor cell differentiation.

This article reports a case of progressive glaucomatous optic neuropathy and visual field loss that occurred in a patient who practiced the Sirsasana (headstand) yoga posture on a daily basis for many years. Visual field analysis was performed through standard automated perimetry. Intraocular pressure (IOP) was measured through pneumotonometry in the sitting position and in the head-down position. Stereo-optic disc photographs were obtained. IOP increased significantly in the head-down position. Optic disc evaluation revealed a new disc hemorrhage in the left eye. Visual field analysis over a period of 2 y showed progression of a superior arcuate defect in the left eye. Transient increases in IOP associated with the yoga headstand posture may lead to progressive glaucomatous optic nerve damage and visual field loss.

Background Interleukin 7 (IL-7) signals via the IL-7 receptor (IL-7R) and drives homeostatic T-cell proliferation in patients after allogeneic hematopoietic stem cell transplantation (aHSCT). Purpose We performed a prospective study in adults (n = 33) and children (n = 29) undergoing aHSCT measuring plasma IL-7 and soluble IL-7R (sIL-7R) concentrations between 1 and 12 months after HSCT in order to investigate the link between sIL-7R and clinical events after aHSCT. Results sIL-7R, but not IL-7, increased with time after HSCT in plasma from all patients enrolled in the study. sIL-7R values were higher at 2, 3, and 6 months (p 0.01) and sibling donors (RH: 2.23, p = 0.004). Recipients of sibling grafts showed high levels of IL-7 (RH: 1.38, p

Background A better understanding of the quality of cellular immune responses directed against molecularly defined targets will guide the development of TB diagnostics and identification of molecularly defined, clinically relevant M.tb vaccine candidates. Methods Recombinant proteins (n = 8) and peptide pools (n = 14) from M. tuberculosis (M.tb) targets were used to compare cellular immune responses defined by IFN-γ and IL-17 production using a Whole Blood Assay (WBA) in a cohort of 148 individuals, i.e. patients with TB + (n = 38), TB- individuals with other pulmonary diseases (n = 81) and individuals exposed to TB without evidence of clinical TB (health care workers, n = 29). Results M.tb antigens Rv2958c (glycosyltransferase), Rv2962c (mycolyltransferase), Rv1886c (Ag85B), Rv3804c (Ag85A), and the PPE family member Rv3347c were frequently recognized, defined by IFN-γ production, in blood from healthy individuals exposed to M.tb (health care workers). A different recognition pattern was found for IL-17 production in blood from M.tb exposed individuals responding to TB10.4 (Rv0288), Ag85B (Rv1886c) and the PPE family members Rv0978c and Rv1917c. Conclusions The pattern of immune target recognition is different in regard to IFN-γ and IL-17 production to defined molecular M.tb targets in PBMCs from individuals frequently exposed to M.tb. The data represent the first mapping of cellular immune responses against M.tb targets in TB patients from Honduras.

Nature Nanotechnology 9, 639–647 (2014); published online 3 August 2014; corrected after print 14 August 2014. In the version of this Article previously published, the name of the co-author Kevan C. Herold was spelt incorrectly. This has now been corrected in the online versions of the Article.