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Effects of Mung Bean (Vigna radiata) Protein Isolate on Rheological, Textural, and Structural Properties of Native Corn Starch

It is critical to understand the starch–protein interactions in food systems to obtain products with desired functional properties. This study aimed to investigate the influence of mung bean protein isolate (MBPI) on the rheological, textural, and structural properties of native corn starch (NCS) and their possible interactions during gelatinization. The dynamic rheological measurements showed a decrease in the storage modulus (G’) and loss modulus (G”) and an increase in the loss factor (tan δ), by adding MBPI to NCS gels. In addition, the textural properties represented a reduction in firmness after the addition of MBPI. The Scanning electron microscope (SEM) images of the freeze-dried NCS/MBPI gels confirmed that the NCS gel became softer by incorporating the MBPI. Moreover, X-ray diffraction (XRD) patterns showed a peak at 17.4°, and the relative crystallinity decreased with increasing MBPI concentrations. The turbidity determination after 120 h refrigerated storage showed that the addition of MBPI could reduce the retrogradation of NCS gels by interacting with leached amylose. Additionally, the syneresis of NCS/MBPI gels decreased at 14 days of refrigerated storage from 60.53 to 47.87%.

Introduction
Starch and protein are two abundant and nutritionally important constituents of the human diet that have different physicochemical and functional characteristics and play an important role in the quality and nutritional properties of food products [1][2][3]. Starch is a tasteless and odorless polysaccharide that is cultivated to produce more than 50 million tons/year globally. It has a semi-crystalline structure that is typically made of amylose (70-85%) and amylopectin (15-30%) macromolecules [4][5][6]. Starch is widely utilized in many food and non-food industries, due to its low-cost, easy availability, biodegradability, and non-toxicity characteristics. Moreover, it is the main component of starchy foods, such as pasta, bread, and noodle [3,7]. However, native starch has some limitations that restrict its application in the food industry, such as high retrogradation tendency and poor shear and thermal stability [8]. Moreover, protein-energy malnutrition is becoming a serious issue, especially in developing countries, due to insufficient protein intake and poor food quality [9]. To meet the consumer demands for improving the textural and the nutritional (high-protein foods) quality of the products, some proteins from different sources (cereals, legumes, milk, meat, and fish), as a safe supplement, have been added to the starch-based foods [1,3,10]. Therefore, proteins have technological and nutritional roles in high-protein starchy products [11].
The second-highest ingredient in most of the starchy foods is typically protein (4-20% w/w) that is naturally present in the food matrix or added to improve the physicochemical Polymers 2022, 14, 3012 3 of 12 neutralized with 0.1 N NaOH, and freeze-dried. The protein was 89%, as determined by the micro-Kjeldahl method [23].

Dynamic Rheological Properties
NCS/MBPI slurry (6%, w/w, dry basis) was gelatinized in a water bath for 30 min at 95 • C and then cooled to 25 • C. The viscoelastic behavior of the NCS/MBPI gels was determined with a rheometer (MCR-302, Anton Paar, Graz, Austria), equipped with a cone (1 • cone angle) and plate geometry (1 mm gap and 25 mm cone diameter) at 25 • C. The dynamic rheological properties, storage modulus (G'), loss modulus (G") and loss factor (tan δ = G"/G') were recorded in the linear viscoelastic region with a frequency sweep range of 0 rad/s to 62.83 rad/s (0.01 to 10 Hz) at 1.0% strain.

Textural Properties
The NCS/MBPI gels were prepared according to a previous test (Section 2.4) and placed in plexiglass holders (20 mm diameter and 10 mm height), then covered and held at 4 • C overnight. The textural properties of gels were analyzed using a TA-XT texture analyzer (Stable Micro Systems, Surrey, England), as described by Hedayati and Niakousari [24]. The gels were compressed with a 40-mm flat probe at the speed of 1 mm/s and the deformation level of 25%.

Morphological Properties
Gels were prepared as described in Section 2.5 and then freeze-dried. The morphological properties of the freeze-dried gels were observed by SEM (TESCAN-Vega 3, Brno, Czech Republic). The cross-section of samples was fixed on SEM stub and coated with gold using a sputter coater (Q150R-ES, Quorum Technologies Ltd, Lewes, UK). The SEM micrographs were taken at an accelerating voltage of 20 kV and a 300× magnification.

X-ray Diffraction (XRD) Analysis
The freeze-dried samples were prepared according to Section 2.6, and the XRD experiment was performed using an X-ray diffractometer (D8-Advance, Bruker, Karlsruhe, Germany) at 40 kV and 40 mA. The diffraction angle was 5 to 30 • , and the scanning speed of 4 • /min. The relative crystallinity (RC) of the samples was evaluated by the software (OriginPro v9.8.0.200, Thermo Fisher Scientific Inc., Waltham, MA, USA).

Turbidity Measurements
Turbidity of different gel samples was evaluated as described by Singh et al. [25]. Briefly, 1% aqueous suspensions of NCS/MBPI blends (Section 2.3) were heated for 30 min in a water bath (90 • C). The samples were stored at 4 • C and the turbidity was measured every 24 h at 640 nm using a double beam UV-vis spectrophotometer (Halo DB-20R, Dynamica, Livingston, UK).

Syneresis
The syneresis of the NCS/MBPI blends was determined by the method of Shams-Abadi and Razavi [8] with some modifications. Next, 10 g of NCS/MBPI gels (4%) were prepared in 50 mL centrifuge tubes, as described before. Then, the centrifuge tubes were stored for 1, 7, and 14 days at 4 • C. The samples were centrifuged (Sigma 3-30 KS, Osterode am Harz, Germany) at 8000× g for 5 min. The syneresis of each sample was measured by the following Equation (1)

Statistical Analyses
All experiments were performed in triplicate. Analysis of variance (ANOVA) was performed (p < 0.05) between means using the Duncan's multiple range test by SPSS software, version 26 (IBM Company, Chicago, IL, USA).

Dynamic Rheological Properties
Dynamic modulus is a suitable tool for studying the interactions between dispersed and continuous phases in polymer solutions, which plays an important role in the sensory and quality evaluation of starch-based products [3,26]. The changes in storage modulus (G'), loss modulus (G"), and loss factor (tan δ) in the frequency range of 0.01-10 Hz are shown in Figure 1. By adding MBPI to NCS, G' and G" of the system decreased, and tan δ increased, which indicates the weakening of the gels' network [27]. In addition, with an increase in frequency, all rheological parameters (G', G" and tan δ) increased, indicating a weak gel network of NCS with/or without the addition of MBPI. However, by adding MBPI, the NCS/MBPI gels presented a higher sensitivity to frequency changes, indicating the weaker gel structure of NCS/MBPI than the NCS gel [26,27]. In addition, all gel samples had a tan δ less than 1 ( Figure 1C). This phenomenon indicates the solid (viscoelastic) behavior of gels throughout the frequency region, which increased with increasing MBPI concentration, and the viscoelastic behavior of the gels shifted from solid-like to liquid-like [12,19]. Previously, Sang et al. [28] reported that adding a small amount of ovalbumin to wheat dough could reduce the dough's G' and G", but increase the tan δ. They suggested that the ovalbumin might serve as a lubricating agent in doughs so that the increasing ovalbumin content makes doughs softer and less elastic. Zhou et al. [27] demonstrated that by increasing the whey protein ratio in wheat doughs, the G' and G" decreased and tan δ increased. However, an opposite trend was observed by the increasing soy protein isolate ratio in the system, which may be due to the aggregation of soy protein isolate and increase in disulfide bonds in the system. Furthermore, Niu et al. [29] suggested that the reduction in G' after adding protein may be due to its interactions, especially the interactions of carboxyl groups of amino acids with leached amylose, which inhibited the amylose gelation and reduced its G'. Moreover, Yang et al. [12] reported that whey protein isolate could act as an inactive filler in corn starch gel and prevent amylose rearrangement, which decreases the elastic modulus (G' and G") and weakenes the corn starch gel network. These observations suggest that the MBPI could weaken the gel network of NCS by acting as a lubricant and inactive filler in the NCS/MBPI system because of its high WHC or/and interact with leached amylose during gelatinization via its carboxyl groups and retard the amylose rearrangement.

Textural Properties
The effects of MBPI on the textural properties (hardness, cohesiveness, springiness, and gumminess) of NCS gels are shown in Table 1. The hardness of the gel samples displays gel strength under compression, which indicates the physical characteristics of foodstuffs [30]. The hardness of NCS/MBPI gels was significantly decreased by increasing MBPI concentrates (p < 0.05). Sun and Xiang [20] and Kumar et al. [31] showed the reduction in the gel hardness of starch-protein compositions after substituting the starch with the protein, which could be due to the decrease in amylose content. Because the amylose content of the system plays an important role in hardness determination, and the reduction in amylose content causes lower firmness and weakens the gel structure [19,32]. In addition, Liu et al. [33] reported that the gel firmness is primarily influenced by leached amylose rearrangement rather than amylopectin retrogradation. In the present study, the starch content of the system was constant, but the gel hardness decreased after the MBPI addition. This phenomenon indicates the interaction of MBPI with leached amylose during the gelatinization and disturbing the cross-linking interactions between starch molecules. The same results for starch-protein systems have been reported previously [29,34]. Furthermore, Anbarani et al. [32] demonstrated that the protein was located between the starch Polymers 2022, 14, 3012 5 of 12 granules in the continuous phase and reduced the starch rearrangement, which caused gel softening. Previously, the reduction in the starch gel harness after the addition of protein has been reported in several studies [2,9,11]. Cohesiveness indicates the required energy to tolerate the deformation within the food, and the rearrangement of leached amylose plays a significant role in starch gel cohesiveness [24]. The addition of MBPI generally decreased the cohesiveness of NCS/MBPI gels, which suggested that the lower energy is required to deform the NCS gels in the presence of MBPI [32]. Springiness represents the foodstuff's elasticity [24]. The springiness of NCS gels did not show a regular trend with the addition of MBPI. However, previous studies showed the reduction in springiness value in starch-protein systems by increasing the protein content [20,35]. Gumminess shows the required energy for semi-solid food disintegration [9]. Gumminess of the NCS gels showed the same trend as hardness and decreased with the addition of MBPI, which could be because of the impact of hardness in determining this parameter [24].

