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Fruits and vegetables are major sources of nutrients for the majority of the population across the globe. With the rapid increase in population, the objectives of the future agro-industry are to reduce product loss while increasing product quality and productivity considerably. Consequently, farmers need to be assisted with cutting-edge technologies for sustainable, eco-friendly, and efficient farming. Smart farming for early disease recognition and control is the current hot-spot research objective in the fruitage domain. The precision agriculture era has been revolutionized by federating cutting-edge technologies like machine learning, deep learning, and, the Internet-of-Things. However, the existing studies focused on the impact of individual technology on single or multiple cultivars of edible fruits or vegetables. Limited areas of the fruitage disease remain explored, necessitating further investigation into the research gaps and challenges identified for implementing the smart practices in real-field farmlands. In this paper, a comprehensive survey of recent advancements in fruit and vegetable disease identification and classification is presented. The technology-wise state-of-the-art findings, gaps, challenges, and future opportunities for fruitage disease recognition have been presented, covering 99 research articles. Moreover, the corpus of publicly available fruit and vegetable datasets has been investigated, with the existing gaps, improvements, and future requirements. The research paper concludes with challenges and a future outlook that promises to be a very significant and valuable resource for researchers working in the area of agronomic disease monitoring. [ABSTRACT FROM AUTHOR]

[Display omitted] • A library of sulfonated polystyrenes differing in MW and polarity was synthesized. • Polymers effective in solubilizing model cell membranes were identified. • The lead polymer forms stable particles (nanodiscs) from model DMPC liposomes. • Polymers remain soluble at low pH and in the presence of Mg2+ ions. Amphiphilic polymers are increasingly applied in the detergent-free isolation and functional studies of membrane proteins. However, the carboxylate group present in the structure of many popular variants, such as styrene-maleic acid (SMA) copolymers, brings limitations in terms of polymer sensitivity to precipitation at acidic pH or in the presence of divalent metal cations. Herein, we addressed this problem by replacing carboxylate with the more acidic sulfonate groups. To this end, we synthesized a library of amphiphilic poly[styrene- co -(sodium 4-styrene sulfonate)] copolymers (termed SSS), differing in their molecular weight and overall polarity. Using model cell membranes (Jurkat), we identified two copolymer compositions (SSS-L30 and SSS-L36) that solubilized membranes to an extent similar to SMA. Interestingly, the density gradient ultracentrifugation/SDS-PAGE/Western blotting analysis of cell lysates revealed a distribution of studied membrane proteins in the gradient fractions that was different than for SMA-solubilized membranes. Importantly, unlike SMA, the SSS copolymers remained soluble at low pH and in the presence of Mg2+ ions. Additionally, the solubilization of DMPC liposomes by the lead materials was studied by turbidimetry, DLS, SEC, and high-resolution NMR, revealing, for SSS-L36, the formation of stable particles (nanodiscs), facilitated by the direct hydrophobic interaction of the copolymer phenyls with lipid acyl chains. [ABSTRACT FROM AUTHOR]

Purpose: To report our institutional experience of targeted massively parallel sequencing (MPS) testing in children with epilepsy.Method: We retrospectively analysed the yield of targeted epileptic encephalopathy (EE) panel of 71 known EE genes in patients with epilepsy of unknown cause, who underwent clinical triage by a group of neurologists prior to the testing. We compared cost of the EE panel approach compared to traditional evaluation in patients with identified pathogenic variants.Results: The yield of pathogenic variants was 28.5% (n = 30/105), highest in early onset EE <3 months including Ohtahara syndrome (52%, n = 10/19) and lowest in generalized epilepsy (0/17). Patients identified with pathogenic variants had earlier onset of seizures (median 3.6 m vs 1.1y, p < 0.001, OR 0.6/year, P < 0.02) compared to those without pathogenic variants. Pathogenic/likely pathogenic variants were found in ALDH7A1 (2), CACNA1A (1), CDKL5 (3), FOXG1 (2), GABRB3 (1), GRIN2A (1), KCNQ2 (4), KCNQ3 (1), PRRT2 (1), SCN1A (6), SCN2A (2), SCN8A (2), SYNGAP1 (1), UBE3A (2) and WWOX (1) genes. This study expands the inheritance pattern caused by KCNQ3 mutations to include an autosomal recessive severe phenotype with neonatal seizures and severe developmental delay. The average cost of etiological evaluation was less with early use of EE panel compared to the traditional investigation approach ($5990 Australian dollars (AUD) vs $13069 AUD ; p = 0.02) among the patients with identified pathogenic variants.Conclusion: Targeted MPS testing is a comprehensive and economical investigation that enables early genetic diagnosis in children with EE. Careful clinical triage and selection of patients with young onset EE may maximize the yield of EE panel testing. [ABSTRACT FROM AUTHOR]

