Recent years have witnessed the huge popularity of Information-Centric Networking (ICN) and its realization as Named Data Networking (NDN) in the context of wireless sensor networks (WSNs). The participating nodes in WSNs are usually equipped with a single radio interface. The existing solutions lack in providing the efficient next forwarder selection in NDN-based single radio WSNs. In this work, we propose a collaborative Interest and Data Forwarding (CIDF-WSN) Strategy for Named Data Wireless Sensor Networks. CIDF-WSN develop a Neighbor Information Base (NFIB) which enables the node to select the optimal next-hop relay in Interest packet forwarding. An efficient Interest packet processing mechanism assisted by the Interest Cache Table (ICT) is provided to avoid Interest packets loss and frequent re-transmissions. In addition, CIDF-WSN also provides a robust Data packet transfer mechanism accompanied by the Temp Cache Table (TCT) to avoid Data packet losses and to ensure well-timed content delivery. Simulation results reveal that CIDF-WSN outperforms the recently published works in terms of Interest satisfaction rate, total energy consumption, Data retrieval delays, and communication overhead. The rapid proliferation of the Internet of Things (IoT) has gained significant attention in recent times as it transforms the modern world into a vast interconnected network of things . These objects have been equipped with the capability to generate data, exchange information, and interact with the environment. Wireless sensor networks (WSNs) are deemed as a promising component of IoTs as their environmental monitoring capability accompanied by the data collection, storage, and processing capacity make them fully compliant with the expectations of IoT. These networks have been utilized in a variety of applications such as smart grids , smart aCommunication between the nodes in conventional WSNs is carried out through an address-based communication model . HoweverTo address the intrinsic restraints of address-based architecture while taking into account the requirements of WSNs, a potential flavor of Information-centric networking architecture referred to as Named Data Networking (NDN) has garnered significant attention specifically in terms of IoT. Unlike the traditional address-based networks, NDN considers the content as a first-class citizen of the network . The conNDN was originally developed for the communication between the devices at an internet scale where the devices are usually equipped with multiple radio interfaces. In contrast, the WSNs are usually supplied with a single radio transceiver and all the transmissions and receptions are carried out via that interface. Several efforts have been devoted to the literature to investigate how to effectively utilize the NDN in WSNs  and how In this paper, we exploit the single radio WSN nodes in layered-based WSNs and propose CIDF-WSN, an efficient collaborative Interest and Data packet forwarding mechanism. CIDF-WSN develops a neighbor information base (NFIB) table which adapts a list of potential virtual faces–next-hop node Ids, based on their associated cost and enable the consumer to select the optimal next-hop forwarder node in its Interest packet forwarding phase. In order to update the cost associated with each virtual face entry of NFIB, we propose robust downstream Interest processing and upstream Interest processing mechanisms. In downstream interest processing, the downstream node in the lower layer overhears the transmission of the upper layer forwarder and update its associated virtual face cost in the NFIB table. The updated NFIB allows the node to designate the optimal node in future Interest forwarding. In addition to NFIB cost maintenance, the upstream Interest processing procedure allows the immediate neighbors of the nominated node to take part in communication and play a pivotal role in minimizing the redundant broadcasts when the designated node does not have information about the potential next hop or fail to further forward the Interest packets. Furthermore, the CIDF-WSN’s unique Data forwarding procedure reduces the incoming Data packet loss due to unpredictable channel variations and upholds the QoS of the network. Finally, the cache tables, such as Temp Cache Table (TCT) and Interest Cache Table (ICT), developed by the CIDF-WSN play an indispensable role and decrease the overall Interest and Data packet losses.Layered Architecture: CIDF-WSN puts forward a layer-based architecture for Interest and Data packet forwarding for resource-constrained Named Data Internet of Things (IoT).NFIB for single radio WSN nodes:We extend the existing NDN architecture and propose a new table named NFIB which enable the node to select the optimal next forwarder in Interest forwarding.Interest packet processing: CIDF-WSN develop an efficient Interest processing mechanism to avoid Interest packets loss and to update the cost associated with the NFIB entries.Data packet recovery:CIDF-WSN presents a robust Data packet recovery mechanism to avoid Data packet loss when the designated lower layer node in the breadcrumb path fails to forward a packet.Interest Cache Table (ICT) and Temp Cache Table (TCT):we devise an Interest Cache Table (ICT) and Temp Cache Table (TCT) to avoid transmission(s) losses.The main contributions of our proposed work are summarized as follows:Through the extensive NDNSim  based siThe rest of this paper is organized as follows: The conventional host-centric IP-based architecture faces several challenges, especially in the domain of resource-constrained IoTs. Some of the major challenges in address-based architecture are summarized as follows.Scalability: The current WSNs deployment utilizes the conventional address-based solution(s) which may induce the efficiency and scalability issues. For instance, extracting the PH value of soil from a single sensor in the agricultural field requires the address and type information of each sensor . Therefore, managing the enterprise level deployment of nodes becomes a significantly complex as well as time-consuming operation.Data Unavailability: Considering the low battery and low memory constraints of WSN nodes, the Data unavailability issue may arise. To cope with data unavailability problems, an in-network caching-like solution is required. Unfortunately, the conventional IP architecture provides no support for in-network caching [ caching .End-to-End connectivity: In most IoT applications, the consumers are interested in updated Data rather than the location of the producer of that specific Data item [ata item . For insMobility Support: There is a lack of native mobility support in IP and to overcome mobility constraint(s), IP utilizes several additional protocols to support the mobility of nodes in dynamic environments.The idea of NDN was initially proposed by Van Jacobson in  and is bEach NDN node carries the following Data structures: (1) Pending Interest table (2) Content store (CS), and (3) Forwarding Information Base (FIB). The Interest and Data packet forwarding is carried out by consulting the PIT, FIB, and CS. The PIT table records the forwarded Interests, the CS caches the incoming Data packets to fulfill future requests, and the FIB relays the Interest packet(s) towards the potential provider node.Ease of application development: In WSNs the consumer usually demands the Data or content regardless of the location or identifier of the producer node. The consumer application may demand the Data in an information-centric manner irrespective of the physical location of producing entity.Data availability: The enchanting in-network caching feature of NDN overcomes the problem of information loss due to unavailability of the node which may happen due to several uncertainties, such as battery depletion and sudden hardware or software failure in resource-restrained WSNs.Ease of Data retrieval: The consumer simply requests the content by issuing an Interest packet that carries the name of the desired content, whereas any receiving node which has already cached the requested content replies with the Data packet regardless of the actual producer of the content. In the network of a large number of wireless sensor nodes, the hierarchical naming provided by the NDN enables easier request/Data aggregation and accelerates the search and information retrieval time. Moreover, the name-based routing mechanism resolves the issue of address space scarcity which may arise in assigning the unique addresses to the huge number of WSN nodes.Scalability: NDN inherently supports scalability and allows multiple nodes to take part in the Data forwarding process. It greatly improves the quality of communication with an increase in the number of nodes in the given area via intrinsic forwarding procedures.The core features of NDN, such as in-network caching, requests aggregation, and content level security, match with the WSNs applications and efficiently handle the underlying constraints. Some of the benefits that NDN can offer in the domain of WSNs are defined as follows:Information-centric WSNs correspond to the category of WSNs which mainly focuses on information retrieval rather than establishing the point-to-point (P2P) communication setup between the objects. In this regards several efforts have been devoted to literature to provide seamless and efficient information sharing.In , BLOOGO—Authors in  developeIn , the autIn , the autSobia et al.  