Robot-assisted rehabilitation therapy has been proven to effectively improve upper-limb motor function in stroke patients. However, most current rehabilitation robotic controllers will provide too much assistance force and focus only on the patient’s position tracking performance while ignoring the patient’s interactive force situation, resulting in the inability to accurately assess the patient’s true motor intention and difficulty stimulating the patient’s initiative, thus negatively affecting the patient’s rehabilitation outcome. Therefore, this paper proposes a fuzzy adaptive passive (FAP) control strategy based on subjects’ task performance and impulse. To ensure the safety of subjects, a passive controller based on the potential field is designed to guide and assist patients in their movements, and the stability of the controller is demonstrated in a passive formalism. Then, using the subject’s task performance and impulse as evaluation indicators, fuzzy logic rules were designed and used as an evaluation algorithm to quantitively assess the subject’s motor ability and to adaptively modify the stiffness coefficient of the potential field and thus change the magnitude of the assistance force to stimulate the subject’s initiative. Through experiments, this control strategy has been shown to not only improve the subject’s initiative during the training process and ensure their safety during training but also enhance the subject’s motor learning ability. With the aging of the population and changes in lifestyle and dietary habits, the incidence of stroke is increasing annually and affecting younger individuals. Stroke can lead to brain damage, which causes a significant number of people to experience motor impairment, which is the primary cause of long-term disability . In partIn recent decades, a plethora of rehabilitation techniques have been developed to facilitate the recovery of motor functions, thereby enhancing patients’ quality of life ,5,6,7. TRehabilitation robots are typically categorized into two main types based on their connection to the user: end-effector type robots and exoskeleton type robots. In the end-effector type ,13,14, tOne of the most well-known robotic systems in upper limb rehabilitation research is the MIT-MANUS, which was developed by Krebs et al. . This roHowever, conventional controllers have fixed parameters that cannot be adapted to changes in the environment or changed according to the patient’s status. Alireza et al.  proposedFor upper limb rehabilitation, different rehabilitation strategies are required because the severity of injury varies from patient to patient. Passive training strategies are mainly used for patients in the acute phase of stroke when the patient’s arm is completely flaccid and unable to perform movements. However, passive training strategies may not be effective in subjects who have regained partial upper limb motor function . For thiTherefore, for patients in the intermediate to advanced stages of rehabilitation who have regained partial upper limb motor function, it is crucial that the robotic assistance provided is limited to only when the patient is unable to complete the task on their own. Completely restricting patients from following a predetermined trajectory to complete their rehabilitation provides no benefits. Thus, a controller that enables the patient to optimize the rehabilitation effect is needed. Such a controller, referred to as an assist-as-needed (AAN) controller or assistive/corrective controller, can offer the necessary assistance or correction to complete the rehabilitation training task in accordance with the patient’s functional ability .Currently, AAN controllers have been applied to upper ,29,30 anMartinez et al.  proposedIn this paper, we propose and implement a fuzzy adaptive passive control strategy for an end-effector bilateral upper limb rehabilitation robot (EBULRR) based on task performance and impulse evaluation. The controller adjusts the assisted force according to the patient’s task performance and exertion to achieve effective training of the patient’s arm. Our controller offers minimal intervention during the rehabilitation process to maximize patient initiative while ensuring safety and compliance in robot-assisted rehabilitation. Compared to previous studies, the main contributions of this research can be summarized as follows: (1) This study proposes a fuzzy adaptive passive controller based on a potential field that is designed according to the desired motion trajectory, which can provide a normal force pointing towards the desired trajectory. (2) The controller uses the subject’s task performance and impulses as evaluation indices of the subject’s motor ability. (3) The controller adaptively adjusts the stiffness coefficient of the potential field using fuzzy logic, thereby adaptively changing the magnitude of the assisting force.The design of the EBULRR platform is shown in The design of the control system must rely on hardware components to be realized, mainly the upper computer, lower computer, motors, force sensors, data acquisition cards, etc. The kinematics of the robot is intended to describe the mapping relationship between the joint space of the robot and the cartesian space at the end of the robot. To facilitate the establishment of the kinematics of the robot, the origin of the base coordinate system is set at joint 1. In this paper, the standard D-H parameter method is used for kinematic analysis, and the coordinate system and D-H parameters of each linkage of the robot arm are shown in The dynamics model of the robot arm can be obtained by the Lagrange–Euler equation, and the dynamics equation of the robot can be described as:The joint space control law can be expressed as:Thus, the robot dynamics equation can be rewritten as:To achieve assisted rehabilitation training of the upper extremity, this paper constructs a potential field on the desired trajectory. During the training process, a normal force pointing to the desired trajectory is provided by the deviation between the patient’s hand and the desired trajectory, and the larger the deviation of the trajectory, the larger the assisted force that needs to be applied to the potential field. The potential energy of the desired trajectory is globally equal and minimal, i.e., the potential field gradient is zero at the desired trajectory. Therefore, the potential energy field can be designed as follows:N points are uniformly sampled from the designed trajectory:ith point in the Cartesian space. The energy element at point The energy element N sampling points using the Gaussian kernel function:Calculate the weight of the potential energy at point Define the normalized weights The total potential energy of point In the potential field, the potential energy is the same at each sampling point. The gradient of the potential field:Using the circle as the desired trajectory, the gradient of the designed potential energy field on the sampled data set The designed potential field is shown in The proposed FAP controller in this paper utilizes fuzzy logic to adaptively modify the stiffness of the potential field, thereby changing the magnitude of the assisting force. Fuzzy logic employs the subject’s task performance and impulse as inputs, with the calculation formula as follows:The design of the stiffness-adaptive law Based on the above principles, fuzzy logic rules were designed and used as evaluation algorithms with evaluation indicators (1) Defining the inputs and outputs: The average score ((2) Perform fuzzification: Perform fuzzification to obtain the input and output membership functions. The inputs and outputs are fuzzified by the membership functions shown in (3) Inference process: This process generates the membership degrees of the output linguistic variables based on the fuzzified inputs. The key to the inference process is the establishment of fuzzy rules. In the fuzzy inference stage, the “If-Then” type of language is used based on fuzzy rules. (4) Defuzzification is the conversion of the inferred fuzzy values into explicit control signals as input values to the system. The FAP controller based on the above design can adaptively adjust the potential field stiffness coefficient The simplified control block diagram of FAP proposed in this paper is shown in The FAP control strategy proposed in this paper is stable because it is modeled by potential functions and dissipative fields . The staThe robot dynamics equation can be defined as follows:The torque The control term We will prove the stability of the controller using the passivity formalism , giving Theorem 1. A system with input effort and output flow is passive if it satisfies:A system that satisfies Equation (20) and has a lower bound on Proof. Set the effort to Differentiating Equation (21) yields:Bringing Equations (18) and (19) into Equation (23) yields:Then, bringing Equation (24) into Equation (22) yields:Since Finally, bringing Equations (12) and (13) into Equation (26) yields:Since In the realm of rehabilitation robotics, ensuring the safety of the robot is of utmost importance since a significant proportion of robot users are individuals suffering from physical disabilities and functional impairments. It is essential to prevent any potential harm or secondary injuries that may occur during the use of the robot. Conventional impedance controllers allow the robot arm to track changing target positions within a certain range of tracking errors. However, external factors, such as sudden spasticity in the patient, can cause the robotic arm to