The unscientific application of synthesized/botanical pesticides has not only brought the resistance of plant pathogens and pests, but also led to serious environmental pollution. In recent years, various nano-delivery systems have been used for the development of environmental-friendly pesticides with improved efficacy. Herein, the current study constructed an efficient avermectin B1a (AVM) nano-delivery system based on a star polyamine (SPc) and focused on the characterization and bioactivity of SPc-loaded AVM at various mass ratios. The hydroxyl groups of AVM could assemble with carbonyl groups of SPc through hydrogen bond and van der Waals forces, and the self-assembly of AVM and SPc formed nearly spherical particles of AVM/SPc complex with nanoscale size. The contact angle of SPc-loaded AVM decreased with the increasing mass ratio of SPc, revealing the easier distribution and spreading of the AVM/SPc complex. Furthermore, the stomach and contact toxicity of AVM/SPc complex also increased along with the increasing SPc mass ratio, which could be attributed to the enhanced systemic transportation in plants, enlarged contact area to insect pests and stronger permeability across the insect cuticle. The current study provides an efficient nano-delivery system for increasing stomach and contact toxicity of pesticides with wide applications in the agricultural field. The utilization of synthesized/botanical pesticides can promote the sustained and stable growth of agricultural production to satisfy the global food demand for growing world population ,2. HowevIn recent years, various nanoparticles have been widely used for the development of environmentally friendly pesticides with improved efficacy, targeted-delivery, controlled-release and enhanced stability characteristics ,9,10,11.Streptomyces avermitilis [Avermectins refer to a new class of bio-insecticide macrocyclic lactones that are derived from the secondary metabolites of rmitilis . As a marmitilis . AVM hasrmitilis ,27,28,29In this context, an efficient AVM nano-delivery system was constructed to reduce the particle size and contact angle as well as to enhance plant uptake for improvement of pest stomach and contact toxicity. The current study focused on the characterization and bioactivity of SPc-loaded AVM at various mass ratios. We determined the pesticide loading content of SPc, analyzed the particle size and morphology of AVM/SPc complex at various mass ratios, and tested the interaction between AVM and SPc to elucidate the self-assembly mechanism of AVM/SPc complex. The contact angle of SPc-loaded AVM at various mass ratios was also evaluated, and their stomach and contact toxicity against green peach aphids was also determined through root application and immersion methods, respectively. The outcome of this study provides an efficient nano-approach for improving stomach and contact toxicity of pesticides with wide applications in the agricultural field.Pure AVM B1a (≥97%) was purchased from Macklin Inc. . For SPc synthesis, the N,N,N′,N′,N″-Pentamethyl diethylenetriamine  and CuBr (99.999%) were purchased from Sigma-Aldrich , the 2-bromo-2-methylpropionyl bromide and triethylamine were purchased from Heowns BioChem Technologies , and the 2-(Dimethyl amino) ethyl methacrylate  was purchased from Energy Chemical . Other agents such as ethanol, methanol, etc. were purchased from Beijing Chemical Works .1H NMR . For the further synthesis of SPc, a flask equipped with a magnetic stirrer was charged with the Pt-Br , DMAEMA  and dry THF (8 mL), and the mixture was degassed by nitrogen for 30 min. The CuBr  and PMDETA  were then added, and the polymerization was carried out in an oil bath at 60 °C for 7 h. The reaction was quenched by cooling and air exposure, and the THF was removed and recycled for the next polymerization. The crude polyamine was purified by dialysis in water, and the white powder of SPc was finally obtained. The structure of SPc was also confirmed by 1H NMR .The SPc was synthesized based on the commercial and cheap material sources through two reaction steps according to the method described by Li et al.  Figure . For the2O), respectively to prepare the mother solutions of AVM and SPc (10 mg/mL). The AVM solution was mixed with SPc solution at mass ratios of 1:1, 1:3 and 1:9, and the mixture was incubated for 15 min at room temperature to prepare the AVM nano-delivery system. The further dilutions were performed using ddH2O. The SPc could spontaneously assemble with AVM to form AVM/SPc complex in aqueous solution similarly to other synthetic/botanical pesticides [As shown in sticides ,19.2O to the total volume of 10 mL, and then dialyzed using the regenerated cellulose with a molecular weight cutoff of 1000 Da  for 12 h. The freeze-dried mixture was dissolved in 18 mL ethanol, and the absorbance at 245 nm was measured to determine AVM concentration. The pesticide-loading content (PLC) was calculated using the formula of PLC (%) = weight of AVM loaded in complex ÷ weight of AVM-loaded complex × 100%. Each treatment was repeated 3 times. Meanwhile, the AVM was mixed with SPc according to the PCL, and the ultraviolet absorbance of mixture of AVM and SPc was also tested.Pure AVM was dissolved in ethanol to prepare a series of AVM dilutions with the concentration of 0, 10, 15, 20, 25 and 30 μg/mL, and the ultraviolet absorbance of each sample was determined via UV-vis spectrophotometry . The AVM concentration was proportional to the ultraviolet absorbance at 245 nm; thus, the standard calibration curve was constructed using the absorbance at 245 nm. The 0.2 mL of excess AVM (10 mg/mL) was mixed with 0.5 mL of SPc aqueous solution (10 mg/mL). The mixture incubated for 15 min was diluted with ddHThe morphologies of AVM and AVM/SPc complex at the mass ratios of 1:1, 1:3 and 1:9 were examined using a scanning electron microscopy  with an accelerating voltage of 3 kV. Each sample was dropped on the surface of silica, dried naturally, and coated with a thin layer of platinum for 30 s with ETD-800 sputter coater . The particle sizes and polydispersity of above samples were also measured using a Particle Sizer and Zeta Potential Analyzer  at 25 °C. Each treatment contained 3 independent samples.The binding force between AVM and SPc was detected to illustrate the self-assembly mechanism using ITC that is regarded as a high-accuracy method for measuring binding affinities ,31. The 2O = 1:9) was dripped onto the glass slide, and the image of the contact angle was collected when the droplet became stable. The ellipse fitting algorithm was used to analyze the contact angle [The utilization efficiency of pesticides with efficient deposition and strong adhesion to the leaf surface is usually high in the actual production . The conct angle . The alg2O were used as controls. The number of dead aphids was recorded on 1, 2 and 3 d after the treatment, and the corrected mortality (CM) was calculated using the formula of CM (%) =  ÷  × 100%. Each treatment was repeated 5 times.Systematic transmission of pesticides is important for controlling aphids that mainly pierce the phloem and indirectly transmit plant virus in many crops . Thus, t2O were also used as controls. The number of dead aphids was recorded, and the corrected mortality was calculated similarly as above. Each treatment was repeated 5 times.The contact toxicity of SPc-loaded AVM was determined against green peach aphids using leaf immersion method as described by Yan et al. . The tobp = 0.05 level of significance. The descriptive statistics were shown as the mean value and standard errors of the mean.The statistical analysis was performed using the SPSS 26.0 software . The ANOVA with Tukey HSD test was used to analyze the data at the The UV absorbance at 245 nm is usually used to monitor AVM concentration . In the Based on the representative SEM images A, AVM sea of 5.221 × 105 M−1 and a low dissociation constant Kd of 1.915 × 10−6 M suggested that there was an effective and strong interaction between AVM and SPc, and this self-assembly was automatic due to the negative value of ΔG. The negative values of ΔH and ΔS demonstrated that the self-assembly was through hydrogen bond and van der Waals forces. Based on the chemical structures of AVM and SPc, the potential interaction groups might be the hydroxyl groups of AVM and carbonyl groups of SPc. Similar to our previous study, the SPc can conjugate with chitosan and dinotefuran through hydrogen bond and van der Waals forces [The hydrophobic core of SPc is designed to assemble with hydrophobic AIs, and its hydrophilic shell is beneficial for improving the water solubility and dispersion stability of loaded AIs ,40. AVM s forces ,20. Means forces ,39,40.Both efficient deposition and strong adhesion to the foliage surface are of great importance to minimize loss and raise the utilization efficiency of pesticides, and the contact angle of the pesticide droplet on the foliage surface can reflect its wettability. As shown in 50 decreasing from 199.89 to 106.41 mg/L [AVM has stomach and contact toxicity against Coleoptera, Lepidoptera and other pests; however, its hydrophobic character constrains the diffusion during the application ,50,51,52.41 mg/L .CHMP5, Epsin, Rab7 gene, etc., to improve the delivery efficiency of loaded cargo [The systemic transportation of AVM in plants is relatively weak, and only a small amount of AVM can be detected in treated leaves, whereas the AVM can be detected in stems and all leaves of rice plants treated with nanocarrier-loaded AVM, revealing the enhanced transportation . Our resed cargo ,17. ThusThe immersion method was used to evaluate the contact toxicity of SPc-loaded AVM against the green peach aphids . The deaIn the current study, a star polyamine was used to successfully construct an efficient AVM nano-delivery system. The AVM could assemble with SPc through hydrogen bond and van der Waals forces, and the potential interaction groups might be the hydroxyl groups of AVM and carbonyl groups of SPc. The interaction between AVM and SPc disturbed the self-aggregated large structure of AVM and thus formed nearly spherical nanoparticles with much smaller particle size. The contact angle of SPc-loaded AVM decreased with the increasing SPc mass ratio, suggesting the easier distribution and spreading of the AVM/SPc complex. Furthermore, the stomach and contact toxicity of AVM/SPc complex increased remarkably compared to AVM alone, and its toxicity increased along with the increasing SPc mass ratio. The potential synergistic mechanism might include the enhanced systemic transportation in plants, enlarged contact area to insect pests and stronger permeability across the insect cuticle, etc. The current study has provided a safe pesticide nano-delivery system for improved stomach and contact toxicity, which is beneficial for pesticide reduction in sustainable agriculture. Extensive studies suggest that the SPc is suitable as a pesticide nanocarrier/adjuvant for green pest management.