proposedIn this section, to elucidate the features of the CIDF-WSN, we first provide the motivation behind our proposed scheme and some network assumptions. Subsequent to that, a detailed explanation of CIDF-WSN is provided.The rapid development of IoT-based technologies has revamped almost every industry including the agriculture sector. The IoT revolution has revamped the conventional agriculture methods with new technologies such as “WSNs” to reduce the environmental impact on crops, attain large production, maintain high quality with minimum labor cost. The WSNs play a backbone role in the agriculture sector, as the sensor nodes sense the environmental conditions such as temperature, humidity, soil moisture level, soil pH, light conditions, and carbon dioxide, etc., and transfer the readings to the monitoring station. These aforementioned readings highly affect the productivity of the crop and require well-timed delivery at the control center to yield better growth. This is because any change in these parameters may directly affect the growth of underlying crop and may require immediate attention  . TherefoThe conventional WSNs-based agricultural monitoring systems employ the traditional address-based mechanisms which may induce scalability and efficiency issues in the network. For instance, a user requiring the temperature information from a sensor node in the monitoring region requires complete knowledge of all the deployed sensor nodes along with their data type. This procedure makes the overall management of large-scale networks significantly complex and time-consuming. Moreover, in single-channel WSNs, most of the schemes lack in providing efficient next forwarder selection, Interest, and Data Packet recovery mechanisms. Thus, the main motivation of the proposed scheme is to put forward a robust NDN-based IoT-based agricultural monitoring system that eliminates the aforementioned constraints and in the meantime fulfills the fundamental requirements of the agricultural monitoring system in order to yield better growth.The CIDF-WSN system is composed of several heterogeneous sensor nodes that are deployed in the Region of Interest (RoI). The concept of heterogeneity is defined in terms of the nodes’ application type and their available resources, such as storage, battery, and computation resources, among others. The region is further partitioned into several layers and each layer is composed of multiple nodes, as depicted in Before diving deep into the operational details of the CIDF-WSN, we first shed light on the two main data structures of the NDN forwarding daemon—FIB and PIT—and why they need to be tuned for efficient operation in WSNs. Later, we discuss how the CIDF-WSN ensures effective forwarding in the resource-constrained environment where the nodes are usually equipped with a single radio interface.The FIB is utilized to forward the Interest packets towards the potential data source that resembles the requested content. As discussed earlier, the FIB table holds the list of outgoing interfaces, and the incoming Interest packets are forwarded towards the potential face by consulting with FIB. The PIT incorporates the information about the Interest that is forwarded towards the upstream  nodes and waiting for the response. Upon receiving an Interest packet, the router first enquires its PIT to ensure whether the same name Interest packet has already been forwarded or not. If the PIT entry already exists, the router adds the face identifier of the incoming Interest packet. Contrarily, the router checks its FIB and selects the face with minimum route cost to forward the Interest packet. Once the forwarding completes, the router inserts the new outgoing face entry in the PIT table (containing the incoming Face address and outgoing Face address of the forwarded Interest packet) representing the forwarded Interest packet. Finally, the Data packets arrive on the reverse path guided by the matching PIT entries.The aforementioned procedure is utilized for the communication between the devices on the internet where the routers are mostly equipped with an array of interfaces . HoweverIt is worth mentioning here that the existing FIB Data structure is used to forward the Interest packet towards the face selected from NFIB on AdHoc Interface. The complete procedure of NFIB-based next-hop selection is depicted in The NFIB is comprised of a collection of virtual faces  and their associated cost. An NFIB entry with a certain prefix denotes that given an Interest packet with that prefix, a possible source(s) of requested data can be reached through the face provided by the virtual face Id(s) in the NFIB entry. The associated cost can be computed based on the current conditions of the node, such as workload, number of successful transmissions by the node, available resources, and residual energy reserve, by adopting suitable multi-criteria decision-making (MCDM) procedure  and it iThe detailed description of NFIB cost maintenance and the Interest processing mechanism is presented as follows.When the interest packet arrives at the designated node, the following Interest packet processing mechanisms, such as Normal Mode Interest Packet processing and recovery mode Interest packet processing, can be executed.Normal Mode Interest Packet Processing: The Normal Mode Interest Packet Processing occurs during stable network conditions. When the lower-layer node forwards the Interest packet to the designated upper-layer node, the packet is also received by the immediate neighbors of the designated node due to the shared medium. The immediate neighboring nodes temporarily store the Interest packet in their ICT (discussed in (a)Normal Mode Downstream Interest packet processing: In the Normal Mode Downstream Interest packet processing, when the upper-layer designated forwarder forwards the Interest packet further, the forwarded Interest packet is also received by the downstream forwarder node (in the lower layer) due to the shared medium. When the downstream node receives the forwarded interest, it increases the number of successful transmission parameters of the upstream forwarder and updates the associated virtual face cost of the upstream forwarder node in the NFIB table by utilizing the cost updating mechanism. The updated NFIB table allows the node to select an upstream relay that has the highest associated cost for the future interest forwarding process.(b)Normal Mode Upstream Interest packet processing: In the Normal Mode Upstream Interest packet processing, upon the designated node Interest packet transmission, the immediate neighbors of the designated node (in the upper layer) also receive the Interest packet. Upon receipt, these immediate neighbor nodes stop their forwarder timer and purge their ICT to avoid cache pollution and to vacate the ICT to accommodate future Interest requests.The complete procedure of Normal Mode Downstream Interest packet processing and Normal Mode Upstream Interest packet processing is shown in Consider a scenario where the node N1 of layer 1 Recovery Mode Downstream Interest packet processing: In Recovery Mode Sownstream Interest packet processing, if the designated node fails to further transmit the Interest packet, the immediate neighbors of the designated node continue the transmissions. As the Interest packet is also received by the immediate neighbors of the forwarder node, these nodes store the Interest packet in ICT and associate the forwarder timer to the received Interest packet. Therefore, on nominated forwarder failure, the immediate neighbor facing the destined provider forwards the Interest packet upon the expiration of the forwarder timer in order to avoid the Interest packet loss. When the neighbor node of the designated node forwards the Interest packet, the downstream forwarder node located in the lower layer overhears the packet, updates the number of successful Interest transmission parameter, and computes the costs of virtual faces in NFIB accordingly.(b)Recovery Mode Upstream Interest packet processing: As discussed earlier, the medium is shared and the forwarded Interest is also received by other immediate neighbors of the designated node. The nodes (which cache the forwarded Interest packet in the ICT), upon receiving the same forwarded Interest packet, purge their ICT entry and stop their forwarder timer to avoid the ICT redundancy and allocate the space for the future Interest packets.The example scenario for both aforementioned mechanisms is depicted in As shown in Once the Interest packet reaches the producer node, the producer node verifies the Interest packet authenticity, assembles the Data packet of the requested content, and forwards it towards the consumer. In the conventional NDN, the Data packet follows the reverse path of PIT entries maintained in the PIT table of downstream nodes. This means that the Data are forwarded back to the consumer node by following the breadcrumb path of the received Interest packet. Following the breadcrumb path, the Data delivery at the consumer end may fail due to poor channel conditions. To cope with these constraints, CIDF-WSN offers a robust Data packet forwarding mechanism to avoid Data packet loss. The Data packet forwarding includes (1) Normal Mode Data Forwarding and (2) Recovery Mode Data Forwarding.The detailed operational description of the above mentioned Data Forwarding modes assisted by the illustrative example depicted in Normal Mode Data