Let the algorithm speak: How to use neural networks for automatic item generation in psychological scale development.

Measurement is at the heart of scientific research. As many-perhaps most-psychological constructs cannot be directly observed, there is a steady demand for reliable self-report scales to assess latent constructs. However, scale development is a tedious process that requires researchers to produce good items in large quantities. In this tutorial, we introduce, explain, and apply the Psychometric Item Generator (PIG), an open-source, free-to-use, self-sufficient natural language processing algorithm that produces large-scale, human-like, customized text output within a few mouse clicks. The PIG is based on the GPT-2, a powerful generative language model, and runs on Google Colaboratory-an interactive virtual notebook environment that executes code on state-of-the-art virtual machines at no cost. Across two demonstrations and a preregistered five-pronged empirical validation with two Canadian samples (NSample 1 = 501, NSample 2 = 773), we show that the PIG is equally well-suited to generate large pools of face-valid items for novel constructs (i.e., wanderlust) and create parsimonious short scales of existing constructs (i.e., Big Five personality traits) that yield strong performances when tested in the wild and benchmarked against current gold standards for assessment. The PIG does not require any prior coding skills or access to computational resources and can easily be tailored to any desired context by simply switching out short linguistic prompts in a single line of code. In short, we present an effective, novel machine learning solution to an old psychological challenge. As such, the PIG will not require you to learn a new language-but instead, speak yours. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Connectomic comparison of mouse and human cortex.

The human cerebral cortex houses 1000 times more neurons than that of the cerebral cortex of a mouse, but the possible differences in synaptic circuits between these species are still poorly understood. We used three-dimensional electron microscopy of mouse, macaque, and human cortical samples to study their cell type composition and synaptic circuit architecture. The 2.5-fold increase in interneurons in humans compared with mice was compensated by a change in axonal connection probabilities and therefore did not yield a commensurate increase in inhibitory-versus-excitatory synaptic input balance on human pyramidal cells. Rather, increased inhibition created an expanded interneuron-to-interneuron network, driven by an expansion of interneuron-targeting interneuron types and an increase in their synaptic selectivity for interneuron innervation. These constitute key neuronal network alterations in the human cortex.

Connectomic analysis of thalamus-driven disinhibition in cortical layer 4.

Sensory signals are transmitted via the thalamus primarily to layer 4 (L4) of the primary sensory cortices. While information about average neuronal connectivity in L4 is available, its detailed higher-order circuit structure is not known. Here, we used three-dimensional electron microscopy for a connectomic analysis of the thalamus-driven inhibitory network in L4. We find that thalamic input drives a subset of interneurons with high specificity, which in turn target excitatory neurons with subtype specificity. These interneurons create a directed disinhibitory network directly driven by the thalamic input. Neuronal activity recordings show that strong synchronous sensory activation yields about 1.5-fold stronger activation of star pyramidal cells than spiny stellates, in line with differential windows of opportunity for activation of excitatory neurons in the thalamus-driven disinhibitory circuit model. With this, we have identified a high degree of specialization of the microcircuitry in L4 of the primary sensory cortex.

FcMBL magnetic bead-based MALDI-TOF MS rapidly identifies paediatric blood stream infections from positive blood cultures.

Rapid identification of potentially life-threatening blood stream infections (BSI) improves clinical outcomes, yet conventional blood culture (BC) identification methods require ~24-72 hours of liquid culture, plus 24-48 hours to generate single colonies on solid media suitable for identification by mass spectrometry (MS). Newer rapid centrifugation techniques, such as the Bruker MBT-Sepsityper® IVD, replace culturing on solid media and expedite the diagnosis of BCs but frequently demonstrate reduced sensitivity for identifying clinically significant Gram-positive bacterial or fungal infections. This study introduces a protocol that utilises the broad-range binding properties of an engineered version of mannose-binding lectin linked to the Fc portion of immunoglobulin (FcMBL) to capture and enrich pathogens combined with matrix-assisted laser desorption-ionisation time-of-flight (MALDI-TOF) MS for enhanced infection identification in BCs. The FcMBL method identified 94.1% (64 of 68) of clinical BCs processed, with a high sensitivity for both Gram-negative and Gram-positive bacteria (94.7 and 93.2%, respectively). The FcMBL method identified more patient positive BCs than the Sepsityper® (25 of 25 vs 17 of 25), notably with 100% (3/3) sensitivity for clinical candidemia, compared to only 33% (1/3) for the Sepsityper®. Additionally, during inoculation experiments, the FcMBL method demonstrated a greater sensitivity, identifying 100% (24/24) of candida to genus level and 9/24 (37.5%) top species level compared to 70.8% (17/24) to genus and 6/24 to species (25%) using the Sepsityper®. This study demonstrates that capture and enrichment of samples using magnetic FcMBL-conjugated beads is superior to rapid centrifugation methods for identification of BCs by MALDI-TOF MS. Deploying the FcMBL method therefore offers potential clinical benefits in sensitivity and reduced turnaround times for BC diagnosis compared to the standard Sepsityper® kit, especially for fungal diagnosis.

Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA.

Widespread acceptance of a vaccine for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will be the next major step in fighting the coronavirus disease 2019 (COVID-19) pandemic, but achieving high uptake will be a challenge and may be impeded by online misinformation. To inform successful vaccination campaigns, we conducted a randomized controlled trial in the UK and the USA to quantify how exposure to online misinformation around COVID-19 vaccines affects intent to vaccinate to protect oneself or others. Here we show that in both countries-as of September 2020-fewer people would 'definitely' take a vaccine than is likely required for herd immunity, and that, relative to factual information, recent misinformation induced a decline in intent of 6.2 percentage points (95th percentile interval 3.9 to 8.5) in the UK and 6.4 percentage points (95th percentile interval 4.0 to 8.8) in the USA among those who stated that they would definitely accept a vaccine. We also find that some sociodemographic groups are differentially impacted by exposure to misinformation. Finally, we show that scientific-sounding misinformation is more strongly associated with declines in vaccination intent.

Dense connectomic reconstruction in layer 4 of the somatosensory cortex.

The dense circuit structure of mammalian cerebral cortex is still unknown. With developments in three-dimensional electron microscopy, the imaging of sizable volumes of neuropil has become possible, but dense reconstruction of connectomes is the limiting step. We reconstructed a volume of ~500,000 cubic micrometers from layer 4 of mouse barrel cortex, ~300 times larger than previous dense reconstructions from the mammalian cerebral cortex. The connectomic data allowed the extraction of inhibitory and excitatory neuron subtypes that were not predictable from geometric information. We quantified connectomic imprints consistent with Hebbian synaptic weight adaptation, which yielded upper bounds for the fraction of the circuit consistent with saturated long-term potentiation. These data establish an approach for the locally dense connectomic phenotyping of neuronal circuitry in the mammalian cortex.