Alcohol is the most used substance by women of childbearing age. Alcohol exposed pregnancies can have serious consequences to the fetus, and the UK has one of the highest rates of drinking during pregnancy. Alcohol use during motherhood is also a public health concern, linked with potential harms to the woman and child. This qualitative study investigated the attitudes and experiences of pregnant/parenting women and healthcare professionals regarding maternal drinking. A semi-structured focus group and interviews were conducted in the North West of England with pregnant women, mothers, and healthcare professionals. Quantitative measures captured demographics, alcohol use, and screened for mental ill-health for pregnant women and mothers. Reflexive thematic analysis was used to analyse narratives. Findings revealed that most participants believed avoiding alcohol during pregnancy is the safest option. However, some pregnant women and mothers stated that there was insufficient evidence to demonstrate the harms of low-level drinking and that abstinence guidelines were patronising. All participants reported that low-level drinking during motherhood was acceptable. Heavy drinking was believed to pose serious harm during pregnancy and motherhood to the baby and mother, in addition to damaging relationships. Strong motives were revealed for choosing and avoiding to drink, such as coping with the difficulties of motherhood and parental responsibilities, respectively. Contradictions were found across quantitative and qualitative self-reports of consumption, reflecting potential underreporting of alcohol use. Additionally, drinking levels were discussed in extremes only (low/heavy) without considering ‘grey area’ drinking. Clear, consistent advice and guidelines are needed to support women in reducing their alcohol use during pregnancy and motherhood. These should include the unique potential risks regarding maternal drinking, and the harm attributable to non-clinically dependent alcohol use. The maternal participants in this study were middle-class, therefore, research is needed to capture the views and experiences of women of all socioeconomic backgrounds. Within the UK, the decreasing gender gap in alcohol use is being driven by an increase in women’s drinking and, worldwide , nearly Importantly, women’s alcohol use is also associated with key secondary harms and alcohol is the most commonly used substance by women of childbearing age . The UK Transition into motherhood can be associated with reduced alcohol use, attributable to changing social norms and increasing responsibility . Yet it Importantly, drinking does not have to reach clinically-dependent levels to be harmful , 23, yetGiven the significant potential primary and secondary harms associated with maternal drinking (i.e. drinking during both pregnancy and motherhood), we need a better understanding of the attitudes and experiences of pregnant women and mothers, and how this compares to the views of relevant healthcare professionals ). This understanding should help overcome the current limitations we have in providing appropriate and effective support in helping pregnant women and mothers to reduce their alcohol consumption . This stThe study was conducted in the North West of England and involved pregnant women, mothers with dependent children (defined as children under 18 years of age living at home), and relevant healthcare professionals. To be eligible to participate, participants had to be 18 years or older, be pregnant and/or the mother of one or more children of dependent age and/or a healthcare professional, be a fluent English speaker, and be able to provide informed consent. If a woman was pregnant and had children of dependent age, she was grouped with the pregnant participants. Healthcare professionals were recruited to represent the key health contacts for women during pregnancy and motherhood  and for women experiencing alcohol use problems (substance misuse practitioners). All individuals who requested to participate were included.The study received ethical approval from the University of Liverpool Central University Research Ethics Committee. Participants of this study provided written informed consent.The research team included female researchers with PhD qualifications. The researchers collecting and analysing data had no prior relationship with the participants of the study. The principal investigator  has substantial experience of researching alcohol use and wellbeing, and the research associate (KUG) has expertise in qualitative methodologies and analysis. The co-authors  are experienced academics in the field of mental health and qualitative research methods, respectively. The focus group and majority of interviews were conducted by KUG, with three interviews with mothers conducted by two psychology master’s students (after receiving training and supervision from AR and KUG).Multiple qualitative methods investigated the attitudes and experiences of women and healthcare professionals (N = 21) to accommodate availability. This included a semi-structured focus group with midwives and a substance use worker (N = 4) and one-to-one semi-structured interviews with general practitioners (N = 3), pregnant women (N = 6), and mothers (N = 8). The schedule for the interviews and the focus group were consistent, but tailored to the participant group , annual household income, and residing area. Participants also provided information regarding the number and age of their children, and whether they were currently pregnant and if so their due date. All quantitative scales were self-report measures.The 10-item AUDIT  was usedDepression was measured by the short 2-item version of PHQ-9 using the first two items of the original scale inquiring about frequency of depressed mood and anhedonia over the past two weeks. Scores range between 0–6 with a score of three or higher indicating likely depressive disorder . GAD 2 mIrrespective of whether you sought help or have people around you that understand your experience, do you think you have experienced a traumatic birth?”. Mothers and pregnant women also reported whether they had experienced any other traumatic life event (not related to childbirth) using the initial screening question of the Primary Care PTSD Screen for DSM-5 [Mothers and pregnant women who had previously given birth reported their subjective individual experience regarding birth trauma by responding to the question “or DSM-5 .The study was advertised on social media and through distributing posters/flyers at public establishments in advantaged and disadvantaged neighbourhoods. Pregnant women and mothers were recruited from a local university, children centres, and family friendly pubs. Healthcare professionals were recruited from the local women’s hospital (midwives), healthcare centres , and a drug and alcohol service (substance misuse worker) by email invitation.Prior to the interviews and the focus group, participants were provided with a Participant Information Sheet and gave their written informed consent. Pregnant women and mothers completed the quantitative measures  and demographic information was collected from all participants. All data were anonymised to protect participant identity by assigning a letter and a number to each participant: M# = mother, P# = pregnant woman, MW# = midwife, SMP# = substance misuse practitioner, GP# = general practitioner.The focus group and most interviews took place on campus, with some conducted in the participants’ home and healthcare centres. One interview was conducted at the local library as per the participant’s request. To accommodate the availability of healthcare professionals, a focus group was conducted first with the midwives and a substance misuse practitioner, which lasted approximately 70 minutes. During the focus group, a researcher, not involved in the project, was present to provide support if needed and took notes. This was followed by three separate interviews with GPs, who were unable to attend the focus group due to work commitments, providing complimentary material. Making the pragmatic decision of using focus groups in combination with interviews has been previously recognised . InterviData saturation in the form of ‘informational redundancy’ , 34, wheThe focus group and interviews were audio recorded and transcribed verbatim. The Nvivo 12 qualitative software programme was used as an aid in data analysis. The researcher conducting the data analysis (KUG) has training and experience of content and thematic analysis of data. Transcriptions were coded and categorised using content analysis and reflexive thematic analysis , 38 wherDifferent data collection methods were used for healthcare professionals. Midwives and a substance misuse practitioner attending a focus group, and GPs attended one-to-one interviews at a later time. All healthcare professionals were requested to answer the same questions and responses were consistent, therefore the data was deemed adequate for combined analysis.A total of six pregnant women, eight mothers, and seven healthcare professionals participated. The majority of pregnant and parenting women were white British , 30 years and older , completed a bachelor’s degree or above  with a household income of higher than £31000 . All participants, but one GP, were female. Most pregnant women and mothers were co-habiting or married . Half of the mothers  had experienced childbirth as traumatic and half of the pregnant  women had non-birth related traumatic life experience(s). Only one pregnant woman, but most mothers  reported any alcohol consumption. All pregnant participants scored zero on the AUDIT scale, however, the reporting of one participant (P2) was difficult to interpret due to unclear and contradictory responses. The score of most mothers demonstrated lower risk drinking (≥7) in general with two participants drinking during breastfeeding (6–9 units/week). Anxiety and depression levels were low across participants, except for one pregnant participant screening positive for probable depression. Healthcare professionals included three midwives, one substance use worker, and three GPs with substantial professional experience. The majority of professional participants were from a white ethnic background and one of them was a male. Four of the healthcare professionals had children themselves. Although they were interviewed in their capacity as a healthcare professional, their experiences as mothers permeated their responses. Full participant characteristics are included in Upon analysing the narratives, substantial similarity was discovered between the themes generated from the different participant groups with differences appearing in terms of views of healthcare professionals on alcohol consumption during pregnancy . To avoiTo drink or not to drink; 2) Drinking during pregnancy is associated with high risk; and 3) Strong behavioural motives.The attitudes and experiences of women and healthcare professionals are presented in three overarching themes in relation to alcohol consumption during pregnancy: 1) Women’s attitudes were split based on whether or not