Over the past few years, the awful COVID-19 pandemic effect has become a lethal sickness. The processing of the gathered samples requires extra time due to the use of medical diagnostic equipment, methodologies, and clinical testing procedures for the early diagnosis of infected individuals. An innovative multimodal paradigm for the early diagnosis and precise categorization of COVID-19 is put up as a solution to this issue. To extract distinguishing features from the prepared chest X-ray picture and cough (audio) database, chest X-ray-based and cough-based model are used here. Other public chest X-ray image datasets, and the Coswara cough (audio) dataset containing 92 COVID-19 positive, and 1079 healthy subjects (people) using the deep Uniform-Net, and Convolutional Neural Network (CNN). The weighted sum-rule fusion method and ensemble deep learning algorithms are utilized to further combine the extracted features. For the early diagnosis of patients, the framework offers an accuracy of 98.67%. [Display omitted] • A novel multimodal framework is proposed for the early diagnosis and accurate classification of COVID-19. • The extracted features are fused using the weighted sum-rule fusion technique for early diagnosis and accurate prediction. • The framework provided an accuracy of 98.67% (X-ray based) and 86.53% (cough based diagnosis) for early diagnosis of patients. [ABSTRACT FROM AUTHOR]

Particulate Matter (PM) levels in the ambient air of three urban sites of strategic importance in Punjab (second largest agricultural state of India) were monitored from September 2013 to June 2014 covering two seasons of crop residue burning episodes of rice and wheat, respectively. During both crop periods, the levels varied from 71 to 167 μgm −3 (PM 10 ) and 43–107 μgm −3 (PM 2.5 ), which were observed to be much beyond the permissible limits of 100 μgm −3 for PM 10 and 60 μgm −3 for PM 2.5 set by state pollution control agencies. Simultaneously, Pulmonary Function Test (PFT) parameters of children (age group 10–16 years) residing at these sites were monitored. PFT parameters like Forced Vital Capacity (FVC) and Peak Expiratory Flow (PEF) were monitored on 50 healthy subjects at each site. The results indicated an alarming fall in FVC (5%–7%) and in PEF (4%–6%) parameters during the crop residue burning period’s .The decrement was almost same for all age groups of children. The adverse effect was more on female subjects than male subjects. The impact of particulate matter pollution was more prominent in industrially important site (7%, SIS) as compared to historically (3%, HIS) and commercially (4%, CAS) important sites. [ABSTRACT FROM AUTHOR]

Polycrystalline HoSn 1.1 Ge 0.9 and DySn 1.1 Ge 0.9 compounds have been studied by means of different experimental probes. Both the compounds are antiferromagnetic and show metamagnetic transition at low temperatures. HoSn 1.1 Ge 0.9 shows a sign change in magnetocaloric effect (MCE) and magnetoresistance (MR) with field, which is attributed to the metamagnetic transition. DySn 1.1 Ge 0.9 shows characteristics of a typical antiferromagnet, as evidenced by magnetization, MCE and MR data. 119 Sn Mössbauer studies show hyperfine splitting at low temperatures, consistent with magnetization data. Thermoelectric power and resistivity measurements reveal metallic behavior in these compounds. Magnetic, magnetocaloric and the magnetoresistance data clearly show that the antiferromagnetic coupling in DySn 1.1 Ge 0.9 is stronger than in HoSn 1.1 Ge 0.9 . [ABSTRACT FROM AUTHOR]