packet forwarding: The Normal Mode Data packet forwarding refers to the Data packet forwarding towards the lower-layer downstream virtual face (node) from where the Interest request arrives. Upon the arrival of the Data packet, the designated downstream node verifies the received Data packet and forwards it towards its designated lower-layer Forwarder. The immediate neighboring nodes of the designated node in the same layer also store the Data packet in their TCT  and Temp Cache Table (TCT). The former avoids the interest packet failure and retransmissions when the nominated forwarder node fails to further relay the packet while the latter targets to minimize the Data packet failures in vulnerable network conditions. The detailed descriptions of both ICT and TCT are as follows.Interest Cache Table (ICT): The Interest Cache Table (ICT) is maintained by the immediate neighbors of the designated forwarder node in the upper layer to avoid Interest Packet failure. The immediate neighbor(s) temporarily saves the incoming Interest packet in the ICT and forwards it if the designated node fails to further transmit the Interest Packet towards the potential data source.Temp Cache Table (TCT): When the Interest packet arrives successfully at the destination of matching content, the provider node, after verifying the authenticity of Interest, sends back the requested content in the breadcrumb path. The breadcrumb path may crash and the requested content may be delayed or fail to reach the destined consumer. The delayed delivery of requested Data may highly degrade the network performance and it may cause havoc, as concerned departments may never take the action on time.To cope with the above-mentioned situation, CIDF-WSN develop a Temp Cache Table (TCT) which is enabled at the immediate neighbors of the designated forwarder node to ensure seamless communication and avoid packet losses in case the nominated node fails to perform the further Data packet transmissions. In this approach, when the node forwards the Data packet towards the designated node, the packet is also received by the immediate neighbors of the designated node due to shared medium. The immediate neighboring nodes keep these packets in their TCT, assign a forwarder timer to the packet, and wait for the transmission of the designated node. If the designated node fails to forward the packet, the neighbor node with the smallest forwarder timer value forwards the packet to streamline the communication process. In contrast, if the designated node successfully forwards the received Data packet, the upstream node(s) purges the TCT to allow future Data packet caching.It is worth mentioning that in both aforementioned Data forwarding modes, the TCT purge operation is always triggered when the downstream node forwards the data towards the consumer in the lower layer(s). The mechanism avoids TCT pollution and enables the node to allocate its TCT for future Data packets.In this section, we evaluate the trade-offs of CIDF-WSN via software-based simulations and compare the performance with the most recent state-of-the-art scheme developed for NDN-based WSNs—geographic interest forwarding (GIF) . We alsoIn the simulations, CIDF-WSN consider several heterogeneous wireless sensor nodes deployed in the monitoring area of 100 m × 100 m with a total number of 100 nodes which are organized into five layers with an equal number of nodes. These nodes are static, and each node is equipped with a GPS module to acquire its physical location. The transmission range of nodes is restricted to one hop . The simulations are conducted in ndnSim  : The Interest satisfaction rate is a measure of the total number of Data packets Interest satisfaction delay: Interest satisfaction delay is defined as the time (t) taken by the Interest (I) to reach the provider (P), i.e., Total number of packets processed: It corresponds to the total number of Interests and Data packets processed in the network.Total energy consumption: The total energy consumption is defined as the amount of energy consumed by all the nodes (N) in Interest and Data packet forwarding.Communication overhead: The communication load refers to the overall bandwidth consumption that may happen due to Interest or Data packet transmissions in the network.The performance evaluation results are presented as follows.To analyze the Interest satisfaction rate (ISR), CIDF-WSN vary the number of Interest packets between 5 to 30 Interest packets/sec. In contrast, the GIF may involve several retransmissions in vulnerable conditions when the designated node fails to transmit the Interest packet. In addition, if the potential node fails to find the next-hop forwarder, it reinitiates the neighbor table development to assign the potential forwarder to the received Interest for further transmissions. All these factors induce congestion and collisions and increase the overall traffic in the network. In comparison with the vanilla NDN, the CIDF-WSN shows much better results. The minimized Interest and Data transmissions avoid the congestion in the network and increase the performance in terms of ISR. Whereas, in NDN the blind Interest and Data packets transmissions turn into the broadcast storm and collisions which, as a result, lower the ISR.The total number of packets processed as a function of the number of Interest packets is shown in On the other hand, in NDN, each node rebroadcasts the received packet which significantly increases the overall transmissions in the network which in turn results in the wastage of network resources, congestion, and eventually increases the number of processed packets in the network.Wireless transmissions in terms of Interest and Data packet disseminations account for the majority of a node’s energy consumption. Interest Energy consumption: To study the impact of Interest energy consumption, CIDF-WSN vary the Interest packet frequency between 5 and 30 packets/sec and compute the total Interest energy consumption of all schemes, as shown in It is evident from the results that the overall energy consumption in all the schemes increases with an increase in the Interest packet frequency. However, the CIDF-WSN lowers more than 50% of energy consumption compared with GIF, while reducing the energy consumption several times in comparison with the vanilla NDN. This is because that the CIDF-WSN avoids the blind Interest broadcasts by utilizing the ICT of the neighboring node(s) of the nominated relay to forward the Interest packet in vulnerable conditions. If the designated node fails to reply to the Interest packet, the immediate neighbors of the designated node forward the Interest packet by consulting their ICT to avoid the blind broadcast of the Interest packet. These procedures highly reduce the retransmissions and conserve the transmission energy of the node.In GIF, if the forwarder fails due to a certain reason, the content consumer restarts the Interest transmissions upon the Interest timeout. In addition, If a node receives an Interest packet and there is no information on sender in its neighbor table, the node re-initiates the neighbor table development procedure which involves multiple Interest packets transmissions which enhances the overall energy consumption of underlying nodes. Moreover, there is no procedure provided by the GIF which may allow the immediate neighbors of the selected relay to continue the Interest forwarding process in traffic-intensive conditions. All these factors result in increasing the total Interest energy consumption of the network. Analyzing the Interest energy consumption of vanilla NDN shown in  comparedData Energy consumption: Data packets have a major impact on total energy consumption due to their large size. The collaboration of immediate neighbors in the Data packet forwarding process reduces the overall Data packet retransmissions and conserves the energy of the nodes. In contrast, the GIF starts over the entire communication process when the packet has failed to receive at the consumer end. Another important reason is that the lack of involvement of immediate neighboring nodes of the potential forwarder to carry the Data packet forwarding in case of designated node failure leads towards frequent packet retransmissions and consumes a substantial amount of energy. On the other hand, similar to the GIF, the vanilla NDN rebroadcasts the incoming Data packets to the downstream neighboring nodes. The frequent Data packet transmissions increase the overall energy consumption of the network which can be visualized in In this paper, we propose a new next forwarder selection and an efficient Interest and Data transfer mechanism for single radio WSNs. CIDF-WSN develop a NFIB table for optimal next-hop selection. In addition, to reduce the interest packets loss and Interest retransmissions, an efficient Interest packet transmission mechanism based on ICT is proposed. Furthermore, to avoid Data packet losses, we devise a robust Data recovery mechanism accompanied by the TCT. The experimental results confirm that the CIDF-WSN magnifies the ISR, and minimizes the Interest satisfaction delay, the communication overhead, and total energy consumption of the network.In future work, CIDF-WSN aims to include the mobility aspect and analyze the performance in the mobile environment by simulating various large and complex scenarios. Moreover, we will adopt AI schemes for optimal cache decisions regarding ICT and TCT mechanisms.