Textural Properties
The effects of MBPI on the textural properties (hardness, cohesiveness, springiness, and gumminess) of NCS gels are shown in Table 1. The hardness of the gel samples dis-

Morphological Properties
The SEM micrographs of the freeze-dried NCS/MBPI gels are shown in Figure 2. The NCS gel (Figure 2A) showed a dense, porous, and honeycomb-liked network structure. This three-dimensional network structure formed due to the swelling and amylose leaching of starch granules into a continuous starch network during the gelatinization [24,36]. The pores of the starch-protein complex gels became larger and thicker, and the structure of the gels became looser with the increasing MBPI concentrates, which was more noticeable in samples containing 8% MBPI ( Figure 2E). The larger pore sizes and lower structural cohesion indicate the weakening of the gel structure [8]. These results were consistent with the results of the rheological and textural tests, which showed that the structure of the NCS gel became weaker by the addition of the MBPI. In addition, Joshi et al. [19] and Sun and Xiong [20] reported similar results for starch-protein composites. Moreover, Li et al. [37] observed smaller pores and denser structures in corn starch gels compared to starch-soy protein concentrate composite gels.

XRD Analysis
The XRD patterns and RC of samples are presented in Figure 3. The NCS showed the A-type crystalline pattern with strong single diffraction peaks at 15.4 • and 23.2 • and obvious doublet peaks at 17.4 • and 18.1 • [38]. After pasting, NCS showed only a single diffraction peak at 17.4 • . Compared with NCS, the addition of MBPI did not affect the crystal type but decreased the RC of the starch-protein composites, especially at higher concentrates. These results could indicate that the addition of MBPI disturbs the crystalline region of NCS [39]. Similar results for starch-protein mixtures were shown by Niu et al. [29] and Zhang et al. [40]. Furthermore, Zheng et al. [18] reported that the protein could delay the recrystallisation of amylopectin. Chen et al. [39] demonstrated that the soy protein could reduce the RC of corn starch by absorbing the granule starch moisture and interrupting the crystalline region of starch.

Turbidity Measurements
The turbidity of NCS and NCS/MBPI gels at different MBPI concentrates are presented in Figure 4. According to Berski et al. [41], turbidity evaluates the early physical changes in gelatinized starch and its retrogradation during storage, which is attributed to the starch molecular rearrangements, especially amylose. The NCS gel had the highest initial turbidity compared to the NCS/MBPI gels. Moreover, for all samples, the turbidity was increased during storage. Previously, Ghumman et al. [42] reported that the albumin could decrease the starch's turbidity; however, globulin increased it. The reduction in the sample's turbidity in the present study may be due to the higher ratio of albumin/globulin in the MBPI (0.42), compared to the other legumes proteins, such as lupine (0.16), pea (0.32), and soybean (0.11) [43]. Moreover, the excellent WHC of MBPI could promote the integrity of starch granules and stabilize the starch gels against retrogradation [44]. These observations indicated that the MBPI could retard the retrogradation of the NCS gel, which were in accordance with our previous results. In addition, the clarity of starch-based foodstuffs is an important factor in attracting customers [45]. Therefore, adding the MBPI to starch-based products, in addition to improving its nutritional value and retrogradation properties, can also fulfil the customers' demands.  gion of NCS [39]. Similar results for starch-protein mixtures were shown by Niu et al. [29] and Zhang et al. [40]. Furthermore, Zheng et al. [18] reported that the protein could delay the recrystallisation of amylopectin. Chen et al. [39] demonstrated that the soy protein could reduce the RC of corn starch by absorbing the granule starch moisture and interrupting the crystalline region of starch.

Turbidity Measurements
The turbidity of NCS and NCS/MBPI gels at different MBPI concentrates are presented in Figure 4. According to Berski et al. [41], turbidity evaluates the early physical changes in gelatinized starch and its retrogradation during storage, which is attributed to the starch molecular rearrangements, especially amylose. The NCS gel had the highest initial turbidity compared to the NCS/MBPI gels. Moreover, for all samples, the turbidity was increased during storage. Previously, Ghumman et al. [42] reported that the albumin could decrease the starch's turbidity; however, globulin increased it. The reduction in the sample's turbidity in the present study may be due to the higher ratio of albumin/globulin in the MBPI (0.42), compared to the other legumes proteins, such as lupine (0.16), pea (0.32), and soybean (0.11) [43]. Moreover, the excellent WHC of MBPI could promote the integrity of starch granules and stabilize the starch gels against retrogradation [44]. These observations indicated that the MBPI could retard the retrogradation of the NCS gel, which were in accordance with our previous results. In addition, the clarity of starchbased foodstuffs is an important factor in attracting customers [45]. Therefore, adding the MBPI to starch-based products, in addition to improving its nutritional value and retrogradation properties, can also fulfil the customers' demands.

Syneresis
Syneresis is water separation of the system during storage and happens when the leached starch molecules begin to reassociate. Consequently, the gel network shrinks and becomes rigid. This phenomenon occurs along with the reduction in the WHC of the system, which is suggested as the rate of a gel's retrogradation during refrigerated storage [8,32]. According to Figure 5, the addition of MBPI decreased the syneresis of NCS gels after 1, 7, and 14 days from 47. 16-37.98, 52.64-42.13, and 60.53-47.87%, respectively. The reduction in syneresis in the first 7 days could correlate with the amylose reassociation and rapid retrogradation of NCS, while the syneresis after 14 days of storage might indicate the amylopectin rearrangement and long-term retrogradation of NCS [32]. These observations were correlated with previous studies about starch-protein systems. Colombo et al. [36] and Ribotta and Rosell [46] showed that soy protein and peanut protein isolates could reduce the syneresis of corn starch by trapping the water molecules in the system,

Syneresis
Syneresis is water separation of the system during storage and happens when the leached starch molecules begin to reassociate. Consequently, the gel network shrinks and becomes rigid. This phenomenon occurs along with the reduction in the WHC of the system, which is suggested as the rate of a gel's retrogradation during refrigerated storage [8,32]. According to Figure 5, the addition of MBPI decreased the syneresis of NCS gels after 1, 7, and 14 days from 47. 16-37.98, 52.64-42.13, and 60.53-47.87%, respectively. The reduction in syneresis in the first 7 days could correlate with the amylose reassociation and rapid retrogradation of NCS, while the syneresis after 14 days of storage might indicate the amylopectin rearrangement and long-term retrogradation of NCS [32]. These observations were correlated with previous studies about starch-protein systems. Colombo et al. [36] and Ribotta and Rosell [46] showed that soy protein and peanut protein isolates could reduce the syneresis of corn starch by trapping the water molecules in the system, due to its high-water retention capacity. Moreover, some interactions developed between the leached amylose and amylopectin during the storage, which caused syneresis. The presence of MBPI in the continuous phase of the system and its interaction with leached molecules could retard these interactions and delay the system's retrogradation. According to the results, addition of MBPI could reduce both short-and long-term retrogradation of NCS, which is important to its usage in starch-based products, such as pudding and custard.

Conclusions
This study demonstrated that MBPI could act as a lubricant in the NCS/MBPI sy and weaken the gel network of NCS by representing the higher sensitivity of rheolo parameters to frequency changes, as well as the lower gel firmness and lower struct cohesion after the MBPI addition. In addition, MBPI can interact with leached macro ecules (amylose and amylopectin) and retard the short-and long-retrogradation o NCS gel. Moreover, MBPI could trap the water molecules in the system because of its WHC, improve the system's clarity, reduce the gel's syneresis, and extend the shelf li the products. Therefore, the interactions of MBPI and leached macromolecules and w molecules during gelatinization could affect the rheological, textural, and structural p erties of NCS gels in different ways. Based on the results of this study, MBPI can be u in starch-based products to reduce the retrogradation, and improve the functional p erties.