RTX ( R = rare earths, T = 3 d /4 d /5 d , transition metals such as Sc, Ti, Mn, Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag, Os, Ir, Pt, Au, and X = p -block elements such as Al, Ga, In, Si, Ge, Sn, As, Sb, Bi) series is a huge family of intermetallics compounds. These compounds crystallize in different crystal structures depending on the constituents. Though these compounds have been known for a long time, they came to limelight recently in view of the large magnetocaloric effect (MCE) and magnetoresistance (MR) shown by many of them. Most of these compounds crystallize in hexagonal, orthorhombic and tetragonal crystal structures. Some of them show crystal structure modification with annealing temperature; while a few of them show iso-structural transition in the paramagnetic regime. Their magnetic ordering temperatures vary from very low temperatures to temperatures well above room temperature (∼510 K). Depending on the crystal structure, they show a variety of magnetic and electrical properties. These compounds have been characterized by means of a variety of techniques/measurements such as X-ray diffraction, neutron diffraction, magnetic properties, heat capacity, magnetocaloric properties, electrical resistivity, magnetoresistance, thermoelectric power, thermal expansion, Hall effect, optical properties, XPS, Mössbauer spectroscopy, ESR, μSR, NMR, and NQR. Some amount of work on theoretical calculations on electronic structure, crystal field interaction and exchange interactions has also been reported. The interesting aspect of this series is that they show a variety of physical properties such as Kondo effect, heavy fermion behavior, spin glass state, intermediate valence, superconductivity, multiple magnetic transitions, metamagnetism, large MCE, large positive as well as negative MR, spin orbital compensation, magnetic polaronic behavior, and pseudo gap effect. Except Mn, no other transition metal in these compounds possesses considerable magnetic moments. Because of this R Mn X compounds in general have high ordering temperatures. Interstitial modification using hydrogen is found to alter their crystal structures and magnetic properties considerably. RTX compounds also show interesting pressure effects on their structural and magnetic properties. In summary, these compounds show variety of physical properties over a wide range of temperatures. This review is intended to cover all the important results obtained in this family, particularly in the last few years. [ABSTRACT FROM AUTHOR]

A comprehensive study on the hydrostatic implosion of carbon fiber reinforced epoxy composite tubes is conducted experimentally to examine the failure and damage mechanisms of collapse. Experiments are performed in a pressure vessel designed to provide constant hydrostatic pressure through the collapse. Filament-wound, braided, and roll-wrapped carbon-fiber/epoxy tubes are studied to explore the effect of geometry and reinforcement architecture on the modes of failure. 3-D Digital Image Correlation technique, which is first calibrated for the underwater environment, is used to capture the full-field deformation and velocities during the implosion event. Dynamic pressure transducers are employed to measure the pressure pulses generated by the event and evaluate its damage potential. The results show that composites with braided fabric reinforcements are found to have more damage potential to adjacent structures than those containing unidirectional reinforcements, as they release pressure waves with significantly greater impulse. [ABSTRACT FROM AUTHOR]

A fundamental experimental investigation, with corresponding computational simulations, was conducted to understand the physical mechanisms of implosions of cylindrical shells occurring within a tubular confining space which has a limited potential energy reservoir. In particular, attention was focused on studying the generation of pressure waves from the implosion, the interaction of the pressure waves with the confining tube walls and end caps, and the collapse mechanisms of the implodable volume. Experiments were conducted with three implodable volume geometries which had similar critical collapse pressures. The implodable volumes were aluminum 6061-T6 cylindrical tubing and were placed concentrically within the confining tube. Pressure histories recorded along the length of the confining tube during the experiments were utilized to analytically evaluate the deformation of the implodable volume using fluid–structure coupled deformation models. Computational simulations were conducted using a coupled Eulerian–Lagrangian scheme to explicitly model the implosion process of the tubes along with the resulting compressible fluid flow. The numerical model developed in this study is shown to have high correlation with the experimental results and will serve as a predictive tool for the simulation of the implosion of different cylindrical geometries as well as various tube-in-tube implosion configurations. The experimental results show that the limited hydrostatic potential energy available in a confined environment, as compared to a free field, significantly influences the implosion process. The wall velocities of the implodable volume during the collapse, as well as the extent of the collapse progression, are largely affected by the sudden decrease in the available hydrostatic potential energy. This energy is shown to be partially transformed into elasto-plastic strain energy absorbed in the deformation of the implodable volume, as well as the kinetic energy of the water during the implosion process. Experiments also show that the extent of the collapse progression of an implodable volume can potentially be inhibited within a closed environment, which can lead to the arresting of an implosion event prior to completion for larger implodable volumes. The pressure waves generated during collapse comprise of waves emitted due to the impact of the implodable volume walls, the arrest of rushing water and contact propagation along the walls. These processes later evolve into water hammer type axial wave behavior. [ABSTRACT FROM AUTHOR]

