Radio Frequency Fingerprinting (RFF) is often proposed as an authentication mechanism for wireless device security, but application of existing techniques in multi-channel scenarios is limited because prior models were created and evaluated using bursts from a single frequency channel without considering the effects of multi-channel operation. Our research evaluated the multi-channel performance of four single-channel models with increasing complexity, to include a simple discriminant analysis model and three neural networks. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models can lead to a deterioration in performance from MCC > 0.9 (excellent) down to MCC < 0.05 (random guess), indicating that single-channel models may not maintain performance across all channels used by the transmitter in realistic operation. We proposed a training data selection technique to create multi-channel models which outperform single-channel models, improving the cross-channel average MCC from 0.657 to 0.957 and achieving frequency channel-agnostic performance. When evaluated in the presence of noise, multi-channel discriminant analysis models showed reduced performance, but multi-channel neural networks maintained or surpassed single-channel neural network model performance, indicating additional robustness of multi-channel neural networks in the presence of noise. Physical-layer emitter identification, known as RFF or Specific Emitter Identification (SEI), is often proposed as a means to bolster communications security . The undPrior related RFF research has trained ML models to identify devices by using bursts received on a single frequency channel. However, modern communications protocols often employ multiple frequency channels to enable simultaneous users and interference avoidance. For example, WiFi IEEE 802.11 b/g/n) .11 b/g/n and WireWhen the channel changes, the carrier frequency used by both the transmitter and receiver shifts to the center of the new channel. This change in carrier frequency affects the signal distortions because radio hardware components such as Phase-Locked Loops (PLLs), amplifiers, and antennas operate irregularly at different frequencies. Furthermore, the Radio Frequency (RF) environment also varies with frequency channel because different sources of interference are present at different frequencies. Since most RFF research predominantly considers bursts received on a single channel, it is not clear whether the performance achieved by those research efforts generally extends to multiple frequency channel operation.For instance, when researchers in ,8,9 collIn works providing single channel demonstrations, channel carrier frequency details are often omitted, given that the RF signals are commonly down-converted to accommodate baseband fingerprint generation. For example, ZigBee research in ,11,12,13Finally, there are researchers who implicitly use collections from multiple carrier frequencies but make no explicit declaration in their work. For instance, any researchers using the Defense Advanced Research Projects Agency (DARPA) Radio Frequency Machine Learning (RFML) WiFi dataset, such as ,16,17, hTo our knowledge, no prior work has considered the sensitivity of RFF performance to different frequency channels; our results suggest that frequency channel must be considered. Signal bursts were collected using a wideband SDR receiver, which captured signals from eight IEEE 802.15.4-based devices communicating across 15 frequency channels. Each individual burst was filtered and categorized based on the frequency channel within which it was received. RFF models were trained and tested using data from different channel combinations to evaluate the effects of frequency channel to performance, which was reported using the multi-class MCC. Machine learning models included a Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) model with expert-designed features, a shallow fully-connected Artificial Neural Network (ANN), a Low-Capacity Convolutional Neural Network (LCCNN), and a High-Capacity Convolutional Neural Network (HCCNN).A first-of-its-kind evaluation of the sensitivity of single-channel models to multi-channel datasets. The evaluation suggests that failing to account for frequency channel during training can lead to a deterioration in performance from MCC > A training data selection technique to construct multi-channel models that can outperform single-channel models, with average cross-channel MCC improving from 0.657 to 0.957. The findings indicate that frequency-agnostic variability can be learned from a small subset of channels and can be leveraged to improve the generalizability of RFF models across all channels (Experiment B).An assessment of multi-channel models against Additive White Gaussian Noise (AWGN) that demonstrated the advantage of multi-channel models in noise performance depended on model type and noise level. Multi-channel neural networks approximately maintained or surpassed single-channel performance, but multi-channel MDA/ML models were consistently outperformed by their single-channel counterparts (Experiment C).The key contributions of our work include:The rest of this paper is structured as follows: an overview of the state-of-the-art in RFF is provided in RFF is fundamentally an ML classification problem, where discriminative features are leveraged to distinguish individual devices. Generation and down-selection of the best features remains an open area of research . For insAnother area of research focuses on bolstering the practicality of deploying RFF mechanisms in operational environments. Notably, ref.  exploresFinally, the sensitivity to deployment variability is considered extensively in recent works. For instance, variability stemming from time, location, and receiver configuration are studied extensively in , and varOur work extends the research in deployment variability by exploring the impact to model performance from the use of different frequency channels. Consistent with the works by ,25, we cRFF and SEI are broad areas of research that include everything from the fingerprinting of personal and industrial communications devices, to radar and satellite identification. Our work leverages communications devices, but it is likely that any transmitter which operates across multiple carrier frequencies would exhibit the effects highlighted in this research.The protocol used in this study was WirelessHART, which implements the PHY-layer in the IEEE 802.15.4 specification. That specification divides the 2.4-GHz Industrial, Scientific and Medical (ISM) band into 15 × 5 MHz channels, each with a different carrier frequency. It is not clear whether the performance improvements of the multi-channel models shown in this research depend on the bandwidth of the frequency channel. For instance, models for Bluetooth, which employs narrower 80 × 1 MHz channels, may need more frequency channels or further-spaced channels in the training set to achieve the same levels of performance improvements. The impact of channel bandwidth to multi-channel models is left as future work.Another key WirelessHART feature is that it allows the use of mesh networking, whereby each device can act as a relay of data for neighboring devices. Mesh networking is also becoming increasingly popular in home automation, particularly with the adoption of new Internet of Things (IoT)-centric protocols such as Thread . The disAlthough our work is limited to preamble-based fingerprinting, in part due to its recent success in classifying WirelessHART devices , anotherIn the wideband collection for this work, only one WirelessHART device was configured to communicate at a time. Under real operational conditions, multiple devices would be able to communicate simultaneously on separate frequency channels, introducing the potential for Adjacent Channel Interference (ACI). ACI occurs when energy emitted on one frequency channel leaks into adjacent frequency channels. At a minimum, this energy leakage could raise the noise floor, reducing the Signal-to-Noise Ratio (SNR) and potentially degrading model performance. A study of the specific effects of ACI to model performance is left as future work.In the end, our goal was to demonstrate that frequency channel can have a significant effect on RFF models in the hopes of encouraging future researchers to take it into consideration.A multi-channel WirelessHART dataset was collected and validated for the purpose of this study. Precautions were taken to minimize the effects stemming from the RF environment and receiver, as our focus was the study of transmitter effects. This Section covers the methodology used for collection and pre-processing and includes a description of the training, validation, and evaluation dataset(s).The WirelessHART communications protocol, used by industrial sensors to transmit stateful information  between industrial sensors and to human-machine interfaces, implements the Offset-Quadrature Phase Shift Keying (O-QPSK) physical layer from IEEE 802.15.4, the standard for low-rate personal area networks [WirelessHART was selected as the candidate protocol for collection for several reasons. First, the IEEE 802.15.4-based protocol is representative of many of the basic low-power IoT devices being deployed across the globe at an exponentially increasing rate. Second, it operates in a common frequency band using a manageable number of channels. And finally, recent research in  employedWirelessHART devices were allowed to communicate directly with the WirelessHART gateway one at a time, while an SDR captured device emissions. During collection, the device was placed 8 ft. from the gateway, and the SDR antenna was positioned 18 in. from the device. The eight devices observed are listed in Bursts were captured using a USRP X310 with a 100 MHz bandwidth centered at 2.440 GHz, enabling simultaneous collection of all 15 WirelessHART channels. Burst detection for the wideband data was performed “on-the-fly” by thresholding the power of the received signal. This enabled the collector to be efficient with its limited hard disk space. In particular, given a received signal, Three precautions were taken in an attempt to minimize effects from the RF environment. First, collections were done within a ranch-style suburban household, far away from other wireless emitters relative to the distance between emitter and receiver. Second, all collections were performed in the same physical location, meaning that any RF effects due to interference with nearby non-emitters would likely manifest in the same way for all devices. Finally, collections were performed in sets of 10 K bursts, started at random times during the day throughout the course of two weeks. This ensured any potential time-dependent sources of interference were well distributed across devices.Collecting data in sets of 10 K bursts also forced the WirelessHART gateway to assign a new frequency hopping scheme to each device when it re-established communication with the gateway (per the WirelessHART specification ). This rOur approach for burst detection did not guarantee that the received signal came from the expected WirelessHART device; it could be the case that strong interference triggered the burst detector. Further protocol-specific analysis was performed to ensure the validity of each collected burst. The most straightforward way to do this was to detect and verify the structure of the preamble, and subsequently read message-level bits to verify that the transmitted source address corresponded to that listed in Unlike , which lAll frequency correction was achieved through multiplication by complex sinusoid. In particular, for a given burst After frequency correction, a 2-MHz 4th order Butterworth low pass filter was applied to each burst for noise suppression outside of baseband.Preamble detection was done in the time domain by correlating the peak-normalized filtered burst with a generated preamble. The index with maximum-amplitude correlation was assumed to be the starting index of the burst preamble. As a measure of similarity, the corresponding correlation coefficient was also used for WirelessHART preamble verification; if the coefficient was too low, the burst was discarded.The angle of the correlation coefficient was also an estimate of the phase offset between the burst preamble and the generated preamble. It was used to correct phase through multiplication by complex exponential. Given a burst A note on frequency and phase correction: in operational receivers, both phase and frequency correction are done via PLLs . PLLs coThe downconverted, phase-corrected burst was demodulated, and the symbols were mapped to message bits. The source address was extracted from the parsed message and compared to the known device addresses listed in SNR estimation of the validated bursts was necessary to enable model evaluation against noise. All SNR estimates were performed after the 2-MHz low pass filter was applied.Noise power and signal-plus-noise power were estimated on a per-burst basis directly from the captured burst. The signal-plus-noise region from which signal-plus-noise power  assess the sensitivity of single-channel models to a multi-channel dataset, (ii) determine whether carefully-constructed multi-channel models generalized better than single-channel models, and (iii) determine whether multi-channels models gained more resiliency to noise. This section provides a description of the four models used across all tests, followed by individual methodologies, results, and analyses for the three experiments.Four typical RFF models with varying levels of complexity were selected as candidates for experimentation in this work. All four model types were used in all three experiments. This section describes the structure of each model in detail.Multiple Discriminant Analysis/Maximum Likelihood. This model type leverages the commonly-used Time-Domain Distinct Native Attribute (TD-DNA) feature set ,34,35, aFully-Connected Artificial Neural Network. Low-Capacity Convolutional Neural Network. Like