abruptly halt during rehabilitation training. This occurrence may result in the impedance controller generating excessive force that could lead to secondary injuries.For patients in the middle to late stages of rehabilitation who have regained some upper extremity motor abilities, it is critical that the robot only assists the patient if he or she is unable to complete the task on a predetermined trajectory, rather than completely preventing the patient from completing the task. Depending on the patient’s functional ability, it is better to provide only the necessary assistance or correction to allow the patient to be more active in completing the rehabilitation training tasks. Since the FAP controller does not provide assistance forces in the tangential direction of the target trajectory, this means that the patient can be motivated to exert more effort during rehabilitation training.To investigate the effect of the FAP controller on the subject’s task trajectory and initiative, we will design two modes, passive mode and active mode, and the FAP controller for comparison experiments. The control strategy of the passive mode consists of robot inverse dynamics feedforward plus a PD controller to track the target trajectory, in which the robot drives the subject completely to complete the task. The active mode control strategy provides only gravity compensation and friction compensation for the robot, and the subject completes the task by himself in this mode.During the experiment, the subject was holding an ASM end-effector and needed to complete a two-dimensional planar circle drawing task based on the visual feedback provided by the display, as shown in The experiments were conducted by eight healthy volunteers . The experiment was conducted in the Rehabilitation Robotics Laboratory of the Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, and approved by the Ethics Committee of Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences. Before starting the trial, each subject was fully informed about the trial procedure and signed an informed consent form.To investigate the effect of the FAP controller on the subjects’ initiative, we analyzed the subjects’ interactive forces during the circle drawing process, and To demonstrate that the proposed FAP controller is effective for motor learning, a crossover experiment is designed to compare which mode of KA and KF is better for motor learning, where the potential field stiffness coefficient K of the KA mode is adaptively updated by fuzzy rules in the range of , i.e., the higher the subject’s score, the smaller the impulse, the smaller the value of K; the lower the subject’s score, the larger the impulse, the larger the value of K. Additionally, the potential field stiffness coefficient K of the KF mode is fixed and takes the value of 12. Before the experiment starts, the subjects are instructed on how to complete the circle drawing task. The experiment was conducted by eight healthy volunteers , and the subjects were randomly and equally divided into two groups (A and B) before the experiment.The subjects were required to conduct the experiment for two days, and the first day of the experiment was divided into two stages: (1) the training stage, in which the potential field provided an assistance force to help the subjects complete the training, where group A performed a 20-turn circle drawing task in KA mode and group B performed a 20-turn circle drawing task in KF mode, and (2) the evaluation stage, in which the subjects rested for one minute after completing the training stage, during which the robot did not provide assistance force, and both groups A and B were required to perform 10 revolutions of the circle drawing task.After a three-day washout period, subjects underwent a second experiment: (1) At the training stage, in contrast to the first day, group B performed a 20-turn circle drawing task in KF mode, and group A performed a 20-turn circle drawing task in KF mode. (2) At the evaluation stage, the same as the first day, both groups A and B were required to perform 10 revolutions of the circle drawing task. This was to eliminate the effect of the order on the experiment. We abbreviated the training stage with the circle drawing task in KA mode as KATS, the training stage with the circle drawing task in KF mode as KFTS, the evaluation stage with the circle drawing task in KA mode as KAES, and the evaluation stage with the circle drawing task in KF mode as KFES.t-test was conducted on the results of two experimental groups, KA and KF. The group mean scores of eight subjects are presented in A paired-sample With the development of science and technology, the application of robotics has widely increased in various industries, and there are more and more examples of robots moving out of the factory environment and joining the real-life human environment, so human–robot interaction technology  has beenIn addition, the majority of patients tend to slack off and take little initiative with the help of rehabilitation robots, which leads to poor rehabilitation results. Therefore, there is a need to develop an adaptive AAN controller that assists only when the subject needs help and stimulates the subject’s subjectivity. Most current on-demand assistive controllers can only vary the amount of assistive force based on task performance, which can ignore the patient’s motor intentions. Additionally, some controllers can provide too much assistance, thus reducing the patient’s initiative. The FAP controller proposed in this paper can adaptively adjust the size of the assistance force through fuzzy rules based on the patient’s task performance and impulse, providing minimal intervention during the rehabilitation process and fully stimulating the patient’s initiative. As shown in Motor learning is crucial for humans to interact with their surroundings and independently perform activities of daily living, and it has long been recognized that the fields of motor learning and neurological rehabilitation are interconnected and that a full understanding of human motor learning can help patients with neurological injuries achieve better rehabilitation . In thisIn addition, after discussion with clinical therapists, it is found that Brunnstrom stages are very commonly adopted clinically to classify stroke patients’ rehabilitation progress due to its simplicity and validity. The method divides the motor recovery process into six stages, from the period of complete flaccidity, which begins immediately after stroke, to the disappearance of spasticity when the patient is able to perform near-normal to normal movement . Each stIn response to the issue that most rehabilitation robot control strategies can lead to low patient initiative and thus affect rehabilitation effectiveness, this paper proposes a fuzzy adaptive passive control strategy based on task performance and impulse. To ensure the safety of the subjects, a passive controller based on the potential field is designed to guide and assist patients in their movements, and the stability of the controller is demonstrated in a passive formalism. Then, using the subject’s task performance and impulse as evaluation indicators, fuzzy logic rules were designed and used as an evaluation algorithm to quantitively assess the subject’s motor ability and to adaptively modify the stiffness coefficient of the potential field and thus change the magnitude of the assistance force to stimulate the subject’s initiative. Through experiments, this control strategy has been shown to not only improve the subject’s initiative during the rehabilitation process and ensure their safety during training but also enhance the subject’s motor learning ability. However, this paper has certain limitations in that only healthy subjects were recruited for experimental testing to validate the effectiveness of the proposed controller. In the next step, this controller is planned to be used for stroke patients to investigate its impact on patient rehabilitation effectiveness.


Medicago sativa L.) as an important legume plant can quickly produce adventitious roots (ARs) to form new plants by cutting. But the regulatory mechanism of AR formation in alfalfa remains unclear.Alfalfa  were selected to explore their roles in AR formation. The numbers of differentially expressed genes (DEGs) of Y-vs-C  and P-vs-Y groups  were larger than that of S-vs-P group (150), indicating highly active in the early AR formation during the complicated development process. Pathways related to cell wall and sugar metabolism, root development, cell cycle, stem cell, and protease were identified, indicating that these genes were involved in AR production. A large number of hormone-related genes associated with the formation of alfalfa ARs have also been identified, in which auxin, ABA and brassinosteroids are thought to play key regulatory roles. Comparing with TF database, it was found that AP2/ERF-ERF, bHLH, WRKY, NAC, MYB, C2H2, bZIP, GRAS played a major regulatory role in the production of ARs of alfalfa. Furthermore, three identified genes showed significant promotion effect on AR formation.To better understand the rooting process of alfalfa cuttings, plant materials from four stages, including initial separation stage (C stage), induction stage (Y stage), AR primordium formation stage (P stage) and AR maturation stage (S stage) were collected and used for RNA-Seq. Meanwhile, three candidate genes (Stimulation of stem basal cells in alfalfa by cutting induced AR production through the regulation of various hormones, transcription factors