they believed drinking was acceptable during pregnancy. Most mothers and pregnant women reported that drinking alcohol during pregnancy should be strictly limited or should not occur as it was the safer option. With the exception of one, these women did not drink once they were aware of their pregnancy or reported to have drunk the occasional ‘sip’ or ‘once or twice’ during pregnancy. The remaining mothers and pregnant women believed low level drinking was acceptable based on the lack of convincing evidence to show the harmful effects of low-level drinking. These women felt judged, stigmatised, and their autonomy threatened. One pregnant woman believed that low level consumption may not be harmful, although she did not drink herself due to negative experiences of alcohol-related problems in her family. All healthcare professionals reported that they advise women to abstain from alcohol through pregnancy as that is the safest option, however, recognising from their own experiences as mothers that women may find this difficult.Three sub-themes were identified in terms of perceived risks of drinking in pregnancy. Drinking during pregnancy was primarily associated with harms to the fetus in addition to some negative impact on the mother’s psychological and physical health, as well as the potential to have a negative effect on relationships. Healthcare professionals’ narratives closely resembled the data obtained from women.Sub-theme 1: ‘Consequences to Baby’. Perceived harms of excessive drinking to the baby included developmental issues, birth defects, foetal alcohol spectrum disorder, miscarriage or stillbirth, addiction for the child, neurobiological delays, low birth weight, heart and other organ problems, interference with gestational time, cognitive defects, and behavioural issues later in life.Sub-theme 2: ‘Consequences to Mother’. Women associated higher level drinking with problems with decision making ability, risky behaviours, hangover, decreased awareness of own well-being, health, and the ability to look after themselves, and difficulties raising an unwell child later on. Physical issues of excessive alcohol intake were reported as heart palpitations, hepatitis, and liver damage, while a range of psychological problems were also mentioned: developing alcohol dependence, lower well-being, increased mental health problems , and feelings of guilt and self-blame for problems with the baby. Drinking at low levels and perhaps without harming the baby may also cause feeling uncomfortable and regretful about drinking. Healthcare professionals highlighted the general risks of alcohol consumption without being pregnant that also apply during this period and the additional strain that may be placed on the body by alcohol consumption while pregnant.Sub-theme 3: ‘Damage to Relationships’. Narratives related alcohol use to potential disrupted family dynamics and isolation. Women felt that family members may have differing views on drinking during pregnancy and even a judgmental attitude towards the mother, leading to disagreements and friction. Having an unhealthy child could put strains on the entire family due to, for example, the child’s behavioural problems and may lead to family members feeling angry and blaming the mother for any negative outcomes.Two sub-themes were generated to demonstrate drinking- and abstinence or reduction-related motives.Sub-theme 1: Motives to drink. Five motives to drink were identified. Pregnant women and mothers reported that women drink alcohol during pregnancy to cope and relax, because they may not be aware of the serious consequences, and to gain a sense of normality. In addition to coping and awareness about consequences, healthcare professionals identified some social aspects of drinking and the challenges of changing health behaviour. The most mentioned reasons for drinking included coping and social aspects.‘Drinking as Coping Strategy’: Some women drink alcohol to relax, aid sleep, ease stress, deal with their emotions, and ‘medicate’ psychological issues. Moreover, an existing alcohol dependence and/or mental health problem may be the reason why women are unable to stop when they get pregnant. Healthcare professionals suggested that women may also turn to alcohol to rebel against an unwanted pregnancy or to cope with domestic violence.‘Awareness and beliefs about consequences’: Women may drink being unaware of their pregnancy. Others may not appreciate how severe potential consequences may be due to misconceptions, lack of clear evidence and inconsistent advice, which may provide a rationalisation for women who choose to drink.: Data from women suggest a social aspect to drinking. The focus, however, is primarily on feeling normal; continuing with usual activities as much as possible without losing lifestyle choices.‘Drinking Provides Normality’‘Social Aspects’: Although healthcare professionals’ narratives included discussion around normality, they focused more on alcohol having a major, engrained role within the UK’s socialising culture. Pregnant women may feel left out if they can’t enjoy a drink with others at social gatherings, such as weddings. They desire ‘being normal’ and to fit in with the crowd as pregnancy tends to lead to major changes in one’s life in terms of appearance and expected behaviour. Drinking at social events may also help to feel more relaxed.‘Challenges of changing health behaviour’: This was an additional theme that emerged for health care professionals. When a woman becomes pregnant, she is expected to change her behaviour and entire lifestyle all at once to protect the baby and have a healthy pregnancy. This may feel overwhelming, particularly at the very early stages of pregnancy when women are getting used to the idea of being pregnant. They may struggle to give up behaviours they are accustomed to, such as having a drink at the weekends. As the pregnancy progresses and attachment to the fetus develops, it becomes easier to adhere to these expectations of lifestyle change.Sub-theme 2: Motives against drinking. Reasons reported by women for avoiding drinking is presented within two sub-themes describing the negative consequences to baby and mother, and women’s personal preference to abstain. In addition to a preference to abstain, healthcare professionals focused on the consequences of drinking to the baby and mothers’ fear of being stigmatised for which a separate theme emerged.‘Negative Consequences’: Participants argued strongly against drinking due to its damaging effects on the fetus  and a desire to have a healthy pregnancy. This reflects the narratives of healthcare professionals, some of whom were also mothers with similar opinions. This knowledge was reported to be the result of receiving health advice from professionals to avoid alcohol owing to the lack of evidence on safe-level drinking. Women reported physical safety and health as a reason to avoid drinking alcohol, in addition to the potential feelings of guilt of harming the baby. Finally, social stigma appears to influence drinking behaviour with social norms dictating not to drink during pregnancy.‘Personal Preference’: In line with previous research , women w‘Fear of Stigma’: While women reported several negative consequences that may affect them, stigma and judgemental attitudes emerged as a full theme from the narratives of healthcare professionals as a motive against drinking. Healthcare professionals include the negative consequences highlighted by women, as risks of drinking as opposed to reasons for not to drink. Themes, sub-themes, and associated example quotes are presented in Low-level drinking is acceptable; 2) Excessive drinking poses risks; and 3) Strong behavioural motives.With regard to alcohol consumption during motherhood, three overarching themes emerged representing the attitudes and experiences of women and healthcare professionals: 1) Women and healthcare professionals believed that drinking alcohol at a low level/in moderation/occasionally is acceptable. However, they felt that drinking to excess and becoming intoxicated in general, and particularly when around their child(ren), could result in negative consequences. With reference to breastfeeding, most women chose not to consume alcohol, with some believing a limited amount was acceptable. However, it is evident that these women may consume up to 9 units of alcohol a week as demonstrated by their scores on the alcohol measures used. Although this is within lower risk guidelines for adults, NCT breastfeeding guidelines recommend no more than 1–2 units up to twice a week .“A glass of wine with a meal…” (P2), “social drinking of a glass of wine here or there” (P2), “…a glass of wine every other night and couple of glasses on the weekend…” (M5). In five of eight of the mothers, contradictions appeared within interview narratives, and when comparing narrative responses to quantitative responses around alcohol use .The definition of acceptable drinking level varied: Participants believed that drinking alcohol can: have serious health consequences; result in poor parenting, and; potentially cause damage to relationships.Sub-theme 1‘Health Consequences’. Participants believed that drinking can lead to physical and mental health problems, particularly in early motherhood when women are vulnerable to common mental illnesses . It may also contribute to feelings of guilt if the drinking leads to child neglect. Participants also considered alcohol’s high unhealthy calorie content as important. Physical health issues mentioned include cirrhosis, liver, heart, and kidney damage. There were concerns regarding drinking becoming more regular leading to alcohol dependence, as well as its cumulative effect on one’s personal and work life.Sub-theme 2: ‘Poor Parenting’. Alcohol consumption may bring about poor parenting, a factor that influences many women to avoid drinking, as will be demonstrated in a later theme ‘Parental Responsibilities’. However, these risk factors do not necessarily stop all women from drinking. Women mentioned the dangers of alcohol use during the breastfeeding period. Despite potential risks, some chose to drink at a low level as they believe that a small amount of alcohol is not harmful.There were extensive reports on issues with the child’s physical and emotional needs and safety, such as being physically and emotionally (un)available, having the capacity to look after their children, and the issue of co-sleeping. From a professional perspective, alcohol may raise issues in terms of safeguarding children, such as whether there may be a case of neglect. Drinking was seen as an issue that may lead to emotional and physical unavailability of the mother, negatively affecting the child’s behaviour. Looking after children with a hangover was reported as a factor that may lead to poor parenting. Providing the wrong example was also a concern, which discouraged some women not to drink in front of their children. Although, responsible drinking behaviour was thought to provide a good example of safe drinking in some cases, other children may engage in problematic drinking witnessing their parents’ excessive alcohol use.Sub-theme 3: ‘Damage to Relationships’. According to narratives, drinking excessively may disrupt the family dynamics and prevent women from performing their role as a mother becoming reliant on the support of others. Participants also related excessive drinking to domestic violence and other conflicts within the family unit due to impulsive and reckless behaviour., and family members disagreeing with drinking around children and/or being concerned about the mother. Dealing with children’s behavioural problems as a consequence of the damage of alcohol on the child’s health may also produce familial issues. Additionally, due to regular and problematic alcohol use, mothers may fail to be emotionally available which will negatively influence the relationship between mother and child, leading to attachment problems.Two sub-themes were identified that represent motives in relation to alcohol use.Sub-theme 1: Motives to drink. Within this sub-theme, two further sub-themes emerged explaining that parenting women use alcohol as a coping strategy and due to its cultural nature as a tool to socialise.