Mucosal surface layers are the critical borders throughout epithelial membranes. These epithelial cells segregate luminal material from external environments. However, mucosal linings are also accountable for absorbing nutrients and requiring specific barrier permeability. These functional acts positioned the mucosal epithelium at the epicenter of communications concerning the mucosal immune coordination and foreign materials, such as dietary antigens and microbial metabolites. Current innovations have revealed that external stimuli can trigger several mechanisms regulated by intestinal mucosal barrier system. Crucial constituents of this epithelial boundary are physical intercellular structures known as tight junctions (TJs). TJs are composed of different types transmembrane proteins linked with cytoplasmic adaptors which helps in attachment to the adjacent cells. Disruption of this barrier has direct influence on healthy or diseased condition, as barrier dysfunctions have been interrelated with the initiation of inflammation, and pathogenic effects following metabolic complications. In this review we focus and overview the TJs structure, function and the diseases which are able to influence TJs during onset of disease. We also highlighted and discuss the role of phytochemicals evidenced to enhance the membrane permeability and integrity through restoring TJs levels. The gastrointestinal inner membranous arrangements sketch out the human body's principal boundary with the outside world. The epithelium permits the nutrient absorption, supporting a physical barrier to the dispersal of proinflammatory components, like microorganisms, foreign antigens and toxins from the luminal milieu to the adjoining mucosal tissues  and the strength of paracellular channels for small molecules like mannitol, dextran, and inulin was studied using permeability efflux. This review covers the structural component of apical junctional complex and summarizes the role of tight junction in regulating the barrier permeability and its relation with health consequences. This review also discusses about the selective participation of phytochemicals among intestinal TJs in well-being and disease progression.The main concern for barrier function at mucosal surfaces inhabits with the plasma membrane of epithelial cells, which is resistance to many hydrophilic solutes due to lack of specific transporters. There are many direct exposures which induce damage to epithelial cell for example, mucosal antigens or cytotoxic agents, such as drugs used for chemotherapy during cancer treatment result in a significant loss of barrier function. Along with the existence of a firm epithelial monolayer, the paracellular pathway between cells must be preserved and functions well. This function is regulated by the apical junctional complex, an assembly of the tight junction and adjacent adherens junctions (AJs) . The adhTJs are manifold protein multiplexes positioned on the intestinal epithelial cell membrane at the apical side . They coin-vitro models as well as in in-vivo models  is an internal protein of the TJs and recognized as a cytoskeleton-related actin protein. Investigators claimed that these proteins form a homodimer with huge N-terminal head and tiny globular C-terminal tail with coiled rod domain . High rate of epithelial apoptosis is seen  and 46 were also significantly increased. Earlier published study enlightens the fact that upregulated Cx 43 lead to monocyte adherence to vascular endothelial cells progressing further into atherosclerosis  and animal models (in-vivo). At the time of microbial sepsis membrane integrity is lost affecting specific TJ proteins, such as the integral membrane protein occludin , Science and Engineering Research Board (SERB), Govt. of India, and CSIR-Indian Institute of Toxicology Research, Govt. of India. SS was supported by a post-doctoral fellowship women (PDFW) awarded by the University Grant Commission . Both the authors had a significant role in content, structure, and review of manuscript. The CSIR-IITR manuscript number is IITR/SEC/2021-2022/40.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Pediatric bilateral elbow dislocation is an infrequent injury. This is a report of a seven-year-old girl, the youngest patient ever reported, with simultaneous isolated bilateral traumatic elbow dislocation without fracture, treated with closed reduction under sedation. Elbow trauma in children is one of the most commonly encountered musculoskeletal injuries in pediatric patients. Supracondylar fracture is the most common type of elbow fracture in children  occurring in the ages of five to 10 years old with a typical mechanism of falling on an outstretched hand with extended elbow [A seven-year-old otherwise healthy female sustained bilateral elbow trauma after a fall with outstretched elbows and landing with force on the floor (kindergarten facility at the climbing frame).The neurovascular status of both upper extremities was intact upon the arrival of the patient to the Trauma Unit. Clinical examination revealed loss of any active movement in both elbow joints in every plane. The joint was locked in a relatively extended position with the forearm neutral to a slightly supinated position. The patient had no sign of swelling or hematoma. Clinical suspicion was guided to a complex elbow injury, possibly with the participation of various bony structures.A gross estimation of the patient's potential hyperlaxity was performed except for the elbow joints using the Beighton scale without significant clinical findings . NeurovaPlain radiographs with standard projections  confirmed posterolateral elbow dislocation bilaterally with no signs of evident fractures. Identification of the bony structures was performed, and meticulous control and confirmation of the secondary ossification centers expected for the patient's age was done to exclude any secondary damage Figures -6.In the emergency department, the upper limbs were immobilized in a provisional plaster with the elbows in a light hyperextension and neutral rotation of the forearms to reduce any movement and relieve the pain. The reduction was achieved under sedation in the operating theater and muscle relaxation with the patient in the beach chair position and with access to fluoroscopy during the whole procedure. The maneuver included gentle manipulation of the joints by slightly rotating, distracting and giving a flexion jerk to the joint. The audible and palpable "click" sign and the complete restoration of the arch of motion with the appropriate imaging confirmed the reduction as well as achievement of ligamentotaxis. Postoperatively, the patient retained both elbows in a functional position, stabilized with dorsal braces and collar and cuff to suspend the extremities Figures -10.Removal of any support took place in three weeks post-reduction, followed by mild kinesiotherapy and gradual return to moderate athletic activities. The patient achieved a full range of motion with no pain and excellent ligament stability. At the six-week follow-up, new radiographs were obtained, with normal findings.Unilateral elbow dislocation is an infrequent injury accounting for only 3%-6% of all pediatric elbow injuries. The average age of pediatric patients at the time of injury is 9.9 years, with a gender ratio of 3.8, favoring females -9. The lBilateral elbow dislocations happen mostly in athletes, especially during gymnastics ,7. The uBilateral elbow dislocations have also been related to hormonal changes during the menstrual period or adolescence before the first menstruation occurs . There hThe management of bilateral elbow dislocation is similar to the treatment of unilateral elbow dislocation. The operative treatment seems to offer no statistically different outcomes in pediatric patients than nonoperative treatment in Mayo Elbow Performance score (MEPS), Quick Disabilities of the Arm, Shoulder and Hand (QuickDASH) Sport and Music Module scores . A shortThe long-term outcomes of bilateral elbow dislocation cases are promising, with reports of full recovery and return to recreation and sports activities. From the six pediatric cases, the two reported full range of motion ,7, threeBilateral elbow dislocation is a very scarce injury with limited reported cases in the literature especially in the pediatric population. The initial diagnostic approach should be very cautious and examine meticulously the joint as well as the neurovascular structures. Especially for young pediatric patients, the secondary ossification centers should be evaluated with scrutiny according to the respective age group. At the follow-up, estimation of the range of motion should be made as well as the muscle strength and the patient should slowly return to activities usually after kinesiotherapy sessions.Overall, bilateral elbow dislocation is a rare injury that requires specialized care and proper treatment. We report the youngest patient with this infrequent injury, and we hope to offer some insight on dealing with this kind of injury.


The gut microbiota  of fishes is known to play an essential role in diverse aspects of their life. The gut microbiota of fish is affected by various environmental parameters, including temperature changes, salinity and diet. This study characterised the microbial composition in gut samples of Nile Tilapia collected from Lake Tana and the Bahir Dar aquaculture facility centre applying modern molecular techniques. The results show clear differences in the gut microbiota in fish from the Lake Tana and the ones from aquaculture. Further, also significant differences were observed on the composition of the gut microbiota across sampling months. Samples from the aquaculture centre displayed a higher diversity than the wild catch Nile tilapia from Lake Tana even though there is also an overlapping of the detected microbial groups. Overall, this is the first study on the effects of sampling season and catching site on the gut microbiota of Nile tilapia in Ethiopia. Future work will help to precisely explain the causes of these changes and their influence of the health and growth of Nile tilapia in Ethiopian lakes as well as under aquaculture conditions.The gut microbiota of fishes is known to play an essential role in diverse aspects of host biology. The gut microbiota of fish is affected by various environmental parameters, including temperature changes, salinity and diet. Studies of effect of environment on gut microbiota enables to have a further understanding of what comprises a healthy microbiota under different environmental conditions. However, there is insufficient understanding regarding the effects of sampling season and catching site (wild and aquaculture) on the gut microbiota of Nile tilapia. This study characterised gut microbial composition and diversity from samples collected from Lake Tana and the Bahir Dar aquaculture facility centre using 16S rDNA Illumina MiSeq platform sequencing. Firmicutes and Fusobacteria were the most dominant phyla in the Lake Tana samples, while Proteobacteria was the most dominant in the aquaculture samples. The results of differential abundance testing