Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Harm Reduction Treatment with or without Pharmacotherapy on Polysubstance Use among People Experiencing Homelessness and Alcohol Use Disorder

Objectives A prior randomized controlled trial showed behavioral harm reduction treatment for alcohol use disorder (AUD), or HaRT-A, was effective in improving alcohol outcomes and quality of life for people experiencing homelessness and AUD when provided with or without pharmacotherapy (ie, extended-release naltrexone). Because nearly 80% of the sample also reported baseline polysubstance use, this secondary study tested whether HaRT-A also positively impacted other substance use. Methods In the parent study, 308 adults with AUD and homelessness were randomized to receive HaRT-A plus intramuscular injections of 380-mg extended-release naltrexone (HaRT-A + extended-release naltrexone), HaRT-A plus placebo (HaRT-A + placebo), HaRT-A alone, or community-based services as usual (control). In this secondary study, we used random intercept models to detect changes in other substance use after exposure to any of the HaRT-A conditions. For less prevalent behaviors, outcomes included past-month use (cocaine, amphetamines/methamphetamines, opioids). For more prevalent behaviors (polysubstance, cannabis), outcomes were past-month use frequency. Results Compared with controls, participants who received HaRT-A showed significantly reduced 30-day frequency of cannabis use (incident rate ratio = 0.59, 95% CI = 0.40–0.86, P = 0.006) and polysubstance use (incident rate ratio = 0.65, 95% CI = 0.43–0.98, P = 0.040). No other significant changes were detected. Conclusions Compared with services as usual, HaRT-A is associated with reduced cannabis and polysubstance use frequency. The benefits of HaRT-A may thus extend beyond its impact on alcohol and quality of life outcomes to positively reshape overall substance use patterns. A randomized controlled trial is needed to further investigate the efficacy of such combined pharmacobehavioral harm reduction treatment for polysubstance use.
COVID-19 Knowledge and Prevention Behaviors in Rural Zambia: A Qualitative Application of the Information-Motivation-Behavioral Skills Model

ABSTRACT. In early 2020, the Zambian Ministry of Health instituted prevention guidelines to limit spread of COVID-19. We assessed community knowledge, motivations, behavioral skills, and perceived community adherence to prevention behaviors (i.e., hand hygiene, mask wearing, social distancing, and limiting gatherings). Within a cluster-randomized controlled trial in four rural districts, in November 2020 and May 2021, we conducted in-depth interviews with health center staff (N = 19) and community-based volunteers (N = 34) and focus group discussions with community members (N = 281). A content analysis was conducted in Nvivo v12. Data were interpreted using the Information-Motivation-Behavioral Skills Model. Generally, respondents showed good knowledge of COVID-19 symptoms, spread, and high-risk activities, with some gaps. Prevention behavior performance was driven by personal and social factors. Respondents described institutional settings (e.g., clinics and church) having higher levels of perceived adherence due to stronger enforcement measures and clear leadership. Conversely, informal community settings (e.g., weddings, funerals, football matches) lacked similar social and leadership expectations for adherence and had lower perceived levels of adherence. These settings often involved higher emotions (excitement or grief), and many involved alcohol use, resulting in community members “forgetting” guidelines. Doubt about disease existence or need for precautions persisted among some community members and drove non-adherence more generally. Although COVID-19 information successfully penetrated these very remote rural communities, more targeted messaging may address persistent COVID-19 doubt and misinformation. Engaging local leaders in religious, civic, and traditional leadership positions could improve community behaviors without adding additional monitoring duties on an already overburdened, resource-limited health system.
Design and Performance of Selected Classes of