‘Drinking as a Coping Strategy’: Motherhood was described as a stressful and challenging period for women which requires coping strategies. Alcohol is primarily used as an aid to relaxation, stress relief, and sleep. Some women may also struggle with negative emotions and mental health problems due to a lack of support and feelings of isolation and loneliness. Drinking may also occur due to alcohol dependence.“make the most of that night out” (P4), it is part of their lifestyle, and it provides them with a feeling of normality engaging in activities unrelated to motherhood. Alcohol also helps social participation. In Britain drinking alcohol is viewed as the norm with not participating in drinking being considered “weird” (P3). Participants reported that having alcoholic drinks forms a major part of the general culture, but recently, it has also been normalised among mothers and advocated in the media.‘Social and Cultural Aspects’: Alcohol was reported to have a significant role in society, where culture supports social activities revolving around drinking and binge drinking. For women, and for mothers in particular, drinking has become the social norm. It provides mothers with the ability to fit in, to have a social life, and to reclaim their identity. Mothers would consume alcohol because they find it enjoyable, a rare opportunity to Sub-theme 2: Motives against drinking. Two sub-themes were generated explaining that women would avoid drinking alcohol when their children are young to meet their parental responsibilities and because of personal choice.‘Parental responsibilities’: The infant’s physical dependence on the mother was mentioned as one of the main factors that stops women from consuming alcohol. Some women are concerned about the transfer of alcohol during breastfeeding and the physical safety of the baby co-sleeping with a parent whose sleep might be affected by alcohol. However, narratives also revealed that in the case of co-sleeping this fear may reduce when mothers have more children and become more experienced in child rearing. Later the focus shifts to the parents’ responsibility to model appropriate drinking behaviour to their children whether it is no drinking or moderate drinking on special occasions and to maintain their children’s perception of them as reliable caregivers. Anxieties about being a new mother are often heightened and there are concerns about being available physically and emotionally for the baby and around the child’s physical safety near intoxicated people. They feel that they want to be in full control when having to attend their children’s needs and to avoid potential feelings of guilt if something goes wrong. Moreover, many women are uncomfortable with the idea of looking after a child with a hangover. One mother felt that a mother drinking was viewed as inappropriate behaviour as opposed to the father having a drink. Pregnant women talked about becoming a mature adult as a result of having children. Interestingly, only pregnant women without children reported that they would lack the opportunity to socialise and consume alcohol due to the changes of circumstances that having a child brings about. Mothering women reported to have found ways of drinking other than during social outings, such as in their home.“Well it’s not sensible…really isn’t because like my situation because I had a drink and my son got took into care…”.One of the pregnant participants (P1) came from a lower socioeconomic background and had been diagnosed with alcohol use disorder (AUD). Her report was inconsistent at times with the narratives of other participants. For example, her reason for abstaining was a fear of losing custody of her children: ‘Personal Preference’: Women chose not to drink as a personal preference in terms of leading a healthier lifestyle, financial considerations, disliking alcohol or being unable to tolerate its effects on the body, and wanting to be mindful of every moment of motherhood. They have less opportunity to socialise which would be their reason to drink. Participants assumed that for those recovering from an alcohol problem, abstinence may serve to avoid relapse. Furthermore, having negative previous alcohol experiences, such as witnessing parental alcoholism, was also reported to be a contributing factor to abstaining. This study investigated the attitudes and experiences of pregnant and parenting women and healthcare professionals using multiple qualitative methods. The UK Chief Medical Officer recommends abstinence during pregnancy and many women comply, recognising the potential risks drinking poses to the fetus, the mother, and those around them. Nevertheless, low level drinking was viewed as acceptable by some women due to insufficient evidence concerning the damaging effects of alcohol use at low levels. Women and healthcare professionals agreed that drinking in moderation is acceptable once the baby has been born, but that excessive drinking can carry significant risks.Although data from the different populations were analysed separately, they are reported together due to a high degree of similarity in the responses. Any differences in the views of pregnant women, mothers, and healthcare professionals have been highlighted. This close resemblance may be the result of many of the healthcare professionals having children themselves and thus providing a combination of perspectives as professionals and as mothers.Women and healthcare professionals associated drinking while pregnant with high risk. Direct harm to the baby was identified including numerous developmental issues and birth defects. Excessive drinking was related to mental and physical health consequences to the mother, with psychological problems potentially arising even at lower level drinking . Any level of drinking was found to be a potential source of disruption in the family due to differing opinions between family members  about alcohol consumption and the responsibility of the mother carrying a child. This awareness of alcohol-related harm resembles previous research findings .In line with previous research on motives for alcohol use , narratiWomen’s uncertainty regarding whether low-level drinking during pregnancy is acceptable has also been suggested by previous research , 44. AltDuring motherhood, low-level drinking was reported as acceptable but drinking to excess was not, due to its potentially serious consequences while carrying out parental responsibilities. However, the definition of the differing levels was unclear, leading to contradictions in findings when comparing the qualitative narratives and quantitative responses. These conflicts suggest that women underreport their level of alcohol use, perhaps as an implicit or explicit way to protect their own or others’ perceptions of them. Under reporting is observed by previous research, due to fear of stigmatisation and disclosure consequences , and socAlthough UK Chief Medical Officer guidelines recommend limiting weekly alcohol consumption to 14 units , it alsoReflecting existing evidence , 13, 56,Despite efforts to recruit participants with different economic and social positions, through visiting children (community) centres in a range of neighbourhoods, we were unable to recruit women from lower socioeconomic backgrounds. Convenience sampling resulted in the recruitment of primarily white, middle-class women aged 30+, the population most associated with alcohol use in pregnancy . HoweverAlthough contradictions in relation to alcohol use found in the narratives reflect a certain degree of potential underreporting, participants of this study primarily reported good levels of mental health and low levels of alcohol use. Understanding such types of alcohol-related behaviours is important because it contributes to preventative work. But data from a more diverse sample would be beneficial, providing information on a wider range of behaviours related to alcohol use and their social and environmental influences. This will increase the information power  of futurAcquiring the view of a range of healthcare professionals provided a more comprehensive picture of alcohol consumption in motherhood. However, due to work commitments, we provided the opportunity for healthcare professionals to take part in the research through a focus group or individual interviews. While focus groups and individual interviews are independent data collection methods and may be challenging to combine the resulting data, this combination can be advantageous in generating complementary views of a given phenomenon . The datDrinking during pregnancy and motherhood is associated with unique and significant primary and secondary harms to the mother and fetus/child, respectively, as well as the wider family. This study highlights some degree of inconsistency in women’s attitudes on drinking during pregnancy and motherhood. Additionally, findings support previous work that mothers may underreport their drinking behaviour, that women may not accurately recognise how much they drink, or how harmful alcohol use can be at non-dependent drinking levels. Moving forward, clear and consistent advice, given in a compassionate way, is needed around drinking during pregnancy, breastfeeding, and motherhood in order to motivate and support change in maternal drinking behaviours.S1 FigThemes in black illustrate views of pregnant women and mothers overall with those of the healthcare professionals, and them in white illustrate where they differ.(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 TableP = pregnant woman; M = mother; GP = general practitioner; MW = midwife; SMP = substance misuse practitioner.(DOCX)Click here for additional data file.S2 TableP = pregnant woman; M = mother. AUDIT score of 1–6 indicates low-risk drinking; AUDIT score of 7 or more in women indicates a strong likelihood of hazardous or harmful drinking; AUDIT score of 20 or above suggests alcohol dependence.(DOCX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S3 File(DOCX)Click here for additional data file.


