Philippa Kaur

Humans have infected a wide range of animals with SARS-CoV-2 viruses1–5, but the establishment of a new natural animal reservoir has not been observed. Here, we document that free-ranging white-tailed deer (Odocoileus virginianus) are highly susceptible to infection with SARS-CoV-2 virus, are exposed to a range of viral diversity from humans, and are capable of sustaining transmission in nature. SARS-CoV-2 virus was detected by rRT-PCR in more than one-third (129/360, 35.8%) of nasal swabs obtained from Odocoileus virginianus in northeast Ohio (USA) during January-March 2021. Deer in 6 locations were infected with 3 SARS-CoV-2 lineages (B.1.2, B.1.582, B.1.596). The B.1.2 viruses, dominant in humans in Ohio at the time, infected deer in four locations. Probable deer-to-deer transmission of B.1.2, B.1.582, and B.1.596 viruses was observed, allowing the virus to acquire amino acid substitutions in the spike protein (including the receptor-binding domain) and ORF1 that are infrequently seen in humans. No spillback to humans was observed, but these findings demonstrate that SARS-CoV-2 viruses have the capacity to transmit in US wildlife, potentially opening new pathways for evolution. There is an urgent need to establish comprehensive “One Health” programs to monitor deer, the environment, and other wildlife hosts globally. More

Isolates and typingThe isolates characterised as well as strain affiliations, geographic origins and clinical presentations are summarised in Table 1. Autopsy images showing typical aspects of putrid infections in some animals are shown in Fig. 1. The complete microarray hybridisation patterns are provided as Supplemental file 2 and some relevant features will be discussed in the descriptions of the respective strains. While all German isolates yielded hybridisation signals for lukF/S-PV, frequently only weak positive or ambiguous results for the lukS-PV probe were observed. This prompted further investigations, including the detection of PVL by lateral flow assay21 (Table 1) and whole genome sequencing (see below).Table 1 Details of animals, isolates and strains.Full size tableFigure 1Pathological lesions of Eurasian beavers (C. fiber) infected with BVL-positive S. aureus. (A) Severe suppurative necrotizing pneumonia (animal B); (B) severe suppurative pyelonephritis (animal G); (C) caseous lymphadenitis, popliteal lymph node (animal E); (D) urinary bladder with pyuria (animal C).Full size imagePhenotypic and genotypic resistance properties of the S. aureus isolatesAntimicrobial susceptibility testing revealed that all beaver isolates from Germany were susceptible to all antimicrobial agents tested. The distribution of minimal inhibitory concentration (MIC) values and test ranges are displayed in Supplemental File 3a. The phenotypic data corresponded well with microarray data, since none of the corresponding resistance genes was identified. In contrast, two of the Austrian isolates showed macrolide resistance with one of them also being lincosamide resistant. One isolate also exhibited tetracycline resistance. These phenotypes corresponded with the detection of genes erm(A), erm(C) and tet(M), respectively (Supplemental file 2 and 3b).The chromosomal variant of the metallothiol transferase gene fosB was present in all CC1956 isolates. Sequence analysis revealed a frame shift at position 108 creating a stop codon at positions (pos.) 146.0.148 compared to the reference sequence (N315, GenBank BA000018.3 [2,389,328.0.2,389,747]). This resulted in a truncated protein of 48 amino acids (aa) rather than 139 aa as for the original fosB gene product. The mutation was present in all available sequences (i.e., Oxford Nanopore and Illumina of WT19 as well as Illumina of WT63, WT64, WT66, WT67a, WT67b, WT68, WT69, WT70, WT71, WT110 and WT111). While fosB was originally implicated in fosfomycin resistance, it appears to be linked to certain CCs. Indeed, it was also present in the CC8 and CC12 beaver isolates (B2, B3, B4) as well as in the reference sequences of the respective CCs (Supplemental File 2). The fosB gene was absent from the CC49 isolate WT65 and from the CC49 reference sequence of Tager 104, GenBank CP012409.1, as well as from the CC398 isolate B1. Moreover, all sequenced isolates (from animals A to G) harboured a gene designated tet(38), encoding a major facilitator superfamily permease. While this gene was implicated in low-level tetracycline resistance when overexpressed22, its mere presence certainly is not associated with phenotypic tetracycline resistance as it can be found in virtually every S. aureus genome.Biocide susceptibility testing of the CC49/1956 isolates revealed unimodal MIC distributions (Supplemental File 3b), with ranges encompassing not more than three to four dilution steps for each of the biocides (benzalkonium chloride, 0.00003–0.00025%; polyhexanide, 0.000125–0.0005%; chlorhexidine, 0.00006–0.00025% and octenidine, 0.00006–0.00025% with percentages given as mass per volume). The four remaining isolates showed MIC values of 0.0000125–0.00025% for benzalkonium chloride, 0.0005–0.001% for polyhexanide, 0.00006–0.000125% for chlorhexidine, and 0.000125–0.00025% for octenidine.The chromosomal heavy metal resistance markers arsB/R and czrB were detected by hybridisation