the ANN, the LCCNN presented in High-Capacity Convolutional Neural Network. This high-capacity model was inspired by ORACLE, a CNN that extracts and classifies injected I-Q imbalance . This moMDA/ML models were trained by fitting a QDA model onto the dimensionally-reduced TD-DNA features. All neural networks were trained using Stochastic Gradient Descent with the Adam optimizer and learning rate of K classes, per-class classification accuracy degrades to Performance for RFF models is typically reported as per-class classification accuracy, which is the accuracy averaged across all classes. For K classes, MCC is calculated ask was predicted, c is the total number of correct predictions, and s is the total number of predictions [MCC is a performance metric first designed for binary class classification models, derived by Matthews as a discrete version of the correlation coefficient . An MCC Gorodkin , preservdictions . For ourThe goal in this experiment was to determine whether the performance of models trained on a single channel  extends to other frequency channels. To that end, single-channel models were evaluated against a dataset containing bursts from multiple channels, and the per-channel evaluation performance was reported.The training and validation datasets described in As expected, none of the models generalized across all channels when trained on data from a single channel, but some models did generalize better than others. In particular, when the models were trained on data from Channel 7, they generalized well across a wide swath of channels , whereas when the models were trained on Channel 14, performance only roughly generalized to Channel 13 and only for MCA/ML and HCCNN. In the worst case, signals from distant channels were classified by the Channel 14 model with success no better than random guess.Between the model types, the MDA/ML model and the HCCNN model were the most competitive, with the MDA/ML model performing best most of the time. The LCCNN and ANN performed similarly most of the time, but the LCCNN was especially bad at generalizing when it was trained on Channel 14. These observations suggest that the MDA/ML and HCCNN model were better at inherently targeting variability that existed irrespective of frequency channel.In general, frequency-dependent signal distortions appeared to be on a spectrum, where signals from nearby channels exhibited similar distortions. However, it is not clear how the channel width might affect those distortions. It could be the case that with narrower channels, like the 1 MHz channels in Bluetooth, performance extends across more channels. Such exploration of the effects of channel width are interesting future work.Additionally, recall that the transmitter, RF environment, and receiver distortions were not decoupled. Thus, it is possible that a non-trivial part of the variability between frequency channels was imposed by non-transmitter sources. Finding a way to decouple (or at least reduce the coupling) across the three sources would enable more flexible applications  and could help researchers more precisely target the signal alterations imposed by hardware components in the transmitter. Regardless of its source, the variability must be considered to achieve good RFF performance.A natural follow-up experiment was to include data from multiple channels in the training set to determine if this improved model generalizability. This experiment is covered in the following section.The focus of Experiment A was to demonstrate that single-channel models did not always perform well across all frequency channels. In Experiment B, the goal was to determine whether including bursts from multiple channels during training improves performance. During training, we deliberately use data from an increasing number of channels, relatively spread throughout the 80 MHz band to create “multi-channel models.” These models were tested against the same evaluation set from Experiment A, which included bursts from all 15 channels.The same four models presented in Generally, the performance of ML models is influenced by the number of observations provided during training. To enable comparison between multi-channel models from Experiment B and single-channel models from Experiment A, the size of the training datasets was limited to no more than 5000 observations per device . Concretely, two-channel data subsets contained 2500 observations per device per channel , three-channel data subsets contained 1666 observations/device per channel (4998 observations/device), four-channel subsets had 1250 observations per device per channel (5000 observations/device), and the all-channel  subset contained only 333 observations per device per channel (4995 observations/device). Channel combinations were selected to explore how channel coverage impacted performance. The 11 data subsets used for Experiment B are listed in A representative sample of the performance results for the multi-frequency models are depicted in Comparing the Channel 14 single-channel models from Experiment A in Two trends are evident from these metrics: (i) model performance generally improved when channels were added to the training set, and (ii) the performance of 4-channel models approached the performance of 15-channel models. Note that even the worst performing single-channel model type, i.e., the LCCNN, improved its Regardless, these trends again support the existence of frequency-irrespective variability. Multi-channel models were better suited to learn that variability, even with limited  exposure to the spectrum. Furthermore, the MDA/ML model consistently generalized better than its neural network counterparts, suggesting that the frequency-irrespective variability in our particular experimental setup can be effectively extracted through the TD-DNA fingerprint generation process.In practice, it is desirable for RFF models to perform well across all frequency channels, but that is not the only requirement. Models should also perform well under the presence of environmental noise. Thus, researchers often report model performance across multiple levels of noise, modeled as AWGN. Experiment C explores whether multi-channel models gain any performance advantages over single-channel models under varying noise conditions.The goal of the final experiment was to determine whether multi-channel models gained any performance advantages over single-channel models in noisy RF environments. Tested models included the Channel 7 single-channel models from Experiment A, and new “all-channel/all-data” multi-channel models built exclusively for this experiment. All models were evaluated with bursts from Channel 7 with varying SNR levels adjusted synthetically through the addition of power-scaled AWGN.Two datasets were used for training: a single-channel set, where the models were only exposed to Channel 7 data (5 K observations/device), and a multi-channel set, which included the full training dataset (75 K observations/device). Performance was captured on a subset of the full evaluation set with only Channel 7 bursts (1 K observations/device). The full training dataset was used to maximize the exposure of multi-channel models to Channel 7 data, enabling them to better compete against the single-channel models. Multi-channel models were at an inherent disadvantage because they had to overcome both channel and noise, whereas single-channel models only had to overcome noise.Each model was trained and evaluated at the same SNR level, which was adjusted to simulate increasingly harsh operational environments. Individual bursts were adjusted to the desired SNR through the addition of AWGN. For each burst, a noise realization was generated from a normal distribution and filtered using the same 2-MHz low pass filter from burst processing. Then, the power of the noise realization was scaled such that when it was added to the burst, the desired SNR was achieved. Models were re-trained at each SNR level, and MCC was calculated after aggregating the results across 100 noise realizations. Performance was reported for Note that since noise could only be added (not removed), the per-burst SNR could only ever be decreased. As an example, The advantage of multi-channel models depended on model type and SNR level. At high SNR levels (SNR > 20 dB), For the neural networks , the frequency-irrespective variability learned by the multi-channel models enabled them to approximately maintain or surpass single-channel model performance. Conversely, the single-channel MDA/ML models consistently outperformed their multi-channel counterparts in the presence of noise. It could be that frequency-specific variability, which was deliberately ignored by multi-channel models, allowed some of the single-channel MDA/ML models to overfit the training channel, giving them an advantage against random noise.Modern communications protocols often employ multiple frequency channels to enable simultaneous user operation and mitigate adverse interference effects. Although recent RFF research targets devices that implement these protocols, the direct applicability of these proof-of-concept works is generally limited given that they train RFF models using bursts received on a single channel. Because the signal distortions leveraged by RFF models are linked to the radio hardware components, and those components operate irregularly across different frequencies, practical RFF models must account for multiple frequency channels. Using WirelessHART signal bursts collected with a wideband SDR, our work demonstrated that RFF model performance depends on the frequency channels used for model training.Candidate models, including MDA/ML using expert-aided features, a fully-connected ANN and two CNNs, were evaluated across several training-evaluation channel combinations. Performance of single-channel models did not always generalize to all frequency channels. In the most disparate case, one of the single-channel models performed almost perfectly . In the worst case, the average MCC for LCCNN models improved from 0.657 in the single-channel configuration to 0.957 in the 4-channel configuration, again implying bolstered performance across all channels. This finding suggests that there existed frequency-irrespective variability that could be learned by the models and used for RFF.The performance advantage of multi-channel models under noisy conditions depended on model type and SNR level. Multi-channel neural networks  were able to approximately maintain or surpass single-channel model performance across most SNR levels, but multi-channel MDA/ML models were consistently outperformed by their single-channel counterparts. It could be that the frequency-specific variability available to the single-channel MDA/ML models caused them to overfit the training channel, giving them an advantage against random noise.One interesting area of future work would be to explore how the bandwidth and spectrum location of the frequency channels used in training affects multi-channel performance. Each additional channel included in the training set exposed the RFF models to an additional 5-MHz “chunk” of that spectrum. This additional exposure enabled models to learn frequency-agnostic variability, making them generalize better across all frequencies. We found that 20 MHz  of exposure spread throughout the 80 MHz band was enough to achieve frequency channel-agnostic performance. Other common communications protocols employ channels of different sizes; e.g., Bluetooth channels are 1 MHz wide, and typical WiFi channels are up to 20 MHz wide. It could be the case that more Bluetooth channels and fewer WiFi channels would be needed to achieve generalizable multi-channel model performance because of the difference in channel bandwidth. Further study of the effects of channel bandwidth and spectrum location to frequency-irrespective variability remains an area of future work.Another area of future work would be to address the radio limitations for practical RFF applications. As discussed, modern IoT protocols enable mesh networking, whereby each endpoint in the network can relay data to and from its neighbors. A practical RFF solution must be able to target all of these data transfers to be useful for security. One solution would be to include RFF capabilities within the individual endpoints, as proposed by researchers in . To thatA stopgap solution would be to deploy more RFF-capable SDRs, forming multi-receiver RFF systems. Multi-receiver systems could also be useful in non-mesh configurations if individual SDRs cannot cover all frequency channels. The key challenge would be to find a way to share RFF models across radios to avoid the tedious collection and training effort that would come with scale. One approach may be to combine bursts collected from multiple receivers, similar to our multi-channel approach, whereby the RFF models could learn receiver-irrespective variability. Notably, this effort would also aid in decoupling signal distortions imposed by the receiver from those imposed by the transmitter and RF environment, further adding to its value as future work.Finally, with the extension of RFF to multi-channel configurations, the effects of ACI to RFF model performance should be explored. When multiple devices communicate simultaneously on different frequency channels, the potential exists for some of the energy in one channel to leak to adjacent channels. At a minimum, this energy leakage could raise the noise floor, reducing SNR and potentially degrading model performance. Understanding the extent to which ACI can affect model performance will therefore be critical in the deployment of RFF models to real operational environments.RFF models continue to offer an attractive out-of-band method for wireless device authentication, especially as a component in the defense-in-depth security paradigm. As modern protocols grow in operational complexity, the variability of signal distortions across these expanded modes of operation must be considered to achieve the most effective and generalizable RFF systems.


