A Novel Method of Intraoperative Calculation in Follicular Unit Transplantation: 'The Sequential Strip and FUE Method'

Hair loss, in particular androgenetic alopecia, has troubled humans since the dawn of history. Treatment options for hair restoration have undergone massive transformation from punch grafting to follicular unit transplantation. Current surgical treatment options in hair restoration fall broadly under two categories, follicular unit transplantation most commonly known as strip method and follicular unit extraction (FUE). The strip method though widely used initially is not so common now due to its fair share of disadvantages ranging from linear donor scar, scar widening to strip overharvesting and wastage of grafts. Follicular unit excision (FUE) was introduced as an alternative method for extraction of grafts to combat the donor linear scar produced by strip method but the disadvantages of FUE include the number of grafts harvested in a single session, moth eaten appearance of donor area caused by over extraction of grafts and harvesting from outside the safe zone. Newer developments like extraction of axillary hair, body hair and pubic hair have been sought to overcome the limitations of number of grafts harvested in a single session of FUE. With more patients now affected by alopecia in their early 20s, there is an ever-increasing demand from the patients for the youthful hairline and hence the focus has shifted towards mega and giga sessions of hair transplantation which pose danger of over extraction of grafts leading to depletion of available donor sites. This article elaborates the combined sequential strip and FUE method along with an intraoperative calculation model to overcome the limitations of over extraction and wastage of grafts. (1) Combination of techniques Strip method with FUE. (2) An intraoperative calculation model that aids in limiting over extraction and wastage of grafts. (3) It is a real time model which can be applied in practice with ease.Level of Evidence IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Quantifying forest resilience post forest fire disturbances using time-series satellite data

Quantification of forest resilience will help us to manage the sustainability of the forest environment and the safety of biodiversity. Measuring forest resilience is crucial for ensuring long-term health of the forest ecosystem in the face of ongoing environmental changes and disturbances. This study focuses on providing a framework to estimate forest resilience scores to assess the vegetation condition after a disturbance. The resilience calculation framework provided uses number of recovery days, the phenological performance level of vegetation in the year when the disturbance took place, long-term mean phenological performance, and greenness levels in subsequent year to calculate the final resilience score at each pixel. Recovery of forests using Landsat data with the help of Normalized Difference Vegetation Index or Normalized Burn Ratio poses a challenge for continuous monitoring of forested landscapes due to cloud cover and availability of scenes at continuous intervals in Landsat datasets. In this regard, MODIS 16-day EVI products were used in this study (2001 to 2020) for monitoring vegetation health before, during, and after the disaster. Bandhavgarh National Park (BNP) located in Madhya Pradesh, India is considered for this study as it witnessed major forest fire breakouts in the second half of March 2018. The objectives of the study are the following: (1) to estimate post-fire recovery days and (2) to formulate new resilience index. The study revealed that the northern part of BNP is more vulnerable and shows slow recovery. The relationship between occupation of people living inside and in the neighboring area with forest resilience is also investigated in this study.

Analysing the spatio-temporal patterns of vegetation dynamics and their responses to climatic parameters in Meghalaya from 2001 to 2020

Quantification of the spatio-temporal trends in vegetation dynamics and its drivers is crucial to ensure sustainable management of ecosystems. The north-eastern state of Meghalaya possessing an idiosyncratic climatic regime has been undergoing tremendous pressure in the past decades considering the recent climate change scenario. A robust trend analysis has been performed using the MODIS NDVI (MOD13Q1) data (2001-2020) along with multi-source gridded climate data (precipitation and temperature) to detect changes in the vegetation dynamics and corresponding climatic variables by employing the Theil-Sen Median trend test and Mann-Kendall test (τ). The spatial variability of trends was gauged with respect to 7 major forest types, administrative boundaries and different elevational gradients found in the area. Results revealed a large positive inter-annual trend (85.48%) with a minimal negative trend (14.52%) in the annual mean NDVI. Mean Annual Precipitation presents a negative trend in 66.97% of the area mainly concentrated in the eastern portion of the state while the western portion displays a positive trend in about 33.03% of the area. Temperature exhibits a 98% positive trend in Meghalaya. Pettitt Change Point Detection revealed three major breakpoints viz., 2010, 2012 and 2014 in the NDVI values from 2001 to 2020 over the forested region of Meghalaya. A consistent future vegetation trend (87.78%) in Meghalaya was identified through Hurst Exponent. A positive correlation between vegetation and temperature was observed in about 82.81% of the area. The western portion of the state was seen to reflect a clear correlation between NDVI and rainfall as compared to the eastern portion where NDVI is correlated more with temperature than rainfall. A gradual deviation of rainfall towards the west was identified which might be feedback of the increasing significant greening observed in the state in the recent decades. This study, therefore, serves as a decadal archive of forest dynamics and also provides an insight into the long-term impact of climate change on vegetation which would further help in investigating and projecting the future ecosystem dynamics in Meghalaya.