Conclusions
This study demonstrated that MBPI could act as a lubricant in the NCS/MBPI system and weaken the gel network of NCS by representing the higher sensitivity of rheological parameters to frequency changes, as well as the lower gel firmness and lower structural cohesion after the MBPI addition. In addition, MBPI can interact with leached macromolecules (amylose and amylopectin) and retard the short-and long-retrogradation of the NCS gel. Moreover, MBPI could trap the water molecules in the system because of its high WHC, improve the system's clarity, reduce the gel's syneresis, and extend the shelf life of the products. Therefore, the interactions of MBPI and leached macromolecules and water molecules during gelatinization could affect the rheological, textural, and structural properties of NCS gels in different ways. Based on the results of this study, MBPI can be used in starch-based products to reduce the retrogradation, and improve the functional properties.


provenance: AgriculturalAndFoodSciences,2022-2022-Agricultural And Food Sciences-2022.jsonl.gz.gz

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Transcriptome Analysis of Citrus Dwarfing Viroid Induced Dwarfing Phenotype of Sweet Orange on Trifoliate Orange Rootstock

Dwarfed citrus trees for high-density plantings or mechanized production systems will be key for future sustainable citrus production. Citrus trees consist of two different species of scion and rootstock. Therefore, any observed phenotype results from gene expression in both species. Dwarfed sweet orange trees on trifoliate rootstock have been produced using citrus dwarfing viroid (CDVd). We performed RNA-seq transcriptome analysis of CDVd-infected stems and roots and compared them to non-infected controls. The identified differentially expressed genes validated with RT-qPCR corresponded to various physiological and developmental processes that could be associated with the dwarfing phenotype. For example, the transcription factors MYB13 and MADS-box, which regulate meristem functions and activate stress responses, were upregulated in the stems. Conversely, a calcium-dependent lipid-binding protein that regulates membrane transporters was downregulated in the roots. Most transcriptome reprogramming occurred in the scion rather than in the rootstock; this agrees with previous observations of CDVd affecting the growth of sweet orange stems while not affecting the trifoliate rootstock. Furthermore, the lack of alterations in the pathogen defense transcriptome supports the term “Transmissible small nuclear ribonucleic acid,” which describes CDVd as a modifying agent of tree performance with desirable agronomic traits rather than a disease-causing pathogen.

Introduction
Citrus (family Rutaceae) is an economically important crop with several high-value cultivars, including oranges, mandarins, grapefruits, and lemons. The citrus industry was recently valued at USD3.63 billion in California alone, with an estimated economic impact of USD7.6 billion [1]. Citrus fruits and flavors have a wide range of usage and are important sources of vitamins, antioxidants, minerals, and dietary fiber essential for overall nutritional wellbeing [2,3]. Citrus trees are produced by grafting the desired scion variety onto a suitable rootstock species and then planting it in commercial citrus orchards. Tree spacing in citrus orchards has reduced over time in favor of higher tree densities to maintain yield on the reduced available land for agriculture, increase economic returns, and combat the growing threat of citrus huanglongbing (HLB). However, high-density citrus plantings cannot be achieved without the use of dwarfed citrus trees [4][5][6][7].
In addition, CDVd-induced symptoms on citrus hosts depend on species, variety, and rootstock. CDVd infection of navel orange trees [Citrus sinensis (L.) Osbeck] propagated on trifoliate rootstock [C. trifoliata (L.), syn. Poncirus trifoliata (L.) Raf.] has shown that the observed stunting phenotype induced by CDVd-infection reduces canopy volume by approximately 50% (Figure 1), and the reduction in the canopy volume is a result of a >20% decrease in the apical growth of the shoots [19][20][21]. Therefore, there is a need to elucidate the molecular mechanism of the dwarfing phenotype, so the valuable information can be used to produce dwarfed trees for highdensity plantings.
Currently, identifying potential targets of CDVd is challenging due to the nature of viroids, which do not encode for proteins, limiting the experimental approaches. In addition, most of the data on symptom induction by single viroids refer to experimental tests on the biological indicator 'Etrog' citron (C. medica L.). Previously, studies on CDVdinduced differentially expressed genes were performed on 'Etrog' citron in growth chamber conditions; microarray analysis identified mainly genes related to the cell wall structure, amino acid transport, signal transduction, and plant defense/stress response [22]. To this date, there are no transcriptome studies on mature dwarfed citrus field trees infected with CDVd [9].
To further explore the global effect of CDVd-infection on citrus host mRNAs and gain insight into the symptom development mechanism leading to the dwarfed phenotype observed in field plantings, we performed a transcriptome analysis of CDVd-infected dwarf citrus trees using a high throughput sequencing (HTS) approach. There has been limited transcriptomic research on citrus host response to viroid infection using the bioindicator 'Etrog' citron [23], while other viroid studies have used model plant systems such as Solanum lycopersicum and Nicotiana tabacum [11,24,25].
The improved availability of citrus genome and annotation has enabled the widespread use of HTS studies to develop a global understanding of the molecular machinery behind the response to pathogen infection [26]. Furthermore, HTS allows the discovery of rare transcripts, which would be difficult to identify using traditional methods such as microarrays, and is more time efficient than an RT-qPCR approach. More specifically, in citrus, HTS analysis of mRNA has been widely used to understand host responses to pathogens such as 'Candidatus' Liberibacter asiaticus [27][28][29][30] and citrus tristeza virus [28,31].
In this study, we performed a comparative analysis of differentially expressed genes in the stems and roots of CDVd-infected and non-infected sweet orange scion on trifoliate orange rootstock. This analysis provides valuable molecular information to understand the mechanisms responsible for the citrus dwarf phenotype in the field.

Plant Materials and RNA Isolation
Plant materials were collected in April 2016 from 18-year-old 'Parent Washington' navel orange on 'Rich 16-6 trifoliate orange trees. All trees were planted in the same east-west running orchard located at the University of California, Agriculture and Natural Resources, Lindcove Research and Extension Center (LREC) in Exeter, CA, USA. CDVdinfected trees were planted at high density (3 × 6.7 m), whereas non-infected control trees were spaced at standard density (6.1 × 6.7 m).
Stem and root samples were collected from the south side of the dwarfed CDVdinfected (n = 3) and the north and south sides of the full-size non-infected trees (n = 3). This type of sampling was necessary to represent the different sizes of trees (i.e., dwarfed trees 61.2% smaller than full size, [21]) and the different light distribution within their canopies due to the size and east-west orientation of the tree rows, in the transcriptome analysis.
After leaves and petioles were removed, the stems were roughly chopped into approximately 0.5-1 cm pieces, placed into 50 mL conical tubes, and flash-frozen with liquid nitrogen in the field. Feeder roots were sampled approximately 1 m away from the trunk and 20 cm deep, near the irrigation emitters, using a 10 cm diameter corer. The feeder root samples were washed thoroughly with water, gently blotted dry with paper towels, chopped into 0.5-1 cm pieces, placed into 50 mL conical tubes, and flash-frozen with liquid nitrogen in the field. Between each sample collection and processing, cutting tools and working surfaces were sanitized with 10% bleach solution (0.5-1% sodium hypochlorite) and then rinsed with water. New sterile disposable plasticware and razor blades were used to chop each sample. The frozen samples were transported to the Citrus Clonal Protection Program (CCPP), Citrus Diagnostic Therapy and Research Laboratory at the UC Riverside (Riverside, CA, USA) on dry ice and stored at −80 • C for downstream analysis (CDFA permit 3477).
Total RNA was isolated using the TRIzol ® reagent (Thermo Fisher Scientific, Waltham, MA, USA). For each sample, 300 mg of frozen tissue were ground in liquid nitrogen with sterilized mortar and pestle. The ground material was transferred to a 5 mL Eppendorf tube, and 3 mL of TRIzol ® reagent was added immediately. RNA extraction was performed according to the manufacturer's instructions. The eluted RNA was aliquoted into four 1.5 mL microcentrifuge tubes to prevent freezing-thawing cycles during downstream analysis. The RNA concentration and quality were assessed with a spectrophotometer and the Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA) using the Plant RNA Nano assay. The presence or absence of CDVd, as well as that of other graft-transmissible pathogens of citrus endemic to California, was confirmed in each sample by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) as previously described [21,32].

Library Preparation and High Throughput Sequencing
Eighteen cDNA libraries were prepared for each sample using the Illumina TruSeq Stranded mRNA Kit (San Diego, CA, USA), following the manufacturer's recommended protocol for Low Sample (LS) throughput. The libraries were quantified with the Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA) using the High Sensitivity DNA Kit. The libraries were quantified using the in-house SeqMatic HT1 quantitative polymerase chain reaction (qPCR) assay (SeqMatic, Fremont, CA, USA) and pooled equimolar. The libraries were sequenced using an Illumina HiSeq TM 4000 (San Diego, CA, USA) instrument with paired-end 100 bp reads (SeqMatic, Fremont, CA, USA). Raw reads were trimmed and demultiplexed for subsequent bioinformatic analysis.

Bioinformatic Analysis
The raw sequencing data of the north and south samples from each of the three non-infected full-size trees were concatenated into one file for analysis. Bioinformatic analyses were performed using the OmicsBox software suite version 2.0.36 (Cambridge, MA, USA) using the reference Valencia orange (version 1.0) (GCF_000317415.1) and trifoliate (v. 1.3.1) [33] genomes. RNA-seq alignment was performed using Spliced Transcript Alignment to a Reference (STAR v. 2.7.8a) [34] with default parameters. The quantification of transcript expression levels was performed using the HTSeq (v. 0.90) package [35], while the differential expression analysis was performed edgeR (v. 3.28.0) [36] with default parameters. Functional analysis was performed using the Blast2GO [37] tool within OmicsBox Suite. BLAST [38] was performed on the DEG sequences, and the gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) functions [39] were assigned. Protein functionality was confirmed with InterPro [40]. The dataset was uploaded into NCBI Sequence Read Archive under the accession numbers SAMN26677719 to SAMN26677722. All figures were created using GraphPad Prism version 9.3.0 (San Diego, CA, USA).