in all four CC1956 isolates tested as well as in the CC49 isolate. This was confirmed by sequencing. There was no evidence for plasmid- or SCC-borne heavy metal resistance markers.The sequence of the phage-borne leukocidin genes in WT19 and WT65As mentioned above, CC49/CC1956 beaver isolates yielded occasionally ambiguous hybridisation intensities for lukS-PV probes prompting further investigation assuming that the specifically designed oligonucleotides were not able to bind optimally at the target due to mismatches, i.e., allelic variants. Sequencing revealed the presence of distinct alleles of phage-borne leukocidin genes (Figs. 2a/b and 3a/b). The sequences from the two sequenced beaver isolates were identical to each other despite their origin from different prophages in different CCs. In general, the beaver alleles, hitherto referred to as “Beaver Leukocidin” or BVL, lukF/S-BV, appeared to be closer related to the PVL genes from human strains of S. aureus than to those from ruminants and horses (see Figs. 2a/b and 3a/b and the percentages of homologies as provided in Supplemental File 4). There was no evidence for recombination/chimerism in lukF-BV and lukS-BV as mismatches compared to other sequences were evenly distributed across the entire sequences. Sequences of lukF-BV and lukS-BV were also related but clearly distinct from core genomic lukF/S-int of S. intermedius/pseudintermedius.Figure 2(a) Alignment of the lukF-BV sequences, of other phage-borne leukocidin F component sequences from S. aureus and of lukF-int from S. intermedius/pseudintermedius. (b) Alignment of the amino acid sequences of the corresponding lukF gene products.Full size imageFigure 3(a) Alignment of the lukS-BV sequences, of other phage-borne leukocidin S component sequences from S. aureus and of lukS-int from S. intermedius/pseudintermedius. (b) Alignment of the amino acid sequences of the corresponding lukS gene products.Full size image
lukF/S-BV and the agr locusTwo isolates from one animal, WT110 and WT111 (Table 1), differed in hemolysis on Columbia blood agar and were thus handled separately although array analysis eventually revealed the same strain affiliations. They also differed in BVL production as shown by lateral flow tests. Sequencing using both, Illumina and Oxford nanopore technologies, revealed a substitution from A to T in position 706 of the agrA gene that results in a premature stop codon at position 236 of the agrA gene product (Supplemental File 5) suggesting that agr played a role in the observed phenotype and the regulation of BVL.Core genome and genomic islands of the CC1956 isolate WT19As revealed by array experiments (Supplemental File 1) and confirmed by genome sequencing of WT19, CC1956 isolates presented with agr IV alleles and capsule type 5. They were positive for cna, but they lacked seh and egc enterotoxin genes, ORF CM14 as well as sasG. Leukocidin genes lukX/Y, lukD/E and lukF/S-hlg were present. This is also in accordance with previously sequenced BVL-negative CC1959 isolates (SAMEA3251370, SAMEA3251372, SAMEA3251377, SAMEA3251376, SAMEA3251380; Supplemental File 2).The WT19 genome (Supplemental Files 6a and 6b) harboured two uncharacterised enterotoxin genes (pos. 1,940,148..1,940,900 and pos. 1,939,378..1,940,121). Both were also found in DAR4145 (CC772) where they also formed a genomic island at approximately the same position within the genome (GenBank CP010526.1: RU53_RS09775, pos. 1,968,336..1,969,061 and RU53_RS09780, pos. 1,969,088..1,969,840). One of these two genes (“seu2” = RU53_RS09780) was covered by the second array-based assay23 and it was found in all four isolates tested with this array.Mobile genetic elements in the CC1956 isolate WT19The lukF/S-BV prophage was integrated into the lipase 2 gene (lip2, “geh”, “sal3”, “salip35”, GenBank CP000253.1 [314,326..316,398]), and spanned pos. 322,629 to 365,636. Besides leukocidin genes, it also included genes associated with the different modules of a typical Siphoviridae genome (lysogeny, DNA metabolism, packaging and capsid morphogenesis, tail morphogenesis, host cell lysis24,25; see Supplemental File 7/Fig. 4).Figure 4Schematic representation of the aligned sequences of the lukF/S-BV prophages from WT19 and WT65.Full size imageFurthermore, there was a small pathogenicity island at pos. 869,706 to 884,748 that included pif encoding a phage interference protein, a gene for a small terminase subunit, genes for “putative proteins” as well as a gene (scn2) coding for a paralog of a complement inhibitor SCIN family protein and a gene for a variant of the von Willebrand factor binding protein Vwb (vwb3). Thus, it is considered a staphylococcal pathogenicity island (SaPI) related to the one in S0385, GenBank AM990992.1.Another prophage integrated between rpmF and isdB, pos. 1,107,447 to 1,146,132. A third prophage was located between a truncated nikB and Q5HG37, pos. 1,425,279 to 1,481,870. Finally, there was a forth prophage between Q5HDU4 and sarV (actually interrupting an MFS transporter between those genes), pos. 2,340,832 to 2,386,591. This prophage sequence corresponded to the phage that was detected by nanopore sequencing after induction by Mitomycin C (see below and Supplemental File 8).Phage morphology and sequencing of phages from the CC1956 isolate