High-quality chromosome-scale haplotype sequences of diploid genomes, polyploid genomes, and metagenomes provide important insights into genetic variation associated with disease and biodiversity. However, whole-genome short read sequencing does not yield haplotype information spanning whole chromosomes directly. Computational assembly of shorter haplotype fragments is required for haplotype reconstruction, which can be challenging owing to limited fragment lengths and high haplotype and repeat variability across genomes. Recent advancements in long-read and chromosome-scale sequencing technologies, alongside computational innovations, are improving the reconstruction of haplotypes at the level of whole chromosomes. Here, we review recent and discuss methodological progress and perspectives in these areas.The online version contains supplementary material available at 10.1186/s13059-021-02328-9. Haplotypes are combinations of alleles from multiple genetic loci on the same chromosome that are inherited together; the term haplotype can encompass as few as two loci or refer to a whole chromosome . For diploid genomes, a given length of chromosomal DNA will have two haplotypes, one inherited from each parent, whereas several haplotypes exist for any given chromosomal region at the population level or for polyploid genomes. DNA microarrays and short-read sequencing can determine the collection of alleles at genetic loci  but provide no information at the level of haplotypes, whether alleles are co-located on the same copy of a chromosome, or which of the parental chromosomes harbors a particular allele. Hence, computational reconstruction of haplotypes using upcoming sequencing technologies, by either read mapping to a reference genome or de novo assembly, is required.cis- and trans-acting variants between homologous chromosomes, that is, the phase of variants, can affect gene expression; chromosome-scale haplotypes help study interactions between variants in regulatory elements (long-range promoter-enhancer interactions) [Haplotype information is fundamental for medical and population genetics , 2, wheractions) . Anotheractions)  also reqactions) . Anotheractions) , where hactions) , 14.https://www.dnazoo.org/), Darwin Tree of Life (https://www.darwintreeoflife.org/), the Human Microbiome Project (https://www.hmpdacc.org/), and the Human Pangenome Project (https://humanpangenome.org/), have begun to leverage diverse recent sequencing data types  and genome-wide (long-range) information must be computationally integrated to assemble chromosome-scale haplotypes . The intes Table  to reconIn this Review, we discuss the bioinformatic methods—reference-based de novo and strain-resolved metagenome assembly—to reconstruct haplotypes in diploids, polyploids, and microbial communities. We present the strengths and weaknesses of these methods, alongside examples of their biological applications. Finally, we conclude with challenges and future directions, with an emphasis on both the algorithmic and technological advances required to achieve routine high-quality haplotypes for further biological discoveries.Early advancements in sequencing technologies , such asIn the era of third-generation sequencing technologies, we define short-range  sequencing that produces genomic fragments (reads) spanning up to megabases of the genome, but cannot connect across multi-megabase sized regions on the whole-genome scale. For example, long-read sequencing technologies such as single-molecule real time sequencing from Pacific Biosciences (PacBio)  and nanoLong-range  sequencing consists of technologies that produce genomic fragments spanning across centromeres, thereby providing information to connect p- and q- arms over the entire genome, and connecting multi-megabases regions. Chromosome conformation capture methods such as Hi-C and related chromatin crosslinking protocols and produce long-range, mate-pair data for short-read sequencing  Fig. 1)1). Hi-C Strand-seq is a recent single-cell sequencing advancement that allows independent sequencing of parental template strands and thereby characterization of individual homologues , 42. SpeThese sequencing methods provide long-range information on genomic structure across centromeres, and they can be computationally assembled into chromosome-scale  at low cWhen a reference genome is available, haplotype reconstruction of the target sample comprises identifying co-occurring alleles of paternal and maternal copies over variant sites from sequencing data aligned to the reference. The process of obtaining these haplotypes is known as haplotype phasing .Traditionally, reference panels of more than 100,000 individuals  are genotyped and used to assign, probabilistically, the most likely local phase of the target sample based on the underlying evolutionary model –74. ThisReconstruction of haplotypes depends on how the heterozygous sites are connected on the chromosome-scale. If there are no reads that connect these sites, then the phasing is fragmented. Thus, the sites must be connected directly or indirectly via sequencing reads to achieve chromosome-scale phasing Fig. . Long- aThe most widely used, state-of-the-art phasing methods are WhatsHap , HapCut2Genome-wide molecular phasing, a task that computes combinatorial solutions at chromosome-scale by using long-range sequencing technologies such as Hi-C and Strand-seq Fig. , is a moHybrid approaches (combining long or 10xG reads with Strand-seq or Hi-C datasets ) for sinBeyond the above bulk sequencing methods, single-cell phasing  has recek-ploid sample, k–1 haplotypes need to be computed before the final haplotype can be inferred. For example, there are k! possibilities (instead of two in diploid) to connect a pair of SNPs in the polyploid. A higher number of haplotypes also requires a greater overall sequencing depth, resulting in a larger number of reads per genome to be processed. This additional complexity requires specialized, highly optimized algorithms to resolve polyploid phasing  using tools such as Racon .However, for diploid and polyploid genomes, most standard de novo assemblers collapse the haplotypes into a single consensus sequence, but it is important to realize that all haplotype information is ignored. Nevertheless, the consensus assembly is useful for de novo haplotype assembly approaches as discussed below.Reconstructing every individual haplotype from sequencing data instead is known as de novo haplotype assembly Fig.  and is eIn de novo haplotype assembly, there are two major related challenges: finding ordering of sequencing reads and distinguishing reads to haplotypes. To find ordering of reads, the brute-force approach is to align all reads to all other reads, where the performance is directly proportional to the square of the number of reads. In repetitive regions, finding alignments of reads is even more expensive. For systematic study, overlap-based  or de BrAlgorithms for long-read sequencing are now able to produce megabase contigs for haplotypes and improve the availability of reference-quality genomes for humans and various other eukaryotic organisms , 104. ThIn collapsed diploid assembly, generic de novo assemblers are used to generate a consensus sequence. Subsequently, by using heterozygous SNP information from reads aligned to the consensus sequence, long-read and chromosome-scale sequencing reads are partitioned into haplotype-specific read sets, which are then separately assembled into haplotypes. This technique is used by tools such as DipAsm  or PorubAlternatively, the widely used FALCON-unzip  method uhttps://github.com/chhylp123/Hifiasm), HiCanu [k-mer based strategies for haplotype-aware error correction of phased contigs, which can be completed in a few hours for human-scale genomes. Similar to semi-collapsed approaches, these phased contigs can be combined into phased scaffolds using long-range information to produce a chromosome-scale diploid assembly. For phased scaffolding, one of the largest challenges is the development of computational models that combine both phasing and scaffolding information together, an approach that is recently explored by the pstools method (https://github.com/shilpagarg/pstools) in the graph sequence space. The preferred input data types are PacBio Hifi and Hi-C.The recent invention of PacBio HiFi technology has made the diploid assembly process, that entains ordering as well as the phasing in the assembly process, easier . A whole, HiCanu , and SDi, HiCanu , produciWhen trios are available, methods such as TrioCanu  search fPolyploid assembly is in principle an immediate extension of diploid assembly; however, an increase in the number of haplotypes inflates the search space dramatically on the whole-genome scale. Some progress has been made in computational approaches for local haplotype assembly in polyploids as a potential step towards chromosome-scale polyploid assembly. For example, using Illumina short-read sequencing reads, POLYTE  performsHaplotype reconstruction plays an important role in strain-resolved metagenome assembly, that is, the computational reconstruction of haplotypes from pooled sequencing to identify microbial strains Fig. a. VariatThe bioinformatic approaches for metagenome assembly are highly related to haplotype reconstruction in diploids and polyploids, as noted by Kolmogorov et al.  and NichFrom an author’s perspective, we present a general workflow for metagenome assembly that consists of several steps –116 Figb: (1) dek-mers to identify  bacterial strains in short-read sequencing data with relative abundances as low as 0.00001%. Other de novo approach tools are MEGAHIT [The commonly used data structures for metagenome assembly are de Bruijn and overlap graphs with special tuning of parameters related to sequencing depth, variations, and errors. For example, IDBA-UD  is the f MEGAHIT  and meta MEGAHIT , but the MEGAHIT  for a de MEGAHIT  and Stra MEGAHIT , both of MEGAHIT , which i MEGAHIT  is one s MEGAHIT   was proposed that combines short-read contig assembly with long-read scaffolding and binning to obtain high-quality, strain-resolved metagenomes . OPERA-MAlternatively, the combination of Hi-C and shotgun sequencing enables chromosome-scale, strain-resolved metagenome assembly through improved clustering of metagenome-assembled contigs at strain level, as well as linking of plasmid sequences to the chromosomes of their hosts , 70, 71.For complex repetitive metagenomes, HiFi reads, in combination with Hi-C, have the ability to become the strategy of choice to produce complete, strain-level resolved metagenome assemblies in the near future.Haplotype reconstruction remains challenging in multi-megabase complex repetitive regions. Despite considerable time and effort, the current version of the human reference genome either contains gaps or is collapsed in these regions without haplotype-level resolution. These regions include tandem repeats , segmentRecent developments in long, accurate long reads (Hifi) as well as ultra-long nanopore reads could pave the way for new advancements in finishing centromeric and other highly repetitive regions in humans and polyploids. Computationally, Hifi reads can be decomposed into monomers  that areDevelopments are required to scale haplotype reconstruction efforts to overcome current limitations and enable routine application to more than hundreds of genomes at a time. Such developments require innovations in technologies that are cheaper and easy to use than long-read, HiFi and Hi-C sequencing. Alternatively, a further reduction in sequencing costs of existing technologies will be required to scale up efforts.https://vgp.github.io/genomeark/). Building a collaborative haplotyping platform that can serve as a repository of data and computational tools and enable exchange of ideas for the scientific community may help to usher in a new era of biological discoveries.With an exponential growth in datasets, the real