Cavia porcellus)  and 194 fleas collected from human bedding and clothing  for the presence of Bartonella DNA. We also tested 83 blood spots collected on Flinders Technology Associates (FTA) cards from guinea pigs inhabiting 338 Peruvian households. Bartonella DNA was detected in 81 (20.7%) of 391 guinea pig fleas, in five (2.6%) of 194 human fleas, and in 16 (19.3%) of 83 guinea pig blood spots. Among identified Bartonella species, B. rochalimae was the most prevalent in fleas (89.5%) and the only species found in the blood spots from guinea pigs. Other Bartonella species detected in fleas included B. henselae (3.5%), B. clarridgeiae (2.3%), and an undescribed Bartonella species (4.7%). Our results demonstrated a high prevalence of zoonotic B. rochalimae in households in rural areas where the research was conducted and suggested a potential role of guinea pigs as a reservoir of this bacterium.In the present study, we tested 391 fleas collected from guinea pigs ( Bartonella bacilliformis. This bacterium is transmitted to humans via the bite of a sand fly (Lutzomyia verrucarum). In addition to B. bacilliformis, other zoonotic Bartonella species  have also been reported in Peru.4 Some of these Bartonella species are hosted by small carnivorous mammals  and can be transmitted to humans by exposure to an infected animal and/or its ectoparasites.5 In humans, the symptoms associated with these Bartonella species are frequently nonspecific.9 For example, B. rochalimae isolated from an American tourist after her travel to Peru caused fever, bacteremia, and splenomegaly.1The Andes region of Peru, Colombia, and Ecuador is endemic for Carrión’s disease, a bacterial infection caused by Bartonella species because of the frequent contact with various livestock and domestic animals. One type of livestock unique to these areas is guinea pigs (Cavia porcellus). They are kept for domestic consumption and sale, and are commonly used in folk medicine and religious ceremonies.10 Most guinea pigs are raised in the kitchen and can roam around freely in the household, whereas others are kept in pens adjacent to the houses. In addition to guinea pigs, other animals such as dogs, cats, and chickens can move freely inside and around the houses . Traditionally, families in rural areas have an average of 20 guinea pigs per household.12 According to the Ministry of Agriculture and Irrigation of Peru, Peruvians alone consume an estimate of 65 million guinea pigs each year.13People in small rural communities in the Andean and Amazonian region of Peru still live a very traditional lifestyle. As a result, they could be exposed to multiple zoonotic 14 Diagnosis can also be determined based on blood culture of B. bacilliformis, which is best grown in 5% rabbit or sheep blood agar incubated at 28°C for up to 8 weeks.16 Other techniques, such as Western blot and polymerase chain reaction (PCR), are both more sensitive and specific, but the cost and lack of infrastructure restrict their use to larger laboratories in Lima.17 Techniques for diagnosing other Bartonella species rely on similar diagnostic tests but not on symptomatology.15In rural areas endemic for Carrión’s disease, diagnosis is typically based on clinical symptoms and Giemsa-stained blood smears. Although this assay has a high specificity, without proper training, the sensitivity can be as low as 36%.Bartonella species circulating in guinea pigs and fleas within rural households from several parts of Peru. We used molecular techniques to test guinea pig blood spots and fleas found in households for Bartonella species.The objective of the present study was to identify This research was conducted in compliance with the Animal Welfare Act and other federal statutes and regulations relating to animals and experiments involving animals and adheres to principles stated in the Guide for the Care and Use of Laboratory Animals, National Research Council Publication (1996 edition).18The study was carried out in 36 localities of six provinces, belonging to the departments of Amazonas, Ancash, and Cajamarca  in 2012.In total, 338 households were selected by convenience for this study . The houses were built with adobe material with internal and external walls not pasted. A smaller number of houses had reed walls pasted with mud. All the houses had tin roofs and dirt floors throughout the rooms. The number of rooms per household varied from 2 to 4, including a kitchen/dining room, bedroom, and storage room. All of the houses were located in rural areas, close to farmland.People raise guinea pigs in the kitchen of the houses. Other animals, including chickens, dogs, and cats, move freely inside the houses all day round. Outside the homes, people raise larger livestock, including turkeys, ducks, sheep, goats, rabbits, pigs, cattle, horses, and donkeys.Fleas were collected by hand from the guinea pigs and placed in 2-mL vials containing 70% alcohol. Each vial contained approximately 20 fleas. The vials were stored at room temperature at the Tropical Medicine Institute “Daniel A. Carrion” of the Universidad Nacional Mayor de San Marcos before shipment to CDC, Fort Collins, where they were identified and processed.Fleas were collected by hand from underwear, shirts, pants, socks, blankets, bed sheets, and pillowcases. For the purpose of our study, we combined the fleas for testing and defined them as fleas from human bedding and clothes.Available blood of guinea pigs was spotted on Whatman Non-Indicating FTA Classic Cards . The FTA cards were then dried and kept at room temperature until shipped to CDC, Fort Collins, for processing. No inclusion/exclusion criteria were used for selecting the guinea pigs.20 Because of the continuing debate of taxonomic differences between Pulex irritans and Pulex simulans,21 fleas of these species were grouped by genus only and labeled as Pulex spp.Fleas were identified to genus and species level using a dissecting microscope and taxonomic keys.Bartonella spp. DNA by amplification of the 16S–23S intergenic spacer region (ITS) using primers 325f and 1100r as described by Diniz et al.,22 and the citrate synthase gene (gltA), using primers CS443f and CS1210r, as described by Birtles and Raoult.23 Nuclease-free water was used as a negative control and Bartonella doshiae DNA as a positive control. All PCR-positive samples were purified using a QIAquick PCR purification kit (Qiagen) according to the manufacturer’s instructions and sequenced with the Applied Biosystems 3130 Genetic Analyzer . Sequences were analyzed using Lasergene 14 software , and phylogenetic analysis was performed using the CLUSTAL W alignment with Lasergene 14 software (DNASTAR). Only samples that were sequence positive with ITS and gltA were considered positive.Individual fleas were placed in a 1.5-mL sterile Navy Snap Cap microcentrifuge tube  with 400 mL of brain heart infusion media (BHI). One tube containing no flea but only BHI was included as a negative control. The tubes were placed in a Bullet Blender Gold homogenizer (Next Advance) for 15 minutes, centrifuged at 3,000 rpm for 1 minute, and 200 µL of the supernatant transferred to a new 1.7-mL sterile tube. DNA was extracted using a QIAxtractor  according to the tissue protocol provided by the manufacturer. A PCR assay was used to detect the presence of ® DNA Mini Kit extraction kits  following the dried blood spot protocol provided by the manufacturer. A blank card was included in the extraction as a negative control. DNA samples were tested by PCR for the presence of Bartonella spp. using ITS and gltA and sequenced as described earlier. Only samples that were sequence positive with both ITS and gltA and the sequences of two targets that correspond to each other were considered positive.DNA extractions were performed using QIAampPulex spp., Tiamastus cavicola, and Ctenocephalides felis and were tested by PCR for Bartonella DNA  collected from guinea pigs and 194 (33.2%) collected from human bedding and clothes, belonged to ella DNA .Bartonella DNA were amplified from 81 fleas collected from guinea pigs, including 74 Pulex spp., one T. cavicola, and six C. felis . The ITS sequences were either 100% identical or 99.7% similar to a previous described variant (DQ683199), whereas the gltA sequences also showed 99.7–100% similarity/identity to a previously described variant (DQ683195). The remaining three (4.1%) Bartonella DNA in Pulex spp. were identical to each other by both ITS and gltA but distant from other Bartonella spp., and presumably represent a new Bartonella species  C. felis–positive fleas, only one (16.7%) was positive for B. rochalimae. Three sequences (50%) belonged to B. henselae and were identical to KC331013 by ITS and L38987 by gltA. The last two (33.3%) sequences belonged to B. clarridgeiae and were identical to C331014 by ITS and U84386 by gltA.Of the six Pulex spp. and two C. felis) were positive for Bartonella DNA by ITS and gltA  fleas . Of the two positive C. felis fleas, one belonged to B. rochalimae and the other one was identical