Evaluating the climatic and socio-economic influences on the agricultural drought vulnerability in Jharkhand

Environmental hazards like drought lead to degrading food production and adversely impact the agro-economy. This study investigates the contributions of different climatic and socio-economic variables to agricultural drought in Jharkhand. The three primary criteria, i.e., exposure (E), sensitivity (S), and adaptive capacity (AC), responsible for agricultural drought vulnerability, were examined to identify the drought-prone areas. Long-term (1958-2020) gridded climatic datasets obtained from the Terra-climate global dataset, MODIS vegetation index dataset (MOD13Q1) for the years 2001-2020, different soil parameters obtained from the ISRIC global soil database and state agricultural portal of Jharkhand, and different socio-economic datasets obtained from census data (2011) provided by Govt. of India, were utilized for this study. Analytic Hierarchy Process (AHP) was used to estimate the weighted contribution of the indicator variables falling under each criterion (E, S, and AC), and three criteria index maps were generated. These separate maps were further integrated to generate the final vulnerability index map. Finally, the study area was categorized into different zones based on the drought vulnerability index value ranging from 0 to 1, according to the severity of the drought. It was observed that about 4.05%, 28.12%, and 37.07% of the total geographical area is very highly, highly, and moderately vulnerable to agricultural drought, respectively. Amongst the three primary criteria, exposure showed a significant positive correlation (R = 0.61), and sensitivity showed a strong positive correlation (R = 0.55) with vulnerability. The adaptive capacity was negatively correlated (R =  -0.75) with the vulnerability. However, putting equal weights to the variables to calculate the vulnerability, the exposure and sensitivity indicators showed a significant positive correlation with the vulnerability, with an R-value of 0.82 and 0.79, respectively. In contrast, the adaptive capacity showed a negative correlation with the vulnerability with R =  -0.75.

Improved NDVI based proxy leaf-fall indicator to assess rainfall sensitivity of deciduousness in the central Indian forests through remote sensing

Quantifying the leaf-fall dynamics in the tropical deciduous forest will help in modeling regional energy balance and nutrient recycle pattern, but the traditional ground-based leaf-fall enumeration is a tedious and geographically limited approach. Therefore, there is a need for a reliable spatial proxy leaf-fall (i.e., deciduousness) indicator. In this context, this study attempted to improve the existing deciduousness metric using time-series NDVI data (MOD13Q1; 250 m; 16 days interval) and investigated its spatio-temporal variability and sensitivity to rainfall anomalies across the central Indian tropical forest over 18 years (2001-2018). The study also analysed the magnitude of deciduousness during extreme (i.e., dry and wet) and normal rainfall years, and compared its variability with the old metric. The improved NDVI based deciduousness metric performed satisfactorily, as its observed variations were in tandem with ground observations in different forest types, and for different pheno-classes. This is the first kind of study in India revealing the spatio-temporal character of leaf-fall in different ecoregions, elevation gradients and vegetation fraction.

Tunable photovoltaic performance of preferentially oriented rutile TiO2 nanorod photoanode based dye sensitized solar cells with quasi-state electrolyte

Photoanodes made of highly oriented TiO₂ nanorod (NR) arrays with different aspect ratios were synthesized via a one-step hydrothermal technique. Preferentially oriented single crystalline rutile TiO₂ was confirmed by the single peak in an XRD pattern (2θ = 63°, (0 0 2)). FESEM images evidenced the growth of an array of NRss having different geometries with respect to reaction time and solution refreshment rate. The length, diameter and aspect ratio of the NRs increased with reaction time as 4 h (1.98 μm, 121 nm, 15.32), 8 h (4 μm, 185 nm, 22.70), 12 h (5.6 μm, 242 nm, 27.24) and 16 h (8 μm, 254 nm, 38.02), respectively. Unlike a conventional dye-sensitized solar cell (DSSC) with a liquid electrolyte, DSSCs were fabricated here using one-dimensional rutile TiO₂ NR based photoanodes, N719 dye and a quasi-state electrolyte. The charge transport properties were investigated using current-voltage curves and fitted using the one-diode model. Interestingly the photovoltaic performance of the DSSCs increased exponentially with the length of the NR and was attributed to a higher surface to volume ratio, more dye anchoring, and channelized electron transport. The higher photovoltaic performance (J_sc = 5.99 mA cm^-2, V_oc = 750 mV, η = 3.08%) was observed with photoanodes (16 h) made with the longer, densely packed TiO₂ NRs (8 μm, 254 nm).