clearly indicated significant differences for Firmicutes, Fusobacteria, Bacteroidetes and Cyanobacteria across sampling months. However, Proteobacteria, Chloroflexi, Fusobacteria and Cyanobacteria were significantly enriched in the comparison of samples from the Lake Tana and aquaculture centre. Significant differences were observed in microbial diversity across sampling months and between wild and captive Nile tilapia. The alpha diversity clearly showed that samples from the aquaculture centre (captive) had a higher diversity than the wild Nile tilapia samples from Lake Tana. The core gut microbiota of all samples of Nile tilapia used in our study comprised Firmicutes, Proteobacteria and Fusobacteria. This study clearly showed the impact of sampling season and catching site (wild and aquaculture) on the diversity and composition of bacterial communities associated with the gut of Nile tilapia. Overall, this is the first study on the effects of sampling season and catching site on the gut microbiota of Nile tilapia in Ethiopia. Future work is recommended to precisely explain the causes of these changes using large representative samples of Nile tilapia from different lakes and aquaculture farms. Oreochromis niloticus) is widely distributed in Africa and is one of the most preferred aquaculture fish species in the world [Nile tilapia , common carp (Cyprinus carpio), channel catfish  and deep bodied crucian carp (Carassius cuvieri) [Season is considered as the major factor affecting the composition of gut microbiota in various fish species . Al-Harbcuvieri) . Water tcuvieri) . The comcuvieri) ,18 and dcuvieri) . Moreovecuvieri) ,21,22.Lake Tana, the largest lake in Ethiopia, is situated on the basaltic plateau of the northwestern highlands of the country. Abundant wetlands, swamps, marshes and floodplains can be found all around the shores of the lake and its tributaries . Lake TaGiven the important roles of gut microbiota in host health, the assessment of gut microbiota may constitute an important aspect to manage the health of Nile tilapia and hence reduces production losses during aquaculture practices. Baseline information on the variations in gut microbiota in Nile tilapia sampled in different months representing different seasons is necessary to understand which microbial communities are dominant and most beneficial. However, there is no such study for Lake Tana, the lake with impressive fish diversity and uniqueness . Therefo2. Its trophic status changed to mesotrophic and eutrophic due to nutrient loads [The specimens were collected from Lake Tana and the Bahir Dar aquaculture facility centre at the Amhara Regional Agricultural Research Institute (ARARI), which is located close to Lake Tana. The aquaculture centre gets water from the lake. Lake Tana is a high-altitude lake (1800 m above sea level) and covers a surface area of 3200 kmnt loads . Lake Tant loads . The meant loads . The lakA total of 47 adult male Nile tilapia samples were collected from the landing site of Lake Tana, and seven samples were supplied by the Bahir Dar aquaculture facility centre . For the® DNA Isolation Kit  with some modifications. Two-step PCR was conducted to amplify the V3–V4 hypervariable region of the 16S rRNA gene for the Illumina MiSeq system  following Shokralla et al. [DNA extraction of gut contents was performed using the PowerFecala et al. . Detailsa et al. . High-thSequences were quality controlled with Cutadapt v. 0.11.1  by removThe gut microbiota structure between sampling months and habitat was assessed using the nonparametric univariate Mann–Whitney test. Comparison of bacterial taxa abundance between sampling months and between Lake Tana (collected in August) and the Bahir Dar aquaculture facility centre was performed using the linear discriminant analysis effect size (LEfSe). A nonparametric Kruskal–Wallis (KW) sum-rank test was used to detect features with significant differential abundance with respect to the groups compared, followed by linear discriminant analysis (LDA) to estimate the effect size of each differentially abundant feature . Core miAlpha diversity and beta diversity statistics were performed using the phyloseq package as used in MicrobiomeAnalyst . The alpA total of 130,848 sequences were obtained from the tilapia gut microbiome by Illumina MiSeq sequencing. Overall, 1055 OTUs were identified from all samples analysed. Rarefaction curves approached the saturation phase in all samples . Cetobacterium, Clostridium_sensu_stricto and Clostridium_XI , Fusobacteria , Bacteroidetes  and Cyanobacteria  across sampling months. The abundance of Fusobacteria was higher during April and August, and Firmicutes was lower during these months  clearly indicated significant differences for the phyla Firmicutes  effect size (LEfSe) analysis of the gut bacteria of Nile tilapia from Lake Tana at a default logarithmic LDA score of 2 showed that the taxon contributing most to the dissimilarity (effect size) for April was milarity a.p-values: 0.00020397, 0.00017971 and 8.96×10-05, respectively)  of the gut microbiota of samples from Lake Tana showed that the gut microbiota diversity varied significantly across the sampling months (ctively) a–c. We fp-value < 0.001 for PERMANOVA . In addition, the nonsignificant results of the PERMDISP test of the sampling months  indicated that group dispersions were similar to each other.To visually display patterns of beta diversity, principal coordinate analysis (PCoA) plots were made using the Bray-Curtis index distance method. Beta diversity analysis revealed a clear separation of samples according to sampling months (from April to August) with a global ERMANOVA a. FurtheIn our study, the core microbiota of all samples from Lake Tana and the Bahir Dar aquaculture facility centre comprised three phyla  a. The nuTo assess the gut microbiota differences between wild and aquaculture Nile tilapia samples, samples of Lake Tana were compared with Bahir Dar aquaculture facility centre samples. As indicated in p-value: 3.97×10-05), Chloroflexi , Fusobacteria  and Cyanobacteria , were found to be significantly different in Mann–Whitney test. The abundances of Proteobacteria, Chloroflexi and Cyanobacteria were higher in samples taken from the aquaculture centre than those taken from Lake Tana, and in contrast, the abundance of Fusobacteria was found to be higher in the Lake Tana samples  clearly showed that samples from the aquaculture facility centre had higher diversity than the wild Nile tilapia samples from Lake Tana  . In addition, the PERMDISP test showed a nonsignificant result . From this, the PERMANOVA result was found to be due to the average community composition differences.The Beta diversity analysis revealed a clear separation of samples from Lake Tana and the Bahir Dar aquaculture facility centre (PERMANOVA < 0.001) b. The ANFrom the comparison of Lake Tana and aquaculture facility centre samples, a total of 26 core genera were detected . At the The gut microbiota of fish is involved in the digestion of food materials and can influence the nutrition, growth, reproduction, general population dynamics and health status of the host fish . These mProteobacteria are often facultatively or obligately anaerobic and capable of tolerating a range of toxic conditions and are thought to contribute to the homeostasis of the anaerobic environment of the gut [Cetobacterium, which can produce vitamin B12 [12 requirement [The concept of a core gut microbiota has been suggested for certain fish species ,43,44. T the gut . Firmicu the gut ,47. Fuso the gut . Due to  the gut . In our amin B12 . Enrichmuirement .Cetobacterium, Clostridium_sensu_stricto and Clostridium_XI, which was different from other studies. Lukassen et al. [Cetobacterium as one of the predominant genera from the intestinal digesta of tilapia from a Brazilian reservoir. The observed discrepancies in microbiota composition may be due to several factors, including rearing environmental conditions, diet composition and genetic lineage. The gut bacterial communities of Nile tilapia larvae were significantly affected by the rearing environment (recirculating or active suspension systems) [The results of this study showed that the gut microbiota of Nile tilapia from Lake Tana were dominated by the phyla Firmicutes and Fusobacteria. These results were in agreement with the findings of a gut microbiota study performed on Nile tilapia of Lake Nasser in Egypt, where they reported that Fusobacteria was the dominant phylum . Ray et n et al.  showed Csystems) . Diet tysystems) ,58. Moresystems) .Our data further confirm the temporal variation in terms of bacterial abundance in accordance with a number of previous studies on catfish  and NileCetobacterium (Fusobacteria) and Paludibacter (Bacteroidetes) were found to be the main contributors to the variation in August  has also been reported by Dulski et al. [The monthly variation in gut microbiota diversity observed could be associated with the availability of food sources and physicochemical variation in the lake. The effect of the rearing environment on the gut microbiota of Nile tilapia was reported by Giatitis et al. , and likr season . Diversir season  for catfi et al. .O. niloticus [In our study, the aquaculture samples were dominated by Proteobacteria. Similar to our study, Proteobacteria was reported as the dominant phylum in the gut of genetically improved farmed tilapia  and juveiloticus . Proteobiloticus . The higiloticus ,74,75.In this study, there were significant differences between samples from Lake Tana and the Bahir Dar aquaculture facility centre in terms of the abundance of the phyla Proteobacteria, Chloroflexi, Fusobacteria and Cyanobacteria. In addition, a total of 30 significantly different genera were detected . The abuFundulus heteroclitus [Paralichthys adspersus [Tor tambroides [The alpha diversity clearly showed that the samples from the aquaculture facility centre had a higher diversity than the wild Nile tilapia samples from Lake Tana . This coroclitus , flatfisdspersus  and Malambroides  had a himbroides . For exambroides . The autmbroides . To the best of our knowledge, this is the first characterization of the gut microbiota of Nile tilapia using a 16S rRNA sequencing approach for Lake Tana. This study indicates evidence of an impact of season and captive effect on the gut microbiota of Nile tilapia. In our study, the diversity and composition of bacteria associated with the gut of Nile tilapia varied between sampling months and habitat types (wild and aquaculture centre). Firmicutes, Proteobacteria and Fusobacteria were abundant in the guts of all Nile tilapia samples, indicating that these phyla are members of the core microbiota. To precisely explain the role of seasonality, rearing habitat, physico-chemical parameters of the environment and food source diversity in shaping gut microbiota composition and diversity, further studies are needed with large representative samples of Nile tilapia from different lakes and aquaculture farms. However, this study significantly extends the base knowledge of the effect of seasonality and captivity on the gut microbiota of Nile tilapia.


