Miracle-dz/UniFAR-FTUs huggingface dataset:
Adoption of improved cultivation practices of Bt. cotton by the farmers in distress prone area of Vidarbha

Study entitled “Involvement of Farmers in Feedback Management with Reference to Bt cotton” was undertaken in Umarkhed Panchayat samiti in Yavatmal district of Vidarbha region of Maharashtra state. Ex-post facto research design was used for the same. The farmers growing Bt cotton since last three years consecutively were identified and from each selected village 12 Bt cotton growers as respondents were selected randomly to constitute sample size of 120 respondents. The data from Bt cotton growers were collected by personal interview method. Relatively higher proportion of Bt cotton growers (58.34%) were in medium adoption level followed by more than one fifth of them (23.33%) were in low level of adoption of recommended package of practices whereas, 18.33 per cent Bt cotton growers who were in high level of adoption of recommended package of practices by Bt cotton growers.
Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine-Learning

COVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with instrumental analysis using mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cohort of 728 patients (369 confirmed COVID-19 and 359 controls) was enrolled from three Brazilian epicentres (Sao Paulo capital, Sao Paulo countryside and Manaus) in the months of April, May, June and July 2020. We were able to elect and identify 21 molecules that are related to the disease's pathophysiology and 26 features to patient's health-related outcomes. With specificity >97% and sensitivity >83% from blinded data, this screening approach is understood as a tool with great potential for real-world application.
Anatomy and relationships of †Aipichthys pretiosus and †‘Aipichthys’ nuchalis (Acanthomorpha: Lampridomorpha), with a review of Late Cretaceous relatives of oarfishes and their allies

The Cenomanian acanthomorph teleost †Aipichthys is best known from two species: †Aipichthys minor and †Aipichthys velifer. Two poorly studied species of †Aipichthys, †Aipichthys pretiosus from Slovenia and †Aipichthys nuchalis from the English Chalk, have been excluded from all recent studies of the genus. Previous accounts of †A. pretiosus – the type species of †Aipichthys – are inadequate, while †A. nuchalis has been described as too incomplete to interpret. Here we completely redescribe †A. pretiosus and utilize computed tomography to reveal previously hidden skeletal anatomy of †A. nuchalis. †Aipichthys pretiosus has a long sagittal crest, with the supraoccipital, frontal and mesethmoid bones contributing to this structure. Based on this and other features, we find that †A. pretiosus belongs to a clade that is more closely related to extant Lampridiformes than all other nominal species of †Aipichthys. As †A. pretiosus is the type species for the genus, we recommend all other species of the genus should be temporarily referred to as †‘Aipichthys’. †‘Aipichthys’ nuchalis possesses a concave posterior margin and a thickened anterior margin to the supraoccipital crest, and is placed in a polytomy alongside †‘A. ’ minor, †‘A. ’ velifer, †‘Aipichthys’ oblongus and †Freigichthys. Further investigation is needed to determine whether †‘Aipichthys’ is congeneric with and can be reassigned to †Freigichthys, or whether a new generic name is needed for these taxa. We also include two additional taxa in our analysis: †Nardovelifer altipinnis, the oldest fossil previously attributed to the lampridiform crown, and †Gigapteryx tethyestris, an incertae sedis acanthopterygian. We find that †Nardovelifer has a number of primitive characters not found in crown Lampridiformes, and resolve it as a stem lampridiform. We find that †Gigapteryx is closely related to †Pycnosteroides, together forming the sister clade to all other lampridomorphs. Finally, we examine the diversity of Cretaceous lampridomorphs and discuss their palaeobiogeography and palaeoecology.
An effective COVID-19 response in South America: the Uruguayan Conundrum