Heavy alcohol use is associated with increased risk of alcohol-related health consequences. Alcohol consumption has increased in females in the last fifteen years and females are more likely to experience exacerbated health risks due to drinking. Our group identified that females with AUD were more likely to report respiratory conditions or cancers compared to their male counterparts. This analysis sought to further examine relationships between sex and alcohol use on medical conditions by using the new 2020 U.S. Dietary Guidelines risk drinking levels.n = 36,309) was used to evaluate associations between sex  and alcohol risk drinking levels  on past year self-reported doctor-confirmed medical conditions).Data from the U.S. National Epidemiologic Survey on Alcohol and Related Conditions  vs. males. Significant interactions demonstrated that heavy drinking females or extreme binge drinking females were 2 to 3 times more likely to have cancers or other conditions (OR=1.95–2.69) vs. males at the same risk drinking level. Female abstainers were more likely than male abstainers to have other medical conditions (OR=1.77).Females were 1.5 to 2 times more likely to have pain, respiratory, or other medical conditions in the past year (odds ratio [Consistent with our previous findings, results identify that higher risk drinking levels are associated with the presence of past year self-reported doctor-confirmed medical conditions spanning organ systems, particularly in females. Treatment for high-risk drinking should be considered in the clinical care of individuals with significant medical conditions. The CenThere has been a substantial increase in rates of alcohol use over the last decade, primarily in women . A recenPrevious findings from our group identified that females with an ongoing Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) alcohol use disorder (AUD) were 2 to 3 times more likely to report doctor- or health professional-confirmed respiratory conditions or cancers compared to males with AUD . Thus, DThe aim of this analysis was to examine whether progressively increasing risk drinking levels, set forth by the new 2020 U.S. Dietary Guidelines , and sex22.1n = 36,309 non-institutionalized adults living in the United States. All individuals who agreed to participate completed an in-person computer-assisted interview consisting of the NIAAA Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (AUDADIS-5) and other questions pertaining to alcohol use. Hispanic, African American, and Asian respondents were oversampled. Data were adjusted for oversampling and non-response, then weighted to represent the U.S. civilian population. Methodology used in the NESARC-III survey are detailed elsewhere (The NESARC-III data was used for this study. NESARC-III (2012–2013) was carried out by the National Institute on Alcohol Abuse and Alcoholism (NIAAA). The sample consisted of lsewhere .2.2except in instances where the interviewer could not make a reasonable determination based on the way the respondent presented themselves. If not apparent, NESARC-III asked “What is your sex?” and recorded each respondent's sex as either male or female. Gender identity was not queried.The NESARC-III did not ask respondents their sex 2.3abstainer, former drinker or lifetime abstainer; moderate, ≤2 drinks/day for males and ≤1 drink/day for females; binge, 5+ drinks/2 h for males and 4+ drinks/2 h for females; heavy, 4+ drinks/day for males and 3+ drinks/day for females; extreme binge, 8+ drinks/day for females and 12+ drinks/day for males. NESARC-III queried participants on quantity of alcohol consumption on days when drank in the last 12 months.Risk drinking levels followed the 2020 U.S. Dietary Guidelines for alcohol use, and we included heavy drinking and extreme binge drinking. We coded the NESARC-III data into the following categories: 2.4last 12 months and whether the diagnosis was confirmed by a doctor or other health professional. For the present investigation, we used doctor- or health professional-confirmed diagnoses for each medical condition, except for HIV/AIDS. For HIV/AIDS, respondents were asked if they ever tested positive for HIV or AIDS? NESARC-III did not ask whether HIV/AIDS status was confirmed by a doctor or health professional. The NESARC questions regarding whether health conditions were diagnosed by a doctor or other health professional were self-reported by respondents. Medical conditions spanned liver, cardiovascular, and respiratory conditions, cancers, pain disorders, seizure disorders, HIV/AIDS, insomnia, bowel problems, sexually transmitted diseases, traumatic brain injuries, etc. A complete list of medical conditions from NESARC-III by category can be found in The NESARC recorded the presence or absence of 32 medical conditions in the 2.5p ≤ 0.05.Data were analyzed using PROC SURVEYLOGISTIC in SAS, version 9.4 , which allowed for incorporating the stratification, clustering ), and unequal weighting of the sampling design. Binary logistic regression modeling was used to examine associations between sex  and risk drinking levels  with the presence of medical conditions (yes vs. no) by broad disease category: liver, cardiovascular, cancer, pain, respiratory, and other. Due to small cell sizes, additional models with individual medical conditions within each category as dependent variables could not be analyzed. The main effect of each variable of interest on any given outcome was interpreted relative to our chosen reference . Covariates relevant to health outcomes, including age, race/ethnicity, body mass index (BMI), smoking status, other drug use, and income, were included in the analysis. Relationships between sex and risk drinking levels were assessed in terms of odds ratios and were considered significant at 3p<0.001, except age (p = 0.07). Males had higher rates of moderate (39.8% vs. 32.8%), binge (4.3% vs. 2.8%), and extreme binge (15.2% vs. 9.0%) drinking compared to females. More females reported alcohol abstinence (39.6% vs. 26.9%) and heavy drinking (15.8% vs. 13.9%) compared to males . Alcohol abstainers were more likely to have a past year liver condition compared to moderate drinkers . A significant interaction (p = 0.003) between sex and risk drinking levels demonstrated that female abstainers were less likely to have past year liver conditions compared to male abstainers  was included as a covariate in the analysis.3.1.2p>0.05; see Main effects of sex and risk drinking levels as well as the interaction between sex and risk drinking levels were not significant (3.1.3p = 0.003) between sex and risk drinking levels demonstrated that heavy drinking females were more likely to have past year cancers compared to heavy drinking males . Further, females who extreme binge drank in the last year were more likely to have past year cancers compared to extreme binge drinking males  and risk drinking levels (p = 0.013) were associated with past year pain conditions. Females were more likely than males to have past year pain conditions . Binge drinkers and extreme binge drinkers were less likely than moderate drinkers to have past year pain conditions  and risk drinking levels (p<0.0001) were associated with past year respiratory conditions. Females were more likely than males to have past year respiratory conditions . Abstainers were more likely than moderate drinkers to have past year pain conditions  and risk drinking levels (p<0.0001) were associated with the presence of other conditions in the past year. Females were more likely to have other conditions in the past year compared to males . Abstainers were more likely to have other conditions in the past year compared to moderate drinkers . Binge drinkers and heavy drinkers were less likely to have other conditions in the past year compared to moderate drinkers . A significant interaction (p = 0.030) between sex and risk drinking levels demonstrated that females were more likely to have other medical conditions across all risk drinking levels. Female abstainers were more likely than male abstainers to have past year other conditions . Further, moderate drinking females and binge drinking females were more likely to have past year other conditions compared to males drinking at the same levels . Heavy drinking females and females who extreme binge drank had higher odds of having other conditions in the past year compared to heavy drinking males and extreme binge drinking males . Females who engaged in heavy drinking or extreme binge drinking in the past year were 2 to 3 times more likely to have a past year doctor- or health-professional confirmed medical condition compared to males engaging in heavy drinking or extreme binge drinking, respectively. Main effects of risk drinking levels demonstrated that alcohol abstainers were 1.5 to 2 times more likely to have liver, respiratory, or other medical conditions in the past year compared to moderate drinkers. When examining main effects of sex, females were 1.5 to 2 times more likely to have pain, respiratory, and other medical conditions compared to males.Results suggest that problematic drinking may be related to negative health outcomes, especially in females. This is consistent with findings from our group and others demonstrating that females have higher odds of alcohol-related health consequences relative to males , even thWorth noting, results may reflect the increased likelihood of females to seek medical care for health problems. Research suggests that males are less likely to seek medical help compared to their female counterparts . TreatmeIn the present investigation, individuals engaging in binge drinking or extreme binge drinking had 18.6 – 20.6% lower odds of past year pain disorders. While we cannot identify causal or temporal relationships in the current study, previous work suggests a link between chronic pain and alcohol use. Alcohol has analgesic properties; thus, alcohol use may be used as a form of self-medication . ExtremeIt should be noted that alcohol abstinence was associated with increased odds of liver, respiratory, and other medical conditions compared to moderate drinking. This effect also extended to a significant interaction between sex and risk drinking levels, such that females abstaining from alcohol were 2 times more likely to have other significant medical conditions compared to males. Thus, moderate alcohol consumption may be associated with less health consequences than alcohol abstinence. Some research has pointed to beneficial effects of moderate drinking on health and mortality, including protective effects on Alzheimer's disease and cardiovascular health . Nonethe4.1n<50) for some sex by risk drinking level by medical condition categories. Future work should seek to examine these relationships in larger samples of drinkers across risk drinking levels and doctor-confirmed medical diagnoses beyond self-report.Limitations are discussed in detail in the first manuscript . In brie4.2This report examined relationships between sex, the new 2020 U.S. Dietary Guidelines risk drinking levels, and doctor- or health professional-confirmed medical disease categories in a nationally representative sample of U.S. adults. To our knowledge, this is the first investigation to examine such relationships using NESARC-III. Results suggest that females as well as alcohol abstainers may be more likely to have a past year doctor- or health professional-confirmed medical diagnoses, such as liver, pain, and other medical conditions compared to males and moderate drinkers, respectively. Significant interactions identified that females who drank heavily or engaged in extreme binge drinking were 2 – 3 times more likely to have cancers or other medical conditions compared to males with similar drinking habits. It is important to note that findings do not imply causal or temporal relationships; however, results are largely consistent with findings from our first manuscript examining associations between sex and ongoing AUD on cancers and respiratory and other medical conditions  and withTerril Verplaetse and Sherry McKee conceptualized the study. All authors contributed to study design. Terril Verplaetse and Brian Pittman conducted the statistical analysis. Terril Verplaetse wrote the first draft of the manuscript and all authors contributed to and have approved the final manuscript.10.13039/100000027NIAAA grants K01AA025670 (TLV), R03AA028361 (TLV), K23AA026890 (WR), P01AA027473 (SAM), and U54AA027989 (SAM).This work was supported by All authors declare that they have no conflicts of interest.