and kinases. This study provides new insights of AR formation in alfalfa and enriches gene resources in crop planting and cultivation.The online version contains supplementary material available at 10.1186/s12870-023-04168-0. Adventitious root (AR) formation from cutting is a method of asexual propagation of plants. AR production during cutting process involves four phases: Stage 1, the stem cutting isolated from the mature plant; Stage 2 , the initial cell reprogramming; Stage 3, primordium formation at the base of the stem cutting; Stage 4, initiation of ARs and white bulges formation [Arabidopsis thaliana and Oryza sativa, the understanding of the auxin rooting mechanism regulating plant AR production, including auxin biosynthesis, metabolism, transport, and signaling pathways, was increased [Auxin is an endogenous hormone containing an unsaturated aromatic ring and an acetic acid side chain, which is synthesized mainly in the young tissues of plants, such as buds and young leaves , and widncreased .In recent years, research on the regulation mechanism of AR production has made great progress at the molecular level. The use of exogenous auxin has a significant effect on the induction of ARs. Compared with indole-3-acetic acid (IAA), indole-3-butyric acid (IBA) has a better induction effect and is more widely used , 12. ExoMedicago sativa L.) is a perennial leguminous plant, which is known as "the king of pasture" [Acyrthosiphon pisum, and inoculation of Rhizophagus intraradices increased the expression of genes related to drug resistance of alfalfa, such as "WRKY transcription factor" and "Kunitz trypsin inhibitor." However, research on AR development in alfalfa is very limited. The developmental mechanism of alfalfa ARs involving the regulation by phytohormones remains unknown. RNA-seq has become a revolutionary tool for better understanding the expression and regulatory mechanisms of differential genes. The method has no strict requirements on the reference genome sequence and is applicable to model species and non-model species [Panax ginseng [Populus [Camellia sinensis [Alfalfa , induction stage (Y stage), AR primordium formation stage (P stage) and AR maturation stage (S stage) Fig. . The secAfter filtering the raw data sequenced by the Illumina platform, a total of 80.42 Gb Clean Data was obtained, including 96.72% Q20 bases with a 41.84% GC content , ribosome , cell wall , microtubule  and kinesin complex . GO items significantly enriched in down-regulated DEGs mainly include integral component of membrane , chloroplast , NAD(P)H dehydrogenase complex (plastoquinone) , photosystem I  and photosystem II .There were 107 and 163 GO items in biological process significantly enriched in up-regulated and down-regulated DEGs, respectively. Among them, GO items significantly enriched in up-regulated DEGs mainly include carbohydrate metabolic process , response to oxidative stress , abscisic acid-activated signaling pathway , gibberellin catabolic process GO:0,045,487), auxin-activated signaling pathway , microtubule-based movement , spermine and spermidine biosynthetic process  and inositol catabolic process . GO items significantly enriched in down-regulated DEGs mainly include photosynthesis , cell redox homeostasis , oxylipin biosynthetic process , chlorophyll biosynthetic process , phloem development , abscisic acid homeostasis , regulation of abscisic acid-activated signaling pathway , and sphingolipid biosynthetic process  , porphyrin and chlorophyll metabolism (ko00860), photosynthesis-antenna proteins (ko00196), MAPK signaling pathway-plant (ko04016), carbon metabolism (ko01200), glycolysis/Gluconeogenesis (ko00010), ABC transporters (ko02010), glycosphingolipid biosynthesis-ganglio series (ko00604), peroxisome (ko04146), sphingolipid metabolism (ko00600) and so on , plasma membrane , plasmodesma , extracellular region  and plant-type cell wall . GO items significantly enriched in down-regulated DEGs mainly include integral component of plasma membrane , integral component of mitochondrial inner membrane , chloroplast thylakoid membrane , mitochondrial proton-transporting ATP synthase complex, coupling factor F(o)  and small ribosomal subunit .There were 101 and 94 GO items in biological process significantly enriched in up-regulated and down-regulated DEGs, respectively. Among them, GO items significantly enriched in up-regulated DEGs mainly include cell wall biogenesis , lignin catabolic process , phloem development , regulation of meristem growth , regulation of salicylic acid biosynthetic process , glycolytic process , regulation of beta-glucan biosynthetic process  and sterol transport . GO items significantly enriched in down-regulated DEGs mainly include response to auxin , photosynthetic electron transport in photosystem II , salicylic acid metabolic process , regulation of cysteine-type endopeptidase activity , proline catabolic process , jasmonic acid metabolic process , spermine biosynthetic process  and sphingolipid metabolic process  , starch and sucrose metabolism (ko00500), MAPK signaling pathway-plant (ko04016), glycolysis / Gluconeogenesis (ko00010), pentose phosphate pathway (ko00030), nitrogen metabolism (ko00910), sphingolipid metabolism (ko00600), inositol phosphate metabolism (ko00562) and so on , sucrose synthase (SUS), beta-D-xylosidase (BXL), cellulose synthase A catalytic subunit (CESA), pectinesterase (PME), sucrose-phosphate synthase (SPS), expansin (EXP), lateral root primordium 1 (LRP1), cyclin (CYC), WUSCHEL-related homeobox (WOX), shoot meristemless (STM) and S-adenosylmethionine synthase (SAMS) genes , ABC transporters, sphingolipid metabolism, isoflavonoid and flavonoid biosynthesis, peroxisome and so on, in Y-vs-C by RNA-Seq analysis. Therefore, it is reasonably believed that these genes were also involved in alfalfa AR production. Eight genes were selected for qRT-PCR analysis, and the results showed that their expression patterns were the same as the transcriptome results Fig. a-h Tabl.Fig. 6ReThere were 350 and 297 DEGs involved in plant hormones in the Y-vs-C and P-vs-Y groups, respectively, including auxin, ABA, brassinosteroid, cytokinin, ethylene, GA, JA and SA Table S, S5, S6.Transcription factors (TFs) revealed 501 TF genes in 58 TF families in the Y-vs-C group, among which 213 were up-regulated and 288 were down-regulated Fig. a, and thSAUR), MsG0780040671.01 (VAN3) and MsG0380016110.01 (EGLC) were significantly up-regulated in Y-vs-C group , AR primordium raised from interfascicular cambium cells closest to phloem cells [Pistacia chinensis Bunge originated from callus, medullary ray, xylem, vascular cambium, secondary phloem, cortex, and the junction of medullary ray and cambium, which belonged to multi-locus occurrence mode and multi-type rooting mode [The location of AR formation depends on the species or organ or tissue. The sites where root primordium can occur include cortex, phloem, cambium, medullary rays, xylem and callus. The types of AR formation can be divided into cutaneous rooting, callus rooting and mixed rooting according to where the root primordium is produced –33. In Aem cells . AR priming mode . The preThe results showed that there were four key nodes in the alfalfa AR production. The results that the stem base tissues of four stages of alfalfa were sliced and stained showed that cells at the base of the stem have been changing in response to stimuli and environmental changes since cutting. Therefore, it was speculated that cutting was the most direct and effective stimulation method for alfalfa AR production. The rapid response of stem basal cells to stimulation leads to a dynamic process of cell rearrangement rather than a sequential process. In the induction stage, the internal cells are constantly increasing and accumulating, which is a quantitative accumulation process, although no apparent changes was observed in the stem base morphology. Compared with the two later stages, the most DEGs  were identified in the induction stage, and the cells were rearranged at this stage. Therefore, the induction period was considered as the key stage to promote the AR production of alfalfa. In the AR primordium formation stage, 6,836 DEGs were identified, while only 150 DEGs were obtained in AR maturation stage. Therefore, AR primordium formation is the critical stage of the successful generation of ARs.LAC17) was significantly down-regulated during the induction phase  hypocotyl found [WOX gene can regulate AR development by regulating hormone transduction related genes, such as ARF, and root development related genes, such as CYCD3. The study of Liu and Xu [CrWOXA gene was specifically expressed in root founding cells during AR initiation in fern Ceratopteris richardii. In Arabidopsis thaliana, the expression switch from WOX11 to WOX5 is essential for AR initiation [WOX11 were significantly up-regulated , which regulates auxin polar transport by altering the abundance of PIN at the plasma membrane, thereby positively regulating the initiation of ARs [GH3 gene that are required for AR initiation in Arabidopsis hypocotyl. Meanwhile, Lakehal, et al. [Arabidopsis Thaliana. In the induction phase of AR production, LAX, ILR and ARF were mainly down-regulated. SAUR, PIN and GH3 were mainly up-regulated. In the root primordium formation stage, LAX, PIN, TIR1, ILR and ARF were mainly up-regulated, and SAUR was mainly down-regulated. Therefore, it is concluded that the production of alfalfa ARs is achieved through two pathways: IAA transport, distribution and IAA signal response. And LAX and PIN play important