Background: South America has become the new epicenter of the COVID-19 pandemic with more than 1.1M reported cases and >50,000 deaths (June 2020). Conversely, Uruguay stands out as an outlier managing this health crisis with remarkable success. Methods: We developed a molecular diagnostic test to detect SARS-CoV-2. This methodology was transferred to research institutes, public hospitals and academic laboratories all around the country, creating a COVID-19 diagnostic lab network. Uruguay also implemented active epidemiological surveillance following the Test, Trace and Isolate (TETRIS) strategy coupled to real-time genomic epidemiology. Results: Three months after the first cases were detected, the number of positive individuals reached 826 (23 deaths, 112 active cases and 691 recovered). The Uruguayan strategy was based in a close synergy established between the national health authorities and the scientific community. In turn, academia rapidly responded to develop national RT-qPCR tests. Consequently, Uruguay was able to perform ~1,000 molecular tests per day in a matter of weeks. The COVID-19 diagnostic lab network performed more than 54% of the molecular tests in the country. This, together with real-time genomics, were instrumental to implement the TETRIS strategy, helping to contain domestic transmission of the main outbreaks registered so far. Conclusions: Uruguay has successfully navigated the first trimester of the COVID-19 health crisis in South America. A rapid response by the scientific community to increase testing capacity, together with national health authorities seeking out the support from the academia were fundamental to successfully contain, until now, the COVID-19 outbreak in the country.
Nutrient diffusing substrata: a field comparison of commonly used methods to assess nutrient limitation

Abstract Nutrient limitation plays an important role in shaping community structure and ecosystem processes in aquatic environments. Many types of nutrient diffusing substrata (NDS) have been used to estimate nutrient limitation in lotic systems. However, whether these various NDS methods produce comparable results is unknown. We evaluated the 3 most commonly used NDS methods—clay pots, plastic cups, and periphytometers—in a single stream to determine if they gave qualitatively similar results. We also examined the effects of initial nutrient ratios on diffusion rates in all 3 types of NDS and periphyton stoichiometry on clay pots. The largest response in chlorophyll a biomass consistently occurred on substrata that simultaneously diffused both inorganic N and P. However, each NDS method produced a significantly different picture of limitation. Clay pots showed that primary producers were colimited by N and P, plastic cups showed primary limitation by N and secondary limitation by P, and periphytometers showed primary limitation by P and secondary limitation by N. Nutrient diffusion rates were very different among methods. Effects of different N∶P ratios were only seen in clay pots. When N∶P was 16∶1, chlorophyll a biomass was low. When N∶P was 1∶1, periphyton had greater %C and %P and low C∶P and N∶P. Our results indicate that further research is required to clarify methodological differences between the types of NDS. Until such discrepancies are addressed, the results obtained with NDS methods should be interpreted with caution.
Incidence, risk factors and impact of seasonal influenza in pregnancy: A national cohort study

Background Pregnant women are particularly vulnerable to severe infection from influenza resulting in poor neonatal outcomes. The majority of evidence relates to pandemic 2009 A/H1N1 influenza. The objective of this study was to describe the characteristics and outcomes of pregnant women hospitalised with seasonal influenza. Methods This national, prospective, observational cohort study used the UK Obstetric Surveillance System (UKOSS) to identify all pregnant women admitted to hospital between 01/11/2016 and 31/10/2018 with laboratory confirmed influenza together with a comparison group of pregnant women. Baseline characteristics, immunisation status, maternal and perinatal outcomes were compared. Results There were 405 women admitted to hospital with laboratory confirmed influenza in pregnancy: 2.7 per 10,000 maternities. Compared to 694 comparison women, women with influenza were less likely to be professionally employed (aOR 0.59, 95%CI 0.39-0.89) or immunised in the relevant season (aOR 0.59, 0.39-0.89) and more likely to have asthma (aOR 2.42, 1.30-4.49) or have had a previous pregnancy complication (aOR 2.47, 1.33-4.61). They were more likely to be admitted to intensive care (aOR 21.3, 2.78-163.1) and to have a caesarean birth (aOR 1.42, 1.02-1.98). Their babies were more likely to be admitted to neonatal intensive care (aOR 1.86, 1.01-3.42). Conclusions Immunisation reduces the risk of hospitalisation with influenza in pregnancy which is associated with increased risk of morbidity for both the mother and baby. There is a continued need to increase awareness of safety and effectiveness of immunisation in pregnancy and provision within antenatal care settings, especially for high risk groups.
Sensitivity of Rhizoctonia species to different fungicides