challenge will be to store and access haplotyping data in an efficient way, which can potentially be achieved by applying massive parallelism . IFurther integration of datasets using scalable bioinformatics approaches will be important. Innovative algorithm engineering  could ek-mer approaches) that improve the capability for assessment and biological validation could benefit from a public collection of high-quality benchmarks, for example in the form of a community-driven assessment initiative similar to the Critical Assessment of Metagenome Interpretation [For the final haplotype assemblies of diploid genomes, many high-quality benchmarks are available, and validation is done with standardized evaluation metrics as a standard practice for non-repetitive regions. This is not the case for polyploids, tumors, and complex repetitive regions in diploids. Innovations in algorithms (beyond retation  (CAMI), retation , 145, anretation  (GAGE). retation  to enablAnother challenge is the visualization of large-scale haplotyping raw sequencing datasets and haplotype sequences from multiple species. The combination of long haplotype sequences and divergence across or within genomes and the large diversity of haplotyping data types pose numerous visualization challenges. While a number of tools exist , nChromosome-scale haplotype reconstruction has yielded new insights into the genetic underpinnings of disease pathogenesis, evolution, and comparative biology. To overcome the limitations of sequencing reads to cover genomic repeats, chromosome-scale haplotype reconstruction using a combination of long-read (HiFi and ultra-long ONT) and chromosome-scale sequencing (Hi-C) datasets, along with integrative algorithms, has become a common strategy to produce haplotypes in diploids, but not polyploids yet.k-mers and data-driven) will be state-of-the-art to reconstruct high-quality haplotypes with fewer gaps in the near future. Both fragment accuracy and length—a few megabases size with accuracy of > 98%—could be important to finish haplotypes. Major reductions in sequencing and computing costs will be critical to scale efforts to thousands of genomes at a time. In the next decade, algorithmic and technological advances, paired with the incorporation of haplotypes with disparate layers of biological information, could mark a new era of gapless end-to-end haplotypes and further our understanding of complex biological phenomena.Improvements in fragment lengths and combining complementary technologies through innovative algorithms (graphs, Additional file 1. Review history.


Percutaneous coronary intervention (PCI) is the mainstay treatment of acute myocardial infarction (AMI); however, many clinicians are reluctant to perform PCI in the elderly population. This study aimed to compare the clinical outcomes of PCI versus medical therapy in nonagenarian Korean patients with AMI. We compared the clinical outcomes of nonagenarian patients with AMI with or without PCI. From the pooled data, based on a series of Korean AMI registries during 2005–2020, 467 consecutive patients were selected and categorized into two groups: the PCI and no-PCI groups. The primary endpoint was 1-year major adverse cardiac event (MACE), a composite of all-cause death, non-fatal myocardial infarction, and any revascularization. Among the 467 participants, 68.5% received PCI. The PCI group had lower proportions of Killip classes III-IV, previous heart failure, and left ventricular ejection fraction <40%, but had higher proportions of all prescribed medications and STEMI diagnosis. The 1-year MACE and all-cause death were higher in the no-PCI group, although partially attenuated post-IPTW. Our study showed that nonagenarian patients with AMI undergoing PCI had better clinical outcomes than those without PCI. Nonetheless, further investigation is needed in the future to elucidate whether PCI is beneficial for this population. Acute myocardial infarction (AMI), an urgent or emergent medical condition, is a leading cause of morbidity and mortality worldwide . The mulThe characteristics and outcomes in this population remain poorly understood as they represent a very small portion of the overall AMI population ,5 and arHere, we aimed to compare the clinical outcomes of PCI versus medical therapy in nonagenarian Korean patients with AMI.The clinical information of the study participants was extracted from the Korea Acute Myocardial Infarction Registry (KAMIR)-I, KAMIR-II, Korea Working Group on Myocardial Infarction (KorMI), KAMIR-National Institute of Health (KAMIR-NIH), and KAMIR-V registries, to collect the nationwide data and standardize all clinical practices with respect to AMI in South Korea. These registries collected the data of patients with AMI from November 2005 to June 2020 and are nationwide, multicentered, and web-based prospective observational cohorts supported by the Korean Working Group of Acute Myocardial Infarction ,20. Thesn = 270) and no-PCI (n = 118) groups. As illustrated in The patients’ demographic and clinical characteristics were recorded. AMI was defined according to contemporary guidelines ,24,25,26In the present study, clinical follow-up was conducted for 12 months. The primary endpoint was the occurrence of 1-year major adverse cardiac events (MACEs), which is a composite of all-cause death, non-fatal myocardial infarction (NFMI), and any revascularization. All-cause death includes both cardiac and non-cardiac death. Any revascularization is a composite of any repeat PCI and coronary artery bypass graft. The secondary endpoints included all-cause death, NFMI, and any revascularization.t-test was used to analyze normally distributed continuous variables. Pearson’s chi-squared test or Fisher’s two-by-two exact test were used to analyze discrete  variables. Continuous variables were described as mean ± standard deviation and discrete  variables were described as percentages with numbers. Statistical significance was set at a two-sided p < 0.05. To explore the differences in clinical outcomes between the two groups with or without PCI, we performed statistical analysis. Student’s Inverse probability of treatment weighting (IPTW) was utilized to minimize the selection bias. We constructed the propensity score using multiple logistic regression with 19 covariates, which included sex , Killip classification (I-II versus III-IV), body mass index, previous medical history (six items), smoking history, family history of coronary artery disease, prescribed medications (five items), thrombolysis, LVEF <40%, and STEMI diagnosis. All patients with missing covariate data were excluded from the IPTW-adjusted statistical analysis.The trends in patient volume and PCI rates in Korean nonagenarian patients with AMI are illustrated in As explained in the study scheme in As for in-hospital complications , the PCISince elderly individuals constitute an increasing proportion of the overall population , patientIn the present study, we performed a comparative analysis of the clinical outcomes among nonagenarian patients with AMI, depending on the treatment strategy. We analyzed the clinical data of 467 consecutive patients derived from the database of the KAMIR-I, KAMIR-II, KorMI, KAMIR-NIH, and KAMIR-V registries. Our results demonstrated that the PCI group showed better 1-year clinical outcomes than the no-PCI group, with lower incidences of MACE and all-cause death. However, after IPTW adjustment, these findings were statistically attenuated, showing relatively similar outcomes in both groups.According to the baseline characteristics described in In the study population, the PCI group had a higher frequency of STEMI diagnosis. Since primary PCI should be performed for timely revascularization in the case of STEMI, these findings are sufficiently predictable. Despite this finding, the PCI group had a relatively lower disease severity with respect to several clinical variables. The no-PCI group had a greater Killip functional class and lower LVEF than the PCI group. Moreover, in terms of comorbidities, the no-PCI group had a higher proportion of previous heart failure. These characteristics make interventional cardiologists reluctant to perform PCI. In terms of in-hospital outcomes and complications, the PCI group experienced cardiogenic shock or cardiac arrest and arrhythmic events, including atrioventricular block, ventricular tachycardia or fibrillation, and atrial fibrillation, at a higher rate. They also received cardiopulmonary resuscitation and intra-aortic balloon pumps at a higher rate. However, although the PCI group was generally more exposed to dangerous clinical situations, these findings did not contribute to a significant difference in in-hospital death in either group.In the 1-year clinical outcomes, MACE was higher in the no-PCI group than in the PCI group. This between-group difference is driven mainly by all-cause mortality. These findings support the argument that PCI should be performed in elderly patients with AMI. Notably, the PCI group tended to receive more stringent medical treatments. According to the information on prescribed medications in In the multivariable logistic regression analysis, both Killip class III-IV and diabetes mellitus were found as predictors for MACE. High Killip class is an independent predictor of mortality in acute coronary syndrome ,32. TypeWe further investigated the angiographic and procedural characteristics in the PCI group to evaluate whether they received high-quality PCI . ApproxiThrough literature review, several foreign studies on clinical outcomes of nonagenarian patients with AMI undergoing PCI were found ,39. MostThis study has some limitations when interpreting the results of this study. The participating institutions in the KAMIR, KorMI, and KAMIR-NIH registries were tertiary cardiovascular institutions with higher volumes of patients with AMI and higher annual PCI rates than average medical centers. Hence, it is difficult to generalize the clinical patterns and outcomes of our study. In addition, as this was not a randomized study, there were some problems pertaining to selection bias. Statistical adjustment using IPTW was performed to overcome this limitation; however, a large-scale multicentered randomized controlled trial must be conducted in the future to draw in-depth conclusions on the clinical outcomes of PCI in the nonagenarian AMI population. Moreover, this may be considered a notable analysis of nonagenarian patients with AMI; however, most participants were enrolled and analyzed before the COVID-19 pandemic. Since there are expected characteristic differences in the clinical features, treatment quality, and outcomes of the AMI population during the COVID-19 pandemic, further studies are needed. In addition, since Korea is one of the fastest-aging countries, further prospective and systematic research on nonagenarian AMI populations is needed in the future.Nonagenarian patients with AMI who underwent PCI may have better clinical outcomes than those treated with medical therapy only, although it is relatively more dangerous during the initial hospitalization. Nevertheless, whether PCI is beneficial in this age group remains inconclusive. Therefore, further investigation is needed in the future.Key messageNonagenarian patients with AMI who underwent PCI appeared to have better clinical outcomes than those who were conservatively treated with no-PCI.Nonagenarian patients with AMI who underwent PCI also tended to receive more optimal medical therapy than those who were conservatively treated with no-PCI.Nonagenarian patients with AMI received high-quality PCI. Despite dangerous post-PCI clinical situations, in-hospital death was comparable in both groups.In routine practice, many clinicians are still reluctant to implement PCI in nonagenarian patients with AMI.Whether PCI is truly beneficial in nonagenarian AMI patients remains controversial, and further investigation is needed in the future.