We study the design of structured Tanner codes with low error-rate floors on the binary additive white Gaussian noise channel (BAWGNC) and the binary erasure channel (BEC). The design technique involves the "doping" of standard LDPC protographs, meaning Hamming or recursive systematic convo- lutional (RSC) code constraints are used together with single- parity-check (SPC) constraints to construct a code's protograph. We show that the doping of a "good" graph with Hamming or RSC codes is a pragmatic approach that frequently results in a code with a good threshold and very low error-rate floor. We focus on low-rate Tanner codes, in part because the design of low-rate, low-floor LDPC codes is particularly difficult. approach involves inserting Hamming constraint nodes into a Tanner graph or a protograph (11) in place of selected SPC nodes. (A protograph will be defi ned in Section III.) In this paper, we consider the doping of protographs using either Hamming nodes or RSC nodes; we will call the latter code type RSC-LDPC codes. When referring generically to such a code, we will use doped LPDC code and Tanner code interchangeably. We will also refer to a code that resides at a constraint node as a component code (in contrast with Tanner's "subcode"), and we use constraint node and component- code node interchangeably. Low-rate codes find application in deep-space communications, wireless communications, and file transfer over packet-erasure channels. We demonstrate via computer simulations low-rate HD-LDPC and RSC-LPDC codes with very low error floors on the BAWGNC and the BEC, even for code lengths less than 1000 bits. The paper proceeds as follows. In the next section, we present an overview of the construction of Hamming- and RSCC-doped LDPC codes based on protographs. Section III presents four example code family designs. In Section IV, we present simulation results, for the BAWGNC and BEC, of the codes that we have designed.
Geoforensic search to crime scene: Remote sensing, geophysics, and dogs

In the absence of surface indications of burial sites, law enforcement or humanitarian organizations are faced with the difficult task of focusing large‐scale ground searches to a manageable excavation area. A geoforensic‐based survey may exclude parts of the landscape for reasons such as diggability or viewshed analysis but leave areas still too large for invasive exploration. This work examines how drone‐based remote sensing, geophysics, and search dogs may be combined to narrow such searches. Here, we ask the reader to consider two examples where forensic geomorphology and land use provided a range of possible burial locations. Following this is a multi‐proxy approach to similar dilemma, with a search‐to‐scene case study using remote sensing (drone photography), geophysics, ground probes, and search dogs. This approach is not presented as a definitive guide, but serves as an example of the conjunctive use of well‐studied methods to approach a common problem in geoforensics.
Theoretical and Technical Issues Concerning the Measurement of Alpha Frequency and the Application of Signal Detection Theory: Comment on Buergers and Noppeney (2022)

Abstract Classical and recent evidence has suggested that alpha oscillations play a critical role in temporally discriminating or binding successively presented items. Challenging this view, Buergers and Noppeney [Buergers, S. , & Noppeney, U. The role of alpha oscillations in temporal binding within and across the senses. Nature Human Behaviour, 6, 732–742, 2022] found that by combining EEG, psychophysics, and signal detection theory, neither prestimulus nor resting-state alpha frequency influences perceptual sensitivity and bias in the temporal binding task. We propose the following four points that should be considered when interpreting the role of alpha oscillations, and especially their frequency, on perceptual temporal binding: (1) Multiple alpha components can be contaminated in conventional EEG analysis; (2) the effect of alpha frequency on perception will interact with alpha power; (3) prestimulus and resting-state alpha frequency can be different from poststimulus alpha frequency, which is the frequency during temporal binding and should be more directly related to temporal binding; and (4) when applying signal detection theory under the assumption of equal variance, the assumption is often incomplete and can be problematic (e.g., the magnitude relationships between individuals in parametric sensitivity may change when converted into nonparametric sensitivity). Future directions, including solutions to each of the issues, are discussed.
The bacterial effector SidN/Lpg1083 promotes cell death by targeting Lamin-B2

