Objective: To determine the difference in effectiveness of positional release therapy (PRT) compared with therapeutic massage (TM) in treating trigger and tender points in the upper trapezius muscle. Background: Trigger points in the upper trapezius muscle are common and can be painful. Therapeutic massage is a more traditional treatment method for this condition while PRT is relatively new. Design and Setting: A randomized-group design was used to examine the differences between the 2 treatments for reducing pain and muscle tension. Subjects: Sixty healthy subjects (males = 24, females = 36; age = 27.1 ± 8.8 years; wt = 75.2 ± 17.9 kg; ht = 172.8 ± 9.7 cm) presenting with upper trapezius pain and a trigger point. Subjects were randomly assigned to the TM group or the PRT group. Measurements: Presence of upper trapezius trigger points was found via palpation by a clinician. Level of pain was measured by a visual analog scale (VAS) and pain pressure threshold (PPT) was assessed by a pressure algometer. Muscle thickness was measured by B-mode ultrasound (US) and muscle tension was measured by shear-wave elastography (SWE). Subjects were measured pretreatment and posttreatment and 48 hours later. Results: All measurements showed significant improvements for both treatments. Positional release therapy was more effective (p = 0.05) at reducing pain at day 2 and was able to maintain the pain loss. The SWE and US showed no difference between the treatment groups. There was no significant difference in PPT, but PRT PPT increased each visit while TM dropped significantly at day 2 (p = .003). Conclusion: Both treatments showed a significant ability to reduce pain and acutely decrease muscle stiffness (as measured by SWE) but there were few differences between the treatments. However, there appeared to be a slight benefit for pain reduction with PRT up to 2 days posttreatment.

Objective: To determine the difference in effectiveness of positional release therapy (PRT) compared with therapeutic massage (TM) in treating trigger and tender points in the upper trapezius muscle. Background: Trigger points in the upper trapezius muscle are common and can be painful. Therapeutic massage is a more traditional treatment method for this condition while PRT is relatively new. Design and Setting: A randomized-group design was used to examine the differences between the 2 treatments for reducing pain and muscle tension. Subjects: Sixty healthy subjects (males = 24, females = 36; age = 27.1 ± 8.8 years; wt = 75.2 ± 17.9 kg; ht = 172.8 ± 9.7 cm) presenting with upper trapezius pain and a trigger point. Subjects were randomly assigned to the TM group or the PRT group. Measurements: Presence of upper trapezius trigger points was found via palpation by a clinician. Level of pain was measured by a visual analog scale (VAS) and pain pressure threshold (PPT) was assessed by a pressure algometer. Muscle thickness was measured by B-mode ultrasound (US) and muscle tension was measured by shear-wave elastography (SWE). Subjects were measured pretreatment and posttreatment and 48 hours later. Results: All measurements showed significant improvements for both treatments. Positional release therapy was more effective (p = 0.05) at reducing pain at day 2 and was able to maintain the pain loss. The SWE and US showed no difference between the treatment groups. There was no significant difference in PPT, but PRT PPT increased each visit while TM dropped significantly at day 2 (p = .003). Conclusion: Both treatments showed a significant ability to reduce pain and acutely decrease muscle stiffness (as measured by SWE) but there were few differences between the treatments. However, there appeared to be a slight benefit for pain reduction with PRT up to 2 days posttreatment.

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No description available.

Trigger Factor (TF) is a prokaryotic chaperone protein that exerts its major chaperone activity while associated with translating ribosomes, assisting de novo folding of the emerging nascent chain. Although much is known about the kinetics behind TF-ribosome binding, most results are based on in vitro experiments which fail to mimic the cellular environment. Single-particle approaches have gained increasing power for studying binding kinetics of biomolecules in living cells. One such method is single-particle tracking by super-resolution fluorescence microscopy, where the position of a fluorescently labelled particle is recorded over time, giving information about the movement of the particle inside the cell. Changes in diffusion behaviour is then used as an indicator of changes in biological activities. In this work, a diffusion model that qualitatively and quantitatively describes TF’s binding to ribosomes is presented. The model was obtained by single-particle tracking of TF labelled with HaloTag. Particle movements were analysed with a Hidden Markov Model-based algorithm that fit the trajectories to a defined set of different diffusion states, where fast diffusion could be related to free TF and slow diffusion to a ribosome-bound state. Moreover, the model could distinguish between two types of ribosome interactions: TF’s stable binding to ribosomes and a faster sampling behaviour. The average time spent stably bound to ribosomes is 670 ms and these interactions account for 53% of TF’s activity. TF is one of many processing proteins that interact with the emerging peptide chain during translation. By using the same approach on more of these factors, the interplay between them and the growing nascent chain can be characterized, giving an increased understanding of the highly complex translation machinery.