to the new sequence identified in C. felis collected from guinea pigs mentioned earlier   samples were by gltA) .B. rochalimae was the main Bartonella species found in fleas from guinea pigs and fleas from human bedding and clothes (89.5%) and the only Bartonella spp. found in guinea pig blood samples. B. rochalimae has previously been detected in several animals such as coyotes (Canis latrans), striped skunks (Mephitis mephitis), red foxes (Vulpes vulpes), and raccoons (Procyon lotor) from the United States,5 and brown rats (Rattus norvegicus) from Taiwan.24 Our results suggest that guinea pigs may be an additional animal reservoir for B. rochalimae.In the present study, B. rochalimae was in a Pulex spp. flea from Peru in 2002.25 Later B. rochalimae was isolated from an American tourist after her return home from a trip to Peru. The patient presented symptoms similar to those of Oroya fever .1 In our study, Pulex spp. was the most abundant flea species, and 96% of the fleas tested were positive for B. rochalimae. This flea species often feeds on a variety of mammals, including guinea pigs, cats, dogs, and humans,29 and it is a known vector of plague and murine typhus.32 In these regions of Peru, where people live in close contact with guinea pigs, flea infestations of animals living in the household can be a source of exposure to zoonotic pathogens.The first description of Bartonella spp. besides B. bacilliformis, such as B. rochalimae and B. ancashensis, may be the cause of milder cases of Carrión’s disease.33 In 2014, Mujica et al.34 conducted a retrospective study where they identified B. rochalimae in the blood of a Peruvian patient originally diagnosed with Carrión’s disease. It can be difficult to diagnose Bartonella if diagnostic techniques rely on culture and clinical symptoms alone. Bartonella is known to be difficult to culture. They are slow growing and there are different growth requirements for B. bacilliformis (5% rabbit or sheep blood agar at 28°C) compared with other Bartonella spp., including B. henselae, Bartonella elizabethae, and Bartonella quintana (5% rabbit or sheep blood at 5% CO2 and 35°C). Moreover, some Bartonella spp., such as B. rochalimae and B. clarridgeiae, have an especially hard time growing on blood agar.35 These issues may result in a misdiagnosis or a missed diagnosis of Bartonella, and molecular testing should be considered as an additional diagnostic tool in these areas.Some researchers have proposed that other Bartonella spp. (B. henselae and B. clarridgeiae) found in the fleas from our study were mainly detected in C. felis, which is also known as the cat flea. Despite its common name, this flea readily feeds on other mammals available, including humans. Most likely, the positive C. felis from our study had previously fed on cats because it is known that cats are the host of B. henselae and B. clarridgeiae.38The remaining B. rochalimae in guinea pigs, their fleas, and fleas from human bedding and clothes, as well as B. henselae and B. clarridgeiae from cat fleas should be taken into consideration when diagnosing Bartonella in these areas. Further research is needed to define the role of the guinea pigs and their ectoparasites in the transmission cycle in Peruvian rural households.The detection of


Attention-Deficit Hyperactivity Disorder (ADHD) as a most common, impairing and complex Neuro-behavioral disorder, is characterized by deficits in behavioral features including inattention, too much activity and impulsiveness that affect daily life of children. This mental health condition is diagnosed based on clinical evaluation of such symptoms in children. Untreated children can be faced with a number of difficulties in educational, social and other aspects of life, especially in their future. Therefore, the ideal rehabilitation of children with ADHD can improve the quality of their life and prevent many anomalies in adulthood. Previous studies and clinical experiences have proven the combination of behavioral and pharmacological interventions in the effective rehabilitation of children with ADHD , 2. CompThe impairment in cognitive skills such as attention is a common symptom in children with ADHD. Therefore, computerized cognitive training programs as supplementary interventions can provide promising results in the rehabilitation of them . Each coThe role of clinical decision support systems in the management of rehabilitation of children with ADHD is very effective and future studies should pay attention to this issue.


Attention deficit hyperactivity disorder (ADHD), one of the most prevalent childhood disorders today, is generally more likely to be diagnosed and treated in boys than in girls. However, gender differences in ADHD are currently poorly understood, partly because previous research included only a limited proportion of girls and relied mainly on subjective measures of ADHD, which are highly vulnerable to reporter’s bias. To further examine gender differences in ADHD and to address some of the shortcomings of previous studies, this study examined gender differences in subjective and objective measures of ADHD among clinic-referred children with ADHD. Participants were 204 children aged 6–17 years-old with ADHD . A retrospective analysis was conducted using records of a clinical database. Obtained data included parent and teacher forms of the Conners ADHD rating scales, Child Behavior Checklist (CBCL), Teacher’s Report Form (TRF), and child’s continuous performance test (CPT) scores. Results showed that according to parents’ and teachers’ reports of ADHD-related symptoms , girls had more inattention problems than boys, but no differences were identified in the level of hyperactivity and impulsivity symptoms. CPT data, however, revealed higher impulsivity among boys. We did not find gender differences in the level of distractibility during CPT performance. Specifically, the effects of distractors type  and distractors load (one or two distracting stimuli at a time) on CPT performance did not differ between boys and girls with ADHD. These findings suggest that gender effects on ADHD symptoms may differ between subjective and objective measures. Understanding gender differences in ADHD may lead to improved identification of girls with the disorder, helping to reduce the gender gap in diagnosis and treatment. Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent childhood disorders today Barkley, , with anResearch has consistently shown that boys are more likely to be diagnosed and treated for ADHD-related symptoms than girls . Patient fees are equivalent across all four HMOs, and all HMOs provide equivalent medical services that are based on national health regulations. The diagnosis of ADHD in Israel is usually given by a psychiatrist or a neurologist and includes the use of the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria and a formal diagnostic questionnaire for parents and teachers Hezi, .The current study included 204 children diagnosed with ADHD (63% boys), referred to an outpatient pediatric neurologic clinic, affiliated with the second-largest HMO. Children were referred to the clinic for ADHD evaluation between January 2014 and December 2017. Participating children and their families were all of Jewish background, lived in rural and urban areas in Northern Israel, and had medium-high or high socioeconomic status, based on a social scale that divides geographic locations into different socioeconomic categories  = 1.01, N.S).Children’s age ranged between 6 and 17 years . No age differences were found between girls and boys , and psychosis. The protocol for the research project conforms to the provisions of the Declaration of Helsinki, approved by the Institutional Review of Board of Maccabi health services.Background variables included the child’s age and gender, ethnicity, socio-economic status, place of residence, and school type.ADHD-related symptoms were assessed by the parent and teacher forms of the Conners ADHD Index Rating scales, 3rd edition, short-form , and the Teacher’s Report Form ; each consisted of 53 trials (33 target and 20 non-target stimuli) and lasted 114.15 s. The total duration of the test was 15.2 min. On each trial, a stimulus (target or non-target) was presented in the middle of the screen for 0.5, 1, or 3 s and was followed by a “void” of the same length . Each stTarget and non-target stimuli were cartoon pictures. Given that ADHD often co-occurs with specific learning disabilities that may be confounded with CPT performance, all stimuli were free of letters or numbers , pure auditory , or as a combination of visual and auditory stimuli (birds singing and simultaneously moving their wings). Distractor presentation time varied between 3.5 and 14.8 s, with a fixed interval of 0.5 s between two distractors. There were six various visual distractors: a bowling ball (presented for 3.5 s), warrior (Jedi) with a saber, a gong (6.8 s), birds (9.25 s), (14.8 s), saber (6.8 s), and a flying airplane (8.6 s). Auditory distractors included the six corresponding sounds of the visual distractors.The test included eight levels, each included different distractors set: Levels 1 and 8 did not include any distractors. Levels 2 and 3 included pure visual stimuli, levels 4 and 5 included pure auditory stimuli, and levels 6 and 7 included a combination of visual and auditory stimuli. During levels 2, 4, and 6, only one distractor was presented at a time. During levels 3, 5 and 7, two distractors were presented simultaneously.Performance indices—The MOXO-CPT measured four performance indices:(1)Attention: the number of correct responses (pressing the key in response to a target stimulus), which were conducted either during the stimulus presentation or during the void period that followed. This method allows the test to evaluate whether the participant responded correctly to the target (was attentive to the target) independently of his/her RT. The number of omission errors were also calculated . The score in the Attention index was calculated as the average of correct responses throughout the eight test levels.