Expression Analysis of Citrus mRNA Target Genes Using RT-qPCR
RT-qPCR assays were designed to verify the expression levels of the predicted target genes (Supplementary Table S1). Actin was used as an internal control gene to determine the relative abundance of the target mRNA expression levels by the comparative Cq method. Reverse transcription was performed using the SuperScript TM II Reverse Transcriptase (RT) (Thermo Fisher Scientific). The reactions were performed using the manufacturer's recommended protocol as follows: 2 µL of oligo (dT) (500 µg/mL), 2 µL of dNTP (10 mM), 4 µL of total RNA (diluted to 100 µg/µL), and 16 µL of nuclease-free water with a final volume of 24 µL. All samples were standardized to the same concentration to ensure equal representation, and incubation steps were performed using the ProFlex thermal cycler (Thermo Fisher Scientific). The mixture was incubated for 5 min at 65 • C and subsequently chilled on ice. The first strand synthesis was prepared with 8 µL of 5x First-Strand Buffer, 4 µL of 0.1 M DTT, and 2 µL of RNaseOUT TM (40 units/µL) (Thermo Fisher Scientific) and then incubated for 2 min at 42 • C. Finally, 2 µL of SuperScript TM II RT (200 units) were added, and the reaction was incubated at 42 • C for 50 min followed by 15 min at 70 • C. Downstream qPCR was performed in triplicates, according to the MIQE guidelines [41], using the TaqMan TM Fast Advanced Master Mix Kit (Thermo Fisher Scientific) as follows: 10µL of master mix, 8 µL of nuclease-free water, 1 µL of primer and probe mixture, and 1 µL of cDNA. FAM fluorophore was used for all qPCR probes. The qPCR was performed using the QuantStudio 12K Flex Real-Time PCR (Thermo Fisher Scientific) with the following conditions: 50 • C for 2 min, 95 • C for 2 min, and 95 • C for 1 s, 60 • C for 20 s for 40 cycles.

Transcriptome Assembly and Annotation
To understand the citrus host transcriptome response to CDVd infection, we prepared and analyzed RNA-seq libraries from the stem and root samples of non-infected and CDVd-infected navel orange citrus trees on trifoliate orange rootstock. Overall, stems generated more reads than roots for non-infected and CDVd-infected trees (Table 1). A high percentage of reads (72-88%) from both the non-infected and CDVd-infected trees were mapped to their relative genomes using the STAR alignment software ( Table 1). The average length of mapped reads in all samples tested was close to the expected size of 200 nt (Table 1).

Differentially Expressed Genes (DEGs) Analysis and Identification
The multidimensional scaling (MDS) plot showed distinct differences between the non-infected and CDVd-infected trees in stem and root tissues (Figure 2A,B). Non-infected stems and roots were similar and clustered closer together than the CDVd-infected tissues (Figure 2A,B).  Table 2). Heat maps of the top 50 DEGs identified from the stem and root tissues of non-infected and CDVd-infected trees were generated based on FDR ( Figure 4). Results indicated a relatively even clustering of stem upregulated and downregulated DEGs in the non-infected and CDVd-infected samples. In contrast, the roots showed clustering skewed towards the DEGs that were significantly downregulated in the CDVd-infected and upregulated in the non-infected samples ( Figure 4A,B).  DEG analysis identified up-and downregulated targets. In the stem, the probable 28S rRNA (cytosine-C(5))-methyltransferase (FDR = 5.05 × 10 −11 ; logFC = 8.83) and the pentatricopeptide repeat-containing protein (FDR = 4.29 × 10 −7 ; logFC = −2.95) had the most statistically significant fold change (Table 3).

Confirmation of Candidate DEGs by RT-qPCR Analysis
To confirm the accuracy of Illumina RNA-seq results, RT-qPCR was performed on selected candidate DEGs. The logFC values of the RNA-seq and RT-qPCR were consistent and showed similar trends for both up-and downregulated stem and root DEGs ( Figure 5).

Functional Classification of DEGs
The RNA-seq data were annotated based on the gene ontology (GO) terms, which categorized the DEGs in response to CDVd infection for both stems and roots into various biological, molecular, and cellular processes ( Figure 6). Among the upregulated DEGs in the stems, there were 143 associated with biological process (BP), 82 14). Only two GO terms were associated with CC (i) cellar anatomical entity (up 26 and down 110) and (ii) protein-containing complex (up 2 and down 8) ( Figure 6B). A list of all GO term annotations for the root DEGs can be found in Supplementary Tables S8 and S9. Kyoto Encyclopedia of Genes and Genome (KEGG) analysis was performed on upand downregulated DEGs from non-infected and CDVd-infected trees from the stem and root tissues (Figure 7). The KEGG results for stem upregulated DEGs identified 82 pathways and 25 sequences linked to specific pathways. The pathways with the most sequences from upregulated stem DEGs were starch and sucrose metabolism (3) (ko00500), NOD-like receptor signaling pathway (2) (ko04621), tryptophan metabolism (2) (ko00980), and protein processing in the endoplasmic reticulum (2) (ko04141). KEGG analysis also identified 96 pathways and 25 sequences in stem downregulated DEGs, with the highest downregulated pathway being the plant circadian rhythm (3) (ko04712) ( Figure 7A). KEGG analysis for roots upregulated DEGs found 73 pathways and 26 sequences and 118 pathways and 53 sequences from downregulated roots. The highest number of sequences associated with upregulated roots were protein processing in the endoplasmic reticulum (10 sequences) (ko04141). In contrast to the downregulated roots, the NOD-like receptor signaling pathway (ko04621) (7), MAPK signaling pathway (7) (ko04010), and plant hormone signal transduction (5) (ko04075) were the highest ( Figure 7B). A complete list of all KEGG results can be found in Supplementary Tables S10-S13.