WT19In three separate preparations, large numbers of phages were observed that were well contrasted with uranyl acetate and with phosphotungstic acid. Phages had elongated capsids. The non-contractile thin tails were straight or slightly curved and ended in a bulb-shaped base plate. Based on these characteristics, they were assigned to the order Caudovirales, family Siphoviridae.Capsids were measured in 40 phages, tails in 34 and base plates in 33 phages. Based on these measurements, two distinct populations could be differentiated (Fig. 5). In one (Fig. 5A), the prolate, distinctly pentagonal capsids averaged 39 ± 5 nm (range 32–46 nm) in diameter and 92 ± 8 nm (range 80–104 nm) in length. Tails were 276 ± 20 nm (range 243–310 nm) long, had a diameter of 11 ± 1 nm (range 10–12 nm) and had a stacked discs appearance. Their baseplates were 16 nm (range 16–31 nm) by 27 nm (range 19–33 nm). The other population (Fig. 5B) had elongated oval capsids with a maximal diameter of 55 ± 2 nm (range 51–60 nm) diameter and 93 ± 5 nm (range 85–100 nm) length. Their tails measured 287 ± 12 nm (range 275–313 nm) in length and 9 ± 1 nm (8–10) in diameter and had a rail-road-track morphology. Dimensions of baseplates were 25 nm (range 21–30 nm) by 29 nm (range 23–39 nm).Figure 5Transmission electron micrograph of two distinct prolate phages resulting from Mitomycin C treatment of S. aureus CC1956 isolate WT19. A, Phage particle with pentagonal 38 nm in diameter capsid and a 12 nm thick tail with stacked disc appearance; B, Two phage particles (1, 2) with oval capsids of 55 nm in diameter and 9 nm thick tails with rail-road-track morphology. The base plate is separated from the tail by a transversal disc (arrow). Negative contrast preparation with uranyl acetate. Bars = 100 nm.Full size imageOxford Nanopore sequencing of one of these phage preparations (Supplemental File 8) yielded just one circular contig with a coverage of 724. Its sequence was identical to that of the forth prophage, between Q5HDU4 and sarV, except for a loss of a single triplet out of a total length of 46,387 nt.Core genome and genomic islands of the CC49 isolate WT65The CC49 isolate carried agr group II alleles and capsule type 5. It was positive for sasG, but lacked seh and egc enterotoxin genes, ORF CM14 and the collagen adhesion gene cna. A truncated copy of the enterotoxin S gene (GenBank CP000046, pos. 2,203,972.0.2,204,196) was found as well as leukocidin genes lukG/H = lukX/Y, lukD/E and lukF/S-hlg. With regard to presence and alleles of chromosomal markers such as MSCRAMM or ssl genes, the genome of WT65 (Supplemental Files 7a and 7b) is closely related to the CC49 reference sequences such as Tager 104, GenBank CP012409.1 (Supplemental File 2).Mobile genetic elements in the CC49 isolate WT65One prophage was integrated into the lip2 gene spanning pos. 311,401 to 354,724. The prophage included the lukF/S-BV genes as well as genes associated with the different modules of a typical Siphoviridae genome (Supplemental File 7/Fig. 4). Sequences corresponding to the lysogeny and replication modules were clearly different compared to the lukF/S-BV-prophage in the CC1956 isolate WT19 while approximately the second half of the two respective prophage sequences (the lower part of the alignment in Fig. 4) were virtually identical in gene content, order and orientation.Other mobile genetic elements (Supplemental File 9a/b) included a small pathogenicity island, pos. 402,133 to 416,237 (between rpsR encoding 30S ribosomal protein S18 and its terminator), that included hypothetical proteins, a gene of a terminase small subunit, vwb3 (encoding a “von Willebrand factor” binding protein) and the scn2 gene (putative paralog of complement inhibitor). Between the genes ktrB and groL, pos. 2,029,208 to 2,042,866, another SaPI was identified that contained additional, slightly different copies of vwb3 and scn2 genes as well as terminase small subunit, integrase and excisionase (xis-AIO21657) genes. Finally, five genes between pos.1,334,169 and 1,339,503 were annotated as phage capsid genes although no other phage-related genes were found in this region.Phage morphology and sequencing of phages from the CC49 isolate WT65Four separate phage preparations were examined. In one of them, few phage-like structures were detected. These findings could not be confirmed in the following preparations. Thus, they were interpreted as artefacts, also given that it was not possible to induce a sufficient amount of phages for Oxford Nanopore sequencing. More

NATURE PODCAST
22 December 2021

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In this episode:01:12 “Oh powered flight”In the first of our festive songs, We pay tribute to NASA’s Ingenuity craft — which took the first powered flight on another planet earlier this year. Lyrics by Noah Baker and performed by The Simon Langton School choir, directed by Emily Renshaw-Kidd.Scroll to the bottom of the page for the lyrics.Video: Flying a helicopter on Mars: NASA’s IngenuityNews: Lift off! First flight on Mars launches new way to explore worlds07:40 Communicating complex science with common wordsIn this year’s festive challenge, our competitors try to describe some of the biggest science stories of the year, using only the 1,000 most commonly used words in the English language. Find out how they get on …Test your skills communicating complex science with simple words with the Up-Goer Five Text Editor18:04 Alphafold oh AlphafoldOur second song brings some Hanukkah magic to Deep Mind’s protein-solving algorithm Alphafold. Lyrics by Kerri Smith and Noah Baker, arranged and performed by Phil Self.Scroll to the bottom of the page for the lyrics.News: ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures21:01 Nature’s 10Every year, Nature’s 10 highlights some of the people who played key roles in science. We hear about a few of the people who made the 2021 list.Nature’s 10 — Ten people who helped shape science in 2021