regulatory roles in the formation of ARs.Phytohormones play a key role in AR formation. Numerous studies have confirmed that auxin plays a central role in AR formation . Auxin in of ARs –54. In tn of ARs . The IAAn of ARs , 57. Thin of ARs . Gutierrn of ARs  demonstr, et al.  believedPYL1) was significantly up-regulated and MsG0080048230.01 (PP2C) was significantly down-regulated , was significantly down-regulated, and MsG0080048230.01 (PP2C) were significantly up-regulated in AR primordium formation, indicating that ABA signal transduction was inhibited. ABA plays an important role in abiotic stress [PYR/PYL family protein is a receptor of ABA, which can bind to ABA to inhibit the activity of PP2Cs , 62. Thec stress . The actCYP734A1 encodes a photochrome B activator marker inhibitor that inactivates BR [CYP90A1 and CYP92A6 were significantly down-regulated, while CYP734A1 was significantly up-regulated, indicating that brassinolide biosynthesis was inhibited. In root primordium formation, CYP92A6 and CYP85A1 were up-regulated, which promoted brassinolide biosynthesis. BRs regulates the expression of downstream CYCD3 and TCH4 through signaling pathways transduction, CYCD3 mainly regulates cell division [TCH4, encodes Xyloglucan endotransglycosylase (XET) enzymes, mainly regulates cell elongation [TCH4. However, in the root primordium production, BRs promoted cell elongation by inducing the expression of TCH4, which may be one of the reasons for the expansion of the stem base. These results demonstrated that BR promoted the production of alfalfa ARs mainly by regulating cell division and elongation  play a role in promoting plant cell elongation, cell division, plant light morphogenesis, plant yield, and enhancing plant tolerance to environmental stress . Brassinvates BR , 71. In division , and TCHPopulus is controlled by the AP2/ERF family. In this study, the ERF accounted for the largest proportion in the two groups of TFs, respectively. Zhang [CaGRAS genes was induced in the process of hypocotyl AR development of pepper. CaGRAS1, CaGRAS2, CaGRAS21, CaGRAS34 and CaGRAS49 may be important regulators of AR formation. In this study, 19 and 17 GRAS were identified in the Y-vs-C and P-vs-Y groups, respectively, indicating that ERF and GRAS play important regulatory roles in the AR formation of alfalfa.In this study, 501 and 362 TFs were differentially expressed in Y group and P-vs-Y groups, respectively. TFs play a crucial role in regulating AR formation in alfalfa. ERFs can regulate ethylene response genes, whose expression responds to ethylene and a variety of extracellular stimuli, including abiotic stress, thereby affecting the formation of ARs . Trupiany. Zhang  found thMsWRKY100 could significantly improve the root length and drought resistance of transgenic Arabidopsis thaliana by reducing the malondialdehyde content and ion leakage rate, significantly increasing the activities of antioxidant enzymes and the sensitivity of Arabidopsis seedlings to abscisic acid. The relative expression of WRKY was significantly up-regulated during AR formation in control apple rootstock [WRKY were up-regulated and 14 were down-regulated, which was consistent with the results of [WRKY may alleviate the water shortage and promote the formation of alfala ARs. Therefore, the TFs among DEGs may be the key factor triggering the generation of ARs.The results of Mao, et al.  showed tootstock . In thissults of . It is dSAUR has positive effects on cell expansion, shade avoidance response, high-temperature induced growth, tip development, leaf growth, and auxin transport during plant growth and development [AtSAUR19 results in hypocotyl elongation and increased leaf area. Overexpression of AtSAUR41 significantly increased hypocotyl epidermal cells [Three DEGs were selected for transformation and rooting tests. As one of the three auxin early response genes, elopment –80. In Aal cells . In thisVAN3, encoding an adenosine diphosphate (ADP)-ribosylation factor-guanosine triphosphatase (GTPase)-activating protein (ARF–GAP), not only plays an important role in the construction of the vascular system, but also is related to auxin signaling and polar transport. The auxin-dependent induction of VAN3 may be positively regulated by VAN3 itself [3 itself . In thisMsG0380016110.01, encoding Glucan endo-1, 3-beta-glucosidase 4, was also up-regulated  and P-vs-Y groups , and the qRT-PCR results were consistent with the transcriptional sequencing results. Cluster analysis showed that there were 4,122 identical genes between the Y-vs-C and P-vs-Y groups, and 3,839 (93%) had the opposite expression trend. There were 4,873 identical genes between Y-vs-C and P-vs-C groups including 4,795 (98%) had the same trend in expression. Thus, Y stage is the key stage of alfalfa AR formation. Many genes and pathways related to AR production of alfalfa were identified by GO and KEGG enrichment analysis. Meanwhile, various hormones and TFs also play vital roles in regulating AR formation, Three DEGs were selected for transient transformation of alfalfa stem base, the result showed that they all promoted the generation and elongation of ARs in alfalfa cutting, but their regulatory mechanisms need to be further studied. In conclusion, the outcomes of this study enhances our understanding of the regulatory mechanism of AR formation of alfalfa cuttings.Medicago sativa L.) ‘Zhongmu No.1’ obtained from the Chinese Academy of Agricultural Sciences, Beijing, China were sown and cultured in a greenhouse with 16 h of light, 25 ℃ of temperature, 8 h of darkness, 23 ℃ of temperature, 14,000 lx of light intensity, 35–60% of air humidity, and watered every three days. After three months, shoots with buds from three individual alfalfa plants of the same growth state were selected for cuttings. The stems were cut into 8–10 cm segments and inserted into the nutrition soil containing peat, vermiculite and perlite (V:V:V = 1:1:1), each branch has one or two nodes and three to five leaves. Samples were taken at the base of 5 mm of stem segment during the four stages of alfalfa cutting according to the method of Brinker, Zyl, Liu, Craig, Sederoff, Clapham and Arnold [Seeds of alfalfa  with the following parameters: adjusted p-value < 0.01 and |log2 FC|> = 2 on BMKCloud platform. And the DESeq2 software was used to analyze DEGs [Total RNA was extracted from each sample and then treated with gDNA Eraser  to remove contaminated genomic DNA. RNA degradation and contamination were monitored on 1% agarose gels and RNA Purity was checked using a NanoPhotometer spectrophotometer . RNA concentration was measured using a Qubit RNA Assay Kit in a Qubit 2.0 Fluorometer , and RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system . A total amount of 3 μg RNA per sample was used to construct a sequencing library . Sequencyze DEGs .−∆∆Ct method [To verify the results of DEG analysis, 17 genes with differential expression were selected and detected by qRT-PCR according to the method of Li, et al. . The relt method . Actin g−5. The KEGG Automatic Annotation Server (KAAS1) was used to obtain the KEGG pathway annotation [The unigene sequences were aligned to databases, including the Nonredundant protein (NR), SwissProt, Pfam, Gene Ontology (GO), Cluster of Orthologous Groups (COG), Kyoto Encyclopedia of Genes and Genomes (KEGG) , Evolutinotation –95, and http://planttfdb.cbi.pku.edu.cn/index.php [−5. The Arabidopsis Hormone Database (AHD) was downloaded from http://ahd.cbi.pku.edu.cn/. To analyze the plant hormone-related DEGs, all DEGs from the Y-vs-C , P-vs-Y groups were compared with protein sequences from AHD using Blastx .A comprehensive plant TF database was downloaded from ndex.php , and allThree DEGs, MsG0780035932.01, MsG0780040671.01 and MsG0380016110.01 identified in the Y-vs-C group were used as candidates to analyze their function in the AR formation. MsG0780035932.01, with a 498 bp CDS, encodes a small auxin-up RNA (SAUR). MsG0780040671.01, with a 1443 bp CDS, encodes an adenosine diphosphate (ADP)-ribosylation factor-guanosine triphosphatase (GTPase)-activating protein (ARF-GAP). And MsG0380016110.01, with a 1362 bp CDS, encodes glucan endo-1, 3-beta-glucosidase 4 (EGLC). PCR was carried out to clone the full length of these genes (Fig. STobacco seeds were grown in the same way as those of alfalfa. MsG0780035932.01, MsG0780040671.01 and MsG0380016110.01 was linked to 3302Y vector containing YFP gene and transferred to Agrobacterium EHA105, respectively. When tobacco grew to 5–6 weeks, three genes was transformed into tobacco leaves by agrobacterium-mediated transient transformation, respectively. After two days of dark culture, subcellular localization was observed under the YFP channel of confocal microscope.Additional file 1: Table S1. Primers sequence used in the experiment. Table S2. Sequence alignment of sample sequencing data with the reference genome. Table S3. Number of DEGs annotated in RNA-Seq. Table S4. Number of DEGs involved in plant hormones.Additional file 2: Table S5. List of annotated hormone related DEGs up-regulated or down-regulated in induction stage verse initial separation stage.Additional file 3: Table S6. List of annotated hormone related DEGs up-regulated or down-regulated in AR primordium formation stage verse induction stage.Additional file 4: Figure S1. Full length of MsG0780035932.01, MsG0380016110.01 and MsG0780040671.01 genes. Figure S2. Circuit diagram of Plant hormone signal transduction, including auxin, abscisic acid, brassinosteroid, cytokinin, ethylene, gibberellin, jasmonic acid and salicylic acid.