Discussion
Citrus is among the most economically valuable fruit crops. In 2019, citrus production exceeded 157.9 million tons in over 9.8 million hectares worldwide [1]. The ever-reducing global natural resources (i.e., decreased arable land and water availability) combined with climate change, agricultural labor shortages, and the increased disease pressure of deadly citrus diseases such as HLB demand the adoption of novel citriculture practices. Highdensity citrus plantings, the possible implementation of mechanized citrus harvest, and citrus production under protective structures are some of the most promising trends for sustainable citriculture. The ability to produce dwarf citrus trees is critical for implementing these innovations and maximizing returns on investments [19,[42][43][44][45][46].
Dwarfed sweet orange trees propagated on trifoliate orange rootstock, where reduced vegetative growth is achieved without compromising fruit yield or quality, have been produced using CDVd. CDVd is part of a group of so-called "graft-transmissible dwarfing agents," the use of which has been explored for almost fifty years since it was initially proposed [16,18,[47][48][49][50]. Previously, we observed CDVd significantly reduced sweet orange canopy volume on trifoliate rootstock by reducing vegetative growth [19][20][21]. Long-term field observations indicated that CDVd might be used as a possible tool for high-density plantings of citrus. In addition, key findings on the possible biological mechanism through which CDVd affects specific rootstock scion combinations to reduce tree canopy volume were obtained [14]. Understanding the detailed molecular mechanisms leading to reduced canopy volume of commercial citrus tree varieties in response to CDVd infection is critical. Indeed, it would provide the information to produce reduced-size citrus trees without a graft-transmissible viroid agent.
Transcriptomic profiling provides a rapid and cost-effective approach to identifying target genes responsible for the observed reduced vegetative growth of CDVd-infected sweet orange trees. Commercial field citrus trees consist of two different species: the scion, producing the canopy of the tree (i.e., branches, stems, and leaves), and the rootstock, from which the root system of the tree (i.e., taproot, lateral, and feeder roots) develops [51]; therefore, any observed tree phenotype is a result of gene expression not in one but two species that closely interact with each other. In this study, we performed a transcriptome analysis of CDVd-infected stems and roots from sweet orange on trifoliate rootstock trees grown in high-density plantings and compared them with non-infected controls. Here, we showed that CDVd infection affects a wide range of biological functions via different stem and root mRNA transcripts. Furthermore, it complements recent molecular studies targeting CDVd-derived small RNAs on dwarfed citrus [32]. As a result of differential expression analysis, a total of 409 CDVd-dependent differentially expressed genes (DEGs) were deregulated (131 upregulated and 278 downregulated), with the majority of upregulated genes in the stems (83/131), and most downregulated genes in the roots (186/278) ( Table 2).
The five most significant upregulated genes in CDVd-infected stems include: LOC102607140, a probable 28S rRNA (cytosine-C(5))-methyltransferase, an ortholog of the Arabidopsis thaliana (AT5G26180), which plays a role in regulating translation in response to stress [52]; LOC102611719, a germin-like protein subfamily T member. Germin-like proteins (GLPs) are encoded by multigene families in several plant species, and some subfamily members play a role in defense against pathogen attack [53,54]; LOC102610118, a pentatricopeptide repeatcontaining protein (ortholog of AT2g17525). Pentatricopeptide repeat (PPR) proteins belong to a large gene family in plants. Some PPR proteins play important roles in organellar RNA metabolism and organ development in Arabidopsis and rice, while their functions remain unknown in woody species [55]; LOC102616051, cytosolic sulfotransferase 15-like, an ortholog of the Arabidopsis cytosolic sulfotransferase genes. In Arabidopsis, a hydroxyjasmonate sulfotransferase was reported to participate in the hydroxylation and sulfonation reactions that might be components of a pathway that inactivates excess jasmonic acid in plants [56]. The LOC102614284 is associated with aspartic proteinase Asp1-like, and plant aspartic proteinases have been implicated in diverse cellular functions, including protein processing and/or degradation, plant senescence, stress responses, programmed cell death, and reproduction [57].
The five most significantly downregulated genes in CDVd-infected stems include: LOC102627502, a pentatricopeptide repeat-containing protein, an ortholog of At4g21300; LOC102620512, SKP1-like protein 21, which is involved in ubiquitination and subsequent proteasomal degradation of target proteins [58]; LOC102610900, SHORT-ROOT-like protein, which controls radial patterning of the Arabidopsis root, and therefore root growth [59], which is consistent with reduced root mass in viroid infected plants [9]; LOC102620992, an amino-acid permease BAT1-like, which plays a role in primary carbon metabolism and plant growth by mediating the transport of gamma amino butyric acid from the cytosol to mitochondria in Arabidopsis [60]; LOC102628393, serine/threonine-protein kinase ATG1a, involved in autophagy in a nutritional condition-dependent manner [61].
In CDVd-infected roots, the five most significantly upregulated genes include: Ptrif.0009s1914.1 (ortholog of AT5G62350/LOC_Os06g49760), a plant invertase/pectin methylesterase inhibitor superfamily protein, which inhibits pectin methylesterases (PMEs) and invertases (PMIs) and has been implicated in the regulation of fruit development, carbohydrate metabolism, and cell wall extension as well as inhibition of microbial pathogen PMEs. The interplay between PME and PMEI is a determinant of cell adhesion, cell wall porosity, and elasticity, as well as a source of signaling molecules released upon cell wall stress [62]. The Ptrif.0004s1154.1 (ortholog of AT5G07310/Cre14.g620500/LOC_Os04g32620) is an integrase-type DNA-binding superfamily protein involved in the regulation of transcription and recently reported to integrate the jasmonate and cytokinin signaling pathways to repress adventitious rooting in Arabidopsis [63]; Ptrif.0005s1389.1, an ortholog of Cre05.g240400/LOC_Os12g25450, S-adenosyl-L-methionine-dependent O-methyltransferase/ ethylene-responsive transcription factor, which methylates proteins, small molecules, lipids, and nucleic acids [64]; Ptrif.0005s2465.1 (ortholog of AT5G52010/LOC_Os06g07020) C2H2like zinc finger protein, which contains one of the most common domains found in the transcription factors of higher eukaryotes [65]; and Ptrif.0006s1595.1 (ortholog of AT5G03800) another pentatricopeptide repeat (PPR) superfamily protein.
Among the additional interesting CDVd-responsive stem genes whose differential expression was verified by RT-qPCR ( Figure 5A), several were found including the plant defense receptor-like protein 9DC3 (LOC102630240) [70]; the MADS-box transcription factor 23-like (LOC102630220) [71], which is part of the transcription factor (TF) families (WRKY, MADS-box and MYB) that activate unique abiotic and biotic stress-responsive strategies considered as key determinants for defense and developmental processes in most eukaryotic plants [72]; the phospholipase A1-Ibeta2 (LOC102609966) [73], the chloroplast import protein TIC 55 (LOC102614849), the chloroplast import protein [74]; the chloroplast pentatricopeptide repeat-containing protein ortholog of At1g08070 (LOC102619843), which is involved in RNA editing events within the chloroplast [75]; the transcription factor MYB13 (LOC102616893), which regulates meristem function by being a component of a regulatory network controlling the establishment and/or development of the shoot system in Arabidopsis [76]; protein IQ-DOMAIN 1, transcript variant X1 (LOC102628194) which plays a role with calmodulins or calmodulin-like proteins as well as involved in scaffolding in cellular signaling and trafficking [77,78]; hydroxyproline O-galactosyltransferase GALT3, transcript variant X3 (LOC102626136), which commits arabinogalactan proteins to the first step in arabinogalactan polysaccharide addition. AGP glycans play essential roles in both vegetative and reproductive plant growth [79]; cytochrome P450 82C4-like, which is involved in the early Fe deficiency response in Arabidopsis [80]; LOC102622795; thaumatinlike protein 1 (LOC102619709), involved in local responses of roots to colonization by non-pathogenic plant growth-promoting rhizobacteria [81].
Gene ontology (GO) assignments were used to classify the function of the identified DEGs. GO term enrichment analysis showed that in both stems and roots of CDVd-infected trees, for the biological process category, the GO terms corresponding to metabolic process and cellular process were the most enriched terms in response to CDVd; for molecular function, the catalytic activity and binding are the most enriched, and for the cellular component category, the cellular anatomical entity term was most enriched in the stems ( Figure 6A,B).
In order to gain further insight into the function of DEGs correlating to a reduced vegetative growth as observed in CDVd-infected trees, the KEGG pathway classification was performed. Overall, circadian rhythms (ko04712), starch and sucrose metabolism (ko00500), and protein processing in the endoplasmic reticulum (ko04141) were most altered in response to CDVd infection. Circadian rhythms (ko04712), downregulated in CDVdinfected stems, are known to cross-talk with defense signaling against bacteria in plants [85] and to be important determinants in the outcome of plant-pathogen interactions [86]. Starch and sucrose metabolism (ko00500) were upregulated in both CDVd-infected stems and roots. Sucrose, the main form of assimilated carbon produced during photosynthesis, has important roles as a signaling molecule, and it is involved in many metabolic processes in plants; it is essential for plant growth and development and plays a role in plant defense by activating plant immune responses against pathogens. Upon infection, pathogens hijack the plant metabolism to access the plant sugars and, in doing so, trigger plant defense responses. Invertases appear to be involved in establishing plant defense responses [87].
Protein processing in the endoplasmic reticulum (ko04141) was upregulated in roots and stems, consistent with higher metabolic activity levels. Tryptophan metabolism (ko00380) was up-and downregulated in stems. Tyrosine and tryptophan are precursors for the plant defense compounds dhurrin and indole glucosinolates, respectively. In addition, tryptophan is a precursor for the essential phytohormone indole-3-acetic acid [88]. Other KEGG pathway classification terms that were altered in response to CDVd infection include: Lipid metabolism (glycerophospholipid (ko00564), which was downregulated in roots and stems; ether lipid (ko00565) was downregulated in stems; ABC transporters (ko 02010), Ras signaling pathway, mTOR signaling pathway (ko04150), which were downregulated in stems.
To date, only one previous citrus gene expression profiling study in response to CDVd infection has been reported [22], and it differs from ours in many important aspects. Indeed, it predates the widespread use of HTS applications and the availability of reference genomes for C. sinensis and P. trifoliata. Instead, the authors profiled the transcriptome using the differential display technique (DDRT-PCR). Most importantly, however, the authors studied the sensitive viroid bioindicator 'Etrog' citron, Arizona 861-S1, newly inoculated with CDVd (18 months old) grown under growth chamber conditions and expressing symptoms of leaf drooping and petiole necrosis. Provided these experimental conditions, as expected, among the upregulated genes, a suppressor of RNA silencing was identified [22]. In contrast, our study used HTS technologies to analyze 18-year-old, field-grown commercial citrus tree varieties that do not express typical viroid symptoms of leaf epinasty and midvein and petiole necrosis [89]. Therefore, it is not surprising that the transcriptome profiling was different.
When taken together, our results indicate that CDVd modulates the expression profile of important citrus growth and developmental processes that may participate in the cellular changes leading to the observed phenotype of reduced vegetative growth and overall smaller tree size. In this study, most of the transcriptome expression reprogramming appeared to occur in the stems (C. sinensis) rather than in the roots (C. trifoliata), which is consistent with the phenotypic observation of the reduced stem vegetative growth of the stems and striking dwarfed citrus tree phenotype in CDVd-infected trees [21]. It is also in agreement with the observation that the trifoliate orange rootstock does not display major symptoms in response to CDVd infection [90][91][92]. Even though our study demonstrates the potential use of modern molecular technologies to decipher complex plant responses to biotic factors using plants growing in agricultural production systems, future studies employing transgenics and model plant systems could provide additional evidence to dissect further the genes and pathways participating in the development of the dwarf phenotype identified in this study.
Lastly, the lack of major alterations in the pathogen defense transcriptome profile in our study is in contrast to studies with other viroids that demonstrated altered expression of plant defense genes [11,24]. This finding supports the idea that when viroid RNAs such as CDVd are used for horticultural purposes, such as reducing tree canopy volume, perhaps they are better described as "Transmissible small nuclear ribonucleic acids" (TsnRNAs). This term provides a more detailed description of the TsnRNA's hallmark properties (i.e., transmissibility, small genome size, site of replication, and RNA nature) as well as dissociation from a generic virology term, such as "viroid", which implies disease and crop loss. Therefore TsnRNAs that do not express a disease syndrome but rather act as modifying agents of tree performance resulting in desirable agronomic traits, such as reduced tree size for high-density plantings with potential economic and sustainability advantages, can be distinguished from the pathogenic viroids [18,20].