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This study applies the SVM, ANN, RT, and GP, for forecasting monthly reservoirs inflow with 1- and 2-month time lags. The historical data for inflow to the Dez, Karkheh, and Gotvand reservoirs were collected and used to build the ML algorithms. The inputs to the algorithms for the Dez, Karkheh, and Gotvand reservoirs are the monthly inflows for 1965–2019, 1957–2019, and 1961–2019, respectively. Four projections were designed for the 1-month time lag and the 2-month time lag patterns based on the input and output months, as depicted in Fig. 1. Figure 2 displays the flowchart of this paper’s methodology.Figure 1Schematic of projections of 1-month and 2-month time-lag patterns.Full size imageFigure 2Flowchart of this study’s methodology.Full size imageSupport vector machineSupport Vector Machine was introduced by Vapnik et al.43. SVM performs classification and regression based on statistical learning theory44. The regression form of SVM is named support vector regression (SVR). Vapnik et al.45 defined two functions for SVR design. The first function is the error function. (Eq. (1), see Fig. 3). The second function is a linear function that calculates output values for input, weight, and deviation values (Eq. 2):$$ left| {y – fleft( x right)} right| = left{ {begin{array}{ll} 0 & {if;left| {y – fleft( x right)} right| le varepsilon } \ {left| {y – fleft( x right)} right| – varepsilon = xi } & {otherwise} \ end{array} } right. $$
(1)
$$ fleft( x right) = W^{T} x + b $$
(2)
Figure 3Illustration of the error function of SVR.Full size image
where (y), (f(x)), (varepsilon), (xi), (W), (b), (T) denote respectively the observational value, the output value calculated by SVR, a function sensitivity value, a model penalty, the weight applied to the variable (x), the deviation of (W^{T} x) from the (y), and the vector/matrix transpose operator.It is seen in Fig. 3 that the first function (Eq. 1) does not apply a penalty to the points where the difference between the observed value and the calculated value falls within the range of (( – varepsilon , + varepsilon )). Otherwise, a penalty (xi) is applied. SVR solves an optimization problem that minimizes the forecast error (Eq. 3) to improve the model’s forecast accuracy. Equations (4) and (5) represent the constraints of the optimization problem.$$ minimizefrac{1}{2}left| W right|^{2} + Csumlimits_{{i = 1}}^{m} {left( {xi _{i}^{ – } + xi _{i}^{ + } } right)} $$
(3)
Subject to:$$ (W^{T} x + b) – y_{i} < varepsilon + xi_{i}^{ + } ;;;i = 1, , 2, ldots , , m $$ (4) $$ y_{i} - left( {W^{T} x + b} right) le varepsilon + xi_{i}^{ - } ;;;i = 1, , 2, ldots , , m $$ (5) where (C), m, (xi_{i}^{ - }), (xi_{i}^{ + }), (y_{i}), and || || denote respectively the penalty coefficient, the number of input data to the model in the training phase, the penalty for the lower bound (( - varepsilon , + varepsilon )), the penalty for the upper bound (( - varepsilon , + varepsilon )), the i-th observational value, and vectorial magnitude. The values of W and b are calculated by solving the optimization problem embodied by Eqs. (3)–(5) with the Lagrange method, and they are substituted in Eq. (2) to calculate the SVR output. SVR is capable of modeling nonlinear data, in which case it relies on transfer functions to transform the data to such that linear functions can be fitted to the data. Reservoirs inflow is forecasted with SVR was performed with the Tanagra software. The transfer function selected and used in this study is the Radial Basis Function (RBF), which provided better results than other transfer functions. The weight vector W is calculated using the Soft Margin method46, and the optimal values of the parameters (xi_{i}^{ - } , + xi_{i}^{ + }) and C were herein estimated by trial and error.Regression tree (RT)RT involves a clustering tree with post-pruning processing (CTP). The clustering tree algorithm has been reported in various articles as the forecasting clustering tree47 and the monothetic clustering tree48. The clustering tree algorithm is based on the top-down induction algorithm of decision trees49; This algorithm takes a set of training data as input and forms a new internal node, provided the best acceptable test can be placed in a node. The algorithm selects the best test scores based on their lower variance. The smaller the variance, the greater the homogeneity of the cluster and the greater the forecast accuracy. If none of the tests significantly reduces the variance the algorithm generates a leaf and tags it as being representative of data47,48.The CTP algorithm is similar