(2)Timing: the number of correct responses (pressing the key in response to a target stimulus) that were given while the target stimulus was still presented on the screen. This index excluded responses that were performed during the void period (after the stimulus has disappeared). This method allowed the test to differentiate between the overall rate of correct responses (measured by the Attention index) and the rate of correct responses that were given only on the right timing (measured by the Timing index). These two aspects of RT correspond to two different deficits typical to ADHD: difficulty to provide an accurate response and difficulty to respond on time Impulsivity: the number of commission errors performed only when a non-target stimulus was present on the screen. Other types of non-inhibited responses  were not considered as impulsive responses (as will describe in the next paragraph). Score in this index was calculated as the average of impulsive responses throughout the eight test levels.(4)Hyperactivity: the total number of commission responses that were not coded as impulsive responses . Differentiating between commission errors that were conducted due to impulsive behavior and commission errors that were conducted due to motor hyper-responsivity allowed the identification of multiple sources of response disinhibition. The score in this index was calculated as the average of hyperactive responses in the eight test levels.The MOXO-CPT version for adolescents and adults, that was administered to participants aged 13 and above, differed from the children’s version in several aspects. First, in each trial, the stimulus (target/non-target) is presented for 0.5, 1 or 4 s, followed by a “void” period of the same duration. Second, eight different distractors were used instead of six. Distractors were based on adults’ and adolescents’ everyday life, including car driving, a crying baby, and arguing people. Third, all distractors were presented for 8 s, with a fixed interval of 0.5 s between two distractors. Finally, each level consisted of 59 trials (34 targets and 25 non-targets) and lasted 136.5 s, so that the total duration of the test was longer (18.2 min). All other test’s characteristics were identical to the children’s version.To address gender differences in teachers’ and parents’ rating scale of child’s behavior , we performed multivariate analysis of variance with covariates (MANCOVA). In these analyses, gender served as the independent variable, and the eight CBCL or TRF subscales were used as dependent variables.To examine gender differences in teachers’ and parents’ reports of ADHD symptoms , we performed two-way repeated-measures ANOVA. Symptom type (inattention or hyperactivity/impulsivity) and informant role (teacher or parent) were the within-subject factors, and gender was the between-subject factor. Of interest were the interaction effects of gender * symptom type, gender * informant role, and the three-way interaction (gender * symptom type * informant role). These effects may provide evidence that gender differences in ADHD symptoms vary as a function of symptom type, informant role or both.Gender differences in the agreement rates between teacher and parents rating of ADHD-related symptoms were examined with chi-square tests.Further, we examined gender differences in the four MOXO-CPT performance indices, using a one-way repeated measures ANOVA, with test levels as the within-subject factor and gender as the between-subject factor. In addition, we examined gender effects on the difference between the first and the last test levels (for each CPT index) in order to explore whether boys and girls are differently affected by time on the task.α = 0.05, and power = 0.80  was able to provide adequate power to detect a medium effect size (Cohen d = 0.5). All multivariate analyses were followed by posthoc analyses with Bonferroni correction for multiple comparisons. Analyses were conducted with SPSS software for Windows Version 25 .Finally, to examine gender differences in distractibility levels during CPT performance, we first calculated the difference between the mean score in the no-distractor level (base-line) and the mean score in each distractor type . This calculation was conducted separately for each CPT index. The outcome of this calculation is considered a measure of the distractibility level. Next, we conducted two-way repeated-measures ANOVA. Distractor type  and distractibility load (low or high distractibility) was the within-subject factors, and gender was the between-subject factor. Of interest were the two-way interactions (gender * distractor type and gender * distractibility load) as well as the three-way interaction (gender * distractor type * distractibility load). Such interactions would provide evidence for differential patterns of sensitivity to environmental distractors between boys and girls. Participants’ age served as a covariate in all analyses. Power analysis calculation revealed that using a two-tailed test, 0 Cohen, , a minimTo test gender differences in parents’ and teachers’ reports of child’s behavior we conducted a one-way MACNOVA for the CBCL and the TRF. The total scores in the CBCL and the TRF were the dependent variables, and gender was the independent variable. Age served as a covariate variable. The results of the analyses are presented in F = 1.62, p = 0.12. MANCOVA results of the TRF subscales yielded a significant overall main effect of gender, Wilks’ Lamda = 0.792, F = 6.32, p < 0.001. Univariate comparisons revealed a main effect for gender so that according to teachers’ reports, boys had more anxiety/depression symptoms, F = 4.81, p = 0.03, and more rule-breaking behaviors, F = 11.89, p = 0.001 than girls. Girls, on the other hand, were more likely to present attention difficulties/hyperactivity symptoms, F = 5.96, p = 0.02. Because inattention and hyperactivity are included in the same subscale of the TRF, it was impossible to identify whether teachers perceived girls as more inattentive or more hyperactive than boys.Overall, MANCOVA results of the CBCL subscales indicated that the effect of gender was not significant, Wilks’ Lamda = 0.937, F = 1.53, p = 0.22, or for informant role, Wilks’ Lamda value = 0.997, F = 0.61, p = 0.43. Gender interacted with symptom type, Wilks’ Lamda = 0.981, F = 3.91, p = 0.049, but not with informant role, Wilks’ Lamda = 0.997, F = 0.52, p = 0.47. Between subject analysis revealed effect for gender, F = 10.04, p = 0.002. Post hoc analysis of the interaction effect yielded a mean difference of 2.99, p = 0.002. As depicted in To examine gender differences in teachers’ and parents’ reports of ADHD symptoms , we performed two-way repeated-measures ANOVA. Symptom type (inattention or hyperactivity/impulsivity) and informant role (teacher or parent) were the within-subject factors, and gender was the between-subject factor. χ2N = 204) = 9.49, p < 0.001. Gender did not interact with test level, Wilks’ Lamda = 0.986, F = 0.39, p = 0.91. Between-subject analyses revealed no effect for gender, F = 0 0.38, p = 0.54.Analyses of within subject effects on the Attention index revealed main effect of test level, Wilks’ Lamda = 0.738, F = 6.14, p < 0.001. Gender did not interact with test level, Wilks’ Lamda = 0.969, F = 0.84, p = 0.55. Between-subject analyses revealed no effect for gender, F = 1.93, p = 0.17.Similar patterns were identified in the Timing and Hyperactivity indices. Analyses of within-subject effects in the Timing index revealed a main effect for test level, Wilks’ Lamda = 0.813, F = 10.24, p < 0.001. Gender did not interact with test level, Wilks’ Lamda = 0.955, F = 1.27, p = 0.27. Between-subject analysis revealed no effect for gender, F = 2.96, p = 0.09.Analyses within subject effects on the Hyperactivity index revealed main effect for test level, Wilks’ Lamda = 0.723, F = 5.67, p = 0.018. Gender did not interact with test level, Wilks’ Lamda = 0.968, F = 0.88, p = 0.52. However, between-subject analysis revealed a main effect of gender, F = 4.63, p = 0.03. Post hoc analyses with Bonferroni correction for multiple comparisons indicated that boys  conducted more impulsive responses than girls , regardless of test level (p = 0.03).Analyses of within-subject effects on the Impulsivity index revealed a main effect for test level, Wilks’ Lamda = 0.971, The effect of the test level that was observed in all CPT indices reflects the variation between levels in the presence, type, or load of distractors. These effects will be described in the next section.Finally, we wished to examine whether boys and girls were differently affected by time on the task. Therefore, we compared boys and girls on the difference between the first and the last level of every CPT index, using two-way repeated-measures ANOVA. For these analyses, test level (first and last) and CPT index  were the within-subject factors and gender was the between-subject factor.F = 250.77, p < 0.001 but not for the test level, Wilks’ Lamda = 0.998, F = 0.34, p = 0.56. Gender did not interact with CPT index, Wilks’ Lamda = 0.994, F = 0.41, p = 0.75 or with test level, Wilks’ Lamda = 0.996, F = 0.87, p = 0.35. The three-way interaction was not significant as well, Wilks’ Lamda = 0.991, F = 0.56, p = 0.64. The between subject analysis did not reveal a main effect for gender, F = 2.39, p = 0.12.Within subject analysis revealed a main effect for CPT index, Wilks’ Lamda = 0.20, To study gender differences in distractibility level during CPT performance, a series of two-way repeated-measures ANOVAs were conducted. Separate analyses were conducted for each one of the four MOXO-CPT performance indices. For these analyses, distractor type  and distractibility load (low or high distractibility) was the within-subject factors, and gender was the between-subject factor. The results are shown in F = 22.38, p < 0.001, and distractibility load, Wilks’ Lamda = 0.975, F = 4.97, p = 0.03. Gender did not interact with distractor type, Wilks’ Lamda = 1.00, F = 0.04, p = 0.96, or with distractibility load, Wilks’ Lamda = 0.996, F = 0 0.77, p = 0.38. The three-way interaction between distractor type, distractibility load and gender was not significant, Wilks’ Lamda = 0.995, F = 0 0.51, p = 0.60. Between-subject analysis revealed no effect for gender, F = 0 0.33, p = 0.57.Analyses of within subject effects on the Attention index revealed main effects of distractor type, Wilks’ Lamda = 0.811, Post hoc analysis with Bonferroni correction of the main effect of distractor type on the Attention index showed that participants were more distracted by the combination of visual and auditory distractors than by the presence of pure visual  or pure auditory distractors . Post hoc analysis of the effect of distractibility load did not reveal significant differences.F = 17.97, p < 0.001, but not of distractibility load, Wilks’ Lamda = 0.995, F = 0.93, p = 0.34. Gender did not interact with distractor type, Wilks’ Lamda = 0.998, F = 0.19, p = 0.83, and not with distractibility load, Wilks’ Lamda = 0.996, F = 0 0.80, p = 0.37. The three-way interaction between distractor type, distractibility load, and gender was not significant, Wilks’ Lamda = 0.984, F = 1.51, p = 0.22. Between-subject analysis revealed no effect for gender, F = 2.31, p = 0.13. Post hoc analysis with Bonferroni correction of the main effect of distractor type on the Timing index showed that participants were more distracted by the combination of visual and auditory distractors than by pure visual  or pure auditory  distractors. Pure visual distractors were more distracting than pure auditory distractors .Analyses of within-subject effects on the Timing index revealed a main effect of distractor type, Wilks’ Lamda = 0.842, F = 19.06, p < 0.001, but not for distractor type, Wilks’ Lamda = 0.990, F = 0.99, p = 0.38. Gender did not interact with distractor type, Wilks’ Lamda = 0.993, F = 0.71, p = 0.49 or with distractibility load, Wilks’ Lamda = 0.983, F = 3.29, p = 0.07. The three-way interaction between distractor type, distractibility load and gender was not significant, Wilks’ Lamda = 0.994, F = 0.54, p = 0.58. Between-subject analysis revealed no effect for gender, F = 2.14, p = 0.15. Post hoc analysis with Bonferroni correction of the main effect of distractibility load on the Hyperactivity index showed that CPT performance was worse when two distractors were simultaneously presented than when only one distractor was presented .Analyses of within subject effects on the Hyperactivity index revealed main effect of distractibility load, Wilks’ Lamda = 0.910, F = 5.67, p = 0.018, but not of distractor type, Wilks’ Lamda, 0.994, F = 0.59, p = 0.56. Gender did not interact with distractor type, Wilks’ Lamda = 0.997, F = 0.34, p = 0.72 or with distractibility load, Wilks’ Lamda = 0.998, F = 0.23, p = 0.79. The three-way interaction between distractor type, distractibility load, and gender was not significant, Wilks’ Lamda = 0.997, F = 0.34, p = 0.72. The between-subject analysis did not reveal a main effect for gender, F = 2.40, p = 0.12. Post hoc analysis with Bonferroni correction of the main effect of distractor ability load on the Impulsivity index showed that CPT performance was worse when two distractors were simultaneously presented than when only one distractor was presented .Analyses of within-subject effects on the Impulsivity index revealed a main effect of distractibility load, Wilks’ Lamda = 0.971, This study systematically examined gender effects on ADHD manifestations in a clinic-referred sample of children with ADHD aged 6–17 years, as obtained through subjective and objective measures of ADHD symptoms. To reduce a reporter’s bias, the current study used the CPT as an objective laboratory-based measure of ADHD symptoms. To the best of our knowledge, this is the first study that focused on gender differences in distractibility in children with ADHD.Examination of gender differences in parent and teacher reports on ADHD-related symptoms, according to the Conners rating scales, showed that the level of inattention symptoms was higher among referred girls. However, boys and girls were equally impaired in terms of impulsivity and hyperactivity. A similar pattern emerged in the TRF, where teachers reported more inattention problems for girls, but higher levels of depression, anxiety, and rule-breaking behaviors for boys. Consistent with previous studies, these findings suggest that clinically diagnosed males and females showed similar symptom severity except for higher inattention scores in females  as distractors (Gordon and Mettelman, Several limitations of this study should be considered. The first limitation is associated with the limited generalizability of the study, due to relative ethnic and geographic homogeneity of the sample, its limited size, and the fact that all children were recruited from a single neuro-pediatric clinic. Given that sustained attention is related to socio-cultural factors and even to gender equality in the country (Riley et al., Our results may offer further insight into the various effects of gender on rating and objective measures of ADHD. The current study suggests that attention deficits according to parents’ and teacher’s rating scales were stronger predictors of ADHD referral among girls, whereas externalization behavior were stronger predictors of ADHD referral among boys. However, when assessed on objective measures of ADHD, boys were more impulsive than girls, but no other gender differences were observed. The fact that boys and girls did not differ on most CPT outcomes, including attention performance, timing, motor activity, distractibility, and time-on-task, may indicate that the severity of their ADHD symptoms is overall similar. These results are in line with previous research in clinically ascertained samples, showing similar levels of ADHD symptoms in boys and girls in ADHD rating scales with the exception of inattention for which females had higher ratings Gershon, .Although the utility of the CPT as a stand-alone diagnostic tool for ADHD is currently limited (Berger et al., The present study provides insight into gender differences in ADHD symptoms using subjective and objective measures of ADHD. It demonstrated that parents and teachers were more likely to identify girls as having inattention problems than boys. Teachers were more likely to identify rule-breaking and anxiety/depression symptoms in boys than in girls. CPT analysis revealed higher impulsivity among boys. Gender did not interact with distractors type or load to affect CPT performance, suggesting that deficits in inhibition control and self-regulation might be considered a key aspect of ADHD in both boys and girls Barkley, , 1999. TThe datasets generated for this study are available on request to the corresponding author.The studies involving human participants were reviewed and approved by Ethics Committee of Maccabi Health Services. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.MD recruited the patients and performed clinical evaluations. OS and MD contributed equally to the development of study design, data analysis, integration of findings, and writing the manuscript.OS has previously served on the scientific advisory board of NeuroTech Solutions Limited. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Multivariate latent class growth analysis was applied to parent- and self-ratings of stress exposure, core ADHD problems , effortful control, emotion dysregulation , and internalizing problems . Results: Seven distinct developmental courses in stress exposure and psychopathology were discerned, of which four related to ADHD. Two persistent ADHD courses of severely affected adolescents were associated with very high curvilinear stress exposure peaking in mid-adolescence: (1) Severe combined type with ongoing, severe emotional dysregulation, and (2) combined type with a high and increasing internalization of problems and elevated irritability; two partly remitting ADHD courses had low and declining stress exposure: (3) inattentive type, and (4) moderate combined type, both mostly without comorbid problems. Conclusions: High-stress exposure between childhood and young adulthood is strongly intertwined with a persistent course of ADHD and with comorbid problems taking the form of either severe and persistent emotion dysregulation  or elevated and increasing irritability, anxiety, and depression. Conversely, low and declining stress exposure is associated with remitting ADHD and decreasing internalizing and externalizing problems. Stress exposure is likely to be a facilitating and sustaining factor in these two persistent trajectories of ADHD with comorbid problems into young adulthood. Our findings suggest that a bidirectional, continuing, cycle of stressors leads to enhanced symptoms, in turn leading to more stressors, and so forth. Consideration of stressful conditions should, therefore, be an inherent part of the diagnosis and treatment of ADHD, to potentiate prevention and interruption of adverse trajectories.Background: Compared to typically developing individuals, individuals with attention-deficit-hyperactivity disorder (ADHD) are on average more often exposed to stressful conditions . We hypothesized that high exposure to stress relates to a more persistent and complex  form of ADHD, while low-stress exposure relates to remitting ADHD over the course of adolescence. Method: Longitudinal data  came from the Tracking Adolescents’ Individual Life Survey (TRAILS). We selected children diagnosed with ADHD ( Over the past years, the focus of research on attention-deficit-hyperactivity disorder (ADHD) has shifted towards its developmental course rather than its childhood characteristics. While the significance of ADHD beyond childhood has become clearer ,2, insigTraditionally, stress–psychopathology relationships are studied in the context of internalizing problems, in particular depression, anxiety disorders, and medically unexplained somatic complaints ,7,8 rathIn the present study, we investigate the manifestation of differential developmental trajectories, in which individual differences in stress exposure, core ADHD symptoms, internalizing problems, and emotional regulation problems are clustered. We hypothesize that high exposure to stressful life events is related to a more persistent and complex form of ADHD , while low-stress exposure relates to remitting ADHD over the course of adolescence. We focus on four waves of longitudinal data collected between age 11–19 from a large number of individuals derived from the TRAILS population and clinical cohorts . This den = 2230) and clinic-referred (n = 543) cohorts of TRAILS . TRAILS is a prospective study of Dutch adolescents with bi- and triennial measurements since age 11 [Participants were a subsample from the population . In the present study, we sampled the children with ADHD from the TRAILS clinic-referred cohort. These participants had a preadolescent lifetime diagnosis of ADHD according to the internet version of the Diagnostic Interview Schedule for Children ,17) admiThe TRAILS population sample was divided into screen positives and screen negatives  using the 90th percentile of the DSM based ADHD scale based on either parent or teacher report. This yielded 365 children from the general population who were screen positive for ADHD. Their average scores on the parent and teacher-rated ADHD problems of current complaints were comparable to the average scores of children diagnosed with lifetime ADHD from TRAILS CC. Likewise, the gender distribution was highly similar . n = 1873), all male participants (n = 831) were included in the current study and we randomly selected 391 of the screen-negative female participants. This yielded a matched sex distribution of 68% male participants in the screen negative subsample (n = 1222). Thus, the total of ADHD (n = 244), screen positive (n = 365), and screen negative participants (n = 1222) added up to 1831 participants that were part of the current study.From the participants from the general population sample who were screen negative  . AttentiAnxiety, depression, and medically unexplained somatic complaints were measured with the DSM based scales of the Youth Self Report  at T1, TIrritability was measured at T1, T2, T3, and T4 and was tapped by a subset of items from the aggression scale of the YSR  comprisiExtreme reactivity was measured by the “not tuned to the social situation” subscale of the parent-rated Children’s Social Behavior Questionnaire  at T1, TFrustration was measured by a subscale of the Early Adolescent Temperament Questionnaire—Revised (EATQ-R)  rated byEffortful control also came from the EATQ-R  and was Stress exposure was the sum of chronic difficulties that were present as indicated by the parents in an interview (T1) and by means of a questionnaire . These included chronic illnesses or handicaps in the child or of other immediate family member(s), high work pressure at school, problems at home, neighborhood problems, unemployment, financial difficulties, having fewer friends than the child would like, child being bullied, child having enduring conflicts with family member(s), child having enduring conflicts with a person outside of the family, family member(s) having enduring conflicts.The Vocabulary and Block Design subtests from the Revised Wechsler Intelligence Scales for Children (WISC-R) administered at T1 were used to estimate full scale IQ for all children  and was To identify the developmental course of stress exposure, ADHD, and related psychopathology, we used multivariate latent class growth analysis (LCGA) ,22. In tThe trajectories were modeled using a polynomial function of age, thereby accounting for the varying specific ages of adolescents at each of the four measurement waves. The number of polynomials considered was restricted to two, to avoid overfitting. Visual inspection of individual trajectories of the outcome variables suggested linear patterns of change over time for most outcomes, with the exception of stress exposure. For the outcomes with linear trends, we modeled linear trajectories that characterized the level of the first wave  and the overall increase or decrease over time . For stress exposure, we observed in a subset of our sample an inverted U-shape with a peak in mid-adolescence. Trajectories of stress within each class were therefore modeled either as a linear trajectory, a curvilinear trajectory, or an inverted U-shape with an optimum between waves 2 and 3. For the latter, we used a polynomial function of age including an intercept and a quadratic function of age minus 14.4. This ensures that the intercept expresses the level of stress exposure at the age of 14.4 years, corresponding with the age with the average highest value of stress, and the quadratic term the levels of decrease around that age. For each class and for each outcome variable, the two polynomial parameters  were estimated. It follows that the classes were distinguished on the basis of the nature of the trajectories for all outcome variables, as expressed by the intercept and the linear or quadratic term (with the quadratic term only possible for the stress exposure).We determined the optimal number of classes and the function for stress for each class  based on the statistical model fit and the interpretability of the classes. As a statistical index of model fit, we used the Bayesian Information Criterion (BIC), a widely accepted index for latent class models that penalizes model complexity and increased sample size ,24. ThatTo relate the classes identified by the LCGA to individual demographic and clinical characteristics of the adolescents, we used the three-step approach that takes into account the uncertainty of allocating individuals to the most likely class . In the The lowest BIC was found for the LGCA model with 9 classes , with 3 classes having a quadratic term and 6 classes having a linear term for stress. We selected the LGCA model