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Hypoallergenicity assessment of an extensively hydrolyzed whey‐protein formula in cow’s milk allergic infants

Abstract Background Extensively hydrolyzed formulas are recommended for the dietary management of infants with cow's milk allergy (CMA). Objectives Hypoallergenicity, growth, and gastrointestinal (GI) tolerability of a new extensively hydrolyzed whey‐protein formula (eHWF) in CMA children were assessed. Methods In this prospective, randomized, international, multi‐center study (Trial NL3889), 34 children with confirmed CMA (74% IgE‐mediated) underwent a double‐blind, placebo‐controlled food challenge (DBPCFC) with an eHWF developed with non‐porcine enzymes, supplemented with prebiotic short‐chain galacto‐ and long‐chain fructo‐oligosaccharides (0.8 g/L, ratio 9:1), arachidonic acid (0.35/100 g), and docosahexaenoic acid (0.35/100 g). If tolerant to the eHWF, children participated in a 7‐day open food challenge with this eHWF. Anthropometrics and GI tolerability were assessed in an optional 16‐weeks follow‐up. Results Of the 34 children who started the DBPCFC with the eHWF, 25 subjects (19 boys, mean age: 61 weeks, 18 with IgE‐mediated CMA) completed the DBPCFC and 7‐day open challenge without major protocol deviations and tested negative at both challenges. One child experienced a late moderate eczematous allergic reaction in the optional follow‐up period, indicating the need for close monitoring of subjects starting new formula. Weight and length gain followed the World Health Organization growth curves. Changes in frequency and consistency of stools upon test formula intake were transient. Conclusions The newly developed eHWF is a suitable option in CMA treatment as all subjects tolerated the product. This result is in line with the international criteria for hypoallergenicity (American Academy of Pediatrics) that state that more than 90% of CMA children must tolerate the formula. Use of the formula is also associated with normal growth curves and GI tolerability. Trial registration Trial NL3889, https://www.trialregister.nl/trial/3889.

| INTRODUC TI ON
Over the last decades, food allergies have become an emerging healthcare issue not only in the western world but also in developing countries. 1 It is estimated that 6-8% of infants under 3 years of age have a food allergy, among which cow's milk allergy (CMA) is the most common. [2][3][4] Clinical symptoms of CMA may start during the first weeks of life, shortly after the introduction of cow's milk proteins (CMP), characterized by cutaneous, gastrointestinal (GI), and/or respiratory symptoms and pain behavior. 2 CMA can be classified as immunoglobulin E (IgE)-mediated, usually characterized by the occurrence of symptoms immediately after antigen exposure, or non-IgE mediated, characterized by delayed allergic symptoms, or a combination of both. Its management in infants and young children requires total avoidance of CMP and, when breastfeeding is not possible, the use of "hypoallergenic" formulas. Hypoallergenic formulas based on extensively hydrolyzed proteins or amino acid mixtures are the only safe option. 2 These infant formula substitutes must be proven to be hypoallergenic and have good tolerability, safety, and nutritional adequacy. The nutritional quality is crucial since studies have shown that the growth of children with CMA may be compromised, 5,6 potentially because the dietary intake of macro-and micronutrients is below the recommendations compared to healthy children. 7 The aim of this study was to investigate the hypoallergenicity and safety (growth and GI tolerability) of an extensively hydrolyzed whey-protein formula (eHWF), according to international guidelines (American Academy of Pediatrics [AAP], European Society for Paediatric Allergology and Clinical Immunology [ESPACI], and the European Society for Paediatric Gastroenterology, Hepatology and Nutrition [ESPGHAN]). [8][9][10] 2 | ME THODS This prospective, randomized, controlled, multi-center study was conducted in the Netherlands, Italy, and Poland. Our primary objective was to assess the hypoallergenicity of a newly developed extensively hydrolyzed whey-protein infant formula in children with CMA according to international guidelines. [8][9][10] We also aimed to assess the effects on growth and tolerability of the formula in a 16-week follow-up. The study was registered in the Dutch Trial Register on 1 July 2013 (registration number NTR4051).

| Study population
We included infants and children with proven CMA from January out under the supervision of a pediatrician. We excluded infants and children with a confirmed history of an anaphylactic reaction to cow's milk, those with intolerance to lactose or any other component of the study formula, those with a previous allergic reaction to any existing eHWF, and those fed any amino acid-based formula (for more than 6 weeks preceding the first part of the DBPCFC) due to potentially severe CMA. Children with major congenital malformations and/or existing illnesses that could interfere with formula acceptance or identification of allergic reaction, children breastfed more than twice daily, participating in another clinical study, or whose parents were judged unable to comply with the protocol requirements were also excluded. The minimum expected consumption of the study formula during the open challenge phase had to be 250 ml per day.

| Study design
The AAP guidelines for clinical testing of hypoallergenic formulas, which have been supported by the ESPACI and ESPGHAN, suggest that the number of subjects must be sufficient to demonstrate with 95% confidence that 90% of the subjects will not react to the formula. [8][9][10] The number of subjects needed to project with 95% confidence (one-sided interval) that less than 10% of subjects will react to the product is 25 subjects if zero clinical reactions are observed, and 43 subjects if one clinical reaction is observed. These sample size estimates were derived based on binomial distribution techniques using Wilson's method for deriving confidence intervals for single proportions.
Prior to the DBPCFC, a blood sample was drawn from all participating children to determine total IgE and allergen-specific IgE levels. In addition, data on anthropometrics and recent GI symptoms were collected.

Key Message
This study describes the safety and hypoallergenicity of a new extensively hydrolyzed whey-protein formula, derived by non-porcine enzyme hydrolysis, for infants with diagnosed cow's milk allergy (CMA). The use of this formula does not impact growth and is a suitable nutritional option for the dietary management of CMA in infants.
In the challenge, the new formula (manufactured by Nutricia Research, Cuijk, The Netherlands) was tested by a DBPCFC followed by an open food challenge for 1 week in case of a negative DBPCFC.
The DBPCFC consisted of two separate hospital visits (visit 1 and visit 2) within a 1-week time frame; children were randomized to receive either: (1) the test formula on the first test day and the reference formula (amino acid formula) on the second test day or (2) the reference (amino acid formula) on the first test day and the test formula on the second test day. Randomization was computergenerated (using SAS Proc Plan) and stratified by the study site.
Participating clinicians received the treatment order within sealed opaque envelopes.
The reference formula and the eHWF were reconstituted according to the manufacturer's instructions; for blinding, the latter was mixed with the placebo formula at a 1:1 ratio. The prescribed schedule was 1, 5, 10, 25, 50, 80 and 100 ml. The administered formulas were prepared by a staff member who was not involved in the patient's care. The investigator, the nursing staff and the family were therefore not informed of what formula the child was being fed.

| Formulae
The test formula was a nutritionally complete, powdered, newly designed; non-porcine enzymes derived, extensively hydrolyzed wheybased infant formula for infants/children with CMA (see Table 1).
The formula was enriched with long-chain polyunsaturated fatty acids (arachidonic acid and docosahexaenoic acid) and a mixture of prebiotic short-chain galacto-oligosaccharides, and long-chain fructo-oligosaccharides (further characterization of the new extensively hydrolyzed protein will be described in Knipping et al. manuscript in preparation). The reference formula used in this study was a commercially available, amino acid-based, nutritionally complete, powdered, hypoallergenic infant formula for the management of (severe) CMA (Neocate, Nutricia Advanced Medical Nutrition).

| Ethics
The approval of the relevant ethics committees in the participating countries was obtained before the start of the study. The study was conducted according to ICH-GCP principles and in compliance with the principles of the "Declaration of Helsinki" (59th WMA General Assembly, Seoul, October 2008) and with the local laws and regulations of the country where the study was performed. Written informed consent was obtained from all parent(s)/guardian(s) before enrolment in the study.

| Statistical analysis
A challenge was considered positive when objective symptoms appeared and when there were severe, persisting (more than 40 min) subjective symptoms. 11 Based on the results of the DPBCFC and subsequent open challenge, the proportion of children tolerating the test formula was calculated with a one-sided 95% Wilson's score confidence interval (for a single proportion).
If the lower confidence limit of the one-sided 95% confidence interval for the population fraction of children with tolerance to the test formula is larger than or equal to 90%, we can claim with 95% confidence that 90% or more of the subjects will tolerate the formula. For anthropometric data, length-for-age and weight-for-age zscores were calculated according to the World Health Organization (WHO) reference values. 12 Other data were described and/or summarized either by means and standard deviations or medians and interquartile ranges as appropriate for continuous data, or by number and percentages for categorical data.

| Demographics and baseline characteristics
The children in the PP population were recruited in Italy (72%) and the Netherlands (28%). A 76% of the recruited children were males and their age at baseline ranged from 15.1 to 142.7 weeks of age.
Their gestational age, weight for age z-score, and length for age zscore at screening were all within the normal range.
In the PP population, 80% of the children reported skin symptoms and 68% GI symptoms when consuming CMP. At study entry, the majority of the children were fed an eHWF (n = 14), others reported consumption of rice hydrolysate (n = 3), soy (n = 6), and amino acid-based formulae consumption outside exclusion criterium of severe allergy (n = 2; Table 2). Ten children were mixed-fed with a combination of formula feeding and breastfeeding at the time of enrolment in the study. Data are depicted in Table 2.
The demographics and baseline characteristics for AST can be found in Table S1.

| Allergic reactions
None of the 25 children in the PP population showed allergic reactions to the test formula during the DBPCFC, nor in the open challenge (see Table 3). No reaction was reported among the 9 children that were excluded from the PP analysis. In accordance with the international guidelines, [8][9][10]

| Anthropometrics
Because of the wide variation in baseline age of the children individual length-for-age and weight-for-age growth data per gender were evaluated (Table S2). In the follow-up phase, weight and length gain followed the normal WHO growth curves, within the WHO ranges (The WHO Multicentre Growth Reference Study (MGRS)).  c One subject consumed 2 types of formula.