to the clustering tree algorithm, except that its post-pruning process is performed with a pruning set to create the right size of the tree50.RT used in this study is programmed in the MATLAB software. The minimum leaf size, the minimum node size for branching, the maximum tree depth, and the maximum number of classification ranges are set by trial and error in this paper’s application.Genetic programming (GP)GP, developed by Cramer51 and Koza52, is a type of evolutionary algorithm that has been used effectively in water management to carry out single- and multi-objective optimization53. GP finds functional relations between input and output data by combining operators and mathematical functions relying on structured tree searches44. GP starts the searching process by generating a random set of trees in the first iteration. The tree's length creates a function called the depth of the tree which the greater the depth of the tree, the more accurate the GP functional relation is54. In a tree structure, all the variables and operators are assumed to be the terminal and function sets, respectively. Figure 4 shows mathematical relational functions generated by GP. Genetic programming consists of the following steps: Select the terminal sets: these are the problem-independent variables and the system state variables. Select a set of functions: these include arithmetic operators (÷ , ×, −, +), Boolean functions (such as "or" "and"), mathematical functions (such as sin and cos), and argumentative expressions (such as if–then-else), and other required statements based on problem objectives. Algorithmic accuracy measurement index: it determines to what extent the algorithm is performing correctly. Control components: these are numerical components, and qualitative variables are used to control the algorithm's execution. Stopping criterion: which determines when the execution of the algorithm is terminated. Figure 4Example of mathematical relations produced by GP based on a tree representation for the function:(fleft( {X_{1} , X_{2} ,X_{3} } right) = left( {5 X_{1} /left( {X_{2} X_{3} } right)} right)^{2}).Full size imageThe Genexprotools software was implemented in this study to program GP. The GP parameters, operators, and linking functions were chosen based on the lowest RMSE in this study. The GP model's parameters and operators applied in this study are listed in Table 1.Table 1 Operators and range of parameters used in GP.Full size tableArtificial Neural Network (ANN)ANN, developed by McCollock and Walterpits55, is an artificial intelligence-based computational method that features an information processing system that employs interconnected data structures to emulate information processing by the human brain56. A neural network does not require precise mathematical algorithms and, like humans, can learn through input/output analysis relying on explicit instructions57. A simple neural network contains one input layer, one hidden layer, and one output layer. Deep-learning networks have multiple hidden layers58. ANN introduces new inputs to forecast the corresponding output with a specific algorithm after training the functional relations between inputs and outputs.This study applies the Multi-Layer Perceptron (MLP). A three-layer feed-forward ANN that features a processing element, an activation function, and a threshold function, as shown in Fig. 5. In MLP, the weighted sum of the inputs and bias term is passed to activation level through a transfer function to create the one output.Figure 5The general structure of a three-layer feed forward ANN and processing architecture.Full size imageThe output is calculated with a nonlinear function as follows:$$ Y = fleft( {mathop sum limits_{i = 1}^{n} W_{i} X_{i} + b} right) $$ (6) where (W_{i}), (X_{i}), (b), (f), and (Y) denote the i-th weight factor, the i-th input vector, the bias, the conversion function, and the output, respectively.The ANN was coded in MATLAB. The number of epochs, the optimal number of hidden layers, and the number of neurons of the hidden layers were found through a trial-and-error procedure. The model output sensitivity was assessed with various algorithms; however, the best forecasting skill was achieved with the Levenberg–Marquardt (LM) algorithm59, and the weight vector W is calculated using the Random Search method60. Furthermore, the Tangent Sigmoid and linear transfer function were chosen by trial and error and used in the hidden and output layers, respectively.70% of the total data were randomly selected and used for training SVM, ANN, RT, and GP. The remaining 30% of the data were applied for testing the forecasting algorithms.Performance-evaluation indicesThe forecasting skill of the ML algorithms (SVM, ANN, RT, and GP) was evaluated with the Correlation Coefficient (R), the Nash–Sutcliffe Efficiency (NSE), the Root Mean Square Error (RMSE), and the Mean Absolute Error (MAE) in the training and testing phases. The closer the R and NSE values are to 1, and the closer the RMSE and MAE values are to 0, the better the performance of the algorithms20. Equations (7)–(10) describe the performance indices:$$ NSE = 1 - frac{{mathop sum nolimits_{i = 1}^{n} left( {Q_{fore,;i} - Q_{obs,i} } right)^{2} }}{{mathop sum nolimits_{i = 1}^{n} left( {Q_{obs,i} - Q_{mean ; obs} } right)^{2} }} $$ (7) $$ R = frac{{mathop sum nolimits_{i = 1}^{n} left( {Q_{fore,i} - Q_{mean ; fore} } right)left( {Q_{obs,i} - Q_{mean ; obs} } right)}}{{sqrt {mathop sum nolimits_{i = 1}^{n} left( {Q_{fore,i} - Q_{mean ; fore} } right)^{2} } sqrt {mathop sum nolimits_{i = 1}^{n} left( {Q_{obs,i} - Q_{mean ; obs} } right)^{2} } }} $$ (8) $$ MAE = frac{1}{n}mathop sum limits_{i = 1}^{n} left| {Q_{fore,i} - Q_{obs,i} } right| $$ (9) $$ RMSE = sqrt {frac{{mathop sum nolimits_{i = 1}^{n} left( {Q_{fore,i} - Q_{obs,i} } right)^{2} }}{n}} $$ (10) in which ( Q_{fore,i}), (Q_{obs,i}), (Q_{mean ; fore}), (Q_{mean ; obs}), (i), and (n) denote the forecasted inflow, observed inflow, mean forecasted inflow, mean observed inflow, time step, and the total number of time steps during training and testing phases, respectively.Ethics approvalAll authors complied with the ethical standards.Consent to participateAll authors consent to participate.Consent for publishAll authors consent to publish. 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RearingAll mosquitoes used in these experiments were derived from a laboratory-reared colony of An. stephensi is initially established (six generations) in University College Agriculture, University of Sargodha. Uninfected mosquitoes were maintained in the laboratory; in gauze-covered boxes (30 cm wide × 30 cm high × 50 cm deep) under control condition 27 ± 2 °C temperature and 75–80% relative humidity. Auto ON/OFF switches with the timer were used to break the scotophase (dark) period in the control conditions of the laboratory with the light: dark cycle set to 12:12 h48. A 10% fructose solution supplemented with 0.05% para-minobenzoic acid (PABA) was provided to mosquitoes. The adult mosquitoes were reared on blood provided via an in situ electronically derived artificial membrane feeder, set at 37 ± 1 °C, and offered twice a week, and offered twice a week given their need for another blood meal approximately 5–6 h after the first49. Multiple blood feeding is vital for Anopheline species as it has been demonstrated as influencing reproductive behavior50. Oviposition cups were provided two days following the second blood meal. The larvae were reared under laboratory conditions described above and provided a certified Laboratory Rodent Diet (LRD) Lab Diet 500151.Fecundity and fertilityTo determine differences in fecundity (number of eggs) and fertility (percentage of fertile eggs) in An. stephensi mosquitoes, cages of mosquitoes were provided ABO blood groups and control (distilled water) via artificial membrane feeders (as described above for mosquito rearing). The blood was obtained from the blood bank of DHQ (one batch of each blood group was used for each replication), Chakwal Punjab, Pakistan. A total of 10 replicate cages were used for each blood type and control, so 10 batches of each blood group (ABO) (just to reduce the potential individual to individual variation) were obtained from the DHQ. After each replication along with the new batch of each blood group, a new strain of mosquitoes was used just to reduce the learning behavior of mosquitoes. Feeding success was determined by calculating the percentage of fed mosquitoes, and also, the numbers of fully engorged female mosquitoes were recorded.To determine fecundity, females from each blood group were removed, killed, and dissected under a microscope, and the numbers of eggs per female were counted 60 h post-blood-feeding. Additionally, to determine oviposition and larval development, 40 fully fed female mosquitoes were caged in one of three replicate glass cages with gauze (25 cm wide × 25 cm high × 25 cm deep) and provided wet filter papers were placed for egg-laying. The total number of eggs was counted after every 12 h from 48 h until 96 h post-blood-feeding under a light microscope. The total numbers of eggs/40 females/box for each human blood group for 10 replicates were calculated.For fertility estimation, an additional 40 gravid An. stephensi from each treatment and replication, including the control group, the females were gently transferred to the cages with triangular Whatman filter paper No. 1 using the mouth aspirator. The egg laid in each experimental and control group was reared in plastic trays filled with distilled water. The numbers of hatched larvae were recorded for a fertility test. While the eggs that could not be hatched into larvae up to day 7 were considered as infertile. The number of fertile and infertile eggs was recorded from all experimental and control cages.The collected eggs from each experimental box were placed into the plastic trays (24 cm wide × 12 cm high × 7 cm deep) with water, and the development of mosquitoes was observed until adult mosquitoes had emerged from all pupae for each blood group. The water in these plastic trays was maintained at a constant level throughout immature mosquito rearing. The larvae were fed a certified Laboratory Rodent Diet (LRD) Lab Diet 500151. The rearing was done according to the standard mass rearing of Anopheles techniques52. Pupae were counted and removed from the tray and placed in cages according to each human blood group type fed to allow emergence, and the percent of male and female mosquitoes was recorded. Adult mosquitoes were maintained on a 10% fructose