Sensory perception is dramatically influenced by the context. Models of contextual neural surround effects in vision have mostly accounted for Primary Visual Cortex (V1) data, via nonlinear computations such as divisive normalization. However, surround effects are not well understood within a hierarchy, for neurons with more complex stimulus selectivity beyond V1. We utilized feedforward deep convolutional neural networks and developed a gradient-based technique to visualize the most suppressive and excitatory surround. We found that deep neural networks exhibited a key signature of surround effects in V1, highlighting center stimuli that visually stand out from the surround and suppressing responses when the surround stimulus is similar to the center. We found that in some neurons, especially in late layers, when the center stimulus was altered, the most suppressive surround surprisingly can follow the change. Through the visualization approach, we generalized previous understanding of surround effects to more complex stimuli, in ways that have not been revealed in visual cortices. In contrast, the suppression based on center surround similarity was not observed in an untrained network. We identified further successes and mismatches of the feedforward CNNs to the biology. Our results provide a testable hypothesis of surround effects in higher visual cortices, and the visualization approach could be adopted in future biological experimental designs. Neural responses and perception of a visual stimulus are influenced by the context, such as what spatially surrounds a given feature. Contextual surround effects have been extensively studied in the early visual cortex. But the brain processes visual inputs hierarchically, from simple features up to complex objects in higher visual areas. Contextual effects are not well understood for higher areas of cortex and for more complex stimuli. Utilizing artificial deep neural networks and a visualization technique we developed, we found that deep networks exhibited a key signature of surround effects in the early visual cortex, highlighting center stimuli that visually stand out from the surround and suppressing responses when the surround stimulus is similar to the center. We found in some neurons, especially in late layers, when the center stimulus was altered, the most suppressive surround could surprisingly follow. This is a generalization of known surround effects for more complex stimuli that has not been revealed in the visual cortex. Our findings relate to notions of efficient coding and salience perception, and emerged without incorporating specialized nonlinear computations typically used to explain contextual effects in the early cortex. Our visualization approach provides a new experimental paradigm and a testable hypothesis of surround effects for more complex stimuli in higher cortical areas; the visualization approach could be adopted in biological experimental designs. Both biological and artificial systems seek to make sense of complex structured information in the world. A key aspect of sensory input is that its interpretation at a given point depends on the context, for example, what surrounds a given feature or object. Spatial context in vision plays a role in perceptual grouping  and segmA rich set of surround effects have been documented in the Primary Visual Cortex (V1) in neurophysiology experiments and respective modeling studies –29. In tSurround effects are less well understood in cortical areas beyond V1 . MoMoreover, feature visualization techniques have become popular in neurophysiology experiments –44 and iUtilizing feedforward deep neural networks and developing a novel gradient-based visualization technique, we found that CNN neurons exhibit a key signature of surround suppression, namely that on average they are most suppressed when the surround matches the center and less suppressed when the surround differs from the center; and that this can even follow when the center orientation is altered. Suppression based on center surround similarity is known for V1 data , but hasAlthough neurons in feedforward neural networks do not have a clear separation between the center and surround region, we suggest that at a computational level they nevertheless exhibit some surround effects that are prototypical in primary visual cortex neurons. Our results can partly be attributed to the weight kernel of the CNN neurons that includes positive and negative weights, and from the spatial integration of rectified neurons in the previous layer. While this is not recurrent connections as a mechanism, this serves as including lateral inputs that could be positive or negatively weighted from neurons in the previous layer. This might be a way for feedforward CNNs to achieve some of the noted surround suppression effects observed biologically. In contrast, we did not find such effects in an untrained CNN network, suggesting that the architecture alone was not sufficient and that learning on images was important.We also found mismatches to the biology, highlighting the limitations of the feedforward architectures, and identifying the need to further incorporate nonlinear computations and circuitry into deep neural networks –54.Before studying the surround effects in CNN neurons, we first defined the center and surround region through a method inspired by neurophysiology studies  Fig 1)..Fig 1). By the conventional definition used in neuroscience, a stimulus placed outside the classical receptive field by itself does not elicit any neural responses Figs  and 2B aThere is a widespread belief that surround effects are due to recurrent and feedback connections, though feedforward mechanisms have also been noted in previous studies –63. EvenFirst, we tested one of the most well-known surround effects found in V1, namely the surround induces the largest response suppression when the grating orientations of the center and surround are the same , 17, 25.In neurophysiology studies, the most suppressive surround has the same orientation as the optimal center orientation, and the surround can be facilitative when it differs from the center , 17, 25 We found that the surround suppression in the early layers was weaker than in the late layers. Regarding the amount of surround suppression, Alexnet layer 3 and VGG16 layer 10 were closest to the V1 data. In the neurophysiology data, the strongest responses in the center tuning curve are aligned with the strongest suppression in the surround suppression tuning curve. To examine this quantitatively in the CNN, we measured the negative correlation between the two curves. Consistent with the neurophysiology observations (-0.858 in ), all laNote that the averaged tuning curves in By screening individual neurons, we found that there were a variety of interesting surround suppression behaviors that had not been documented in neurophysiology studies . This inIn this and the following analysis, we wanted to use only the few necessary neuron selection criteria to show the population results of as many neurons as possible. On average, the population showed the effects, and there were individual neurons that showed clean effects. However, there were also many neurons with noisy tuning curves that showed less/no effect, like the neuron Alexnet_L3_N97 in In both neuroscience and machine learning, there is interest in understanding what visual features neurons are sensitive to. Indeed, with recent advances in deep neural networks, there has been some focus on visualizing what input features induce the most or the least responses in CNN neurons, for instance using gradient based optimization methods . We werehttps://gin.g-node.org/xupan/CNN_surround_effects_visualization). We selected them to show the variety. In general, the most suppressive surround looked similar to the center, whereas the most facilitative surround looked dissimilar to the center. Based on the visual appearance, we found several typical patterns. We observed visual similarity along various features, such as color and spatial frequency; the most suppressive surround could have similar color or spatial frequency to the center . This was observed in both the averaged diameter tuning curves  and scatThough in the above experiments we found some striking commonalities between the CNN neurons and cortical neurons regarding surround effects, we also found failures of the CNNs. One mismatch we found is related to the geometric structure of the surround. Cortical neurons show the largest suppression when the stimulus in the surround is in the location that aligns with the orientation  Fig 9A)Fig 9A). Other mismatches we found are related to the contrast. Neurophysiology studies have shown that the grating diameter tuning curve peaks later when the contrast is low , 16 Fig. Though We studied visual contextual surround effects in CNN neurons. First, we simulated a classic visual surround effects experiment in CNN neurons and found that the most suppressive surround grating orientation matches the optimal center orientation . Second,In recent years, optimization-based neuron control techniques have been used in neuroscience experiments to find stimuli that elicit the strongest neural responses and even control the population activation pattern –44. OptiHow can a generic feedforward CNN capture a signature of surround suppression? Since surround effects are thought to arise from nonlinear computations such as divisive normalization and recurrent and feedback connections, feedforward CNNs without those connections are not supposed to capture such effects. However, CNNs can potentially capture surround effects by stacking layers. From a statistical perspective, this could relate to computational studies showing that in deeper layers of CNNs, the activations of neighboring neurons are less statistically dependent, thereby achieving some of the statistical properties that have been attributed to divisive normalization . SurrounIs the surround suppression we observe in standard feedforward architectures subtractive or divisive? Some studies have shown that surround effects in neurophysiology are better fit with a divisive than subtractive model , 70, 71.Studies in V2 have shown that there are additional factors that influence surround suppression, namely whether the stimulus itself is naturalistic or noise. In particular, there is less surround suppression for extended natural textures than for spectrally matched noise  Fig 8),,Fig 8), In addition to the success cases, we also found important mismatches between CNN neurons and cortical neurons. First, we point out that in the generic CNN model, there is not a clear separation of center and surround regions as in the visual cortex see . The surThe connection between surround effects and neural coding has been studied in theoretical works , 22, 24.Surround suppression has been considered to have a number of beneficial roles in neural computation, for example, reducing coding redundancy and highlighting salient features , 28, 72.Our findings overall demonstrate that standard feedforward architectures exhibit surround suppression based on the similarity between center and surround stimuli, suggesting that such architectures can capture and generalize an important characteristic of surround effects in cortical neurons. The visualization approach could be adopted to study surround effects in high visual areas. The mismatches we found may inspire future studies of contextual effects in deep neural networks with more sophisticated circuitry, including the role of divisive normalization –53, 76, We trained an Alexnet-style and a VGG16-style network on the Imagenet dataset mostly following the original papers respectively , 56. ModSince the feature maps of all the convolutional layers in our study have an even number of neurons in height and width, the center neurons we selected are actually a half unit away from the image center. And these half unit displacements in the feature maps correspond to different pixel numbers when tracing back to the input image. In this study, we always put stimuli at the true center of each neuron. That means for each layer, the displacement of the stimuli from the image center was adjusted based on the shape of the feature maps. The displacement in pixels is calculated by the formula:ri, the kernel size ki, and the