F1 and ACC are 87.54% and 92.88% on ACL14, respectively.In the process of semantic capture, traditional sentence representation methods tend to lose a lot of global and contextual semantics and ignore the internal structure information of words in sentences. To address these limitations, we propose a sentence representation method for character-assisted construction-Bert (CharAs-CBert) to improve the accuracy of sentiment text classification. First, based on the construction, a more effective construction vector is generated to distinguish the basic morphology of the sentence and reduce the ambiguity of the same word in different sentences. At the same time, it aims to strengthen the representation of salient words and effectively capture contextual semantics. Second, character feature vectors are introduced to explore the internal structure information of sentences and improve the representation ability of local and global semantics. Then, to make the sentence representation have better stability and robustness, character information, word information, and construction vectors are combined and used together for sentence representation. Finally, the evaluation and verification are carried out on various open-source baseline data such as ACL-14 and SemEval 2014 to demonstrate the validity and reliability of sentence representation, namely, the  As one of the important operations of natural language processing, sentence embedding representation is widely used in many tasks such as text classification , semantiUnlike convolutional neural networks , which aWhen understanding a sentence, the weight of the word processed by BERT is not directly used to explain the sentence, but a slice attention enhancement network is designed to explain these behaviors, assigning salient words in the sentence to the sentence. Higher weight coefficients, meanwhile, explore the channel dependencies and spatial correlations of different salient words in the sentence.Based on our sentence construction, we design a bidirectional independent recurrent neural network to explore the construction vector of sentences, alleviate the ambiguity of the same word in different sentences, and promote long-term learning of the network to establish effective long-term dependencies, it realizes the interaction between forward and backward semantic information and improves the model’s ability to perceive contextual details.A construction-based character graph convolutional network is designed to explore the internal structural information of salient words in sentences, that is, there is a strong correlation between adjacent characters in each salient word. Features strengthen construction information to improve the ability of construction information to distinguish the basic structure of sentences. Furthermore, we design a triple loss function to better tune and optimize the network to learn better sentence representations.To alleviate the ambiguity of the same word in different sentences and combine the construction to explore the internal structure information of the sentence, a better sentence representation can be obtained. We propose a sentence representation framework of character-assisted construction Bert to improve the representation of global and contextual semantic information in sentiment texts. The approach has three main contributions:The rest of this paper is organized as follows: Sentiment classification usually maps emotional sentences into a dense vector space and determines the author’s subjective attitude, i.e., positive, negative or neutral, by solving the sentence representation. How to improve the performance of sentence representation to obtain complete and accurate semantic information and improve the downstream task has been a widespread concern of researchers.For several traditional machine learning algorithms, Bayhaqy et al. , respectWith the development of the machine learning model, the accuracy of the sentence representation model is gradually improved, but there is still the problem of incomplete sentence semantic expression. With the emergence of deep learning, the unique learning model of the neural network provides a distributed lexical expression for sentences, which further promotes the development of this field. Ma et al.  proposedContrastive learning has made great progress in sentence representation. Zhang et al.  proposedTo alleviate the ambiguity of the same word in different sentences and explore the internal structure information of salient words in sentences by combining construction information, we developed a sentence representation framework of character-assisted construction-Bert (CharAs-CBert) to improve the classification performance of downstream tasks such as emotion classification. The overall network structure of CharAs-CBert is shown in The CharAs-CBert sentence representation framework is mainly composed of three parts, namely the BERT initial embedding module with character and construction information, the characters auxiliary module, and the downstream sentiment classification module. Among them, the BERT embedding module of characters and constructions aims to map words and the characters that make up words into a low-dimensional space according to the sentence construction, making it easier to represent sentences; the character auxiliary module uses characters graph convolution (CharGCM) starting from the smallest unit that constitutes a sentence, that is, characters, it explores the internal structural information of sentences and improves the representation ability of salient words. Based on the sentence construction, we model its long-term dependencies with the help of a bidirectional independent recurrent neural network (BIRM), and realize information interaction from forward and reverse; in addition, we utilize the slice attention module (SAM) from two perspectives, such as channel and space. Model the importance of each word in a sentence and assign a higher weight ratio to keywords. At the same time, considering that the prior knowledge contains rich underlying semantics, we introduce prior word vector knowledge to improve the sentence representation ability. SoftMax classifiers are designed to perform downstream tasks such as sentiment classification.As the most basic components of a sentence, the representation effect of a word directly affects the representation of the entire sentence and the character is the smallest unit of a word, so obtaining an effective character vector is helpful for sentence representation. Therefore, to improve the representation of sentences, we use the pre-trained language model BERT ,10 to emN words, which is represented as M characters. At the same time, the sentence may contain S different constructions, such as the sentence “But the staff was so horrible to us.” subjective attitude is “negative”, but it has “CC -1 NP -1 .” and “DT -1 NP -1 .” and other construction structures, so to obtain an effective sentence construction vector, improve the ability of semantics to distinguish sentence structure, and promote sentence representation, we adopt an average weighting strategy to count the words corresponding to “CC”, “NP” and “DT” in the construction and obtain the mean vector feature, where “CC” indicates coordinating conjunction; “DT” indicates determiner; and “NP” indicates noun phrase or noun; in the sentence “But the staff was so horrible to us.” “But” is used as a “connector (CC)” or “determiner (DT)” to the noun phrase “the staff” (NP). Limit the scope of the situation or connect with other words to form a specific state. It is worth noting that the words corresponding to “−1” in the construction are not calculated for the mean value. The character vector, word vector, and construction vector of the corresponding word in the sentence are shown in the following equation.T represents the number of constructions in the Suppose that each sentiment sentence consists of x represents the input sentence; M characters, and M in different words is different; N represents the number of words contained in the sentence.A word is composed of several characters. We input the characters that make up the word into BERT for learning and training to obtain character features. Secondly, the construction of a sentence is also composed of specific words, and these specific words are input into BERT to obtain a vector feature. We think this vector feature is a construction vector that contains construction information.This module explores the global and contextual semantics of sentences from different perspectives and levels by obtaining word vectors, character vectors, and construction vectors of emotional sentences. The character vector and construction vector can start from the internal structure of the words in the sentence and the basic structure of the sentence, effectively reducing the ambiguity of the same word in different sentences. Meanwhile, it is helpful for sentence representation.Although the word vector feature of the sentence is effectively obtained by using the BERT  languageb indicates bias matrix; · means dot product; For a given feature vector ttention ,25 compof and c are aggregated together for the attention feature of this slice group. Specifically as shown asChannel attention Considering that in the process of sentence extraction, the recurrent neural network is prone to problems such as gradient disappearance and explosion. At the same time, it is difficult to establish effective long-term dependencies, and it is difficult to explain the input behavior due to the entanglement of neurons. Although Long Short-Term Memory (LSTM)  can effeAmong them, Each neuron in Characters are the smallest units that make up a word, and the adjacent relationship between them reflects the internal structural information of the word. A sentence usually contains multiple constructions, and the constructions of different sentences may also be the same. When the constructions of different sentences are the same, it is not conducive to distinguishing between different sentences. Therefore, obtaining character information of words in a sentence is helpful for sentences. The distinction between representation and basic structure. For example, the subjective attitude of the sentence “We would return to this place again!” is “volution  to optimW in the input sentence x consists of M characters and can be expressed as j represents the Assuming that each word M characters, such as the word “g represents the number of edges; the adjacency matrix Then, we construct a topology graph A, and then, to obtain the character node information, the optimization process of the topology map I denotes the unit adjacency matrix; To obtain better character representations, we use two layers of graph convolutions to aggregate and transfer this graph node information. In the process of transfer and aggregation, to prevent the network from falling into local optimality, Laplace renormalization is introduced to operate