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C21H23ClN2O4, monoclinic, P21 (no. 4), a = 10.9270(4) Å, b = 8.0274(3) Å, c = 12.9691(6) Å, β = 114.914(1)°, V = 1031.73(7) Å3, Z = 2, Rgt (F) = 0.0362 wRref (F 2) = 0.0898, T = 150 K.

C21H16ClFeNO, monoclinic, P21/c (no. 14), a = 10.3273(5) Å, b = 23.0536(11) Å, c = 7.2835(4) Å, β = 106.078(2)°, V = 1666.24(15) Å3, Z = 4, R gt(F) = 0.0558, wR ref(F 2) = 0.1219, T = 170 K.

C25H37Cl2N5O6, triclinic, P1‾ $P\overline{1}$ (no. 2), a = 8.115(4) Å, b = 8.656(4) Å, c = 22.641(9) Å, α = 81.344(9)°, β = 80.173(9)°, γ = 65.833(8)°, V = 1423.9(10) Å3, Z = 2, R gt(F) = 0.0652, wR ref(F 2) = 0.2010, T = 173 K.

C17H19F2N3O5S, monoclinic, P21/c (no. 14), a = 14.157(7) Å, b = 11.133(6) Å, c = 11.400(6) Å, β = 91.965(13)°, V = 1795.6(16) Å3, Z = 4, Rgt(F) = 0.0611 wRref(F2) = 0.1809, T = 205 K.

C20H18F2N2O3, monoclinic, P21/n (no. 14), a = 7.5573(10) Å, b = 14.519(2) Å, c = 16.245(2) Å, β = 98.187(3)°, V = 1764.4(4) Å3, Z = 4, Rgt(F) = 0.0420 wRref(F2) = 0.1142, T = 205 K.

C13H10ClNO, monoclinic, P21 (no. 4), a = 4.3011(7) Å, b = 11.2236(19) Å, c = 11.776(2) Å, β = 94.281(5)°, V = 566.90(16) Å3, Z = 2, Rgt(F) = 0.0420, wRref(F2) = 0.0827, T = 170 K.

C19H14Cl2N2O3, monoclinic, P21/c (no. 14), a = 10.635(3) Å, b = 8.772(3) Å, c = 19.656(6) Å, β = 98.256(10)°, V = 1814.8(10) Å3, Z = 4, Rgt(F) = 0.0602, wRref(F2) = 0.1547, T = 205 K.

C23H21ClN2O5, monoclinic, P21/c (no. 14), a = 9.191(3) Å, b = 10.725(5) Å, c = 11.2390(10) Å, β = 109.869(13)°, V = 1041.9(6) Å3, Z = 2, Rgt(F) = 0.0609, wRref(F2) = 0.1695, T = 293 K.

C12H15NO3, monoclinic, P21/c (no. 14), a = 6.7230(2) Å, b = 11.1182(3) Å, c = 14.4996(5) Å, β = 94.8870(10)°$94.8870(10){}^{\circ}$, V = 1079.87(6) Å3, Z = 4, Rgt(F) = 0.0511, wRref(F2${F}^{2}$) = 0.1525, T = 170 K.

C11H13NO3, monoclinic, P21/n (no. 14), a = 8.0478(3) Å, b = 9.8093(4) Å, c = 13.1602(5) Å, β = 104.712(2)°, V = 1004.85(7) Å3, Z = 1, Rgt(F) = 0.0395, wRref(F2) = 0.1010, T = 170 K.

C10H10O4, monoclinic, P21/c (no. 14), a = 9.464(6) Å, b = 10.302(7) Å, c = 10.589(7) Å, β = 114.174(11)°, V = 941.8(10) Å3, Z = 4, Rgt(F) = 0.0453, wRref(F2) = 0.1394, T = 173 K.

C10H6BrCl2N3, monoclinic, P21/c (no. 14), a = 9.2229(13) Å, b = 14.709(2) Å, c = 8.6472(13) Å, β = 104.851(3)°, V = 1133.9(3) Å3, Z = 4, R gt(F) = 0.0312, wRref (F 2) = 0.0839, T = 173 K.