stride size si. In order to calculate r recursively, we need another attribute, jump size ji in previous layers which is the distance in pixels between two adjacent units. For the input layer, j0 = 1, and r0 = 1. j can be recursively computed, ji = ji−1 * si−1. Then r can also be recursively computed, ri = ri−1 + (ki−1 − 1) * ji−1. The above two recurrence equations can be solved, For each CNN neuron, we can derive a theoretical receptive field by tracing the feedforward computations . Note thA key component of our simulations was to define the diameter that separates the center and surround; in other words, an analogy to the classical receptive field in neurophysiology settings. We tried to mimic the neurophysiology experiments that define these diameters as much as possible  Fig 1A)Fig 1A). We did in-silico simulations on CNN neurons following the experimental neurophysiology paradigms as much as possible. For each neural feature map in each layer, we only selected the center neuron (see the section “CNN models”) to do the simulation. The method we used to get optimal grating parameters and define the center region are described in the previous sections.We did not include all the neurons in the analysis due to either an unsuitable center and surround ratio or lack of response variation in the orientation tuning curve. In detail, in Figs The center and surround border has been described in the previous sections. In more detail, for our simulations, we chose the center diameter according to the grating summation field; the inner diameter of the surround as the grating summation field plus 4 pixels; and the outer diameter of the surround according to the theoretical receptive field size Figs , 4 and 9Some neurophysiology studies use the diameter tuning curve for the annular stimuli to determine the inner diameter of the surround stimuli , 16. We One can visualize the features in CNNs by finding the optimal inputs that lead to the maximum activations –47. ThisThe innovation of our visualization method is to visualize the surround effects in two steps: first find the most facilitative center image; then find the most suppressive or facilitative surround image.In detail, to find the most facilitative center image, the center image parameters were used to construct an image; the surround region (see the previous section for the definition) of the image was replaced by gray; then the resulting image was passed to the CNN. We computed the gradient of a center neuron’s response with respect to the center image parameters. The gradient was used to optimize the center image parameters by the Adam optimizer. This optimization step was repeated for 500 iterations for each neuron.To find the most suppressive or facilitative surround image, the surround image parameters were used to construct an image; then the center region of this image was replaced by the most facilitative center image described in the previous paragraph; then the optimization procedure for the surround image parameters was the same as the procedure for the center.We used the Adam optimizer for all the layers in both networks. We found that the learning rate that can generate visualizations with vivid color and clear patterns varies across layers. In Alexnet, the first three layers are 0.001; the latter two layers are 0.005. In VGG16, layers 1 to 5 are 0.0005; layers 6 to 9 are 0.001; layers 10 to 11 are 0.0025, layers 12 to 13 are 0.005.In the naturalistic and spectrally matched noise simulation, we synthesized 225 naturalistic texture images from 15 original texture images and their corresponding spectrally matched noise images , 85. We The naturalistic and spectrally matched noise images used in this study are generated according to . We usedS1 TableSilent neurons and neurons with too large or small center were excluded from the orientation suppression simulations. But the visualization experiments only excluded neurons with too large or small center and included silent neurons. Silent neurons were defined whose center orientation tuning curve has less than 0.001 variances. Center that is larger than 70% of the theoretical receptive field size was considered too large; smaller than 30% of the theoretical receptive field size was considered too small.(PDF)Click here for additional data file.S1 FigSee Methods for more details.(TIF)Click here for additional data file.S2 FigA. Stimuli used in the experiments. The surround is either at the optimal orientation (preferred surround) or orthogonal to the optimal orientation . The center contrast is either at the regular pixel range (high contrast) or 17% of the regular range (low contrast). B. Scatter plots of the responses for preferred surround versus orthogonal surround. Plots with black frames used high contrast center; plots with gray frames used low contrast center. P values inside the plots were calculated from the paired sample t-test . Points below the diagonal lines indicate more suppression when the surround is at the optimal orientation than when it is at the orthogonal orientation. This effect is more pronounced when the center contrast is high. P values outside the plots were calculated from the paired sample t-test of the difference between the preferred surround and orthogonal surround in high contrast and low contrast conditions.(TIF)Click here for additional data file.S3 FigGrating summation field is defined by the smallest diameter that elicits at least 95% of the maximum response. Suppression Index is a metric of surround suppression with grating stimuli which is defined as 1—(suppressed response / peak response). Circular variance is a metric that characterizes orientation selectivity and is defined in . Small v(TIF)Click here for additional data file.S4 FigIn the main experiments, we focused our analysis on the neurons with sufficiently large center and surround sizes. In some layers, especially middle layers, neurons with negative tuning curve correlation are concentrated at a center-surround ratio of 0.2 to 0.5.(TIF)Click here for additional data file.S5 FigWe found the distributions are multi-modal. Many neurons with negative tuning curve correlation are concentrated below 30-pixel spatial period. There are some neurons that have large spatial periods, e.g. 50 pixels. Those neurons are likely to be tuned to large color patches or complex patches beyond simple gratings.(TIF)Click here for additional data file.S6 FigFeature correlation of the center and surround depends on the choice of the reference feature maps. Though the absolute value varies with the feature map choice, they share similar trends that the most suppressive surround has higher feature correlations than the most facilitative surround in early/middle layers in VGG16. Note that a proper choice of feature map should not be a layer that is too deep and has large receptive fields, in which case the surround feature also “sees” the center. For details on how to compute feature correlation, see (TIF)Click here for additional data file.S7 FigSame measure as in Figs (TIF)Click here for additional data file.S8 FigThe blue background denotes the most suppressive surround; the pink background denotes the most facilitative surround. These neurons do not have clear center-surround contrastive features; they are likely to include surround features that are geometrically arranged rather than uniform across the surround or features that are arranged as object-like shapes.(TIF)Click here for additional data file.S9 FigOrientation deviation is defined as the difference between the most suppressive surround orientation and the optimal orientation. To plot this histogram, we included another selection criterion that the surround suppression tuning curve must have at least 0.001 variation, because if a surround suppression tuning curve is flat, there is not a meaningful orientation that has maximum suppression; therefore, it is not informative including in the histogram. In later layers (layer 8 and beyond) in VGG16, orientation deviations match the center orientation better on average.(TIF)Click here for additional data file.S10 FigEach dot represents a neuron. In most middle and later layers, neurons have higher suppression indexes with noise images than with naturalistic images, as indicated by the positive t-value and small p value. T and P values are from paired t-test.(TIF)Click here for additional data file.S11 FigA. Strength of the surround suppression depends on the location of the surround stimuli. Embed images show the stimuli used in this experiment. The center was fixed at the optimal orientation. The surround had two patches at different locations relative to the center stimuli. The surround was either at optimal orientation or orthogonal orientation. Surround patches that align with the center stimuli induce the strongest suppression when it is at optimal orientation and the strongest facilitation when it is at orthogonal orientation. The polar radius represents the normalized response whereas the gray circle represents 1 (reproduced from ). B. Ave(TIF)Click here for additional data file.S12 FigIn neurophysiology studies, a low contrast grating causes the peak of the diameter tuning curves to shift to a larger size. We tested this effect in CNN neurons. A. Stimuli used in the simulation. Low contrast stimuli are at 17% of the regular pixel value range. B. Averaged diameter tuning curves with regular and low contrast stimuli. The black line denotes the regular contrast stimuli; the gray line denotes the low contrast stimuli. C. Histograms of the peak shift values. Positive values indicate low contrast stimuli shifted peak to a larger size, which is seen in cortical data. CNN neurons did not consistently show similar effects. In later layers, the low contrast peak even shifted to a smaller size significantly. Shaded area indicates s.e.m. P values are from paired t-test.(TIF)Click here for additional data file.S13 FigContrast values were normalized to the regular pixel value range. We fixed the surround contrast at 1, changed the center contrast, and measured the contrast response function. We then fitted two curves with subtractive and divisive models. The subtractive model is described as Rs = max, where Rc is the responses of the center stimuli; Rc is the responses of the center stimuli with preferred surround; a is a subtractive parameter that is to be fitted. The divisive model is described as Rs = Rc/b, where b is a divisive parameter that is to be fitted. A. An example V1 neuron from a reference neurophysiology study (reproduced from ). Black (TIF)Click here for additional data file.S14 FigTo determine if the surround suppression effect is more likely to be in a subtractive form or divisive form, we fitted contrast curves in (TIF)Click here for additional data file.S15 FigThe homogeneous suppressive surround could be implemented by the center surround kernel weights structure. A subtractive form of surround suppression can be seen as the kernel weights having different signs in the center and surround. We show three example neurons in Alexnet layer 2. From left to right: tuning curves and visualization of the example neurons, six most contributing first-layer filters and corresponding kernel weights to the example neurons, and averaged kernel weights of kernels with positive or negative center weight. The most contributing first-layer filters are found by sorting the sum of absolute values of kernel weights. Most of the kernels have a center-surround structure. This indicates a subtractive form of surround suppression.(TIF)Click here for additional data file.S16 FigSuppression index, surround suppression curves and visualizations are shown for untrained Alexnet layer 3 and untrained VGG16 layer 9. While most neurons in trained networks showed close to 1 suppression index (strong surround suppression), neurons in untrained networks had bimodal distribution with the majority having close to 0 suppression index (no surround suppression). This was also reflected in the surround suppression curves (cyan), which in untrained networks were flat. On the right is the visualization of several example neurons in the untrained networks. There was no effects like most suppressive surround matches center. All the visualizations look like natural spectrum noise with no high-order textural appearance.(TIF)Click here for additional data file.