Gene expression in trypanosomes is mainly regulated post-transcriptionally. Genes are transcribed as polycistronic mRNAsthat are dissected by the concerted action of trans-splicing and polyadenylation. In trans-splicing, a common exon, the spliced leader, is added to all mRNAs from a small RNA. In this study, we examined by microarray analysis the transcriptome following RNAi silencing of the basal splicing factors U2AF65, SF1, and U2AF35. The transcriptome data revealed correlations between the affected genes and their splicing and polyadenylation signaling properties, suggesting that differential binding of these factors to pre-mRNA regulates trans-splicing and hence expression of specific genes. Surprisingly, all these factors were shown to affect not only splicing but also mRNA stability. Affinity purification of SF1 and U2AF35 complexes supported their role in mRNA stability. U2AF35 but not SF1 was shown to bind to ribosomes. To examine the role of splicing factors in mRNA stability, mutations were introduced into the polypyrimidine tract located in the 3' UTR of a mini-gene, and the results demonstrate that U2AF65 binds to such a site and controls the mRNA stability. We propose that transcripts carrying splicing signals in their 3'UTR bind the splicing factors and control their stability. [ABSTRACT FROM AUTHOR]

In trypanosomatids, all mRNAs are processed via trans-splicing, although cis-splicing also occurs. In trans-splicing, a common small exon, the spliced leader (SL), which is derived from a small SL RNA species, is added to all mRNAs. Sm and Lsm proteins are core proteins that bind to U snRNAs and are essential for both these splicing processes. In this study, SmD3- and Lsm3-associated complexes were purified to homogeneity from Leishmania tarentolae. The purified complexes were analyzed by mass spectrometry, and 54 and 39 proteins were purified from SmD3 and Lsm complexes, respectively. Interestingly, among the proteins purified from Lsm3, no mRNA degradation factors were detected, as in Lsm complexes from other eukaryotes. The U 1A complex was purified and mass spectrometry analysis identified, in addition to U1 small nuclear ribonucleoprotein (snRNP) proteins, additional co-purified proteins, including the polyadenylation factor CPSF73. Defects observed in cells silenced for U1 snRNP proteins suggest that the U1 snRNP functions exclusively in cis-splicing, although U1A also participates in polyadenylation and affects trans-splicing. The study characterized several trypanosome-specific nuclear factors involved in snRNP biogenesis, whose function was elucidated in Trypanosoma brucei. Conserved factors, such as PRP19, which functions at the heart of every cis-spliceosome, also affect SL RNA modification; GEMIN2, a protein associated with SMN (survival of motor neurons) and implicated in selective association of U snRNA with core Sm proteins in trypanosomes, is a master regulator of snRNP assembly. This study demonstrates the existence of trypanosomatid-specific splicing factors but also that conserved snRNP proteins possess trypanosome- specific functions. [ABSTRACT FROM AUTHOR]

Objectives: To examine whether individuals with low short-term risk of coronary heart disease but high lifetime predicted risk of cardiovascular disease (CVD) have greater prevalence of left ventricular (LV) hypertrophy and increased aortic wall thickness (AWT) than those with low short-term and low lifetime risk. Background: Lifetime risk prediction can be used for stratifying individuals younger than 50 years of age into 2 groups: low short-term/high lifetime and low short-term/low lifetime predicted risk of CVD. Individuals with low short-term/high lifetime risk have a greater burden of subclinical atherosclerosis as measured by coronary artery calcium and carotid intima-media thickness. However, >75% of individuals with low short-term/high lifetime risk do not have detectable coronary artery calcium, suggesting the presence of alternative subclinical abnormalities. Methods: We stratified 1,804 Dallas Heart Study subjects between the ages of 30 and 50 years who had cardiac magnetic resonance into 3 groups: low short-term (<10% 10-year risk of coronary heart disease)/low lifetime predicted risk (<39% lifetime risk of CVD), low short-term (<10%)/high lifetime risk (≥39%), and high short-term risk (≥10%, prevalent diabetes, or previous stroke, or myocardial infarction). In those with low short-term risk, we compared measures of LV hypertrophy and AWT between those with low versus high lifetime risk. Results: Subjects with low short-term/high lifetime risk compared with those with low short-term/low lifetime risk had increased LV mass (men: 95 ± 17 g/m2 vs. 90 ± 12 g/m2 and women: 75 ± 14 g/m2 vs. 71 ± 10 g/m2, respectively; p < 0.001 for both). LV concentricity (mass/volume), wall thickness, and AWT were also significantly greater in those with high lifetime risk in this comparison (p < 0.001 for all), but LV end-diastolic volume was not (p > 0.3). These associations persisted among participants without detectable coronary artery calcium. Conclusions: Among individuals 30 to 50 years of age with low short-term risk, a high lifetime predicted risk of CVD is associated with concentric LV hypertrophy and increased AWT. [Copyright &y& Elsevier]