Most current research in Tibetan speech synthesis relies primarily on autoregressive models in deep learning. However, these models face challenges such as slow inference, skipped readings, and repetitions. To overcome these issues, we propose an enhanced non-autoregressive acoustic model combined with a vocoder for Tibetan speech synthesis. Specifically, we introduce the mixture alignment FastSpeech2 method to correct errors caused by hard alignment in the original FastSpeech2 method. This new method employs soft alignment at the level of Latin letters and hard alignment at the level of Tibetan characters, thereby improving alignment accuracy between text and speech and enhancing the naturalness and intelligibility of the synthesized speech. Additionally, we integrate pitch and energy information into the model, further enhancing overall synthesis quality. Furthermore, Tibetan has relatively smaller text-to-audio datasets compared to widely studied languages. To address these limited resources, we employ a transfer learning approach to pre-train the model with data from resource-rich languages. Subsequently, this pre-trained mixture alignment FastSpeech2 model is fine-tuned for Tibetan speech synthesis. Experimental results demonstrate that the mixture alignment FastSpeech2 model produces higher-quality speech compared to the original FastSpeech2 model, particularly when pre-trained on an English dataset, resulting in further improvements in clarity and naturalness. [ABSTRACT FROM AUTHOR]

BACKGROUND: Adelphocoris suturalis is a destructive pest that attacks > 270 plants, including cotton, maize, soybean, and fruit trees. Adelphocoris suturalis can cause tremendous crop losses when the density exceeds economic thresholds, but because it can be both phytophagous and zoophytophagous it can serve as a natural enemy of other pests when the density is below the economic threshold. Effective control of its population is beneficial for maximizing yield and profits. RNA interference (RNAi) has potential to be a viable alternative to conventional pesticide‐based pest management, but the lack of efficient double‐stranded RNA (dsRNA) delivery systems and candidate genes are currently limiting factors for field applications. RESULTS: In this study, RNAi of juvenile hormone (JH) receptor components methoprene‐tolerant (Met)/Taiman (Tai) in Adelphocoris suturalis reduced fertility. Based on this reproductive role, we targeted Adelphocoris suturalis Met and Tai for knockdown by coupling nanomaterial‐dsRNA complexes with a transdermal spray delivery system. Within 12 h of adult emergence, females were sprayed with star polycation (SPc)–dsRNA formulations and the RNAi effects were assessed over time. RNAi knockdown efficiencies of 39–58% were observed at 5 days post‐treatment and abnormal ovarian development was apparent by 10 days post‐treatment. CONCLUSION: Our results show that spray‐induced and nanocarrier‐delivered gene silencing (SI‐NDGS) system targeting JH signal genes effectively impaired oviposition, thus developing a novel RNA fertility inhibitor to control Adelphocoris suturalis populations. These results give new perspective on pest management and suggest broad prospects for field applications. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

π-stacking interaction, as a fundamental type of intermolecular interaction, plays a crucial role in generating new functional molecules, altering the optoelectronic properties of materials, and maintaining protein structural stability. However, regulating intermolecular π–π interactions at the single-molecule level without altering the molecular conformation as well as the chemical properties remains a significant challenge. To this end, via conductance measurement with thousands of single molecular junctions employing a series of aromatic molecules, we demonstrate that the π–π coupling between neighboring aromatic molecules with rigid structures in a circuit can be greatly enhanced by increasing the bias voltage. We further reveal that this universal regulating effect of bias voltage without molecular conformational variation originates from the increases of the molecular dipole upon an applied electric field. These findings not only supply a non-destructive method to regulate the intermolecular interactions offering an approach to modulate the electron transport through a single molecular junction, but also deepen the understanding of the mechanism of π–π interactions. [ABSTRACT FROM AUTHOR]

C21H23ClN2O4, monoclinic, P21 (no. 4), a = 10.9270(4) Å, b = 8.0274(3) Å, c = 12.9691(6) Å, β = 114.914(1)°, V = 1031.73(7) Å3, Z = 2, Rgt(F) = 0.0362 wRref(F2) = 0.0898, T = 150 K. [ABSTRACT FROM AUTHOR]

C21H23ClN2O4, monoclinic, P21 (no. 4), a = 10.9270(4) Å, b = 8.0274(3) Å, c = 12.9691(6) Å, β = 114.914(1)°, V = 1031.73(7) Å3, Z = 2, Rgt(F) = 0.0362 wRref(F2) = 0.0898, T = 150 K. [ABSTRACT FROM AUTHOR]