Comprehensive and integrated district health systems strengthening: the Rwanda Population Health Implementation and Training (PHIT) Partnership

Background: Nationally, health in Rwanda has been improving since 2000, with considerable improvement since 2005. Despite improvements, rural areas continue to lag behind urban sectors with regard to key health outcomes. Partners In Health (PIH) has been supporting the Rwanda Ministry of Health (MOH) in two rural districts in Rwanda since 2005. Since 2009, the MOH and PIH have spearheaded a health systems strengthening (HSS) intervention in these districts as part of the Rwanda Population Health Implementation and Training (PHIT) Partnership. The partnership is guided by the belief that HSS interventions should be comprehensive, integrated, responsive to local conditions, and address health care access, cost, and quality. The PHIT Partnership represents a collaboration between the MOH and PIH, with support from the National University of Rwanda School of Public Health, the National Institute of Statistics, Harvard Medical School, and Brigham and Women’s Hospital. Description of intervention The PHIT Partnership’s health systems support aligns with the World Health Organization’s six health systems building blocks. HSS activities focus across all levels of the health system — community, health center, hospital, and district leadership — to improve health care access, quality, delivery, and health outcomes. Interventions are concentrated on three main areas: targeted support for health facilities, quality improvement initiatives, and a strengthened network of community health workers. Evaluation design The impact of activities will be assessed using population-level outcomes data collected through oversampling of the demographic and health survey (DHS) in the intervention districts. The overall impact evaluation is complemented by an analysis of trends in facility health care utilization. A comprehensive costing project captures the total expenditures and financial inputs of the health care system to determine the cost of systems improvement. Targeted evaluations and operational research pieces focus on specific programmatic components, supported by partnership-supported work to build in-country research capacity. Discussion Building on early successes, the work of the Rwanda PHIT Partnership approach to HSS has already seen noticeable increases in facility capacity and quality of care. The rigorous planned evaluation of the Partnership’s HSS activities will contribute to global knowledge about intervention methodology, cost, and population health impact

Oral literature in South Africa: 20 years on

I offer a retrospective on the field of orality and performance studies in South Africa from the perspective of 2016, assessing what has been achieved, what may have happened inadvertently or worryingly, what some of the significant implications have been, what remain challenges, and how we may think of, or rethink, orality and performance studies in a present and future that are changing at almost inconceivable pace.DHE

Learning to Produce Syllabic Speech Sounds via Reward-Modulated Neural Plasticity.

At around 7 months of age, human infants begin to reliably produce well-formed syllables containing both consonants and vowels, a behavior called canonical babbling. Over subsequent months, the frequency of canonical babbling continues to increase. How the infant's nervous system supports the acquisition of this ability is unknown. Here we present a computational model that combines a spiking neural network, reinforcement-modulated spike-timing-dependent plasticity, and a human-like vocal tract to simulate the acquisition of canonical babbling. Like human infants, the model's frequency of canonical babbling gradually increases. The model is rewarded when it produces a sound that is more auditorily salient than sounds it has previously produced. This is consistent with data from human infants indicating that contingent adult responses shape infant behavior and with data from deaf and tracheostomized infants indicating that hearing, including hearing one's own vocalizations, is critical for canonical babbling development. Reward receipt increases the level of dopamine in the neural network. The neural network contains a reservoir with recurrent connections and two motor neuron groups, one agonist and one antagonist, which control the masseter and orbicularis oris muscles, promoting or inhibiting mouth closure. The model learns to increase the number of salient, syllabic sounds it produces by adjusting the base level of muscle activation and increasing their range of activity. Our results support the possibility that through dopamine-modulated spike-timing-dependent plasticity, the motor cortex learns to harness its natural oscillations in activity in order to produce syllabic sounds. It thus suggests that learning to produce rhythmic mouth movements for speech production may be supported by general cortical learning mechanisms. The model makes several testable predictions and has implications for our understanding not only of how syllabic vocalizations develop in infancy but also for our understanding of how they may have evolved

Mindfulness-related differences in neural response to own infant negative versus positive emotion contexts

Mindfulness is thought to promote well-being by shaping the way people respond to challenging social-emotional situations. Current understanding of how this occurs at the neural level is based on studies of response to decontextualized emotion stimuli that may not adequately represent lived experiences. In this study, we tested relations between mothers' dispositional mindfulness and neural responses to their own infant in different emotion-eliciting contexts. Mothers (n = 25) engaged with their 3-month-old infants in videorecorded tasks designed to elicit negative (arm restraint) or positive (peekaboo) emotion. During a functional MRI session, mothers were presented with 15-s clips from these recordings, and dispositional mindfulness scores were used to predict their neural responses to arm restraint > peekaboo videos. Mothers higher in nonreactivity showed relatively lower activation to their infants’ arm restraint compared to peekaboo videos in hypothesized regions—insula and dorsal prefrontal cortex—as well as non-hypothesized regions. Other mindfulness dimensions were associated with more limited areas of lower (nonjudgment) and higher (describing) activation in this contrast. Mothers who were higher in mindfulness generally activated more to the positive emotion context and less to the negative emotion context in perceptual and emotion processing areas, a pattern that may help to explain mindfulness-related differences in well-being. Keywords: Mindfulness, fMRI, Mother, Infant, Emotio

Increase in salience and syllabicity over time.