Despite a broad heterogenicity of included studies, evidence of functional changes in brain activity using hypnosis was identified. Electromyography (EMG) startle amplitudes result in greater activity in the frontal brain area; amplitudes using Somatosensory Event-Related Potentials (SERPs) showed similar results. Electroencephalography (EEG) oscillations of θ activity are positively associated with response to hypnosis. EEG results showed greater amplitudes for highly hypnotizable subjects over the left hemisphere. Less activity during hypnosis was observed in the insula and anterior cingulate cortex (ACC).Hypnosis has proven a powerful method in indications such as pain control and anxiety reduction. As recently discussed, it has been yielding increased attention from medical/dental perspectives. This systematic review (PROSPERO-registration-ID-CRD42021259187) aimed to critically evaluate and discuss functional changes in brain activity using hypnosis by means of different imaging techniques. Randomized controlled trials, cohort, comparative, cross-sectional, evaluation and validation studies from three databases—Cochrane, Embase and Medline via PubMed from January 1979 to August 2021—were reviewed using an ad hoc prepared search string and following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. A total of 10,404 articles were identified, 1194 duplicates were removed and 9190 papers were discarded after consulting article titles/abstracts. Ultimately, 20 papers were assessed for eligibility, and 20 papers were included after a hand search ( Hypnosis is defined as “a state of consciousness involving focused attention and reduced peripheral awareness characterized by an enhanced capacity for response to suggestion,” according to the American Psychological Association (APA) Division 30 . HypnosiWith various imaging methods, such as EEG , fMRI , fNIRS (near-infrared spectroscopy), PET (positron emission tomography), SPECT (single-photon emission computed tomography) and CT (computer tomography), functional, metabolic and structural information about the brain can be obtained. Using hypnosis, brain activity can be demonstrated using these imaging methods.The aim of the present study was to perform a systematic review of articles published in the last four decades, investigating the functional changes in brain activity using hypnosis by means of different imaging methods. A broad review of the use of different imaging methods demonstrating plastic brain-activity changes during hypnosis should be provided to gain information for future study designs and to review whether similarities or differences exist regarding medical applications in the literature. The hypothesis of this systematic review is that differences can be observed in functional changes in brain activity when comparing the normal/resting state and the hypnotic state.https://www.crd.york.ac.uk/prospero; last access: 12 January 2022). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were adopted throughout the process of the present systematic review [The review protocol was registered in the international prospective register of systematic reviews, PROSPERO, on 5 July 2021 with ID-CRD42021259187 https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools; last access: 12 January 2022) of the National Heart, Lung and Blood Institute was used to rate the quality of the included papers.Intervention: Medical subject headings (MeSH) terms and keywords related to the topic studied were applied. The Study Quality-assessment tool , electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), magnetic resonance imaging (MRI), positron emission tomography (PET), regional cerebral blood flow (rCBF) and single-photon emission computer tomography (SPECT).Outcome: The quality-control tool of the National Herat, Lung and Blood Institute included criteria about adequate randomization, participation rate, similarity of groups/population and adherence to the intervention protocols, as well as sources of bias . For each part, yes/no/cannot determine was selected. In the end, each study/paper was scored as good if the study had the least risk for bias, fair if the study was predisposed to some bias and poor if it was possible that the study was biased.In this systematic review, randomized controlled trials, cohort, comparative, cross-sectional, evaluation and validation studies reporting brain-activity changes using hypnosis by means of imaging methods such as CT, EEG, EMG, EOG, fMRI, fNIRS, MRI PET, rCBF or SPECT were reviewed. Only human studies published in English from 1 January 1979 to 31 August 2021 were collected and evaluated.http://www.opengrey.eu; last access: 12 January 2022).Electronic databases, such as Cochrane, Embase and MEDLINE via PubMed, were taken into consideration and screened for articles. Grey literature was retrieved via opengrey.eu  terms and keywords was used:  AND  AND . Using the bibliographies of full-text articles, cross-referencing was performed. Via opengrey.eu, grey literature was also retrieved.After the comparison of the various string research studies with cross-referencing, all duplicates were excluded. Titles and abstracts of all references were read independently by two authors (K.A.F. and T.G.W.). The full texts of articles with titles and abstracts that appeared to fit the eligibility criteria were then assessed by the same authors. References for which the full texts fulfilled the eligibility criteria were included in this systematic review. Any disagreement in the selection process was resolved in a discussion between peers. In the case of continuous controversy, a third author (G.C.) was consulted.For each included reference, data collection and synthesis were carried out by two authors (K.A.F. and T.G.W.). The excluded and included articles are summarized in tables . The folhttps://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools; last access: 12 January 2022). Quality control included criteria about adequate randomization, participation rate, similarity of groups/population, adherence to the intervention protocols and sources of bias . For each part, yes/no/cannot determine was selected. Each study/paper was scored as good if the study had the least risk for bias, fair if the study was predisposed to some bias and poor if it was possible that the study was biased.The quality and choice of the included papers were obtained by two authors (K.A.F. and G.C.) according to the respective customized quality-assessment tool generated by the National Heart, Lung, and Blood Institute for controlled intervention studies, systematic reviews and meta-analyses, observational cohort and cross-sectional studies, case-control studies, pre-post studies with no control group and case series studies, as well as the accompanying quality-assessment tool guidance for assessing the quality of controlled intervention studies , and during hypnosis/analgesia, highly hypnotizable subjects displayed significant reductions in pain [1 amplitude over the left frontal compared to the right hemisphere and in the posterior areas, as well as more θ1 activity in the left and right frontal areas and in the right posterior area compared to the low-hypnotizability group [1, more activity was observed in the left hemisphere over the frontal region as well as for α2  under hypnosis than normal wakefulness and an increased functional connectivity between S1 and distant insular and prefrontal cortices . Highly When comparing these results with a recent study from De Souza et al. , a justiPositron emission tomography (PET) analysis showed that a hypnotic state induced a significant activation of a right-sided extrastriate area and the anterior cingulate cortex. This activation is related to pain perception and unpleasantness during hypnotic states . SignifiOur primary aim was to focus on functional brain changes during hypnosis in dental applications. However, to the best of our knowledge, there is a limited number of papers available concerning dental applications . Using dIt is difficult to generalize the results due to the various imaging techniques employed and the different application areas of hypnosis. Due to the heterogeneity of the studies, the numerous imaging methods employed and the lack of comparability, a more uniform description is recommended in the design of future hypnosis studies investigating brain activity by means of imaging methods. Hypnotizability does not play a role in patient-specific individual hypnosis treatment, but it is used as a criterion in many studies and examined with validated questionnaires. In the future investigation not only of highly hypnotizable but also medium- and low-hypnotizability subjects is recommended in order to provide a better evaluation of the effectiveness of hypnosis on the whole population.Despite a broad heterogenicity of included studies, evidence of functional changes in brain activity using hypnosis could be determined.EMG startle amplitudes indicate higher activity over the frontal brain area; amplitudes using SERP showed similar results.EEG oscillations of θ activity are positively associated with response to hypnosis; EEG results showed greater amplitudes for highly hypnotizable subjects over the left hemisphere.Less ACC and insula activity was observed during hypnosis.