Of 14 fungicides with different modes of action, cyproconazole and tolclofos-methyl were generally inhibitory both in vitro and in vivo against all tested isolates of five Rhizoctonia species belonging to the teleomorphs Thanatephorus cucumeris, Waitea circinata or Ceratobasidium cereale. Triadimenol and carboxin provided considerable variation in activity against different species and isolates, whereas prochloraz was ineffective against all isolates. Imazalil and fenarimol showed moderate control, whereas flusilazole, propiconazole, fenpropimorph and benomyl showed strong activity against R. zeae and R. oryzae, but were much less effective against R. sasakii, R. cerealis and R. solani. Benodanil and iprodione controlled all isolates of R. cerealis and R. solani, but were not very effective against R. zeae and R. oryzae. Pencycuron showed strong activity against R. sasakii and most R. solani isolates, moderate activity against R. zeae, and was ineffective against R. oryzae and R. cerealis.
Differentiable Manifold Reconstruction for Point Cloud Denoising

3D point clouds are often perturbed by noise due to the inherent limitation of acquisition equipments, which obstructs downstream tasks such as surface reconstruction, rendering and so on. Previous works mostly infer the displacement of noisy points from the underlying surface, which however are not designated to recover the surface explicitly and may lead to sub-optimal denoising results. To this end, we propose to learn the underlying manifold of a noisy point cloud from differentiably subsampled points with trivial noise perturbation and their embedded neighborhood feature, aiming to capture intrinsic structures in point clouds. Specifically, we present an autoencoder-like neural network. The encoder learns both local and non-local feature representations of each point, and then samples points with low noise via an adaptive differentiable pooling operation. Afterwards, the decoder infers the underlying manifold by transforming each sampled point along with the embedded feature of its neighborhood to a local surface centered around the point. By resampling on the reconstructed manifold, we obtain a denoised point cloud. Further, we design an unsupervised training loss, so that our network can be trained in either an unsupervised or supervised fashion. Experiments show that our method significantly outperforms state-of-the-art denoising methods under both synthetic noise and real world noise. The code and data are available at https://github.com/luost26/DMRDenoise
Feynman checkers: towards algorithmic quantum theory

We survey and develop the most elementary model of electron motion introduced by R.Feynman. It is a game, in which a checker moves on a checkerboard by simple rules, and we count the turns. It is also known as a one-dimensional quantum walk or an Ising model at imaginary temperature. We solve mathematically a problem by R.Feynman from 1965, which was to prove that the model reproduces the usual quantum-mechanical free-particle kernel for large time, small average velocity, and small lattice step. We compute the small-lattice-step and the large-time limits, justifying heuristic derivations by J.Narlikar from 1972 and by A.Ambainis et al. from 2001. For the first time we observe and prove concentration of measure in the former limit. We perform the second quantization of the model. The main tools are the Fourier transform and the stationary phase method.
Sequential Multi-Hypothesis Testing in Multi-Armed Bandit Problems: An Approach for Asymptotic Optimality

We consider a multi-hypothesis testing problem involving a $K$ -armed bandit. Each arm’s signal follows a distribution from a vector exponential family. The actual parameters of the arms are unknown to the decision maker. The decision maker incurs a delay cost for delay until a decision and a switching cost whenever he switches from one arm to another. His goal is to minimise the overall cost until a decision is reached on the true hypothesis. Of interest are policies that satisfy a given constraint on the probability of false detection. This is a sequential decision making problem where the decision maker gets only a limited view of the true state of nature at each stage, but can control his view by choosing the arm to observe at each stage. An information-theoretic lower bound on the total cost (expected time for a reliable decision plus total switching cost) is first identified, and a variation on a sequential policy based on the generalised likelihood ratio statistic is then studied. Due to the vector exponential family assumption, the signal processing at each stage is simple; the associated conjugate prior distribution on the unknown model parameters enables easy updates of the posterior distribution. The proposed policy, with a suitable threshold for stopping, is shown to satisfy the given constraint on the probability of false detection. Under a continuous selection assumption, the policy is also shown to be asymptotically optimal in terms of the total cost among all policies that satisfy the constraint on the probability of false detection.
Effect of plant growth promoting rhizobacteria, nematode parasitic fungi and root-nodule bacterium on root-knot nematodes Meloidogyne javanica and growth of chickpea