Abstract To facilitate survival, replication, and dissemination, the intracellular pathogen Legionella pneumophila relies on its unique type IVB secretion system (T4SS) to deliver over 330 effectors to hijack host cell pathways in a spatiotemporal manner. The effectors and their host targets are largely unexplored due to their low sequence identity to the known proteins and functional redundancy. The T4SS effector SidN (Lpg1083) is secreted into host cells during the late infection period. However, to the best of our knowledge, the molecular characterization of SidN has not been studied. Herein, we identified SidN as a nuclear envelope-localized effector. Its structure adopts a novel fold, and the N-terminal domain is crucial for its specific subcellular localization. Furthermore, we found that SidN is transported by eukaryotic karyopherin Importin-13 into the nucleus, where it attaches to the N-terminal region of Lamin-B2 to interfere with the integrity of the nuclear envelope, causing nuclear membrane disruption and eventually cell death. Our work provides new insights into the structure and function of an L. pneumophila effector protein, and suggests a potential strategy utilized by the pathogen to promote host cell death and then escape from the host for secondary infection.
LSTM-AE for Domain Shift Quantification in Cross-Day Upper-Limb Motion Estimation Using Surface Electromyography

Although deep learning (DL) techniques have been extensively researched in upper-limb myoelectric control, system robustness in cross-day applications is still very limited. This is largely caused by non-stable and time-varying properties of surface electromyography (sEMG) signals, resulting in domain shift impacts on DL models. To this end, a reconstruction-based method is proposed for domain shift quantification. Herein, a prevalent hybrid framework that combines a convolutional neural network (CNN) and a long short-term memory network (LSTM), i.e. CNN-LSTM, is selected as the backbone. The paring of auto-encoder (AE) and LSTM, abbreviated as LSTM-AE, is proposed to reconstruct CNN features. Based on reconstruction errors (RErrors) of LSTM-AE, domain shift impacts on CNN-LSTM can be quantified. For a thorough investigation, experiments were conducted in both hand gesture classification and wrist kinematics regression, where sEMG data were both collected in multi-days. Experiment results illustrate that, when the estimation accuracy degrades substantially in between-day testing sets, RErrors increase accordingly and can be distinct from those obtained in within-day datasets. According to data analysis, CNN-LSTM classification/regression outcomes are strongly associated with LSTM-AE errors. The average Pearson correlation coefficients could reach $-0.986\,\,\pm $ 0.014 and $-0.992\,\,\pm $ 0.011, respectively.
Escalated police stops of Black men are linguistically and psychologically distinct in their earliest moments

Significance Amid calls for police officers to de-escalate encounters with Black citizens, this work sheds light on when and how car stops escalate, as well as their psychological impact on Black men. Our analysis of police body-worn camera footage reveals that stops ultimately resulting in escalation differ in their conversational structure in the earliest moments of the encounter: in as little as the first 45 words the officer speaks. Listening to these escalated encounters evoked anxiety, suspicion, and worry about officer use of force for Black men, who are disproportionately subjected to escalated outcomes. The findings reported here not only inform approaches to de-escalation but also demonstrate the power and promise of systematic footage review more broadly. To improve police–community interactions, we could start by examining them.
Effects of Anticholinergic Burden on Verbal Memory Performance in First-Episode Psychosis

Objectives Antipsychotics are widely used to treat first-episode psychosis but may have an anticholinergic burden, that is, a cumulative effect of medications that block the cholinergic system. Studies suggest that a high anticholinergic burden negatively affects memory in psychosis, where cognitive deficits, particularly those in verbal memory, are a core feature of the disease. The present study sought to replicate this in a large cohort of well-characterized first-episode psychosis patients. We expected that patients in the highest anticholinergic burden group would exhibit the poorest verbal memory compared to those with low anticholinergic burden and healthy controls at baseline (3 months following admission). We further hypothesized that over time, at month 12, patients’ verbal memory performance would improve but would remain inferior to controls. Methods Patients (n = 311; low anticholinergic burden [n = 241] and high anticholinergic burden [n = 70], defined by a Drug Burden Index cut-off of 1) and healthy controls (n = 128) completed a clinical and neurocognitive battery including parts of the Wechsler Memory Scale at months 3 and 12. Results Cross-sectionally, using an analysis of variance, patients in the highest anticholinergic burden group had the poorest performance in verbal memory when compared to the other groups at month 3, F(2,430) = 52.33, P < 0.001. Longitudinally, using a Generalized Estimating Equation model, the verbal memory performance of all groups improved over time. However, patients’ performance overall remained poorer than the controls. Conclusion These findings highlight the importance of considering the anticholinergic burden when prescribing medications in the early stages of the disease.
Multiple spectral similarity metrics for surface materials identification using hyperspectral data