Objectives: The purpose of this study was to determine the association between fitness and lifetime risk for cardiovascular disease (CVD). Background: Higher levels of traditional risk factors are associated with marked differences in lifetime risks for CVD. However, data are sparse regarding the association between fitness and the lifetime risk for CVD. Methods: We followed up 11,049 men who underwent clinical examination at the Cooper Institute in Dallas, Texas, before 1990 until the occurrence of CVD death, non-CVD death, or attainment of age 90 years (281,469 person-years of follow-up, median follow-up 25.3 years, 1,106 CVD deaths). Fitness was measured by the Balke protocol and categorized according to treadmill time into low, moderate, and high fitness, with further stratification by CVD risk factor burden. Lifetime risk for CVD death determined by the National Death Index was estimated for fitness levels measured at ages 45, 55, and 65 years, with non-CVD death as the competing event. Results: Differences in fitness levels (low fitness vs. high fitness) were associated with marked differences in the lifetime risks for CVD death at each index age: age 45 years, 13.7% versus 3.4%; age 55 years, 34.2% versus 15.3%; and age 65 years, 35.6% versus 17.1%. These associations were strongest among persons with CVD risk factors. Conclusions: A single measurement of low fitness in mid-life was associated with higher lifetime risk for CVD death, particularly among persons with a high burden of CVD risk factors. [ABSTRACT FROM AUTHOR]

• Numerical investigations to find the optimum configuration of punched out rectangular winglet with hole at their surface for performance improvement. • Critical comparison is presented between punched and attached cases of rectangular winglet. • Punched-out winglet gives up to 34% augmentation in heat transfer for common flow up configuration located upstream. • Punched-out winglet enhances the augmentation by 73% over baseline case for common flow down configuration at upstream location. Thermohydraulic performance of fin-tube heat exchanger can be augmented by using longitudinal vortex generators. Numerical simulations have been performed in the present work for investigating the effect of punching a rectangular winglet having hole from fin surface, on the heat transfer and flow resistance characteristics in a fin-tube heat exchanger. The mentioned concept is being studied in two configurations namely, common flow down and common flow up. Comparisons on the basis of heat transfer and flow resistance characteristics have been drawn between punched and non-punched cases of rectangular winglet with hole for all the configurations under consideration using Colburn's factor (j), friction factor (f) and performance evaluation criterion (PEC) also known as JF factor (j 1 /j 0)/(f 1 /f 0)1/3. Investigations have been performed considering Reynolds number in the range of 1500 to 9000 and angle of attack as 45°. Numerical simulations have been performed using SST k-ω turbulence model which has demonstrated capabilities of capturing separated flows. There is a heat transfer augmentation of up to 34% for the considered range of Reynolds number in case of fin-tube heat exchanger employing punched rectangular winglet with hole having Common Flow Up configuration located in the upstream location, over the non-punched case of winglet with hole in the same configuration and location. The result clearly indicates that punching the rectangular winglet having hole considerably improves thermohydraulic performance in case of winglet placed in common flow up configuration at upstream location. [ABSTRACT FROM AUTHOR]

All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method. [ABSTRACT FROM AUTHOR]

• Systematic framework is established that formalizes the scope of the smart grids. • A deep learning-based approach is proposed for predicting load in smart grids quite accurately with use of features of various neural networks for efficient load balancing in smart grids. • Use of the proposed approach to efficiently determine the load forecasting characteristics in macro grids and microgrids. • Comparative analysis of proposed work with state-of-the-art for load forecasting and feature extraction in smart grids. Load forecasting plays an essential role in effective energy planning and distribution in a smart grid. However, due to the unpredictable and non-linear structure of smart grids and large datasets' complex nature, accurate load forecasting is still challenging. Statistical techniques are being used for a long time for load forecasting, but it is inefficient. This paper tries to resolve challenges imposed by conventional methods like mean and mode by suggesting an ANN model for accurate load forecasting. Specifically, the LSTM and random forest approach has been used here. We compared our model to other models that use similar parameters and found that ours is more reliable and can be used for long-term forecasting. Our model has achieved an average overall accuracy of 96% and an average MSE of 4.486 with average CPU time consumption of 904.47 s, 872.43 s, and 908.32 s, respectively. Hence, the present model outperforms other existing methods. [ABSTRACT FROM AUTHOR]