Abstract Effects of plant growth promoting rhizobacteria (Pseudomonas putida MTCC No. 3604 and Pseudomonas alcaligenes MTCC No. 493) and parasitic fungi (Pochonia chlamydosporia KIA and Paecilomyces lilacinus KIA) were studied, alone and together with Rhizobium sp. (charcoal commercial culture) on the growth of chickpea and multiplication of Meloidogyne javanica. Individually, P. putida 3604, P. alcaligenes 493 and Rhizobium caused a significant increase in the growth of chickpea in both nematode inoculated and uninoculated plants. Inoculation of Rhizobium with a parasitic fungus or with plant growth promoting rhizobaterium caused a greater increase in the growth of plants inoculated with nematodes than caused by either of them singly. Individually, P. lilacinus KIA caused a greater increase in the growth of nematode inoculated plants than caused by P. putida 3604 or P. alcaligenes 493. P. lilacinus KIA caused a greater reduction in galling and nematode multiplication followed by P. chlamydosporia KIA, P. putida 3604 and P. alcaligenes 493. Combined use of P. lilacinus KIA with Rhizobium was better in reducing galling and nematode multiplication than any other treatment. P. putida 3604 caused a greater colonization of root than P. alcaligenes 493 while P. lilacinus KIA was isolated from more nematodes than P. chlamydosporia KIA.
Diplodia theobromae associated with sudden decline of mango in the Sultanate of Oman

Mango ( Mangifera indica ) is an important crop in the Sultanate of Oman, producing 12 000 t from 1500 ha annually. Grafted and nongrafted trees are cultivated, the former using exotic scions on local rootstocks; nongrafted trees are selections from trees with good fruit qualities. Since 1999 a new disease has appeared, with up to 60% of trees affected in parts of the Al Batinah region. Trees show gummosis from the trunk, wilting and eventual browning of leaves on a single branch. As the infection progresses tree death occurs, perhaps 6 months from the first appearance of symptoms. The wood is stained dark brown, spreading from a point of infection. Damage caused by a bark beetle ( Cryphalus scabrecollis ) is frequently associated with the disease. Pycnidia are observed in lesions on infected bark and the pathogen Diplodia theobromae (IMI Reference W6341) has been isolated from affected trees. When inoculated onto healthy seedlings, the pathogen fulfils Koch’s postulates. The pathogen has been isolated from bark beetles, suggesting the insect may act as a vector for dispersal. Non-grafted (Omani) trees show higher levels of disease severity than exotic scions (65 and 14%, respectively). Insect damage is significantly less on exotic scions than on local, nongrafted trees. Insect log traps protected with polyester fleece to exclude the beetle showed no symptom development, while logs left unprotected were quickly infested with beetles and showed wood staining. Diplodia theobromae causing citrus dieback has been reported from Oman (Waller & Bridge, 1978). Research continues to evaluate varietal resistance to the disease and vector, potential management techniques, and the relationship with Ceratocystis fimbriata , a pathogen that has occasionally been isolated from trees showing mango decline symptoms.
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics

While deep neural networks (DNNs) are an increasingly popular way to query large corpora of data, their significant runtime remains an active area of research. As a result, researchers have proposed systems and optimizations to reduce these costs by allowing users to trade off accuracy and speed. In this work, we examine end-to-end DNN execution in visual analytics systems on modern accelerators. Through a novel measurement study, we show that the preprocessing of data (e.g., decoding, resizing) can be the bottleneck in many visual analytics systems on modern hardware. To address the bottleneck of preprocessing, we introduce two optimizations for end-to-end visual analytics systems. First, we introduce novel methods of achieving accuracy and throughput trade-offs by using natively present, low-resolution visual data. Second, we develop a runtime engine for efficient visual DNN inference. This runtime engine a) efficiently pipelines preprocessing and DNN execution for inference, b) places preprocessing operations on the CPU or GPU in a hardware- and input-aware manner, and c) efficiently manages memory and threading for high throughput execution. We implement these optimizations in a novel system, Smol, and evaluate Smol on eight visual datasets. We show that its optimizations can achieve up to 5.9X end-to-end throughput improvements at a fixed accuracy over recent work in visual analytics.
Self-Attentive Multi Layer Aggregation with Feature Recalibration and Normalization for End-to-End Speaker Verification System

One of the most important parts of an end-to-end speaker verification system is the speaker embedding generation. In our previous paper, we reported that shortcut connections-based multi-layer aggregation improves the representational power of the speaker embedding. However, the number of model parameters is relatively large and the unspecified variations increase in the multi-layer aggregation. Therefore, we propose a self-attentive multi-layer aggregation with feature recalibration and normalization for end-to-end speaker verification system. To reduce the number of model parameters, the ResNet, which scaled channel width and layer depth, is used as a baseline. To control the variability in the training, a self-attention mechanism is applied to perform the multi-layer aggregation with dropout regularizations and batch normalizations. Then, a feature recalibration layer is applied to the aggregated feature using fully-connected layers and nonlinear activation functions. Deep length normalization is also used on a recalibrated feature in the end-to-end training process. Experimental results using the VoxCeleb1 evaluation dataset showed that the performance of the proposed methods was comparable to that of state-of-the-art models (equal error rate of 4.95% and 2.86%, using the VoxCeleb1 and VoxCeleb2 training datasets, respectively).
Many-body chaos and anomalous diffusion across thermal phase transitions in two dimensions

Chaos is an important characterization of classical dynamical systems. How is chaos linked to the long-time dynamics of collective modes across phases and phase transitions? We address this by studying chaos across Ising and Kosterlitz-Thouless transitions in classical XXZ model. We show that spatio-temporal chaotic properties have crossovers across the transitions and distinct temperature dependence in the high and low-temperature phases which show normal and anomalous diffusions, respectively. Our results also provide new insights into the dynamics of interacting quantum systems in the semiclassical limit.
MATURITY EFFECTS ON AVOCADO POSTHARVEST PHYSIOLOGY IN FRUITS PRODUCED UNDER COOL ENVIRONMENTAL CONDITIONS

The influence of cold storage and increasing fruit maturity on ripening physiology was investigated. Fruits that were cold-stored for 28 days at 5,5°C before ripening, always ripened faster than non-stored fruits of a similar maturity. Non-stored fruit showed a decrease in ripening time with increasing maturity. In cold-stored fruit the relationship between ripening time and maturity was less clear. Cold-stored fruit lost less water during ripening than non-stored fruit of similar maturity, but lost water at a faster rate than non-stored fruit. Increasing maturity reduced the total amount of water lost during ripening. Vascular browning appears to be maturity (time of harvest) related with similar results in both cold-stored and non-stored fruit. Cold storage increased the incidence of mesocarp discolouration which became more acute with increasing fruit maturity
NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination

Greedy-NMS inherently raises a dilemma, where a lower NMS threshold will potentially lead to a lower recall rate and a higher threshold introduces more false positives. This problem is more severe in pedestrian detection because the instance density varies more intensively. However, previous works on NMS don't consider or vaguely consider the factor of the existent of nearby pedestrians. Thus, we propose \heatmapname (\heatmapnameshort ), which pinpoints the objects nearby each proposal with a Gaussian distribution, together with \nmsname, which dynamically eases the suppression for the space that might contain other objects with a high likelihood. Compared to Greedy-NMS, our method, as the state-of-the-art, improves by $3.9%$ AP, $5.1%$ Recall, and $0.8%$ MR\textsuperscript-2 on CrowdHuman to $89.0%$ AP and $92.9%$ Recall, and $43.9%$ MR\textsuperscript-2 respectively.