Abstract Modern hyperspectral imaging and non-imaging spectroradiometer has the capability to acquire high-resolution spectral reflectance data required for surface materials identification and mapping. Spectral similarity metrics, due to their mathematical simplicity and insensitiveness to the number of reference labelled spectra, have been increasingly used for material mapping by labelling reflectance spectra in hyperspectral data labelling. For a particular hyperspectral data set, the accuracy of spectral labelling depends considerably upon the degree of unambiguous spectral matching achieved by the spectral similarity metric used. In this work, we propose a new methodology for quantifying spectral similarity for hyperspectral data labelling for surface materials identification. Developed adopting the multiple classifier system architecture, the proposed methodology unifies into a single framework the differential performances of eight different spectral similarity metrics for the quantification of spectral matching for surface materials. The proposed methodology has been implemented on two types of hyperspectral data viz. image (airborne hyperspectral images) and non-image (library spectra) for numerous surface materials identification. Further, the performance of the proposed methodology has been compared with the support vector machines (SVM) approach, and with all the base spectral similarity metrics. The results indicate that, for the hyperspectral images, the performance of the proposed methodology is comparable with that of the SVM. For the library spectra, the proposed methodology shows a consistently higher (increase of about 30% when compared to SVM) classification accuracy. The proposed methodology has the potential to serve as a general library search method for materials identification using hyperspectral data.
Proximity to criticality predicts surface properties of biomolecular condensates

Significance Self-organization through the phase separation of biomolecular condensates is ubiquitous in living cells. The macroscopic physical properties of these condensates play an essential role in their organization and function—what general principles relate these macroscopic properties to the underlying microscopic features of biomolecules? By using universal ratios of thermodynamic quantities in the vicinity of a critical point, condensate physical properties can be inferred from a small number of thermodynamic parameters, which we demonstrate in the case of surface tension. Importantly, we confirm via simulation that the range of validity of the critical region is large enough to cover the physiologically relevant range in living cells. This simplification allows us to identify key features of biomolecules that influence surface tension.
Automatic Generalization of Sounding in Digital Chart

According to the basic principle of sounding generalization in chart, a new pattern of sounding generalization is presented. That is simulating the thinking of human when he is generalizing, including representation, identification and generalization.Ultimately, its practicability and validity is tested. This will bring a new theory and research method for automatic generalization.
Fast stochastic inversion based on simulated annealing algorithm

Seismic resolution is the key factor to the extraction of stratigraphic information from seismic data and it is very important for the thin layer description. Due to the band limited nature of seismic data, conventional inversion methods are unable to meet the needs of current exploration and exploitation demands. Compared to the deterministic inversion, stochastic inversion can effectively integrate the high frequency information of well-logging data and have a higher resolution. This paper starts from the original framework of stochastic inversion, combined with FFTMoving Average (FFT-MA) simulation and Simulated Annealing (SA) algorithm, proposes a new fast stochastic inversion method. According to the Bayesian theory, using well-logging data as conditional data and seismic data as constraints, we integrate geostatistical priori information into the posteriori probability density function of the model. Finally, through the numerical calculations, we can conclude that FFT-MA simulation after conditional processing, combined with SA algorithm, can reduce the time consumption. The inversion result can be converged to the seismic data, and the final results match the model very well, which demonstrates the effectiveness of the proposed inverdion method.
Magnetic properties induced by the Yb-valence inhomogeneity in the layered Kondo-lattice compound Yb4RuGe8