TA B L E 2 Demographic data and subject characteristics at baseline for PP population
d The extensively hydrolyzed formula could be either whey or casein-based. Thirteen severe AEs were reported in 4 subjects. These were GI disorders, infections, investigations, nervous system disorders, and skin and subcutaneous tissue disorders. None of these events were indicated by the investigator to be related to the test formula intake.

| Safety outcomes
Most of them had a short duration and did not require medication.
No serious adverse events were reported in this study.

| GI tolerability
By collecting parent-reported, non-allergy-related GI symptoms from the first day of the DBPCFC (hospital visit 2) up to and including the 16-week follow-up ( Figure S1), the GI tolerability of the test formula was assessed. At baseline, the percentage of children with GI symptoms was low (<20%). At baseline, the majority of the children had stool consistency reported as "soft" or "formed." The use of an infant formula or changing to another infant formula can cause transient changes in stool patterns as also seen in other studies, 14,15 reflected in Figure 1 showing that the first couple of weeks of 16- week follow-up (weeks 3-8) the consistency slightly differed compared to baseline (predominantly softened). This observation seems to be transient as the consistency was back to baseline level by the end of the 16-week follow-up. Other GI symptoms were hardly reported during the first weeks of the follow-up period. Overall, almost all parent-reported symptoms were mild. Stool frequency was very consistent throughout the study (Table S4).  (Table S5).

| DISCUSS ION
This study was designed according to international guidelines (AAP,  among infants with more complex forms of the disease. 18,19 Infants, who react to eHWFs, develop delayed non-IgE-mediated GI reactions to cow's milk and severe eczema more frequently than those who do not react to eHWFs. 20 The one late allergic reaction in the follow-up phase is in line with this information from the literature. The IgE sensitization patterns of the subjects as determined by the ISAC method (see Table S5) could roughly be divided into It has long been known that there is a broad range of attributes among hydrolyzed formulas with significant differences, especially in their taste. 24 In this study only one parent reported aversion to the taste of the test product. This is in line with the observation of Maslin et al., who described that whey-based lactose-containing eHWFs are generally considered to be more palatable and accepted in comparison to other hydrolysates (non-lactose, casein-based). 25,26 The main strength of the present study was that it has been designed according to the AAP guidelines for testing hypoallergenicity of a hydrolyzed formula, 8 which have also been adopted by the A limitation of the study is the exclusion of infants that were on the amino acid formula for scientific and safety reasons. So, this study does not completely represent the total, global population of cow's milk allergic infants and children.
In conclusion, the study formula was tolerated by more than 90% of infants with a diagnosed CMA, with a 95% confidence interval, and is therefore in line with the criteria of hypoallergenicity and safety of the international guidelines (i.e., AAP) for eHWF. The formula also supported proper growth and GI tolerability in those infants. The palatability of the formula tested makes it a suitable option in the treatment of CMA in terms of efficacy, nutritional adequacy, acceptance, and tolerance.

CO N FLI C T O F I NTE R E S T
Johan Garssen is parttime employee of Danone Nutricia Research as well as Utrecht University.

PEER R E V I E W
The peer review history for this article is available at https://publo ns.com/publo n/10.1111/pai.13814.


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Combination of high order kernel estimators for estimation of fruit trees biomass on critical land

This study aims to estimate fruit tree biomass with a high-order Gaussian kernel estimator. The source of the data was obtained by estimating the relationship between tree diameter (x) and tree biomass (y) which was obtained through the allometric equation y = 0.11ρx2.62 for branched trees. Data samples were taken as many as 250 obtained from 5 sub-district locations in critical land on longan and mango fruit tree vegetation. The relationship between x and y is then estimated in the form of an equation yi = m (xi) + εi where, i = 1, 2, n. The results of the analysis using the kernel estimator showed that the tree biomass estimation resulted in a smooth regression curve at an optimal bandwidth (h) score of 1.3 (GCV = 122.92) longan and 2.6 (GCV = 3523.25) mango. The relationship between MSE and bandwidth shows a very strong negative correlation with a score of r = -0.959 (p.value = 0.04) in the estimated longan biomass and r = -0.964 (p.value = 0.03) in the estimated mango biomass and is significant (α = 0.05). This means that a parallel increase in the bandwidth score will decrease the MSE score when GCV is controlled.

Introduction
Indonesia has been designated as the country with the second-largest vegetation diversity in the world [1]. As a tropical area, Indonesia is a potential source of new species that have more than half the diversity of flora and fauna in the world, so it is suspected as the richest source of discovery of new types of macro-organisms [2]. At this time, the condition of land resources and the environment in Indonesia is increasingly alarming, indicated by the wide area of critical land that has an impact on the decline in vegetation diversity. Critical lands are generally more fragile, prone to erosion, less productive, and not easy to manage, thus leading to a decline in land quality [3]. The Ministry of Environment and Forestry (2018) reports that the area of critical and very critical land in Indonesia in 2015 without DKI Jakarta was approximately 24.303.294 ha consisting of 19.564.911 ha critical and 4,738,384 ha [4]. The expansion of critical land is caused by several things, including forest destruction, expansion of agricultural areas that are not in accordance with the carrying capacity of the environment, increasing population pressure, and uncontrolled fires [5].
The development of a conservation success model must be able to take an approach to save the environment from ecological damage, one of which is by estimating tree biomass. The concept of IOP Publishing doi: 10.1088/1742-6596/2157/1/012035 2 developing a conservation success model that combines the architectural characteristics of trees with the biomass potential of each individual tree so that with a practical method of measuring biomass estimation, the potential of the land can be known quickly so that efforts to save the environment are immediately resolved. Estimation of tree biomass can be done using the analytical method of a combination of a High Order Kernel estimator with dendrometric analysis. The dendrometric analysis method is the concept of measuring tree biomass using tree sample measurements on the trunk, branches, and canopy [6]. This method is expected to provide more accurate and practical tree biomass estimation results and not leave damage to trees and forests. The conservation success model in critical land that was built is to establish a relationship between fruit tree productivity and growth limiting factors in the environmental and a biotic and socio-economic factors and culture in the research area so that it is hoped that a conservation success model will be formed as a recommendation for stakeholders to make policies for the program conservation in the future.
Measurement of tree biomass, especially to measure tree biomass components such as leaves, branches, and roots so that it does not take a long time and reduces costs. Empirical relationships can be used to estimate the total biomass of biometric variables such as diameter at breast height or tree height [7], where the empirical relationship between the components of tree biomass forms an allometric equation. Because the measurement of tree biomass in the field takes a long time, especially to measure tree biomass components such as leaves, branches, and roots, and is not small, empirical relationships can be used to estimate total biometric biomass. Variables such as diameter at breast height or tree height [8]. Therefore, it is natural that the estimation of the tree and forest biomass has become a topic of long-term research such as that of Kunze in 1873 and Burger in 1929 [9]. Krisnawati et al. has compiled 807 biomass allometric models and tree volume allometric models in several types of forest ecosystems, of which 437 allometric models are used for estimating tree biomass components and 370 allometric models for estimating several types of tree volume [10]. Almost all types of major forest ecosystems in Indonesia are available with allometric models of tree biomass and/or volume, although they are not evenly distributed across major islands in Indonesia. Thus, research on the formulation of an allometric equation model for estimating the biomass of a tree species in an ecosystem type is still needed to enrich the existing data throughout the archipelago.
Almost all estimates of biomass studies focused on the application of linear and nonlinear regression models [11][12][13]. However, the mapping between ground surface parameters and SAR imagery is always very complex because of the strong nonlinearity. Regression models based on real data measurements cannot provide a clear enough relationship. The traditional allometric method in the form of the Schumacher-Hall equation provides less accurate estimates of biomass, where the logarithmic transformation has a weakness that cannot increase the validity of the estimation [14]. Therefore, an estimation method is needed that is able to provide more accurate results with large amounts of data, besides that the estimation technique must be flexible and does not require regression assumptions. The kernel estimator is a regression approximation technique that does not require an assumption of normality. Zulfikar states that the High Order Kernel Estimator has a smaller MSE value and is an indicator for choosing the best estimator [15].
Growth factors are simpler than constructing and implementing, but allometric models are preferred because of the increased flexibility to describe tree architectural variations and biomass compartmentalization [16]. An important aspect in the quantification of individual tree biomass is the large natural variability in the data, especially for species native to tropical and subtropical regions. A single mathematical formulation may not be able to reproduce such a great natural variation. These factors affect the quality of the fit model and can give incorrect estimates. Another feature of the allometric model is that when using the regression technique several assumptions must be reached. These assumptions are as follows: additive and linearity, residual independence, homoscedasticity, and residual normality [17]. Related to this, this study aims to estimate fruit tree biomass on critical land using a high-order kernel estimator based on stem diameter to the amount of plant biomass to obtain a regression curve estimator obtained by estimating these parameters [18].