<p>A: Average auditory salience of the sounds produced by the model as a function of simulation time in seconds and whether the simulation was reinforced based on auditory salience or was a yoked control. B: Number of vowel nuclei, i.e. number of syllables, estimated to be contained within the sounds produced by the model as a function of simulation time in seconds and whether the simulation was reinforced based on auditory salience or was a yoked control. Lines are generalized additive model fits and dark gray shading gives 95% confidence intervals around those fits. When reinforced for auditory salience, the model increases both the salience of its vocalizations and the number of syllables contained within those vocalizations, while the yoked controls do not show such increases.</p

Vocalization examples.

<p>Three examples of vocalizations produced by the model. The left column shows a vocalization that contains no consonants and would not be considered canonical or syllabic babbling. The associated WAV file is available for listening in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145096#pone.0145096.s001" target="_blank">S1 Sound</a>. The middle column shows a vocalization that contains one consonant and the right column shows a vocalization that contains three consonants. The middle and right vocalizations would qualify as canonical babbling (the associated WAV files are available for listening in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145096#pone.0145096.s002" target="_blank">S2 Sound</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145096#pone.0145096.s003" target="_blank">S3 Sound</a>, respectively). The vocalizations were all produced by fully trained versions of the primary version of the model. A: Raster plots of the 1 s of reservoir neuron activity associated with the vocalization. B: motor neuron raster plots. C: Smoothed motor neuron activity for the agonist and antagonist groups as well as the difference between the smoothed agonist and antagonist activities. This difference was what was input as muscle activity to the vocalizations synthesizer. D: Waveforms (cyan), salience traces (black) and overall salience estimates (titles) for each example vocalization. Note that positive values of the salience trace represent detection of onsets of patterns in the auditory stimulus and negative values represent offsets of patterns. E: Spectrograms of the vocalizations; these provide visual evidence of the vocalization’s harmonic frequencies and of formant transitions associated with the production of consonants.</p

Synaptic weights after learning.

<p>A: Example of the synapse strengths from each reservoir output neuron to each motor neuron after learning. The left plot shows the synapses for the first simulation of the 200 motor neuron <i>m</i> = 2 model reinforced for high-salience vocalizations. The right plot shows the synapses for the corresponding yoked control simulation. Yellow indicates greater connection strengths; blue indicates weaker synapses. The stronger synapses on the left half of the left plot as compared to the right half of that same plot reflect the greater connection of reservoir neurons to agonist motor neurons promoting mouth closure than to antagonist motor neurons promoting mouth opening. Note that this bias is not present in the connection weights of the yoked control simulation shown on the right. B: Across all simulations of the 200 motor neuron <i>m</i> = 2 model, the total strength of the connections from the reservoir to the agonist motor neurons divided by the total strength of the connections from the reservoir to the antagonist motor neurons. Bar height indicates the mean across the five simulations and the error bars represent 95% confidence intervals. C: Across all simulations of the 200 motor neuron <i>m</i> = 2 model, the standard deviation of the connection strengths from the reservoir to the motor neurons. Bar height indicates the mean standard deviation across the five simulations.</p

Overview of the model.

<p>A: Schematic depiction of the groups of neurons in the spiking neural network and how they are connected. There is a reservoir of 1000 recurrently connected neurons, with 200 of those being inhibitory (red) and the rest excitatory (blue and black). 200 of the reservoir’s excitatory neurons are designated as output neurons (black). These output neurons connect to two groups of motor neurons, agonist motor neurons (blue) and antagonist motor neurons (red). The connection weights within the reservoir are set at the start of the simulation to random values and do not change over the course of the simulation. The connection weights from the reservoir output neurons to the motor neurons are initially set to random values and are modified throughout the simulation by dopamine (DA)-modulated STDP. All reservoir and motor neurons receive random input current at each time step (not shown). B: Raster plot of spikes in the reservoir over a 1 s time period. C: Raster plot of spikes in the motor neuron groups over the same 1 s time period. The agonist and antagonist motor neuron spikes are summed at each time step then are smoothed using a 100 ms moving average. The smoothed antagonist activity is subtracted from the smoothed agonist activity, creating a net smoothed muscle activity that is sent to the orbicularis and masseter muscles. D: The smoothed agonist, antagonist, and net activity for the same 1 s as in the raster plots. E: Effects of the orbicularis oris and masseter on the vocal tract’s shape (reprinted with permission from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145096#pone.0145096.ref061" target="_blank">61</a>]). Orbicularis oris activity tends to round and close the lips and masseter activity tends to raise the jaw. F: Schematic illustration that the vocal tract is modeled as an air-filled tube bounded by walls made up of coupled mass-spring systems (reprinted with permission from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145096#pone.0145096.ref061" target="_blank">61</a>]). The orbicularis oris and masseter affect the equilibrium positions at the front parts of the tube. The air pressure over time and space in the tube is calculated, and the air pressure at the lip end of the tube forms the sound waveform. The vocal tract shape is modeled more realistically than depicted here and also contains a nasal cavity that is not depicted. G: The sound synthesized by the vocal tract model is input to an algorithm that estimates auditory salience. The plot shows, for the same 1 s as in B–D, the synthesized vocalization waveform (in cyan) and the salience of that waveform over time (in black). Apart from a peak in salience at the sound’s onset, the most salient portion of the sound is around the place where the sound’s one consonant can be heard. The overall salience of this particular sound is 10.77. If the salience of the sound is above the model’s current threshold, a reward is given, which causes an increase in dopamine concentration in the neural network.</p