We have successfully grown single crystals of a novel ytterbium-based layered compound Yb4RuGe8 and studied its structural, magnetic, thermal, and transport properties. The magnetic susceptibility has a broad peak caused by the Kondo effect at approximately 40 K and is enhanced below 15 K owing to the development of additional magnetic correlations. An analysis with the grand-Kadowaki–Woods relation reveals that the low-temperature state except for the effect of the additional magnetic correlations is a heavy-mass Fermi liquid.
Climate-smart forestry through innovative wood products and commercial afforestation and reforestation on marginal land

Significance Afforestation and reforestation (AR) are nature-based solutions to climate change. However, the greenhouse gas (GHG) mitigation efficacy of protection or commercial AR is under debate. This study develops a dynamic life cycle assessment to quantify the GHG mitigation potential of protection and commercial AR on marginal land in the southeastern United States. We found that commercial AR with cross-laminated timber and biochar production generally mitigates more GHGs across 100 y than protection AR and commercial AR with traditional lumber production. Protection AR could mitigates more GHGs in a shorter timeframe (≤50 y). These results highlight the role of synergizing protection AR, innovative wood utilization, and strategic forest plantation management in supporting short- and long-term climate change mitigation goals.
A convolutional neural network‐based, quantitative complete blood count scattergram‐mapping framework promptly screens acute promyelocytic leukemia with high sensitivity

Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia (AML) characterized by its rapidly progressive and fatal clinical course if untreated, although it is curable if treated in a timely manner. Promptly screening patients who have results that are suspicious for APL is vital to overcome early death.
A (S)-3-Hydroxybutyrate Dehydrogenase Belonging to the 3-Hydroxyacyl-CoA Dehydrogenase Family Facilitates Hydroxyacid Degradation in Anaerobic Bacteria

(R)-3-hydroxybutyrate is well studied as a component of ketone bodies produced by the liver and of bacterial polyesters. However, the biochemistry of its enantiomer (S)-3-hydroxybutyrate is poorly understood. ABSTRACT Ketone bodies, including acetoacetate, 3-hydroxybutyrate, and acetone, are produced in the liver of animals during glucose starvation. Enzymes for the metabolism of (R)-3-hydroxybutyrate have been extensively studied, but little is known about the metabolism of its enantiomer (S)-3-hydroxybutyrate. Here, we report the characterization of a novel pathway for the degradation of (S)-3-hydroxybutyrate in anaerobic bacteria. We identify and characterize a stereospecific (S)-3-hydroxylbutyrate dehydrogenase (3SHBDH) from Desulfotomaculum ruminis, which catalyzes the reversible NAD(P)H-dependent reduction of acetoacetate to form (S)-3-hydroxybutyrate. 3SHBDH also catalyzes oxidation of d-threonine (2R, 3S) and l-allo-threonine (2S, 3S), consistent with its specificity for β-(3S)-hydroxy acids. Isothermal calorimetry experiments support a sequential mechanism involving binding of NADH prior to (S)-3-hydroxybutyrate. Homologs of 3SHBDH are present in anaerobic fermenting and sulfite-reducing bacteria, and experiments with Clostridium pasteurianum showed that 3SHBDH, acetate CoA-transferase (YdiF), and (S)-3-hydroxybutyryl-CoA dehydrogenase (Hbd) are involved together in the degradation of (S)-3-hydroxybutyrate as a carbon and energy source for growth. (S)-3-hydroxybutyrate is a human metabolic marker and a chiral precursor for chemical synthesis, suggesting potential applications of 3SHBDH in diagnostics or the chemicals industry. IMPORTANCE (R)-3-hydroxybutyrate is well studied as a component of ketone bodies produced by the liver and of bacterial polyesters. However, the biochemistry of its enantiomer (S)-3-hydroxybutyrate is poorly understood. This study describes the identification and characterization of a stereospecific (S)-3-hydroxylbutyrate dehydrogenase and its function in a metabolic pathway for the degradation of (S)-3-hydroxybutyrate as a carbon and energy source in anaerobic bacteria. (S)-3-hydroxybutyrate is a mammalian metabolic marker and a precursor for chemical synthesis and bioplastics, suggesting potential applications of these enzymes in diagnostics and biotechnology.