the adjacency matrix To improve the representation accuracy of sentences, we fuse character features, word vector features, and constructional information for sentence representation, and in addition, considering that the prior knowledge contains rich low-level semantic details, we embed it into the sentence representation. The specific operations are as follows.Among them, Finally, the sentence vectors generated after fusion are applied to the downstream sentiment classification task to demonstrate the effect of sentence representation. Specifically as shown asAmong them, To promote the proposed CharAs-CBert framework to obtain better sentence representation and alleviate the error caused by data imbalance, we embed class weights in the loss function, and the specific operations are as followsIn this section, we conduct rich experiments to demonstrate the effectiveness of the proposed CharAs-CBert framework in sentence representation and provide a detailed analysis of the experiments.SemEval2014: This dataset is mainly used for fine-grained sentiment classification, including two domains, Laptop and Restaurant, where each domain involves three categories: “positive”, “negative” and “neutral”, at the same time, the dataset of each domain is divided into training samples, validation samples, and test samples.ACL14: The dataset mainly includes negative, neutral, and positive reviews of celebrity ties, products, and companies, with negative, neutral, and positive sentiment sentences accounting for 25%, 50%, 25%, respectively. Meanwhile, the number of training samples is 6248, and the number of test samples is 692. It is worth noting that each sentiment sentence in these datasets contains multiple different constructions, and ACC) and F1 values were adopted as evaluation indexes, as shown in the equationTP (true positive) indicates that the sample originally belongs to the positive class is divided into positive classes. TN (true negative) indicates that the sample originally belonged to the negative category and is divided into the negative category. FP  indicates that errors that originally belong to the negative class are divided into positive classes. FN  indicates that the error that originally belonged to the positive class is classified into the negative class.To demonstrate the effectiveness of the CharAs-CBert sentence representation framework and ensure the smooth progress of subsequent experiments, accuracy  the initial vector set to 3 × Environment configuration. The paper uses the To demonstrate the effectiveness of the proposed CharAs-CBert sentence representation framework, we conducted evaluation and verification on open-source baseline data such as SemEval2014 and ACL14. The overall performance of our proposed CharAs-CBERT sentence representation framework on the three baseline datasets outperforms other representation models, such as Compared with BERTS-LSTM and Tree-LSTM sentence representation models, TG-HTreeLSTM and TE-DCNN have certain competitive advantages in three types of data. For example, on the Laptop data, the Capsule-B is improved by 0.98%, 1.42%, and 2.01%, respectively, compared with In the ACL14 baseline data, the ACC of CNN-LSTM is 1.09% higher than that of the LSTM method. The possible reason is that CNN obtains the local spatial features of sentences, LSTM encodes the time series and establishes a complementary relationship between the spatial and temporal features. Improved sentence representation. Thus, the sentence representation accuracy is improved. In contrast, Self-Att achieves better competitive advantages in three sets of open-source baseline datasets, mainly since self-attention focuses on key information and effectively models the local and global semantics of sentences.According to To intuitively understand the operating efficiency of different sentence representation models, we give the parameters of different models and the visualization effect of FLOPs, as shown in According to To demonstrate whether the components in the proposed CharAs-CBert sentence representation framework play a positive role in the overall performance of the model, we evaluate and verify different components on three types of data. Compared with the single-structure sentence representation, the multi-feature co-representation method shows better performance. Such as CharAs-CBert (On the Laptop baseline data, CharAs-CBert (Although According to To verify whether the loss function we designed has a positive effect on the CharAs-CBert frameworks and the impact of the class weight (As can be seen in To demonstrate the influence of the number of layers in BIRM and CharGCM on the overall performance of the model, we conduct evaluation and verification on the Restaurant baseline datasets. From In this paper, we design a new character-assisted structure BERT sentence representation framework, which utilizes words, structures, and characters to explore the context and global semantics of sentences from different perspectives and helps to capture the sentence’s meaning based on structure and character information. The internal structure information improves the ability to distinguish between different sentences. At the same time, due to the complementary and interactive relationship between different feature vectors, the ambiguity of the same word in different sentences is reduced. Finally, the evaluation results on baseline data such as ACL14 and SemEval 2014 show that the proposed CharAs-CBert sentence representation framework has good robustness and effectiveness, that is, the experimental results on different baseline datasets are superior to other sentence display methods.Although the proposed CharAs-CBert sentence representation framework has achieved good representation performance, we found that this method still has shortcomings during the experiment, such as the complex model structure, and different feature vectors having some redundant information in the fusion stage. At the same time, there is still a lot of room for improvement in time efficiency. Therefore, in the following, we will design an efficient and concise semantic-guided sentence representation framework from the perspective of model structure and exploration of more effective detail semantics to obtain semantic details while ensuring accuracy and improving model efficiency.


Adversarial examples have aroused great attention during the past years owing to their threat to the deep neural networks (DNNs). Recently, they have been successfully extended to video models. Compared with image cases, the sparse adversarial perturbations in the videos can not only reduce the computation complexity, but also guarantee the crypticity of adversarial examples. In this paper, we propose an efficient attack to generate adversarial video perturbations with large sparsity in both the temporal (inter-frames) and spatial (intra-frames) domains. Specifically, we select the key frames and key pixels according to the gradient feedback of the target models by computing the forward derivative, and then add the perturbations on them. To overcome the problem of dimensional explosion in the video, we introduce super-pixels to decrease the number of pixels that need to compute gradients. The proposed method is finally verified under both the white-box and black-box settings. We estimate the gradients using natural evolution strategy (NES) in the black-box attacks. The experiments are conducted on two widely used datasets: UCF101 and HMDB51 versus two mainstream models: C3D and LRCN. Results show that compared with the state-of-the-art method, our method can achieve the similar attacking performance, but it pollutes only <1% pixels and costs less time to finish the attacks. The development of DNNs brings significant convenience to people’s lives. However, recently, researchers have found that DNNs are vulnerable to adversarial examples . The worThe low-cost adversarial examples will make the DNNs return the wrong output, and they thus bring great damage and harm to the applications based on DNNs. Adding perturbations to the road signs would cause the auto-driving system to make a wrong decision . WearingThe concerns about the video models’ security cause the focus of AEs to turn to adversarial video examples (AVEs). Recently, the key applications based on video classification models have begun to be applied to some critical systems. Those AI systems are directly related to personal safety and property security. For example, they are widely used in the fields of smart home , automatAccording to the number of perturbations added to the inputs, the current attacks by AVEs can be divided into two classes according to their settings. One is the sparse AVEs, and the other is dense AVEs. The sparse AVEs only add perturbations on several frames , not allIn the sparse AVEs, they use spatial sparsity and temporal sparsity to define the number of perturbed frames and pixels. Specifically, temporal sparsity means the proportion of clean frames versus all frames of a video. The spatial sparsity means the proportion of clean pixels versus all pixels of a video. A higher temporal sparsity and spatial sparsity mean fewer frames and pixels being perturbed. The detailed definitions of them are described in the following section.Compared to the dense AVEs, the sparse AVEs avoid the redundancy of the adversarial perturbations and have the following advantages: (1) Sparse video attack can reduce the computational complexity. Sparse video attack only adds perturbations on several frames and has a lower total value of adversarial perturbations. There is no need to take the computational cost to generate complex perturbations. According to our experimental results, our sparse attack costs less time to generate adversarial videos compared with other sparse attacks; (2) Sparse video attack can improve the imperceptibility to human observers. Our sparse attack has higher spatial sparsity and temporal sparsity than other sparse adversarial video attacks. This means that it only needs to perturb fewer pixels of videos to generate adversarial videos. Meanwhile, for human observers, it has better invisibility; (3) Sparse video attack gives a better interpretation of adversarial video attack. The perturbation positions reveal which frame and which part of that frame are important but also vulnerable for the prediction by the video classifier; (4) Sparse video attack can decrease the queries of adversarial video attack under black-box settings. Sparse video attack generates adversarial videos in a low-dimensional manifold. It decreases the high dimension of videos. Compared to dense attacks, the more the sparsity of the adversarial video attack, the fewer queries it needs to query the results from the black-box models.However, the current sparse video attack is not sparse enough. First, they only focus on generating AVEs with temporal sparsity and ignore the spatial sparsity. Specifically, they only consider how to