Chromosomal evolution is a driving force underlying diversification and speciation in plants. Chromosome numbers and morphology, as well as nuclear DNA amount and composition, are very diverse in plants. During the last few decades, a range of new tools has been developed that allow for better insights into chromosomal and genome evolution in plants, far beyond chromosome counting and establishing karyotype structure. One of the most important tools is next generation sequencing (NGS) technology which has recently revolutionized nearly all biological disciplines. Genome skimming and whole-genome sequencing elevated classical cytogenetics to a new level of modern evolutionary cytogenomics  has become a prominent model system for studying plant genome and chromosomal evolution due to the availability of abundant genomic resources, such as the high-quality reference genome of Aethionema, is the sister clade to all remaining crucifer lineages. The evolution of the genomes and phylogenetic relationships of this tribe within the Brassicaceae is addressed by Walden et al. Crucifer genomic blocks (GBs) were used to infer syntenies between the genome sequences of Ae. arabicum and other crucifer genomes. In contrast to the largely conserved genomic structure of most non-polyploid Brassicaceae lineages, GBs in Aethionema were highly rearranged, similar to genomes in the tribe Arabideae. Thus, Arabideae  might also have diverged before the other major crucifer lineages.The Brassicaceae tribe Aethionemeae, containing a single genus Mandáková et al. report here on analyses of the genome structure of seven genera in this tribe using comparative chromosome painting. The ancestral Boechereae genome of n = 7 is inferred to be derived from an n = 8 genome by descending dysploidy. Although most of the Boechereae genera have a conserved genomic structure, chromosomal rearrangements (most often inversions) have accompanied the divergence of some genera and species within the tribe.The Brassicaceae tribe Boechereae comprises about 130 species in nine genera including a number of apomictic species of hybrid origin. Šimoníková et al. report on the development of chromosome/chromosome-arm specific oligo painting probes that allow for the identification of all chromosomes in Musa spp. and for physically anchoring pseudomolecules of the reference genome sequence to individual chromosomes. This has, for the first time, allowed us to gain deeper insight into the evolution of A, B, and S banana genomes, as well as identify chromosomal translocations that have accompanied genome evolution and speciation in Musa.Elucidating chromosomal rearrangements leading to karyotype restructuring requires the use of appropriate genomic tools, as has been elegantly shown for numerous crucifers. Another family for which such tools have now been developed is the Musaceae (bananas). Here, Costa et al. in their analysis of the Amaryllidaceae subfamily Allioideae . The three tribes of this subfamily  have evolved toward their current intercontinental disjunctions to the Northern Hemisphere, South America, and Southern Africa, respectively. Using a dated phylogeny, together with models of chromosome number evolution and diversification rate analysis, the authors reconstruct the biogeography of the Allioideae to be the result of vicariance due to the break-up of Gondwana; centric fissions are identified as the most likely mechanism of karyotypic diversification in the tribe Gilliesieae.Phylogenetic hypotheses and molecular clock analyses have become essential components of studies on karyotype evolution, as demonstrated by Udall et al. investigate the chromosomal rearrangements mediating n−1 descending dysploidy in two genera (Gossypioides and Kokia), sister to the cotton genus (Gossypium), using high-quality genome assemblies and Hi-C data. These genome comparisons show that the two descending dysploidies were not mediated by simple recombination between two ancestral chromosomes, but that multiple steps were required to generate the extant genome structures; the evolution of plant chromosomes can be complex and does not necessarily follow the most parsimonious pathways.Chromosome number variation in eukaryotes results from frequent, sometimes very complex, chromosomal rearrangements, which may eventually lead to the reduction of chromosomes, i.e., genetic linkage groups (descending dysploidy). Although such reductions are usually easily detectable in comparative analyses, reconstructing the mechanism underlying these changes is more challenging. Here, Huang and Rieseberg review the latest research on chromosome inversions in plant genomes, focusing on the role of inversions in speciation in the presence of gene flow and models of inversion fixation. The authors also discuss sequential inversions and the purported functional link between suppressed recombination and the differentiation of sex chromosomes in plants, such as in the dioecious Silene species .As plant genome assemblies are steadily being improved by the increasing read length, we are obtaining a better view of the incidence, scale and structure of chromosomal rearrangements in plant genomes. Inversions are probably the most common chromosomal rearrangements and are ubiquitous across the plant kingdom. Here timely, Whole genome duplications (WGD) have accompanied the evolution of angiosperms since the beginning. Past and ongoing rounds of polyploidization continue to shape the majority of extant plant genomes.Levin offers a personal view on the continued impact of polyploidization on plant diversification. Specifically, the author focuses on mechanisms of polyploid diversification, reasons why WGDs are not followed by immediate diversification thrusts, and on polyploidy going forward. The polyploid wave which began roughly 60 million years ago will, as Levin suggests, continue to rise in the coming millennia, in part due to increasing climate and environmental changes and the impact of other anthropogenic drivers. The proportion of polyploid species might also increase as polyploids are likely to gain an advantage over diploids in changing environments. The increase in polyploidy in the coming millennia might also involve the frequent formation of higher level polyploids and post-polyploid dysploids.In a perspective article, Glombik et al. The genomes of newly established interspecific hybrids often undergo dramatic changes, including chromosomal rearrangements, changes in the amount and localization of repetitive DNAs, and gene expression modifications. Successful hybridizations and WGD events are followed by genomic and cytological diploidization events. One of the most significant aspects of this process is establishment of diploid-like chromosome pairing. The authors provide an overview of the current knowledge of genomic changes in interspecific homoploid and alloploid hybrids, focusing on chromosome pairing, and discuss parental genome dominance at various levels of organization in relation to the stability of hybrid genomes.Another process that has a significant impact on plant genome evolution is hybridization. The genomic consequences of such genome mergers alone and in combination with WGD are addressed here in a review paper by Hardigan et al. analyse the genome structure and diversity of the allo-octoploid cultivated strawberry (Fragaria × ananassa). Comparative genomic analyses show that geographically diverse wild octoploids were diploidized, nearly completely collinear, and retained strong macro-synteny with their diploid progenitor taxa. The conserved genome structure of octoploid Fragaria species allows for unimpeded gene flow during repeated cycles of homoploid hybridization without the formation of reproductive barriers or loss of fertility.Domesticated plant species often have polyploid ancestry. Here, Silene  includes both dioecious and gynodioecious species. Two dioecious species, Silene latifolia and S. dioica, possess heteromorphic sex chromosomes and an XX/XY sex determination system. Here, Bačovský et al. addressed the origin of X/Y sex chromosomes in Silene by mapping an oligo painting probe enriched for X-linked scaffolds to chromosomes of two dioecious and two gynodioecious species. The results suggest shared ancestry of the sex chromosomes in the two dioecious species, accompanied by subsequent extensive chromosomal rearrangements.Dioecy is found in 5–6% of angiosperms genera  is an example of one of the families in which holocentricity has been demonstrated and is shown to be associated with frequent dysploid and polyploidization events. Here, Cuscuta . In most organisms, centromeres are determined epigenetically by the presence of the centromere-specific histone variant CENH3. In holocentrics, CENH3 is typically distributed along the entire chromosome length. Here, Oliveira et al. identified CENH3 genes in the holocentric plant Cuscuta europaea and found a unique pattern of CENH3 distribution along mitotic chromosomes in this species. Two major CENH3 variants were expressed and co-localized in one to three discrete heterochromatic regions per chromosome that contained the satellite repeat CUS-TR24, whereas the rest of the chromatin appeared to be devoid of CENH3. In contrast, the mitotic spindle microtubules attached at uniform density along the entire chromosome length. The data suggest that CENH3 either lost its function or acts in parallel to an additional CENH3-free mechanism of kinetochore assembly.Another genus that harbors species with both monocentric and holocentric chromosomes is parasitic The tremendous amount of NGS data available allows for detailed insights into plant genome composition and the dynamics of different types of repetitive DNA during diversification and speciation. Apart from a