Effect of FSH and LH on testis during nonbreeding season in Calotes versicolor (Daud.)

Histological and biochemical studies carried out on the male reproductive organs of the Indian garden lizard, Calotes versicolor after the treatment with pituitary gonadotrophins (FSH and LH), showed a significant increase in the weight, protein content and diameter of testis, but decrease in its cholesterol. The spermatogonia, spermatocytes and spermatids increased significantly in the germinal epithelium of seminiferous tubule, and spermatozoa appeared in its lumen. The Leydig and Sertoli cells were hypertrophied with increase in their nuclear diameter. The epidymal weight, diameter and protein content also increased after gonadotrophins treatment. There was a significant decrease in the testicular cholesterol indicating the utilization of cholesterol for steroid hormone synthesis. The combined gonadotrophin (FSH + LH) treatment was more effective than the individual gonadotrophin treatment.     

Recognize fish as food in policy discourse and development funding

The international development community is off-track from meeting targets for alleviating global malnutrition. Meanwhile, there is growing consensus across scientific disciplines that fish plays a crucial role in food and nutrition security. However, this ‘fish as food’ perspective has yet to translate into policy and development funding priorities. We argue that the traditional framing of fish as a natural resource emphasizes economic development and biodiversity conservation objectives, whereas situating fish within a food systems perspective can lead to innovative policies and investments that promote nutrition-sensitive and socially equitable capture fisheries and aquaculture. This paper highlights four pillars of research needs and policy directions toward this end. Ultimately, recognizing and working to enhance the role of fish in alleviating hunger and malnutrition can provide an additional long-term development incentive, beyond revenue generation and biodiversity conservation, for governments, international development organizations, and society more broadly to invest in the sustainability of capture fisheries and aquaculture.Electronic supplementary materialThe online version of this article (10.1007/s13280-020-01451-4) contains supplementary material, which is available to authorized users.     

Bayesian approach to estimating margin of safety for total maximum daily load development

A Bayesian-updating approach is presented to the estimation of total uncertainty-based Margin of Safety (MOS) for Total Maximum Daily Load (TMDL) calculations. Probability distributions are presented to construct the likelihood function, the prior probability distribution, and the posterior (total uncertainty) probability distribution. The Bayesian-updating approach is demonstrated through a case study for the Lower Amite River, Louisiana. The posterior probability distribution-based on the Bayesian approach updates the standard deviation of summer dissolved oxygen in the Amite River from 1.88 mg/L to 2.10 mg/L when the total uncertainty is considered. Results from the Bayesian-updating approach are compared with two conventional methods. The dissolved oxygen reserve based on a conventional margin of safety of 20% is estimated to be 45,682.26 kg/Day. The second conventional method, where we consider the standard deviation of 1.88 mg/L, produces a dissolved oxygen reserve of 40,516.09 kg/Day. The Bayesian approach yields the dissolved oxygen reserve of 38,614.43 kg/Day with the first level (μ-σ) of MOS, producing a deficit of 5606.65 kg/Day in dissolved oxygen. The dissolved oxygen reserve deficit increases to 23,895.13 kg/Day when the second level (μ-2σ) of MOS is used, which escalated to 42,383.52 kg/Day when the highest level (μ-3σ) of MOS is used. While the total uncertainty-based Bayesian approach is demonstrated for a TMDL development on the Amite River, the overall approach could be applied in any river system with similar available data.     

Upper level set scan statistic for detecting arbitrarily shaped hotspots

A declared need is around for geoinformatic surveillance statistical science and software infrastructure for spatial and spatiotemporal hotspot detection. Hotspot means something unusual, anomaly, aberration, outbreak, elevated cluster, critical resource area, etc. The declared need may be for monitoring, etiology, management, or early warning. The responsible factors may be natural, accidental, or intentional. This proof-of-concept paper suggests methods and tools for hotspot detection across geographic regions and across networks. The investigation proposes development of statistical methods and tools that have immediate potential for use in critical societal areas, such as public health and disease surveillance, ecosystem health, water resources and water services, transportation networks, persistent poverty typologies and trajectories, environmental justice, biosurveillance and biosecurity, among others. We introduce, for multidisciplinary use, an innovation of the health-area-popular circle-based spatial and spatiotemporal scan statistic. Our innovation employs the notion of an upper level set, and is accordingly called the upper level set scan statistic, pointing to a sophisticated analytical and computational system as the next generation of the present day popular SaTScan. Success of surveillance rests on potential elevated cluster detection capability. But the clusters can be of any shape, and cannot be captured only by circles. This is likely to give more of false alarms and more of false sense of security. What we need is capability to detect arbitrarily shaped clusters. The proposed upper level set scan statistic innovation is expected to fill this need     

Use of Hydrologic Landscape Classification to Diagnose Streamflow Predictability in Oregon

We implement a spatially lumped hydrologic model to predict daily streamflow at 88 catchments within the state of Oregon and analyze its performance using the Oregon Hydrologic Landscape (OHL) classification. OHL is used to identify the physio‐climatic conditions that favor high (or low) streamflow predictability. High prediction catchments (Nash‐Sutcliffe efficiency of (NS) > 0.75) are mainly classified as rain dominated with very wet climate, low aquifer permeability, and low to medium soil permeability. Most of them are located west of the Cascade Mountain Range. Conversely, most low prediction catchments (NS < 0.6) are classified as snow‐dominated with high aquifer permeability and medium to high soil permeability. They are mainly located in the volcano‐influenced High Cascades region. Using a subset of 36 catchments, we further test if class‐specific model parameters can be developed to predict at ungauged catchments. In most catchments, OHL class‐specific parameters provide predictions that are on par with individually calibrated parameters (NS decline < 10%). However, large NS declines are observed in OHL classes where predictability is not high enough. Results suggest higher uncertainty in rain‐to‐snow transition of precipitation phase and external gains/losses of deep groundwater are major factors for low prediction in Oregon. Moreover, regionalized estimation of model parameters is more useful in regions where conditions favor good streamflow predictability.     