decrease the number of perturbed frames and do not consider how to decrease the number of perturbed pixels among the perturbed frames. However, different from images, the video has two-dimensional features: the temporal and the spatial features. Therefore, it is crucial to consider the temporal sparsity and spatial sparsity simultaneously. Second, the temporal sparsity is not enough. Current sparse AVEs still need to perturb nearly half of the video frames to acquire a successful attack. However, with the development of video classification models, they need to input more frames to obtain better performance, such as increasing inputs from 16 frames as input to 48 frames extracted from a video. Therefore, the cost would significantly increase, and current sparse AVEs still need great cost to generate successful AVEs. Therefore, there still is a gap between the temporal sparsity and the real requirements, and the key point in a sparse attack is working out how to increase the temporal and spatial sparsity of an adversarial video simultaneously as much as possible. Therefore, in this paper, we present a method to generate sparse adversarial videos, which can significantly improve both the temporal and spatial sparsity at the same time. We are motivated by the physical meaning of the forward derivative. According to its definition, the forward derivative of each pixel can reflect the contribution of that pixel to the output of video models. Thus, we calculate the forward derivative of the frames. It can also reflect the contribution of that frame to the output. We select the key frames and key pixels according to their forward derivative, respectively, and then only add perturbations on the selected key frames and pixels. The method of selecting key frames and pixels can significantly improve the temporal sparsity and spatial sparsity.However, compared with image data, video data have the problem of dimension explosion when computing the forward derivative. To solve this problem, we introduce the super-pixels , and comMoreover, different from other adversarial generation methods that can only adapt to one setting, either the white-box setting or the black-box setting, we also devise a framework to generate adversarial examples under the two settings. In the white-box video attack, the attacker knows all the knowledge of a model, while in the black-box video attack, the attacker can only access the model’s outputs.Specifically, in the white-box attack, we first use the super-pixels to reduce the dimension of a video and compute each super-pixel’s forward derivative as the contribution. Then we sum that value within a frame as the contribution of that frame and search for the key frames, key super-pixels, and key pixels by ranking those contributions. Lastly, we can acquire the adversarial videos by only perturbing these key pixels. We can find the closest adversarial videos to the original sample, which has the lowest number of disturbed pixels, through this method.In the black-box attack, we cannot directly calculate the contribution of frames or pixels due to the lack of models’ details. Therefore, we choose to estimate the forward derivative based on natural evolution strategy (NES) . HoweverWe propose a novel method to generate adversarial videos with large sparsity in both the temporal and spatial domains. It adds perturbations only on the key pixels of the key frames and finally generates an adversarial video with fewer pixels disturbed in a short time.We introduce super-pixels to solve the dimension explosion problem that exists in attacking video data. This method can decrease the number of pixels whose gradients need to be computed, and thus improve the efficiency of generating adversarial examples.Our algorithm can work in different two settings: the white-box attack and the black-box attack. We test its attacking performance against two mainstream models on two public datasets. Results show that compared with the state-of-the-art video attacking methods, our method can achieve a similar attacking performance, but it only pollutes <1% pixels and costs less time.Our major contributions can be summarized as follows:The rest of this paper is organized as follows: In Many works have focused on generating adversarial samples for images in recent years. These works can be divided into two categories due to their attacking condition: white-box and black-box attacks.White-box attack assumes that the structure and parameters of a target model are known to the attacker. In white-box attack, it is easy to perform attacks according to the gradient of objective function of attack or the forward derivative of model computed via backpropagation. Various types of attacks have been proposed. For example, L-BFGS , fast grF on the jth category when inputting X, and ith input feature. The forward derivative gives an instruction of the input that has the greatest impact on the specific output of the classifier. Therefore, adding perturbations to that input can significantly influence the output and help to generate effective AEs.In Equation , Fj(X) rContrarily, the black-box setting remains the model with information unknown to attacker, which makes generation of adversarial samples more challenging. Existing works in this setting include zeroth-order optimization (ZOO) , autoencThese adversarial attacks can generate AEs for image models, and they would bring much harm to the systems based on the models. For example, the work  proposesIn 2019, some works focused on how to generate adversarial samples of video models. In a white-box attack, an ognition  utilizedognition  achievesognition  heuristiUnlike the algorithms mentioned above, our method computes key frames based on forward derivatives and only adds perturbations on key pixels among the key frames to generate adversarial videos. Our method can generate adversarial videos with fewer pixels perturbed. Finally, we successfully implement it both in black-box and white-box settings.F, but in the black-box attack, the only knowledge of the model we have is the outputs.Our sparse attack of video models can be implemented both in white-box and black-box conditions, and generate the target and non-target AVEs in the two conditions. In the the white-box setting, it calculates the super-pixels to reduce dimension of input video, and then it calculates the forward derivative of each super-pixel as the contribution of video to the output. According to the forward derivatives of each super-pixel, it can calculate the contribution of the frames when all derivatives in total are among a frame. The forward derivative is the contribution of a frame to the output. Then, we construct the saliency map to show the different inputs that have the greatest impact on the specific output of the classifier. Through the saliency map, we select the key frames, key super-pixels, and key pixels. Finally, it adds adversarial perturbations to the key pixels to generate effective AVEs. Through this method, we can generate the AVEs close to the original example.In the black-box attack, we first select the key frame according to the output of each frame. Then, it uses super-pixels to reduce the dimension of the key frame, and due to the parameters being inaccessible, we estimate the forward derivative based on NES instead. Finally, we select the key pixels according to the forward derivative and add adversarial perturbations to the key pixels.Above all, the key differences of the algorithm between the two conditions are how to acquire the forward derivative of the super-pixel among a video and the node for dimension reduction. We detail the difference in the following section.F, a clean video input as X is defined as C is the number of classes. i-th frame of X. We use j-th super-pixel of frame k-th pixel within the g is the target label. If a frame Denotation: In this setting, we define the classifier function of the threat model as The main flow of the algorithm under the white-box setting isDetermine the label of attack target.Reduce dimension for the video.Compute forward derivative and saliency map.Search for key frames and key pixels.Add perturbations.g. Compared with selecting a label as target randomly, it can cost the minimum resources to generate the AVEs that can make the model give a wrong label. The equation is Equation Fi=FIn black-box attack, as we cannot access the full knowledge of the model, we cannot compute the forward derivative directly, so we consider another method to acquire it. We estimate the forward derivative function , we maxiEquation  15]:(8):(8)λcij M is the number of super-pixels of With a trick similar to , we chooEquation :(9)∇xiF: the percentage of adversarial videos that are successfully misclassified . A largeZ is the total number of pixels in a video and Perceptibility : the perceptibility score of an adversarial video, as L is the number of perturbed frames, which is length of the set of the key frames Temporal sparsity (TS): the proportion of clean frames versus all frames of a video . TS=1−L/Spatial sparsity (SS): the proportion of clean pixels versus all pixels of a video. Query (Q): in black-box attack, the number of queries of the threat model. A small value means fast attack.Time (Ti): in white-box attack, the time consumed when attacking a video. A small value means fast attack.In this section, we set the parameters used in two algorithms. We show the different performance of different parameters in n with setting n increases, the effect on FR of different n is small, but the other metrics will increase, and the line shows that ℵ, and the line k with the parameters selected above, and when k. That analysis is under the normal pursuit of better According to Above all, we know that there will be a better performance when In the white-box setting, we compared our method with the sparse attack  which usD attack , which eIn this section, we test the difference between our algorithm with SLIC and that without SLIC. The results are shown in As In this paper, we propose an algorithm to generate sparse adversarial videos in both black-box setting and white-box setting. In order to improve the temporal and spatial sparsity, we search for key pixels and key frames and only add perturbations on these selected pixels. In addition, to solve the problem of dimension explosion, we utilize super-pixels to decrease the number of pixels needed to be computed in white-box while estimated in black-box. Our algorithm can adapt to multiple video models and datasets. The experimental results show that our algorithm can attack a model with only <1% polluted pixels in a shorter time. However, though the sparse attack has many advantages, as discussed before, it cannot be generalized to the physical world. The reason is that it is hard to realize adding sparse perturbations in the physical world. In the future, we will explore the adversarial attack in the physical world to help to evaluate the robustness of the models.