solution supplemented with 0.05% para-minobenzoic acid (PABA) but were not provided with a blood meal. The mortality of adult mosquitoes was recorded daily until total mortality reached 100%.Digestibility testsThe precipitin and benzidine tests were used to test the effect of human blood groups ABO (on the rate of digestion in mosquitoes). The experiment was conducted in controlled laboratory conditions where the temperature, humidity, and day and night periods were maintained as described above. Mosquitoes (10 mosquitoes were used for each blood group and the same experiment was repeated 10 times) that had not been fed previously on either a sugar solution or blood were used in experiments. Mosquitoes were provided one of four different human blood group types, as previously described. After feeding the female were kept in the same boxes without any further food and water, and boxes were placed in an incubator where the temperature and the relative humidity was at a constant level (28 ± 2 °C and 80 ± 5%). The engorged female adult mosquitoes were killed at 8 h intervals, rubbed over the filter paper53, and the filter papers were placed inside the refrigerator until the test could be conducted. Approximately 48 female mosquitoes were used in each boxed marked for each blood group. The rate of blood digestion in the engorged blood was classified according to the Sella scale, following Detinova et al.54.To perform the precipitin test, the physiological saline and the filter paper smears were extracted in a small capillary tube. The specific antiserum was also extracted in the same capillary tube at the end; the change in color, clumping, and cloudiness of the solution indicates the presence of human blood in the tissue smears55. The collected material was heated in a steam oven for 10–12 min to apply the benzidine test at 108–110 °C. The test was used to check the traces of iron porphyrins in the abdomen of mosquitoes.Effect of blood groups on oogenesisTo test the blood-specific effects on the development of the ovaries of An. stephensi, the ovaries of fully fed female mosquitoes were collected separately from the box of each blood group 36 h post engorgement. For scanning electron microscopy (SEM), the whole female mosquitoes were selected from each box of every blood group separately (10 females for each blood group ABO). In preparing specimens for the scanning electron microscope, the process is divided into two fixations, the primary and the secondary fixation. For the primary fixing process, the 2.5% glutaraldehyde in 0.1 M cacodylate buffer was used for the period of 2 h, followed by the three consecutive washing with the same buffer for the 30 min. While for the secondary fixation process, 1% osmium tetroxide was used for the 2 h. Then the samples were rinsed for the final time with the 0.1 M cacodylate buffer three times for 30 min.The ovaries were dehydrated by using the graded series of acetone (50%, 70%, 80%, 90%, and 100%). The dehydrated ovaries were then transferred to the critical point drying apparatus. The recommended quantity of acetone solution was also poured into the drying chamber to avoid over-drying. Liquid nitrogen was also added into the drying critical point drying chamber. The CO2 and acetone were allowed to be mixed freely; the same process was repeated eight times to confirm the drying of the specimen. The dried mosquito specimens were mounted over the stubs, and the specimens tubes were coated with a thin layer of silver. Gold-spotted SCD005 was used, and then the samples were photographed with SEM.Electroantennography (EAG)To measure the response of mosquitoes to each human blood group type, EAG recordings from one antenna of An. stephensi female mosquito was made. Unfed female mosquitoes were anesthetized by the use of CO2 and were permanently fixed with the reference electrode by the use of spectra 360 electrode gel. It was made sure that the mosquito was completely immobile except for the antennae. The tips of the antennae were pressed into the small drop of electrode gel on the recording electrode. Both of the electrodes are silver wires coated with silver chloride with a diameter of 0.2 mm. The experimental preparations were done in continuous airflow (600 mL/min, 1.5 m/s) by the Teflon tube of 0.7–0.8 cm diameter, containing about 100 mL/min dry air and the 600 mL/min moist air passed through the charcoal filter. At this stage, little modification was done in the structure of the electroantennogram, and an artificial blood feeder of mosquitoes with the membrane was attached to the system.The blood feeder was packed in a glass jar through which continuous airflow was passed, and this air flow ends as stimuli near the mounted mosquito. The diameter of the glass tube was 0.5 mm, and the flow of air was controlled by using an ON/OFF switch; three bursts of 0.5 s of air from the blood jar were provided as a stimulus to host-seeking mosquitoes. All the blood groups were tested systematically together with the control group. The amplifier amplified the generated signals while the well-known software decoded the recordings (EAG 2000, Syntech, Hilversum, and the Netherlands). All of the test blood groups were also dissolved separately in tetryl-butyl-methyl ether (MTBE), and about 30 uL of this