general characterization of the repeat types, NGS data analyses provide the means to conduct comprehensive comparative analyses on the genomic repeat composition of any plant species and their contribution to genome size change, as well as to analyse the evolution of individual repeat families.McCann et al. analyse the evolution of repeats in a group of 13 closely related diploid species of the genus Melampodium  differing ca. 4.5-fold in genome size despite possessing the same base chromosome number of x = 10. Analyses of genome skimming data using the RepeatExplorer pipeline . Both species possess karyotypes with one large and five small chromosome pairs. The large chromosome pair of E. bulbosa additionally carries pericentric inversion in permanent heterozygosity in one of its chromosomes. Here, Báez et al. investigate the repeatomes of both species to test whether the permanent chromosomal inversion in the large chromosome influenced the dynamics of repetitive DNA sequences. While the accumulation of repeats differed between large and small chromosomes, the most abundant repeats showed a similar chromosomal distribution in both homologs of the large pair, regardless of the presence of the permanent inversion.Several angiosperm families have evolved biomodal karyotypes that consist of chromosomes in two contrasting size classes. One such system is found in the neotropical genus Jedlicka et al. measure the LTR divergence of thousands of LTR retrotransposons to determine their age and evolutionary dynamics in 15 plant species representing major lineages across Viridiplantae and ranging nearly 10-fold in genome size. The authors hypothesize that gene conversion might have contributed to the higher observed DNA sequence divergence of LTR regions of nested retrotransposon copies compared to the divergence of LTRs of pre-existing retrotransposons into which they had been inserted. The negative correlation between the frequency of gene conversion and the abundance of solo LTRs suggests interference between gene conversion and ectopic recombination.Long terminal repeat (LTR) retrotransposons constitute the most significant part of most plant genomes and the periodical bursts of their activity play an important role in genome size changes, among others. LTR regions are used to estimate the age of retrotransposons, and thus, to date periods of their activity. Here, n. The review article by Peska and Garcia provides an up-to-date overview of the diversity of plant telomeres following recent discoveries of novel telomeric variants in various plant groups. The authors also provide an overview of methods used for identification of telomeric motifs.Another type of tandem repeat that is ubiquitous in nearly all known eukaryotic genomes is found in the telomeres, structures that protect and maintain the ends of linear chromosomes. Telomeric DNA sequences are usually tandemly arranged minisatellites, typically following the formula (TxAyGz)Garcia et al. analyzed 5S rDNA arrays in over 80 homoploid and alloploid hybrid species of different evolutionary ages using graph clustering implemented in the RepeatExplorer pipeline. Comparative analysis of 5S rDNAs in hybrids and their progenitor species allowed for identification of homoeologous 5S rRNA gene families in both evolutionarily young and older taxa, thereby facilitating inferences of their origin. The shapes of cluster graphs robustly reflect the organization and homogeneity of 5S rDNA repeats within each of the parental subgenomes. The authors proposed that this approach, together with cytogenetic analyses, might assist with inferring parental origin of the hybrid taxa.Tandemly repeated ribosomal genes (rRNA genes) are widely used in molecular and cytogenetic studies, including analyses of parental (sub)genomes in homoploid and allopolyploid hybrids. Here, This Research Topic presents most recent advances in the field of chromosomal plant evolution, broadly covering most important topics centered around plant genome evolution on all levels of its organization as well as its temporal and spatial aspects. This collection of papers clearly demonstrates that the rapid development of genomic tools and approaches, such as NGS, high throughput data analyses and novel cytogenomic techniques will continue to facilitate the analyses of more wild plant groups and will allow for in-depth analyses of various aspects of chromosomal and genome evolution. Thus, over the coming years we will continue to test old hypotheses in light of novel methodologies and data, and unravel new patterns and phenomena that contribute to genome evolution in plants.MAL and HW-S drafted the manuscript. Both authors revised and approved the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Seizure detection algorithms are often optimized to detect seizures from the epileptogenic cortex. However, in non-localizable epilepsies, the thalamus is frequently targeted for neuromodulation. Developing a reliable seizure detection algorithm from thalamic SEEG may facilitate the translation of closed-loop neuromodulation. Deep learning algorithms promise reliable seizure detectors, but the major impediment is the lack of larger samples of curated ictal thalamic SEEG needed for training classifiers. We aimed to investigate if synthetic data generated by temporal Generative Adversarial Networks (TGAN) can inflate the sample size to improve the performance of a deep learning classifier of ictal and interictal states from limited samples of thalamic SEEG. Thalamic SEEG from 13 patients (84 seizures) was obtained during stereo EEG evaluation for epilepsy surgery. Overall, TGAN generated synthetic data augmented the performance of the bidirectional Long-Short Term Memory (BiLSTM) performance in classifying thalamic ictal and baseline states. Adding synthetic data improved the accuracy of the detection model by 18.5%. Importantly, this approach can be applied to classify electrographic seizure onset patterns or develop patient-specific seizure detectors from implanted neuromodulation devices. Increass cohort . Accurats cohort –12. Howes cohort .There are multiple challenges in developing seizure detectors from regions like the thalamic subnuclei, i.e., (a) the thalami have lower power spectra, and the spectral contents are significantly different from the epileptogenic cortex during interictal and seizure substages , (b) theSeveral deep learning algorithms have been proposed for automatic seizure detection. These include artificial neural networks, convolution, and deep convolution-based seizure detection systems. Amongst them, a widely popular and high-performing method for seizure classification using EEG is the use of temporal models such as recurrent neural network (RNN) and its variants, including Long-Short Term Memory (LSTM) BiN = 84 from 13 patients) and baseline interictal data (Length: 550 s prior to seizure) were obtained. The demographic details of the study participants are detailed in Patients diagnosed with drug-refractory temporal lobe epilepsy (TLE) who underwent stereoelectroencephalography (SEEG) for localization of seizure focus were included in the study. The indication for SEEG was clinically necessary and determined in a multidisciplinary patient management conference. Within this cohort, consenting adults who had thalamic implantation for research were included in the present analysis. The multi-step consenting and evaluation process has been described in detail in our previous studies , 14, 17.All SEEG implantation procedures were performed using robotic assistance  . Once implanted, the patients were monitored over 4–12 days in the epilepsy monitoring unit (EMU). SEED data as recorded using Natus Quantum . Signals were referenced to a common extracranial electrode placed posteriorly in the occiput near the hairline.The details of the accuracy of the implantation strategy have been reported in our previous study . Here weThe time of the seizure onsets and offsets was annotated by a board-certified epileptologist (SP). Seizure onset in the cortex was marked as “unequivocal EEG onset” (UEO) at the earliest occurrence of rhythmic or repetitive spikes that was distinct from the background activity. SEEG segments were clipped to include 10 min before this UEO and 10 min after seizure termination. Four different clinical seizure types were included for analysis: focal aware seizures (FAS), focal impaired awareness seizures (FIAS), and focal to bilateral tonic clonic seizures (FBTCS) . Epochs The input to the BiLSTM classifier was the interictal baseline and ictal thalamic EEG data. The baseline and the ictal data from the 84 seizures (13 subjects) were initially grouped by the subject identification. Since the length of ictal data was variable, we chose the length of the shortest seizure for any given subject  as the length of the analyzable data. To avoid a discrepancy in the length of data between ictal and baseline segments, we chose a similar 14 s length of SEEG data from the initial segment of the baseline data. The input to the BiLSTM classifier is a 2D array of data. Hence, the 14 s of the data were then clipped into multiple 1 s epochs and rearranged into a two-dimensional array of 14 × 2,048 samples .Principally, the LSTM network only obtains information from the previous input observations but cannot use that information for future input observations. However, the BiLSTM model, composed of two independent LSTM networks, can transmit information bi-directionally and increase the learning ability of the system output  23). Si. Si23). GANs learn and generate synthetic data by preserving the data distributions. For a generation of sequential data, the temporal dynamics need to be preserved. Yoon et al.  proposedR2) values and the percentage