The demand for vegetable powder has been escalating considerably due to its various health benefits and higher shelf life compared to fresh green leafy vegetables. Thus, much research emphasised manufacturing vegetable powder at a lower operational cost and higher efficiency while preserving the nutritive values of the vegetables. In this study, zinc- (Zn-) amaranth puree was liquefied with three types of cell wall degrading enzymes  with varying concentrations (0–3% v/w) and incubation time (0.5–24 h) at pH 5 and 45°C before the drying process. The results showed that enzymatic liquefaction using 1% (v/w) of Viscozyme L for 3 h was the optimal procedure for the reduction of the viscosity of the puree. The liquefied puree was then microencapsulated through either spray- or freeze-drying with different wall materials, e.g., 10% of maltodextrin (MD) DE 10, resistant maltodextrin (RMD), N-octenyl succinate anhydride (OSA) starches from waxy maize, HI CAP 100 (HICAP), Capsul (CAP), and gum Arabic (GA). The results showed that all freeze-dried powders generally had higher process yield (except for that encapsulated by HICAP), higher moisture content (but similar water activities), higher retention of total Zn-chlorophyll derivatives, lower hygroscopicity with slab-like particles, larger particle size, and lower bulk density than those of spray-dried powders. In contrast, the spray-dried powders exhibited irregular spherical shapes with relatively high encapsulation efficiency and antioxidant activities. Nonetheless, encapsulation using different wall materials and drying methods had no significant effect on the powder's cohesiveness and flowability. Amaranthus viridis), commonly known as bayam hijau or bayam panjang in Malay, is one of the most popular leafy vegetables consumed in Malaysia. Aside from A. viridis, there are also other species of amaranth that are abundantly available in the market, namely, bayam itik (Amaranthus blitum), bayam merah (Amaranthus gangeticus), and bayam putih (Amaranthus paniculatus) ,,10], in aw) of the powder was determined using a digital water activity meter .The moisture content of Zn-amaranth powder was determined by oven-drying according to Bhusari et al. . TriplicHygroscopicity of the powder was determined according to Cai and Corke  with somThe EE was calculated as outlined by Idham et al.  with somD, μm)  [36]7) [ [36]7) . Th. Th57]. In general, the process yield of all encapsulated powders was higher than 60%, with the yield of freeze-dried powders being higher than that of spray-dried powders . A lowerMichelia alba) was higher than that of the freeze-dried powder using octenyl succinic anhydride starch (OSA) as an encapsulating agent. It could be due to the droplet-to-droplet interaction in the emulsion during the freeze-drying process, which consumes more time than spray-drying, and therefore resulting in the inconsistency of extract entrapment in the freeze-dried powder [Encapsulation efficiency (EE) is the potential of an encapsulating agent to hold or encapsulate the core component inside the microcapsule . For insd powder . The sprμm) resulted in handling difficulties and poor reconstitution properties. Moreover, powder with a small particle size (and therefore large surface area) is vulnerable to various environmental factors, thus increasing the rate of the degradation of sensitive compounds [μm  has a low bulk density value with a small particle size value. Nonetheless, powder with low bulk density occludes more air within it, and hence, the possibility of oxidative degradation and reduced storage stability is high [As indicated in ompounds . Accordiompounds , the divunds [μm , which c is high . As show is high . As stat is high , a high The effects of wall materials on the microencapsulation (spray- and freeze-drying) methods were also evaluated based on the antioxidant activity and retention of TZCD of Zn-amaranth powder. As shown in In addition, the loss in bioactivity could be associated with material grinding after the drying process in freeze-drying. The grinding process increased the possibility of contact between the powders and the air, resulting in an oxidation reaction leading to the deterioration of the active ingredients . On contvia spray-drying [As demonstrated in y-drying . Furthery-drying –81. Thisy-drying , 82.Meanwhile, spray-dried powders produced by RMD and HICAP exhibited a spherical shape with a smooth surface. As reported by Osorio et al. , smooth The findings of this study indicated that the enzymatic liquefaction process, encapsulation technique (spray- and freeze-drying), and wall material  substantially influenced the physicochemical properties and morphology of the Zn-amaranth microcapsules. The liquefaction process with Viscozyme L (1% v/w) at pH 5, 45°C for 3 h of incubation, significantly reduced the viscosity and highly retained the TZCD of the Zn-amaranth puree. In comparison between the two encapsulation techniques, freeze-dried powders exhibited better properties in terms of yield, hygroscopicity, and total Zn-chlorophyll derivative retention. As for the wall materials, among the five that were studied, Zn-amaranth powders with maltodextrin and resistant maltodextrin produced the highest yield through freeze-drying. Nevertheless, the high operating cost of freeze-drying should be considered to determine the feasibility of this method. Undoubtedly, the spray-dried powders also possessed appreciable quality due to their lower moisture content, higher encapsulation efficiency, and higher antioxidant activity compared to the freeze-dried powders. In comparison to the other wall materials, spray-dried powder with GA had a higher antioxidant content. In general, all powders showed an intermediate cohesiveness with fair flowability. Overall, our studies have provided a comprehensive understanding of the encapsulation techniques and wall materials to be applied in the manufacture of amaranth powder.


Coexistence of cancer and COVID-19 is associated with worse outcomes. However, the studies on cancer-related characteristics associated with worse COVID-19 outcomes have shown controversial results. The objective of the study was to evaluate cancer-related characteristics associated with invasive mechanical ventilation use or in-hospital mortality in patients with COVID-19 admitted to intensive care unit (ICU).We designed a cohort multicenter study including adults with active cancer admitted to ICU due to COVID-19. Seven cancer-related characteristics  were introduced in a multilevel logistic regression model as first-level variables and hospital was introduced as second-level variable (random effect). Confounders were identified using directed acyclic graphs.We included 274 patients. Required to undergo invasive mechanical ventilation were 176 patients (64.2%) and none of the cancer-related characteristics were associated with mechanical ventilation use. Approximately 155 patients died in hospital (56.6%) and poor performance status, measured with the Eastern Cooperative Oncology Group (ECOG) score was associated with increased in-hospital mortality, with odds ratio = 3.54  for ECOG =2 and odds ratio = 3.40  for ECOG = 3 to 4. Cancer status, cancer type, metastatic tumor, lung cancer, and recent chemotherapy or immunotherapy were not associated with in-hospital mortality.In patients with active cancer and COVID-19 admitted to ICU, poor performance status was associated with in-hospital mortality but not with mechanical ventilation use. Cancer status, cancer type, metastatic tumor, lung cancer, and recent chemotherapy or immunotherapy were not associated with invasive mechanical ventilation use or in-hospital mortality. It has been shown that patients with cancer are more susceptible to the disease caused by the new SARS-CoV-2 virus COVID-19)  In some studies, recent chemotherapy –10, receOne plausible explanation for the controversial results is the heterogeneity of the evaluated population. Most studies included outpatients and inpatients in different proportions, including patients with different disease severity, ranging from asymptomatic  to hospiBetter understanding of the cancer-related characteristics associated with worse outcomes can benefit critically ill patients with cancer and COVID-19, providing better ICU triage and, for patients already in the ICU, providing more appropriate therapeutic planning, prognostication and counseling for patients and their relatives.In critically ill patients with active cancer admitted to ICU due to COVID-19, the objective of the present study was to evaluate the association between cancer-related characteristics and MV use or in-hospital mortality.Cohort multicenter study included patients from ICUs of four hospitals located in São Paulo, Brazil. The AC Camargo Cancer Center ethics committees approved this study (2521/18L) and waived the need for informed consent.We included consecutive adult patients (≥18 years old) with solid tumors or hematologic malignancies and ICU admission due to COVID-19. We excluded patients with cancer remission ≥5 years; decision to forego life-sustaining therapies prior to ICU admission; and admissions for elective postoperative care. If a patient had multiple ICU admissions, only the first was considered. COVID-19 was confirmed by a positive SARS-Cov-2 real-time reverse transcription polymerase chain reaction in a patient with compatible symptoms or chest computerized tomography findings suggestive of COVID-19.Variables were prospectively collected. Each center employed a different data entry form. However, all forms had the same fields and used a standardized definition of the variables.The following cancer-related characteristics were recorded: 1. Cancer status related to cancer treatment, categorized as newly diagnosed without treatment, partial or complete response, progressive cancer despite treatment; 2. Type of cancer ; 3. Metastatic tumor; 4. Recent chemotherapy or immunotherapy, defined as therapy in the last 30 days; 5. Lung as the primary site of the solid tumor (pulmonary metastases from other solid tumor sites were not considered as lung cancer); and 6. Performance status, measured with the Eastern Cooperative Oncology Group (ECOG) score.Upon ICU admission, patient’s demographic characteristics, Simplified Acute Physiology Score (SAPS 3) , ECOG peDuring ICU stay, the need for oxygen therapy , MV for more than >24 h, noninvasive mechanical ventilation , vasopressors , and hemodialysis use were recorded. Finally, the in-hospital mortality was recorded.Categorical and continuous data were presented as absolute values (percentages) and median (25–75% interquartile range), respectively. Categorical variables were compared using the Chi-square test or Fisher’s exact test, as appropriate. Continuous variables were compared using the Mann–Whitney test.Multilevel logistic regression models were used to determine the cancer-related characteristics associated with MV use or in-hospital mortality. The cancer-related characteristics were introduced in the multilevel logistic regression model as first-level variables and hospital introduced as second-level variable (random effect) . For eacTo estimate the causal inference of the cancer-related characteristics on MV use, the regression models for cancer status, type of cancer, and performance status were adjusted for age, sex, Charlson comorbidity index and lung cancer. The regression models for recent use of chemotherapy or immunotherapy, and lung cancer were adjusted for age, sex, and Charlson comorbidities index. Finally, for metastatic tumor, the directed acyclic graph method did not detect any confounder, therefore the regression model was adjusted for age and sex, as prespecified , D-dimer (36.5%), and C-reactive protein (5.1%) upon ICU admission. These missing values were not imputed.P-values ≤0.05 were considered significant. The directed acyclic graphs were created using the browser-based environment DAGitty .  