with 7 classes—3 classes with quadratic terms and 4 classes with linear terms—for stress, because this model was simpler to interpret than the 9 class model and it had the lowest BIC among the 7 class models. Compared to the selected 7 class model, the 9 class model showed a bit more refinement in the trends of adolescents with relatively healthy outcomes but not in children with ADHD. The demographic and clinical differences among the four ADHD subgroups, as well as between the ADHD groups and the non-ADHD subgroups, were according to expectation and they strongly supported their validity . The associations and characteristics across subgroups are shown in Seven subgroups were identified that were characterized by differences in the course of stress exposure, core ADHD symptoms, effortful control, and internalizing and emotional regulation problems. The seven subgroups . This can be explained by both heightened sensitivity to and reduced regulation of stress, as these two vulnerabilities hold true particularly for adolescents with combined type ADHD. Children with ADHD enter adolescence with—on average—a less matured prefrontal cortex . AdolescOf strong interest is the finding that high-stress exposure was associated with two persistent ADHD trajectories with diverging comorbid trajectories: one characterized by persistently elevated irritability, as well as elevated and increasing internalizing problems but without the more extreme reactivity and frustration; the other characterized by severe ongoing emotional regulation problems  but without the internalizing problems. This finding supports earlier work showing etiological overlap (in cognitive control) between internalizing and externalizing symptoms ,33. At tIntriguingly, we did not identify subgroups for whom high-stress levels lead to trajectories of internalizing problems or severe emotion dysregulation problems in the absence of ADHD. Only a “mild internalizing” subgroup was found with enhanced but much lower stress levels. This finding may be taken to indicate that stress-exposure and ADHD are even more intertwined than currently acknowledged in the literature which is focused on the role of stress in depression and anxiety. Importantly, the finding should not be interpreted to show the irrelevance of the classic stress–stress cycle. Rather, since we oversampled male participants, this cycle is more likely in our non-sampled screen-negative female participants. It is likely that our identified “mild internalizing” subgroup will develop more severe internalizing problems when they reach their early twenties when depression and several anxiety disorders have their peak incidence. Our findings are suggestive of a different underlying etiology for anxiety-depression with and without ADHD, respectively. We tentatively propose a neurodevelopmental subtype of “internalizing ADHD,” which is different from the adolescent onset of internalizing problems without ADHD. This distinction between “neurodevelopmental” and “traditional” internalizing problems is strongly consistent with a series of recent papers on the role of childhood irritability in depression ,36. ThesOur study has strong assets. We applied multivariate latent class growth modeling that captures this heterogeneity in both stress exposure and behavioral symptoms. This approach makes full use of the repeated simultaneous measurement of all variables by parsing subgroups of adolescents with specific forms of continuity and change . In addiOur study has limitations as well. The current study of trajectories does not allow inferences of causality; rather our analyses show the strong intertwinement of high-stress exposure with two qualitatively different trajectories of persistent ADHD, both different from remitting ADHD. We regard our study, therefore, as the first necessary step in showing the relevance of stress exposure for the course of ADHD, which has been ignored so far in the literature. Future observational and treatment studies may extend our work by focusing on the causal relations among stress exposure and the symptoms of ADHD over time. As the early stages of a progressive course of ADHD were already apparent in pre-adolescents, we recommend to not only study the potential causal role of stress during adolescence but also pre-adolescence and even pre-onset to ADHD. We propose to study the consequences of stress exposure on ADHD symptoms both at the microlevel, capturing day-to-day causal relations, and at the macrolevel for causal changes over longer periods of time.Our findings suggest that taking into account stressful environmental factors should be an inherent part of the diagnosis, prognosis, and treatment of ADHD. The clinical profile and level of stress exposure at the start of adolescence are strong markers for what comes next: preadolescents living in chronic stressful conditions who have combined type ADHD symptoms and enhanced anxiety and depression are likely to have persistent ADHD and future exacerbation of anxiety and depression. Conversely, under similar stressful conditions, high parent-rated compared to self-rated emotion regulation problems mark the highly severe and persistent course of both ADHD and emotion dysregulation symptoms. High-quality normative data for instruments that adequately tap these behaviors, derived from pre-adolescents with ADHD may aid clinical thinking about the prognosis. Importantly, the extent to which individuals expose themselves to stressful conditions and how they manage stress are potentially modifiable, thereby providing a therapeutic angle for prevention or interruption of the negative cycle of persistent ADHD and (development of) comorbid conditions.In sum, we conclude that the stress–psychopathology–stress cycle likely plays an important facilitating and sustaining role in the persistence of ADHD and (development of) comorbid problems from childhood to adolescence. We showed that ADHD combined type with high-stress exposure can either take the form of co-occurring severe and persistent emotion dysregulation  or persistently high irritability and increased anxiety and depression symptoms. The former trajectory fits well with recent literature that emphasizes that emotion dysregulation is present in a substantial subset of children with ADHD and we show that the course of this group into adulthood is unfavorable. The latter trajectory fits well with a recent emphasis on a neurodevelopmental route to depression. Conversely, low and declining stress exposure and remitting or inattentive ADHD may accumulate towards desistence from both internalizing and externalizing problems. Clinicians may, therefore, include the presence of stressful conditions in the diagnostic process for prognosis and potential prevention or interruption of an adverse ADHD course. -Cross-sectional studies have indicated that individuals with ADHD are on average exposed to more stressful conditions than typically developing individuals.-Current findings show that stress exposure is strongly intertwined with a persistent course of ADHD between childhood and young adulthood.-High exposure to stress that peaks in mid-adolescence go jointly with two different persistent courses: combined type ADHD with strong irritability, extreme reactivity, and high frustration and combined type ADHD with elevated and increasing irritability, anxiety, and depression.-Consideration of stressful conditions should be part of the diagnosis and treatment of ADHD for prognosis as well as potential prevention or interruption of adverse trajectories.


The spike encoding properties of two polarization-resolved modes in vertical-cavity surface-emitting laser with an embedded saturable absorber (VCSEL-SA) are investigated numerically, based on the spin-flip model combined with the Yamada model. The results show that the external input optical pulse (EIOP) can be encoded into spikes in X-polarization (XP) mode, Y-polarization (YP) mode, or both XP and YP modes. Furthermore, the numerical bifurcation diagrams show that a lower (higher) strength of EIOP is beneficial for generating tonic (phasic) spikes; a small amplitude anisotropy contributes to wide (narrow) tonic spiking range in XP (YP) mode; a large current leads to low thresholds of EIOP strength for both XP and YP modes. However, the spike encoding properties are hardly affected by the phase anisotropy. The encoding rate is shown to be improved by increasing EIOP strength. Moreover, dual-channel polarization-multiplexed spike encoding can also be achieved in a single VCSEL-SA. To the best of our knowledge, such single channel polarization-resolved and dual-channel polarization-multiplexed spike encoding schemes have not yet been reported. Hence, this work is valuable for ultrafast photonic neuromorphic systems and brain-inspired information processing. In recent years, the nonlinear dynamics, polarization switching and polarization bistability properties of VCSELs have been intensively studied14. Interestingly, numerical and experimental investigations