Study Area
This research was conducted in Jombang Regency, East Java Province, from Mei -July 2021. The study location was determined by purposive sampling, which is one of the techniques using certain considerations [19]. Based on that, the researchers determined the location by considering the conditions of vegetation density and geographical site, by making indirect visual observations using the Google Earth application, while also conducting surveys and direct visualizations. The Research Map Design was generated with the R version 3.6.1 application program, as shown in Figure 1.

Data source and research variable
The data used in this study is primary data in Jombang Regency. The regression model that was built was to obtain the form of the relationship between trunk diameter (X) and tree biomass (Y). Plant parameters measured were stem diameter at a height of 1.3 meters above ground level and stem diameter in centimetres. The fruit trees studied were longan (Dimocarpus longan L) and mango (Mangifera indica L), each of which took 250 data samples. Measurement of tree biomass using the branching tree allometric equation formula [20]: The fruit trees whose biomass was measured were longan and mango with wood densities of 0.91 and 0.58, respectively [21].

Analysis method
The steps taken in this study were to perform the estimation stage, then analyze the kernel regression model using the application program of R 3.61 version. Biomass estimation using nonparametric regression model based on local polynomial kernel estimator with estimation steps as follows: a. observational data are obtained   , ii yx that meet the nonparametric regression; b. plotting data in pairs: c. determines the type and weighted function of the Gaussian Kernel; d. determine the matrix A (h) of size N x N; e. chooses the order of the polynomial p and minimizes the optimal bandwidth value; (3) f. simultaneously models the local polynomial order p and the optimal bandwidth value from step (4); g. calculates the average value of the square of the error; (4) h. gets a biomass estimation model with a local polynomial kernel estimator.

Research Results
To apply the Gaussian kernel estimator, research data from the results of measuring the diameter of the longan and mango tree trunks are used, each with 250 sample data. It will be seen the relationship between trunk diameter (x) and the amount of tree biomass (y) based on equation (2) with m regression curve. The plot between x and y is given in Figure 2 With the Gaussian kernel. The second order Gaussian kernel form is obtained by substituting the kernel function: Into equation (5). First, the optimal bandwidth selection for each kernel is given by using the GCV method. The GCV function is given by: With A (h) obtained from the equation:

Kernel regression function
Kernel order 2 regression used for the estimation of longan biomass was built with the approach of minimizing the GCV (h) function to obtain the optimum bandwidth value. For some bandwidths, the GCV (h) values are listed in Table 1 Table 1 shows that the optimum bandwidth value is 1.3 with a minimum GCV value of 122.9177 in the estimated longan tree biomass. The same thing is also shown in Table 2, where the optimum bandwidth value is 2.6 with a minimum GCV value of 3523,248 in the estimated mango tree biomass as shown in Table 2. The optimum h in kernel regression is used to estimate the regression curve obtained by estimating these parameters. The GCV (h) function given in Figure 2 shows that the GCV (h) function forms a curve at the minimum value to obtain the optimum bandwidth value. This curve image emphasizes that minimizing the score of the GCV (h) function will get the optimum bandwidth (h) value, both shown in the estimated biomass of longan and mango.   Figure 4 shows that the formation of the kernel regression curve for the estimation of longan tree biomass shows that at h = 0.1 it forms a rough curve, while at h = 1.5 the regression curve begins to look smooth. In order to obtain a smooth curve with the optimum h value, it is necessary to minimize the GCV value as shown in Figure 5, where the minimum GCV value in this kernel estimate is obtained with the optimum h value = 1.3. Furthermore, the kernel regression curve with the optimum h is obtained as shown in Figure 6 and becomes the ideal kernel regression function for estimating the amount of longan tree biomass. The same thing is also shown in Figure 7, which is the formation of a smooth kernel regression curve at the optimum bandwidth of 2.6.

Validation Test
The minimum GCV (h) function for determining the optimum bandwidth will get a smooth kernel regression function, which will then form a relationship between MSE and bandwidth. In figure 3 it can be seen that the greater the bandwidth score, the smoother the curve where the limit of the h value cannot be determined to what extent the kernel regression curve is optimal. As a form of validation of the results of using high-order kernel regression, MSE scores are needed and compare them with bandwidth and GCV values. The relationship between MSE and bandwidth to see the validation of the kernel regression used is then carried out by a partial correlation test using the Pearson method where GCV is the control variable as shown in Tables 4 and 5.  Table 4. Partial correlation between MSE and Bandwidth with GCV as a control in the estimation of longan biomass using the Pearson method. The significance of the partial correlation at the level of P-value < (α = 0.05)

Bandwidth
The partial correlation between bandwidth and MSE at a score of -0.9598964 is a strong negative correlation. This shows that the parallel increase in the bandwidth value will decrease the MSE score when GCV is controlled. The amount of p-value in this partial correlation is 0.04010363 which is statistically significant at = 0.05. The same thing is shown from the results of the partial correlation test between bandwidth and MSE at a score of -0.9642895 which is a strong negative correlation. A parallel increase in the score in bandwidth value will decrease the MSE score when GCV is controlled with a p-value of 0.03571046 which is statistically significant at = 0.05. The level of difference in MSE, GCV, and bandwidth values can be seen in Figure 5. The significance of the partial correlation at the level of P-value < (α = 0.05) In Figure 10 and 11, it can be seen that the bar graph formed between MSE, GCV, and bandwidth forms the same pattern both in the estimation of longan and mango biomass. Relationship between MSE, bandwidth and GCV, where MSE and bandwidth showed a strong negative correlation (r = -0.82) for the estimated longan biomass and (r = -0.94) for mango. While the minimum GCV value to show the optimum bandwidth value. The MSE score pattern shows a decrease as the bandwidth value increases, while the GCV score shows a decreasing pattern and then increases with increasing bandwidth scores and decreasing MSE scores. Determining the optimum bandwidth at the minimum GCV value finally determines the MSE value as a valid score for the kernel regression model that formed.

Discussion
The main obstacle in estimating tree biomass is the use of an estimator that is not careful in choosing the method of analysis, where estimation data is often found using the classical regression approach. Moreover, many ecologists conducting studies of almost all biomass estimates focused on applying linear and nonlinear regression models [11][12][13]. It is also added that the results of previous studies show that biomass estimation has parameters that deal with complex environments where the mapping between soil surface parameters and SAR images is always very complex because of the strong nonlinearity. Regression models based on real data measurements cannot provide a clear enough relationship. The traditional allometric method in the form of the Schumacher-Hall equation provides less accurate estimates of biomass, where the logarithmic transformation has a weakness that cannot increase the validity of the estimation [14]. Consequently, the use of nonparametric models, which have several advantages for example no need of normality, should be used within the forestry and environmental purposes of obtaining robust and understandable models [23]. Higher-order kernels have been suggested for use in nonparametric curve estimation, it is seen that they work well when the curvature of the target curve is roughly constant, and work poorly when there are abrupt changes in curvature on neighborhoods that are the size of the bandwidth [24].
The biomass estimation method using a high-order kernel estimator is very helpful in overcoming these obstacles because the variable relationship model formed is flexible without the need to predict the form of the relationship between the two variables. The results of this study indicate that the kernel estimator is able to produce tree biomass estimates for both longan and mango by estimating the regression form by minimizing the GCV score and succeeding in obtaining the optimum bandwidth with 1.3 for longan biomass estimation and 2.6 for mango. The use of optimum h in kernel regression is used to estimate the regression curve obtained by estimating these parameters [18]. The bandwidth selection approach has been found that the GCV method is asymptotically optimal for the mean value in the case of data independence [22]. Hall et al. (1995) added that subsequent studies of asymptotic properties at optimal bandwidth under different levels of dependencies [25]. The direct plug-in approach, where unknown functional that appear in expressions for the asymptotically optimal bandwidths are replaced by kernel estimates, is used [26,27].
The validation of the regression method built with this approach is used by measuring the MSE, which can show a low MSE score, where the use of the Kernel Estimator estimator has a smaller MSE value and is an indicator for choosing the best estimator [17]. The relationship between Bandwidth and MSE partially with controlled GCV shows that a strong negative correlation is obtained, where the greater the bandwidth, the lower the MSE score. The role of bandwidth as a curve smoothing with an increasing score will be controlled by decreasing the MSE value, thus the role of GCV will show the limit of its optimum bandwidth value. In this study, it has been shown that the relationship between bandwidth and MSE has a strong and significant negative correlation at the (α) 0.05 level, with an rvalue of -0.960 in the estimated longan biomass and an r of -0.964 for mangoes. Kim  that the use of low-scoring MSE for regression model validation is more recommended than the standard method. Another advantage of using a high-order kernel estimator is that the method steps are more concise because there is no need to use the classical requirements as in the use of parametric regression. In this nonparametric regression estimation, the regression formed is more indicated in the formation of a regression curve from a rough shape to a smooth limit that is controlled by GCV.

Conclusion
The use of a high-order kernel estimator is able to overcome the difficulties in estimating tree biomass for both longan and mango. The results of the biomass estimation show that the resulting regression curve forms a smooth curve at the optimal bandwidth with a score of 1.3 for longan and 2.6 for mango from the results of minimizing the GCV(h) function. The best estimator validation used the selection of the MSE score and its relationship with Bandwidth on the controlled GCV value. The results show that there is a strong negative correlation where the greater the bandwidth value, the smaller the MSE.