Subgroup analysis revealed that there was no statistically significant difference in the accuracy rates achieved by XVS and Allura ARSN (p = 0.092). AR enables reliable, accurate placement of spinal instrumentation. Future research efforts should focus on comparative studies, cost effectiveness, operative time, and radiation exposure.Technological advancements, particularly in the realm of augmented reality (AR), may facilitate more accurate and precise pedicle screw placement. AR integrates virtual data into the operator’s real-world view, allowing for the visualization of patient-specific anatomy and navigated trajectories. We aimed to conduct a meta-analysis of the accuracy of pedicle screw placement using AR-based systems. A systematic review of the literature and meta-analysis was performed using the PubMed/MEDLINE database, including studies reporting the accuracy of pedicle screw placement using AR. In total, 8 studies with 163 patients and 1259 screws were included in the analysis. XVision (XVS) was the most commonly used AR system (595 screws) followed by the Allura AR surgical navigation system (ARSN) (462 screws). The overall accuracy was calculated as 97.2% (95% CI 96.2–98.1%  Pedicle screw placement is a fundamental spine surgery technique classically performed via an anatomy-based freehand technique or with fluoroscopic guidance . However AND  AND (screw OR pedicle OR fixation OR hardware).A systematic search of the literature was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The MEDLINE database was screened from inception until 31 May 2023, using the search syntax Studies were reviewed after establishing the inclusion and exclusion criteria. We included the studies if they met the following criteria: involved ≥1 patient undergoing pedicle screw fixation using an AR-based system; provided accuracy data on screw placement; reported in English. Studies were excluded if they were conducted on cadavers or phantom models or if they were conference abstracts, reviews, technical notes, commentaries, or case reports. Articles were independently reviewed by the first two authors, using title/abstract screening followed by full-text evaluation. Any disagreements were resolved by the senior author. The reference lists of included studies were also manually screened to capture any additional studies not included by the initial search strategy.The required data were extracted by one author (B.P.) and then independently verified by an additional author (T.D.A.). The data included the patients’ demographics, comorbid medical conditions, number of screws, spinal level of screw placement, type of AR system, accuracy of screw placement, accuracy assessment scale, grade-wise accuracy, complications, and follow-up.The 2011 Oxford Center for Evidence-Based Medicine guidelines were utilized to assess the level of evidence of each study.2 test (I2 = 31.87%). This could be attributed to the utilization of two different AR systems, the type of study design, and the involvement of a variety of spinal levels. A subgroup analysis was conducted to compare the accuracy of the Allura augmented-reality surgical navigation (ARSN) system with that of the XVision system (XVS) using the Student’s t-test; all statistics were two-tailed with a p < 0.05. To assess for publication bias, the Egger’s test was performed.The meta-analysis was conducted using MAJOR . A fixed-effects model was created to study the effect size of the screw accuracy achieved using AR. The fixed-effects model was chosen over the random-effect model because of the moderate heterogeneity detected using the IThe initial search strategy returned a total of 388 potential studies for the meta-analysis . FollowiA total of 1259 screws were placed in 163 patients ) .Spinal deformity was the most common indication  followed by degenerative spine disease . The thoracic spine was the most frequent site of pedicle screw placement using AR  followed by the lumbosacral spine . One study did not mention the spinal level. The most commonly used AR system was the XVS (595 screws) followed by Allura ARSN (426 screws) and mixed reality (MR) (146 screws).Most studies  utilized the Gertzbein and Robbins (GR) classification to assess screw accuracy while two studies utilized the original Gertzbein classification. GR grading is a modification of the Gertzbein classification  as depicp < 0.001) (p < 0.001) (p = 0.092).Six studies deconstructed their data to provide grade-specific accuracy . Placeme< 0.001) a, meanin< 0.001) b. There p value of the Egger’s test was 0.131 (>0.05), suggesting low-to-minimal publication bias (The ion bias .p < 0.001) [Pedicle screw placement can be divided into two major schools of thought, namely freehand and imaging guided. Image guidance, classically, involves fluoroscopic guidance. Technological advancement has seen the emergence of new techniques for intraoperative navigation, i.e., robot assistance and AR . Regardl< 0.001) . Additio< 0.001) . However< 0.001) . In our < 0.001) ,17. Thes< 0.001) ) to 100%< 0.001) ). Emi-Te< 0.001) .Two AR systems have been utilized in spinal screw instrumentation, namely XVision AR-HMD and Allura ARSN. XVS is the only AR-HMD system approved by the Food and Drug Association (FDA), while Allura is currently only used as an off-label prototype . XVS hasComparatively, Gu and colleagues conducted a randomized prospective study to compare outcomes in patients undergoing lumbar pedicle screw insertion using mixed-reality (MR) versus freehand techniques. MR, a hybrid of augmented- and virtual-reality technologies, allows surgeons to superimpose virtual 3D holographic images onto the operative field. Gu and colleagues demonstrated a screw accuracy rate of 96%  when usip =0.31) [Navigated techniques appear to outperform freehand techniques in terms of operative time for actual screw placement. Butler et al. reported that the time taken to place each screw was 3 min 54 s using AR , which wp =0.31) . A similp =0.31) . It is lp = 0.01) [p = 0.06) [Gu et al. reported a reduction in operative blood loss when using the MR system, when compared to the freehand group  . SimilarImportantly, Boyaci et al.  sought tWhile the potential benefits of AR technology in spinal instrumentation are significant, the associated capital costs are likely less than that of robotic systems ,27. ThesFactors such as obesity, age, and osteoporosis are pivotal in such studies since they can affect screw breach ,30. HoweAugmented reality (AR) is an emerging technology with promising results in spinal surgery. The early data indicate that AR navigation for pedicle screw placement is safe and accurate. Future investigations into long-term patient outcomes the cost effectiveness of AR navigation will be important to help determine the best role for this technology in the future of spinal surgery.