Sodium cyanide-induced modulations in the activities of some oxidative enzymes and metabolites in the fingerlings of Cyprinus carpio (Linnaeus)

Carp fingerlings exposed to a sublethal concentration (0.5?mg?L^?1) of sodium cyanide showed a steady decrement over a 7-day period in respiratory rate, rise in lactate dehydrogenase (LDH), and fall in succinate dehydrogenase (SDH) activities followed by variations in lactic and pyruvate levels. Changes in these enzyme activities might be due to impaired oxidative metabolism and severe cellular damage leading to the release of these enzymes. Decline in the activities of SDH and LDH clearly represents a shift from aerobic to anaerobic metabolism as evidenced by elevated lactate and decline in pyruvate levels. The shift to anaerobic metabolism is also reflected by severe drop in the respiratory rate of the fish. This may be a consequence of the blockage of electron transfer from cytochrome c oxidase to molecular oxygen, thus ceasing cellular respiration and it can lead to cellular hypoxia even in the presence of normal hemoglobin oxygenation. Hence, we indirectly reconfirm the inhibition of oxidative metabolism by sodium cyanide. Alterations in behavioral pattern induced by sublethal sodium cyanide exposure may be due to the combination of cytotoxic hypoxia with lactate acidosis, which depresses the central nervous system (CNS); as the brain is the most sensitive site to anoxia, it results in impaired CNS function.     

Multicriteria prioritization and partial order in environmental sciences

The complexity of the present data-centric world finds its expression in the increasing number of multi-indicator systems. This has led to the development of multicriteria ranking systems based on partial orders. Order theory is a main pillar of structural mathematics. Partial orders help to reveal why an object of interest holds a certain ranking position and how much it is subject to change if a composite indicator is upgraded. Order theory helps to derive linear or weak orders without indicator weighting schemes. Hence, rankings obtained from decision support systems (DSS) which depend on many parameters beyond the data matrix can be checked and discrepancies can lead to examine the parameters of the DSS. Order theory helps discover association and implication structures derived from formal concept lattices. Association and implication networks among the attributes of the data matrix allow more insights into multi-indicator systems and lead to new hypotheses and motivate further research. Some new and innovative concepts, like separated subsets, antagonistic indicators, ranking stability fields are rendered. Separated subsets are the typical outcome of a partial order analysis; their identification leads to antagonistic indicators, which are responsible for the separatedness of object’s subsets. Numerical aggregation can be performed step-by-step and the question which values of a weight lead to an order inversion is of high interest. The concept of stability fields is one possible answer, discussed in this paper. After an outline of partial order theory some more specific theoretical results are shown, then we discuss the role of composite indicators in the light of partial order and give some examples of interesting applications of partial order. Finally examples are selected from real life case studies of watersheds, environmental performance evaluations, child well being, geographic and administrative regions and more.     

Physical characterization,magnetic measurements,REE geochemistry and biomonitoring of dust load accumulated during a protracted winter fog period and their implications

The winter fog in India is a recurrent phenomenon for more than a decade now affecting the entire Himalayan and sub-Himalayan regions covering an area of nearly 500,000 km². Every winter (December–January), the air and surface transports in cities of northern India (Amritsar, New Delhi, Agra, Gwalior, Kanpur, Lucknow, and Allahabad) are severely disrupted with visibility reduced to <50 m at times. Since dust particles are known to act as nuclei for the fog formation, this study is aimed to carry out physicochemical characterization of the dust particulates accumulated during a protracted fog period from one of the severely fog affected cities of north India (Allahabad; 25°27′33.40″N–81°52′45.47″E). The dust-loaded tree leaves belonging to Ficus bengalensis and Ficus religiosa from 50 different locations between January 24 and 31, 2010 are sampled and characterized. The mass of dust, color, grain shape, size, phase constituents, and mineral magnetic parameters, such as magnetic susceptibility, SIRM, χ _fd%, and S-ratio, show minor variation and the regional influence outweighs local anthropogenic contributions. The dust compositions show fractionated rare earth element pattern with a pronounced negative Eu anomaly similar to upper continental crust and further suggesting their derivation from sources located in parts of north and central India.     

PM10 in the ambient air of Chandrapur coal mine and its comparison with other environments

This study compares the ambient air particulate matter (PM10) data of 15 different coal mine environments. For most of these mine environments, the monitoring was carried out by different researchers using respirable dust sampler (RDS) that separates PM10 by centrifugal inertial separation. At two sites — Padmapur and Ghugus (Chandrapur, Maharashtra, India) — mass inertial impaction-based sampler was used for PM10 monitoring. It is observed that the spatiotemporal average value of ambient air PM10 monitored using mass inertial impactor reports relatively higher values (240–372 μg/m³) compared to those monitored using RDS (<227 μg/m³). In order to realize the severity of mine area pollution, it is compared with PM10 values found in an urban area (Delhi, India). It is found that PM10 values in Delhi (using mass inertial impactor) are much higher (300–400 μg/m³) than those reported for the mine environment. The data seems to indicate that the mine environment is relatively cleaner than urban air and therefore raises doubt about the appropriateness of using either mass impactor or RDS for PM10 sampling.