För att förstå vad som får en individ att förändra beteenden till mer miljövänliga har studien utgått ifrån Fogg behavior model som menar att faktorerna motivation, förmåga och triggers behövs för att ett beteende ska förändras. Studien har undersökt i hur stor grad individer som har motivation och förmåga, samt fått triggers, uppnått sina miljömässiga beteendeförändringsmål. Det har även undersökts hur stor skillnad triggern gör för att ett beteende ska uppnås. Jag höll fyra, för allmänheten öppna, föreläsningar om kemikalier i vardagen varpå deltagarna satte fem beteendeförändringsmål att uppnå. Hälften fick under två månaders tid kontinuerliga triggers varpå samtliga respondenter fyllde i en uppföljning där de rankade i vilken grad de upplevde att deras beteendemål var uppfyllt. Resultatet visar att gruppen med både motivation, förmåga och triggers i genomsnitt nådde sina mål med 82,6 % och att gruppen med motivation och förmåga, men utan triggers, uppnådde sina mål i genomsnitt med 59,9%. Fishers exakta test visar en signifikansnivå på p < 0,01 vilket innebär en hög signifikant. Detta tyder på att Fogg behavior model är tillförlitlig samt att faktorn trigger är viktig för att en beteendeförändring ska ske. / To understand what makes individuals change their behaviors towards more environmentally friendly, this study started with Fogg behavior model which says that the factors needed to change a behavior is motivation, ability and triggers. This study investigated in which scale individuals who has motivation, ability and triggers achieved their goals of changing behaviors to more environmentally friendly, and how much effect the trigger had. I had four lectures, all opened for the general public, about chemicals in the every day life and afterwords the participants chose five ”behavior-goals” they wanted to achieve. Half of the respondents got triggers every week and after two months everybody made a follow-up where they rated in which scale they experienced that their goals were achieved. The result shows that the group with both motivation, ability and triggers reached their goals in average with 82,6% and the group with motivation and ability but without triggers reached their goals in average with 59,9 %. Fisher ́s Exact Test shows a significance level of p < 0,01 which is the same as a highly significant. This indicates that Fogg behavior model might be reliable and that the trigger is important for a behavior to occur.

<p dir="ltr">Artificial intelligence (AI) is increasingly integrated into critical systems across various sectors, including public surveillance, autonomous driving, and malware detection. Despite their impressive performance and promise, the security and safety of AI-enabled systems remain significant concerns. Like conventional systems that have software bugs or vulnerabilities, applications leveraging AI are also susceptible to such issues. Malicious behaviors can be intentionally injected into AI models by adversaries, creating a backdoor. These models operate normally with benign inputs but consistently misclassify samples containing an attacker-inserted trigger, known as a <i>backdoor attack</i>.</p><p dir="ltr">However, backdoors can not only be injected by an attacker but may also naturally exist in normally trained models. One can find backdoor triggers in benign models that cause any inputs with the trigger to be misclassified, a phenomenon termed <i>natural backdoors</i>. Regardless of whether they are injected or natural, backdoors can take various forms, which increases the difficulty of identifying such vulnerabilities. This challenge is exacerbated when access to AI models is limited.</p><p dir="ltr">This dissertation introduces an optimization-based technique that reverse-engineers trigger patterns exploited by backdoors, whether injected or natural. It formulates how backdoor triggers modify inputs down to the pixel level to approximate their potential forms. The intended changes in output predictions guide the reverse-engineering process, which involves computing the input gradient or sampling possible perturbations when model access is limited. Although various types of backdoors exist, this dissertation demonstrates that they can be effectively clustered into two categories based on their methods of input manipulation. The development of practical reverse-engineering approaches is based on this fundamental classification, leading to the successful identification of backdoor vulnerabilities in AI models.</p><p dir="ltr">To alleviate such security threats, this dissertation introduces a novel hardening technique that enhances the robustness of models against adversary exploitation. It sheds light on the existence of backdoors, which can often be attributed to the small distance between two classes. Based on this analysis, a class distance hardening method is proposed to proactively enlarge the distance between every pair of classes in a model. This method is effective in eliminating both injected and natural backdoors in a variety of forms.</p><p dir="ltr">This dissertation aims to highlight both existing and newly identified security and safety challenges in AI systems. It introduces novel formulations of backdoor trigger patterns and provides a fundamental understanding of backdoor vulnerabilities, paving the way for the development of safer and more secure AI systems.</p>

