UC Berkeley

Background

Study objective

Methods

Results

Conclusion

This study was undertaken to evaluate the effect of a reproductive empowerment contraceptive counselling intervention (ARCHES) adapted to private clinics in Nairobi, Kenya on proximal outcomes of contraceptive use and covert use, self-efficacy, awareness and use of intimate partner violence (IPV) survivor services, and attitudes justifying reproductive coercion (RC) and IPV. We conducted a cluster-controlled trial among female family planning patients (N = 659) in six private clinics non-randomly assigned to ARCHES or control in and around Nairobi, Kenya. Patients completed interviews immediately before (baseline) and after (exit) treatment and at three- and six-month follow-up. We use inverse probability by treatment weighting (IPTW) applied to difference-in-differences marginal structural models to estimate the treatment effect using a modified intent-to-treat approach. After IPTW, women receiving ARCHES contraceptive counselling, relative to controls, were more likely to receive a contraceptive method at exit (86% vs. 75%, p < 0.001) and had a significantly greater relative increase in awareness of IPV services at from baseline to three- (beta 0.84, 95% CI 0.13, 1.55) and six-month follow-up (beta 0.92, 95% CI 0, 1.84) and a relative decrease in attitudes justifying RC from baseline to six-month follow-up (beta -0.34, 95% CI -0.65, -0.04). In the first evaluation of a clinic-based approach to address both RC and IPV in a low- or middle-income country (LMIC) context, we found evidence that ARCHES contraceptive counselling improved proximal outcomes related to contraceptive use and coping with RC and IPV. We recommend further study and refinement of this approach in Kenya and other LMICs.Plain Language Summary Reproductive coercion (RC) and intimate partner violence (IPV) are two forms of gender-based violence that are known to harm womens reproductive health. While one intervention, ARCHES - Addressing Reproductive Coercion in Health Settings, has shown promise to improve contraceptive use and help women cope with RC and IPV in the United States, no approach has been proven effective in a low- or middle-income country (LMIC) context. In the first evaluation of a reproductive empowerment contraceptive counselling intervention in an LMIC setting, we found that ARCHES contraceptive counselling, relative to standard contraceptive counselling, improved proximal outcomes on contraceptive uptake, covert contraceptive use, awareness of local violence survives, and reduced attitudes justifying RC among women seeking contraceptive services in Nairobi, Kenya. Distal outcomes will be reported separately. Findings from this study support the promise of addressing RC and IPV within routine contraceptive counselling in Kenya on womens proximal outcomes related to contraceptive use and coping with violence and coercion and should be used to inform the further study of this approach in Kenya and other LMICs.

Machine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.

Defect engineering is foundational to classical electronic device development and for emerging quantum devices. Here, we report on defect engineering of silicon with ion pulses from a laser accelerator in the laser intensity range of 1019 W cm−2 and ion flux levels of up to 1022 ions cm−2 s−1, about five orders of magnitude higher than conventional ion implanters. Low energy ions from plasma expansion of the laser-foil target are implanted near the surface and then diffuse into silicon samples locally pre-heated by high energy ions from the same laser-ion pulse. Silicon crystals exfoliate in the areas of highest energy deposition. Color centers, predominantly W and G-centers, form directly in response to ion pulses without a subsequent annealing step. We find that the linewidth of G-centers increases with high ion flux faster than the linewidth of W-centers, consistent with density functional theory calculations of their electronic structure. Intense ion pulses from a laser-accelerator drive materials far from equilibrium and enable direct local defect engineering and high flux doping of semiconductors.

Non-stationary non-Gaussian random vibration problems of structures are challenging and drawing increasing attention. In the present study, firstly, an explicit time-domain method (ETDM) is proposed to determine the higher-order response statistics of linear systems subjected to non-stationary non-Gaussian random excitations, in which the first four orders of cumulants of dynamic responses are directly formulated through the cumulant operation rule based on the explicit expressions of responses. Secondly, an equivalent linearization – explicit time-domain method (EL-ETDM) is further developed to solve the non-stationary non-Gaussian random vibration problems of Duffing systems, in which the equivalent linear system is derived discarding the assumption of Gaussian response, and the corresponding higher-order cumulant analyses of the linearized system are accomplished by the efficient ETDM. The present approach can account for non-Gaussian random excitations with arbitrary forms, and two specific applications to the Poisson white noise and the square form of Gaussian random process are investigated. Four numerical examples are presented to demonstrate the effectiveness of the proposed methods.