difference between original and synthetic data (t-distributed stochastic neighbor embedding (t-SNE) analysis was performed to visualize if the ictal and baseline data could be better segregated using the original or the synthetic data. T-SNEs were generated in MATLAB using the “exact” algorithm, with Mahalanobis distance, the perplexity of 50, and PCA dimensions of 3.The second level BiLSTM analysis classifies ictal and baseline states based on the synthetic TGAN data . Hence itic data . FinallyTo estimate the performance of the BiLSTM following metrics were computed: sensitivity , specificity, accuracy , positive predictive value (PPV or precision), F1-score, and area under the curve (AUC). The difference in the performance of the BiLSTM classifiers for original and synthetic data was visualized using ROC (receiver operating characteristic) curve by testing the relationship between sensitivity and 1-specificity.The BiLSTM and TGAN models were tested in Python, and t-SNE analysis was performed in MATLAB. We utilized Keras , scikit-Thirteen subjects were included in the study, with 10 had electrodes localized to the anterior nucleus of the thalamus (ANT) and 3 in the centrolateral thalamic nuclei . CT braiR2) of the original and synthetic data . Similarly, there was no difference in the mean absolute error (MAE) of original and synthetic data . Similarly, the TGAN augmented data synthesized during the ictal period did not differ from the original data in R2  and the MAE .The TGAN generated synthetic data was similar to the original SEEG data. At baseline, there was no difference between the mean coefficient of determination  is the Responsive neurostimulation. To date this device has been extensively used to target neuromodulation in the cortical regions. This device uses amplitude threshold and line-length as the main seizure detection algorithms. There have been anecdotal reports of implanting the human thalamus with RNS, where the seizures were still detected in the cortex but the stimulation was performed in the ANT. There has been growing literature that thalamus is involved early in focal seizures, particularly in TLE. Some studies have also tried to detect seizures from human thalamus. This detection of seizures from the human thalamus and understanding the pattern of involvement of thalamus on focal seizures is of utmost importance while developing closed-loop DBS systems. To date, it has been shown that ANT DBS (open loop) has had great success in patients with drug resistant epilepsy particularly those patients who are negative for a lesion on the MRI, with a median seizure frequency reduction of 75% at 7 years of therapy.Newer sensing-enabled DBS systems have been approved by the FDA since 2018 in the practice of epilepsy and since 2002 for Parkinson's disease. These devices offer closed loop sensing and diary functions (record of events) to monitor symptoms and tailor therapeutic stimulation. A recent study has shown that closed loop neurostimulation within the human thalamus has shown a ≥50% reduction in seizure frequency with no adverse effects on mood, memory or behavior. With the advent of such sensing-enabled closed loop systems, there is a clinical need to develop seizure detection algorithms from the thalamic SEEG and not just from the cortical seizure onset zone. One of the most critical steps in enabling sensing, is to develop patient-specific detection based on individual subject's thalamic seizure patterns. Often, the data obtained from a single subject is limited and hence the translation of deep learning approaches has been hindered by the lack of larger samples of curated ictal thalamic SEEG needed for training these classifiers. Here, we demonstrate the utility of generating synthetic data using GAN that can augment the sample size and improve the performance of BiLSTM. Importantly, this approach can be applied to classify electrographic seizure onset patterns or develop patient-specific seizure detectors from implanted neuromodulation devices. In summary, we found that Time-GAN helps generate synthetic time series that resemble the original data, with a very small mean absolute error rate of 0.1 ± 0.5% between the original and the augmented data. In fact, when this time-GAN augmented data was used in BiLSTM classifier to detect the ictal state, we noticed that the accuracy of the classifier improved by 18.5%, sensitivity by 13% and PPV by 10% when compared to classifying using the original data. Though marginal, such an improvement is promising and further refinement of such models are required to optimize seizure detection in the thalamus.As expected, the performance of these classifiers was higher with biosignals obtained directly from the cortex than from the scalp which is likely to be closer to the seizure focus. In those studies, the sample size for deep learning consisted of over 100 ictal EEG data. The main motivation of this study is to highlight how data augmentation techniques can improve the accuracy of the classifier, albeit a lower overall performance of our classifier in comparison to other studies. Even when data is smaller (84 seizures), we can use data-augmentation methods to enhance the performance of the classifier (accuracy improved by 18% in our current study) and improve the detection performed in subcortical neuromodulatory targets such as the thalamus, which are distant and outside the seizure cortex. Wei et al.  were amoGANs, are deep-learning algorithms where two competing networks, namely the generator and the discriminator, compete against each other until the generator generates artificial data of high quality. According to Goodfellow et al. , “the geThere is a proof-of-concept study evaluating the use of synthetic data in augmenting sample size for deep learning. The study needs to be extended to a larger cohort with thalamic recordings of seizures and interictal baseline. One major challenge of the temporal GAN model is that it is computationally intense and consumes time to learn or converge to local minima and hence slows the training process. Another limitation of GAN is that the presence of discontinuous  data may synthesize incorrect data. In our study, the duration of the data used for time-GAN analysis was 14 s based on the shortest duration of the ictal event from our cohort and in the future the results need to be optimized to individual patients, in whom the seizure durations are likely to vary significantly. This will also have a bearing on minimizing the detection latencies in the future models. Such sophisticated models will help build closed-loop neuromodulation strategies where early seizure detection can be used to pace the brain to abort seizures. Regarding the size of our data-set, we used 63 seizures from 11 patients' data for training and 21 seizures from 2 patients's data for testing the BiLSTM and Time-GAN models. While our study did show a reasonable improvement in the accuracy of the BiLSTM models and can be used as a proof of concept, in the future it is essential to validate this using random sampling of the patients' data and at individual subject level to emphasize and validate its clinical use. Also, cross validation across all patients' data would further strengthen the validity of the model. Since GAN is time consuming and resource demanding, our purpose was not to run it on all subjects but show how even in few subjects an improved accuracy can be obtained. Another limitation is that we did not determine the exact cause of improved accuracy and test the fidelity, diversity, and generalization of the data augmentation method, i.e., TGAN. These measures help determine the point at which the generative model surpasses and fools the discriminative network. Once the TGAN augmented data robustly mimics the real data, the TGAN-output is then used as the input in BiLSTM models improve accuracy. TGANs supersede BiLSTMs at finding a better low dimensional representation and hence may contribute to improved accuracy. In our study, we were interested in showing if the TGAN is able to produce synthetic data effectively from available limited samples and whether that use of the synthetic data shows elevated performance as compared to the using the original data only and a more detailed validation of TGAN was to voluminous for this study.The ability to detect seizures from the thalamus- a structure remote to the seizure focus is clinically necessary for monitoring seizure burden in drug-resistant epilepsies where seizure foci are non-localizable. In this study, we demonstrate the use of synthetic data to augment sample size and improve deep learning performance in detecting seizures from the human thalamic SEEG. The proposed framework should be extended to a larger cohort of patients with thalamic DBS in multifocal epilepsies.patilabuab@gmail.com.The datasets presented in this article are not readily available because the data is controlled by IRB. Requests to access the datasets should be directed to The studies involving human participants were reviewed and approved by University of Alabama Birmingham Medical Center. The patients/participants provided their written informed consent to participate in this study.BG and GC analyzed, interpreted, and helped in writing the mansucript. KB supervised data analysis, interpretation, and revised the mansucript. RB and NN provided analytical tools and helped in analysis of the data. SP obtained IRB approval, designed the study, interpreted the data, and wrote and revised the manuscript. All authors approved the final version of the manuscript.SP would like to acknowledge the USA National Science Foundation Grant (NSF RII-2 FEC OIA-1632891) that provided protected time for research.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.