Rehabilitation has a fundamental role in the management of rotator cuff pathology whether the final choice is conservative or surgical treatment. Conservative treatment can give excellent results in cases of rotator cuff tendinopathies without rupture, partial tears less than 50% of the thickness of the tendon, chronic full-thickness tears in elderly patients and irreparable tears. It is an option prior to reconstructive surgery in non-pseudo paralytic cases. When surgery is indicated, adequate postoperative rehabilitation is the best complement to obtain a successful result. No consensus has still been established on the optimal postoperative protocol to follow. No differences were found between delayed, early passive and early active protocols after rotator cuff repair. However, early motion improved the range of motion in the short and mid-term, allowing faster recovery. A 5-phase postoperative rehabilitation protocol is described. Rehabilitation is also an option in specific failed surgical procedures. To choose a therapeutic strategy in these cases, it is reasonable to differentiate between Sugaya type 2 or 3 (tendinopathy of the tendon) and type 4 or 5 (discontinuity/retear). The rehabilitation program should always be tailored to the individual patient. Rehabilitation has a fundamental role in the management of rotator cuff pathology, whether the choice is conservative or surgical treatment. Rehabilitation by itself is enough to solve a significant percentage of habitual pathology. In cases of surgery, it is a very important coadjuvant not only in the postoperative period but also in many cases it is used preoperatively.Much progress has been made in the conservative therapeutic approach to shoulder injuries, however, for many years the issue has been addressed mostly by surgeons dedicated to the treatment of the pathology of the shoulder girdle –4. In reIt is the surgeon who has at his disposal all the diagnostic tools and manages all the therapeutic options. In many cases, rehabilitation-focused management does not start from a specific diagnosis and simply treats symptoms. This approach, however, is at risk of serious diagnostic errors. The enthusiastic incorporation of ultrasound by physiotherapists and other medical specialities, does not guarantee a correct diagnosis . There mDecision-making must be based on the never-so-undervalued “clinical-surgical criteria”. We will never agree that there is “too much medicine in musculoskeletal practice”  when theAnother factor that has complicated the panorama is the entry into the therapeutic circuit of professionals who, without having a specific training base in musculoskeletal pathology, begin to treat shoulder injuries just because they implement some of the new “regenerative” treatments in vogue . These are situations in which treatment adapts to the professionals’ preferences rather than the patients’ pathology . For theThis review aims to analyze the rehabilitation alternatives in three different scenarios: rehabilitation as the unique treatment for rotator cuff tendinopathies, postoperative rehabilitation as a complement to tendon repair surgery, and the role of rehabilitation in failed rotator cuff surgeries, using a consensus approach between rehabilitators and surgeons.The initial indication for conservative treatment in rotator cuff pathology includes tendinopathies without rupture, partial lesions less than 50% of the thickness of the tendon, chronic full-thickness tears in elderly patients, and irreparable tears as an option prior to reconstructive surgery in non-pseudo paralytic cases.It has been demonstrated that in the case of tendinopathies without rupture, the results of surgery and conservative treatment are similar , 10. EveThe Rockwood program is based on four fundamental goals: 1) pain control, 2) mobility recovery, 3) the development of muscular strength and 4) long-term maintenance.The strengthening process involves the scapular stabilizers, the humeral head depressors and finally the deltoid muscle. These postulates today sound too basic in relation to the degree of rehabilitation development reached and the appearance of new, more sophisticated approaches, however in daily practice they are not always applied.In the case of partial rotator cuff injuries that do not exceed 50% of the thickness of the tendon, it is justified to exhaust the chances of a conservative treatment since the risk of fatty infiltration, muscular atrophy, and severe tear extension are relatively low . ConservIn a total series of 76 patients with partial rotator cuff injuries, Lo et al.  treated The initial indication in patients older than 70 years old with full-thickness rupture and low functional demands is conservative, not only because of the high rate of positive results but also because the biological healing capacity of the repaired tendon is greatly diminished. The indication for surgery in the elderly is linked to pain control when conservative treatment has failed. Some authors have proposed extending the conservative indication to younger patients with specific tears. Kukkonen et al. in a randomized controlled trial  found noIn cases of full-thickness ruptures in young and active patients, and especially when they are acute, the results of surgery are superior to conservative treatment , 20.In irreparable massive ruptures, rehabilitation also has an important role, especially in cases that do not present pseudoparalysis and that have controllable painful symptoms –23.Christensen et al.  reportedLevy et al.  used a sPatients with a nonoperatively managed, moderately symptomatic massive RCT can maintain satisfactory shoulder function for at least four years .An RCT can be surgically treated with an open, mini-open or arthroscopic repair. According to Liu et al. , arthrosTo provide a secure and customized rehabilitation treatment, it is critical to understand the different timings and biological events of the healing sequence . FollowiThe main objectives of the rehabilitation process after ARCR are promoting healing of the repaired tendons, restoring function and regaining the range of motion and strength of the shoulder while minimizing shoulder stiffness and muscle atrophy.To date, the literature presents just a few high-level evidence-based studies focusing in detail on postoperative rehabilitation regimens and no consensus has still been established on the optimal postoperative protocol to follow .Three postoperative protocols have been described after ARCR: strict immobilization, early passive motion and early assisted active motion. It might be reasonable to assume that early motion, whether passive or active, increases the range of motion (ROM) after ARCR, while the risk of re-tear could be significantly higher if compared to delayed motion. On the other hand, immobilization could decrease the likelihood of re-tear, but could also result in shoulder stiffness, which can cause functional limitations, pain, and frustration.For convenience, results will be subdivided into early versus delayed motion analyzing a range of motion, functional outcome, repair integrity, tear size, loading and strengthening .The early passive motion protocol includes passive shoulder ROM activities starting on the first postoperative day. The delayed passive motion protocol consists of sling immobilization, except pendulum exercises, for the first 4–6 postoperatively weeks.The current, best available evidence –33 suggeAt 6 postoperative months, strict immobilization was associated with lower flexion, abduction, external and internal rotation than EPM or EAM , 32, 33.Strict immobilization reduced flexion at 1 year compared to passive or active motion , 29, 33.At a 2-year follow-up, no significant differences between the two EPM and EAM protocols in terms of ROMs were found, however, this result may be influenced by the lack of studies with long-term follow-up .A most recent meta-analysis –30, 33 rNo statistical difference was found regarding shoulder stiffness at 2 years follow-up either .Anatomic failure, non-healing or re-tear, after ARCR is not uncommon (25–60%) and it tends to occur in the first 3–6 months after surgery . The mosThe risks of failure after ARCR are well documented and include in addition to larger tear size –38, poorThe literature lacks strong evidence about the correlation between different postoperative rehabilitation protocols and RCT size.Raschhofer et al. , investiPhase 1 (weeks 0–2): strict immobilization. Distal hand and wrist active motion is allowed.Phase 2 (weeks 2–6): continue immobilization. A protected passive range of motion (PROM) is initiated by the patients in the scapular plane. It is allowed the elbow’s active range of motion (AROM) with the arm at the side. During weeks 4–6, it is allowed to move from PROM to an active-assistive range of motion (AAROM) by supervised physiotherapy. Criteria to advance to the next step are pain-free PROM, external rotation >30° and forward flexion >120°.Phase 3 (weeks 6–12): progress from AAROM to AROM with <15% supraspinatus electromyographic activity level. Criteria to advance are full and pain-free PROM and AROM without compensation, no shoulder “shrug”, and pain-free isometric exercises.Phase 4 (weeks 12–20): strengthening and endurance with 30–49% supraspinatus electromyographic activity level.Phase 5 (weeks 20–26): strengthening with >50% supraspinatus electromyographic activity level.According to current literature , 33 and Rotator cuff repair may be considered “failed” for various reasons: persisting pain or loss of function, inability to return to work or previous sports level, anterosuperior escape, cuff tear arthropathy or perioperative complications such as infection. With the exception of infection, most of these conditions are due to retears. Perioperative complications aside, most of these problems are due to retears of the rotator cuff. Retear rates depend on patient-related  and not patient-related  factors. They are reported inconsistently and may be around a considerable 20% . This inTo choose a therapeutic strategy , it is rPersisting tendinopathy after rotator cuff repair (Sugaya 2 or 3) does not seem to correlate with pre-existing tendinopathy in concordance with the initial  but may In contrast to the first group, in patients with retear (Sugaya 4 or 5), the treating physician or surgeon may consider surgical options such as a further attempt of reconstruction, latissimus dorsi or lower trapezius transfer, supra capsular reconstruction, subacromial balloon interposition, biological augmentation or reverse shoulder arthroplasty. For rotator cuff repairs, it was shown that patients undergoing nonoperative treatment had improved outcomes in the initial follow-up period compared with patients undergoing a surgical procedure, but this trend reversed in the longer term , 66. ThiIf re-operation is not in favour, non-operative treatment vastly depends on the type of retear. In that perspective, it is not the pain that correlates with the type of re-rupture but the functional impairment , 68. WalIn contrast to patients with postoperative tendinopathy, the rehabilitation cannot aim for tendon healing, instead, it should focus to compensate for the missing part of the rotator cuff. Levy et al. propose that the anterior deltoid is the pillar for successful rehabilitation . FollowiRehabilitation has a fundamental role in the management of rotator cuff pathology. Numerous cuff injuries can be treated with good results by applying for only an adequate rehabilitation program.The role of rehabilitation in the postoperative period of rotator cuff repairs is essential. There are different protocols with theoretical advantages and disadvantages, but in practice, it has not been possible to demonstrate statistically significant differences between them.In cases of failed repair surgeries and complications, except in infections, rehabilitation is a primary indication.In all scenarios, it is very useful to follow a defined rehabilitation protocol adapted to the characteristics of each patient, including a supervised home program.All authors certify that they have no financial conflict of interest  in connection with this article.This research did not receive any specific funding.Ethical approval was not required.This article does not contain any studies involving human subjects. Written informed consent was obtained from all patients and/or families of the cases presented in All authors contributed to writing original drafts, reviewing and editing. Fabio V. Sciarretta wrote “Rehabilitation after failed rotator cuff repair” section. Daniel Moya wrote “Introduction” and “Rehabilitation as an alternative to surgical treatment” sections. Kilian List wrote “Rehabilitation after failed rotator cuff repair” section.