User surveys alone do not accurately measure the actual use of improved cookstoves in the field. We present the results of comparing survey-reported and sensor-recorded cooking events, or durations of use, of improved cookstoves in two monitoring studies, in rural Maharashtra, India. The first was a free trial of the Berkeley-India Stove (BIS) provided to 159 households where we monitored cookstove usage for an average of 10 days (SD = 4.5) (termed “free-trial study”). In the second study, we monitored 91 households' usage of the BIS for an average of 468 days (SD = 153) after they purchased it at a subsidized price of about one third of the households' monthly income (termed “post-purchase study”). The studies lasted from February 2019 to March 2021. We found that 34% of households (n = 88) over-reported BIS usage in the free-trial study and 46% and 28% of households over-reported BIS usage in the first (n = 75) and second (n = 69) surveys of the post-purchase study, respectively. The average over-reporting in both studies decreased when households were asked about their usage in a binary question format, but this method provided less granularity. Notably, in the post-purchase study, sensors showed that most households dis-adopted the cookstove even though they purchased it with their own money. Surveys failed to detect the long-term declining trend in cookstove usage. In fact, surveys indicated that cookstoves’ adoption remained unchanged during the study. Households tended to report nominal responses for use such as 0, 7, or 14 cooking events per week (corresponding to 0, 1, or 2 times per day), indicating the difficulty of recalling exact days of use in a week. Additionally, we found that surveys may also provide misleading qualitative findings on user-reported cookstove benefits without the support of sensor data, causing us to overestimate impact. Some households with zero sensor-recorded usage reported cookstove fuel savings, quick cooking, and less smoke. These findings suggest that surveys may be unreliable or insufficient to provide solid foundational data for subsidies based on the ability of a stove to reduce damage to health or reduce emissions in real-world implementations.

Myosins are required for clathrin-mediated endocytosis, but their precise molecular roles in this process are not known. This is, in part, because the biophysical properties of the relevant motors have not been investigated. Myosins have diverse mechanochemical activities, ranging from powerful contractility against mechanical loads to force-sensitive anchoring. To better understand the essential molecular contribution of myosin to endocytosis, we studied the in vitro force-dependent kinetics of the Saccharomyces cerevisiae endocytic type I myosin called Myo5, a motor whose role in clathrin-mediated endocytosis has been meticulously studied in vivo. We report that Myo5 is a low-duty-ratio motor that is activated ∼10-fold by phosphorylation and that its working stroke and actin-detachment kinetics are relatively force-insensitive. Strikingly, the in vitro mechanochemistry of Myo5 is more like that of cardiac myosin than that of slow anchoring myosin-1s found on endosomal membranes. We, therefore, propose that Myo5 generates power to augment actin assembly-based forces during endocytosis in cells.

This cross-sectional study examined the associations between parent-reported, perceptions of changes in school-aged childrens (ages 5-18) school meal participation, household cooking, fast food consumption, dietary intake, and weight during the COVID-19 pandemic. Respondents with low-income and school-aged children (n = 1040) were enrolled using quota sampling to approximate the distribution of low-income households and race/ethnicity among California residents who completed an on-line questionnaire developed by the authors. Adjusted multinomial models examined associations between parent-reported changes in school meal participation and time spent cooking, with parent-reported changes in child diet and body weight during COVID-19 (from before March 2020 to January-March 2021). During the pandemic, decreased school meal participation was associated with decreased childs fast food intake (OR[95 %CI] = 1.47[1.04-2.07]); conversely, increased school meal participation was associated with increased childs fast food intake (OR[95 %CI] = 1.71[1.09-2.68]). Decreased cooking at home was associated with decreased fruit and vegetable intake (OR[95 %CI] = 2.71[1.62-4.53]), increased sugar-sweetened beverage intake (OR[95 %CI] = 3.83[2.16-6.81]), and increased fast food intake (OR[95 %CI] = 4.09[2.45-6.84]); while increased cooking at home was associated with increased fruit and vegetable (OR[95 %CI] = 2.26[1.59-3.20]), sugar-sweetened beverage (OR[95 %CI] = 1.88[1.20-2.94]), sweets (OR[95 %CI] = 1.46[1.02-2.10]), and salty snack food intake (OR[95 %CI] = 1.87[1.29-2.71]). These parent-reported perceived changes in meal sources during the pandemic for children from low-income California households, and the mixed results in their associations with changes in parent-reported child dietary intake, suggest the need for strengthening policies and programs to support both access to, and healthfulness of, meals from school and home during prolonged school closures.