The prevalence of distressing content on social media raises concerns about users' mental well-being, prompting the use of trigger warnings (TW) and content warnings (CW). However, varying practices across platforms indicate a lack of clarity among users regarding these warnings. To gain insight into how users experience and use these warnings, we conducted interviews with 15 regular social media users. Our findings show that users generally have a positive view of warnings, but there are differences in how they understand and use them. Challenges related to using TW/CW on social media emerged, making it a complex decision when dealing with such content. These challenges include determining which topics require warnings, navigating logistical complexities related to usage norms, and considering the impact of warnings on social media engagement. We also found that external factors, such as how the warning and content are presented, and internal factors, such as the viewer's mindset, tolerance, and level of interest, play a significant role in the user's decision-making process when interacting with content that has TW/CW. Participants emphasized the need for better education on warnings and triggers in social media and offered suggestions for improving warning systems. They also recommended post-trigger support measures. The implications and future directions include promoting author accountability, introducing nudges and interventions, and improving post-trigger support to create a more trauma-informed social media environment. / Master of Science / In today's world of social media, you often come across distressing content that can affect your mental well-being. To address this concern, platforms and content authors use something called trigger warnings (TW) and content warnings (CW) to alert users about potentially upsetting content. However, different platforms have different ways of using these warnings, which can be confusing for users. To better understand how people like you experience and use these warnings, we conducted interviews with 15 regular social media users. What we found is that, in general, users have a positive view of these warnings, but there are variations in how they understand and use them. Using TW/CW on social media can be challenging because it involves deciding which topics should have warnings, dealing with the different rules on each platform, and thinking about how warnings affect people's engagement with content. We also discovered that various factors influence how people decide whether to engage with warned content. These factors include how the warning and content are presented and the person's own mindset, tolerance for certain topics, and level of interest. Our study participants highlighted the need for better education about warnings and triggers on social media. They also had suggestions for improving how these warnings are used and recommended providing support to users after they encounter distressing content. Looking ahead, our findings suggest the importance of holding content creators accountable, introducing helpful tools and strategies, and providing better support to make social media a more empathetic and supportive place for all users.

No / The use of many common clinically relevant chemotherapeutics is often limited due to insufficient delivery to the tumor and dose-limiting systemic toxicities. Therefore, therapeutics that specifically target tumor cells and are nontoxic to normal cells are required. Here, we report the development of a novel class of liposomes composed of lipid prodrugs, which use the increased secretory phospholipase A2 type IIA (sPLA2) activity of the tumor microenvironment as a trigger for the release of anticancer etherlipids (AEL). Treatment of sPLA2-secreting tumor cells in vitro with liposomes consisting of proAELs resulted in growth inhibition comparable with addition of the AELs alone. Using a specific sPLA2 inhibitor, we showed the low cytotoxicity of the nonhydrolyzed proAEL liposomes and have proven the sPLA2 dependency of the activation of proAELs to cytotoxic AELs. In addition, we showed that our proAEL liposomes circumvent the inherent hemolytic toxicities associated with the use of etherlipids, thereby allowing i.v. administration of such therapeutics as nontoxic prodrug liposomes. Furthermore, using a sPLA2-secreting human colon cancer xenograft model, we showed that the proAEL liposomes are capable of inducing a tumor growth delay in vivo. Taken together, these data support the validity of this novel tumor-selective liposomal prodrug delivery strategy. This new approach also provides a promising system for tumor-selective delivery and release of conventional chemotherapeutics encapsulated in the sPLA2-degradable prodrug liposomes.

Social media can trigger past traumatic memories in viewers when posters post sensitive content. Strict content moderation and blocking/reporting features do not work when triggers are nuanced and the posts may not violate site guidelines. Viewer-side interventions exist to help filter and hide certain content but these put all the responsibility on the viewer and typically act as 'aftermath interventions'. Trigger and content warnings offer a unique solution giving viewers the agency to scroll past content they may want to avoid. However, there is a lack of education and awareness for posters for how to add a warning and what topics may require one. We conducted this study to determine if poster-side interventions such as a nudge algorithm to add warnings to sensitive posts would increase social media users' knowledge and understanding of how and when to add trigger and content warnings. To investigate the effectiveness of a nudge algorithm, we designed the TWIST (Trigger Warning Includer for Sensitive Topics) app. The TWIST app scans tweet content to determine whether a TW/CW is needed and if so, nudges the social media poster to add one with an example of what it may look like. We then conducted a 4-part mixed methods study with 88 participants. Our key findings from this study include (1) Nudging social media users to add TW/CW educates them on triggering topics and raises their awareness when posting in the future, (2) Social media users can learn how to add a trigger/content warning through using a nudge app, (3) Researchers grew in understanding of how a nudge algorithm like TWIST can change people's behavior and perceptions, and (4) We provide empirical evidence of the effectiveness of such interventions (even in short-time use). / Master of Science / Social media can trigger past traumatic memories in viewers when posters post sensitive content. Strict content moderation and blocking/reporting features do not work when triggers are nuanced and the posts may not violate site guidelines. Viewer-side interventions exist to help filter and hide certain content but these put all the responsibility on the viewer and typically act as 'aftermath interventions'. Trigger and content warnings offer a unique solution giving viewers the agency to scroll past content they may want to avoid. However, there is a lack of education and awareness for posters for how to add a warning and what topics may require one. We conducted this study to determine if poster-side interventions such as a nudge algorithm to add warnings to sensitive posts would increase social media users' knowledge and understanding of how and when to add trigger and content warnings. To investigate the effectiveness of a nudge algorithm, we designed the TWIST (Trigger Warning Includer for Sensitive Topics) app then conducted a 4-part mixed methods study with 88 participants. Our findings from this study show that nudging social media users to add TW/CW educates them on triggering topics and raise their awareness when posting in the future. It also shows social media users can learn how to add a trigger/content warning through using a nudge app.