test solution was applied onto a piece of filter paper (1.5–2 cm). About 20 min was given to the TMBE solution to evaporate from the filter paper leaving behind the blood; then, this piece of filter paper was placed into the Pasteur pipette. In the case of the control treatment, distilled water was used, and the same treatment was applied with the distilled water for the test compounds. The stimulus controller C5-01/b, Syntech, was used to inject the odor cues originating from the treated filter paper in the Pasteur pipette into the humidified and filtered air stream directed towards the antennae of immobilized mosquitoes. Olfactory stimuli were tested randomly against different mosquito specimens with a total of five specimens exposed to each of the human blood type groups and control.To minimize the chances of error and to test the electrophysiological activity of the stuck female mosquito, lactic acid, 1-octen-3-01, and isovaleric acid were used as known stimulants41,56. After that, each blood group was replicated three times to record the activity of the olfactory neurons of antennae. All the treatments of blood groups were tested randomly, and a regular interruption of control stimulus (0.1% lactic acid) was done. The regular interruption of the control stimulus was used to control the activity of the antennae. All the stimulants were expressed as a mean percent response to the control treatment. The response of different female mosquitoes to human blood groups was indicated as a mean percent response. The results were analyzed by the use of the Student’s t-test.Wind tunnel bioassaysWind tunnel bioassays were used to determine the response of An. stephensi to four human blood groups (A, B, AB, and O) and control stimuli (distilled water). Wind tunnel bioassays have been used to evaluate the response of Ae. aegypti, Cx. quinquefasciatus and Cx. nigripalpus towards the blood volatiles44. A dual choice wind tunnel was converted into a “five-choice” tunnel with all five glass tubes having glass jars at their end with openings to accommodate an artificial blood feeder. A continuous flow of warm water ensured the blood remained in liquid and produced its specific smell.A batch of approximately 100 female mosquitoes was released at the downwind end of the tunnel in the air stream coming from the five upwind end chambers. After 30 min, the numbers of mosquitoes in each of the five glass jars were counted. The mosquitoes were then sent back towards the downwind end of the wind tunnel, and the positions of odor cues were changed, including the control. Before the second time release, the fresh air was passed through the tunnel. Again the mosquitoes were released from the releasing box, and the response of the mosquitoes towards the new cues and the number of mosquitoes in each chamber at the upwind end was counted after 30 min; the same process was repeated, and for the third time with randomization. The same process was repeated 10 times with each blood group and with a new batch of mosquitoes each time.To test the response of female mosquitoes towards human-emitted olfactory cues, an olfactometer was used in previous studies41,43. The olfactometer test was conducted in the control room; the temperature was 27 ± 2 °C with 70–80% relative humidity. The optimum activity of the An. stephensi was observed late at night, so the experiment was conducted at 2–6 AM57.Steel balls rubbed in the hands of persons (ten volunteers per blood group) of having ABO blood groups along with the few drops of blood group-specific sweat were placed in the glass jars at the upwind end of the olfactometer. Approximately 100 female mosquitoes were released at the downwind end of the olfactometer from the releasing cage. After 30 min, the total number of mosquitoes in each box at the upwind end was counted, including the control. After that, the mosquitoes were returned to the releasing cage; then, the positions of steel balls at the upwind end of the glass jar of the olfactometer were changed randomly to decrease the biases from the data. To remove the smell of a sweat from the olfactory tube after cleaning, the fresh air was passed for about 10 min continuously. Mosquitoes were then again allowed to enter into the olfactometer, and after 30 min, the total number of mosquitoes was counted. The same process was repeated for the third time. The same experiment was repeated 10 times with different persons and mosquitoes to decrease the chances of error. A new batch of mosquitoes was selected for each replication.Statistical analysisThe mean number of eggs of An. stephensi were evaluated with the help of a linear model (ANOVA) and Tukey’s test on Minitab® software (12.2, version, Minitab). Before performing the ANOVA, with the help of angular transformation (arcsine √x), egg viability was also transformed58 for fertility. Data obtained were analyzed using R 3.2.2 software. The Shapiro–Wilk normality test was carried out, which showed that the data were not normally distributed. Hence, the Kruskal–Wallis Chi-square test was used to compare the averages of the responses of An. stephensi in relation to blood-group treatments.Ethics statementAll applicable international, national, and/or institutional guidelines for the care and use of animals were followed. More