demonstrate that, by injecting external input optical pulse (EIOP), VCSELs can reproduce different behaviors exhibited by biological neurons including phasic spiking and tonic spiking, but on a much faster timescale22. Therefore, the VCSELs can be regarded as photonic neurons in the neuromorphic systems20.Vertical-cavity surface-emitting lasers (VCSELs) are popular candidates for many potential applications, including optical communication, optical signal processing, and optical computation, as they exhibit many advantages, such as low manufacturing cost, easy to integrate into two-dimensional arrays, and high energy efficiency21. Based on a two-section rate equation model derived from the well-known Yamada model, some representative cortical spiking algorithms have been demonstrated numerically in small circuits consisting of excitable VCSELs-SA, including multistable circuit, synfire chain, and spatiotemporal pattern recognition circuit16. In addition, our previous numerical findings indicate that the spike codes can be stored successfully in the mutually coupled VCSELs-SA system21. However, the effects of polarization dynamics of VCSEL-SA on the spiking properties are not addressed in these works.Compared with conventional VCSELs, the VCSEL with an embedded saturable absorber (VCSEL-SA) can offer the excitability threshold for photonic neuron23. For example, single-channel all-optical digital-to-spike conversion was realized in the photonic neuron based on graphene excitable laser23. Hence, it is still open to explore whether dual-channel spike encoding can be achieved in a single VCSEL-SA taking advantage of the polarization dynamics.Moreover, the previous works mainly focused on single-channel spike encoding in the photonic neuronsIn this paper, we focus on the numerically realization of dual-channel spike encoding in VCSEL-SA, and concentrate on the effects of polarization dynamics on the spiking encoding. By combining the well-known spin-flip model (SFM) and the Yamada model, the spike encoding properties in two polarization-resolved modes, including X-polarization (XP) and Y-polarization (YP) modes of the VCSEL-SA are investigated numerically in detail. The roles of EIOP strength, amplitude anisotropy, phase anisotropy and two pump currents are considered. Furthermore, we explore the dual-channel polarization-multiplexed spiking encoding in a single VCSEL-SA for the first time.24, which is composed of two InGaAs/AlGaAs quantum wells for the gain section, and one InGaAs/AlGaAs quantum well for the SA section, can be employed in our proposed scheme. The two polarization-resolved modes of the VCSEL-SA are then separated via a PBS for achieving two channel outputs. In a possible experiment, the strength of EIOP can be easily controlled by VOA.The schematic diagram of dual-channel spike encoding based on VCSEL-SA is presented in Fig. 4. The well-known Yamada model is successful for expressing a semiconductor laser with an embedded saturable absorber25. Our model is based on the combination of SFM and Yamada models27. We use two orthogonal linear components to replace two circularly polarized components asThe polarization dynamics of VCSEL can be described in the framework of the standard SFMFx,y represent the slowly varying complex amplitudes of the two linear polarized components of the electric field. D1,2 represent the total carrier inversion between the conduction and valence bands related to the transparency carrier density. d1,2 represent the carrier inversions with opposite spin orientations. εa (εp) is the amplitude (phase) anisotropy. The terms kinjx,y are the input strength, Finjx,y characterize the optical pulse injection, and c12D2 and c21D1 are the carrier diffusion. α is the linewidth enhancement factor, μ1,2 are the injection currents, γ1,2 are the total carrier decay rates, and γs1,2 are the effective spin-flip rates. a1,2 are the differential gains. Here, we define γp denotes the birefringence, and κ is the cavity decay rate. The equations are written in a dimensionless form so the time is measured in units of κ−1. The following parameters are used in simulation27: α = 3, μ1 = 2.1, μ2 = −6.1, γ1 = 1.09 × 10−3, γ2 = 1.13 × 10−3, γs1 = γs2 = 0.25, εa = 0, γp = 15 ns−1, κ = 390 ns−1, a1 = 1, a2 = 8.7, c12 = 2.84 × 10−2, c21 = 1.91. With these parameters, the VCSEL-SA operates right below the lasing threshold μth = 2.6. Thus, without any external optical injection, no laser light is emitted24.By including the polarized EIOPs, the rate equations can be written as follows:kinjx ≠ 0, kinjy = 0; (ii) the EIOP is injected in YP mode, i.e., kinjx = 0, kinjy ≠ 0. Then, the effects of EIOP strength, amplitude anisotropy, phase anisotropy and pump currents are studied. The distribution of time intervals between the encoding spikes is investigated.In this section, we first consider polarization-resolved spike encoding in VCSEL-SA for two injection cases, (i) the EIOP is injected in XP mode, i.e., kinjx,y = 0.5. The output intensities are denoted as τFWHM, and is τFWHM = 17 ps as shown in the inset of Fig. τFWHM is the same as that in Fig. τFWHM is also 17 ps. That is to say, the EIOP can be successfully encoded into spikes in two polarization-resolved modes, respectively. Besides, a large Δτ leads to more spikes in the polarization-resolved modes for both injection cases, while the FWHM of the generated spikes is hardly affected by Δτ.Figure kinjx = 0.25, no spike is generated, indicating that the strength is below the excitability threshold. From Fig. kinjx = 0.28, 0.4 and 0.6, the responses of VCSEL-SA during the EIOP are multiple periodic spikes, and the corresponding τFWHM is 19 ps, 18 ps and 17 ps, respectively. Interestingly, such multiple periodic spikes are similar to the tonic spiking obtained experimentally and numerically based on the SFM in conventional VCSEL17. Moreover, the intervals between two consecutive spikes decrease with the increase of kinjx. Also, the intensity of the first spike is increased with the increasing kinjx. The response for kinjx = 0.75 is presented in Fig. τFWHM = 16 ps) and some damped spikes. Eventually, the intensity is about 1 in the remaining duration of the EIOP, as shown in the inset. When the kinjx is further increased, only one large intensity spike (with τFWHM = 15 ps) is generated in XP mode upon the arrival of EIOP as shown in Fig. kinjx = 1.2, which is similar to the phasic spiking reported in conventional VCSEL17. It also can be seen from the inset, the intensity is about 2 in the remaining duration of the EIOP. Note that, the stable states shown in Fig. 15.Next, the effect of EIOP strength on the spiking encoding in the polarization-resolved modes is studied. As the response properties in YP mode are similar to those in XP mode, we only present the spike encoding properties in XP mode for simplicity. The responses in XP mode of VCSEL-SA for different EIOP strengths are presented in Fig. 22. The phasic spiking and tonic spiking have been identified in conventional VCSEL20. We also present the bifurcation diagram, i.e., the maximum of Ix as a function of kinjx, in Fig. kinjx = 0.26, corresponding to the red dot G1. When kinjx < 0.26, no spike is generated, corresponding to the response shown in Fig. kinjx = 0.25 shown by arrow A. When 0.26 < kinjx < 0.69 (red dot G2), for the pointed arrows B, C and D corresponding to the kinjx used in Fig. kinjx < 0.98 (red dot G3), a large intensity spike followed by damped spikes is observed, a representative case is shown in Fig. kinjx = 0.75. When kinjx < 0.98, phasic spiking is obtained, which can be seen clearly from Fig. It has already been shown that, the bifurcation diagram is a useful tool to characterize different neuron-like dynamicskinjx,y under different cases of amplitude anisotropy, phase anisotropy and injection currents. The other parameters are identical to those in Fig. kinjx under different amplitude anisotropy εa are presented in Fig. kinjx = 0.26 for all different cases of εa. Besides, the range of kinjx corresponding to tonic spiking is 0.26 < kinjx < 0.74, for εa = −0.1, 0.26 < kinjx< 0.72 for εa = −0.05, 0.26 < kinjx < 0.66 for εa = 0.05 and 0.27 < kinjx < 0.62 for εa = 0.1. In addition, the range of kinjx corresponding to phasic spiking is kinjx > 1.06 for εa = −0.1, kinjx > 1.05 for εa = −0.05, kinjx > 0.94 for εa = −0.05 and kinjx > 0.84 for εa = 0.1. Namely, for a larger εa, the range of kinjx corresponding to tonic spiking is narrower, and the minimum kinjx corresponding to the onset of phasic spiking moves to a lower value. Correspondingly, for injection case (ii), the bifurcation diagrams for YP mode as a function of kinjy under different εa are presented in Fig. εa = −0.1, −0.05, 0.05 and 0.1, the ranges of kinjy corresponding to the tonic spiking are 0.24 < kinjx < 0.66, 0.24 < kinjx < 0.67, 0.24 < kinjx < 0.72, and 0.23 < kinjx < 0.77, respectively. Additionally, the minimum value of kinjy corresponding to the onset of phasic spiking is 0.81, 0.89, 1.06 and 1.14, respectively. That is to