DAGitty . We foll DAGitty  and the  DAGitty   with a mean duration of 9 days . Fifty pIn a multivariable model adjusting for confounders, none of the cancer-related characteristics were associated with MV use 5 also sApproximately 134 patients died in ICU . Among the 140 patients discharged alive from ICU, 21 died in hospital, resulting an in-hospital mortality of 56.6% .In the adjusted models, a poor performance status (ECOG ≥2) was associated with increased in-hospital mortality 8 showedWe showed that cancer-related characteristics were not associated with MV use in critically ill patients with COVID-19. We also showed that poor performance status was associated with in-hospital mortality.P-based select confounders, what is not in accordance with the best practices to control of confounding and reporting of results in causal inference studies . We know that SARS-CoV-2 has specific virulence mechanisms that, combined with personal innate antiviral response, account for the heterogeneous clinical evolution of patients with COVID-19 , 38. ThiIn the present study, no cancer-related characteristic, except performance status, was associated with in-hospital mortality. One could expect that hematologic malignancies (type of cancer) would be associated with higher in-hospital mortality because critically ill patients with hematologic malignancies have presented higher short-term mortality compared with patients with solid tumors . HoweverWe showed that poor performance status was associated with in-hospital mortality. Poor performance status has been associated with in-hospital mortality in patients with cancer and acute respiratory failure due to COVID-19  or otherIn critically ill patients with COVID-19, the knowledge of cancer-related characteristics associated with worse outcomes can be used to prompt a closer monitoring, avoiding ICU admission delay, which has been associated with higher mortality in patients with cancer and acute respiratory failure . AdditioOur study has limitations inherent to observational studies. The care of patients across different centers might not be comparable. However, we employed a statistical analysis designed to deal with heterogeneity among centers , and theIn patients with active cancer admitted to ICU due to COVID-19, a poor performance status is associated with increased in-hospital mortality. Metastatic tumor and recent chemotherapy are associated with approximately twofold increase in hospital mortality, but the estimates are imprecise. The cancer status, type of cancer, lung cancer, and recent immunotherapy are not associated with increased in-hospital mortality. Cancer-related characteristics are not associated with increased invasive mechanical ventilation use. The results, however, should be cautiously interpreted due to the observational design of the study.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The AC Camargo Cancer Center ethics committees approved this study (2521/18L) and waived the need for informed consent. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.PC, RT, and AP: Substantial contributions to the conception of the work, acquisition, and interpretation of data. Revised the manuscript critically for important intellectual content. IF, VO, PS, RC, AK, RF, WP, EC, TM, FC, and JF: Acquisition and interpretation of data and revised the manuscript critically for important intellectual content. All authors contributed to the article and approved the submitted version.This study was funded by the intensive care unit of the AC Camargo Cancer Center.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.


Additionally, we categorize TL techniques in DL-based BCIs into explicit and implicit methods. Our systematization reveals advances in the DA and TL methods. Among the studies reviewed herein, ~45% of DA studies used generative model-based techniques, whereas ~45% of TL studies used explicit knowledge transferring strategy. Moreover, based on our literature review, we recommend an appropriate DA strategy for DL-based BCIs and discuss trends of TLs used in DL-based BCIs.Brain–computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG) is one of the most widely used measurements for BCI. However, EEG has complex patterns and EEG-based BCIs mostly involve a cost/time-consuming calibration phase; thus, acquiring sufficient EEG data is rarely possible. Recently, deep learning (DL) has had a theoretical/practical impact on BCI research because of its use in learning representations of complex patterns inherent in EEG. Moreover, algorithmic advances in DL facilitate short/zero-calibration in BCI, thereby suppressing the data acquisition phase. Those advancements include data augmentation (DA), increasing the number of training samples without acquiring additional data, and transfer learning (TL), taking advantage of representative knowledge obtained from one dataset to address the so-called data insufficiency problem in other datasets. In this study, we review DL-based short/zero-calibration methods for BCI. Further, we elaborate methodological/algorithmic trends, highlight intriguing approaches in the literature, and discuss directions for further research. In particular, we search for  In the past decade, human-centered BCIs, such as those in mental fatigue detection tasks   , which have been well-suited for structural pattern representation and are thus widely used to learn spatio-spectral-temporal patterns of EEG  methodologies    and/or three-dimensional (height × width × depth) convolutional kernels. A CNN learns local features by sharing kernel weights, thus translational invariance is naturally followed. In other words, it represents patterns regardless of the position of the object in an input image. In contrast, for raw EEG analysis, DL-based BCIs   . Further, they shifted the spectrogram and filled the remaining space with random noise and finally, perturbed the RGB values of the STFT image in the color space. Shovon et al. -based spectral transformation. Finally, Fan et al.   denotes a random number drawn from a uniform distribution. Owing to its simplicity and power, some DL-based BCI studies have used SMOTE to augment the imbalanced training data. Lee T. et al.  paradigm. Similarly, Romaissa et al. x, where C ∈ ℝ is a predefined amplification-control hyperparameter. Freer and Yang  all-time data and (ii) near-peak data. In the second strategy, Sakai et al. only multiplied (1±C) to near-peak data.In addition, some studies amplified given EEG samples to augment them. Amplification-based DA can be performed by using and Yang  amplifiei et al.  amplifieMixup xj, and its label is defined as yaug = λyi + (1 − λ)yj, where λ ∈  is a random number. In case of DL-based BCI, Kostas and Rudzicz  EEG samples and improved the BCI performance. Rudzicz  used mixRaw data modification-based DA methods are easily applicable and do not require any further networks optimization. Meanwhile, because of the EEG data's spectro-spatio-temporal properties, these methods barely improve the performance and make model interpretation complicated.empirical mode decomposition (EMD)  without leaving the time domain by EMD. Similar to the segmentation and recombination, EMD-based DA first estimates IMFs of EEG signals, and IMFs are then recombined to create artificial EEG samples. Importantly, the mode of each IMF used in the DA does not overlap. Dinarès-Ferran et al.  . The weights of the BMU and neurons that are close to it in the SOM grid are adjusted to the input data. When adjusting, the magnitude of the change decreases with time and the grid-distance from the BMU. In this regard, Liu et al. , the Jensen-Shannon distance (JSD) is used for estimating the distance between the real sample distribution and the generated sample distribution. Here, where MIEEG-GAN. Roy et al. developed an LSTM-based generator and an LSTM-based discriminator to augment both raw motor imagery EEG signals and spectrum images generated by STFT. Further, Roy et al. analyzed generated samples both qualitatively and quantitatively. Similarly, Krishna et al. (gated recurrent unit (GRU) . This LSGAN objective function gives a larger gradient to fake samples farther from the real samples decision boundary, thereby suppressing the gradient vanishing phenomenon. In case of DA for BCI, Pascual et al.  adopted Meanwhile, Radford et al.  focused Similar to LSGAN  and improved the BCI performance. Panwar et al. . In Equation (4), the first term represents a negative log-likelihood of the latent code, z, and can be considered as a reconstruction error. The second term is a regularization term to constrain the variational distribution, where KLD denotes the In this regard, some DL-based BCIs use AEs Ballard,  and VAEsIn recent years, efforts have been made to take advantage of other real EEG samples  to train deep neural networks that decode EEG samples, thereby mitigating the data insufficiency problem  intra- and inter-subject variabilities and (ii) negative transfer. First, as brain signals contain their inherent background activities and vary according to their conditions, e.g., fatigue, drowsiness, excitation, and agitation, high variabilities have been observed for different subjects and even for sessions of the same subjects   reproducing kernel Hilbert space (RKHS) and nS and nT denote the number of samples drawn from the S and T distributions, respectively. In terms of TL for DL-based BCIs, Hang et al. utilized MMD to minimize the distance between the source and target domains in features extracted from fully-connected layers after convolutional layers. They deployed another loss function named the center-based discriminative feature learning (CDFL) method. CDFL is referred to as a regularization technique, that compels the distance between each sample feature and the corresponding class center point to become less than thresholds for better separability between different classes. As a result, Hang et al. acquired a domain-invariant feature of motor imagery EEG signals at the class level by minimizing MMD as well as CDFL. Chai et al. also minimized MMD in a hidden feature space among source and target samples during training an AE and obtained a domain-invariant subspace for the emotion recognition task. However, the classifier was not jointly trained with the AE.where ϕ denotes a mapping function for Similar to Chai et al. 