say, the excitability threshold is also approximately the same for different εa in YP mode. Besides, the range of kinjy corresponding to tonic spiking is broadened for a larger εa, while the minimum kinjy corresponding to the onset of phasic spiking moves to higher value, which contrasts with the injection case (i). The numerical bifurcation diagrams for different εp are shown in Fig. εp. The bifurcation diagrams for injection case (i) under different μ1 are presented in Fig. μ1 leads to a lower excitability threshold. More precisely, the value of excitability threshold is 0.44, 0.35, 0.17 and 0.11 for μ1 = 1.9, 2.0, 2.2 and 2.3, respectively. Besides, a larger μ1 contributes to a wider range of kinjx corresponding to tonic spiking. In addition, the kinjx corresponding to the end of tonic spiking is 0.73, 0.71, 0.67 and 0.65 for μ1 = 1.9, 2.0, 2.2 and 2.3, respectively. Note that, the minimum values of kinjx corresponding to the onset of phasic spiking are almost similar for different cases of μ1. The bifurcation diagrams for different μ2 are presented in Fig. μ2 = −5.7, −5.9, −6.3 and −6.5, respectively, indicating that a larger μ2 also leads to a lower excitability threshold. Besides, the ranges of kinjx corresponding to tonic spiking are almost similar for different cases of μ2. The kinjx corresponding to the end of tonic spiking is 0.60, 0.65, 0.74 and 0.77 for μ2 = −5.7, −5.9, −6.3 and −6.5, respectively. In addition, the minimum kinjx corresponding to the onset of phasic spiking is 0.89, 0.94, 1.05 and 1.06 for μ2 = −5.7, −5.9, −6.3 and −6.5, respectively. The bifurcation diagrams of YP mode for injection case (ii) are similar to those of XP mode for different cases of εp, μ1, and μ2, and are not shown here.Next, we use the bifurcation diagram to further analyze the effect of different parameters on spiking coding characteristics. Figure t121) and the time interval between two consecutive output spikes (denoted as Δt221) as functions of kinjx (kinjy) for both injection cases are presented in Fig. kinjx, the values of Δt1 decrease sharply and then converge, while the values of Δt2 decrease sharply and then disappear when kinjx > 0.88. The values of Δt1 are close to 0.27 ns when kinjx approach 1. For clarity, the values of Δt1 and Δt2 versus kinjx are also presented in log-log plots as shown in the inset. Moreover, we find that, the value of Δt2 is always greater than 2 ns (blue dashed line). Hence, the Δτ can be fixed at 2 ns to ensure a single spike output for a given EIOP. Note, the kinjx should be large enough and above the excitability threshold, that is, kinjx = 0.41 for Δτ = 2 ns. On the other hand, it can be seen in Fig. t1, can be achieved in the VCSEL-SA29.The time interval between the onset of EIOP and the first output spike  sequence, whose period is denoted as ΔT, is regarded as EIOP. Here, ten bits of 1 are considered, and the pulse width in one bit is fixed at 2 ns. In this way, the successful one-bit-to-one-spike encoding is identified by ten spikes in the response. For convenience, a successful spike is characterized by max. Ix,y ≥ 20 in the given bit duration. On the one hand, for injection case (i), the output spike numbers as functions of ΔT for four different kinjx are presented in Fig. kinjx = 0.45, at ΔT = 7.6 ns for kinjx = 0.6, at ΔT = 6 ns for kinjx = 0.7 and at ΔT = 3 ns for kinjx = 1.3, respectively. That is to say, the ten bits can be encoded correctly in the XP mode only when ΔT is sufficiently large, which can be attributed to the refractory period29. Besides, for a larger kinjx, the output spike number reaches ten at a smaller ΔT. Thus, the encoding rate, which is equal to 1/ΔT, can be improved by increasing kinjx. Usually, the encoding rate can be further improved by decreasing the volumes of the cavities and material recombination times24. On the other hand, for injection case (ii), the output spike numbers as functions of ΔT for four different kinjy are presented in Fig. kinjy = 0.45, ΔT = 7 ns for kinjy = 0.6, ΔT = 5.5 ns for kinjy = 0.7 and ΔT = 2.9 ns for kinjy = 1.3, respectively. Namely, the ten bits can also be encoded correctly in YP mode for sufficiently large ΔT, and higher encoding rate can be achieved for larger kinjy. In order to intuitively present the encoding process, the time series of responses in the XP mode for three representative ΔT are presented in Fig. kinjx = 0.7. It can be clearly seen that, ten bits are encoded into five spikes for ΔT = 4 ns, and seven spikes for ΔT = 5 ns. Namely, the one-bit-to-one-spike encoding fails due to the refractory period. For ΔT = 8 ns, ten bits are successfully encoded into ten spikes. Correspondingly, the time series of responses in the YP mode for three representative cases of ΔT are presented in Fig. Next, we further discuss the encoding rate, which is closely related to the refractory period of a photonic neuronkinjx ≠ 0, kinjy ≠ 0. Through extensive calculation, we find that when kinjx and kinjy are relative small, the polarization mode competition and multiple polarization switching will occur and seriously affect the tonic spiking dynamics, leading to very complex spike encoding properties. Hence, for simplicity, we only consider large values of kinjx and kinjy corresponding to phasic spiking dynamics. The responses in both XP and YP modes for four representative cases are shown in Fig. kinjx = 1.3 and kinjy = 1.3. It can be seen that, when the time windows of EIOPs are completely overlapped as shown in Fig. 10. The threshold cannot be reached to trigger another spike in the other mode. Interestingly, such behavior is quite similar to a biological behavior of an inhibitory interneuron that hinders the firing of others30. As presented in Fig. kinjx = 1.3, kinjy = 1.3 is considered. It can be seen that, when ΔT > 6 ns (ΔT > 5.7 ns), ten spikes can be generated in XP (YP) mode. Hence dual-channel polarization-multiplexed spike encoding can be achieved in a single VCSEL-SA when ΔT > 6 ns. Besides, to obtain more general results, we also consider the pseudo-random bit sequence as the EIOP. Here, the response outputs for only one representative ΔT is presented. It can be seen from Fig. kinjx = 1.3 and kinjy = 1.3. Note that, the polarization-multiplexed spike encoding scheme guarantees dual-channel parallel information processing in a single VCSEL-SA, which leads to reduced system cost. Hence, it is valuable for the implementation of parallel photonic information processing and photonic neuromorphic systems. Moreover, the polarization-multiplexed spike encoding may also be interesting for two channel vector coding31.Next, we explore the polarization-multiplexed spike encoding in a single VCSEL-SA. That is to say, two different EIOPs are injected in both XP and YP modes, respectively, i.e., 23. The encoding outputs for different signal-to-noise ratios (SNR) are presented in Fig. kinjx = 0.7 and kinjy = 0.7. It can be seen from Fig. 23. The white Gaussian noises induced timing jitters of output spikes are presented in the insets in Fig. 23 of six spikes. It can be seen that, the timing jitter23 is about 2 ps (9 ps) for SNR = 30 dB (20 dB). Compared to the values of Δt1 and Δt2 shown in Fig. Without loss of generality, we also consider the effect of signal noise on the spike encoding properties. Here, white Gaussian noise is added to the RZ sequencesIn the present work, we derive the theoretical model to account for the polarization dynamics, saturable absorber, and EIOP in VCSEL-SA, by combing the well-known SFM and Yamada models. The single-channel spike encoding in the polarization-resolved modes and the dual-channel polarization-multiplexed spike encoding in a single VCSEL-SA are investigated numerically. The results show that the EIOPs can be encoded into spikes in XP mode, YP mode, or both XP and YP mode in VCSEL-SA. Besides, the generated spikes are similar to the tonic spiking and phasic spiking observed in traditional VCSEL. In addition, the numerical bifurcation analyses indicate that a small value of EIOP strength is beneficial for generating tonic spiking; a large value of EIOP strength is beneficial for generating phasic spiking; a small amplitude anisotropy contributes to a wide (narrow) range of tonic spiking in XP (YP) mode; large pump currents lead to low excitability threshold. While the spike encoding properties are hardly affected by the phase anisotropy. In the context of single-channel spike encoding, the encoding rate can be improved by increasing input strength. Furthermore, the polarization-multiplexed spike encoding is achieved in a single VCSEL-SA under proper condition. At last, we find that the polarization-resolved spike encoding as well as the polarization-multiplexed spike encoding are robust to noisy RZ sequences. To the best of our knowledge, such polarization-resolved and polarization-multiplexed spike encoding have not yet been reported, and are interesting and valuable for the ultrafast photonic neuromorphic systems and brain-inspired photonic information processing.The Eqs –5) for  for 5) f