's work, Euclidean alignment (EA) method  in their dataset     where gradients are multiplied by a negative value during the back-propagation process. The objective function of the DANN is defined as follows:Most adversarial learning-based methods adopt DANN  is used to train the feature extractor where Based on DANN, Özdenizci et al.  introducRecently, several methods have shown that the use of only DANN  source selection  scenario to fine-tune the trained parameters totally or partially using new target data . Most existing implicit TL methods have been used in the Fine-tuning is a retraining strategy to initialize parameters of a network as learned parameters of another identical network trained with diverse source datasets to adapt them to the target dataset. Fine-tuning can be regarded as the most naive approach to transfer knowledge. In this respect, many studies have taken advantage of fine-tuning for TL (Andreotti et al., On the contrary to those methods, the existing works (Zhao et al., Several studies have focused on learning better EEG representations to concentrate on more discriminative temporal slices (Zhang et al., K frequency bands in the list and then used them as their CNN input. Ko et al. (Meanwhile, Kwon et al.  applied o et al.  also demo et al.  to them Jeon et al.  proposedThese methods (Kwon et al., learning to learn, which allows a model to learn a method that enables fast adaptation to a new task or environment for a few-shot learning task (Hospedales et al., Meta-learning is known as In section 2, we review many DA methods for DL-based BCIs. From now on, we directly compare generative model-based DA methods and geometric manipulation-based DA methods and recommend a DA method for DL-based BCIs. Approximately 45% of generative model-based DA methods are reviewed, whereas ~55% of geometric manipulation-based methods are reviewed. Interestingly, Zhang et al.  and FahiIn section 3, we summarize various TL approaches for DL-based BCIs. To achieve a short/zero calibration task, many studies performed TL across different subjects/sessions in a single dataset (Fahimi et al., backbone network. Then, DA methods are applied to the target samples to augment them. Finally, these augmented target samples can fine-tune the backbone network to improve the short-calibration BCIs.Based on our survey about many pioneering DA and TL approaches for BCIs, we conclude that both strategies can be beneficial to the short- and/or zero-calibration BCIs. Especially, it can be an interesting future research direction to combine both DA and TL approaches. For instance, before performing any TL strategies, a series of DAs would augment the number of samples, thereby improving the zero-calibration BCIs. Moreover, let us assume that there exist a large amount of source data samples and a few target samples. Then, it can be considered, inter alia, some strategic TL methods to build a good starting In this study, we surveyed recent advances in the field of DL-based BCIs, especially for short/zero-calibration techniques. We focused on several important aspects of the short/zero-calibration techniques. Various generative model-based and geometric manipulation-based DA methods have demonstrated their promising potential in the short-calibration technique. Moreover, we summarized recent trends in TL used in DL-based BCIs. Overall, explicit TL-based and implicit TL-based TL strategies significantly improve the zero-calibration BCIs.real world. Moreover, advancements in other fields, such as computer vision will benefit from more practical and powerful DL-based BCIs. We hope that this review contributes to the BCI field as a good summary of short/zero-calibration techniques for the design of DL-based BCI studies.Presently, increasing interests in DL have considerably increased the use of BCI technologies in the WK, EJ, SJ, and JP performed the literature search. WK and EJ discussed and clustered the results and drafted the manuscript together. H-IS organized the overall structure of the manuscript and revised the manuscript, approved the final version, and agreed to be accountable for all aspects of the work. All authors contributed to the article and approved the submitted 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.


There is growing interest in the role of compassion in promoting health and well-being, with cross-sectional data showing an inverse correlation with loneliness. This is the first longitudinal study examining both compassion toward others (CTO) and compassion toward self (CTS) as predictors of mental and physical health outcomes including loneliness, across adult lifespan. We followed 552 women and 538 men in San Diego County for up to 7.5 (mean 4.8 and SD 2.2) years, using validated rating scales for CTO, CTS, and loneliness. Linear mixed-effects models were employed to examine age- and sex-related trajectories of CTO and CTS over time. Linear regression models were used to evaluate baseline and longitudinal relationships of CTO and CTS with mental well-being, physical well-being, and loneliness. CTS and CTO were weakly intercorrelated. Women had higher baseline CTO than men. While CTO was stable over time and across the lifespan, CTS scores had an inverse U-shaped relationship with age, peaking around age 77. There were significant baseline × slope interactions of both CTO and CTS predicting improvements in physical well-being in adults <60 years old. Increases in CTO and CTS predicted improvements in mental well-being. Higher baseline CTO and CTS as well as increases in CTO and CTS scores predicted lower loneliness scores at follow-up. Thus, CTO and CTS were associated with better mental well-being and loneliness across the adult lifespan, and physical well-being in younger adults, and are promising targets for interventions to improve health outcomes. Pro-social attitudes and behaviors like compassion have been linked to greater well-being and better health in individuals and potentially, in the society , 2. CompFrom an evolutionary perspective, CTO has been purported to support caring for vulnerable offspring , aid mate selection, and increased cooperation between non-related individuals . CTS mayCompared to men, women have been reported to have greater levels of affective and cognitive empathy as well as higher CTO –10, but During recent decades there has been a behavioral pandemic of loneliness, associated with higher morbidity and all-cause mortality , 35. LonCross-sectional studies have reported associations of compassion with better mental health (greater happiness and well-being –45) and In the current study, CTO and CTS were assessed in a relatively large sex-balanced community-based sample across the adult lifespan (age range 27–101 years) that was followed longitudinally for up to 7.5 (mean 4.8 and SD 2.2) years. We sought to examine: (1) the relationship between CTO and CTS; (2) sex differences in CTO and CTS; (3) how CTO and CTS vary by sex across the lifespan; and (4) the relationship of CTO and CTS to physical and mental health including loneliness. We hypothesized that (1) baseline CTO and CTS levels would be highly intercorrelated, and (2) women would have higher levels of CTO than men. We explored (3) how CTO and CTS would change longitudinally by sex and across the age-span. Finally, we hypothesized that (4) higher baseline levels and longitudinal increases in CTO and CTS would predict higher levels of mental well-being including lower loneliness, as well as better physical health at the end of the follow-up period, controlling for smoking, alcohol use, and relevant sociodemographic factors.The study participants were recruited from the UCSD Successful AGing Evaluation (SAGE) study across the adult lifespan, which has been described previously , 57. BriThe study protocol was approved by the UC San Diego Human Research Protections Program (HRPP) and all the participants provided a written informed consent prior to study participation. The data were collected over a period from July 2012 through February 2020.Trained study staff completed a 25-min initial phone interview followed by a survey that was mailed or completed online. Data included sociodemographic information on age, sex, education level, race/ethnicity, current marital status, living situation, and income. Self-administered standardized assessments were completed for depression  , anxietyr = 0.96) with high internal reliability . An example item is “I tend to feel compassion for people, even though I do not know them.” To measure CTS, we used the Neff Self-compassion Scale short-form, a 12-item adaptation of the popular and well-validated 25-item scale .The models of physical well-being differed by age group. In the younger age group (≤60 years old), baseline values as well as changes in CTO and CTS were associated with changes in physical well-being Table . There wIn the older group (>60 years old), baseline and changes in CTS and CTO were not significantly associated with changes in physical well-being Table .Greater increases in CTO and CTS predicted improvements in mental well-being Table . There wHigher baseline CTO and CTS, as well as greater increases in CTO and CTS scores significantly predicted lower loneliness scores at the last follow-up  to be associated with higher CTS but not CTO, the literature linking CTO/CTS to education and socioeconomic status (SES) is complex and mixed. Cross-sectional studies reported that individuals with more education and higher SES had lower CTO  and highCTO and CTS represent distinct constructs. CTO has sometimes been associated with negative outcomes. For instance, one study found that caregivers with higher CTO experienced more intrusive thoughts about their care recipients’ condition than caregivers with lower CTO, regardless of the level of physical suffering that they saw in the care recipient. In concordance with the current findings, three observational studies of adults across the lifespan, college undergraduates, and nursing students reported weak or non-significant correlations between CTS and CTO , 88, 89.The influence of CTO and CTS on physical health was consistent with prior studies which showed that CTS improved lifestyle behaviors and metabolic biomarkers among adults with diabetes , 55, 91.r = 0.51–0.76) between loneliness and compassion or wisdom in at least four different cross-sectional studies [Our finding of both CTO and CTS being predictors of lower loneliness at follow-up provides support to the previous findings of a significant inverse correlation , biological mechanisms linked to developing CTO and CTS , and causal links between CTO/CTS and health (RCTs of compassion-based interventions with adequate follow-up periods).This study has several limitations. The sample was predominantly Caucasian, and the results may not generalize to racial/ethnic minorities or to non-English-speaking adults. All the clinical assessments were self-report-based and did not include objective measures. We did not evaluate empathy in this study. Also, we did not begin assessing loneliness until the last follow-up; therefore, the contribution of CTO and CTS to the longitudinal changes in loneliness is not known. Also, we did not examine biomarkers of inflammation or other markers of stress or aging. The maximum longitudinal follow-up period was 7.5 years, and it is possible that longer follow-up might have produced different results.Nonetheless, the study also has several strengths. To our knowledge, this is the first multi-year longitudinal study of CTO and CTS across the adult lifespan. It was based on a community-based sample selected using random digit dialing, with comparable numbers of women and men. We used validated rating scales and controlled for various sociodemographic factors.Future research is warranted to identify the innate personality features and environmental influences  that may alter CTO and CTS in diverse populations. In that vein, the benefits of CTO and CTS interventions have not been fully characterized—i.e., whether these interventions have lasting effects on health and other outcomes and which individuals benefit most from such interventions. Last, using biomarkers and objective assessments of CTO and CTS can improve our understanding of the biological underpinnings of pro-social behaviors. Efforts to promote compassion at individual and societal levels may help stem the modern behavioral pandemics of loneliness, stress, suicides, and opioid abuse, worsened by the recent Covid-19 pandemic and the necessary social distancing requirements , 96.